From c551fb9fdfac70b73741e328ab465605f257f4a8 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Tue, 18 Aug 2020 18:34:31 +0000 Subject: [PATCH] auto-generating sphinx docs --- docs/stable/.buildinfo | 2 +- docs/stable/_images/add_histogram.png | Bin 48135 -> 0 bytes docs/stable/_images/add_hparam.png | Bin 64086 -> 0 bytes docs/stable/_images/add_image.png | Bin 47119 -> 0 bytes docs/stable/_images/add_images.png | Bin 76694 -> 0 bytes docs/stable/_images/add_scalar.png | Bin 45941 -> 0 bytes docs/stable/_images/add_scalars.png | Bin 99156 -> 0 bytes docs/stable/_modules/index.html | 1 - docs/stable/_modules/torch.html | 4 +- docs/stable/_modules/torch/_jit_internal.html | 4 +- docs/stable/_modules/torch/_lowrank.html | 4 +- .../_modules/torch/autograd/grad_mode.html | 10 +- docs/stable/_modules/torch/functional.html | 20 +- docs/stable/_modules/torch/hub.html | 28 +- docs/stable/_modules/torch/jit.html | 24 +- .../_modules/torch/nn/modules/activation.html | 34 +- 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docs/stable/quantization.html | 161 +- docs/stable/searchindex.js | 2 +- docs/stable/sparse.html | 22 +- docs/stable/tensorboard.html | 557 ------ docs/stable/tensors.html | 67 +- docs/stable/torch.html | 117 +- docs/stable/torchvision/ops.html | 26 +- docs/stable/torchvision/transforms.html | 271 +-- 327 files changed, 5668 insertions(+), 6177 deletions(-) delete mode 100644 docs/stable/_images/add_histogram.png delete mode 100644 docs/stable/_images/add_hparam.png delete mode 100644 docs/stable/_images/add_image.png delete mode 100644 docs/stable/_images/add_images.png delete mode 100644 docs/stable/_images/add_scalar.png delete mode 100644 docs/stable/_images/add_scalars.png delete mode 100644 docs/stable/_modules/torch/utils/tensorboard/writer.html diff --git a/docs/stable/.buildinfo b/docs/stable/.buildinfo index 84cdbd6ab202..3449402f7a16 100644 --- a/docs/stable/.buildinfo +++ b/docs/stable/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the 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All modules for which code is available
  • torch.utils.data.sampler
  • torch.utils.mobile_optimizer
    • -
  • torch.utils.tensorboard.writer
  • torchvision
    • torchvision.datasets.celeba
    • torchvision.datasets.cifar
      • diff --git a/docs/stable/_modules/torch.html b/docs/stable/_modules/torch.html index 4d9ca65bc5c3..4ee4726c9721 100644 --- a/docs/stable/_modules/torch.html +++ b/docs/stable/_modules/torch.html @@ -838,9 +838,9 @@

      Source code for torch

       del _torch_docs, _tensor_docs, _storage_docs
 
 
-
      [docs]def compiled_with_cxx11_abi():
+def compiled_with_cxx11_abi():
     r"""Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1"""
-    return _C._GLIBCXX_USE_CXX11_ABI
      
+    return _C._GLIBCXX_USE_CXX11_ABI
 
 
 # Import the ops "namespace"
diff --git a/docs/stable/_modules/torch/_jit_internal.html b/docs/stable/_modules/torch/_jit_internal.html
index c387da189a52..2f78d151ecd9 100644
--- a/docs/stable/_modules/torch/_jit_internal.html
+++ b/docs/stable/_modules/torch/_jit_internal.html
@@ -710,7 +710,7 @@ 
      Source code for torch._jit_internal
           fn._torchscript_modifier = FunctionModifiers.UNUSED
     return fn
      
 
-def ignore(drop=False, **kwargs):
+
      [docs]def ignore(drop=False, **kwargs):
     """
     This decorator indicates to the compiler that a function or method should
     be ignored and left as a Python function. This allows you to leave code in
@@ -801,7 +801,7 @@ 
      Source code for torch._jit_internal
               else:
             fn._torchscript_modifier = FunctionModifiers.IGNORE
         return fn
-    return decorator
+    return decorator
      
 
 
 def _copy_to_script_wrapper(fn):
diff --git a/docs/stable/_modules/torch/_lowrank.html b/docs/stable/_modules/torch/_lowrank.html
index 40498c24ce7f..34972f7ace7a 100644
--- a/docs/stable/_modules/torch/_lowrank.html
+++ b/docs/stable/_modules/torch/_lowrank.html
@@ -419,7 +419,7 @@ 
      Source code for torch._lowrank
           return Q
 
 
-def svd_lowrank(A, q=6, niter=2, M=None):
+
      [docs]def svd_lowrank(A, q=6, niter=2, M=None):
     # type: (Tensor, Optional[int], Optional[int], Optional[Tensor]) -> Tuple[Tensor, Tensor, Tensor]
     r"""Return the singular value decomposition ``(U, S, V)`` of a matrix,
     batches of matrices, or a sparse matrix :math:`A` such that
@@ -464,7 +464,7 @@ 
      Source code for torch._lowrank
               tensor_ops = (A, M)
         if (not set(map(type, tensor_ops)).issubset((torch.Tensor, type(None))) and has_torch_function(tensor_ops)):
             return handle_torch_function(svd_lowrank, tensor_ops, A, q=q, niter=niter, M=M)
-    return _svd_lowrank(A, q=q, niter=niter, M=M)
+    return _svd_lowrank(A, q=q, niter=niter, M=M)
      
 
 
 def _svd_lowrank(A, q=6, niter=2, M=None):
diff --git a/docs/stable/_modules/torch/autograd/grad_mode.html b/docs/stable/_modules/torch/autograd/grad_mode.html
index 75e0bc5ccf97..22e5db5109a7 100644
--- a/docs/stable/_modules/torch/autograd/grad_mode.html
+++ b/docs/stable/_modules/torch/autograd/grad_mode.html
@@ -369,7 +369,7 @@ 
      Source code for torch.autograd.grad_mode
               return generator_context
 
 
-class no_grad(_DecoratorContextManager):
+
      [docs]class no_grad(_DecoratorContextManager):
     r"""Context-manager that disabled gradient calculation.
 
     Disabling gradient calculation is useful for inference, when you are sure
@@ -406,10 +406,10 @@ 
      Source code for torch.autograd.grad_mode
               torch._C.set_grad_enabled(False)
 
     def __exit__(self, *args):
-        torch.set_grad_enabled(self.prev)
+        torch.set_grad_enabled(self.prev)
      
 
 
-
      [docs]class enable_grad(_DecoratorContextManager):
+
      [docs]class enable_grad(_DecoratorContextManager):
     r"""Context-manager that enables gradient calculation.
 
     Enables gradient calculation, if it has been disabled via :class:`~no_grad`
@@ -448,7 +448,7 @@ 
      Source code for torch.autograd.grad_mode
               torch.set_grad_enabled(self.prev)
      
 
 
-class set_grad_enabled(object):
+
      [docs]class set_grad_enabled(object):
     r"""Context-manager that sets gradient calculation to on or off.
 
     ``set_grad_enabled`` will enable or disable grads based on its argument :attr:`mode`.
@@ -493,7 +493,7 @@ 
      Source code for torch.autograd.grad_mode
               pass
 
     def __exit__(self, *args):
-        torch.set_grad_enabled(self.prev)
+        torch.set_grad_enabled(self.prev)
      
 
      
 
              
diff --git a/docs/stable/_modules/torch/functional.html b/docs/stable/_modules/torch/functional.html
index 7aad23570614..ec4b116dde0e 100644
--- a/docs/stable/_modules/torch/functional.html
+++ b/docs/stable/_modules/torch/functional.html
@@ -402,7 +402,7 @@ 
      Source code for torch.functional
           return _VF.broadcast_tensors(tensors)
 
 
-def split(tensor, split_size_or_sections, dim=0):
+
      [docs]def split(tensor, split_size_or_sections, dim=0):
     r"""Splits the tensor into chunks. Each chunk is a view of the original tensor.
 
     If :attr:`split_size_or_sections` is an integer type, then :attr:`tensor` will
@@ -449,7 +449,7 @@ 
      Source code for torch.functional
           # This dispatches to two ATen functions depending on the type of
     # split_size_or_sections. The branching code is in tensor.py, which we
     # call here.
-    return tensor.split(split_size_or_sections, dim)
+    return tensor.split(split_size_or_sections, dim)
      
 
 # equivalent to itertools.product(indices)
 def _indices_product(indices):
@@ -702,7 +702,7 @@ 
      Source code for torch.functional
           return _VF.meshgrid(tensors)
 
 
-def stft(input, n_fft, hop_length=None, win_length=None, window=None,
+
      [docs]def stft(input, n_fft, hop_length=None, win_length=None, window=None,
          center=True, pad_mode='reflect', normalized=False, onesided=True):
     # type: (Tensor, int, Optional[int], Optional[int], Optional[Tensor], bool, str, bool, bool) -> Tensor
     r"""Short-time Fourier transform (STFT).
@@ -799,10 +799,10 @@ 
      Source code for torch.functional
               pad = int(n_fft // 2)
         input = F.pad(input.view(extended_shape), (pad, pad), pad_mode)
         input = input.view(input.shape[-signal_dim:])
-    return _VF.stft(input, n_fft, hop_length, win_length, window, normalized, onesided)
+    return _VF.stft(input, n_fft, hop_length, win_length, window, normalized, onesided)
      
 
 
-def istft(input, n_fft, hop_length=None, win_length=None, window=None,
+
      [docs]def istft(input, n_fft, hop_length=None, win_length=None, window=None,
           center=True, normalized=False, onesided=True, length=None):
     # type: (Tensor, int, Optional[int], Optional[int], Optional[Tensor], bool, bool, bool, Optional[int]) -> Tensor
     r"""Inverse short time Fourier Transform. This is expected to be the inverse of :func:`~torch.stft`.
@@ -861,7 +861,7 @@ 
      Source code for torch.functional
                       length=length)
 
     return _VF.istft(
-        input, n_fft, hop_length, win_length, window, center, normalized, onesided, length)
+        input, n_fft, hop_length, win_length, window, center, normalized, onesided, length)
      
 
 
 del torch.unique_dim
@@ -1136,7 +1136,7 @@ 
      Source code for torch.functional
       unique_consecutive.__doc__ = _unique_consecutive_impl.__doc__
 
 
-def tensordot(a, b, dims=2):
+
      [docs]def tensordot(a, b, dims=2):
     r"""Returns a contraction of a and b over multiple dimensions.
 
     :attr:`tensordot` implements a generalized matrix product.
@@ -1193,7 +1193,7 @@ 
      Source code for torch.functional
                   raise RuntimeError("tensordot expects dims >= 0, but got dims={}".format(dims))
         dims_a = list(range(-dims, 0))
         dims_b = list(range(dims))
-    return _VF.tensordot(a, b, dims_a, dims_b)
+    return _VF.tensordot(a, b, dims_a, dims_b)
      
 
 def cartesian_prod(*tensors):
     """Do cartesian product of the given sequence of tensors. The behavior is similar to
@@ -1339,7 +1339,7 @@ 
      Source code for torch.functional
           # type: (Tensor, str, Optional[int], bool, Optional[Tensor], Optional[int]) -> Tensor
     pass
 
-def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None):  # noqa: 749
+
      [docs]def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None):  # noqa: 749
     r"""Returns the matrix norm or vector norm of a given tensor.
 
     Args:
@@ -1464,7 +1464,7 @@ 
      Source code for torch.functional
                   if dtype is None:
                 return _VF.norm(input, p, _dim, keepdim=keepdim, out=out)
             else:
-                return _VF.norm(input, p, _dim, keepdim=keepdim, dtype=dtype, out=out)
+                return _VF.norm(input, p, _dim, keepdim=keepdim, dtype=dtype, out=out)
      
 
 def chain_matmul(*matrices):
     r"""Returns the matrix product of the :math:`N` 2-D tensors. This product is efficiently computed
diff --git a/docs/stable/_modules/torch/hub.html b/docs/stable/_modules/torch/hub.html
index 9af33c148526..e25278c652b8 100644
--- a/docs/stable/_modules/torch/hub.html
+++ b/docs/stable/_modules/torch/hub.html
@@ -560,7 +560,7 @@ 
      Source code for torch.hub
           return func
 
 
-def get_dir():
+
      [docs]def get_dir():
     r"""
     Get the Torch Hub cache directory used for storing downloaded models & weights.
 
@@ -576,10 +576,10 @@ 
      Source code for torch.hub
       
     if _hub_dir is not None:
         return _hub_dir
-    return os.path.join(_get_torch_home(), 'hub')
+    return os.path.join(_get_torch_home(), 'hub')
      
 
 
-def set_dir(d):
+
      [docs]def set_dir(d):
     r"""
     Optionally set the Torch Hub directory used to save downloaded models & weights.
 
@@ -587,10 +587,10 @@ 
      Source code for torch.hub
               d (string): path to a local folder to save downloaded models & weights.
     """
     global _hub_dir
-    _hub_dir = d
+    _hub_dir = d
      
 
 
-def list(github, force_reload=False):
+
      [docs]def list(github, force_reload=False):
     r"""
     List all entrypoints available in `github` hubconf.
 
@@ -617,10 +617,10 @@ 
      Source code for torch.hub
           # We take functions starts with '_' as internal helper functions
     entrypoints = [f for f in dir(hub_module) if callable(getattr(hub_module, f)) and not f.startswith('_')]
 
-    return entrypoints
+    return entrypoints
      
 
 
-def help(github, model, force_reload=False):
+
      [docs]def help(github, model, force_reload=False):
     r"""
     Show the docstring of entrypoint `model`.
 
@@ -644,14 +644,14 @@ 
      Source code for torch.hub
       
     entry = _load_entry_from_hubconf(hub_module, model)
 
-    return entry.__doc__
+    return entry.__doc__
      
 
 
 # Ideally this should be `def load(github, model, *args, forece_reload=False, **kwargs):`,
 # but Python2 complains syntax error for it. We have to skip force_reload in function
 # signature here but detect it in kwargs instead.
 # TODO: fix it after Python2 EOL
-def load(github, model, *args, **kwargs):
+
      [docs]def load(github, model, *args, **kwargs):
     r"""
     Load a model from a github repo, with pretrained weights.
 
@@ -691,10 +691,10 @@ 
      Source code for torch.hub
       
     sys.path.remove(repo_dir)
 
-    return model
+    return model
      
 
 
-def download_url_to_file(url, dst, hash_prefix=None, progress=True):
+
      [docs]def download_url_to_file(url, dst, hash_prefix=None, progress=True):
     r"""Download object at the given URL to a local path.
 
     Args:
@@ -753,7 +753,7 @@ 
      Source code for torch.hub
           finally:
         f.close()
         if os.path.exists(f.name):
-            os.remove(f.name)
+            os.remove(f.name)
      
 
 def _download_url_to_file(url, dst, hash_prefix=None, progress=True):
     warnings.warn('torch.hub._download_url_to_file has been renamed to\
@@ -761,7 +761,7 @@ 
      Source code for torch.hub
                   _download_url_to_file will be removed in after 1.3 release')
     download_url_to_file(url, dst, hash_prefix, progress)
 
-def load_state_dict_from_url(url, model_dir=None, map_location=None, progress=True, check_hash=False, file_name=None):
+
      [docs]def load_state_dict_from_url(url, model_dir=None, map_location=None, progress=True, check_hash=False, file_name=None):
     r"""Loads the Torch serialized object at the given URL.
 
     If downloaded file is a zip file, it will be automatically
@@ -829,7 +829,7 @@ 
      Source code for torch.hub
                   extraced_name = members[0].filename
             cached_file = os.path.join(model_dir, extraced_name)
 
-    return torch.load(cached_file, map_location=map_location)
+    return torch.load(cached_file, map_location=map_location)
      
 
      
 
              
diff --git a/docs/stable/_modules/torch/jit.html b/docs/stable/_modules/torch/jit.html
index b03a5de7880d..17f36b5fd7f7 100644
--- a/docs/stable/_modules/torch/jit.html
+++ b/docs/stable/_modules/torch/jit.html
@@ -470,7 +470,7 @@ 
      Source code for torch.jit
       DEFAULT_EXTRA_FILES_MAP = torch._C.ExtraFilesMap()
 
 
-def save(m, f, _extra_files=DEFAULT_EXTRA_FILES_MAP):
+
      [docs]def save(m, f, _extra_files=DEFAULT_EXTRA_FILES_MAP):
     r"""
     Save an offline version of this module for use in a separate process. The
     saved module serializes all of the methods, submodules, parameters, and
@@ -534,9 +534,9 @@ 
      Source code for torch.jit
               m.save(f, _extra_files=_extra_files)
     else:
         ret = m.save_to_buffer(_extra_files=_extra_files)
-        f.write(ret)
+        f.write(ret)
      
 
-def load(f, map_location=None, _extra_files=DEFAULT_EXTRA_FILES_MAP):
+
      [docs]def load(f, map_location=None, _extra_files=DEFAULT_EXTRA_FILES_MAP):
     r"""
     Load a :class:`ScriptModule` or :class:`ScriptFunction` previously
     saved with :func:`torch.jit.save <torch.jit.save>`
@@ -614,7 +614,7 @@ 
      Source code for torch.jit
               cpp_module = torch._C.import_ir_module_from_buffer(cu, f.read(), map_location, _extra_files)
 
     # TODO: Pretty sure this approach loses ConstSequential status and such
-    return torch.jit._recursive.wrap_cpp_module(cpp_module)
+    return torch.jit._recursive.wrap_cpp_module(cpp_module)
      
 
 def validate_map_location(map_location=None):
     if isinstance(map_location, str):
@@ -1460,7 +1460,7 @@ 
      Source code for torch.jit
           return module
      
 
 
-def fork(func, *args, **kwargs):
+
      [docs]def fork(func, *args, **kwargs):
     """
     Creates an asynchronous task executing `func` and a reference to the value
     of the result of this execution. `fork` will return immediately,
@@ -1517,7 +1517,7 @@ 
      Source code for torch.jit
               mod = Mod()
         assert mod(input) == torch.jit.script(mod).forward(input)
     """
-    return torch._C.fork(func, *args, **kwargs)
+    return torch._C.fork(func, *args, **kwargs)
      
 
 
 
      [docs]def wait(future):
@@ -1532,7 +1532,7 @@ 
      Source code for torch.jit
           return torch._C.wait(future)
      
 
 
-def freeze(mod, preserved_attrs : Optional[List[str]] = None):
+
      [docs]def freeze(mod, preserved_attrs : Optional[List[str]] = None):
     r"""
     Freezing a :class:`ScriptModule` will clone it and attempt to inline the cloned
     module's submodules, parameters, and attributes as constants in the TorchScript IR Graph.
@@ -1614,7 +1614,7 @@ 
      Source code for torch.jit
           out = RecursiveScriptModule(torch._C._freeze_module(mod._c, preserved_attrs))
     RecursiveScriptModule._finalize_scriptmodule(out)
 
-    return out
+    return out
      
 
 
 class CompilationUnit(object):
@@ -1700,7 +1700,7 @@ 
      Source code for torch.jit
           _jit_script_class_compile(qualified_name, ast, rcb)
     _add_script_class(obj, qualified_name)
 
-def script(obj, optimize=None, _frames_up=0, _rcb=None):
+
      [docs]def script(obj, optimize=None, _frames_up=0, _rcb=None):
     r"""
     Scripting a function or ``nn.Module`` will inspect the source code, compile
     it as TorchScript code using the TorchScript compiler, and return a :class:`ScriptModule` or
@@ -1888,7 +1888,7 @@ 
      Source code for torch.jit
               # Forward docstrings
         fn.__doc__ = obj.__doc__
         _set_jit_function_cache(obj, fn)
-        return fn
+        return fn
      
 
 def interface(obj):
     if not inspect.isclass(obj):
@@ -2518,9 +2518,9 @@ 
      Source code for torch.jit
       
 else:
     # TODO MAKE SURE THAT DISABLING WORKS
-    class ScriptModule(torch.nn.Module):
+
      [docs]    class ScriptModule(torch.nn.Module):
         def __init__(self):
-            super(ScriptModule, self).__init__()
+            super(ScriptModule, self).__init__()
      
 
 
 class TracedModule(ScriptModule):
diff --git a/docs/stable/_modules/torch/nn/modules/activation.html b/docs/stable/_modules/torch/nn/modules/activation.html
index f7123592d60c..1ec6a88b9ea8 100644
--- a/docs/stable/_modules/torch/nn/modules/activation.html
+++ b/docs/stable/_modules/torch/nn/modules/activation.html
@@ -507,7 +507,7 @@ 
      Source code for torch.nn.modules.activation
               return 'lower={}, upper={}{}'.format(self.lower, self.upper, inplace_str)
 
 
-class Hardtanh(Module):
+
      [docs]class Hardtanh(Module):
     r"""Applies the HardTanh function element-wise
 
     HardTanh is defined as:
@@ -577,7 +577,7 @@ 
      Source code for torch.nn.modules.activation
               inplace_str = ', inplace=True' if self.inplace else ''
         return 'min_val={}, max_val={}{}'.format(
             self.min_val, self.max_val, inplace_str
-        )
+        )
      
 
 
 class ReLU6(Hardtanh):
@@ -636,7 +636,7 @@ 
      Source code for torch.nn.modules.activation
               return torch.sigmoid(input)
 
 
-class Hardsigmoid(Module):
+
      [docs]class Hardsigmoid(Module):
     r"""Applies the element-wise function:
 
     .. math::
@@ -660,7 +660,7 @@ 
      Source code for torch.nn.modules.activation
           """
 
     def forward(self, input: Tensor) -> Tensor:
-        return F.hardsigmoid(input)
+        return F.hardsigmoid(input)
      
 
 
 class Tanh(Module):
@@ -687,7 +687,7 @@ 
      Source code for torch.nn.modules.activation
               return torch.tanh(input)
 
 
-class Hardswish(Module):
+
      [docs]class Hardswish(Module):
     r"""Applies the hardswish function, element-wise, as described in the paper:
 
     `Searching for MobileNetV3`_.
@@ -715,10 +715,10 @@ 
      Source code for torch.nn.modules.activation
           """
 
     def forward(self, input: Tensor) -> Tensor:
-        return F.hardswish(input)
+        return F.hardswish(input)
      
 
 
-
      [docs]class ELU(Module):
+class ELU(Module):
     r"""Applies the element-wise function:
 
     .. math::
@@ -755,7 +755,7 @@ 
      Source code for torch.nn.modules.activation
       
     def extra_repr(self) -> str:
         inplace_str = ', inplace=True' if self.inplace else ''
-        return 'alpha={}{}'.format(self.alpha, inplace_str)
      
+        return 'alpha={}{}'.format(self.alpha, inplace_str)
 
 
 class CELU(Module):
@@ -945,7 +945,7 @@ 
      Source code for torch.nn.modules.activation
               return '{}'.format(self.lambd)
 
 
-class LeakyReLU(Module):
+
      [docs]class LeakyReLU(Module):
     r"""Applies the element-wise function:
 
     .. math::
@@ -992,10 +992,10 @@ 
      Source code for torch.nn.modules.activation
       
     def extra_repr(self) -> str:
         inplace_str = ', inplace=True' if self.inplace else ''
-        return 'negative_slope={}{}'.format(self.negative_slope, inplace_str)
+        return 'negative_slope={}{}'.format(self.negative_slope, inplace_str)
      
 
 
-class LogSigmoid(Module):
+
      [docs]class LogSigmoid(Module):
     r"""Applies the element-wise function:
 
     .. math::
@@ -1016,7 +1016,7 @@ 
      Source code for torch.nn.modules.activation
           """
 
     def forward(self, input: Tensor) -> Tensor:
-        return F.logsigmoid(input)
+        return F.logsigmoid(input)
      
 
 
 class Softplus(Module):
@@ -1105,7 +1105,7 @@ 
      Source code for torch.nn.modules.activation
               return str(self.lambd)
 
 
-class MultiheadAttention(Module):
+
      [docs]class MultiheadAttention(Module):
     r"""Allows the model to jointly attend to information
     from different representation subspaces.
     See reference: Attention Is All You Need
@@ -1200,7 +1200,7 @@ 
      Source code for torch.nn.modules.activation
       
         super(MultiheadAttention, self).__setstate__(state)
 
-    def forward(self, query, key, value, key_padding_mask=None,
+
      [docs]    def forward(self, query, key, value, key_padding_mask=None,
                 need_weights=True, attn_mask=None):
         # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]]
         r"""
@@ -1261,7 +1261,7 @@ 
      Source code for torch.nn.modules.activation
                       self.dropout, self.out_proj.weight, self.out_proj.bias,
                 training=self.training,
                 key_padding_mask=key_padding_mask, need_weights=need_weights,
-                attn_mask=attn_mask)
+                attn_mask=attn_mask)
      
 
 
 class PReLU(Module):
@@ -1507,7 +1507,7 @@ 
      Source code for torch.nn.modules.activation
               return F.softmax(input, 1, _stacklevel=5)
 
 
-class LogSoftmax(Module):
+
      [docs]class LogSoftmax(Module):
     r"""Applies the :math:`\log(\text{Softmax}(x))` function to an n-dimensional
     input Tensor. The LogSoftmax formulation can be simplified as:
 
@@ -1548,7 +1548,7 @@ 
      Source code for torch.nn.modules.activation
               return F.log_softmax(input, self.dim, _stacklevel=5)
 
     def extra_repr(self):
-        return 'dim={dim}'.format(dim=self.dim)
+        return 'dim={dim}'.format(dim=self.dim)
      
 
      
 
              
diff --git a/docs/stable/_modules/torch/nn/modules/batchnorm.html b/docs/stable/_modules/torch/nn/modules/batchnorm.html
index d6f3e4670a92..8801d57e2749 100644
--- a/docs/stable/_modules/torch/nn/modules/batchnorm.html
+++ b/docs/stable/_modules/torch/nn/modules/batchnorm.html
@@ -473,7 +473,7 @@ 
      Source code for torch.nn.modules.batchnorm
                   self.weight, self.bias, bn_training, exponential_average_factor, self.eps)
 
 
-
      [docs]class BatchNorm1d(_BatchNorm):
+class BatchNorm1d(_BatchNorm):
     r"""Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
     inputs with optional additional channel dimension) as described in the paper
     `Batch Normalization: Accelerating Deep Network Training by Reducing
@@ -541,10 +541,10 @@ 
      Source code for torch.nn.modules.batchnorm
           def _check_input_dim(self, input):
         if input.dim() != 2 and input.dim() != 3:
             raise ValueError('expected 2D or 3D input (got {}D input)'
-                             .format(input.dim()))
      
+                             .format(input.dim()))
 
 
-
      [docs]class BatchNorm2d(_BatchNorm):
+class BatchNorm2d(_BatchNorm):
     r"""Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs
     with additional channel dimension) as described in the paper
     `Batch Normalization: Accelerating Deep Network Training by Reducing
@@ -612,10 +612,10 @@ 
      Source code for torch.nn.modules.batchnorm
           def _check_input_dim(self, input):
         if input.dim() != 4:
             raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
      
+                             .format(input.dim()))
 
 
-
      [docs]class BatchNorm3d(_BatchNorm):
+class BatchNorm3d(_BatchNorm):
     r"""Applies Batch Normalization over a 5D input (a mini-batch of 3D inputs
     with additional channel dimension) as described in the paper
     `Batch Normalization: Accelerating Deep Network Training by Reducing
@@ -684,10 +684,10 @@ 
      Source code for torch.nn.modules.batchnorm
           def _check_input_dim(self, input):
         if input.dim() != 5:
             raise ValueError('expected 5D input (got {}D input)'
-                             .format(input.dim()))
      
+                             .format(input.dim()))
 
 
-class SyncBatchNorm(_BatchNorm):
+
      [docs]class SyncBatchNorm(_BatchNorm):
     r"""Applies Batch Normalization over a N-Dimensional input (a mini-batch of [N-2]D inputs
     with additional channel dimension) as described in the paper
     `Batch Normalization: Accelerating Deep Network Training by Reducing
@@ -844,7 +844,7 @@ 
      Source code for torch.nn.modules.batchnorm
                       input, self.weight, self.bias, self.running_mean, self.running_var,
                 self.eps, exponential_average_factor, process_group, world_size)
 
-    @classmethod
+
      [docs]    @classmethod
     def convert_sync_batchnorm(cls, module, process_group=None):
         r"""Helper function to convert all :attr:`BatchNorm*D` layers in the model to
         :class:`torch.nn.SyncBatchNorm` layers.
@@ -890,7 +890,7 @@ 
      Source code for torch.nn.modules.batchnorm
               for name, child in module.named_children():
             module_output.add_module(name, cls.convert_sync_batchnorm(child, process_group))
         del module
-        return module_output
+        return module_output
      
 
      
 
              
diff --git a/docs/stable/_modules/torch/nn/modules/container.html b/docs/stable/_modules/torch/nn/modules/container.html
index 3557d0e3b04c..c60fd1f7ad72 100644
--- a/docs/stable/_modules/torch/nn/modules/container.html
+++ b/docs/stable/_modules/torch/nn/modules/container.html
@@ -362,7 +362,7 @@ 
      Source code for torch.nn.modules.container
                   self.add_module(key, value)
 
 
-
      [docs]class Sequential(Module):
+class Sequential(Module):
     r"""A sequential container.
     Modules will be added to it in the order they are passed in the constructor.
     Alternatively, an ordered dict of modules can also be passed in.
@@ -452,10 +452,10 @@ 
      Source code for torch.nn.modules.container
           def forward(self, input):
         for module in self:
             input = module(input)
-        return input
      
+        return input
 
 
-class ModuleList(Module):
+
      [docs]class ModuleList(Module):
     r"""Holds submodules in a list.
 
     :class:`~torch.nn.ModuleList` can be indexed like a regular Python list, but
@@ -531,7 +531,7 @@ 
      Source code for torch.nn.modules.container
               keys = [key for key in keys if not key.isdigit()]
         return keys
 
-    def insert(self, index: int, module: Module) -> None:
+
      [docs]    def insert(self, index: int, module: Module) -> None:
         r"""Insert a given module before a given index in the list.
 
         Arguments:
@@ -540,18 +540,18 @@ 
      Source code for torch.nn.modules.container
               """
         for i in range(len(self._modules), index, -1):
             self._modules[str(i)] = self._modules[str(i - 1)]
-        self._modules[str(index)] = module
+        self._modules[str(index)] = module
      
 
-    def append(self: T, module: Module) -> T:
+
      [docs]    def append(self: T, module: Module) -> T:
         r"""Appends a given module to the end of the list.
 
         Arguments:
             module (nn.Module): module to append
         """
         self.add_module(str(len(self)), module)
-        return self
+        return self
      
 
-    def extend(self: T, modules: Iterable[Module]) -> T:
+
      [docs]    def extend(self: T, modules: Iterable[Module]) -> T:
         r"""Appends modules from a Python iterable to the end of the list.
 
         Arguments:
@@ -563,13 +563,13 @@ 
      Source code for torch.nn.modules.container
               offset = len(self)
         for i, module in enumerate(modules):
             self.add_module(str(offset + i), module)
-        return self
+        return self
      
 
     def forward(self):
-        raise NotImplementedError()
+        raise NotImplementedError()
      
 
 
-class ModuleDict(Module):
+
      [docs]class ModuleDict(Module):
     r"""Holds submodules in a dictionary.
 
     :class:`~torch.nn.ModuleDict` can be indexed like a regular Python dictionary,
@@ -638,12 +638,12 @@ 
      Source code for torch.nn.modules.container
           def __contains__(self, key: str) -> bool:
         return key in self._modules
 
-    def clear(self) -> None:
+
      [docs]    def clear(self) -> None:
         """Remove all items from the ModuleDict.
         """
-        self._modules.clear()
+        self._modules.clear()
      
 
-    def pop(self, key: str) -> Module:
+
      [docs]    def pop(self, key: str) -> Module:
         r"""Remove key from the ModuleDict and return its module.
 
         Arguments:
@@ -651,27 +651,27 @@ 
      Source code for torch.nn.modules.container
               """
         v = self[key]
         del self[key]
-        return v
+        return v
      
 
-    @_copy_to_script_wrapper
+
      [docs]    @_copy_to_script_wrapper
     def keys(self) -> Iterable[str]:
         r"""Return an iterable of the ModuleDict keys.
         """
-        return self._modules.keys()
+        return self._modules.keys()
      
 
-    @_copy_to_script_wrapper
+
      [docs]    @_copy_to_script_wrapper
     def items(self) -> Iterable[Tuple[str, Module]]:
         r"""Return an iterable of the ModuleDict key/value pairs.
         """
-        return self._modules.items()
+        return self._modules.items()
      
 
-    @_copy_to_script_wrapper
+
      [docs]    @_copy_to_script_wrapper
     def values(self) -> Iterable[Module]:
         r"""Return an iterable of the ModuleDict values.
         """
-        return self._modules.values()
+        return self._modules.values()
      
 
-    def update(self, modules: Mapping[str, Module]) -> None:
+
      [docs]    def update(self, modules: Mapping[str, Module]) -> None:
         r"""Update the :class:`~torch.nn.ModuleDict` with the key-value pairs from a
         mapping or an iterable, overwriting existing keys.
 
@@ -704,10 +704,10 @@ 
      Source code for torch.nn.modules.container
                           raise ValueError("ModuleDict update sequence element "
                                      "#" + str(j) + " has length " + str(len(m)) +
                                      "; 2 is required")
-                self[m[0]] = m[1]
+                self[m[0]] = m[1]
      
 
     def forward(self):
-        raise NotImplementedError()
+        raise NotImplementedError()
      
 
 
 class ParameterList(Module):
diff --git a/docs/stable/_modules/torch/nn/modules/conv.html b/docs/stable/_modules/torch/nn/modules/conv.html
index bc80ef42c898..9ee2f7fccfa6 100644
--- a/docs/stable/_modules/torch/nn/modules/conv.html
+++ b/docs/stable/_modules/torch/nn/modules/conv.html
@@ -449,7 +449,7 @@ 
      Source code for torch.nn.modules.conv
                   self.padding_mode = 'zeros'
 
 
-class Conv1d(_ConvNd):
+
      [docs]class Conv1d(_ConvNd):
     r"""Applies a 1D convolution over an input signal composed of several input
     planes.
 
@@ -591,7 +591,7 @@ 
      Source code for torch.nn.modules.conv
                                   self.weight, self.bias, self.stride,
                             _single(0), self.dilation, self.groups)
         return F.conv1d(input, self.weight, self.bias, self.stride,
-                        self.padding, self.dilation, self.groups)
+                        self.padding, self.dilation, self.groups)
      
 
 
 
      [docs]class Conv2d(_ConvNd):
diff --git a/docs/stable/_modules/torch/nn/modules/distance.html b/docs/stable/_modules/torch/nn/modules/distance.html
index 6dc27a616f07..d6914cf2d86f 100644
--- a/docs/stable/_modules/torch/nn/modules/distance.html
+++ b/docs/stable/_modules/torch/nn/modules/distance.html
@@ -341,7 +341,7 @@ 
      Source code for torch.nn.modules.distance
       from torch import Tensor
 
 
-class PairwiseDistance(Module):
+
      [docs]class PairwiseDistance(Module):
     r"""
     Computes the batchwise pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm:
 
@@ -376,10 +376,10 @@ 
      Source code for torch.nn.modules.distance
               self.keepdim = keepdim
 
     def forward(self, x1: Tensor, x2: Tensor) -> Tensor:
-        return F.pairwise_distance(x1, x2, self.norm, self.eps, self.keepdim)
+        return F.pairwise_distance(x1, x2, self.norm, self.eps, self.keepdim)
      
 
 
-
      [docs]class CosineSimilarity(Module):
+class CosineSimilarity(Module):
     r"""Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along dim.
 
     .. math ::
@@ -409,7 +409,7 @@ 
      Source code for torch.nn.modules.distance
               self.eps = eps
 
     def forward(self, x1: Tensor, x2: Tensor) -> Tensor:
-        return F.cosine_similarity(x1, x2, self.dim, self.eps)
      
+        return F.cosine_similarity(x1, x2, self.dim, self.eps)
 
      
 
              
diff --git a/docs/stable/_modules/torch/nn/modules/dropout.html b/docs/stable/_modules/torch/nn/modules/dropout.html
index 9385e2dd3817..8f1d1a623938 100644
--- a/docs/stable/_modules/torch/nn/modules/dropout.html
+++ b/docs/stable/_modules/torch/nn/modules/dropout.html
@@ -358,7 +358,7 @@ 
      Source code for torch.nn.modules.dropout
               return 'p={}, inplace={}'.format(self.p, self.inplace)
 
 
-
      [docs]class Dropout(_DropoutNd):
+class Dropout(_DropoutNd):
     r"""During training, randomly zeroes some of the elements of the input
     tensor with probability :attr:`p` using samples from a Bernoulli
     distribution. Each channel will be zeroed out independently on every forward
@@ -392,10 +392,10 @@ 
      Source code for torch.nn.modules.dropout
           """
 
     def forward(self, input: Tensor) -> Tensor:
-        return F.dropout(input, self.p, self.training, self.inplace)
      
+        return F.dropout(input, self.p, self.training, self.inplace)
 
 
-
      [docs]class Dropout2d(_DropoutNd):
+class Dropout2d(_DropoutNd):
     r"""Randomly zero out entire channels (a channel is a 2D feature map,
     e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
     batched input is a 2D tensor :math:`\text{input}[i, j]`).
@@ -434,10 +434,10 @@ 
      Source code for torch.nn.modules.dropout
           """
 
     def forward(self, input: Tensor) -> Tensor:
-        return F.dropout2d(input, self.p, self.training, self.inplace)
      
+        return F.dropout2d(input, self.p, self.training, self.inplace)
 
 
-
      [docs]class Dropout3d(_DropoutNd):
+class Dropout3d(_DropoutNd):
     r"""Randomly zero out entire channels (a channel is a 3D feature map,
     e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
     batched input is a 3D tensor :math:`\text{input}[i, j]`).
@@ -476,10 +476,10 @@ 
      Source code for torch.nn.modules.dropout
           """
 
     def forward(self, input: Tensor) -> Tensor:
-        return F.dropout3d(input, self.p, self.training, self.inplace)
      
+        return F.dropout3d(input, self.p, self.training, self.inplace)
 
 
-class AlphaDropout(_DropoutNd):
+
      [docs]class AlphaDropout(_DropoutNd):
     r"""Applies Alpha Dropout over the input.
 
     Alpha Dropout is a type of Dropout that maintains the self-normalizing
@@ -518,7 +518,7 @@ 
      Source code for torch.nn.modules.dropout
           """
 
     def forward(self, input: Tensor) -> Tensor:
-        return F.alpha_dropout(input, self.p, self.training)
+        return F.alpha_dropout(input, self.p, self.training)
      
 
 
 class FeatureAlphaDropout(_DropoutNd):
diff --git a/docs/stable/_modules/torch/nn/modules/fold.html b/docs/stable/_modules/torch/nn/modules/fold.html
index a4344a8aebe1..5027366d086f 100644
--- a/docs/stable/_modules/torch/nn/modules/fold.html
+++ b/docs/stable/_modules/torch/nn/modules/fold.html
@@ -343,7 +343,7 @@ 
      Source code for torch.nn.modules.fold
       from ..common_types import _size_any_t
 
 
-
      [docs]class Fold(Module):
+class Fold(Module):
     r"""Combines an array of sliding local blocks into a large containing
     tensor.
 
@@ -485,7 +485,7 @@ 
      Source code for torch.nn.modules.fold
               return 'output_size={output_size}, kernel_size={kernel_size}, ' \
             'dilation={dilation}, padding={padding}, stride={stride}'.format(
                 **self.__dict__
-            )
      
+            )
 
 
 
      [docs]class Unfold(Module):
diff --git a/docs/stable/_modules/torch/nn/modules/instancenorm.html b/docs/stable/_modules/torch/nn/modules/instancenorm.html
index b291becfd26e..083310962935 100644
--- a/docs/stable/_modules/torch/nn/modules/instancenorm.html
+++ b/docs/stable/_modules/torch/nn/modules/instancenorm.html
@@ -394,7 +394,7 @@ 
      Source code for torch.nn.modules.instancenorm
      self.training or not self.track_running_stats, self.momentum, self.eps)
 
 
-class InstanceNorm1d(_InstanceNorm):
+
      [docs]class InstanceNorm1d(_InstanceNorm):
     r"""Applies Instance Normalization over a 3D input (a mini-batch of 1D
     inputs with optional additional channel dimension) as described in the paper
     `Instance Normalization: The Missing Ingredient for Fast Stylization
@@ -472,10 +472,10 @@ 
      Source code for torch.nn.modules.instancenorm
      )
         if input.dim() != 3:
             raise ValueError('expected 3D input (got {}D input)'
-                             .format(input.dim()))
+                             .format(input.dim()))
      
 
 
-class InstanceNorm2d(_InstanceNorm):
+
      [docs]class InstanceNorm2d(_InstanceNorm):
     r"""Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs
     with additional channel dimension) as described in the paper
     `Instance Normalization: The Missing Ingredient for Fast Stylization
@@ -546,7 +546,7 @@ 
      Source code for torch.nn.modules.instancenorm
      def _check_input_dim(self, input):
         if input.dim() != 4:
             raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
+                             .format(input.dim()))
      
 
 
 
      [docs]class InstanceNorm3d(_InstanceNorm):
diff --git a/docs/stable/_modules/torch/nn/modules/linear.html b/docs/stable/_modules/torch/nn/modules/linear.html
index 946022a7b73f..9542e5ae4d88 100644
--- a/docs/stable/_modules/torch/nn/modules/linear.html
+++ b/docs/stable/_modules/torch/nn/modules/linear.html
@@ -345,7 +345,7 @@ 
      Source code for torch.nn.modules.linear
       from .module import Module
 
 
-class Identity(Module):
+
      [docs]class Identity(Module):
     r"""A placeholder identity operator that is argument-insensitive.
 
     Args:
@@ -365,10 +365,10 @@ 
      Source code for torch.nn.modules.linear
               super(Identity, self).__init__()
 
     def forward(self, input: Tensor) -> Tensor:
-        return input
+        return input
      
 
 
-class Linear(Module):
+
      [docs]class Linear(Module):
     r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
 
     Args:
@@ -430,7 +430,7 @@ 
      Source code for torch.nn.modules.linear
           def extra_repr(self) -> str:
         return 'in_features={}, out_features={}, bias={}'.format(
             self.in_features, self.out_features, self.bias is not None
-        )
+        )
      
 
 
 # This class exists soley for Transformer; it has an annotation stating
@@ -442,7 +442,7 @@ 
      Source code for torch.nn.modules.linear
               super().__init__(in_features, out_features, bias=True)
 
 
-
      [docs]class Bilinear(Module):
+class Bilinear(Module):
     r"""Applies a bilinear transformation to the incoming data:
     :math:`y = x_1^T A x_2 + b`
 
@@ -511,7 +511,7 @@ 
      Source code for torch.nn.modules.linear
           def extra_repr(self) -> str:
         return 'in1_features={}, in2_features={}, out_features={}, bias={}'.format(
             self.in1_features, self.in2_features, self.out_features, self.bias is not None
-        )
      
+        )
 
 # TODO: PartialLinear - maybe in sparse?
 
      
diff --git a/docs/stable/_modules/torch/nn/modules/loss.html b/docs/stable/_modules/torch/nn/modules/loss.html
index aa82db3289db..5dfac6d8a612 100644
--- a/docs/stable/_modules/torch/nn/modules/loss.html
+++ b/docs/stable/_modules/torch/nn/modules/loss.html
@@ -362,7 +362,7 @@ 
      Source code for torch.nn.modules.loss
               self.register_buffer('weight', weight)
 
 
-class L1Loss(_Loss):
+
      [docs]class L1Loss(_Loss):
     r"""Creates a criterion that measures the mean absolute error (MAE) between each element in
     the input :math:`x` and target :math:`y`.
 
@@ -427,7 +427,7 @@ 
      Source code for torch.nn.modules.loss
               super(L1Loss, self).__init__(size_average, reduce, reduction)
 
     def forward(self, input: Tensor, target: Tensor) -> Tensor:
-        return F.l1_loss(input, target, reduction=self.reduction)
+        return F.l1_loss(input, target, reduction=self.reduction)
      
 
 
 class NLLLoss(_WeightedLoss):
@@ -631,7 +631,7 @@ 
      Source code for torch.nn.modules.loss
                                         eps=self.eps, reduction=self.reduction)
 
 
-class KLDivLoss(_Loss):
+
      [docs]class KLDivLoss(_Loss):
     r"""The `Kullback-Leibler divergence`_ Loss
 
     KL divergence is a useful distance measure for continuous distributions
@@ -713,10 +713,10 @@ 
      Source code for torch.nn.modules.loss
               self.log_target = log_target
 
     def forward(self, input: Tensor, target: Tensor) -> Tensor:
-        return F.kl_div(input, target, reduction=self.reduction, log_target=self.log_target)
+        return F.kl_div(input, target, reduction=self.reduction, log_target=self.log_target)
      
 
 
-class MSELoss(_Loss):
+
      [docs]class MSELoss(_Loss):
     r"""Creates a criterion that measures the mean squared error (squared L2 norm) between
     each element in the input :math:`x` and target :math:`y`.
 
@@ -779,7 +779,7 @@ 
      Source code for torch.nn.modules.loss
               super(MSELoss, self).__init__(size_average, reduce, reduction)
 
     def forward(self, input: Tensor, target: Tensor) -> Tensor:
-        return F.mse_loss(input, target, reduction=self.reduction)
+        return F.mse_loss(input, target, reduction=self.reduction)
      
 
 
 class BCELoss(_WeightedLoss):
@@ -968,7 +968,7 @@ 
      Source code for torch.nn.modules.loss
                                                         reduction=self.reduction)
 
 
-class HingeEmbeddingLoss(_Loss):
+
      [docs]class HingeEmbeddingLoss(_Loss):
     r"""Measures the loss given an input tensor :math:`x` and a labels tensor :math:`y`
     (containing 1 or -1).
     This is usually used for measuring whether two inputs are similar or
@@ -1025,10 +1025,10 @@ 
      Source code for torch.nn.modules.loss
               self.margin = margin
 
     def forward(self, input: Tensor, target: Tensor) -> Tensor:
-        return F.hinge_embedding_loss(input, target, margin=self.margin, reduction=self.reduction)
+        return F.hinge_embedding_loss(input, target, margin=self.margin, reduction=self.reduction)
      
 
 
-class MultiLabelMarginLoss(_Loss):
+
      [docs]class MultiLabelMarginLoss(_Loss):
     r"""Creates a criterion that optimizes a multi-class multi-classification
     hinge loss (margin-based loss) between input :math:`x` (a 2D mini-batch `Tensor`)
     and output :math:`y` (which is a 2D `Tensor` of target class indices).
@@ -1089,7 +1089,7 @@ 
      Source code for torch.nn.modules.loss
               super(MultiLabelMarginLoss, self).__init__(size_average, reduce, reduction)
 
     def forward(self, input: Tensor, target: Tensor) -> Tensor:
-        return F.multilabel_margin_loss(input, target, reduction=self.reduction)
+        return F.multilabel_margin_loss(input, target, reduction=self.reduction)
      
 
 
 class SmoothL1Loss(_Loss):
@@ -1191,7 +1191,7 @@ 
      Source code for torch.nn.modules.loss
               return F.soft_margin_loss(input, target, reduction=self.reduction)
 
 
-
      [docs]class CrossEntropyLoss(_WeightedLoss):
+class CrossEntropyLoss(_WeightedLoss):
     r"""This criterion combines :func:`nn.LogSoftmax` and :func:`nn.NLLLoss` in one single class.
 
     It is useful when training a classification problem with `C` classes.
@@ -1282,10 +1282,10 @@ 
      Source code for torch.nn.modules.loss
       
     def forward(self, input: Tensor, target: Tensor) -> Tensor:
         return F.cross_entropy(input, target, weight=self.weight,
-                               ignore_index=self.ignore_index, reduction=self.reduction)
      
+                               ignore_index=self.ignore_index, reduction=self.reduction)
 
 
-class MultiLabelSoftMarginLoss(_WeightedLoss):
+
      [docs]class MultiLabelSoftMarginLoss(_WeightedLoss):
     r"""Creates a criterion that optimizes a multi-label one-versus-all
     loss based on max-entropy, between input :math:`x` and target :math:`y` of size
     :math:`(N, C)`.
@@ -1329,7 +1329,7 @@ 
      Source code for torch.nn.modules.loss
               super(MultiLabelSoftMarginLoss, self).__init__(weight, size_average, reduce, reduction)
 
     def forward(self, input: Tensor, target: Tensor) -> Tensor:
-        return F.multilabel_soft_margin_loss(input, target, weight=self.weight, reduction=self.reduction)
+        return F.multilabel_soft_margin_loss(input, target, weight=self.weight, reduction=self.reduction)
      
 
 
 class CosineEmbeddingLoss(_Loss):
@@ -1379,7 +1379,7 @@ 
      Source code for torch.nn.modules.loss
               return F.cosine_embedding_loss(input1, input2, target, margin=self.margin, reduction=self.reduction)
 
 
-class MarginRankingLoss(_Loss):
+
      [docs]class MarginRankingLoss(_Loss):
     r"""Creates a criterion that measures the loss given
     inputs :math:`x1`, :math:`x2`, two 1D mini-batch `Tensors`,
     and a label 1D mini-batch tensor :math:`y` (containing 1 or -1).
@@ -1423,10 +1423,10 @@ 
      Source code for torch.nn.modules.loss
               self.margin = margin
 
     def forward(self, input1: Tensor, input2: Tensor, target: Tensor) -> Tensor:
-        return F.margin_ranking_loss(input1, input2, target, margin=self.margin, reduction=self.reduction)
+        return F.margin_ranking_loss(input1, input2, target, margin=self.margin, reduction=self.reduction)
      
 
 
-class MultiMarginLoss(_WeightedLoss):
+
      [docs]class MultiMarginLoss(_WeightedLoss):
     r"""Creates a criterion that optimizes a multi-class classification hinge
     loss (margin-based loss) between input :math:`x` (a 2D mini-batch `Tensor`) and
     output :math:`y` (which is a 1D tensor of target class indices,
@@ -1487,7 +1487,7 @@ 
      Source code for torch.nn.modules.loss
       
     def forward(self, input: Tensor, target: Tensor) -> Tensor:
         return F.multi_margin_loss(input, target, p=self.p, margin=self.margin,
-                                   weight=self.weight, reduction=self.reduction)
+                                   weight=self.weight, reduction=self.reduction)
      
 
 
 
      [docs]class TripletMarginLoss(_Loss):
diff --git a/docs/stable/_modules/torch/nn/modules/module.html b/docs/stable/_modules/torch/nn/modules/module.html
index 06987a5fc4d3..eef19b781c4d 100644
--- a/docs/stable/_modules/torch/nn/modules/module.html
+++ b/docs/stable/_modules/torch/nn/modules/module.html
@@ -495,7 +495,7 @@ 
      Source code for torch.nn.modules.module
           return handle
 
 
-class Module:
+
      [docs]class Module:
     r"""Base class for all neural network modules.
 
     Your models should also subclass this class.
@@ -571,7 +571,7 @@ 
      Source code for torch.nn.modules.module
           """
     forward: Callable[..., Any] = _forward_unimplemented
 
-    def register_buffer(self, name: str, tensor: Tensor, persistent: bool = True) -> None:
+
      [docs]    def register_buffer(self, name: str, tensor: Tensor, persistent: bool = True) -> None:
         r"""Adds a buffer to the module.
 
         This is typically used to register a buffer that should not to be
@@ -621,9 +621,9 @@ 
      Source code for torch.nn.modules.module
                   if persistent:
                 self._non_persistent_buffers_set.discard(name)
             else:
-                self._non_persistent_buffers_set.add(name)
+                self._non_persistent_buffers_set.add(name)
      
 
-    def register_parameter(self, name: str, param: Parameter) -> None:
+
      [docs]    def register_parameter(self, name: str, param: Parameter) -> None:
         r"""Adds a parameter to the module.
 
         The parameter can be accessed as an attribute using given name.
@@ -660,9 +660,9 @@ 
      Source code for torch.nn.modules.module
                       "as a function of another Tensor, compute the value in "
                 "the forward() method.".format(name))
         else:
-            self._parameters[name] = param
+            self._parameters[name] = param
      
 
-    def add_module(self, name: str, module: 'Module') -> None:
+
      [docs]    def add_module(self, name: str, module: 'Module') -> None:
         r"""Adds a child module to the current module.
 
         The module can be accessed as an attribute using the given name.
@@ -684,7 +684,7 @@ 
      Source code for torch.nn.modules.module
                   raise KeyError("module name can't contain \".\"")
         elif name == '':
             raise KeyError("module name can't be empty string \"\"")
-        self._modules[name] = module
+        self._modules[name] = module
      
 
     def _apply(self, fn):
         for module in self.children():
@@ -735,7 +735,7 @@ 
      Source code for torch.nn.modules.module
       
         return self
 
-    def apply(self: T, fn: Callable[['Module'], None]) -> T:
+
      [docs]    def apply(self: T, fn: Callable[['Module'], None]) -> T:
         r"""Applies ``fn`` recursively to every submodule (as returned by ``.children()``)
         as well as self. Typical use includes initializing the parameters of a model
         (see also :ref:`nn-init-doc`).
@@ -776,9 +776,9 @@ 
      Source code for torch.nn.modules.module
               for module in self.children():
             module.apply(fn)
         fn(self)
-        return self
+        return self
      
 
-    def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:
+
      [docs]    def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:
         r"""Moves all model parameters and buffers to the GPU.
 
         This also makes associated parameters and buffers different objects. So
@@ -792,17 +792,17 @@ 
      Source code for torch.nn.modules.module
               Returns:
             Module: self
         """
-        return self._apply(lambda t: t.cuda(device))
+        return self._apply(lambda t: t.cuda(device))
      
 
-    def cpu(self: T) -> T:
+
      [docs]    def cpu(self: T) -> T:
         r"""Moves all model parameters and buffers to the CPU.
 
         Returns:
             Module: self
         """
-        return self._apply(lambda t: t.cpu())
+        return self._apply(lambda t: t.cpu())
      
 
-    def type(self: T, dst_type: Union[dtype, str]) -> T:
+
      [docs]    def type(self: T, dst_type: Union[dtype, str]) -> T:
         r"""Casts all parameters and buffers to :attr:`dst_type`.
 
         Arguments:
@@ -811,39 +811,39 @@ 
      Source code for torch.nn.modules.module
               Returns:
             Module: self
         """
-        return self._apply(lambda t: t.type(dst_type))
+        return self._apply(lambda t: t.type(dst_type))
      
 
-    def float(self: T) -> T:
+
      [docs]    def float(self: T) -> T:
         r"""Casts all floating point parameters and buffers to float datatype.
 
         Returns:
             Module: self
         """
-        return self._apply(lambda t: t.float() if t.is_floating_point() else t)
+        return self._apply(lambda t: t.float() if t.is_floating_point() else t)
      
 
-    def double(self: T) -> T:
+
      [docs]    def double(self: T) -> T:
         r"""Casts all floating point parameters and buffers to ``double`` datatype.
 
         Returns:
             Module: self
         """
-        return self._apply(lambda t: t.double() if t.is_floating_point() else t)
+        return self._apply(lambda t: t.double() if t.is_floating_point() else t)
      
 
-    def half(self: T) -> T:
+
      [docs]    def half(self: T) -> T:
         r"""Casts all floating point parameters and buffers to ``half`` datatype.
 
         Returns:
             Module: self
         """
-        return self._apply(lambda t: t.half() if t.is_floating_point() else t)
+        return self._apply(lambda t: t.half() if t.is_floating_point() else t)
      
 
-    def bfloat16(self: T) -> T:
+
      [docs]    def bfloat16(self: T) -> T:
         r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype.
 
         Returns:
             Module: self
         """
-        return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)
+        return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)
      
 
     @overload
     def to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ...,
@@ -858,7 +858,7 @@ 
      Source code for torch.nn.modules.module
           def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T:
         ...
 
-    def to(self, *args, **kwargs):
+
      [docs]    def to(self, *args, **kwargs):
         r"""Moves and/or casts the parameters and buffers.
 
         This can be called as
@@ -941,9 +941,9 @@ 
      Source code for torch.nn.modules.module
                       return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format)
             return t.to(device, dtype if t.is_floating_point() else None, non_blocking)
 
-        return self._apply(convert)
+        return self._apply(convert)
      
 
-    def register_backward_hook(
+
      [docs]    def register_backward_hook(
         self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]]
     ) -> RemovableHandle:
         r"""Registers a backward hook on the module.
@@ -977,9 +977,9 @@ 
      Source code for torch.nn.modules.module
               """
         handle = hooks.RemovableHandle(self._backward_hooks)
         self._backward_hooks[handle.id] = hook
-        return handle
+        return handle
      
 
-    def register_forward_pre_hook(self, hook: Callable[..., None]) -> RemovableHandle:
+
      [docs]    def register_forward_pre_hook(self, hook: Callable[..., None]) -> RemovableHandle:
         r"""Registers a forward pre-hook on the module.
 
         The hook will be called every time before :func:`forward` is invoked.
@@ -1000,9 +1000,9 @@ 
      Source code for torch.nn.modules.module
               """
         handle = hooks.RemovableHandle(self._forward_pre_hooks)
         self._forward_pre_hooks[handle.id] = hook
-        return handle
+        return handle
      
 
-    def register_forward_hook(self, hook: Callable[..., None]) -> RemovableHandle:
+
      [docs]    def register_forward_hook(self, hook: Callable[..., None]) -> RemovableHandle:
         r"""Registers a forward hook on the module.
 
         The hook will be called every time after :func:`forward` has computed an output.
@@ -1023,7 +1023,7 @@ 
      Source code for torch.nn.modules.module
               """
         handle = hooks.RemovableHandle(self._forward_hooks)
         self._forward_hooks[handle.id] = hook
-        return handle
+        return handle
      
 
     def _slow_forward(self, *input, **kwargs):
         tracing_state = torch._C._get_tracing_state()
@@ -1211,7 +1211,7 @@ 
      Source code for torch.nn.modules.module
           def state_dict(self, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Tensor]:
         ...
 
-    def state_dict(self, destination=None, prefix='', keep_vars=False):
+
      [docs]    def state_dict(self, destination=None, prefix='', keep_vars=False):
         r"""Returns a dictionary containing a whole state of the module.
 
         Both parameters and persistent buffers (e.g. running averages) are
@@ -1239,7 +1239,7 @@ 
      Source code for torch.nn.modules.module
                   hook_result = hook(self, destination, prefix, local_metadata)
             if hook_result is not None:
                 destination = hook_result
-        return destination
+        return destination
      
 
     def _register_load_state_dict_pre_hook(self, hook):
         r"""These hooks will be called with arguments: `state_dict`, `prefix`,
@@ -1327,7 +1327,7 @@ 
      Source code for torch.nn.modules.module
                           if input_name not in self._modules and input_name not in local_state:
                         unexpected_keys.append(key)
 
-    def load_state_dict(self, state_dict: Union[Dict[str, Tensor], Dict[str, Tensor]],
+
      [docs]    def load_state_dict(self, state_dict: Union[Dict[str, Tensor], Dict[str, Tensor]],
                         strict: bool = True):
         r"""Copies parameters and buffers from :attr:`state_dict` into
         this module and its descendants. If :attr:`strict` is ``True``, then
@@ -1380,7 +1380,7 @@ 
      Source code for torch.nn.modules.module
               if len(error_msgs) > 0:
             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
                                self.__class__.__name__, "\n\t".join(error_msgs)))
-        return _IncompatibleKeys(missing_keys, unexpected_keys)
+        return _IncompatibleKeys(missing_keys, unexpected_keys)
      
 
     def _named_members(self, get_members_fn, prefix='', recurse=True):
         r"""Helper method for yielding various names + members of modules."""
@@ -1395,7 +1395,7 @@ 
      Source code for torch.nn.modules.module
                       name = module_prefix + ('.' if module_prefix else '') + k
                 yield name, v
 
-    def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
+
      [docs]    def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
         r"""Returns an iterator over module parameters.
 
         This is typically passed to an optimizer.
@@ -1417,9 +1417,9 @@ 
      Source code for torch.nn.modules.module
       
         """
         for name, param in self.named_parameters(recurse=recurse):
-            yield param
+            yield param
      
 
-    def named_parameters(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Tensor]]:
+
      [docs]    def named_parameters(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Tensor]]:
         r"""Returns an iterator over module parameters, yielding both the
         name of the parameter as well as the parameter itself.
 
@@ -1443,9 +1443,9 @@ 
      Source code for torch.nn.modules.module
                   lambda module: module._parameters.items(),
             prefix=prefix, recurse=recurse)
         for elem in gen:
-            yield elem
+            yield elem
      
 
-    def buffers(self, recurse: bool = True) -> Iterator[Tensor]:
+
      [docs]    def buffers(self, recurse: bool = True) -> Iterator[Tensor]:
         r"""Returns an iterator over module buffers.
 
         Args:
@@ -1465,9 +1465,9 @@ 
      Source code for torch.nn.modules.module
       
         """
         for name, buf in self.named_buffers(recurse=recurse):
-            yield buf
+            yield buf
      
 
-    def named_buffers(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Tensor]]:
+
      [docs]    def named_buffers(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Tensor]]:
         r"""Returns an iterator over module buffers, yielding both the
         name of the buffer as well as the buffer itself.
 
@@ -1491,18 +1491,18 @@ 
      Source code for torch.nn.modules.module
                   lambda module: module._buffers.items(),
             prefix=prefix, recurse=recurse)
         for elem in gen:
-            yield elem
+            yield elem
      
 
-    def children(self) -> Iterator['Module']:
+
      [docs]    def children(self) -> Iterator['Module']:
         r"""Returns an iterator over immediate children modules.
 
         Yields:
             Module: a child module
         """
         for name, module in self.named_children():
-            yield module
+            yield module
      
 
-    def named_children(self) -> Iterator[Tuple[str, 'Module']]:
+
      [docs]    def named_children(self) -> Iterator[Tuple[str, 'Module']]:
         r"""Returns an iterator over immediate children modules, yielding both
         the name of the module as well as the module itself.
 
@@ -1520,9 +1520,9 @@ 
      Source code for torch.nn.modules.module
               for name, module in self._modules.items():
             if module is not None and module not in memo:
                 memo.add(module)
-                yield name, module
+                yield name, module
      
 
-    def modules(self) -> Iterator['Module']:
+
      [docs]    def modules(self) -> Iterator['Module']:
         r"""Returns an iterator over all modules in the network.
 
         Yields:
@@ -1547,9 +1547,9 @@ 
      Source code for torch.nn.modules.module
       
         """
         for name, module in self.named_modules():
-            yield module
+            yield module
      
 
-    def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = ''):
+
      [docs]    def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = ''):
         r"""Returns an iterator over all modules in the network, yielding
         both the name of the module as well as the module itself.
 
@@ -1585,9 +1585,9 @@ 
      Source code for torch.nn.modules.module
                           continue
                 submodule_prefix = prefix + ('.' if prefix else '') + name
                 for m in module.named_modules(memo, submodule_prefix):
-                    yield m
+                    yield m
      
 
-    def train(self: T, mode: bool = True) -> T:
+
      [docs]    def train(self: T, mode: bool = True) -> T:
         r"""Sets the module in training mode.
 
         This has any effect only on certain modules. See documentations of
@@ -1605,9 +1605,9 @@ 
      Source code for torch.nn.modules.module
               self.training = mode
         for module in self.children():
             module.train(mode)
-        return self
+        return self
      
 
-    def eval(self: T) -> T:
+
      [docs]    def eval(self: T) -> T:
         r"""Sets the module in evaluation mode.
 
         This has any effect only on certain modules. See documentations of
@@ -1620,9 +1620,9 @@ 
      Source code for torch.nn.modules.module
               Returns:
             Module: self
         """
-        return self.train(False)
+        return self.train(False)
      
 
-    def requires_grad_(self: T, requires_grad: bool = True) -> T:
+
      [docs]    def requires_grad_(self: T, requires_grad: bool = True) -> T:
         r"""Change if autograd should record operations on parameters in this
         module.
 
@@ -1641,9 +1641,9 @@ 
      Source code for torch.nn.modules.module
               """
         for p in self.parameters():
             p.requires_grad_(requires_grad)
-        return self
+        return self
      
 
-    def zero_grad(self) -> None:
+
      [docs]    def zero_grad(self) -> None:
         r"""Sets gradients of all model parameters to zero."""
         if getattr(self, '_is_replica', False):
             warnings.warn(
@@ -1655,7 +1655,7 @@ 
      Source code for torch.nn.modules.module
               for p in self.parameters():
             if p.grad is not None:
                 p.grad.detach_()
-                p.grad.zero_()
+                p.grad.zero_()
      
 
     def share_memory(self: T) -> T:
         return self._apply(lambda t: t.share_memory_())
@@ -1663,14 +1663,14 @@ 
      Source code for torch.nn.modules.module
           def _get_name(self):
         return self.__class__.__name__
 
-    def extra_repr(self) -> str:
+
      [docs]    def extra_repr(self) -> str:
         r"""Set the extra representation of the module
 
         To print customized extra information, you should reimplement
         this method in your own modules. Both single-line and multi-line
         strings are acceptable.
         """
-        return ''
+        return ''
      
 
     def __repr__(self):
         # We treat the extra repr like the sub-module, one item per line
@@ -1721,7 +1721,7 @@ 
      Source code for torch.nn.modules.module
               replica._modules = replica._modules.copy()
         replica._is_replica = True
 
-        return replica
+        return replica
      
 
      
 
              
diff --git a/docs/stable/_modules/torch/nn/modules/padding.html b/docs/stable/_modules/torch/nn/modules/padding.html
index 3677724e4116..13501ea0f4d2 100644
--- a/docs/stable/_modules/torch/nn/modules/padding.html
+++ b/docs/stable/_modules/torch/nn/modules/padding.html
@@ -361,7 +361,7 @@ 
      Source code for torch.nn.modules.padding
               return 'padding={}, value={}'.format(self.padding, self.value)
 
 
-
      [docs]class ConstantPad1d(_ConstantPadNd):
+class ConstantPad1d(_ConstantPadNd):
     r"""Pads the input tensor boundaries with a constant value.
 
     For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
@@ -408,10 +408,10 @@ 
      Source code for torch.nn.modules.padding
       
     def __init__(self, padding: _size_2_t, value: float):
         super(ConstantPad1d, self).__init__(value)
-        self.padding = _pair(padding)
      
+        self.padding = _pair(padding)
 
 
-
      [docs]class ConstantPad2d(_ConstantPadNd):
+class ConstantPad2d(_ConstantPadNd):
     r"""Pads the input tensor boundaries with a constant value.
 
     For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
@@ -458,10 +458,10 @@ 
      Source code for torch.nn.modules.padding
       
     def __init__(self, padding: _size_4_t, value: float) -> None:
         super(ConstantPad2d, self).__init__(value)
-        self.padding = _quadruple(padding)
      
+        self.padding = _quadruple(padding)
 
 
-
      [docs]class ConstantPad3d(_ConstantPadNd):
+class ConstantPad3d(_ConstantPadNd):
     r"""Pads the input tensor boundaries with a constant value.
 
     For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
@@ -497,7 +497,7 @@ 
      Source code for torch.nn.modules.padding
       
     def __init__(self, padding: _size_6_t, value: float) -> None:
         super(ConstantPad3d, self).__init__(value)
-        self.padding = _ntuple(6)(padding)
      
+        self.padding = _ntuple(6)(padding)
 
 
 class _ReflectionPadNd(Module):
diff --git a/docs/stable/_modules/torch/nn/modules/pooling.html b/docs/stable/_modules/torch/nn/modules/pooling.html
index 15f33e8f019a..027dac6e752c 100644
--- a/docs/stable/_modules/torch/nn/modules/pooling.html
+++ b/docs/stable/_modules/torch/nn/modules/pooling.html
@@ -372,7 +372,7 @@ 
      Source code for torch.nn.modules.pooling
                   ', dilation={dilation}, ceil_mode={ceil_mode}'.format(**self.__dict__)
 
 
-class MaxPool1d(_MaxPoolNd):
+
      [docs]class MaxPool1d(_MaxPoolNd):
     r"""Applies a 1D max pooling over an input signal composed of several input
     planes.
 
@@ -423,10 +423,10 @@ 
      Source code for torch.nn.modules.pooling
           def forward(self, input: Tensor) -> Tensor:
         return F.max_pool1d(input, self.kernel_size, self.stride,
                             self.padding, self.dilation, self.ceil_mode,
-                            self.return_indices)
+                            self.return_indices)
      
 
 
-class MaxPool2d(_MaxPoolNd):
+
      [docs]class MaxPool2d(_MaxPoolNd):
     r"""Applies a 2D max pooling over an input signal composed of several input
     planes.
 
@@ -493,10 +493,10 @@ 
      Source code for torch.nn.modules.pooling
           def forward(self, input: Tensor) -> Tensor:
         return F.max_pool2d(input, self.kernel_size, self.stride,
                             self.padding, self.dilation, self.ceil_mode,
-                            self.return_indices)
+                            self.return_indices)
      
 
 
-class MaxPool3d(_MaxPoolNd):
+
      [docs]class MaxPool3d(_MaxPoolNd):
     r"""Applies a 3D max pooling over an input signal composed of several input
     planes.
 
@@ -567,7 +567,7 @@ 
      Source code for torch.nn.modules.pooling
           def forward(self, input: Tensor) -> Tensor:
         return F.max_pool3d(input, self.kernel_size, self.stride,
                             self.padding, self.dilation, self.ceil_mode,
-                            self.return_indices)
+                            self.return_indices)
      
 
 
 class _MaxUnpoolNd(Module):
@@ -578,7 +578,7 @@ 
      Source code for torch.nn.modules.pooling
               )
 
 
-class MaxUnpool1d(_MaxUnpoolNd):
+
      [docs]class MaxUnpool1d(_MaxUnpoolNd):
     r"""Computes a partial inverse of :class:`MaxPool1d`.
 
     :class:`MaxPool1d` is not fully invertible, since the non-maximal values are lost.
@@ -644,10 +644,10 @@ 
      Source code for torch.nn.modules.pooling
       
     def forward(self, input: Tensor, indices: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
         return F.max_unpool1d(input, indices, self.kernel_size, self.stride,
-                              self.padding, output_size)
+                              self.padding, output_size)
      
 
 
-class MaxUnpool2d(_MaxUnpoolNd):
+
      [docs]class MaxUnpool2d(_MaxUnpoolNd):
     r"""Computes a partial inverse of :class:`MaxPool2d`.
 
     :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
@@ -721,10 +721,10 @@ 
      Source code for torch.nn.modules.pooling
       
     def forward(self, input: Tensor, indices: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
         return F.max_unpool2d(input, indices, self.kernel_size, self.stride,
-                              self.padding, output_size)
+                              self.padding, output_size)
      
 
 
-class MaxUnpool3d(_MaxUnpoolNd):
+
      [docs]class MaxUnpool3d(_MaxUnpoolNd):
     r"""Computes a partial inverse of :class:`MaxPool3d`.
 
     :class:`MaxPool3d` is not fully invertible, since the non-maximal values are lost.
@@ -787,7 +787,7 @@ 
      Source code for torch.nn.modules.pooling
       
     def forward(self, input: Tensor, indices: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
         return F.max_unpool3d(input, indices, self.kernel_size, self.stride,
-                              self.padding, output_size)
+                              self.padding, output_size)
      
 
 
 class _AvgPoolNd(Module):
@@ -1023,7 +1023,7 @@ 
      Source code for torch.nn.modules.pooling
               self.__dict__.setdefault('count_include_pad', True)
 
 
-
      [docs]class FractionalMaxPool2d(Module):
+class FractionalMaxPool2d(Module):
     r"""Applies a 2D fractional max pooling over an input signal composed of several input planes.
 
     Fractional MaxPooling is described in detail in the paper `Fractional MaxPooling`_ by Ben Graham
@@ -1084,7 +1084,7 @@ 
      Source code for torch.nn.modules.pooling
               return F.fractional_max_pool2d(
             input, self.kernel_size, self.output_size, self.output_ratio,
             self.return_indices,
-            _random_samples=self._random_samples)
      
+            _random_samples=self._random_samples)
 
 
 class FractionalMaxPool3d(Module):
@@ -1169,7 +1169,7 @@ 
      Source code for torch.nn.modules.pooling
                   'ceil_mode={ceil_mode}'.format(**self.__dict__)
 
 
-class LPPool1d(_LPPoolNd):
+
      [docs]class LPPool1d(_LPPoolNd):
     r"""Applies a 1D power-average pooling over an input signal composed of several input
     planes.
 
@@ -1208,10 +1208,10 @@ 
      Source code for torch.nn.modules.pooling
       
     def forward(self, input: Tensor) -> Tensor:
         return F.lp_pool1d(input, float(self.norm_type), self.kernel_size,
-                           self.stride, self.ceil_mode)
+                           self.stride, self.ceil_mode)
      
 
 
-class LPPool2d(_LPPoolNd):
+
      [docs]class LPPool2d(_LPPoolNd):
     r"""Applies a 2D power-average pooling over an input signal composed of several input
     planes.
 
@@ -1263,7 +1263,7 @@ 
      Source code for torch.nn.modules.pooling
       
     def forward(self, input: Tensor) -> Tensor:
         return F.lp_pool2d(input, float(self.norm_type), self.kernel_size,
-                           self.stride, self.ceil_mode)
+                           self.stride, self.ceil_mode)
      
 
 
 class _AdaptiveMaxPoolNd(Module):
diff --git a/docs/stable/_modules/torch/nn/modules/rnn.html b/docs/stable/_modules/torch/nn/modules/rnn.html
index c8156b9fc466..82d9321c8ef6 100644
--- a/docs/stable/_modules/torch/nn/modules/rnn.html
+++ b/docs/stable/_modules/torch/nn/modules/rnn.html
@@ -358,7 +358,7 @@ 
      Source code for torch.nn.modules.rnn
           return tensor.index_select(dim, permutation)
 
 
-
      [docs]class RNNBase(Module):
+class RNNBase(Module):
     __constants__ = ['mode', 'input_size', 'hidden_size', 'num_layers', 'bias',
                      'batch_first', 'dropout', 'bidirectional']
 
@@ -443,7 +443,7 @@ 
      Source code for torch.nn.modules.rnn
                   self._flat_weights[idx] = value
         super(RNNBase, self).__setattr__(attr, value)
 
-
      [docs]    def flatten_parameters(self) -> None:
+    def flatten_parameters(self) -> None:
         """Resets parameter data pointer so that they can use faster code paths.
 
         Right now, this works only if the module is on the GPU and cuDNN is enabled.
@@ -485,7 +485,7 @@ 
      Source code for torch.nn.modules.rnn
                           torch._cudnn_rnn_flatten_weight(
                         self._flat_weights, (4 if self.bias else 2),
                         self.input_size, rnn.get_cudnn_mode(self.mode), self.hidden_size, self.num_layers,
-                        self.batch_first, bool(self.bidirectional))
      
+                        self.batch_first, bool(self.bidirectional))
 
     def _apply(self, fn):
         ret = super(RNNBase, self)._apply(fn)
@@ -627,7 +627,7 @@ 
      Source code for torch.nn.modules.rnn
               # flat weights list.
         replica._flat_weights = replica._flat_weights[:]
         replica._flat_weights_names = replica._flat_weights_names[:]
-        return replica
      
+        return replica
 
 
 class RNN(RNNBase):
@@ -750,7 +750,7 @@ 
      Source code for torch.nn.modules.rnn
       #
 # TODO: remove the overriding implementations for LSTM and GRU when TorchScript
 # support expressing these two modules generally.
-class LSTM(RNNBase):
+
      [docs]class LSTM(RNNBase):
     r"""Applies a multi-layer long short-term memory (LSTM) RNN to an input
     sequence.
 
@@ -922,7 +922,7 @@ 
      Source code for torch.nn.modules.rnn
                   output_packed = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
             return output_packed, self.permute_hidden(hidden, unsorted_indices)
         else:
-            return output, self.permute_hidden(hidden, unsorted_indices)
+            return output, self.permute_hidden(hidden, unsorted_indices)
      
 
 
 class GRU(RNNBase):
@@ -1141,7 +1141,7 @@ 
      Source code for torch.nn.modules.rnn
                   init.uniform_(weight, -stdv, stdv)
 
 
-
      [docs]class RNNCell(RNNCellBase):
+class RNNCell(RNNCellBase):
     r"""An Elman RNN cell with tanh or ReLU non-linearity.
 
     .. math::
@@ -1226,10 +1226,10 @@ 
      Source code for torch.nn.modules.rnn
                   ret = input  # TODO: remove when jit supports exception flow
             raise RuntimeError(
                 "Unknown nonlinearity: {}".format(self.nonlinearity))
-        return ret
      
+        return ret
 
 
-class LSTMCell(RNNCellBase):
+
      [docs]class LSTMCell(RNNCellBase):
     r"""A long short-term memory (LSTM) cell.
 
     .. math::
@@ -1304,7 +1304,7 @@ 
      Source code for torch.nn.modules.rnn
                   input, hx,
             self.weight_ih, self.weight_hh,
             self.bias_ih, self.bias_hh,
-        )
+        )
      
 
 
 class GRUCell(RNNCellBase):
diff --git a/docs/stable/_modules/torch/nn/modules/transformer.html b/docs/stable/_modules/torch/nn/modules/transformer.html
index b168e16dc545..63aab3365a42 100644
--- a/docs/stable/_modules/torch/nn/modules/transformer.html
+++ b/docs/stable/_modules/torch/nn/modules/transformer.html
@@ -350,7 +350,7 @@ 
      Source code for torch.nn.modules.transformer
      from .normalization import LayerNorm
 
 
-
      [docs]class Transformer(Module):
+class Transformer(Module):
     r"""A transformer model. User is able to modify the attributes as needed. The architecture
     is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer,
     Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and
@@ -403,7 +403,7 @@ 
      Source code for torch.nn.modules.transformer
      self.d_model = d_model
         self.nhead = nhead
 
-
      [docs]    def forward(self, src: Tensor, tgt: Tensor, src_mask: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None,
+    def forward(self, src: Tensor, tgt: Tensor, src_mask: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None,
                 memory_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None,
                 tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
         r"""Take in and process masked source/target sequences.
@@ -461,22 +461,22 @@ 
      Source code for torch.nn.modules.transformer
      output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=memory_mask,
                               tgt_key_padding_mask=tgt_key_padding_mask,
                               memory_key_padding_mask=memory_key_padding_mask)
-        return output
      
+        return output
 
-
      [docs]    def generate_square_subsequent_mask(self, sz: int) -> Tensor:
+    def generate_square_subsequent_mask(self, sz: int) -> Tensor:
         r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
             Unmasked positions are filled with float(0.0).
         """
         mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
         mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
-        return mask
      
+        return mask
 
     def _reset_parameters(self):
         r"""Initiate parameters in the transformer model."""
 
         for p in self.parameters():
             if p.dim() > 1:
-                xavier_uniform_(p)
      
+                xavier_uniform_(p)
 
 
 
      [docs]class TransformerEncoder(Module):
@@ -523,7 +523,7 @@ 
      Source code for torch.nn.modules.transformer
      return output
      
 
 
-
      [docs]class TransformerDecoder(Module):
+class TransformerDecoder(Module):
     r"""TransformerDecoder is a stack of N decoder layers
 
     Args:
@@ -546,7 +546,7 @@ 
      Source code for torch.nn.modules.transformer
      self.num_layers = num_layers
         self.norm = norm
 
-
      [docs]    def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
+    def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
                 memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None,
                 memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
         r"""Pass the inputs (and mask) through the decoder layer in turn.
@@ -573,7 +573,7 @@ 
      Source code for torch.nn.modules.transformer
      if self.norm is not None:
             output = self.norm(output)
 
-        return output
      
+        return output
 
 
      [docs]class TransformerEncoderLayer(Module):
     r"""TransformerEncoderLayer is made up of self-attn and feedforward network.
diff --git a/docs/stable/_modules/torch/nn/utils/convert_parameters.html b/docs/stable/_modules/torch/nn/utils/convert_parameters.html
index 5af721f05778..0e800305b71d 100644
--- a/docs/stable/_modules/torch/nn/utils/convert_parameters.html
+++ b/docs/stable/_modules/torch/nn/utils/convert_parameters.html
@@ -338,7 +338,7 @@ 
      Source code for torch.nn.utils.convert_parameters
      import torch
 
 
-def parameters_to_vector(parameters):
+
      [docs]def parameters_to_vector(parameters):
     r"""Convert parameters to one vector
 
     Arguments:
@@ -357,7 +357,7 @@ 
      Source code for torch.nn.utils.convert_parameters
      param_device = _check_param_device(param, param_device)
 
         vec.append(param.view(-1))
-    return torch.cat(vec)
+    return torch.cat(vec)
      
 
 
 
      [docs]def vector_to_parameters(vec, parameters):
diff --git a/docs/stable/_modules/torch/nn/utils/prune.html b/docs/stable/_modules/torch/nn/utils/prune.html
index cc22134fa29c..098e089b462d 100644
--- a/docs/stable/_modules/torch/nn/utils/prune.html
+++ b/docs/stable/_modules/torch/nn/utils/prune.html
@@ -350,7 +350,7 @@ 
      Source code for torch.nn.utils.prune
           ABC = ABCMeta('ABC', (), {})
     from collections import Iterable
 
-class BasePruningMethod(ABC):
+
      [docs]class BasePruningMethod(ABC):
     r"""Abstract base class for creation of new pruning techniques.
 
     Provides a skeleton for customization requiring the overriding of methods
@@ -371,7 +371,7 @@ 
      Source code for torch.nn.utils.prune
               """
         setattr(module, self._tensor_name, self.apply_mask(module))
 
-    @abstractmethod
+
      [docs]    @abstractmethod
     def compute_mask(self, t, default_mask):
         r"""Computes and returns a mask for the input tensor ``t``.
         Starting from a base ``default_mask`` (which should be a mask of ones
@@ -388,9 +388,9 @@ 
      Source code for torch.nn.utils.prune
               Returns:
             mask (torch.Tensor): mask to apply to ``t``, of same dims as ``t``
         """
-        pass
+        pass
      
 
-    def apply_mask(self, module):
+
      [docs]    def apply_mask(self, module):
         r"""Simply handles the multiplication between the parameter being
         pruned and the generated mask.
         Fetches the mask and the original tensor from the module
@@ -412,9 +412,9 @@ 
      Source code for torch.nn.utils.prune
               mask = getattr(module, self._tensor_name + "_mask")
         orig = getattr(module, self._tensor_name + "_orig")
         pruned_tensor = mask.to(dtype=orig.dtype) * orig
-        return pruned_tensor
+        return pruned_tensor
      
 
-    @classmethod
+
      [docs]    @classmethod
     def apply(cls, module, name, *args, **kwargs):
         r"""Adds the forward pre-hook that enables pruning on the fly and
         the reparametrization of a tensor in terms of the original tensor
@@ -528,9 +528,9 @@ 
      Source code for torch.nn.utils.prune
                       del module._parameters[name + "_orig"]
             raise e
 
-        return method
+        return method
      
 
-    def prune(self, t, default_mask=None):
+
      [docs]    def prune(self, t, default_mask=None):
         r"""Computes and returns a pruned version of input tensor ``t``
         according to the pruning rule specified in :meth:`compute_mask`.
 
@@ -547,9 +547,9 @@ 
      Source code for torch.nn.utils.prune
               """
         if default_mask is None:
             default_mask = torch.ones_like(t)
-        return t * self.compute_mask(t, default_mask=default_mask)
+        return t * self.compute_mask(t, default_mask=default_mask)
      
 
-    def remove(self, module):
+
      [docs]    def remove(self, module):
         r"""Removes the pruning reparameterization from a module. The pruned
         parameter named ``name`` remains permanently pruned, and the parameter
         named ``name+'_orig'`` is removed from the parameter list. Similarly,
@@ -575,10 +575,10 @@ 
      Source code for torch.nn.utils.prune
               orig.data = weight.data
         del module._parameters[self._tensor_name + "_orig"]
         del module._buffers[self._tensor_name + "_mask"]
-        setattr(module, self._tensor_name, orig)
+        setattr(module, self._tensor_name, orig)
      
 
 
-class PruningContainer(BasePruningMethod):
+
      [docs]class PruningContainer(BasePruningMethod):
     """Container holding a sequence of pruning methods for iterative pruning.
     Keeps track of the order in which pruning methods are applied and handles
     combining successive pruning calls.
@@ -599,7 +599,7 @@ 
      Source code for torch.nn.utils.prune
                   for method in args:
                 self.add_pruning_method(method)
 
-    def add_pruning_method(self, method):
+
      [docs]    def add_pruning_method(self, method):
         r"""Adds a child pruning ``method`` to the container.
 
         Args:
@@ -620,7 +620,7 @@ 
      Source code for torch.nn.utils.prune
                       + " Found '{}'".format(method._tensor_name)
             )
         # if all checks passed, add to _pruning_methods tuple
-        self._pruning_methods += (method,)
+        self._pruning_methods += (method,)
      
 
     def __len__(self):
         return len(self._pruning_methods)
@@ -631,7 +631,7 @@ 
      Source code for torch.nn.utils.prune
           def __getitem__(self, idx):
         return self._pruning_methods[idx]
 
-    def compute_mask(self, t, default_mask):
+
      [docs]    def compute_mask(self, t, default_mask):
         r"""Applies the latest ``method`` by computing the new partial masks
         and returning its combination with the ``default_mask``.
         The new partial mask should be computed on the entries or channels
@@ -726,10 +726,10 @@ 
      Source code for torch.nn.utils.prune
       
         method = self._pruning_methods[-1]
         mask = _combine_masks(method, t, default_mask)
-        return mask
+        return mask
      
 
 
-class Identity(BasePruningMethod):
+
      [docs]class Identity(BasePruningMethod):
     r"""Utility pruning method that does not prune any units but generates the
     pruning parametrization with a mask of ones.
     """
@@ -740,7 +740,7 @@ 
      Source code for torch.nn.utils.prune
               mask = default_mask
         return mask
 
-    @classmethod
+
      [docs]    @classmethod
     def apply(cls, module, name):
         r"""Adds the forward pre-hook that enables pruning on the fly and
         the reparametrization of a tensor in terms of the original tensor
@@ -751,10 +751,10 @@ 
      Source code for torch.nn.utils.prune
                   name (str): parameter name within ``module`` on which pruning
                 will act.
         """
-        return super(Identity, cls).apply(module, name)
+        return super(Identity, cls).apply(module, name)
      
 
 
-class RandomUnstructured(BasePruningMethod):
+
      [docs]class RandomUnstructured(BasePruningMethod):
     r"""Prune (currently unpruned) units in a tensor at random.
 
     Args:
@@ -793,7 +793,7 @@ 
      Source code for torch.nn.utils.prune
       
         return mask
 
-    @classmethod
+
      [docs]    @classmethod
     def apply(cls, module, name, amount):
         r"""Adds the forward pre-hook that enables pruning on the fly and
         the reparametrization of a tensor in terms of the original tensor
@@ -810,10 +810,10 @@ 
      Source code for torch.nn.utils.prune
               """
         return super(RandomUnstructured, cls).apply(
             module, name, amount=amount
-        )
+        )
      
 
 
-class L1Unstructured(BasePruningMethod):
+
      [docs]class L1Unstructured(BasePruningMethod):
     r"""Prune (currently unpruned) units in a tensor by zeroing out the ones
     with the lowest L1-norm.
 
@@ -855,7 +855,7 @@ 
      Source code for torch.nn.utils.prune
       
         return mask
 
-    @classmethod
+
      [docs]    @classmethod
     def apply(cls, module, name, amount):
         r"""Adds the forward pre-hook that enables pruning on the fly and
         the reparametrization of a tensor in terms of the original tensor
@@ -870,10 +870,10 @@ 
      Source code for torch.nn.utils.prune
                       fraction of parameters to prune. If ``int``, it represents the
                 absolute number of parameters to prune.
         """
-        return super(L1Unstructured, cls).apply(module, name, amount=amount)
+        return super(L1Unstructured, cls).apply(module, name, amount=amount)
      
 
 
-class RandomStructured(BasePruningMethod):
+
      [docs]class RandomStructured(BasePruningMethod):
     r"""Prune entire (currently unpruned) channels in a tensor at random.
 
     Args:
@@ -893,7 +893,7 @@ 
      Source code for torch.nn.utils.prune
               self.amount = amount
         self.dim = dim
 
-    def compute_mask(self, t, default_mask):
+
      [docs]    def compute_mask(self, t, default_mask):
         r"""Computes and returns a mask for the input tensor ``t``.
         Starting from a base ``default_mask`` (which should be a mask of ones
         if the tensor has not been pruned yet), generate a random mask to
@@ -954,9 +954,9 @@ 
      Source code for torch.nn.utils.prune
                   # unstructured) mask
             mask = make_mask(t, self.dim, tensor_size, nparams_toprune)
             mask *= default_mask.to(dtype=mask.dtype)
-        return mask
+        return mask
      
 
-    @classmethod
+
      [docs]    @classmethod
     def apply(cls, module, name, amount, dim=-1):
         r"""Adds the forward pre-hook that enables pruning on the fly and
         the reparametrization of a tensor in terms of the original tensor
@@ -975,10 +975,10 @@ 
      Source code for torch.nn.utils.prune
               """
         return super(RandomStructured, cls).apply(
             module, name, amount=amount, dim=dim
-        )
+        )
      
 
 
-class LnStructured(BasePruningMethod):
+
      [docs]class LnStructured(BasePruningMethod):
     r"""Prune entire (currently unpruned) channels in a tensor based on their
     Ln-norm.
 
@@ -1002,7 +1002,7 @@ 
      Source code for torch.nn.utils.prune
               self.n = n
         self.dim = dim
 
-    def compute_mask(self, t, default_mask):
+
      [docs]    def compute_mask(self, t, default_mask):
         r"""Computes and returns a mask for the input tensor ``t``.
         Starting from a base ``default_mask`` (which should be a mask of ones
         if the tensor has not been pruned yet), generate a mask to apply on
@@ -1073,9 +1073,9 @@ 
      Source code for torch.nn.utils.prune
                   mask = make_mask(t, self.dim, topk.indices)
             mask *= default_mask.to(dtype=mask.dtype)
 
-        return mask
+        return mask
      
 
-    @classmethod
+
      [docs]    @classmethod
     def apply(cls, module, name, amount, n, dim):
         r"""Adds the forward pre-hook that enables pruning on the fly and
         the reparametrization of a tensor in terms of the original tensor
@@ -1096,10 +1096,10 @@ 
      Source code for torch.nn.utils.prune
               """
         return super(LnStructured, cls).apply(
             module, name, amount=amount, n=n, dim=dim
-        )
+        )
      
 
 
-class CustomFromMask(BasePruningMethod):
+
      [docs]class CustomFromMask(BasePruningMethod):
 
     PRUNING_TYPE = "global"
 
@@ -1111,7 +1111,7 @@ 
      Source code for torch.nn.utils.prune
               mask = default_mask * self.mask.to(dtype=default_mask.dtype)
         return mask
 
-    @classmethod
+
      [docs]    @classmethod
     def apply(cls, module, name, mask):
         r"""Adds the forward pre-hook that enables pruning on the fly and
         the reparametrization of a tensor in terms of the original tensor
@@ -1124,10 +1124,10 @@ 
      Source code for torch.nn.utils.prune
               """
         return super(CustomFromMask, cls).apply(
             module, name, mask
-        )
+        )
      
 
 
-def identity(module, name):
+
      [docs]def identity(module, name):
     r"""Applies pruning reparametrization to the tensor corresponding to the
     parameter called ``name`` in ``module`` without actually pruning any
     units. Modifies module in place (and also return the modified module)
@@ -1155,7 +1155,7 @@ 
      Source code for torch.nn.utils.prune
               tensor([1., 1., 1.])
     """
     Identity.apply(module, name)
-    return module
+    return module
      
 
 
 
      [docs]def random_unstructured(module, name, amount):
@@ -1191,7 +1191,7 @@ 
      Source code for torch.nn.utils.prune
           return module
      
 
 
-def l1_unstructured(module, name, amount):
+
      [docs]def l1_unstructured(module, name, amount):
     r"""Prunes tensor corresponding to parameter called ``name`` in ``module``
     by removing the specified `amount` of (currently unpruned) units with the
     lowest L1-norm.
@@ -1221,7 +1221,7 @@ 
      Source code for torch.nn.utils.prune
               odict_keys(['bias', 'weight_orig', 'weight_mask'])
     """
     L1Unstructured.apply(module, name, amount)
-    return module
+    return module
      
 
 
 
      [docs]def random_structured(module, name, amount, dim):
@@ -1297,7 +1297,7 @@ 
      Source code for torch.nn.utils.prune
           return module
      
 
 
-def global_unstructured(parameters, pruning_method, **kwargs):
+
      [docs]def global_unstructured(parameters, pruning_method, **kwargs):
     r"""
     Globally prunes tensors corresponding to all parameters in ``parameters``
     by applying the specified ``pruning_method``.
@@ -1399,10 +1399,10 @@ 
      Source code for torch.nn.utils.prune
               custom_from_mask(module, name, param_mask)
 
         # Increment the pointer to continue slicing the final_mask
-        pointer += num_param
+        pointer += num_param
      
 
 
-def custom_from_mask(module, name, mask):
+
      [docs]def custom_from_mask(module, name, mask):
     r"""Prunes tensor corresponding to parameter called ``name`` in ``module``
     by applying the pre-computed mask in ``mask``.
     Modifies module in place (and also return the modified module)
@@ -1431,7 +1431,7 @@ 
      Source code for torch.nn.utils.prune
       
     """
     CustomFromMask.apply(module, name, mask)
-    return module
+    return module
      
 
 
 
      [docs]def remove(module, name):
@@ -1465,7 +1465,7 @@ 
      Source code for torch.nn.utils.prune
           )
      
 
 
-def is_pruned(module):
+
      [docs]def is_pruned(module):
     r"""Check whether ``module`` is pruned by looking for
     ``forward_pre_hooks`` in its modules that inherit from the
     :class:`BasePruningMethod`.
@@ -1488,7 +1488,7 @@ 
      Source code for torch.nn.utils.prune
               for _, hook in submodule._forward_pre_hooks.items():
             if isinstance(hook, BasePruningMethod):
                 return True
-    return False
+    return False
      
 
 
 def _validate_pruning_amount_init(amount):
diff --git a/docs/stable/_modules/torch/nn/utils/spectral_norm.html b/docs/stable/_modules/torch/nn/utils/spectral_norm.html
index 9600a8f919ac..7dc27bedc10f 100644
--- a/docs/stable/_modules/torch/nn/utils/spectral_norm.html
+++ b/docs/stable/_modules/torch/nn/utils/spectral_norm.html
@@ -592,7 +592,7 @@ 
      Source code for torch.nn.utils.spectral_norm
      return module
      
 
 
-def remove_spectral_norm(module, name='weight'):
+
      [docs]def remove_spectral_norm(module, name='weight'):
     r"""Removes the spectral normalization reparameterization from a module.
 
     Args:
@@ -622,7 +622,7 @@ 
      Source code for torch.nn.utils.spectral_norm
      del module._load_state_dict_pre_hooks[k]
             break
 
-    return module
+    return module
      
 
      
 
              
diff --git a/docs/stable/_modules/torch/nn/utils/weight_norm.html b/docs/stable/_modules/torch/nn/utils/weight_norm.html
index 4fa82dbee997..da0c6ce7be33 100644
--- a/docs/stable/_modules/torch/nn/utils/weight_norm.html
+++ b/docs/stable/_modules/torch/nn/utils/weight_norm.html
@@ -435,7 +435,7 @@ 
      Source code for torch.nn.utils.weight_norm
           return module
      
 
 
-def remove_weight_norm(module, name='weight'):
+
      [docs]def remove_weight_norm(module, name='weight'):
     r"""Removes the weight normalization reparameterization from a module.
 
     Args:
@@ -453,7 +453,7 @@ 
      Source code for torch.nn.utils.weight_norm
                   return module
 
     raise ValueError("weight_norm of '{}' not found in {}"
-                     .format(name, module))
+                     .format(name, module))
      
 
      
 
              
diff --git a/docs/stable/_modules/torch/random.html b/docs/stable/_modules/torch/random.html
index 05e0324ad2e3..d4a8f7ed830a 100644
--- a/docs/stable/_modules/torch/random.html
+++ b/docs/stable/_modules/torch/random.html
@@ -341,18 +341,18 @@ 
      Source code for torch.random
       from torch._C import default_generator
 
 
-def set_rng_state(new_state):
+
      [docs]def set_rng_state(new_state):
     r"""Sets the random number generator state.
 
     Args:
         new_state (torch.ByteTensor): The desired state
     """
-    default_generator.set_state(new_state)
+    default_generator.set_state(new_state)
      
 
 
-def get_rng_state():
+
      [docs]def get_rng_state():
     r"""Returns the random number generator state as a `torch.ByteTensor`."""
-    return default_generator.get_state()
+    return default_generator.get_state()
      
 
 
 def manual_seed(seed):
@@ -371,7 +371,7 @@ 
      Source code for torch.random
           return default_generator.manual_seed(seed)
 
 
-def seed():
+
      [docs]def seed():
     r"""Sets the seed for generating random numbers to a non-deterministic
     random number. Returns a 64 bit number used to seed the RNG.
     """
@@ -381,7 +381,7 @@ 
      Source code for torch.random
           if not torch.cuda._is_in_bad_fork():
         torch.cuda.manual_seed_all(seed)
 
-    return seed
+    return seed
      
 
 
 
      [docs]def initial_seed():
diff --git a/docs/stable/_modules/torch/serialization.html b/docs/stable/_modules/torch/serialization.html
index 9a085646e93b..758c8b3b2e5e 100644
--- a/docs/stable/_modules/torch/serialization.html
+++ b/docs/stable/_modules/torch/serialization.html
@@ -664,7 +664,7 @@ 
      Source code for torch.serialization
                       pickle_module.__version__
             ))
 
-def save(obj, f, pickle_module=pickle, pickle_protocol=DEFAULT_PROTOCOL, _use_new_zipfile_serialization=True):
+
      [docs]def save(obj, f, pickle_module=pickle, pickle_protocol=DEFAULT_PROTOCOL, _use_new_zipfile_serialization=True):
     """Saves an object to a disk file.
 
     See also: :ref:`recommend-saving-models`
@@ -700,7 +700,7 @@ 
      Source code for torch.serialization
                   with _open_zipfile_writer(opened_file) as opened_zipfile:
                 _save(obj, opened_zipfile, pickle_module, pickle_protocol)
                 return
-        _legacy_save(obj, opened_file, pickle_module, pickle_protocol)
+        _legacy_save(obj, opened_file, pickle_module, pickle_protocol)
      
 
 
 def _legacy_save(obj, f, pickle_module, pickle_protocol):
diff --git a/docs/stable/_modules/torch/tensor.html b/docs/stable/_modules/torch/tensor.html
index 2671a5e71429..4132103c7780 100644
--- a/docs/stable/_modules/torch/tensor.html
+++ b/docs/stable/_modules/torch/tensor.html
@@ -489,7 +489,7 @@ 
      Source code for torch.tensor
               # All strings are unicode in Python 3.
         return torch._tensor_str._str(self)
 
-
      [docs]    def backward(self, gradient=None, retain_graph=None, create_graph=False):
+
      [docs]    def backward(self, gradient=None, retain_graph=None, create_graph=False):
         r"""Computes the gradient of current tensor w.r.t. graph leaves.
 
         The graph is differentiated using the chain rule. If the tensor is
@@ -521,7 +521,7 @@ 
      Source code for torch.tensor
               """
         torch.autograd.backward(self, gradient, retain_graph, create_graph)
      
 
-
      [docs]    def register_hook(self, hook):
+
      [docs]    def register_hook(self, hook):
         r"""Registers a backward hook.
 
         The hook will be called every time a gradient with respect to the
@@ -613,7 +613,7 @@ 
      Source code for torch.tensor
           Views cannot be detached in-place.
     """)
 
-
      [docs]    def retain_grad(self):
+
      [docs]    def retain_grad(self):
         r"""Enables .grad attribute for non-leaf Tensors."""
         if not self.requires_grad:
             raise RuntimeError("can't retain_grad on Tensor that has requires_grad=False")
@@ -638,21 +638,21 @@ 
      Source code for torch.tensor
               self.register_hook(retain_grad_hook)
         self.retains_grad = True
      
 
-
      [docs]    def is_shared(self):
+    def is_shared(self):
         r"""Checks if tensor is in shared memory.
 
         This is always ``True`` for CUDA tensors.
         """
-        return self.storage().is_shared()
      
+        return self.storage().is_shared()
 
-
      [docs]    def share_memory_(self):
+    def share_memory_(self):
         r"""Moves the underlying storage to shared memory.
 
         This is a no-op if the underlying storage is already in shared memory
         and for CUDA tensors. Tensors in shared memory cannot be resized.
         """
         self.storage().share_memory_()
-        return self
      
+        return self
 
     def __reversed__(self):
         r"""Reverses the tensor along dimension 0."""
@@ -661,20 +661,20 @@ 
      Source code for torch.tensor
               else:
             return self.flip(0)
 
-
      [docs]    def norm(self, p="fro", dim=None, keepdim=False, dtype=None):
+    def norm(self, p="fro", dim=None, keepdim=False, dtype=None):
         r"""See :func:`torch.norm`"""
-        return torch.norm(self, p, dim, keepdim, dtype=dtype)
      
+        return torch.norm(self, p, dim, keepdim, dtype=dtype)
 
-
      [docs]    def lu(self, pivot=True, get_infos=False):
+    def lu(self, pivot=True, get_infos=False):
         r"""See :func:`torch.lu`"""
         # If get_infos is True, then we don't need to check for errors and vice versa
         LU, pivots, infos = torch._lu_with_info(self, pivot=pivot, check_errors=(not get_infos))
         if get_infos:
             return LU, pivots, infos
         else:
-            return LU, pivots
      
+            return LU, pivots
 
-
      [docs]    def stft(self, n_fft, hop_length=None, win_length=None, window=None,
+    def stft(self, n_fft, hop_length=None, win_length=None, window=None,
              center=True, pad_mode='reflect', normalized=False, onesided=True):
         r"""See :func:`torch.stft`
 
@@ -683,13 +683,13 @@ 
      Source code for torch.tensor
                 the previous signature may cause error or return incorrect result.
         """
         return torch.stft(self, n_fft, hop_length, win_length, window, center,
-                          pad_mode, normalized, onesided)
      
+                          pad_mode, normalized, onesided)
 
-
      [docs]    def istft(self, n_fft, hop_length=None, win_length=None, window=None,
+    def istft(self, n_fft, hop_length=None, win_length=None, window=None,
               center=True, normalized=False, onesided=True, length=None):
         r"""See :func:`torch.istft`"""
         return torch.istft(self, n_fft, hop_length, win_length, window, center,
-                           normalized, onesided, length)
      
+                           normalized, onesided, length)
 
     def resize(self, *sizes):
         warnings.warn("non-inplace resize is deprecated")
@@ -701,7 +701,7 @@ 
      Source code for torch.tensor
               from torch.autograd._functions import Resize
         return Resize.apply(self, tensor.size())
 
-
      [docs]    def split(self, split_size, dim=0):
+    def split(self, split_size, dim=0):
         r"""See :func:`torch.split`
         """
         if isinstance(split_size, int):
@@ -713,21 +713,21 @@ 
      Source code for torch.tensor
                   except ValueError:
                 return super(Tensor, self).split_with_sizes(split_size, dim)
         else:
-            return super(Tensor, self).split_with_sizes(split_size, dim)
      
+            return super(Tensor, self).split_with_sizes(split_size, dim)
 
-
      [docs]    def unique(self, sorted=True, return_inverse=False, return_counts=False, dim=None):
+    def unique(self, sorted=True, return_inverse=False, return_counts=False, dim=None):
         r"""Returns the unique elements of the input tensor.
 
         See :func:`torch.unique`
         """
-        return torch.unique(self, sorted=sorted, return_inverse=return_inverse, return_counts=return_counts, dim=dim)
      
+        return torch.unique(self, sorted=sorted, return_inverse=return_inverse, return_counts=return_counts, dim=dim)
 
-
      [docs]    def unique_consecutive(self, return_inverse=False, return_counts=False, dim=None):
+    def unique_consecutive(self, return_inverse=False, return_counts=False, dim=None):
         r"""Eliminates all but the first element from every consecutive group of equivalent elements.
 
         See :func:`torch.unique_consecutive`
         """
-        return torch.unique_consecutive(self, return_inverse=return_inverse, return_counts=return_counts, dim=dim)
      
+        return torch.unique_consecutive(self, return_inverse=return_inverse, return_counts=return_counts, dim=dim)
 
     def __rsub__(self, other):
         return _C._VariableFunctions.rsub(self, other)
diff --git a/docs/stable/_modules/torch/utils/tensorboard/writer.html b/docs/stable/_modules/torch/utils/tensorboard/writer.html
deleted file mode 100644
index 5906f522dd9d..000000000000
--- a/docs/stable/_modules/torch/utils/tensorboard/writer.html
+++ /dev/null
@@ -1,1652 +0,0 @@
-
-
-
-
-
-  
-
-  
-  
-  
-  
-  
-  torch.utils.tensorboard.writer — PyTorch 1.6.0 documentation
-  
-
-  
-  
-  
-  
-    
-  
-
-  
-
-  
-  
-    
-
-  
-
-  
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-    
-     
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-
-
-
-   
-
-    
-
-    
      
-      Table of Contents
-      
-    
      
-
-    
-
-    
      
-      
      
-        
      
-          
-
-
-
-
-
-
-
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-
-
-
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-
-
-
-
      
-
-  
-
-  
-
      
-        
      
-
-        
      
-          Shortcuts
-        
      
-      
      
-
-      
      
-        
      
-
-        
-          
-          
      
-          
-            
      
-             
      
-              
-  
      Source code for torch.utils.tensorboard.writer
      -"""Provides an API for writing protocol buffers to event files to be
-consumed by TensorBoard for visualization."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import six
-import time
-import torch
-
-from tensorboard.compat import tf
-from tensorboard.compat.proto.event_pb2 import SessionLog
-from tensorboard.compat.proto.event_pb2 import Event
-from tensorboard.compat.proto import event_pb2
-from tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig
-from tensorboard.summary.writer.event_file_writer import EventFileWriter
-
-from ._convert_np import make_np
-from ._embedding import (
-    make_mat, make_sprite, make_tsv, write_pbtxt, get_embedding_info,
-)
-from ._onnx_graph import load_onnx_graph
-from ._pytorch_graph import graph
-from ._utils import figure_to_image
-from .summary import (
-    scalar, histogram, histogram_raw, image, audio, text,
-    pr_curve, pr_curve_raw, video, custom_scalars, image_boxes, mesh, hparams
-)
-
-
-class FileWriter(object):
-    """Writes protocol buffers to event files to be consumed by TensorBoard.
-
-    The `FileWriter` class provides a mechanism to create an event file in a
-    given directory and add summaries and events to it. The class updates the
-    file contents asynchronously. This allows a training program to call methods
-    to add data to the file directly from the training loop, without slowing down
-    training.
-    """
-
-    def __init__(self, log_dir, max_queue=10, flush_secs=120, filename_suffix=''):
-        """Creates a `FileWriter` and an event file.
-        On construction the writer creates a new event file in `log_dir`.
-        The other arguments to the constructor control the asynchronous writes to
-        the event file.
-
-        Args:
-          log_dir: A string. Directory where event file will be written.
-          max_queue: Integer. Size of the queue for pending events and
-            summaries before one of the 'add' calls forces a flush to disk.
-            Default is ten items.
-          flush_secs: Number. How often, in seconds, to flush the
-            pending events and summaries to disk. Default is every two minutes.
-          filename_suffix: A string. Suffix added to all event filenames
-            in the log_dir directory. More details on filename construction in
-            tensorboard.summary.writer.event_file_writer.EventFileWriter.
-        """
-        # Sometimes PosixPath is passed in and we need to coerce it to
-        # a string in all cases
-        # TODO: See if we can remove this in the future if we are
-        # actually the ones passing in a PosixPath
-        log_dir = str(log_dir)
-        self.event_writer = EventFileWriter(
-            log_dir, max_queue, flush_secs, filename_suffix)
-
-    def get_logdir(self):
-        """Returns the directory where event file will be written."""
-        return self.event_writer.get_logdir()
-
-    def add_event(self, event, step=None, walltime=None):
-        """Adds an event to the event file.
-        Args:
-          event: An `Event` protocol buffer.
-          step: Number. Optional global step value for training process
-            to record with the event.
-          walltime: float. Optional walltime to override the default (current)
-            walltime (from time.time()) seconds after epoch
-        """
-        event.wall_time = time.time() if walltime is None else walltime
-        if step is not None:
-            # Make sure step is converted from numpy or other formats
-            # since protobuf might not convert depending on version
-            event.step = int(step)
-        self.event_writer.add_event(event)
-
-    def add_summary(self, summary, global_step=None, walltime=None):
-        """Adds a `Summary` protocol buffer to the event file.
-        This method wraps the provided summary in an `Event` protocol buffer
-        and adds it to the event file.
-
-        Args:
-          summary: A `Summary` protocol buffer.
-          global_step: Number. Optional global step value for training process
-            to record with the summary.
-          walltime: float. Optional walltime to override the default (current)
-            walltime (from time.time()) seconds after epoch
-        """
-        event = event_pb2.Event(summary=summary)
-        self.add_event(event, global_step, walltime)
-
-    def add_graph(self, graph_profile, walltime=None):
-        """Adds a `Graph` and step stats protocol buffer to the event file.
-
-        Args:
-          graph_profile: A `Graph` and step stats protocol buffer.
-          walltime: float. Optional walltime to override the default (current)
-            walltime (from time.time()) seconds after epoch
-        """
-        graph = graph_profile[0]
-        stepstats = graph_profile[1]
-        event = event_pb2.Event(graph_def=graph.SerializeToString())
-        self.add_event(event, None, walltime)
-
-        trm = event_pb2.TaggedRunMetadata(
-            tag='step1', run_metadata=stepstats.SerializeToString())
-        event = event_pb2.Event(tagged_run_metadata=trm)
-        self.add_event(event, None, walltime)
-
-    def add_onnx_graph(self, graph, walltime=None):
-        """Adds a `Graph` protocol buffer to the event file.
-
-        Args:
-          graph: A `Graph` protocol buffer.
-          walltime: float. Optional walltime to override the default (current)
-            _get_file_writerfrom time.time())
-        """
-        event = event_pb2.Event(graph_def=graph.SerializeToString())
-        self.add_event(event, None, walltime)
-
-    def flush(self):
-        """Flushes the event file to disk.
-        Call this method to make sure that all pending events have been written to
-        disk.
-        """
-        self.event_writer.flush()
-
-    def close(self):
-        """Flushes the event file to disk and close the file.
-        Call this method when you do not need the summary writer anymore.
-        """
-        self.event_writer.close()
-
-    def reopen(self):
-        """Reopens the EventFileWriter.
-        Can be called after `close()` to add more events in the same directory.
-        The events will go into a new events file.
-        Does nothing if the EventFileWriter was not closed.
-        """
-        self.event_writer.reopen()
-
-
-
      [docs]class SummaryWriter(object):
-    """Writes entries directly to event files in the log_dir to be
-    consumed by TensorBoard.
-
-    The `SummaryWriter` class provides a high-level API to create an event file
-    in a given directory and add summaries and events to it. The class updates the
-    file contents asynchronously. This allows a training program to call methods
-    to add data to the file directly from the training loop, without slowing down
-    training.
-    """
-
-
      [docs]    def __init__(self, log_dir=None, comment='', purge_step=None, max_queue=10,
-                 flush_secs=120, filename_suffix=''):
-        """Creates a `SummaryWriter` that will write out events and summaries
-        to the event file.
-
-        Args:
-            log_dir (string): Save directory location. Default is
-              runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.
-              Use hierarchical folder structure to compare
-              between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.
-              for each new experiment to compare across them.
-            comment (string): Comment log_dir suffix appended to the default
-              ``log_dir``. If ``log_dir`` is assigned, this argument has no effect.
-            purge_step (int):
-              When logging crashes at step :math:`T+X` and restarts at step :math:`T`,
-              any events whose global_step larger or equal to :math:`T` will be
-              purged and hidden from TensorBoard.
-              Note that crashed and resumed experiments should have the same ``log_dir``.
-            max_queue (int): Size of the queue for pending events and
-              summaries before one of the 'add' calls forces a flush to disk.
-              Default is ten items.
-            flush_secs (int): How often, in seconds, to flush the
-              pending events and summaries to disk. Default is every two minutes.
-            filename_suffix (string): Suffix added to all event filenames in
-              the log_dir directory. More details on filename construction in
-              tensorboard.summary.writer.event_file_writer.EventFileWriter.
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-
-            # create a summary writer with automatically generated folder name.
-            writer = SummaryWriter()
-            # folder location: runs/May04_22-14-54_s-MacBook-Pro.local/
-
-            # create a summary writer using the specified folder name.
-            writer = SummaryWriter("my_experiment")
-            # folder location: my_experiment
-
-            # create a summary writer with comment appended.
-            writer = SummaryWriter(comment="LR_0.1_BATCH_16")
-            # folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/
-
-        """
-        torch._C._log_api_usage_once("tensorboard.create.summarywriter")
-        if not log_dir:
-            import socket
-            from datetime import datetime
-            current_time = datetime.now().strftime('%b%d_%H-%M-%S')
-            log_dir = os.path.join(
-                'runs', current_time + '_' + socket.gethostname() + comment)
-        self.log_dir = log_dir
-        self.purge_step = purge_step
-        self.max_queue = max_queue
-        self.flush_secs = flush_secs
-        self.filename_suffix = filename_suffix
-
-        # Initialize the file writers, but they can be cleared out on close
-        # and recreated later as needed.
-        self.file_writer = self.all_writers = None
-        self._get_file_writer()
-
-        # Create default bins for histograms, see generate_testdata.py in tensorflow/tensorboard
-        v = 1E-12
-        buckets = []
-        neg_buckets = []
-        while v < 1E20:
-            buckets.append(v)
-            neg_buckets.append(-v)
-            v *= 1.1
-        self.default_bins = neg_buckets[::-1] + [0] + buckets
      
-
-    def _check_caffe2_blob(self, item):
-        """
-        Caffe2 users have the option of passing a string representing the name of
-        a blob in the workspace instead of passing the actual Tensor/array containing
-        the numeric values. Thus, we need to check if we received a string as input
-        instead of an actual Tensor/array, and if so, we need to fetch the Blob
-        from the workspace corresponding to that name. Fetching can be done with the
-        following:
-
-        from caffe2.python import workspace (if not already imported)
-        workspace.FetchBlob(blob_name)
-        workspace.FetchBlobs([blob_name1, blob_name2, ...])
-        """
-        return isinstance(item, six.string_types)
-
-    def _get_file_writer(self):
-        """Returns the default FileWriter instance. Recreates it if closed."""
-        if self.all_writers is None or self.file_writer is None:
-            self.file_writer = FileWriter(self.log_dir, self.max_queue,
-                                          self.flush_secs, self.filename_suffix)
-            self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
-            if self.purge_step is not None:
-                most_recent_step = self.purge_step
-                self.file_writer.add_event(
-                    Event(step=most_recent_step, file_version='brain.Event:2'))
-                self.file_writer.add_event(
-                    Event(step=most_recent_step, session_log=SessionLog(status=SessionLog.START)))
-                self.purge_step = None
-        return self.file_writer
-
-    def get_logdir(self):
-        """Returns the directory where event files will be written."""
-        return self.log_dir
-
-
      [docs]    def add_hparams(self, hparam_dict, metric_dict):
-        """Add a set of hyperparameters to be compared in TensorBoard.
-
-        Args:
-            hparam_dict (dict): Each key-value pair in the dictionary is the
-              name of the hyper parameter and it's corresponding value.
-              The type of the value can be one of `bool`, `string`, `float`,
-              `int`, or `None`.
-            metric_dict (dict): Each key-value pair in the dictionary is the
-              name of the metric and it's corresponding value. Note that the key used
-              here should be unique in the tensorboard record. Otherwise the value
-              you added by ``add_scalar`` will be displayed in hparam plugin. In most
-              cases, this is unwanted.
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            with SummaryWriter() as w:
-                for i in range(5):
-                    w.add_hparams({'lr': 0.1*i, 'bsize': i},
-                                  {'hparam/accuracy': 10*i, 'hparam/loss': 10*i})
-
-        Expected result:
-
-        .. image:: _static/img/tensorboard/add_hparam.png
-           :scale: 50 %
-
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
-        if type(hparam_dict) is not dict or type(metric_dict) is not dict:
-            raise TypeError('hparam_dict and metric_dict should be dictionary.')
-        exp, ssi, sei = hparams(hparam_dict, metric_dict)
-
-        logdir = os.path.join(
-            self._get_file_writer().get_logdir(),
-            str(time.time())
-        )
-        with SummaryWriter(log_dir=logdir) as w_hp:
-            w_hp.file_writer.add_summary(exp)
-            w_hp.file_writer.add_summary(ssi)
-            w_hp.file_writer.add_summary(sei)
-            for k, v in metric_dict.items():
-                w_hp.add_scalar(k, v)
      
-
-
      [docs]    def add_scalar(self, tag, scalar_value, global_step=None, walltime=None):
-        """Add scalar data to summary.
-
-        Args:
-            tag (string): Data identifier
-            scalar_value (float or string/blobname): Value to save
-            global_step (int): Global step value to record
-            walltime (float): Optional override default walltime (time.time())
-              with seconds after epoch of event
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            writer = SummaryWriter()
-            x = range(100)
-            for i in x:
-                writer.add_scalar('y=2x', i * 2, i)
-            writer.close()
-
-        Expected result:
-
-        .. image:: _static/img/tensorboard/add_scalar.png
-           :scale: 50 %
-
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_scalar")
-        if self._check_caffe2_blob(scalar_value):
-            scalar_value = workspace.FetchBlob(scalar_value)
-        self._get_file_writer().add_summary(
-            scalar(tag, scalar_value), global_step, walltime)
      
-
-
      [docs]    def add_scalars(self, main_tag, tag_scalar_dict, global_step=None, walltime=None):
-        """Adds many scalar data to summary.
-
-        Args:
-            main_tag (string): The parent name for the tags
-            tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values
-            global_step (int): Global step value to record
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            writer = SummaryWriter()
-            r = 5
-            for i in range(100):
-                writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r),
-                                                'xcosx':i*np.cos(i/r),
-                                                'tanx': np.tan(i/r)}, i)
-            writer.close()
-            # This call adds three values to the same scalar plot with the tag
-            # 'run_14h' in TensorBoard's scalar section.
-
-        Expected result:
-
-        .. image:: _static/img/tensorboard/add_scalars.png
-           :scale: 50 %
-
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_scalars")
-        walltime = time.time() if walltime is None else walltime
-        fw_logdir = self._get_file_writer().get_logdir()
-        for tag, scalar_value in tag_scalar_dict.items():
-            fw_tag = fw_logdir + "/" + main_tag.replace("/", "_") + "_" + tag
-            if fw_tag in self.all_writers.keys():
-                fw = self.all_writers[fw_tag]
-            else:
-                fw = FileWriter(fw_tag, self.max_queue, self.flush_secs,
-                                self.filename_suffix)
-                self.all_writers[fw_tag] = fw
-            if self._check_caffe2_blob(scalar_value):
-                scalar_value = workspace.FetchBlob(scalar_value)
-            fw.add_summary(scalar(main_tag, scalar_value),
-                           global_step, walltime)
      
-
-
      [docs]    def add_histogram(self, tag, values, global_step=None, bins='tensorflow', walltime=None, max_bins=None):
-        """Add histogram to summary.
-
-        Args:
-            tag (string): Data identifier
-            values (torch.Tensor, numpy.array, or string/blobname): Values to build histogram
-            global_step (int): Global step value to record
-            bins (string): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find
-              other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            import numpy as np
-            writer = SummaryWriter()
-            for i in range(10):
-                x = np.random.random(1000)
-                writer.add_histogram('distribution centers', x + i, i)
-            writer.close()
-
-        Expected result:
-
-        .. image:: _static/img/tensorboard/add_histogram.png
-           :scale: 50 %
-
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_histogram")
-        if self._check_caffe2_blob(values):
-            values = workspace.FetchBlob(values)
-        if isinstance(bins, six.string_types) and bins == 'tensorflow':
-            bins = self.default_bins
-        self._get_file_writer().add_summary(
-            histogram(tag, values, bins, max_bins=max_bins), global_step, walltime)
      
-
-    def add_histogram_raw(self, tag, min, max, num, sum, sum_squares,
-                          bucket_limits, bucket_counts, global_step=None,
-                          walltime=None):
-        """Adds histogram with raw data.
-
-        Args:
-            tag (string): Data identifier
-            min (float or int): Min value
-            max (float or int): Max value
-            num (int): Number of values
-            sum (float or int): Sum of all values
-            sum_squares (float or int): Sum of squares for all values
-            bucket_limits (torch.Tensor, numpy.array): Upper value per bucket.
-              The number of elements of it should be the same as `bucket_counts`.
-            bucket_counts (torch.Tensor, numpy.array): Number of values per bucket
-            global_step (int): Global step value to record
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-            see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/README.md
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            import numpy as np
-            writer = SummaryWriter()
-            dummy_data = []
-            for idx, value in enumerate(range(50)):
-                dummy_data += [idx + 0.001] * value
-
-            bins = list(range(50+2))
-            bins = np.array(bins)
-            values = np.array(dummy_data).astype(float).reshape(-1)
-            counts, limits = np.histogram(values, bins=bins)
-            sum_sq = values.dot(values)
-            writer.add_histogram_raw(
-                tag='histogram_with_raw_data',
-                min=values.min(),
-                max=values.max(),
-                num=len(values),
-                sum=values.sum(),
-                sum_squares=sum_sq,
-                bucket_limits=limits[1:].tolist(),
-                bucket_counts=counts.tolist(),
-                global_step=0)
-            writer.close()
-
-        Expected result:
-
-        .. image:: _static/img/tensorboard/add_histogram_raw.png
-           :scale: 50 %
-
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_histogram_raw")
-        if len(bucket_limits) != len(bucket_counts):
-            raise ValueError('len(bucket_limits) != len(bucket_counts), see the document.')
-        self._get_file_writer().add_summary(
-            histogram_raw(tag,
-                          min,
-                          max,
-                          num,
-                          sum,
-                          sum_squares,
-                          bucket_limits,
-                          bucket_counts),
-            global_step,
-            walltime)
-
-
      [docs]    def add_image(self, tag, img_tensor, global_step=None, walltime=None, dataformats='CHW'):
-        """Add image data to summary.
-
-        Note that this requires the ``pillow`` package.
-
-        Args:
-            tag (string): Data identifier
-            img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
-            global_step (int): Global step value to record
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-        Shape:
-            img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
-            convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.
-            Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as
-            corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            import numpy as np
-            img = np.zeros((3, 100, 100))
-            img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
-            img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
-
-            img_HWC = np.zeros((100, 100, 3))
-            img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
-            img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
-
-            writer = SummaryWriter()
-            writer.add_image('my_image', img, 0)
-
-            # If you have non-default dimension setting, set the dataformats argument.
-            writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
-            writer.close()
-
-        Expected result:
-
-        .. image:: _static/img/tensorboard/add_image.png
-           :scale: 50 %
-
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_image")
-        if self._check_caffe2_blob(img_tensor):
-            img_tensor = workspace.FetchBlob(img_tensor)
-        self._get_file_writer().add_summary(
-            image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
      
-
-
      [docs]    def add_images(self, tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW'):
-        """Add batched image data to summary.
-
-        Note that this requires the ``pillow`` package.
-
-        Args:
-            tag (string): Data identifier
-            img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
-            global_step (int): Global step value to record
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-            dataformats (string): Image data format specification of the form
-              NCHW, NHWC, CHW, HWC, HW, WH, etc.
-        Shape:
-            img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be
-            accepted. e.g. NCHW or NHWC.
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            import numpy as np
-
-            img_batch = np.zeros((16, 3, 100, 100))
-            for i in range(16):
-                img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
-                img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i
-
-            writer = SummaryWriter()
-            writer.add_images('my_image_batch', img_batch, 0)
-            writer.close()
-
-        Expected result:
-
-        .. image:: _static/img/tensorboard/add_images.png
-           :scale: 30 %
-
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_images")
-        if self._check_caffe2_blob(img_tensor):
-            img_tensor = workspace.FetchBlob(img_tensor)
-        self._get_file_writer().add_summary(
-            image(tag, img_tensor, dataformats=dataformats), global_step, walltime)
      
-
-    def add_image_with_boxes(self, tag, img_tensor, box_tensor, global_step=None,
-                             walltime=None, rescale=1, dataformats='CHW', labels=None):
-        """Add image and draw bounding boxes on the image.
-
-        Args:
-            tag (string): Data identifier
-            img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
-            box_tensor (torch.Tensor, numpy.array, or string/blobname): Box data (for detected objects)
-              box should be represented as [x1, y1, x2, y2].
-            global_step (int): Global step value to record
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-            rescale (float): Optional scale override
-            dataformats (string): Image data format specification of the form
-              NCHW, NHWC, CHW, HWC, HW, WH, etc.
-            labels (list of string): The label to be shown for each bounding box.
-        Shape:
-            img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformats`` argument.
-            e.g. CHW or HWC
-
-            box_tensor: (torch.Tensor, numpy.array, or string/blobname): NX4,  where N is the number of
-            boxes and each 4 elememts in a row represents (xmin, ymin, xmax, ymax).
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_image_with_boxes")
-        if self._check_caffe2_blob(img_tensor):
-            img_tensor = workspace.FetchBlob(img_tensor)
-        if self._check_caffe2_blob(box_tensor):
-            box_tensor = workspace.FetchBlob(box_tensor)
-        if labels is not None:
-            if isinstance(labels, str):
-                labels = [labels]
-            if len(labels) != box_tensor.shape[0]:
-                labels = None
-        self._get_file_writer().add_summary(image_boxes(
-            tag, img_tensor, box_tensor, rescale=rescale, dataformats=dataformats, labels=labels), global_step, walltime)
-
-
      [docs]    def add_figure(self, tag, figure, global_step=None, close=True, walltime=None):
-        """Render matplotlib figure into an image and add it to summary.
-
-        Note that this requires the ``matplotlib`` package.
-
-        Args:
-            tag (string): Data identifier
-            figure (matplotlib.pyplot.figure) or list of figures: Figure or a list of figures
-            global_step (int): Global step value to record
-            close (bool): Flag to automatically close the figure
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_figure")
-        if isinstance(figure, list):
-            self.add_image(tag, figure_to_image(figure, close), global_step, walltime, dataformats='NCHW')
-        else:
-            self.add_image(tag, figure_to_image(figure, close), global_step, walltime, dataformats='CHW')
      
-
-
      [docs]    def add_video(self, tag, vid_tensor, global_step=None, fps=4, walltime=None):
-        """Add video data to summary.
-
-        Note that this requires the ``moviepy`` package.
-
-        Args:
-            tag (string): Data identifier
-            vid_tensor (torch.Tensor): Video data
-            global_step (int): Global step value to record
-            fps (float or int): Frames per second
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-        Shape:
-            vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_video")
-        self._get_file_writer().add_summary(
-            video(tag, vid_tensor, fps), global_step, walltime)
      
-
-
      [docs]    def add_audio(self, tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None):
-        """Add audio data to summary.
-
-        Args:
-            tag (string): Data identifier
-            snd_tensor (torch.Tensor): Sound data
-            global_step (int): Global step value to record
-            sample_rate (int): sample rate in Hz
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-        Shape:
-            snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1].
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_audio")
-        if self._check_caffe2_blob(snd_tensor):
-            snd_tensor = workspace.FetchBlob(snd_tensor)
-        self._get_file_writer().add_summary(
-            audio(tag, snd_tensor, sample_rate=sample_rate), global_step, walltime)
      
-
-
      [docs]    def add_text(self, tag, text_string, global_step=None, walltime=None):
-        """Add text data to summary.
-
-        Args:
-            tag (string): Data identifier
-            text_string (string): String to save
-            global_step (int): Global step value to record
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-        Examples::
-
-            writer.add_text('lstm', 'This is an lstm', 0)
-            writer.add_text('rnn', 'This is an rnn', 10)
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_text")
-        self._get_file_writer().add_summary(
-            text(tag, text_string), global_step, walltime)
      
-
-    def add_onnx_graph(self, prototxt):
-        torch._C._log_api_usage_once("tensorboard.logging.add_onnx_graph")
-        self._get_file_writer().add_onnx_graph(load_onnx_graph(prototxt))
-
-
      [docs]    def add_graph(self, model, input_to_model=None, verbose=False):
-        # prohibit second call?
-        # no, let tensorboard handle it and show its warning message.
-        """Add graph data to summary.
-
-        Args:
-            model (torch.nn.Module): Model to draw.
-            input_to_model (torch.Tensor or list of torch.Tensor): A variable or a tuple of
-                variables to be fed.
-            verbose (bool): Whether to print graph structure in console.
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_graph")
-        if hasattr(model, 'forward'):
-            # A valid PyTorch model should have a 'forward' method
-            self._get_file_writer().add_graph(graph(model, input_to_model, verbose))
-        else:
-            # Caffe2 models do not have the 'forward' method
-            from caffe2.proto import caffe2_pb2
-            from caffe2.python import core
-            from ._caffe2_graph import (
-                model_to_graph_def, nets_to_graph_def, protos_to_graph_def
-            )
-            if isinstance(model, list):
-                if isinstance(model[0], core.Net):
-                    current_graph = nets_to_graph_def(model)
-                elif isinstance(model[0], caffe2_pb2.NetDef):
-                    current_graph = protos_to_graph_def(model)
-            else:
-                # Handles cnn.CNNModelHelper, model_helper.ModelHelper
-                current_graph = model_to_graph_def(model)
-            event = event_pb2.Event(
-                graph_def=current_graph.SerializeToString())
-            self._get_file_writer().add_event(event)
      
-
-    @staticmethod
-    def _encode(rawstr):
-        # I'd use urllib but, I'm unsure about the differences from python3 to python2, etc.
-        retval = rawstr
-        retval = retval.replace("%", "%%%02x" % (ord("%")))
-        retval = retval.replace("/", "%%%02x" % (ord("/")))
-        retval = retval.replace("\\", "%%%02x" % (ord("\\")))
-        return retval
-
-
      [docs]    def add_embedding(self, mat, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None):
-        """Add embedding projector data to summary.
-
-        Args:
-            mat (torch.Tensor or numpy.array): A matrix which each row is the feature vector of the data point
-            metadata (list): A list of labels, each element will be convert to string
-            label_img (torch.Tensor): Images correspond to each data point
-            global_step (int): Global step value to record
-            tag (string): Name for the embedding
-        Shape:
-            mat: :math:`(N, D)`, where N is number of data and D is feature dimension
-
-            label_img: :math:`(N, C, H, W)`
-
-        Examples::
-
-            import keyword
-            import torch
-            meta = []
-            while len(meta)<100:
-                meta = meta+keyword.kwlist # get some strings
-            meta = meta[:100]
-
-            for i, v in enumerate(meta):
-                meta[i] = v+str(i)
-
-            label_img = torch.rand(100, 3, 10, 32)
-            for i in range(100):
-                label_img[i]*=i/100.0
-
-            writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
-            writer.add_embedding(torch.randn(100, 5), label_img=label_img)
-            writer.add_embedding(torch.randn(100, 5), metadata=meta)
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_embedding")
-        mat = make_np(mat)
-        if global_step is None:
-            global_step = 0
-            # clear pbtxt?
-
-        # Maybe we should encode the tag so slashes don't trip us up?
-        # I don't think this will mess us up, but better safe than sorry.
-        subdir = "%s/%s" % (str(global_step).zfill(5), self._encode(tag))
-        save_path = os.path.join(self._get_file_writer().get_logdir(), subdir)
-
-        fs = tf.io.gfile.get_filesystem(save_path)
-        if fs.exists(save_path):
-            if fs.isdir(save_path):
-                print(
-                    'warning: Embedding dir exists, did you set global_step for add_embedding()?')
-            else:
-                raise Exception("Path: `%s` exists, but is a file. Cannot proceed." % save_path)
-        else:
-            fs.makedirs(save_path)
-
-        if metadata is not None:
-            assert mat.shape[0] == len(
-                metadata), '#labels should equal with #data points'
-            make_tsv(metadata, save_path, metadata_header=metadata_header)
-
-        if label_img is not None:
-            assert mat.shape[0] == label_img.shape[0], '#images should equal with #data points'
-            make_sprite(label_img, save_path)
-
-        assert mat.ndim == 2, 'mat should be 2D, where mat.size(0) is the number of data points'
-        make_mat(mat, save_path)
-
-        # Filesystem doesn't necessarily have append semantics, so we store an
-        # internal buffer to append to and re-write whole file after each
-        # embedding is added
-        if not hasattr(self, "_projector_config"):
-            self._projector_config = ProjectorConfig()
-        embedding_info = get_embedding_info(
-            metadata, label_img, fs, subdir, global_step, tag)
-        self._projector_config.embeddings.extend([embedding_info])
-
-        from google.protobuf import text_format
-        config_pbtxt = text_format.MessageToString(self._projector_config)
-        write_pbtxt(self._get_file_writer().get_logdir(), config_pbtxt)
      
-
-
-
      [docs]    def add_pr_curve(self, tag, labels, predictions, global_step=None,
-                     num_thresholds=127, weights=None, walltime=None):
-        """Adds precision recall curve.
-        Plotting a precision-recall curve lets you understand your model's
-        performance under different threshold settings. With this function,
-        you provide the ground truth labeling (T/F) and prediction confidence
-        (usually the output of your model) for each target. The TensorBoard UI
-        will let you choose the threshold interactively.
-
-        Args:
-            tag (string): Data identifier
-            labels (torch.Tensor, numpy.array, or string/blobname):
-              Ground truth data. Binary label for each element.
-            predictions (torch.Tensor, numpy.array, or string/blobname):
-              The probability that an element be classified as true.
-              Value should in [0, 1]
-            global_step (int): Global step value to record
-            num_thresholds (int): Number of thresholds used to draw the curve.
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            import numpy as np
-            labels = np.random.randint(2, size=100)  # binary label
-            predictions = np.random.rand(100)
-            writer = SummaryWriter()
-            writer.add_pr_curve('pr_curve', labels, predictions, 0)
-            writer.close()
-
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve")
-        labels, predictions = make_np(labels), make_np(predictions)
-        self._get_file_writer().add_summary(
-            pr_curve(tag, labels, predictions, num_thresholds, weights),
-            global_step, walltime)
      
-
-    def add_pr_curve_raw(self, tag, true_positive_counts,
-                         false_positive_counts,
-                         true_negative_counts,
-                         false_negative_counts,
-                         precision,
-                         recall,
-                         global_step=None,
-                         num_thresholds=127,
-                         weights=None,
-                         walltime=None):
-        """Adds precision recall curve with raw data.
-
-        Args:
-            tag (string): Data identifier
-            true_positive_counts (torch.Tensor, numpy.array, or string/blobname): true positive counts
-            false_positive_counts (torch.Tensor, numpy.array, or string/blobname): false positive counts
-            true_negative_counts (torch.Tensor, numpy.array, or string/blobname): true negative counts
-            false_negative_counts (torch.Tensor, numpy.array, or string/blobname): false negative counts
-            precision (torch.Tensor, numpy.array, or string/blobname): precision
-            recall (torch.Tensor, numpy.array, or string/blobname): recall
-            global_step (int): Global step value to record
-            num_thresholds (int): Number of thresholds used to draw the curve.
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-            see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve_raw")
-        self._get_file_writer().add_summary(
-            pr_curve_raw(tag,
-                         true_positive_counts,
-                         false_positive_counts,
-                         true_negative_counts,
-                         false_negative_counts,
-                         precision,
-                         recall,
-                         num_thresholds,
-                         weights),
-            global_step,
-            walltime)
-
-    def add_custom_scalars_multilinechart(self, tags, category='default', title='untitled'):
-        """Shorthand for creating multilinechart. Similar to ``add_custom_scalars()``, but the only necessary argument
-        is *tags*.
-
-        Args:
-            tags (list): list of tags that have been used in ``add_scalar()``
-
-        Examples::
-
-            writer.add_custom_scalars_multilinechart(['twse/0050', 'twse/2330'])
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars_multilinechart")
-        layout = {category: {title: ['Multiline', tags]}}
-        self._get_file_writer().add_summary(custom_scalars(layout))
-
-    def add_custom_scalars_marginchart(self, tags, category='default', title='untitled'):
-        """Shorthand for creating marginchart. Similar to ``add_custom_scalars()``, but the only necessary argument
-        is *tags*, which should have exactly 3 elements.
-
-        Args:
-            tags (list): list of tags that have been used in ``add_scalar()``
-
-        Examples::
-
-            writer.add_custom_scalars_marginchart(['twse/0050', 'twse/2330', 'twse/2006'])
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars_marginchart")
-        assert len(tags) == 3
-        layout = {category: {title: ['Margin', tags]}}
-        self._get_file_writer().add_summary(custom_scalars(layout))
-
-
      [docs]    def add_custom_scalars(self, layout):
-        """Create special chart by collecting charts tags in 'scalars'. Note that this function can only be called once
-        for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called
-        before or after the training loop.
-
-        Args:
-            layout (dict): {categoryName: *charts*}, where *charts* is also a dictionary
-              {chartName: *ListOfProperties*}. The first element in *ListOfProperties* is the chart's type
-              (one of **Multiline** or **Margin**) and the second element should be a list containing the tags
-              you have used in add_scalar function, which will be collected into the new chart.
-
-        Examples::
-
-            layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]},
-                         'USA':{ 'dow':['Margin',   ['dow/aaa', 'dow/bbb', 'dow/ccc']],
-                              'nasdaq':['Margin',   ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
-
-            writer.add_custom_scalars(layout)
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars")
-        self._get_file_writer().add_summary(custom_scalars(layout))
      
-
-
      [docs]    def add_mesh(self, tag, vertices, colors=None, faces=None, config_dict=None, global_step=None, walltime=None):
-        """Add meshes or 3D point clouds to TensorBoard. The visualization is based on Three.js,
-        so it allows users to interact with the rendered object. Besides the basic definitions
-        such as vertices, faces, users can further provide camera parameter, lighting condition, etc.
-        Please check https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene for
-        advanced usage.
-
-        Args:
-            tag (string): Data identifier
-            vertices (torch.Tensor): List of the 3D coordinates of vertices.
-            colors (torch.Tensor): Colors for each vertex
-            faces (torch.Tensor): Indices of vertices within each triangle. (Optional)
-            config_dict: Dictionary with ThreeJS classes names and configuration.
-            global_step (int): Global step value to record
-            walltime (float): Optional override default walltime (time.time())
-              seconds after epoch of event
-
-        Shape:
-            vertices: :math:`(B, N, 3)`. (batch, number_of_vertices, channels)
-
-            colors: :math:`(B, N, 3)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
-
-            faces: :math:`(B, N, 3)`. The values should lie in [0, number_of_vertices] for type `uint8`.
-
-        Examples::
-
-            from torch.utils.tensorboard import SummaryWriter
-            vertices_tensor = torch.as_tensor([
-                [1, 1, 1],
-                [-1, -1, 1],
-                [1, -1, -1],
-                [-1, 1, -1],
-            ], dtype=torch.float).unsqueeze(0)
-            colors_tensor = torch.as_tensor([
-                [255, 0, 0],
-                [0, 255, 0],
-                [0, 0, 255],
-                [255, 0, 255],
-            ], dtype=torch.int).unsqueeze(0)
-            faces_tensor = torch.as_tensor([
-                [0, 2, 3],
-                [0, 3, 1],
-                [0, 1, 2],
-                [1, 3, 2],
-            ], dtype=torch.int).unsqueeze(0)
-
-            writer = SummaryWriter()
-            writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor)
-
-            writer.close()
-        """
-        torch._C._log_api_usage_once("tensorboard.logging.add_mesh")
-        self._get_file_writer().add_summary(mesh(tag, vertices, colors, faces, config_dict), global_step, walltime)
      
-
-
      [docs]    def flush(self):
-        """Flushes the event file to disk.
-        Call this method to make sure that all pending events have been written to
-        disk.
-        """
-        if self.all_writers is None:
-            return
-        for writer in self.all_writers.values():
-            writer.flush()
      
-
-
      [docs]    def close(self):
-        if self.all_writers is None:
-            return  # ignore double close
-        for writer in self.all_writers.values():
-            writer.flush()
-            writer.close()
-        self.file_writer = self.all_writers = None
      
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.close()
      
-
      
-
-             
      
-             
-            
      
-            
      
-  
-
-  
-
-    
      
-
-  
-
-  
      
-    
      
-        © Copyright 2019, Torch Contributors.
-
-    
      
-  
      
-    
-      
      
-        Built with Sphinx using a theme provided by Read the Docs.
-      
      
-     
-
-
      
-
-          
      
-        
      
-
-        
      
-          
      
-            
      
-              
-            
      
-          
      
-        
      
-      
      
-    
      
-
-  
-
-
-  
-
-     
-       
-         
-         
-         
-         
-     
-
-  
-
-  
-  
-  
-  
-
-  
- 
-
-
-
-
-
-
-
-
-
-  
-
-  
      
-    
      
-      
      
-        
      
-          
      Docs
      
-          
      Access comprehensive developer documentation for PyTorch
      
-          View Docs
-        
      
-
-        
      
-          
      Tutorials
      
-          
      Get in-depth tutorials for beginners and advanced developers
      
-          View Tutorials
-        
      
-
-        
      
-          
      Resources
      
-          
      Find development resources and get your questions answered
      
-          View Resources
-        
      
-      
      
-    
      
-  
      
-
-  
-
-  
      
-  
      
-    
      To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.
      
-    
-  
      
-
      
-
-  
-
-  
-
-  
      
-    
      
-      
      
-        
      
-          
-          
-        
      
-      
      
-    
      
-
-    
      
-      
      
-        
-      
      
-    
      
-  
      
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-  
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-
-
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diff --git a/docs/stable/_modules/torchvision/ops/boxes.html b/docs/stable/_modules/torchvision/ops/boxes.html
index 06449403875d..d6b76af74e3c 100644
--- a/docs/stable/_modules/torchvision/ops/boxes.html
+++ b/docs/stable/_modules/torchvision/ops/boxes.html
@@ -341,7 +341,8 @@ 
      Source code for torchvision.ops.boxes
       import torchvision
 
 
-
      [docs]def nms(boxes: Tensor, scores: Tensor, iou_threshold: float) -> Tensor:
+
      [docs]def nms(boxes, scores, iou_threshold):
+    # type: (Tensor, Tensor, float) -> Tensor
     """
     Performs non-maximum suppression (NMS) on the boxes according
     to their intersection-over-union (IoU).
@@ -377,12 +378,8 @@ 
      Source code for torchvision.ops.boxes
       
 
 @torch.jit._script_if_tracing
-def batched_nms(
-    boxes: Tensor,
-    scores: Tensor,
-    idxs: Tensor,
-    iou_threshold: float,
-) -> Tensor:
+def batched_nms(boxes, scores, idxs, iou_threshold):
+    # type: (Tensor, Tensor, Tensor, float) -> Tensor
     """
     Performs non-maximum suppression in a batched fashion.
 
@@ -423,7 +420,8 @@ 
      Source code for torchvision.ops.boxes
               return keep
 
 
-def remove_small_boxes(boxes: Tensor, min_size: float) -> Tensor:
+def remove_small_boxes(boxes, min_size):
+    # type: (Tensor, float) -> Tensor
     """
     Remove boxes which contains at least one side smaller than min_size.
 
@@ -441,7 +439,8 @@ 
      Source code for torchvision.ops.boxes
           return keep
 
 
-def clip_boxes_to_image(boxes: Tensor, size: Tuple[int, int]) -> Tensor:
+def clip_boxes_to_image(boxes, size):
+    # type: (Tensor, Tuple[int, int]) -> Tensor
     """
     Clip boxes so that they lie inside an image of size `size`.
 
@@ -470,7 +469,7 @@ 
      Source code for torchvision.ops.boxes
           return clipped_boxes.reshape(boxes.shape)
 
 
-def box_area(boxes: Tensor) -> Tensor:
+def box_area(boxes):
     """
     Computes the area of a set of bounding boxes, which are specified by its
     (x1, y1, x2, y2) coordinates.
@@ -487,7 +486,7 @@ 
      Source code for torchvision.ops.boxes
       
 # implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
 # with slight modifications
-def box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
+def box_iou(boxes1, boxes2):
     """
     Return intersection-over-union (Jaccard index) of boxes.
 
diff --git a/docs/stable/_modules/torchvision/ops/deform_conv.html b/docs/stable/_modules/torchvision/ops/deform_conv.html
index faf654abce99..070a7493ed72 100644
--- a/docs/stable/_modules/torchvision/ops/deform_conv.html
+++ b/docs/stable/_modules/torchvision/ops/deform_conv.html
@@ -345,15 +345,8 @@ 
      Source code for torchvision.ops.deform_conv
       from torch.jit.annotations import Optional, Tuple
 
 
-
      [docs]def deform_conv2d(
-    input: Tensor,
-    offset: Tensor,
-    weight: Tensor,
-    bias: Optional[Tensor] = None,
-    stride: Tuple[int, int] = (1, 1),
-    padding: Tuple[int, int] = (0, 0),
-    dilation: Tuple[int, int] = (1, 1),
-) -> Tensor:
+
      [docs]def deform_conv2d(input, offset, weight, bias=None, stride=(1, 1), padding=(0, 0), dilation=(1, 1)):
+    # type: (Tensor, Tensor, Tensor, Optional[Tensor], Tuple[int, int], Tuple[int, int], Tuple[int, int]) -> Tensor
     """
     Performs Deformable Convolution, described in Deformable Convolutional Networks
 
@@ -424,17 +417,8 @@ 
      Source code for torchvision.ops.deform_conv
           """
     See deform_conv2d
     """
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        kernel_size: int,
-        stride: int = 1,
-        padding: int = 0,
-        dilation: int = 1,
-        groups: int = 1,
-        bias: bool = True,
-    ):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0,
+                 dilation=1, groups=1, bias=True):
         super(DeformConv2d, self).__init__()
 
         if in_channels % groups != 0:
@@ -460,14 +444,14 @@ 
      Source code for torchvision.ops.deform_conv
       
         self.reset_parameters()
 
-    def reset_parameters(self) -> None:
+    def reset_parameters(self):
         init.kaiming_uniform_(self.weight, a=math.sqrt(5))
         if self.bias is not None:
             fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
             bound = 1 / math.sqrt(fan_in)
             init.uniform_(self.bias, -bound, bound)
 
-    def forward(self, input: Tensor, offset: Tensor) -> Tensor:
+    def forward(self, input, offset):
         """
         Arguments:
             input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
@@ -478,7 +462,7 @@ 
      Source code for torchvision.ops.deform_conv
               return deform_conv2d(input, offset, self.weight, self.bias, stride=self.stride,
                              padding=self.padding, dilation=self.dilation)
 
-    def __repr__(self) -> str:
+    def __repr__(self):
         s = self.__class__.__name__ + '('
         s += '{in_channels}'
         s += ', {out_channels}'
diff --git a/docs/stable/_modules/torchvision/ops/feature_pyramid_network.html b/docs/stable/_modules/torchvision/ops/feature_pyramid_network.html
index aed145365a05..6f28aa49dc69 100644
--- a/docs/stable/_modules/torchvision/ops/feature_pyramid_network.html
+++ b/docs/stable/_modules/torchvision/ops/feature_pyramid_network.html
@@ -341,31 +341,7 @@ 
      Source code for torchvision.ops.feature_pyramid_network
      import torch.nn.functional as F
 from torch import nn, Tensor
 
-from torch.jit.annotations import Tuple, List, Dict, Optional
-
-
-class ExtraFPNBlock(nn.Module):
-    """
-    Base class for the extra block in the FPN.
-
-    Arguments:
-        results (List[Tensor]): the result of the FPN
-        x (List[Tensor]): the original feature maps
-        names (List[str]): the names for each one of the
-            original feature maps
-
-    Returns:
-        results (List[Tensor]): the extended set of results
-            of the FPN
-        names (List[str]): the extended set of names for the results
-    """
-    def forward(
-        self,
-        results: List[Tensor],
-        x: List[Tensor],
-        names: List[str],
-    ) -> Tuple[List[Tensor], List[str]]:
-        pass
+from torch.jit.annotations import Tuple, List, Dict
 
 
 
      [docs]class FeaturePyramidNetwork(nn.Module):
@@ -405,12 +381,7 @@ 
      Source code for torchvision.ops.feature_pyramid_network
              >>>    ('feat3', torch.Size([1, 5, 8, 8]))]
 
     """
-    def __init__(
-        self,
-        in_channels_list: List[int],
-        out_channels: int,
-        extra_blocks: Optional[ExtraFPNBlock] = None,
-    ):
+    def __init__(self, in_channels_list, out_channels, extra_blocks=None):
         super(FeaturePyramidNetwork, self).__init__()
         self.inner_blocks = nn.ModuleList()
         self.layer_blocks = nn.ModuleList()
@@ -432,7 +403,8 @@ 
      Source code for torchvision.ops.feature_pyramid_network
      assert isinstance(extra_blocks, ExtraFPNBlock)
         self.extra_blocks = extra_blocks
 
-    def get_result_from_inner_blocks(self, x: Tensor, idx: int) -> Tensor:
+    def get_result_from_inner_blocks(self, x, idx):
+        # type: (Tensor, int) -> Tensor
         """
         This is equivalent to self.inner_blocks[idx](x),
         but torchscript doesn't support this yet
@@ -450,7 +422,8 @@ 
      Source code for torchvision.ops.feature_pyramid_network
      i += 1
         return out
 
-    def get_result_from_layer_blocks(self, x: Tensor, idx: int) -> Tensor:
+    def get_result_from_layer_blocks(self, x, idx):
+        # type: (Tensor, int) -> Tensor
         """
         This is equivalent to self.layer_blocks[idx](x),
         but torchscript doesn't support this yet
@@ -468,7 +441,8 @@ 
      Source code for torchvision.ops.feature_pyramid_network
      i += 1
         return out
 
-    def forward(self, x: Dict[str, Tensor]) -> Dict[str, Tensor]:
+    def forward(self, x):
+        # type: (Dict[str, Tensor]) -> Dict[str, Tensor]
         """
         Computes the FPN for a set of feature maps.
 
@@ -503,16 +477,31 @@ 
      Source code for torchvision.ops.feature_pyramid_network
      return out
      
 
 
+class ExtraFPNBlock(nn.Module):
+    """
+    Base class for the extra block in the FPN.
+
+    Arguments:
+        results (List[Tensor]): the result of the FPN
+        x (List[Tensor]): the original feature maps
+        names (List[str]): the names for each one of the
+            original feature maps
+
+    Returns:
+        results (List[Tensor]): the extended set of results
+            of the FPN
+        names (List[str]): the extended set of names for the results
+    """
+    def forward(self, results, x, names):
+        pass
+
+
 class LastLevelMaxPool(ExtraFPNBlock):
     """
     Applies a max_pool2d on top of the last feature map
     """
-    def forward(
-        self,
-        x: List[Tensor],
-        y: List[Tensor],
-        names: List[str],
-    ) -> Tuple[List[Tensor], List[str]]:
+    def forward(self, x, y, names):
+        # type: (List[Tensor], List[Tensor], List[str]) -> Tuple[List[Tensor], List[str]]
         names.append("pool")
         x.append(F.max_pool2d(x[-1], 1, 2, 0))
         return x, names
@@ -522,7 +511,7 @@ 
      Source code for torchvision.ops.feature_pyramid_network
      """
     This module is used in RetinaNet to generate extra layers, P6 and P7.
     """
-    def __init__(self, in_channels: int, out_channels: int):
+    def __init__(self, in_channels, out_channels):
         super(LastLevelP6P7, self).__init__()
         self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
         self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
@@ -531,12 +520,7 @@ 
      Source code for torchvision.ops.feature_pyramid_network
      nn.init.constant_(module.bias, 0)
         self.use_P5 = in_channels == out_channels
 
-    def forward(
-        self,
-        p: List[Tensor],
-        c: List[Tensor],
-        names: List[str],
-    ) -> Tuple[List[Tensor], List[str]]:
+    def forward(self, p, c, names):
         p5, c5 = p[-1], c[-1]
         x = p5 if self.use_P5 else c5
         p6 = self.p6(x)
diff --git a/docs/stable/_modules/torchvision/ops/poolers.html b/docs/stable/_modules/torchvision/ops/poolers.html
index 0ebdb8f13de0..9c5f1583601e 100644
--- a/docs/stable/_modules/torchvision/ops/poolers.html
+++ b/docs/stable/_modules/torchvision/ops/poolers.html
@@ -352,7 +352,8 @@ 
      Source code for torchvision.ops.poolers
       # _onnx_merge_levels() is an implementation supported by ONNX
 # that merges the levels to the right indices
 @torch.jit.unused
-def _onnx_merge_levels(levels: Tensor, unmerged_results: List[Tensor]) -> Tensor:
+def _onnx_merge_levels(levels, unmerged_results):
+    # type: (Tensor, List[Tensor]) -> Tensor
     first_result = unmerged_results[0]
     dtype, device = first_result.dtype, first_result.device
     res = torch.zeros((levels.size(0), first_result.size(1),
@@ -369,13 +370,8 @@ 
      Source code for torchvision.ops.poolers
       
 
 # TODO: (eellison) T54974082 https://github.com/pytorch/pytorch/issues/26744/pytorch/issues/26744
-def initLevelMapper(
-    k_min: int,
-    k_max: int,
-    canonical_scale: int = 224,
-    canonical_level: int = 4,
-    eps: float = 1e-6,
-):
+def initLevelMapper(k_min, k_max, canonical_scale=224, canonical_level=4, eps=1e-6):
+    # type: (int, int, int, int, float) -> LevelMapper
     return LevelMapper(k_min, k_max, canonical_scale, canonical_level, eps)
 
 
@@ -391,21 +387,16 @@ 
      Source code for torchvision.ops.poolers
               eps (float)
     """
 
-    def __init__(
-        self,
-        k_min: int,
-        k_max: int,
-        canonical_scale: int = 224,
-        canonical_level: int = 4,
-        eps: float = 1e-6,
-    ):
+    def __init__(self, k_min, k_max, canonical_scale=224, canonical_level=4, eps=1e-6):
+        # type: (int, int, int, int, float) -> None
         self.k_min = k_min
         self.k_max = k_max
         self.s0 = canonical_scale
         self.lvl0 = canonical_level
         self.eps = eps
 
-    def __call__(self, boxlists: List[Tensor]) -> Tensor:
+    def __call__(self, boxlists):
+        # type: (List[Tensor]) -> Tensor
         """
         Arguments:
             boxlists (list[BoxList])
@@ -453,12 +444,7 @@ 
      Source code for torchvision.ops.poolers
               'map_levels': Optional[LevelMapper]
     }
 
-    def __init__(
-        self,
-        featmap_names: List[str],
-        output_size: List[int],
-        sampling_ratio: int,
-    ):
+    def __init__(self, featmap_names, output_size, sampling_ratio):
         super(MultiScaleRoIAlign, self).__init__()
         if isinstance(output_size, int):
             output_size = (output_size, output_size)
@@ -468,7 +454,8 @@ 
      Source code for torchvision.ops.poolers
               self.scales = None
         self.map_levels = None
 
-    def convert_to_roi_format(self, boxes: List[Tensor]) -> Tensor:
+    def convert_to_roi_format(self, boxes):
+        # type: (List[Tensor]) -> Tensor
         concat_boxes = torch.cat(boxes, dim=0)
         device, dtype = concat_boxes.device, concat_boxes.dtype
         ids = torch.cat(
@@ -481,7 +468,8 @@ 
      Source code for torchvision.ops.poolers
               rois = torch.cat([ids, concat_boxes], dim=1)
         return rois
 
-    def infer_scale(self, feature: Tensor, original_size: List[int]) -> float:
+    def infer_scale(self, feature, original_size):
+        # type: (Tensor, List[int]) -> float
         # assumption: the scale is of the form 2 ** (-k), with k integer
         size = feature.shape[-2:]
         possible_scales = torch.jit.annotate(List[float], [])
@@ -492,11 +480,8 @@ 
      Source code for torchvision.ops.poolers
               assert possible_scales[0] == possible_scales[1]
         return possible_scales[0]
 
-    def setup_scales(
-        self,
-        features: List[Tensor],
-        image_shapes: List[Tuple[int, int]],
-    ) -> None:
+    def setup_scales(self, features, image_shapes):
+        # type: (List[Tensor], List[Tuple[int, int]]) -> None
         assert len(image_shapes) != 0
         max_x = 0
         max_y = 0
@@ -513,12 +498,8 @@ 
      Source code for torchvision.ops.poolers
               self.scales = scales
         self.map_levels = initLevelMapper(int(lvl_min), int(lvl_max))
 
-    def forward(
-        self,
-        x: Dict[str, Tensor],
-        boxes: List[Tensor],
-        image_shapes: List[Tuple[int, int]],
-    ) -> Tensor:
+    def forward(self, x, boxes, image_shapes):
+        # type: (Dict[str, Tensor], List[Tensor], List[Tuple[int, int]]) -> Tensor
         """
         Arguments:
             x (OrderedDict[Tensor]): feature maps for each level. They are assumed to have
diff --git a/docs/stable/_modules/torchvision/ops/ps_roi_align.html b/docs/stable/_modules/torchvision/ops/ps_roi_align.html
index fa0b600d35b7..aff93f0c83f4 100644
--- a/docs/stable/_modules/torchvision/ops/ps_roi_align.html
+++ b/docs/stable/_modules/torchvision/ops/ps_roi_align.html
@@ -339,18 +339,13 @@ 
      Source code for torchvision.ops.ps_roi_align
      from torch import nn, Tensor
 
 from torch.nn.modules.utils import _pair
-from torch.jit.annotations import List, Tuple
+from torch.jit.annotations import List
 
 from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
 
 
-
      [docs]def ps_roi_align(
-    input: Tensor,
-    boxes: Tensor,
-    output_size: int,
-    spatial_scale: float = 1.0,
-    sampling_ratio: int = -1,
-) -> Tensor:
+
      [docs]def ps_roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1):
+    # type: (Tensor, Tensor, int, float, int) -> Tensor
     """
     Performs Position-Sensitive Region of Interest (RoI) Align operator
     mentioned in Light-Head R-CNN.
@@ -391,22 +386,17 @@ 
      Source code for torchvision.ops.ps_roi_align
      """
     See ps_roi_align
     """
-    def __init__(
-        self,
-        output_size: int,
-        spatial_scale: float,
-        sampling_ratio: int,
-    ):
+    def __init__(self, output_size, spatial_scale, sampling_ratio):
         super(PSRoIAlign, self).__init__()
         self.output_size = output_size
         self.spatial_scale = spatial_scale
         self.sampling_ratio = sampling_ratio
 
-    def forward(self, input: Tensor, rois: Tensor) -> Tensor:
+    def forward(self, input, rois):
         return ps_roi_align(input, rois, self.output_size, self.spatial_scale,
                             self.sampling_ratio)
 
-    def __repr__(self) -> str:
+    def __repr__(self):
         tmpstr = self.__class__.__name__ + '('
         tmpstr += 'output_size=' + str(self.output_size)
         tmpstr += ', spatial_scale=' + str(self.spatial_scale)
diff --git a/docs/stable/_modules/torchvision/ops/ps_roi_pool.html b/docs/stable/_modules/torchvision/ops/ps_roi_pool.html
index 1cdc5da02358..5783f1818225 100644
--- a/docs/stable/_modules/torchvision/ops/ps_roi_pool.html
+++ b/docs/stable/_modules/torchvision/ops/ps_roi_pool.html
@@ -339,17 +339,13 @@ 
      Source code for torchvision.ops.ps_roi_pool
       from torch import nn, Tensor
 
 from torch.nn.modules.utils import _pair
-from torch.jit.annotations import List, Tuple
+from torch.jit.annotations import List
 
 from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
 
 
-
      [docs]def ps_roi_pool(
-    input: Tensor,
-    boxes: Tensor,
-    output_size: int,
-    spatial_scale: float = 1.0,
-) -> Tensor:
+
      [docs]def ps_roi_pool(input, boxes, output_size, spatial_scale=1.0):
+    # type: (Tensor, Tensor, int, float) -> Tensor
     """
     Performs Position-Sensitive Region of Interest (RoI) Pool operator
     described in R-FCN
@@ -384,15 +380,15 @@ 
      Source code for torchvision.ops.ps_roi_pool
           """
     See ps_roi_pool
     """
-    def __init__(self, output_size: int, spatial_scale: float):
+    def __init__(self, output_size, spatial_scale):
         super(PSRoIPool, self).__init__()
         self.output_size = output_size
         self.spatial_scale = spatial_scale
 
-    def forward(self, input: Tensor, rois: Tensor) -> Tensor:
+    def forward(self, input, rois):
         return ps_roi_pool(input, rois, self.output_size, self.spatial_scale)
 
-    def __repr__(self) -> str:
+    def __repr__(self):
         tmpstr = self.__class__.__name__ + '('
         tmpstr += 'output_size=' + str(self.output_size)
         tmpstr += ', spatial_scale=' + str(self.spatial_scale)
diff --git a/docs/stable/_modules/torchvision/ops/roi_align.html b/docs/stable/_modules/torchvision/ops/roi_align.html
index 6de3f8ba7973..24eb103a8a71 100644
--- a/docs/stable/_modules/torchvision/ops/roi_align.html
+++ b/docs/stable/_modules/torchvision/ops/roi_align.html
@@ -344,14 +344,8 @@ 
      Source code for torchvision.ops.roi_align
       from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
 
 
-
      [docs]def roi_align(
-    input: Tensor,
-    boxes: Tensor,
-    output_size: BroadcastingList2[int],
-    spatial_scale: float = 1.0,
-    sampling_ratio: int = -1,
-    aligned: bool = False,
-) -> Tensor:
+
      [docs]def roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1, aligned=False):
+    # type: (Tensor, Tensor, BroadcastingList2[int], float, int, bool) -> Tensor
     """
     Performs Region of Interest (RoI) Align operator described in Mask R-CNN
 
@@ -392,23 +386,17 @@ 
      Source code for torchvision.ops.roi_align
           """
     See roi_align
     """
-    def __init__(
-        self,
-        output_size: BroadcastingList2[int],
-        spatial_scale: float,
-        sampling_ratio: int,
-        aligned: bool = False,
-    ):
+    def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=False):
         super(RoIAlign, self).__init__()
         self.output_size = output_size
         self.spatial_scale = spatial_scale
         self.sampling_ratio = sampling_ratio
         self.aligned = aligned
 
-    def forward(self, input: Tensor, rois: Tensor) -> Tensor:
+    def forward(self, input, rois):
         return roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, self.aligned)
 
-    def __repr__(self) -> str:
+    def __repr__(self):
         tmpstr = self.__class__.__name__ + '('
         tmpstr += 'output_size=' + str(self.output_size)
         tmpstr += ', spatial_scale=' + str(self.spatial_scale)
diff --git a/docs/stable/_modules/torchvision/ops/roi_pool.html b/docs/stable/_modules/torchvision/ops/roi_pool.html
index b110417b9609..73eb23dd4cb5 100644
--- a/docs/stable/_modules/torchvision/ops/roi_pool.html
+++ b/docs/stable/_modules/torchvision/ops/roi_pool.html
@@ -344,12 +344,8 @@ 
      Source code for torchvision.ops.roi_pool
       from ._utils import convert_boxes_to_roi_format, check_roi_boxes_shape
 
 
-
      [docs]def roi_pool(
-    input: Tensor,
-    boxes: Tensor,
-    output_size: BroadcastingList2[int],
-    spatial_scale: float = 1.0,
-) -> Tensor:
+
      [docs]def roi_pool(input, boxes, output_size, spatial_scale=1.0):
+    # type: (Tensor, Tensor, BroadcastingList2[int], float) -> Tensor
     """
     Performs Region of Interest (RoI) Pool operator described in Fast R-CNN
 
@@ -382,15 +378,15 @@ 
      Source code for torchvision.ops.roi_pool
           """
     See roi_pool
     """
-    def __init__(self, output_size: BroadcastingList2[int], spatial_scale: float):
+    def __init__(self, output_size, spatial_scale):
         super(RoIPool, self).__init__()
         self.output_size = output_size
         self.spatial_scale = spatial_scale
 
-    def forward(self, input: Tensor, rois: Tensor) -> Tensor:
+    def forward(self, input, rois):
         return roi_pool(input, rois, self.output_size, self.spatial_scale)
 
-    def __repr__(self) -> str:
+    def __repr__(self):
         tmpstr = self.__class__.__name__ + '('
         tmpstr += 'output_size=' + str(self.output_size)
         tmpstr += ', spatial_scale=' + str(self.spatial_scale)
diff --git a/docs/stable/_modules/torchvision/transforms/functional.html b/docs/stable/_modules/torchvision/transforms/functional.html
index 5d897b1e147b..865eeeb0e621 100644
--- a/docs/stable/_modules/torchvision/transforms/functional.html
+++ b/docs/stable/_modules/torchvision/transforms/functional.html
@@ -335,47 +335,36 @@
              
      
               
   
      Source code for torchvision.transforms.functional
      -import math
-import numbers
-import warnings
-from typing import Any, Optional
-
-import numpy as np
-from PIL import Image
-
-import torch
+import torch
 from torch import Tensor
-from torch.jit.annotations import List, Tuple
-
+import math
+from PIL import Image, ImageOps, ImageEnhance, __version__ as PILLOW_VERSION
 try:
     import accimage
 except ImportError:
     accimage = None
+import numpy as np
+from numpy import sin, cos, tan
+import numbers
+from collections.abc import Sequence, Iterable
+import warnings
 
 from . import functional_pil as F_pil
 from . import functional_tensor as F_t
 
 
-_is_pil_image = F_pil._is_pil_image
-_parse_fill = F_pil._parse_fill
-
-
-def _get_image_size(img: Tensor) -> List[int]:
-    """Returns image sizea as (w, h)
-    """
-    if isinstance(img, torch.Tensor):
-        return F_t._get_image_size(img)
-
-    return F_pil._get_image_size(img)
+def _is_pil_image(img):
+    if accimage is not None:
+        return isinstance(img, (Image.Image, accimage.Image))
+    else:
+        return isinstance(img, Image.Image)
 
 
-@torch.jit.unused
-def _is_numpy(img: Any) -> bool:
+def _is_numpy(img):
     return isinstance(img, np.ndarray)
 
 
-@torch.jit.unused
-def _is_numpy_image(img: Any) -> bool:
+def _is_numpy_image(img):
     return img.ndim in {2, 3}
 
 
@@ -390,7 +379,7 @@ 
      Source code for torchvision.transforms.functional
          Returns:
         Tensor: Converted image.
     """
-    if not(F_pil._is_pil_image(pic) or _is_numpy(pic)):
+    if not(_is_pil_image(pic) or _is_numpy(pic)):
         raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
 
     if _is_numpy(pic) and not _is_numpy_image(pic):
@@ -445,7 +434,7 @@ 
      Source code for torchvision.transforms.functional
          Returns:
         Tensor: Converted image.
     """
-    if not(F_pil._is_pil_image(pic)):
+    if not(_is_pil_image(pic)):
         raise TypeError('pic should be PIL Image. Got {}'.format(type(pic)))
 
     if accimage is not None and isinstance(pic, accimage.Image):
@@ -497,14 +486,8 @@ 
      Source code for torchvision.transforms.functional
      msg = f"The cast from {image.dtype} to {dtype} cannot be performed safely."
             raise RuntimeError(msg)
 
-        # https://github.com/pytorch/vision/pull/2078#issuecomment-612045321
-        # For data in the range 0-1, (float * 255).to(uint) is only 255
-        # when float is exactly 1.0.
-        # `max + 1 - epsilon` provides more evenly distributed mapping of
-        # ranges of floats to ints.
         eps = 1e-3
-        result = image.mul(torch.iinfo(dtype).max + 1 - eps)
-        return result.to(dtype)
+        return image.mul(torch.iinfo(dtype).max + 1 - eps).to(dtype)
     else:
         # int to float
         if dtype.is_floating_point:
@@ -653,31 +636,41 @@ 
      Source code for torchvision.transforms.functional
      return tensor
      
 
 
-
      [docs]def resize(img: Tensor, size: List[int], interpolation: int = Image.BILINEAR) -> Tensor:
-    r"""Resize the input image to the given size.
-    The image can be a PIL Image or a torch Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]def resize(img, size, interpolation=Image.BILINEAR):
+    r"""Resize the input PIL Image to the given size.
 
     Args:
-        img (PIL Image or Tensor): Image to be resized.
+        img (PIL Image): Image to be resized.
         size (sequence or int): Desired output size. If size is a sequence like
             (h, w), the output size will be matched to this. If size is an int,
             the smaller edge of the image will be matched to this number maintaining
             the aspect ratio. i.e, if height > width, then image will be rescaled to
-            :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
-            In torchscript mode padding as single int is not supported, use a tuple or
-            list of length 1: ``[size, ]``.
-        interpolation (int, optional): Desired interpolation enum defined by `filters`_.
-            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
-            and ``PIL.Image.BICUBIC`` are supported.
+            :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`
+        interpolation (int, optional): Desired interpolation. Default is
+            ``PIL.Image.BILINEAR``
 
     Returns:
-        PIL Image or Tensor: Resized image.
+        PIL Image: Resized image.
     """
-    if not isinstance(img, torch.Tensor):
-        return F_pil.resize(img, size=size, interpolation=interpolation)
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    if not (isinstance(size, int) or (isinstance(size, Iterable) and len(size) == 2)):
+        raise TypeError('Got inappropriate size arg: {}'.format(size))
 
-    return F_t.resize(img, size=size, interpolation=interpolation)
      
+    if isinstance(size, int):
+        w, h = img.size
+        if (w <= h and w == size) or (h <= w and h == size):
+            return img
+        if w < h:
+            ow = size
+            oh = int(size * h / w)
+            return img.resize((ow, oh), interpolation)
+        else:
+            oh = size
+            ow = int(size * w / h)
+            return img.resize((ow, oh), interpolation)
+    else:
+        return img.resize(size[::-1], interpolation)
      
 
 
 def scale(*args, **kwargs):
@@ -686,24 +679,20 @@ 
      Source code for torchvision.transforms.functional
      return resize(*args, **kwargs)
 
 
-
      [docs]def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "constant") -> Tensor:
-    r"""Pad the given image on all sides with the given "pad" value.
-    The image can be a PIL Image or a torch Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]def pad(img, padding, fill=0, padding_mode='constant'):
+    r"""Pad the given PIL Image on all sides with specified padding mode and fill value.
 
     Args:
-        img (PIL Image or Tensor): Image to be padded.
-        padding (int or tuple or list): Padding on each border. If a single int is provided this
+        img (PIL Image): Image to be padded.
+        padding (int or tuple): Padding on each border. If a single int is provided this
             is used to pad all borders. If tuple of length 2 is provided this is the padding
             on left/right and top/bottom respectively. If a tuple of length 4 is provided
-            this is the padding for the left, top, right and bottom borders respectively.
-            In torchscript mode padding as single int is not supported, use a tuple or
-            list of length 1: ``[padding, ]``.
-        fill (int or str or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
+            this is the padding for the left, top, right and bottom borders
+            respectively.
+        fill: Pixel fill value for constant fill. Default is 0. If a tuple of
             length 3, it is used to fill R, G, B channels respectively.
-            This value is only used when the padding_mode is constant. Only int value is supported for Tensors.
+            This value is only used when the padding_mode is constant
         padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
-            Mode symmetric is not yet supported for Tensor inputs.
 
             - constant: pads with a constant value, this value is specified with fill
 
@@ -720,107 +709,142 @@ 
      Source code for torchvision.transforms.functional
                               will result in [2, 1, 1, 2, 3, 4, 4, 3]
 
     Returns:
-        PIL Image or Tensor: Padded image.
+        PIL Image: Padded image.
     """
-    if not isinstance(img, torch.Tensor):
-        return F_pil.pad(img, padding=padding, fill=fill, padding_mode=padding_mode)
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
-    return F_t.pad(img, padding=padding, fill=fill, padding_mode=padding_mode)
      
+    if not isinstance(padding, (numbers.Number, tuple)):
+        raise TypeError('Got inappropriate padding arg')
+    if not isinstance(fill, (numbers.Number, str, tuple)):
+        raise TypeError('Got inappropriate fill arg')
+    if not isinstance(padding_mode, str):
+        raise TypeError('Got inappropriate padding_mode arg')
+
+    if isinstance(padding, Sequence) and len(padding) not in [2, 4]:
+        raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
+                         "{} element tuple".format(len(padding)))
+
+    assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'], \
+        'Padding mode should be either constant, edge, reflect or symmetric'
+
+    if padding_mode == 'constant':
+        if isinstance(fill, numbers.Number):
+            fill = (fill,) * len(img.getbands())
+        if len(fill) != len(img.getbands()):
+            raise ValueError('fill should have the same number of elements '
+                             'as the number of channels in the image '
+                             '({}), got {} instead'.format(len(img.getbands()), len(fill)))
+        if img.mode == 'P':
+            palette = img.getpalette()
+            image = ImageOps.expand(img, border=padding, fill=fill)
+            image.putpalette(palette)
+            return image
+
+        return ImageOps.expand(img, border=padding, fill=fill)
+    else:
+        if isinstance(padding, int):
+            pad_left = pad_right = pad_top = pad_bottom = padding
+        if isinstance(padding, Sequence) and len(padding) == 2:
+            pad_left = pad_right = padding[0]
+            pad_top = pad_bottom = padding[1]
+        if isinstance(padding, Sequence) and len(padding) == 4:
+            pad_left = padding[0]
+            pad_top = padding[1]
+            pad_right = padding[2]
+            pad_bottom = padding[3]
+
+        if img.mode == 'P':
+            palette = img.getpalette()
+            img = np.asarray(img)
+            img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), padding_mode)
+            img = Image.fromarray(img)
+            img.putpalette(palette)
+            return img
+
+        img = np.asarray(img)
+        # RGB image
+        if len(img.shape) == 3:
+            img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), padding_mode)
+        # Grayscale image
+        if len(img.shape) == 2:
+            img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), padding_mode)
 
+        return Image.fromarray(img)
      
 
-
      [docs]def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:
-    """Crop the given image at specified location and output size.
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading
-    dimensions
+
+
      [docs]def crop(img, top, left, height, width):
+    """Crop the given PIL Image.
 
     Args:
-        img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
+        img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
         top (int): Vertical component of the top left corner of the crop box.
         left (int): Horizontal component of the top left corner of the crop box.
         height (int): Height of the crop box.
         width (int): Width of the crop box.
 
     Returns:
-        PIL Image or Tensor: Cropped image.
+        PIL Image: Cropped image.
     """
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
-    if not isinstance(img, torch.Tensor):
-        return F_pil.crop(img, top, left, height, width)
-
-    return F_t.crop(img, top, left, height, width)
      
+    return img.crop((left, top, left + width, top + height))
      
 
 
-
      [docs]def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
-    """Crops the given image at the center.
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]def center_crop(img, output_size):
+    """Crop the given PIL Image and resize it to desired size.
 
     Args:
-        img (PIL Image or Tensor): Image to be cropped.
-        output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int
-            it is used for both directions.
-
+        img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
+        output_size (sequence or int): (height, width) of the crop box. If int,
+            it is used for both directions
     Returns:
-        PIL Image or Tensor: Cropped image.
+        PIL Image: Cropped image.
     """
     if isinstance(output_size, numbers.Number):
         output_size = (int(output_size), int(output_size))
-    elif isinstance(output_size, (tuple, list)) and len(output_size) == 1:
-        output_size = (output_size[0], output_size[0])
-
-    image_width, image_height = _get_image_size(img)
+    image_width, image_height = img.size
     crop_height, crop_width = output_size
-
-    # crop_top = int(round((image_height - crop_height) / 2.))
-    # Result can be different between python func and scripted func
-    # Temporary workaround:
-    crop_top = int((image_height - crop_height + 1) * 0.5)
-    # crop_left = int(round((image_width - crop_width) / 2.))
-    # Result can be different between python func and scripted func
-    # Temporary workaround:
-    crop_left = int((image_width - crop_width + 1) * 0.5)
+    crop_top = int(round((image_height - crop_height) / 2.))
+    crop_left = int(round((image_width - crop_width) / 2.))
     return crop(img, crop_top, crop_left, crop_height, crop_width)
      
 
 
-
      [docs]def resized_crop(
-        img: Tensor, top: int, left: int, height: int, width: int, size: List[int], interpolation: int = Image.BILINEAR
-) -> Tensor:
-    """Crop the given image and resize it to desired size.
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]def resized_crop(img, top, left, height, width, size, interpolation=Image.BILINEAR):
+    """Crop the given PIL Image and resize it to desired size.
 
     Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.
 
     Args:
-        img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
+        img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.
         top (int): Vertical component of the top left corner of the crop box.
         left (int): Horizontal component of the top left corner of the crop box.
         height (int): Height of the crop box.
         width (int): Width of the crop box.
         size (sequence or int): Desired output size. Same semantics as ``resize``.
-        interpolation (int, optional): Desired interpolation enum defined by `filters`_.
-            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
-            and ``PIL.Image.BICUBIC`` are supported.
+        interpolation (int, optional): Desired interpolation. Default is
+            ``PIL.Image.BILINEAR``.
     Returns:
-        PIL Image or Tensor: Cropped image.
+        PIL Image: Cropped image.
     """
+    assert _is_pil_image(img), 'img should be PIL Image'
     img = crop(img, top, left, height, width)
     img = resize(img, size, interpolation)
     return img
      
 
 
 
      [docs]def hflip(img: Tensor) -> Tensor:
-    """Horizontally flip the given PIL Image or Tensor.
+    """Horizontally flip the given PIL Image or torch Tensor.
 
     Args:
-        img (PIL Image or Tensor): Image to be flipped. If img
+        img (PIL Image or Torch Tensor): Image to be flipped. If img
             is a Tensor, it is expected to be in [..., H, W] format,
             where ... means it can have an arbitrary number of trailing
             dimensions.
 
     Returns:
-        PIL Image or Tensor:  Horizontally flipped image.
+        PIL Image:  Horizontally flipped image.
     """
     if not isinstance(img, torch.Tensor):
         return F_pil.hflip(img)
@@ -828,6 +852,43 @@ 
      Source code for torchvision.transforms.functional
      return F_t.hflip(img)
      
 
 
+def _parse_fill(fill, img, min_pil_version):
+    """Helper function to get the fill color for rotate and perspective transforms.
+
+    Args:
+        fill (n-tuple or int or float): Pixel fill value for area outside the transformed
+            image. If int or float, the value is used for all bands respectively.
+            Defaults to 0 for all bands.
+        img (PIL Image): Image to be filled.
+        min_pil_version (str): The minimum PILLOW version for when the ``fillcolor`` option
+            was first introduced in the calling function. (e.g. rotate->5.2.0, perspective->5.0.0)
+
+    Returns:
+        dict: kwarg for ``fillcolor``
+    """
+    major_found, minor_found = (int(v) for v in PILLOW_VERSION.split('.')[:2])
+    major_required, minor_required = (int(v) for v in min_pil_version.split('.')[:2])
+    if major_found < major_required or (major_found == major_required and minor_found < minor_required):
+        if fill is None:
+            return {}
+        else:
+            msg = ("The option to fill background area of the transformed image, "
+                   "requires pillow>={}")
+            raise RuntimeError(msg.format(min_pil_version))
+
+    num_bands = len(img.getbands())
+    if fill is None:
+        fill = 0
+    if isinstance(fill, (int, float)) and num_bands > 1:
+        fill = tuple([fill] * num_bands)
+    if not isinstance(fill, (int, float)) and len(fill) != num_bands:
+        msg = ("The number of elements in 'fill' does not match the number of "
+               "bands of the image ({} != {})")
+        raise ValueError(msg.format(len(fill), num_bands))
+
+    return {"fillcolor": fill}
+
+
 def _get_perspective_coeffs(startpoints, endpoints):
     """Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.
 
@@ -836,7 +897,8 @@ 
      Source code for torchvision.transforms.functional
          Args:
         List containing [top-left, top-right, bottom-right, bottom-left] of the original image,
-        List containing [top-left, top-right, bottom-right, bottom-left] of the transformed image
+        List containing [top-left, top-right, bottom-right, bottom-left] of the transformed
+                   image
     Returns:
         octuple (a, b, c, d, e, f, g, h) for transforming each pixel.
     """
@@ -868,7 +930,7 @@ 
      Source code for torchvision.transforms.functional
              PIL Image:  Perspectively transformed Image.
     """
 
-    if not F_pil._is_pil_image(img):
+    if not _is_pil_image(img):
         raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
     opts = _parse_fill(fill, img, '5.0.0')
@@ -881,7 +943,7 @@ 
      Source code for torchvision.transforms.functional
      """Vertically flip the given PIL Image or torch Tensor.
 
     Args:
-        img (PIL Image or Tensor): Image to be flipped. If img
+        img (PIL Image or Torch Tensor): Image to be flipped. If img
             is a Tensor, it is expected to be in [..., H, W] format,
             where ... means it can have an arbitrary number of trailing
             dimensions.
@@ -895,20 +957,17 @@ 
      Source code for torchvision.transforms.functional
      return F_t.vflip(img)
      
 
 
-
      [docs]def five_crop(img: Tensor, size: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
-    """Crop the given image into four corners and the central crop.
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]def five_crop(img, size):
+    """Crop the given PIL Image into four corners and the central crop.
 
     .. Note::
         This transform returns a tuple of images and there may be a
         mismatch in the number of inputs and targets your ``Dataset`` returns.
 
     Args:
-        img (PIL Image or Tensor): Image to be cropped.
-        size (sequence or int): Desired output size of the crop. If size is an
-            int instead of sequence like (h, w), a square crop (size, size) is
-            made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
+       size (sequence or int): Desired output size of the crop. If size is an
+           int instead of sequence like (h, w), a square crop (size, size) is
+           made.
 
     Returns:
        tuple: tuple (tl, tr, bl, br, center)
@@ -916,44 +975,37 @@ 
      Source code for torchvision.transforms.functional
          """
     if isinstance(size, numbers.Number):
         size = (int(size), int(size))
-    elif isinstance(size, (tuple, list)) and len(size) == 1:
-        size = (size[0], size[0])
-
-    if len(size) != 2:
-        raise ValueError("Please provide only two dimensions (h, w) for size.")
+    else:
+        assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
 
-    image_width, image_height = _get_image_size(img)
+    image_width, image_height = img.size
     crop_height, crop_width = size
     if crop_width > image_width or crop_height > image_height:
         msg = "Requested crop size {} is bigger than input size {}"
         raise ValueError(msg.format(size, (image_height, image_width)))
 
-    tl = crop(img, 0, 0, crop_height, crop_width)
-    tr = crop(img, 0, image_width - crop_width, crop_height, crop_width)
-    bl = crop(img, image_height - crop_height, 0, crop_height, crop_width)
-    br = crop(img, image_height - crop_height, image_width - crop_width, crop_height, crop_width)
+    tl = img.crop((0, 0, crop_width, crop_height))
+    tr = img.crop((image_width - crop_width, 0, image_width, crop_height))
+    bl = img.crop((0, image_height - crop_height, crop_width, image_height))
+    br = img.crop((image_width - crop_width, image_height - crop_height,
+                   image_width, image_height))
+    center = center_crop(img, (crop_height, crop_width))
+    return (tl, tr, bl, br, center)
      
 
-    center = center_crop(img, [crop_height, crop_width])
 
-    return tl, tr, bl, br, center
      
-
-
-
      [docs]def ten_crop(img: Tensor, size: List[int], vertical_flip: bool = False) -> List[Tensor]:
-    """Generate ten cropped images from the given image.
-    Crop the given image into four corners and the central crop plus the
+
      [docs]def ten_crop(img, size, vertical_flip=False):
+    """Generate ten cropped images from the given PIL Image.
+    Crop the given PIL Image into four corners and the central crop plus the
     flipped version of these (horizontal flipping is used by default).
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
 
     .. Note::
         This transform returns a tuple of images and there may be a
         mismatch in the number of inputs and targets your ``Dataset`` returns.
 
     Args:
-        img (PIL Image or Tensor): Image to be cropped.
         size (sequence or int): Desired output size of the crop. If size is an
             int instead of sequence like (h, w), a square crop (size, size) is
-            made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
+            made.
         vertical_flip (bool): Use vertical flipping instead of horizontal
 
     Returns:
@@ -963,11 +1015,8 @@ 
      Source code for torchvision.transforms.functional
          """
     if isinstance(size, numbers.Number):
         size = (int(size), int(size))
-    elif isinstance(size, (tuple, list)) and len(size) == 1:
-        size = (size[0], size[0])
-
-    if len(size) != 2:
-        raise ValueError("Please provide only two dimensions (h, w) for size.")
+    else:
+        assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
 
     first_five = five_crop(img, size)
 
@@ -984,13 +1033,13 @@ 
      Source code for torchvision.transforms.functional
      """Adjust brightness of an Image.
 
     Args:
-        img (PIL Image or Tensor): Image to be adjusted.
+        img (PIL Image or Torch Tensor): Image to be adjusted.
         brightness_factor (float):  How much to adjust the brightness. Can be
             any non negative number. 0 gives a black image, 1 gives the
             original image while 2 increases the brightness by a factor of 2.
 
     Returns:
-        PIL Image or Tensor: Brightness adjusted image.
+        PIL Image or Torch Tensor: Brightness adjusted image.
     """
     if not isinstance(img, torch.Tensor):
         return F_pil.adjust_brightness(img, brightness_factor)
@@ -1002,13 +1051,13 @@ 
      Source code for torchvision.transforms.functional
      """Adjust contrast of an Image.
 
     Args:
-        img (PIL Image or Tensor): Image to be adjusted.
+        img (PIL Image or Torch Tensor): Image to be adjusted.
         contrast_factor (float): How much to adjust the contrast. Can be any
             non negative number. 0 gives a solid gray image, 1 gives the
             original image while 2 increases the contrast by a factor of 2.
 
     Returns:
-        PIL Image or Tensor: Contrast adjusted image.
+        PIL Image or Torch Tensor: Contrast adjusted image.
     """
     if not isinstance(img, torch.Tensor):
         return F_pil.adjust_contrast(img, contrast_factor)
@@ -1020,13 +1069,13 @@ 
      Source code for torchvision.transforms.functional
      """Adjust color saturation of an image.
 
     Args:
-        img (PIL Image or Tensor): Image to be adjusted.
+        img (PIL Image or Torch Tensor): Image to be adjusted.
         saturation_factor (float):  How much to adjust the saturation. 0 will
             give a black and white image, 1 will give the original image while
             2 will enhance the saturation by a factor of 2.
 
     Returns:
-        PIL Image or Tensor: Saturation adjusted image.
+        PIL Image or Torch Tensor: Saturation adjusted image.
     """
     if not isinstance(img, torch.Tensor):
         return F_pil.adjust_saturation(img, saturation_factor)
@@ -1065,7 +1114,7 @@ 
      Source code for torchvision.transforms.functional
      raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
      
 
 
-
      [docs]def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
+
      [docs]def adjust_gamma(img, gamma, gain=1):
     r"""Perform gamma correction on an image.
 
     Also known as Power Law Transform. Intensities in RGB mode are adjusted
@@ -1079,18 +1128,26 @@ 
      Source code for torchvision.transforms.functional
          .. _Gamma Correction: https://en.wikipedia.org/wiki/Gamma_correction
 
     Args:
-        img (PIL Image or Tensor): PIL Image to be adjusted.
+        img (PIL Image): PIL Image to be adjusted.
         gamma (float): Non negative real number, same as :math:`\gamma` in the equation.
             gamma larger than 1 make the shadows darker,
             while gamma smaller than 1 make dark regions lighter.
         gain (float): The constant multiplier.
-    Returns:
-        PIL Image or Tensor: Gamma correction adjusted image.
     """
-    if not isinstance(img, torch.Tensor):
-        return F_pil.adjust_gamma(img, gamma, gain)
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    if gamma < 0:
+        raise ValueError('Gamma should be a non-negative real number')
+
+    input_mode = img.mode
+    img = img.convert('RGB')
 
-    return F_t.adjust_gamma(img, gamma, gain)
      
+    gamma_map = [255 * gain * pow(ele / 255., gamma) for ele in range(256)] * 3
+    img = img.point(gamma_map)  # use PIL's point-function to accelerate this part
+
+    img = img.convert(input_mode)
+    return img
      
 
 
 
      [docs]def rotate(img, angle, resample=False, expand=False, center=None, fill=None):
@@ -1117,7 +1174,7 @@ 
      Source code for torchvision.transforms.functional
          .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
 
     """
-    if not F_pil._is_pil_image(img):
+    if not _is_pil_image(img):
         raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
     opts = _parse_fill(fill, img, '5.2.0')
@@ -1125,9 +1182,7 @@ 
      Source code for torchvision.transforms.functional
      return img.rotate(angle, resample, expand, center, **opts)
      
 
 
-def _get_inverse_affine_matrix(
-        center: List[int], angle: float, translate: List[float], scale: float, shear: List[float]
-) -> List[float]:
+def _get_inverse_affine_matrix(center, angle, translate, scale, shear):
     # Helper method to compute inverse matrix for affine transformation
 
     # As it is explained in PIL.Image.rotate
@@ -1147,6 +1202,14 @@ 
      Source code for torchvision.transforms.functional
      #
     # Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1
 
+    if isinstance(shear, numbers.Number):
+        shear = [shear, 0]
+
+    if not isinstance(shear, (tuple, list)) and len(shear) == 2:
+        raise ValueError(
+            "Shear should be a single value or a tuple/list containing " +
+            "two values. Got {}".format(shear))
+
     rot = math.radians(angle)
     sx, sy = [math.radians(s) for s in shear]
 
@@ -1154,100 +1217,57 @@ 
      Source code for torchvision.transforms.functional
      tx, ty = translate
 
     # RSS without scaling
-    a = math.cos(rot - sy) / math.cos(sy)
-    b = -math.cos(rot - sy) * math.tan(sx) / math.cos(sy) - math.sin(rot)
-    c = math.sin(rot - sy) / math.cos(sy)
-    d = -math.sin(rot - sy) * math.tan(sx) / math.cos(sy) + math.cos(rot)
+    a = cos(rot - sy) / cos(sy)
+    b = -cos(rot - sy) * tan(sx) / cos(sy) - sin(rot)
+    c = sin(rot - sy) / cos(sy)
+    d = -sin(rot - sy) * tan(sx) / cos(sy) + cos(rot)
 
     # Inverted rotation matrix with scale and shear
     # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
-    matrix = [d, -b, 0.0, -c, a, 0.0]
-    matrix = [x / scale for x in matrix]
+    M = [d, -b, 0,
+         -c, a, 0]
+    M = [x / scale for x in M]
 
     # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
-    matrix[2] += matrix[0] * (-cx - tx) + matrix[1] * (-cy - ty)
-    matrix[5] += matrix[3] * (-cx - tx) + matrix[4] * (-cy - ty)
+    M[2] += M[0] * (-cx - tx) + M[1] * (-cy - ty)
+    M[5] += M[3] * (-cx - tx) + M[4] * (-cy - ty)
 
     # Apply center translation: C * RSS^-1 * C^-1 * T^-1
-    matrix[2] += cx
-    matrix[5] += cy
+    M[2] += cx
+    M[5] += cy
+    return M
 
-    return matrix
 
-
-
      [docs]def affine(
-        img: Tensor, angle: float, translate: List[int], scale: float, shear: List[float],
-        resample: int = 0, fillcolor: Optional[int] = None
-) -> Tensor:
-    """Apply affine transformation on the image keeping image center invariant.
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
+
      [docs]def affine(img, angle, translate, scale, shear, resample=0, fillcolor=None):
+    """Apply affine transformation on the image keeping image center invariant
 
     Args:
-        img (PIL Image or Tensor): image to be rotated.
+        img (PIL Image): PIL Image to be rotated.
         angle (float or int): rotation angle in degrees between -180 and 180, clockwise direction.
         translate (list or tuple of integers): horizontal and vertical translations (post-rotation translation)
         scale (float): overall scale
         shear (float or tuple or list): shear angle value in degrees between -180 to 180, clockwise direction.
-            If a tuple of list is specified, the first value corresponds to a shear parallel to the x axis, while
-            the second value corresponds to a shear parallel to the y axis.
+        If a tuple of list is specified, the first value corresponds to a shear parallel to the x axis, while
+        the second value corresponds to a shear parallel to the y axis.
         resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
-            An optional resampling filter. See `filters`_ for more information.
-            If omitted, or if the image is PIL Image and has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
-            If input is Tensor, only ``PIL.Image.NEAREST`` and ``PIL.Image.BILINEAR`` are supported.
+            An optional resampling filter.
+            See `filters`_ for more information.
+            If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
         fillcolor (int): Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)
-
-    Returns:
-        PIL Image or Tensor: Transformed image.
     """
-    if not isinstance(angle, (int, float)):
-        raise TypeError("Argument angle should be int or float")
-
-    if not isinstance(translate, (list, tuple)):
-        raise TypeError("Argument translate should be a sequence")
-
-    if len(translate) != 2:
-        raise ValueError("Argument translate should be a sequence of length 2")
-
-    if scale <= 0.0:
-        raise ValueError("Argument scale should be positive")
-
-    if not isinstance(shear, (numbers.Number, (list, tuple))):
-        raise TypeError("Shear should be either a single value or a sequence of two values")
-
-    if isinstance(angle, int):
-        angle = float(angle)
-
-    if isinstance(translate, tuple):
-        translate = list(translate)
-
-    if isinstance(shear, numbers.Number):
-        shear = [shear, 0.0]
-
-    if isinstance(shear, tuple):
-        shear = list(shear)
-
-    if len(shear) == 1:
-        shear = [shear[0], shear[0]]
-
-    if len(shear) != 2:
-        raise ValueError("Shear should be a sequence containing two values. Got {}".format(shear))
-
-    img_size = _get_image_size(img)
-    if not isinstance(img, torch.Tensor):
-        # center = (img_size[0] * 0.5 + 0.5, img_size[1] * 0.5 + 0.5)
-        # it is visually better to estimate the center without 0.5 offset
-        # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine
-        center = [img_size[0] * 0.5, img_size[1] * 0.5]
-        matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
-        return F_pil.affine(img, matrix=matrix, resample=resample, fillcolor=fillcolor)
+    assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
+        "Argument translate should be a list or tuple of length 2"
 
-    # we need to rescale translate by image size / 2 as its values can be between -1 and 1
-    translate = [2.0 * t / s for s, t in zip(img_size, translate)]
+    assert scale > 0.0, "Argument scale should be positive"
 
-    matrix = _get_inverse_affine_matrix([0, 0], angle, translate, scale, shear)
-    return F_t.affine(img, matrix=matrix, resample=resample, fillcolor=fillcolor)
      
+    output_size = img.size
+    center = (img.size[0] * 0.5 + 0.5, img.size[1] * 0.5 + 0.5)
+    matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
+    kwargs = {"fillcolor": fillcolor} if int(PILLOW_VERSION.split('.')[0]) >= 5 else {}
+    return img.transform(output_size, Image.AFFINE, matrix, resample, **kwargs)
      
 
 
 
      [docs]def to_grayscale(img, num_output_channels=1):
@@ -1262,7 +1282,7 @@ 
      Source code for torchvision.transforms.functional
                  if num_output_channels = 3 : returned image is 3 channel with r = g = b
     """
-    if not F_pil._is_pil_image(img):
+    if not _is_pil_image(img):
         raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 
     if num_output_channels == 1:
@@ -1278,7 +1298,7 @@ 
      Source code for torchvision.transforms.functional
      return img
      
 
 
-
      [docs]def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool = False) -> Tensor:
+
      [docs]def erase(img, i, j, h, w, v, inplace=False):
     """ Erase the input Tensor Image with given value.
 
     Args:
diff --git a/docs/stable/_modules/torchvision/transforms/transforms.html b/docs/stable/_modules/torchvision/transforms/transforms.html
index 18ddb7ac8f93..b233954de0a4 100644
--- a/docs/stable/_modules/torchvision/transforms/transforms.html
+++ b/docs/stable/_modules/torchvision/transforms/transforms.html
@@ -335,22 +335,19 @@
              
      
               
   
      Source code for torchvision.transforms.transforms
      -import math
-import numbers
+import torch
+import math
 import random
-import warnings
-from collections.abc import Sequence
-from typing import Tuple, List, Optional
-
-import numpy as np
-import torch
 from PIL import Image
-from torch import Tensor
-
 try:
     import accimage
 except ImportError:
     accimage = None
+import numpy as np
+import numbers
+import types
+from collections.abc import Sequence, Iterable
+import warnings
 
 from . import functional as F
 
@@ -371,6 +368,15 @@ 
      Source code for torchvision.transforms.transforms
      }
 
 
+def _get_image_size(img):
+    if F._is_pil_image(img):
+        return img.size
+    elif isinstance(img, torch.Tensor) and img.dim() > 2:
+        return img.shape[-2:][::-1]
+    else:
+        raise TypeError("Unexpected type {}".format(type(img)))
+
+
 
      [docs]class Compose(object):
     """Composes several transforms together.
 
@@ -429,7 +435,7 @@ 
      Source code for torchvision.transforms.transforms
      class PILToTensor(object):
     """Convert a ``PIL Image`` to a tensor of the same type.
 
-    Converts a PIL Image (H x W x C) to a Tensor of shape (C x H x W).
+    Converts a PIL Image (H x W x C) to a torch.Tensor of shape (C x H x W).
     """
 
     def __call__(self, pic):
@@ -546,40 +552,31 @@ 
      Source code for torchvision.transforms.transforms
      return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
      
 
 
-
      [docs]class Resize(torch.nn.Module):
-    """Resize the input image to the given size.
-    The image can be a PIL Image or a torch Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]class Resize(object):
+    """Resize the input PIL Image to the given size.
 
     Args:
         size (sequence or int): Desired output size. If size is a sequence like
             (h, w), output size will be matched to this. If size is an int,
             smaller edge of the image will be matched to this number.
             i.e, if height > width, then image will be rescaled to
-            (size * height / width, size).
-            In torchscript mode padding as single int is not supported, use a tuple or
-            list of length 1: ``[size, ]``.
-        interpolation (int, optional): Desired interpolation enum defined by `filters`_.
-            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
-            and ``PIL.Image.BICUBIC`` are supported.
+            (size * height / width, size)
+        interpolation (int, optional): Desired interpolation. Default is
+            ``PIL.Image.BILINEAR``
     """
 
     def __init__(self, size, interpolation=Image.BILINEAR):
-        super().__init__()
-        if not isinstance(size, (int, Sequence)):
-            raise TypeError("Size should be int or sequence. Got {}".format(type(size)))
-        if isinstance(size, Sequence) and len(size) not in (1, 2):
-            raise ValueError("If size is a sequence, it should have 1 or 2 values")
+        assert isinstance(size, int) or (isinstance(size, Iterable) and len(size) == 2)
         self.size = size
         self.interpolation = interpolation
 
-    def forward(self, img):
+    def __call__(self, img):
         """
         Args:
-            img (PIL Image or Tensor): Image to be scaled.
+            img (PIL Image): Image to be scaled.
 
         Returns:
-            PIL Image or Tensor: Rescaled image.
+            PIL Image: Rescaled image.
         """
         return F.resize(img, self.size, self.interpolation)
 
@@ -598,36 +595,28 @@ 
      Source code for torchvision.transforms.transforms
      super(Scale, self).__init__(*args, **kwargs)
      
 
 
-
      [docs]class CenterCrop(torch.nn.Module):
-    """Crops the given image at the center.
-    The image can be a PIL Image or a torch Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]class CenterCrop(object):
+    """Crops the given PIL Image at the center.
 
     Args:
         size (sequence or int): Desired output size of the crop. If size is an
             int instead of sequence like (h, w), a square crop (size, size) is
-            made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
+            made.
     """
 
     def __init__(self, size):
-        super().__init__()
         if isinstance(size, numbers.Number):
             self.size = (int(size), int(size))
-        elif isinstance(size, Sequence) and len(size) == 1:
-            self.size = (size[0], size[0])
         else:
-            if len(size) != 2:
-                raise ValueError("Please provide only two dimensions (h, w) for size.")
-
             self.size = size
 
-    def forward(self, img):
+    def __call__(self, img):
         """
         Args:
-            img (PIL Image or Tensor): Image to be cropped.
+            img (PIL Image): Image to be cropped.
 
         Returns:
-            PIL Image or Tensor: Cropped image.
+            PIL Image: Cropped image.
         """
         return F.center_crop(img, self.size)
 
@@ -635,23 +624,20 @@ 
      Source code for torchvision.transforms.transforms
      return self.__class__.__name__ + '(size={0})'.format(self.size)
      
 
 
-
      [docs]class Pad(torch.nn.Module):
-    """Pad the given image on all sides with the given "pad" value.
-    The image can be a PIL Image or a torch Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]class Pad(object):
+    """Pad the given PIL Image on all sides with the given "pad" value.
 
     Args:
-        padding (int or tuple or list): Padding on each border. If a single int is provided this
+        padding (int or tuple): Padding on each border. If a single int is provided this
             is used to pad all borders. If tuple of length 2 is provided this is the padding
             on left/right and top/bottom respectively. If a tuple of length 4 is provided
-            this is the padding for the left, top, right and bottom borders respectively.
-            In torchscript mode padding as single int is not supported, use a tuple or
-            list of length 1: ``[padding, ]``.
+            this is the padding for the left, top, right and bottom borders
+            respectively.
         fill (int or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
             length 3, it is used to fill R, G, B channels respectively.
             This value is only used when the padding_mode is constant
         padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
-            Default is constant. Mode symmetric is not yet supported for Tensor inputs.
+            Default is constant.
 
             - constant: pads with a constant value, this value is specified with fill
 
@@ -668,32 +654,25 @@ 
      Source code for torchvision.transforms.transforms
                      will result in [2, 1, 1, 2, 3, 4, 4, 3]
     """
 
-    def __init__(self, padding, fill=0, padding_mode="constant"):
-        super().__init__()
-        if not isinstance(padding, (numbers.Number, tuple, list)):
-            raise TypeError("Got inappropriate padding arg")
-
-        if not isinstance(fill, (numbers.Number, str, tuple)):
-            raise TypeError("Got inappropriate fill arg")
-
-        if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
-            raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
-
-        if isinstance(padding, Sequence) and len(padding) not in [1, 2, 4]:
-            raise ValueError("Padding must be an int or a 1, 2, or 4 element tuple, not a " +
+    def __init__(self, padding, fill=0, padding_mode='constant'):
+        assert isinstance(padding, (numbers.Number, tuple))
+        assert isinstance(fill, (numbers.Number, str, tuple))
+        assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric']
+        if isinstance(padding, Sequence) and len(padding) not in [2, 4]:
+            raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
                              "{} element tuple".format(len(padding)))
 
         self.padding = padding
         self.fill = fill
         self.padding_mode = padding_mode
 
-    def forward(self, img):
+    def __call__(self, img):
         """
         Args:
-            img (PIL Image or Tensor): Image to be padded.
+            img (PIL Image): Image to be padded.
 
         Returns:
-            PIL Image or Tensor: Padded image.
+            PIL Image: Padded image.
         """
         return F.pad(img, self.padding, self.fill, self.padding_mode)
 
@@ -791,31 +770,25 @@ 
      Source code for torchvision.transforms.transforms
      return t(img)
      
 
 
-
      [docs]class RandomCrop(torch.nn.Module):
-    """Crop the given image at a random location.
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading
-    dimensions
+
      [docs]class RandomCrop(object):
+    """Crop the given PIL Image at a random location.
 
     Args:
         size (sequence or int): Desired output size of the crop. If size is an
             int instead of sequence like (h, w), a square crop (size, size) is
-            made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
+            made.
         padding (int or sequence, optional): Optional padding on each border
-            of the image. Default is None. If a single int is provided this
-            is used to pad all borders. If tuple of length 2 is provided this is the padding
-            on left/right and top/bottom respectively. If a tuple of length 4 is provided
-            this is the padding for the left, top, right and bottom borders respectively.
-            In torchscript mode padding as single int is not supported, use a tuple or
-            list of length 1: ``[padding, ]``.
+            of the image. Default is None, i.e no padding. If a sequence of length
+            4 is provided, it is used to pad left, top, right, bottom borders
+            respectively. If a sequence of length 2 is provided, it is used to
+            pad left/right, top/bottom borders, respectively.
         pad_if_needed (boolean): It will pad the image if smaller than the
             desired size to avoid raising an exception. Since cropping is done
             after padding, the padding seems to be done at a random offset.
-        fill (int or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
+        fill: Pixel fill value for constant fill. Default is 0. If a tuple of
             length 3, it is used to fill R, G, B channels respectively.
             This value is only used when the padding_mode is constant
-        padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
-            Mode symmetric is not yet supported for Tensor inputs.
+        padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
 
              - constant: pads with a constant value, this value is specified with fill
 
@@ -833,70 +806,60 @@ 
      Source code for torchvision.transforms.transforms
          """
 
+    def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant'):
+        if isinstance(size, numbers.Number):
+            self.size = (int(size), int(size))
+        else:
+            self.size = size
+        self.padding = padding
+        self.pad_if_needed = pad_if_needed
+        self.fill = fill
+        self.padding_mode = padding_mode
+
     @staticmethod
-    def get_params(img: Tensor, output_size: Tuple[int, int]) -> Tuple[int, int, int, int]:
+    def get_params(img, output_size):
         """Get parameters for ``crop`` for a random crop.
 
         Args:
-            img (PIL Image or Tensor): Image to be cropped.
+            img (PIL Image): Image to be cropped.
             output_size (tuple): Expected output size of the crop.
 
         Returns:
             tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
         """
-        w, h = F._get_image_size(img)
+        w, h = _get_image_size(img)
         th, tw = output_size
         if w == tw and h == th:
             return 0, 0, h, w
 
-        i = torch.randint(0, h - th + 1, size=(1, )).item()
-        j = torch.randint(0, w - tw + 1, size=(1, )).item()
+        i = random.randint(0, h - th)
+        j = random.randint(0, w - tw)
         return i, j, th, tw
 
-    def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode="constant"):
-        super().__init__()
-        if isinstance(size, numbers.Number):
-            self.size = (int(size), int(size))
-        elif isinstance(size, Sequence) and len(size) == 1:
-            self.size = (size[0], size[0])
-        else:
-            if len(size) != 2:
-                raise ValueError("Please provide only two dimensions (h, w) for size.")
-
-            # cast to tuple for torchscript
-            self.size = tuple(size)
-        self.padding = padding
-        self.pad_if_needed = pad_if_needed
-        self.fill = fill
-        self.padding_mode = padding_mode
-
-    def forward(self, img):
+    def __call__(self, img):
         """
         Args:
-            img (PIL Image or Tensor): Image to be cropped.
+            img (PIL Image): Image to be cropped.
 
         Returns:
-            PIL Image or Tensor: Cropped image.
+            PIL Image: Cropped image.
         """
         if self.padding is not None:
             img = F.pad(img, self.padding, self.fill, self.padding_mode)
 
-        width, height = F._get_image_size(img)
         # pad the width if needed
-        if self.pad_if_needed and width < self.size[1]:
-            padding = [self.size[1] - width, 0]
-            img = F.pad(img, padding, self.fill, self.padding_mode)
+        if self.pad_if_needed and img.size[0] < self.size[1]:
+            img = F.pad(img, (self.size[1] - img.size[0], 0), self.fill, self.padding_mode)
         # pad the height if needed
-        if self.pad_if_needed and height < self.size[0]:
-            padding = [0, self.size[0] - height]
-            img = F.pad(img, padding, self.fill, self.padding_mode)
+        if self.pad_if_needed and img.size[1] < self.size[0]:
+            img = F.pad(img, (0, self.size[0] - img.size[1]), self.fill, self.padding_mode)
 
         i, j, h, w = self.get_params(img, self.size)
 
         return F.crop(img, i, j, h, w)
 
     def __repr__(self):
-        return self.__class__.__name__ + "(size={0}, padding={1})".format(self.size, self.padding)
      
+        return self.__class__.__name__ + '(size={0}, padding={1})'.format(self.size, self.padding)
      
 
 
 
      [docs]class RandomHorizontalFlip(torch.nn.Module):
@@ -930,7 +893,7 @@ 
      Source code for torchvision.transforms.transforms
      [docs]class RandomVerticalFlip(torch.nn.Module):
-    """Vertically flip the given image randomly with a given probability.
+    """Vertically flip the given PIL Image randomly with a given probability.
     The image can be a PIL Image or a torch Tensor, in which case it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading
     dimensions
@@ -1026,10 +989,8 @@ 
      Source code for torchvision.transforms.transforms
      return self.__class__.__name__ + '(p={})'.format(self.p)
      
 
 
-
      [docs]class RandomResizedCrop(torch.nn.Module):
-    """Crop the given image to random size and aspect ratio.
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+
      [docs]class RandomResizedCrop(object):
+    """Crop the given PIL Image to random size and aspect ratio.
 
     A crop of random size (default: of 0.08 to 1.0) of the original size and a random
     aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
@@ -1037,77 +998,59 @@ 
      Source code for torchvision.transforms.transforms
          This is popularly used to train the Inception networks.
 
     Args:
-        size (int or sequence): expected output size of each edge. If size is an
-            int instead of sequence like (h, w), a square output size ``(size, size)`` is
-            made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
-        scale (tuple of float): range of size of the origin size cropped
-        ratio (tuple of float): range of aspect ratio of the origin aspect ratio cropped.
-        interpolation (int): Desired interpolation enum defined by `filters`_.
-            Default is ``PIL.Image.BILINEAR``. If input is Tensor, only ``PIL.Image.NEAREST``, ``PIL.Image.BILINEAR``
-            and ``PIL.Image.BICUBIC`` are supported.
+        size: expected output size of each edge
+        scale: range of size of the origin size cropped
+        ratio: range of aspect ratio of the origin aspect ratio cropped
+        interpolation: Default: PIL.Image.BILINEAR
     """
 
     def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR):
-        super().__init__()
-        if isinstance(size, numbers.Number):
-            self.size = (int(size), int(size))
-        elif isinstance(size, Sequence) and len(size) == 1:
-            self.size = (size[0], size[0])
-        else:
-            if len(size) != 2:
-                raise ValueError("Please provide only two dimensions (h, w) for size.")
+        if isinstance(size, (tuple, list)):
             self.size = size
-
-        if not isinstance(scale, (tuple, list)):
-            raise TypeError("Scale should be a sequence")
-        if not isinstance(ratio, (tuple, list)):
-            raise TypeError("Ratio should be a sequence")
+        else:
+            self.size = (size, size)
         if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
-            warnings.warn("Scale and ratio should be of kind (min, max)")
+            warnings.warn("range should be of kind (min, max)")
 
         self.interpolation = interpolation
         self.scale = scale
         self.ratio = ratio
 
     @staticmethod
-    def get_params(
-            img: Tensor, scale: Tuple[float, float], ratio: Tuple[float, float]
-    ) -> Tuple[int, int, int, int]:
+    def get_params(img, scale, ratio):
         """Get parameters for ``crop`` for a random sized crop.
 
         Args:
-            img (PIL Image or Tensor): Input image.
-            scale (tuple): range of scale of the origin size cropped
+            img (PIL Image): Image to be cropped.
+            scale (tuple): range of size of the origin size cropped
             ratio (tuple): range of aspect ratio of the origin aspect ratio cropped
 
         Returns:
             tuple: params (i, j, h, w) to be passed to ``crop`` for a random
                 sized crop.
         """
-        width, height = F._get_image_size(img)
+        width, height = _get_image_size(img)
         area = height * width
 
         for _ in range(10):
-            target_area = area * torch.empty(1).uniform_(*scale).item()
-            log_ratio = torch.log(torch.tensor(ratio))
-            aspect_ratio = torch.exp(
-                torch.empty(1).uniform_(log_ratio[0], log_ratio[1])
-            ).item()
+            target_area = random.uniform(*scale) * area
+            log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
+            aspect_ratio = math.exp(random.uniform(*log_ratio))
 
             w = int(round(math.sqrt(target_area * aspect_ratio)))
             h = int(round(math.sqrt(target_area / aspect_ratio)))
 
             if 0 < w <= width and 0 < h <= height:
-                i = torch.randint(0, height - h + 1, size=(1,)).item()
-                j = torch.randint(0, width - w + 1, size=(1,)).item()
+                i = random.randint(0, height - h)
+                j = random.randint(0, width - w)
                 return i, j, h, w
 
         # Fallback to central crop
         in_ratio = float(width) / float(height)
-        if in_ratio < min(ratio):
+        if (in_ratio < min(ratio)):
             w = width
             h = int(round(w / min(ratio)))
-        elif in_ratio > max(ratio):
+        elif (in_ratio > max(ratio)):
             h = height
             w = int(round(h * max(ratio)))
         else:  # whole image
@@ -1117,13 +1060,13 @@ 
      Source code for torchvision.transforms.transforms
      j = (width - w) // 2
         return i, j, h, w
 
-    def forward(self, img):
+    def __call__(self, img):
         """
         Args:
-            img (PIL Image or Tensor): Image to be cropped and resized.
+            img (PIL Image): Image to be cropped and resized.
 
         Returns:
-            PIL Image or Tensor: Randomly cropped and resized image.
+            PIL Image: Randomly cropped and resized image.
         """
         i, j, h, w = self.get_params(img, self.scale, self.ratio)
         return F.resized_crop(img, i, j, h, w, self.size, self.interpolation)
@@ -1147,11 +1090,8 @@ 
      Source code for torchvision.transforms.transforms
      super(RandomSizedCrop, self).__init__(*args, **kwargs)
      
 
 
-
      [docs]class FiveCrop(torch.nn.Module):
-    """Crop the given image into four corners and the central crop.
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading
-    dimensions
+
      [docs]class FiveCrop(object):
+    """Crop the given PIL Image into four corners and the central crop
 
     .. Note::
          This transform returns a tuple of images and there may be a mismatch in the number of
@@ -1161,7 +1101,6 @@ 
      Source code for torchvision.transforms.transforms
          Args:
          size (sequence or int): Desired output size of the crop. If size is an ``int``
             instead of sequence like (h, w), a square crop of size (size, size) is made.
-            If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
 
     Example:
          >>> transform = Compose([
@@ -1176,37 +1115,23 @@ 
      Source code for torchvision.transforms.transforms
          """
 
     def __init__(self, size):
-        super().__init__()
+        self.size = size
         if isinstance(size, numbers.Number):
             self.size = (int(size), int(size))
-        elif isinstance(size, Sequence) and len(size) == 1:
-            self.size = (size[0], size[0])
         else:
-            if len(size) != 2:
-                raise ValueError("Please provide only two dimensions (h, w) for size.")
-
+            assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
             self.size = size
 
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            tuple of 5 images. Image can be PIL Image or Tensor
-        """
+    def __call__(self, img):
         return F.five_crop(img, self.size)
 
     def __repr__(self):
         return self.__class__.__name__ + '(size={0})'.format(self.size)
      
 
 
-
      [docs]class TenCrop(torch.nn.Module):
-    """Crop the given image into four corners and the central crop plus the flipped version of
-    these (horizontal flipping is used by default).
-    The image can be a PIL Image or a Tensor, in which case it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading
-    dimensions
+
      [docs]class TenCrop(object):
+    """Crop the given PIL Image into four corners and the central crop plus the flipped version of
+    these (horizontal flipping is used by default)
 
     .. Note::
          This transform returns a tuple of images and there may be a mismatch in the number of
@@ -1216,7 +1141,7 @@ 
      Source code for torchvision.transforms.transforms
          Args:
         size (sequence or int): Desired output size of the crop. If size is an
             int instead of sequence like (h, w), a square crop (size, size) is
-            made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).
+            made.
         vertical_flip (bool): Use vertical flipping instead of horizontal
 
     Example:
@@ -1232,26 +1157,15 @@ 
      Source code for torchvision.transforms.transforms
          """
 
     def __init__(self, size, vertical_flip=False):
-        super().__init__()
+        self.size = size
         if isinstance(size, numbers.Number):
             self.size = (int(size), int(size))
-        elif isinstance(size, Sequence) and len(size) == 1:
-            self.size = (size[0], size[0])
         else:
-            if len(size) != 2:
-                raise ValueError("Please provide only two dimensions (h, w) for size.")
-
+            assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
             self.size = size
         self.vertical_flip = vertical_flip
 
-    def forward(self, img):
-        """
-        Args:
-            img (PIL Image or Tensor): Image to be cropped.
-
-        Returns:
-            tuple of 10 images. Image can be PIL Image or Tensor
-        """
+    def __call__(self, img):
         return F.ten_crop(img, self.size, self.vertical_flip)
 
     def __repr__(self):
@@ -1709,7 +1623,7 @@ 
      Source code for torchvision.transforms.transforms
      return self.__class__.__name__ + '(p={0})'.format(self.p)
      
 
 
-
      [docs]class RandomErasing(torch.nn.Module):
+
      [docs]class RandomErasing(object):
     """ Randomly selects a rectangle region in an image and erases its pixels.
     'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/pdf/1708.04896.pdf
 
@@ -1736,21 +1650,13 @@ 
      Source code for torchvision.transforms.transforms
          """
 
     def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False):
-        super().__init__()
-        if not isinstance(value, (numbers.Number, str, tuple, list)):
-            raise TypeError("Argument value should be either a number or str or a sequence")
-        if isinstance(value, str) and value != "random":
-            raise ValueError("If value is str, it should be 'random'")
-        if not isinstance(scale, (tuple, list)):
-            raise TypeError("Scale should be a sequence")
-        if not isinstance(ratio, (tuple, list)):
-            raise TypeError("Ratio should be a sequence")
+        assert isinstance(value, (numbers.Number, str, tuple, list))
         if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
-            warnings.warn("Scale and ratio should be of kind (min, max)")
+            warnings.warn("range should be of kind (min, max)")
         if scale[0] < 0 or scale[1] > 1:
-            raise ValueError("Scale should be between 0 and 1")
+            raise ValueError("range of scale should be between 0 and 1")
         if p < 0 or p > 1:
-            raise ValueError("Random erasing probability should be between 0 and 1")
+            raise ValueError("range of random erasing probability should be between 0 and 1")
 
         self.p = p
         self.scale = scale
@@ -1759,18 +1665,13 @@ 
      Source code for torchvision.transforms.transforms
      self.inplace = inplace
 
     @staticmethod
-    def get_params(
-            img: Tensor, scale: Tuple[float, float], ratio: Tuple[float, float], value: Optional[List[float]] = None
-    ) -> Tuple[int, int, int, int, Tensor]:
+    def get_params(img, scale, ratio, value=0):
         """Get parameters for ``erase`` for a random erasing.
 
         Args:
             img (Tensor): Tensor image of size (C, H, W) to be erased.
-            scale (tuple or list): range of proportion of erased area against input image.
-            ratio (tuple or list): range of aspect ratio of erased area.
-            value (list, optional): erasing value. If None, it is interpreted as "random"
-                (erasing each pixel with random values). If ``len(value)`` is 1, it is interpreted as a number,
-                i.e. ``value[0]``.
+            scale: range of proportion of erased area against input image.
+            ratio: range of aspect ratio of erased area.
 
         Returns:
             tuple: params (i, j, h, w, v) to be passed to ``erase`` for random erasing.
@@ -1779,27 +1680,27 @@ 
      Source code for torchvision.transforms.transforms
      area = img_h * img_w
 
         for _ in range(10):
-            erase_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
-            aspect_ratio = torch.empty(1).uniform_(ratio[0], ratio[1]).item()
+            erase_area = random.uniform(scale[0], scale[1]) * area
+            aspect_ratio = random.uniform(ratio[0], ratio[1])
 
             h = int(round(math.sqrt(erase_area * aspect_ratio)))
             w = int(round(math.sqrt(erase_area / aspect_ratio)))
-            if not (h < img_h and w < img_w):
-                continue
-
-            if value is None:
-                v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
-            else:
-                v = torch.tensor(value)[:, None, None]
 
-            i = torch.randint(0, img_h - h + 1, size=(1, )).item()
-            j = torch.randint(0, img_w - w + 1, size=(1, )).item()
-            return i, j, h, w, v
+            if h < img_h and w < img_w:
+                i = random.randint(0, img_h - h)
+                j = random.randint(0, img_w - w)
+                if isinstance(value, numbers.Number):
+                    v = value
+                elif isinstance(value, torch._six.string_classes):
+                    v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
+                elif isinstance(value, (list, tuple)):
+                    v = torch.tensor(value, dtype=torch.float32).view(-1, 1, 1).expand(-1, h, w)
+                return i, j, h, w, v
 
         # Return original image
         return 0, 0, img_h, img_w, img
 
-    def forward(self, img):
+    def __call__(self, img):
         """
         Args:
             img (Tensor): Tensor image of size (C, H, W) to be erased.
@@ -1807,25 +1708,8 @@ 
      Source code for torchvision.transforms.transforms
              Returns:
             img (Tensor): Erased Tensor image.
         """
-        if torch.rand(1) < self.p:
-
-            # cast self.value to script acceptable type
-            if isinstance(self.value, (int, float)):
-                value = [self.value, ]
-            elif isinstance(self.value, str):
-                value = None
-            elif isinstance(self.value, tuple):
-                value = list(self.value)
-            else:
-                value = self.value
-
-            if value is not None and not (len(value) in (1, img.shape[-3])):
-                raise ValueError(
-                    "If value is a sequence, it should have either a single value or "
-                    "{} (number of input channels)".format(img.shape[-3])
-                )
-
-            x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=value)
+        if random.uniform(0, 1) < self.p:
+            x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=self.value)
             return F.erase(img, x, y, h, w, v, self.inplace)
         return img
      
 
      
diff --git a/docs/stable/autograd.html b/docs/stable/autograd.html
index d8dcb62d33a6..1ef84311ab41 100644
--- a/docs/stable/autograd.html
+++ b/docs/stable/autograd.html
@@ -1360,7 +1360,7 @@ 
      Context method mixins
      Numerical gradient checking
      
 
      
 
      
-torch.autograd.gradcheck(func: Callable[[...], Union[torch.Tensor, Sequence[torch.Tensor]]], inputs: Union[torch.Tensor, Sequence[torch.Tensor]], eps: float = 1e-06, atol: float = 1e-05, rtol: float = 0.001, raise_exception: bool = True, check_sparse_nnz: bool = False, nondet_tol: float = 0.0, check_undefined_grad: bool = True) → bool[source]
      
+torch.autograd.gradcheck(func: Callable[..., Union[torch.Tensor, Sequence[torch.Tensor]]], inputs: Union[torch.Tensor, Sequence[torch.Tensor]], eps: float = 1e-06, atol: float = 1e-05, rtol: float = 0.001, raise_exception: bool = True, check_sparse_nnz: bool = False, nondet_tol: float = 0.0, check_undefined_grad: bool = True) → bool[source]
 
      Check gradients computed via small finite differences against analytical
 gradients w.r.t. tensors in inputs that are of floating point or complex type
 and with requires_grad=True.
      
@@ -1408,7 +1408,7 @@ 
      Context method mixins
 
      
-torch.autograd.gradgradcheck(func: Callable[[...], Union[torch.Tensor, Sequence[torch.Tensor]]], inputs: Union[torch.Tensor, Sequence[torch.Tensor]], grad_outputs: Union[torch.Tensor, Sequence[torch.Tensor], None] = None, eps: float = 1e-06, atol: float = 1e-05, rtol: float = 0.001, gen_non_contig_grad_outputs: bool = False, raise_exception: bool = True, nondet_tol: float = 0.0, check_undefined_grad: bool = True) → bool[source]
      
+torch.autograd.gradgradcheck(func: Callable[..., Union[torch.Tensor, Sequence[torch.Tensor]]], inputs: Union[torch.Tensor, Sequence[torch.Tensor]], grad_outputs: Union[torch.Tensor, Sequence[torch.Tensor], None] = None, eps: float = 1e-06, atol: float = 1e-05, rtol: float = 0.001, gen_non_contig_grad_outputs: bool = False, raise_exception: bool = True, nondet_tol: float = 0.0, check_undefined_grad: bool = True) → bool[source]
 
      Check gradients of gradients computed via small finite differences
 against analytical gradients w.r.t. tensors in inputs and
 grad_outputs that are of floating point or complex type and with
diff --git a/docs/stable/community/persons_of_interest.html b/docs/stable/community/persons_of_interest.html
index 900fabc0a847..2f6536a4455a 100644
--- a/docs/stable/community/persons_of_interest.html
+++ b/docs/stable/community/persons_of_interest.html
@@ -182,6 +182,7 @@
 
           
 
+
             
             
               
@@ -250,7 +251,7 @@
 
      
 
      
@@ -866,4 +865,4 @@ 
      Resources
      
     })
   
 
-
+
\ No newline at end of file
diff --git a/docs/stable/complex_numbers.html b/docs/stable/complex_numbers.html
index b97698a73fa1..4d4dd67a8578 100644
--- a/docs/stable/complex_numbers.html
+++ b/docs/stable/complex_numbers.html
@@ -337,11 +337,15 @@
               
   
      
 
      Complex Numbers
      
-
      Complex numbers are numbers that can be expressed in the form a+bja + bj
+
      Complex numbers are numbers that can be expressed in the form a+bja + bj
+
 , where a and b are real numbers,
-and j is a solution of the equation . Complex numbers frequently occur in mathematics and
+and j is a solution of the equation x2=−1x^2 = −1
+
+. Complex numbers frequently occur in mathematics and
 engineering, especially in signal processing. Traditionally many users and libraries (e.g., TorchAudio) have
-handled complex numbers by representing the data in float tensors with shape (...,2)(..., 2)
+handled complex numbers by representing the data in float tensors with shape (...,2)(..., 2)
+
  where the last
 dimension contains the real and imaginary values.
      
 
      Tensors of complex dtypes provide a more natural user experience for working with complex numbers. Operations on
@@ -377,7 +381,8 @@ 
      Creating Complex Tensors
 
      Transition from the old representation
      
-
      Users who currently worked around the lack of complex tensors with real tensors of shape (...,2)(..., 2)
+
      Users who currently worked around the lack of complex tensors with real tensors of shape (...,2)(..., 2)
+
 
 can easily to switch using the complex tensors in their code using torch.view_as_complex()
 and torch.view_as_real(). Note that these functions don’t perform any copy and return a
@@ -456,7 +461,9 @@ 
      Serialization
      
 
      PyTorch supports autograd for complex tensors. The autograd APIs can be
 used for both holomorphic and non-holomorphic functions. For holomorphic functions,
-you get the regular complex gradient. For  real-valued loss functions,
+you get the regular complex gradient. For C→RC → R
+
+ real-valued loss functions,
 grad.conj() gives a descent direction. For more details, check out the note Autograd for Complex Numbers.
      
 
      We do not support the following subsystems:
      
 
      where θ\theta
+
+ are the parameters, α\alpha
+
  is the learning rate,
-rr
- is the reward and p(a∣πθ(s))p(a|\pi^\theta(s))
+rr
+
+ is the reward and p(a∣πθ(s))p(a|\pi^\theta(s))
+
  is the probability of
-taking action aa
- in state ss
- given policy πθ\pi^\theta
+taking action aa
+
+ in state ss
+
+ given policy πθ\pi^\theta
+
 .
      
 
      In practice we would sample an action from the output of a network, apply this
 action in an environment, and then use log_prob to construct an equivalent
@@ -599,12 +609,17 @@ 
      ExponentialFamilyExponentialFamily is the abstract base class for probability distributions belonging to an
 exponential family, whose probability mass/density function has the form is defined below
      
 
      
-pF(x;θ)=exp(⟨t(x),θ⟩−F(θ)+k(x))p_{F}(x; \theta) = \exp(\langle t(x), \theta\rangle - F(\theta) + k(x))
-
      where θ\theta
- denotes the natural parameters, t(x)t(x)
+pF(x;θ)=exp⁡(⟨t(x),θ⟩−F(θ)+k(x))p_{F}(x; \theta) = \exp(\langle t(x), \theta\rangle - F(\theta) + k(x))
+
+
      where θ\theta
+
+ denotes the natural parameters, t(x)t(x)
+
  denotes the sufficient statistic,
-F(θ)F(\theta)
- is the log normalizer function for a given family and k(x)k(x)
+F(θ)F(\theta)
+
+ is the log normalizer function for a given family and k(x)k(x)
+
  is the carrier
 measure.
      
 
      
@@ -902,7 +917,8 @@ 
      Categoricaltorch.multinomial()
 samples from.
      
 
      
-
      Samples are integers from {0,…,K−1}\{0, \ldots, K-1\}
+
      Samples are integers from {0,…,K−1}\{0, \ldots, K-1\}
+
  where K is probs.size(-1).
      
 
      If probs is 1D with length-K, each element is the relative
 probability of sampling the class at that index.
      
@@ -1518,11 +1534,14 @@ 
      Geometrictorch.distributions.distribution.Distribution
      
 
      Creates a Geometric distribution parameterized by probs,
 where probs is the probability of success of Bernoulli trials.
-It represents the probability that in k+1k + 1
+It represents the probability that in k+1k + 1
+
  Bernoulli trials, the
-first kk
+first kk
+
  trials failed, before seeing a success.
      
-
      Samples are non-negative integers [0, inf\inf
+
      Samples are non-negative integers [0, inf⁡\inf
+
 ).
      
 
      Example:
      
 
      >>> m = Geometric(torch.tensor([0.3]))
@@ -2380,14 +2399,18 @@ 
      MultivariateNormalCreates a multivariate normal (also called Gaussian) distribution
 parameterized by a mean vector and a covariance matrix.
      
 
      The multivariate normal distribution can be parameterized either
-in terms of a positive definite covariance matrix Σ\mathbf{\Sigma}
+in terms of a positive definite covariance matrix Σ\mathbf{\Sigma}
+
 
-or a positive definite precision matrix Σ−1\mathbf{\Sigma}^{-1}
+or a positive definite precision matrix Σ−1\mathbf{\Sigma}^{-1}
+
 
-or a lower-triangular matrix L\mathbf{L}
+or a lower-triangular matrix L\mathbf{L}
+
  with positive-valued
 diagonal entries, such that
-Σ=LL⊤\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top
+Σ=LL⊤\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top
+
 . This triangular matrix
 can be obtained via e.g. Cholesky decomposition of the covariance.
      
 
      Example
      
@@ -2804,9 +2827,10 @@ 
      Poissonrate, the rate parameter.
      
 
      Samples are nonnegative integers, with a pmf given by
      
 
      
-rateke−ratek!\mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}
+rateke−ratek!\mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}
+
+
 
-
 
      Example:
      
 
      >>> m = Poisson(torch.tensor([4]))
 >>> m.sample()
@@ -3436,10 +3460,12 @@ 
      Weibull
 
      
 torch.distributions.kl.kl_divergence(p, q)[source]
      
-
      Compute Kullback-Leibler divergence KL(p∥q)KL(p \| q)
+
      Compute Kullback-Leibler divergence KL(p∥q)KL(p \| q)
+
  between two distributions.
      
 
      
-KL(p∥q)=∫p(x)logp(x)q(x)dxKL(p \| q) = \int p(x) \log\frac {p(x)} {q(x)} \,dx
+KL(p∥q)=∫p(x)log⁡p(x)q(x) dxKL(p \| q) = \int p(x) \log\frac {p(x)} {q(x)} \,dx
+
 
      
 
      Parameters
      
 
      
 
 
      
 
      
 class torch.distributions.transforms.PowerTransform(exponent, cache_size=0)[source]
      
-
      Transform via the mapping y=xexponenty = x^{\text{exponent}}
+
      Transform via the mapping y=xexponenty = x^{\text{exponent}}
+
 .
      
 
      
 
 
      
 
      
 class torch.distributions.transforms.SigmoidTransform(cache_size=0)[source]
      
-
      Transform via the mapping y=11+exp(−x)y = \frac{1}{1 + \exp(-x)}
- and x=logit(y)x = \text{logit}(y)
+
      Transform via the mapping y=11+exp⁡(−x)y = \frac{1}{1 + \exp(-x)}
+
+ and x=logit(y)x = \text{logit}(y)
+
 .
      
 
      
 
 
      
 
      
 class torch.distributions.transforms.TanhTransform(cache_size=0)[source]
      
-
      Transform via the mapping y=tanh(x)y = \tanh(x)
+
      Transform via the mapping y=tanh⁡(x)y = \tanh(x)
+
 .
      
 
      It is equivalent to
 `
@@ -3622,14 +3653,16 @@ 
      Weibull
 
      
 class torch.distributions.transforms.AbsTransform(cache_size=0)[source]
      
-
      Transform via the mapping y=∣x∣y = |x|
+
      Transform via the mapping y=∣x∣y = |x|
+
 .
      
 
      
 
 
      
 
      
 class torch.distributions.transforms.AffineTransform(loc, scale, event_dim=0, cache_size=0)[source]
      
-
      Transform via the pointwise affine mapping y=loc+scale×xy = \text{loc} + \text{scale} \times x
+
      Transform via the pointwise affine mapping y=loc+scale×xy = \text{loc} + \text{scale} \times x
+
 .
      
 
      
 
      Parameters
      
@@ -3647,7 +3680,8 @@ 
      Weibull
 
      
 class torch.distributions.transforms.SoftmaxTransform(cache_size=0)[source]
      
-
      Transform from unconstrained space to the simplex via y=exp(x)y = \exp(x)
+
      Transform from unconstrained space to the simplex via y=exp⁡(x)y = \exp(x)
+
  then
 normalizing.
      
 
      This is not bijective and cannot be used for HMC. However this acts mostly
diff --git a/docs/stable/generated/torch.abs.html b/docs/stable/generated/torch.abs.html
index 6bffc9d862e4..927e867d3c57 100644
--- a/docs/stable/generated/torch.abs.html
+++ b/docs/stable/generated/torch.abs.html
@@ -344,9 +344,10 @@ 
      torch.abstorch.abs(input, out=None) → Tensor
 
      Computes the element-wise absolute value of the given input tensor.
      
 
      
-outi=∣inputi∣\text{out}_{i} = |\text{input}_{i}|
+outi=∣inputi∣\text{out}_{i} = |\text{input}_{i}|
+
+
 
-
 
      
 
      Parameters
      
 
        
diff --git a/docs/stable/generated/torch.acos.html b/docs/stable/generated/torch.acos.html
index 1a193bd1bb29..1fee072cd036 100644
--- a/docs/stable/generated/torch.acos.html
+++ b/docs/stable/generated/torch.acos.html
@@ -344,9 +344,10 @@ 
        torch.acostorch.acos(input, out=None) → Tensor
 
        Returns a new tensor with the arccosine  of the elements of input.
        
 
        
-outi=cos−1(inputi)\text{out}_{i} = \cos^{-1}(\text{input}_{i})
+outi=cos⁡−1(inputi)\text{out}_{i} = \cos^{-1}(\text{input}_{i})
+
+
 
-
 
        
 
        Parameters
        
 
          
diff --git a/docs/stable/generated/torch.acosh.html b/docs/stable/generated/torch.acosh.html
index 63e0061356e6..1331f7bb67dd 100644
--- a/docs/stable/generated/torch.acosh.html
+++ b/docs/stable/generated/torch.acosh.html
@@ -349,9 +349,10 @@ 
          torch.acoshNaN, except for + INF for which the output is mapped to + INF.
          
 
      
 
      
-outi=cosh−1(inputi)\text{out}_{i} = \cosh^{-1}(\text{input}_{i})
+outi=cosh⁡−1(inputi)\text{out}_{i} = \cosh^{-1}(\text{input}_{i})
+
+
 
-
 
      
 
      Parameters
      
 
      input (Tensor) – the input tensor.
      
diff --git a/docs/stable/generated/torch.add.html b/docs/stable/generated/torch.add.html
index 4eaa4151e1d5..f6c7c81466f4 100644
--- a/docs/stable/generated/torch.add.html
+++ b/docs/stable/generated/torch.add.html
@@ -345,9 +345,10 @@ 
      torch.addother to each element of the input input
 and returns a new resulting tensor.
      
 
      
-out=input+other\text{out} = \text{input} + \text{other}
+out=input+other\text{out} = \text{input} + \text{other}
+
+
 
-
 
      If input is of type FloatTensor or DoubleTensor, other must be
 a real number, otherwise it should be an integer.
      
 
      
@@ -380,9 +381,10 @@ 
      torch.addinput and other must be
 broadcastable.
      
 
      
-out=input+alpha×other\text{out} = \text{input} + \text{alpha} \times \text{other}
+out=input+alpha×other\text{out} = \text{input} + \text{alpha} \times \text{other}
+
+
 
-
 
      If other is of type FloatTensor or DoubleTensor, alpha must be
 a real number, otherwise it should be an integer.
      
 
      
diff --git a/docs/stable/generated/torch.addbmm.html b/docs/stable/generated/torch.addbmm.html
index bb695b404355..873929fdb7ac 100644
--- a/docs/stable/generated/torch.addbmm.html
+++ b/docs/stable/generated/torch.addbmm.html
@@ -349,18 +349,23 @@ 
      torch.addbmminput is added to the final result.
      
 
      batch1 and batch2 must be 3-D tensors each containing the
 same number of matrices.
      
-
      If batch1 is a (b×n×m)(b \times n \times m)
+
      If batch1 is a (b×n×m)(b \times n \times m)
+
  tensor, batch2 is a
-(b×m×p)(b \times m \times p)
+(b×m×p)(b \times m \times p)
+
  tensor, input must be
-broadcastable with a (n×p)(n \times p)
+broadcastable with a (n×p)(n \times p)
+
  tensor
-and out will be a (n×p)(n \times p)
+and out will be a (n×p)(n \times p)
+
  tensor.
      
 
      
-out=β input+α (∑i=0b−1batch1i@batch2i)out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i)
+out=β input+α (∑i=0b−1batch1i@batch2i)out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i)
+
+
 
-
 
      For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha
 must be real numbers, otherwise they should be integers.
      
 
      
@@ -368,10 +373,12 @@ 
      torch.addbmm
        
 
      • batch1 (Tensor) – the first batch of matrices to be multiplied
        • 
 
      • batch2 (Tensor) – the second batch of matrices to be multiplied
        • 
-
      • beta (Number, optional) – multiplier for input (β\beta
+
      • beta (Number, optional) – multiplier for input (β\beta
+
 )
        • 
 
      • input (Tensor) – matrix to be added
        • 
-
      • alpha (Number, optional) – multiplier for batch1 @ batch2 (α\alpha
+
      • alpha (Number, optional) – multiplier for batch1 @ batch2 (α\alpha
+
 )
        • 
 
      • out (Tensor, optional) – the output tensor.
        • 
 
      
diff --git a/docs/stable/generated/torch.addcdiv.html b/docs/stable/generated/torch.addcdiv.html
index 92159c5181b9..1beb10a20ac6 100644
--- a/docs/stable/generated/torch.addcdiv.html
+++ b/docs/stable/generated/torch.addcdiv.html
@@ -358,9 +358,10 @@ 
      torch.addcdivtensor2).
      
 
      
 
      
-outi=inputi+value×tensor1itensor2i\text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i}
+outi=inputi+value×tensor1itensor2i\text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i}
+
+
 
-
 
      The shapes of input, tensor1, and tensor2 must be
 broadcastable.
      
 
      For inputs of type FloatTensor or DoubleTensor, value must be
@@ -371,7 +372,8 @@ 
      torch.addcdivTensor) – the tensor to be added
      
 
    • tensor1 (Tensor) – the numerator tensor
      • 
 
    • tensor2 (Tensor) – the denominator tensor
      • 
-
    • value (Number, optional) – multiplier for tensor1/tensor2\text{tensor1} / \text{tensor2}
+
    • value (Number, optional) – multiplier for tensor1/tensor2\text{tensor1} / \text{tensor2}
+
 
      • 
 
    • out (Tensor, optional) – the output tensor.
      •
    diff --git a/docs/stable/generated/torch.addcmul.html b/docs/stable/generated/torch.addcmul.html index 2fa6651dc4be..4dc7d1c8d464 100644 --- a/docs/stable/generated/torch.addcmul.html +++ b/docs/stable/generated/torch.addcmul.html @@ -346,9 +346,10 @@

    torch.addcmultensor2, multiply the result by the scalar value and add it to input.

    -outi=inputi+value×tensor1i×tensor2i\text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i +outi=inputi+value×tensor1i×tensor2i\text{out}_i = \text{input}_i + \text{value} \times \text{tensor1}_i \times \text{tensor2}_i + + -

    The shapes of tensor, tensor1, and tensor2 must be broadcastable.

    For inputs of type FloatTensor or DoubleTensor, value must be @@ -359,7 +360,8 @@

    torch.addcmulTensor) – the tensor to be added

  • tensor1 (Tensor) – the tensor to be multiplied

  • tensor2 (Tensor) – the tensor to be multiplied

    • -

  • value (Number, optional) – multiplier for tensor1.∗tensor2tensor1 .* tensor2 +

  • value (Number, optional) – multiplier for tensor1.∗tensor2tensor1 .* tensor2 +

  • out (Tensor, optional) – the output tensor.

    • diff --git a/docs/stable/generated/torch.addmm.html b/docs/stable/generated/torch.addmm.html index 3c6ba275a5f6..eeb7e76bd17b 100644 --- a/docs/stable/generated/torch.addmm.html +++ b/docs/stable/generated/torch.addmm.html @@ -344,20 +344,25 @@

    torch.addmmtorch.addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) → Tensor

    Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.

    -

    If mat1 is a (n×m)(n \times m) +

    If mat1 is a (n×m)(n \times m) + tensor, mat2 is a -(m×p)(m \times p) +(m×p)(m \times p) + tensor, then input must be -broadcastable with a (n×p)(n \times p) +broadcastable with a (n×p)(n \times p) + tensor -and out will be a (n×p)(n \times p) +and out will be a (n×p)(n \times p) + tensor.

    alpha and beta are scaling factors on matrix-vector product between mat1 and mat2 and the added matrix input respectively.

    -out=β input+α (mat1i@mat2i)\text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) +out=β input+α (mat1i@mat2i)\text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) + + -

    For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers.

    @@ -366,10 +371,13 @@

    torch.addmmTensor) – matrix to be added

  • mat1 (Tensor) – the first matrix to be multiplied

  • mat2 (Tensor) – the second matrix to be multiplied

    • -

  • beta (Number, optional) – multiplier for input (β\beta +

  • beta (Number, optional) – multiplier for input (β\beta + )

    • -

  • alpha (Number, optional) – multiplier for mat1@mat2mat1 @ mat2 - (α\alpha +

  • alpha (Number, optional) – multiplier for mat1@mat2mat1 @ mat2 + + (α\alpha + )

  • out (Tensor, optional) – the output tensor.

    • diff --git a/docs/stable/generated/torch.addmv.html b/docs/stable/generated/torch.addmv.html index e08897716924..9a72fa3d4127 100644 --- a/docs/stable/generated/torch.addmv.html +++ b/docs/stable/generated/torch.addmv.html @@ -345,7 +345,8 @@

    torch.addmvmat and the vector vec. The vector input is added to the final result.

    -

    If mat is a (n×m)(n \times m) +

    If mat is a (n×m)(n \times m) + tensor, vec is a 1-D tensor of size m, then input must be broadcastable with a 1-D tensor of size n and @@ -353,9 +354,10 @@

    torch.addmvalpha and beta are scaling factors on matrix-vector product between mat and vec and the added tensor input respectively.

    -out=β input+α (mat@vec)\text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) +out=β input+α (mat@vec)\text{out} = \beta\ \text{input} + \alpha\ (\text{mat} \mathbin{@} \text{vec}) + + -

    For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers

    @@ -364,10 +366,13 @@

    torch.addmvTensor) – vector to be added

  • mat (Tensor) – matrix to be multiplied

  • vec (Tensor) – vector to be multiplied

    • -

  • beta (Number, optional) – multiplier for input (β\beta +

  • beta (Number, optional) – multiplier for input (β\beta + )

    • -

  • alpha (Number, optional) – multiplier for mat@vecmat @ vec - (α\alpha +

  • alpha (Number, optional) – multiplier for mat@vecmat @ vec + + (α\alpha + )

  • out (Tensor, optional) – the output tensor.

    • diff --git a/docs/stable/generated/torch.addr.html b/docs/stable/generated/torch.addr.html index 9e7e6f996d16..005fd8206b83 100644 --- a/docs/stable/generated/torch.addr.html +++ b/docs/stable/generated/torch.addr.html @@ -348,15 +348,18 @@

    torch.addrvec1 and vec2 and the added matrix input respectively.

    -out=β input+α (vec1⊗vec2)\text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) +out=β input+α (vec1⊗vec2)\text{out} = \beta\ \text{input} + \alpha\ (\text{vec1} \otimes \text{vec2}) + + -

    If vec1 is a vector of size n and vec2 is a vector of size m, then input must be broadcastable with a matrix of size -(n×m)(n \times m) +(n×m)(n \times m) + and out will be a matrix of size -(n×m)(n \times m) +(n×m)(n \times m) + .

    For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers

    @@ -366,10 +369,13 @@

    torch.addrTensor) – matrix to be added

  • vec1 (Tensor) – the first vector of the outer product

  • vec2 (Tensor) – the second vector of the outer product

    • -

  • beta (Number, optional) – multiplier for input (β\beta +

  • beta (Number, optional) – multiplier for input (β\beta + )

    • -

  • alpha (Number, optional) – multiplier for vec1⊗vec2\text{vec1} \otimes \text{vec2} - (α\alpha +

  • alpha (Number, optional) – multiplier for vec1⊗vec2\text{vec1} \otimes \text{vec2} + + (α\alpha + )

  • out (Tensor, optional) – the output tensor.

    • diff --git a/docs/stable/generated/torch.allclose.html b/docs/stable/generated/torch.allclose.html index cd887a3b5a29..bed2cfa35f2e 100644 --- a/docs/stable/generated/torch.allclose.html +++ b/docs/stable/generated/torch.allclose.html @@ -344,9 +344,10 @@

    torch.allclosetorch.allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) → bool

    This function checks if all input and other satisfy the condition:

    -∣input−other∣≤atol+rtol×∣other∣\lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert +∣input−other∣≤atol+rtol×∣other∣\lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert + + -

    elementwise, for all elements of input and other. The behaviour of this function is analogous to numpy.allclose

    diff --git a/docs/stable/generated/torch.angle.html b/docs/stable/generated/torch.angle.html index c4a1cf01bef4..aae63c13ab81 100644 --- a/docs/stable/generated/torch.angle.html +++ b/docs/stable/generated/torch.angle.html @@ -344,9 +344,10 @@

    torch.angletorch.angle(input, out=None) → Tensor

    Computes the element-wise angle (in radians) of the given input tensor.

    -outi=angle(inputi)\text{out}_{i} = angle(\text{input}_{i}) +outi=angle(inputi)\text{out}_{i} = angle(\text{input}_{i}) + + -
    Parameters
      diff --git a/docs/stable/generated/torch.arange.html b/docs/stable/generated/torch.arange.html index 4edb45ba64b7..1c4b2c7d6617 100644 --- a/docs/stable/generated/torch.arange.html +++ b/docs/stable/generated/torch.arange.html @@ -342,7 +342,8 @@

      torch.arange
      torch.arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
      -

      Returns a 1-D tensor of size ⌈end−startstep⌉\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil +

      Returns a 1-D tensor of size ⌈end−startstep⌉\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil + with values from the interval [start, end) taken with common difference step beginning from start.

      @@ -350,9 +351,10 @@

      torch.arangeend; to avoid inconsistency, we advise adding a small epsilon to end in such cases.

      -outi+1=outi+step\text{out}_{{i+1}} = \text{out}_{i} + \text{step} +outi+1=outi+step\text{out}_{{i+1}} = \text{out}_{i} + \text{step} + + -
      Parameters
        diff --git a/docs/stable/generated/torch.asin.html b/docs/stable/generated/torch.asin.html index 68ff1cd85adb..5c454f23b236 100644 --- a/docs/stable/generated/torch.asin.html +++ b/docs/stable/generated/torch.asin.html @@ -344,9 +344,10 @@

        torch.asintorch.asin(input, out=None) → Tensor

        Returns a new tensor with the arcsine of the elements of input.

        -outi=sin−1(inputi)\text{out}_{i} = \sin^{-1}(\text{input}_{i}) +outi=sin⁡−1(inputi)\text{out}_{i} = \sin^{-1}(\text{input}_{i}) + + -
        Parameters
          diff --git a/docs/stable/generated/torch.asinh.html b/docs/stable/generated/torch.asinh.html index 27abe2798e3f..dadf0b94f585 100644 --- a/docs/stable/generated/torch.asinh.html +++ b/docs/stable/generated/torch.asinh.html @@ -344,9 +344,10 @@

          torch.asinhtorch.asinh(input, out=None) → Tensor

          Returns a new tensor with the inverse hyperbolic sine of the elements of input.

          -outi=sinh−1(inputi)\text{out}_{i} = \sinh^{-1}(\text{input}_{i}) +outi=sinh⁡−1(inputi)\text{out}_{i} = \sinh^{-1}(\text{input}_{i}) + + -
          Parameters

          input (Tensor) – the input tensor.

          diff --git a/docs/stable/generated/torch.atan.html b/docs/stable/generated/torch.atan.html index 2bf3ff5dd832..d387439e171c 100644 --- a/docs/stable/generated/torch.atan.html +++ b/docs/stable/generated/torch.atan.html @@ -344,9 +344,10 @@

          torch.atantorch.atan(input, out=None) → Tensor

          Returns a new tensor with the arctangent of the elements of input.

          -outi=tan−1(inputi)\text{out}_{i} = \tan^{-1}(\text{input}_{i}) +outi=tan⁡−1(inputi)\text{out}_{i} = \tan^{-1}(\text{input}_{i}) + + -
          Parameters
            diff --git a/docs/stable/generated/torch.atan2.html b/docs/stable/generated/torch.atan2.html index 80a98926b866..02e3c61a1eb9 100644 --- a/docs/stable/generated/torch.atan2.html +++ b/docs/stable/generated/torch.atan2.html @@ -342,15 +342,20 @@

            torch.atan2
            torch.atan2(input, other, out=None) → Tensor
            -

            Element-wise arctangent of inputi/otheri\text{input}_{i} / \text{other}_{i} +

            Element-wise arctangent of inputi/otheri\text{input}_{i} / \text{other}_{i} + with consideration of the quadrant. Returns a new tensor with the signed angles -in radians between vector (otheri,inputi)(\text{other}_{i}, \text{input}_{i}) +in radians between vector (otheri,inputi)(\text{other}_{i}, \text{input}_{i}) + -and vector (1,0)(1, 0) -. (Note that otheri\text{other}_{i} +and vector (1,0)(1, 0) + +. (Note that otheri\text{other}_{i} + , the second -parameter, is the x-coordinate, while inputi\text{input}_{i} +parameter, is the x-coordinate, while inputi\text{input}_{i} + , the first parameter, is the y-coordinate.)

            The shapes of input and other must be diff --git a/docs/stable/generated/torch.atanh.html b/docs/stable/generated/torch.atanh.html index 6744fd08bcb1..62b1029e9a97 100644 --- a/docs/stable/generated/torch.atanh.html +++ b/docs/stable/generated/torch.atanh.html @@ -350,9 +350,10 @@

            torch.atanh -outi=tanh−1(inputi)\text{out}_{i} = \tanh^{-1}(\text{input}_{i}) +outi=tanh⁡−1(inputi)\text{out}_{i} = \tanh^{-1}(\text{input}_{i}) + + -
            Parameters

            input (Tensor) – the input tensor.

            diff --git a/docs/stable/generated/torch.baddbmm.html b/docs/stable/generated/torch.baddbmm.html index 7a168e5ca177..ef2a4de9dcf9 100644 --- a/docs/stable/generated/torch.baddbmm.html +++ b/docs/stable/generated/torch.baddbmm.html @@ -347,20 +347,25 @@

            torch.baddbmminput is added to the final result.

            batch1 and batch2 must be 3-D tensors each containing the same number of matrices.

            -

            If batch1 is a (b×n×m)(b \times n \times m) +

            If batch1 is a (b×n×m)(b \times n \times m) + tensor, batch2 is a -(b×m×p)(b \times m \times p) +(b×m×p)(b \times m \times p) + tensor, then input must be broadcastable with a -(b×n×p)(b \times n \times p) +(b×n×p)(b \times n \times p) + tensor and out will be a -(b×n×p)(b \times n \times p) +(b×n×p)(b \times n \times p) + tensor. Both alpha and beta mean the same as the scaling factors used in torch.addbmm().

            -outi=β inputi+α (batch1i@batch2i)\text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) +outi=β inputi+α (batch1i@batch2i)\text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + -

            For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers.

            @@ -369,10 +374,13 @@

            torch.baddbmmTensor) – the tensor to be added

          • batch1 (Tensor) – the first batch of matrices to be multiplied

          • batch2 (Tensor) – the second batch of matrices to be multiplied

            • -

          • beta (Number, optional) – multiplier for input (β\beta +

          • beta (Number, optional) – multiplier for input (β\beta + )

            • -

          • alpha (Number, optional) – multiplier for batch1@batch2\text{batch1} \mathbin{@} \text{batch2} - (α\alpha +

          • alpha (Number, optional) – multiplier for batch1@batch2\text{batch1} \mathbin{@} \text{batch2} + + (α\alpha + )

          • out (Tensor, optional) – the output tensor.

          diff --git a/docs/stable/generated/torch.bartlett_window.html b/docs/stable/generated/torch.bartlett_window.html index 23714eebeb86..aa57ce6b4de6 100644 --- a/docs/stable/generated/torch.bartlett_window.html +++ b/docs/stable/generated/torch.bartlett_window.html @@ -344,27 +344,32 @@

          torch.bartlett_windowtorch.bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

          Bartlett window function.

          -w[n]=1−∣2nN−1−1∣={2nN−1if 0≤n≤N−122−2nN−1if N−12<n<N,w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} +w[n]=1−∣2nN−1−1∣={2nN−1if 0≤n≤N−122−2nN−1if N−12<n<N,w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\ 2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\ \end{cases}, - -

          where NN + + +

          where NN + is the full window size.

          The input window_length is a positive integer controlling the returned window size. periodic flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like -torch.stft(). Therefore, if periodic is true, the NN +torch.stft(). Therefore, if periodic is true, the NN + in -above formula is in fact window_length+1\text{window\_length} + 1 +above formula is in fact window_length+1\text{window\_length} + 1 + . Also, we always have torch.bartlett_window(L, periodic=True) equal to torch.bartlett_window(L + 1, periodic=False)[:-1]).

          Note

          -

          If window_length =1=1 +

          If window_length =1=1 + , the returned window contains a single value 1.

          @@ -386,7 +391,8 @@

          torch.bartlett_window

          Returns
          -

          A 1-D tensor of size (window_length,)(\text{window\_length},) +

          A 1-D tensor of size (window_length,)(\text{window\_length},) + containing the window

          Return type
          diff --git a/docs/stable/generated/torch.bernoulli.html b/docs/stable/generated/torch.bernoulli.html index c582c39c9f34..693626518a44 100644 --- a/docs/stable/generated/torch.bernoulli.html +++ b/docs/stable/generated/torch.bernoulli.html @@ -346,18 +346,23 @@

          torch.bernoulliinput tensor should be a tensor containing probabilities to be used for drawing the binary random number. Hence, all values in input have to be in the range: -0≤inputi≤10 \leq \text{input}_i \leq 1 +0≤inputi≤10 \leq \text{input}_i \leq 1 + .

          -

          The ith\text{i}^{th} +

          The ith\text{i}^{th} + element of the output tensor will draw a -value 11 - according to the ith\text{i}^{th} +value 11 + + according to the ith\text{i}^{th} + probability value given in input.

          -outi∼Bernoulli(p=inputi)\text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i}) +outi∼Bernoulli(p=inputi)\text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i}) + + -

          The returned out tensor only has values 0 or 1 and is of the same shape as input.

          out can have integral dtype, but input must have floating diff --git a/docs/stable/generated/torch.blackman_window.html b/docs/stable/generated/torch.blackman_window.html index 15224eba783c..fd8dbc5e8aca 100644 --- a/docs/stable/generated/torch.blackman_window.html +++ b/docs/stable/generated/torch.blackman_window.html @@ -344,24 +344,29 @@

          torch.blackman_windowtorch.blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

          Blackman window function.

          -w[n]=0.42−0.5cos(2πnN−1)+0.08cos(4πnN−1)w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right) +w[n]=0.42−0.5cos⁡(2πnN−1)+0.08cos⁡(4πnN−1)w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right) + + + +

          where NN - -

          where NN is the full window size.

          The input window_length is a positive integer controlling the returned window size. periodic flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like -torch.stft(). Therefore, if periodic is true, the NN +torch.stft(). Therefore, if periodic is true, the NN + in -above formula is in fact window_length+1\text{window\_length} + 1 +above formula is in fact window_length+1\text{window\_length} + 1 + . Also, we always have torch.blackman_window(L, periodic=True) equal to torch.blackman_window(L + 1, periodic=False)[:-1]).

          Note

          -

          If window_length =1=1 +

          If window_length =1=1 + , the returned window contains a single value 1.

          @@ -383,7 +388,8 @@

          torch.blackman_window

          Returns
          -

          A 1-D tensor of size (window_length,)(\text{window\_length},) +

          A 1-D tensor of size (window_length,)(\text{window\_length},) + containing the window

          Return type
          diff --git a/docs/stable/generated/torch.bmm.html b/docs/stable/generated/torch.bmm.html index 0d431ec1eba3..19c1783362a6 100644 --- a/docs/stable/generated/torch.bmm.html +++ b/docs/stable/generated/torch.bmm.html @@ -346,16 +346,20 @@

          torch.bmmmat2.

          input and mat2 must be 3-D tensors each containing the same number of matrices.

          -

          If input is a (b×n×m)(b \times n \times m) +

          If input is a (b×n×m)(b \times n \times m) + tensor, mat2 is a -(b×m×p)(b \times m \times p) +(b×m×p)(b \times m \times p) + tensor, out will be a -(b×n×p)(b \times n \times p) +(b×n×p)(b \times n \times p) + tensor.

          -outi=inputi@mat2i\text{out}_i = \text{input}_i \mathbin{@} \text{mat2}_i +outi=inputi@mat2i\text{out}_i = \text{input}_i \mathbin{@} \text{mat2}_i + + -

          Note

          This function does not broadcast. diff --git a/docs/stable/generated/torch.cdist.html b/docs/stable/generated/torch.cdist.html index 2d56d131c797..be92332d7921 100644 --- a/docs/stable/generated/torch.cdist.html +++ b/docs/stable/generated/torch.cdist.html @@ -341,17 +341,20 @@

          torch.cdist

          -torch.cdist(x1: torch.Tensor, x2: torch.Tensor, p: float = 2.0, compute_mode: str = 'use_mm_for_euclid_dist_if_necessary') → torch.Tensor[source]
          +torch.cdist(x1, x2, p=2.0, compute_mode='use_mm_for_euclid_dist_if_necessary')[source]

          Computes batched the p-norm distance between each pair of the two collections of row vectors.

          Parameters

          If upper is True, and AA + is a batch of symmetric positive-definite matrices, then the returned tensor will be composed of upper-triangular Cholesky factors of each of the individual matrices. Similarly, when upper is False, the returned @@ -362,8 +366,10 @@

          torch.cholesky
          Parameters
            -

          • input (Tensor) – the input tensor AA - of size (∗,n,n)(*, n, n) +

          • input (Tensor) – the input tensor AA + + of size (∗,n,n)(*, n, n) + where * is zero or more batch dimensions consisting of symmetric positive-definite matrices.

          • upper (bool, optional) – flag that indicates whether to return a diff --git a/docs/stable/generated/torch.cholesky_inverse.html b/docs/stable/generated/torch.cholesky_inverse.html index db0ad622225a..612b16187581 100644 --- a/docs/stable/generated/torch.cholesky_inverse.html +++ b/docs/stable/generated/torch.cholesky_inverse.html @@ -342,29 +342,36 @@

            torch.cholesky_inverse
            torch.cholesky_inverse(input, upper=False, out=None) → Tensor
            -

            Computes the inverse of a symmetric positive-definite matrix AA +

            Computes the inverse of a symmetric positive-definite matrix AA + using its -Cholesky factor uu +Cholesky factor uu + : returns matrix inv. The inverse is computed using LAPACK routines dpotri and spotri (and the corresponding MAGMA routines).

            -

            If upper is False, uu +

            If upper is False, uu + is lower triangular such that the returned tensor is

            -inv=(uuT)−1inv = (uu^{{T}})^{{-1}} +inv=(uuT)−1inv = (uu^{{T}})^{{-1}} + + + +

            If upper is True or not provided, uu - -

            If upper is True or not provided, uu is upper triangular such that the returned tensor is

            -inv=(uTu)−1inv = (u^T u)^{{-1}} +inv=(uTu)−1inv = (u^T u)^{{-1}} + + -
            Parameters
              -

            • input (Tensor) – the input 2-D tensor uu +

            • input (Tensor) – the input 2-D tensor uu + , a upper or lower triangular Cholesky factor

            • upper (bool, optional) – whether to return a lower (default) or upper triangular matrix

              • diff --git a/docs/stable/generated/torch.cholesky_solve.html b/docs/stable/generated/torch.cholesky_solve.html index 19611bc647ff..4ffd9d9d6f1d 100644 --- a/docs/stable/generated/torch.cholesky_solve.html +++ b/docs/stable/generated/torch.cholesky_solve.html @@ -343,37 +343,48 @@

              torch.cholesky_solve torch.cholesky_solve(input, input2, upper=False, out=None) → Tensor

              Solves a linear system of equations with a positive semidefinite -matrix to be inverted given its Cholesky factor matrix uu +matrix to be inverted given its Cholesky factor matrix uu + .

              -

              If upper is False, uu +

              If upper is False, uu + is and lower triangular and c is returned such that:

              -c=(uuT)−1bc = (u u^T)^{{-1}} b +c=(uuT)−1bc = (u u^T)^{{-1}} b + + + +

              If upper is True or not provided, uu - -

              If upper is True or not provided, uu is upper triangular and c is returned such that:

              -c=(uTu)−1bc = (u^T u)^{{-1}} b +c=(uTu)−1bc = (u^T u)^{{-1}} b + + -

              torch.cholesky_solve(b, u) can take in 2D inputs b, u or inputs that are batches of 2D matrices. If the inputs are batches, then returns batched outputs c

              Parameters
                -

              • input (Tensor) – input matrix bb - of size (∗,m,k)(*, m, k) +

              • input (Tensor) – input matrix bb + + of size (∗,m,k)(*, m, k) + , -where ∗* +where ∗* + is zero or more batch dimensions

                • -

              • input2 (Tensor) – input matrix uu - of size (∗,m,m)(*, m, m) +

              • input2 (Tensor) – input matrix uu + + of size (∗,m,m)(*, m, m) + , -where ∗* +where ∗* + is zero of more batch dimensions composed of upper or lower triangular Cholesky factor

              • upper (bool, optional) – whether to consider the Cholesky factor as a diff --git a/docs/stable/generated/torch.clamp.html b/docs/stable/generated/torch.clamp.html index 90202efc6607..c15bf5abf2d0 100644 --- a/docs/stable/generated/torch.clamp.html +++ b/docs/stable/generated/torch.clamp.html @@ -345,13 +345,14 @@

                torch.clampinput into the range [ min, max ] and return a resulting tensor:

                -yi={minif xi<minxiif min≤xi≤maxmaxif xi>maxy_i = \begin{cases} +yi={minif xi<minxiif min≤xi≤maxmaxif xi>maxy_i = \begin{cases} \text{min} & \text{if } x_i < \text{min} \\ x_i & \text{if } \text{min} \leq x_i \leq \text{max} \\ \text{max} & \text{if } x_i > \text{max} \end{cases} - + +

                If input is of type FloatTensor or DoubleTensor, args min and max must be real numbers, otherwise they should be integers.

                diff --git a/docs/stable/generated/torch.combinations.html b/docs/stable/generated/torch.combinations.html index faa1b5945a1b..7803bc5d5983 100644 --- a/docs/stable/generated/torch.combinations.html +++ b/docs/stable/generated/torch.combinations.html @@ -342,7 +342,8 @@

                torch.combinations
                torch.combinations(input, r=2, with_replacement=False) → seq
                -

                Compute combinations of length rr +

                Compute combinations of length rr + of the given tensor. The behavior is similar to python’s itertools.combinations when with_replacement is set to False, and itertools.combinations_with_replacement when with_replacement is set to True.

                diff --git a/docs/stable/generated/torch.conj.html b/docs/stable/generated/torch.conj.html index 1b5ad6ccf528..739944dea16d 100644 --- a/docs/stable/generated/torch.conj.html +++ b/docs/stable/generated/torch.conj.html @@ -344,9 +344,10 @@

                torch.conjtorch.conj(input, out=None) → Tensor

                Computes the element-wise conjugate of the given input tensor.

                -outi=conj(inputi)\text{out}_{i} = conj(\text{input}_{i}) +outi=conj(inputi)\text{out}_{i} = conj(\text{input}_{i}) + + -
                Parameters
                  diff --git a/docs/stable/generated/torch.cos.html b/docs/stable/generated/torch.cos.html index 0f43a7f0308e..243aa004dc7e 100644 --- a/docs/stable/generated/torch.cos.html +++ b/docs/stable/generated/torch.cos.html @@ -344,9 +344,10 @@

                  torch.costorch.cos(input, out=None) → Tensor

                  Returns a new tensor with the cosine of the elements of input.

                  -outi=cos(inputi)\text{out}_{i} = \cos(\text{input}_{i}) +outi=cos⁡(inputi)\text{out}_{i} = \cos(\text{input}_{i}) + + -
                  Parameters
                    diff --git a/docs/stable/generated/torch.cosh.html b/docs/stable/generated/torch.cosh.html index f8f6a1eee20b..be358df1c1ba 100644 --- a/docs/stable/generated/torch.cosh.html +++ b/docs/stable/generated/torch.cosh.html @@ -345,9 +345,10 @@

                    torch.coshinput.

                    -outi=cosh(inputi)\text{out}_{i} = \cosh(\text{input}_{i}) +outi=cosh⁡(inputi)\text{out}_{i} = \cosh(\text{input}_{i}) + + -
                    Parameters
                      diff --git a/docs/stable/generated/torch.cummax.html b/docs/stable/generated/torch.cummax.html index 9c99386144ac..6cebcc945bbb 100644 --- a/docs/stable/generated/torch.cummax.html +++ b/docs/stable/generated/torch.cummax.html @@ -346,6 +346,10 @@

                      torch.cummaxinput in the dimension dim. And indices is the index location of each maximum value found in the dimension dim.

                      +yi=max(x1,x2,x3,…,xi)y_i = max(x_1, x_2, x_3, \dots, x_i) + + +
                      Parameters
                        diff --git a/docs/stable/generated/torch.cummin.html b/docs/stable/generated/torch.cummin.html index ef6d7e449401..30637225cdbb 100644 --- a/docs/stable/generated/torch.cummin.html +++ b/docs/stable/generated/torch.cummin.html @@ -346,6 +346,10 @@

                        torch.cummininput in the dimension dim. And indices is the index location of each maximum value found in the dimension dim.

                        +yi=min(x1,x2,x3,…,xi)y_i = min(x_1, x_2, x_3, \dots, x_i) + + +
                        Parameters
                          diff --git a/docs/stable/generated/torch.cumprod.html b/docs/stable/generated/torch.cumprod.html index b7183363cdd9..ea5f16d3075d 100644 --- a/docs/stable/generated/torch.cumprod.html +++ b/docs/stable/generated/torch.cumprod.html @@ -347,6 +347,10 @@

                          torch.cumprodinput is a vector of size N, the result will also be a vector of size N, with elements.

                          +yi=x1×x2×x3×⋯×xiy_i = x_1 \times x_2\times x_3\times \dots \times x_i + + +
                          Parameters
                            diff --git a/docs/stable/generated/torch.cumsum.html b/docs/stable/generated/torch.cumsum.html index 15ab224c9d2b..cdfd572c002a 100644 --- a/docs/stable/generated/torch.cumsum.html +++ b/docs/stable/generated/torch.cumsum.html @@ -347,6 +347,10 @@

                            torch.cumsuminput is a vector of size N, the result will also be a vector of size N, with elements.

                            +yi=x1+x2+x3+⋯+xiy_i = x_1 + x_2 + x_3 + \dots + x_i + + +
                            Parameters
                              diff --git a/docs/stable/generated/torch.diag_embed.html b/docs/stable/generated/torch.diag_embed.html index f36ea78221a6..17dfd3fea91c 100644 --- a/docs/stable/generated/torch.diag_embed.html +++ b/docs/stable/generated/torch.diag_embed.html @@ -354,7 +354,8 @@

                              torch.diag_embedoffset other than 00 +Note that for offset other than 00 + , the order of dim1 and dim2 matters. Exchanging them is equivalent to changing the sign of offset.

                              diff --git a/docs/stable/generated/torch.digamma.html b/docs/stable/generated/torch.digamma.html index ced9ce513f04..ab0f4489f96d 100644 --- a/docs/stable/generated/torch.digamma.html +++ b/docs/stable/generated/torch.digamma.html @@ -344,9 +344,10 @@

                              torch.digammatorch.digamma(input, out=None) → Tensor

                              Computes the logarithmic derivative of the gamma function on input.

                              -ψ(x)=ddxln(Γ(x))=Γ′(x)Γ(x)\psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)} +ψ(x)=ddxln⁡(Γ(x))=Γ′(x)Γ(x)\psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)} + + -
                              Parameters

                              input (Tensor) – the tensor to compute the digamma function on

                              diff --git a/docs/stable/generated/torch.div.html b/docs/stable/generated/torch.div.html index 1b0d5849824c..6e50fd94ab7a 100644 --- a/docs/stable/generated/torch.div.html +++ b/docs/stable/generated/torch.div.html @@ -351,9 +351,10 @@

                              torch.divtorch.floor_divide() (// in Python), instead.

                              -outi=inputiother\text{out}_i = \frac{\text{input}_i}{\text{other}} +outi=inputiother\text{out}_i = \frac{\text{input}_i}{\text{other}} + + -

                              If the torch.dtype of input and other differ, the torch.dtype of the result tensor is determined following rules described in the type promotion documentation. If @@ -387,9 +388,10 @@

                              torch.divinput is divided by each element of the tensor other. The resulting tensor is returned.

                              -outi=inputiotheri\text{out}_i = \frac{\text{input}_i}{\text{other}_i} +outi=inputiotheri\text{out}_i = \frac{\text{input}_i}{\text{other}_i} + + -

                              The shapes of input and other must be broadcastable. If the torch.dtype of input and other differ, the torch.dtype of the result tensor is determined following rules described in the type promotion documentation. If out is specified, the result must be diff --git a/docs/stable/generated/torch.eig.html b/docs/stable/generated/torch.eig.html index dc56b8d9f208..99f10c85bdf2 100644 --- a/docs/stable/generated/torch.eig.html +++ b/docs/stable/generated/torch.eig.html @@ -351,7 +351,8 @@

                              torch.eig
                              Parameters
                                -

                              • input (Tensor) – the square matrix of shape (n×n)(n \times n) +

                              • input (Tensor) – the square matrix of shape (n×n)(n \times n) + for which the eigenvalues and eigenvectors will be computed

                              • eigenvectors (bool) – True to compute both eigenvalues and eigenvectors; @@ -363,21 +364,25 @@

                                torch.eig

                                A namedtuple (eigenvalues, eigenvectors) containing

                                  -

                                • eigenvalues (Tensor): Shape (n×2)(n \times 2) +

                                • eigenvalues (Tensor): Shape (n×2)(n \times 2) + . Each row is an eigenvalue of input, where the first element is the real part and the second element is the imaginary part. The eigenvalues are not necessarily ordered.

                                • eigenvectors (Tensor): If eigenvectors=False, it’s an empty tensor. -Otherwise, this tensor of shape (n×n)(n \times n) +Otherwise, this tensor of shape (n×n)(n \times n) + can be used to compute normalized (unit length) eigenvectors of corresponding eigenvalues as follows. If the corresponding eigenvalues[j] is a real number, column eigenvectors[:, j] is the eigenvector corresponding to eigenvalues[j]. If the corresponding eigenvalues[j] and eigenvalues[j + 1] form a complex conjugate pair, then the true eigenvectors can be computed as -true eigenvector[j]=eigenvectors[:,j]+i×eigenvectors[:,j+1]\text{true eigenvector}[j] = eigenvectors[:, j] + i \times eigenvectors[:, j + 1] +true eigenvector[j]=eigenvectors[:,j]+i×eigenvectors[:,j+1]\text{true eigenvector}[j] = eigenvectors[:, j] + i \times eigenvectors[:, j + 1] + , -true eigenvector[j+1]=eigenvectors[:,j]−i×eigenvectors[:,j+1]\text{true eigenvector}[j + 1] = eigenvectors[:, j] - i \times eigenvectors[:, j + 1] +true eigenvector[j+1]=eigenvectors[:,j]−i×eigenvectors[:,j+1]\text{true eigenvector}[j + 1] = eigenvectors[:, j] - i \times eigenvectors[:, j + 1] + .

                                diff --git a/docs/stable/generated/torch.erf.html b/docs/stable/generated/torch.erf.html index bc348fc6ed20..b320c84f9c9b 100644 --- a/docs/stable/generated/torch.erf.html +++ b/docs/stable/generated/torch.erf.html @@ -344,16 +344,21 @@

                                torch.erftorch.erf(input, out=None) → Tensor

                                Computes the error function of each element. The error function is defined as follows:

                                -erf(x)=2π∫0xe−t2dt\mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt - - +erf(x)=2π∫0xe−t2dt\mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt + + +
                                Parameters
                                  diff --git a/docs/stable/generated/torch.erfc.html b/docs/stable/generated/torch.erfc.html index 53bb93dee26d..3acd8dc24ebe 100644 --- a/docs/stable/generated/torch.erfc.html +++ b/docs/stable/generated/torch.erfc.html @@ -345,16 +345,21 @@

                                  torch.erfcinput. The complementary error function is defined as follows:

                                  -erfc(x)=1−2π∫0xe−t2dt\mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt - - +erfc(x)=1−2π∫0xe−t2dt\mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt + + +
                                  Parameters
                                  diff --git a/docs/stable/generated/torch.nn.Module.html b/docs/stable/generated/torch.nn.Module.html index 168da5056bde..6c74247a1df3 100644 --- a/docs/stable/generated/torch.nn.Module.html +++ b/docs/stable/generated/torch.nn.Module.html @@ -380,7 +380,7 @@

                                  Module
                                  -apply(fn: Callable[[Module], None]) → T[source]
                                  +apply(fn: Callable[Module, None]) → T[source]

                                  Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also torch.nn.init).

                                  @@ -841,7 +841,7 @@

                                  Module
                                  -register_forward_hook(hook: Callable[[...], None]) → torch.utils.hooks.RemovableHandle[source]
                                  +register_forward_hook(hook: Callable[..., None]) → torch.utils.hooks.RemovableHandle[source]

                                  Registers a forward hook on the module.

                                  The hook will be called every time after forward() has computed an output. It should have the following signature:

                                  @@ -866,7 +866,7 @@

                                  Module
                                  -register_forward_pre_hook(hook: Callable[[...], None]) → torch.utils.hooks.RemovableHandle[source]
                                  +register_forward_pre_hook(hook: Callable[..., None]) → torch.utils.hooks.RemovableHandle[source]

                                  Registers a forward pre-hook on the module.

                                  The hook will be called every time before forward() is invoked. It should have the following signature:

                                  diff --git a/docs/stable/generated/torch.nn.MultiLabelMarginLoss.html b/docs/stable/generated/torch.nn.MultiLabelMarginLoss.html index a1b084685667..bb4dd613881b 100644 --- a/docs/stable/generated/torch.nn.MultiLabelMarginLoss.html +++ b/docs/stable/generated/torch.nn.MultiLabelMarginLoss.html @@ -343,24 +343,35 @@

                                  MultiLabelMarginLoss class torch.nn.MultiLabelMarginLoss(size_average=None, reduce=None, reduction: str = 'mean')[source]

                                  Creates a criterion that optimizes a multi-class multi-classification -hinge loss (margin-based loss) between input xx +hinge loss (margin-based loss) between input xx + (a 2D mini-batch Tensor) -and output yy +and output yy + (which is a 2D Tensor of target class indices). For each sample in the mini-batch:

                                  -loss(x,y)=∑ijmax(0,1−(x[y[j]]−x[i]))x.size(0)\text{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\text{x.size}(0)} - - -

                                  where x∈{0,⋯,x.size(0)−1}x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\} -, y∈{0,⋯,y.size(0)−1}y \in \left\{0, \; \cdots , \; \text{y.size}(0) - 1\right\} -, 0≤y[j]≤x.size(0)−10 \leq y[j] \leq \text{x.size}(0)-1 -, and i≠y[j]i \neq y[j] - for all ii - and jj +loss(x,y)=∑ijmax⁡(0,1−(x[y[j]]−x[i]))x.size(0)\text{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\text{x.size}(0)} + + + +

                                  where x∈{0,  ⋯ ,  x.size(0)−1}x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\} + +, y∈{0,  ⋯ ,  y.size(0)−1}y \in \left\{0, \; \cdots , \; \text{y.size}(0) - 1\right\} + +, 0≤y[j]≤x.size(0)−10 \leq y[j] \leq \text{x.size}(0)-1 + +, and i≠y[j]i \neq y[j] + + for all ii + + and jj + .

                                  -

                                  yy - and xx +

                                  yy + + and xx + must have the same size.

                                  The criterion only considers a contiguous block of non-negative targets that starts at the front.

                                  @@ -388,14 +399,19 @@

                                  MultiLabelMarginLoss
                                  Shape:
                                    -

                                  • Input: (C)(C) - or (N,C)(N, C) +

                                  • Input: (C)(C) + + or (N,C)(N, C) + where N is the batch size and C is the number of classes.

                                    • -

                                  • Target: (C)(C) - or (N,C)(N, C) +

                                  • Target: (C)(C) + + or (N,C)(N, C) + , label targets padded by -1 ensuring same shape as the input.

                                    • -

                                  • Output: scalar. If reduction is 'none', then (N)(N) +

                                  • Output: scalar. If reduction is 'none', then (N)(N) + .

                                  diff --git a/docs/stable/generated/torch.nn.MultiLabelSoftMarginLoss.html b/docs/stable/generated/torch.nn.MultiLabelSoftMarginLoss.html index 3e0e07c82bb8..93d8801117db 100644 --- a/docs/stable/generated/torch.nn.MultiLabelSoftMarginLoss.html +++ b/docs/stable/generated/torch.nn.MultiLabelSoftMarginLoss.html @@ -343,20 +343,26 @@

                                  MultiLabelSoftMarginLoss class torch.nn.MultiLabelSoftMarginLoss(weight: Optional[torch.Tensor] = None, size_average=None, reduce=None, reduction: str = 'mean')[source]

                                  Creates a criterion that optimizes a multi-label one-versus-all -loss based on max-entropy, between input xx - and target yy +loss based on max-entropy, between input xx + + and target yy + of size -(N,C)(N, C) +(N,C)(N, C) + . For each sample in the minibatch:

                                  -loss(x,y)=−1C∗∑iy[i]∗log((1+exp(−x[i]))−1)+(1−y[i])∗log(exp(−x[i])(1+exp(−x[i])))loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1}) +loss(x,y)=−1C∗∑iy[i]∗log⁡((1+exp⁡(−x[i]))−1)+(1−y[i])∗log⁡(exp⁡(−x[i])(1+exp⁡(−x[i])))loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1}) + (1-y[i]) * \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right) - -

                                  where i∈{0,⋯,x.nElement()−1}i \in \left\{0, \; \cdots , \; \text{x.nElement}() - 1\right\} + + +

                                  where i∈{0,  ⋯ ,  x.nElement()−1}i \in \left\{0, \; \cdots , \; \text{x.nElement}() - 1\right\} + , -y[i]∈{0,1}y[i] \in \left\{0, \; 1\right\} +y[i]∈{0,  1}y[i] \in \left\{0, \; 1\right\} + .

                                  Parameters
                                  @@ -384,11 +390,14 @@

                                  MultiLabelSoftMarginLoss
                                  Shape:
                                    -

                                  • Input: (N,C)(N, C) +

                                  • Input: (N,C)(N, C) + where N is the batch size and C is the number of classes.

                                    • -

                                  • Target: (N,C)(N, C) +

                                  • Target: (N,C)(N, C) + , label targets padded by -1 ensuring same shape as the input.

                                    • -

                                  • Output: scalar. If reduction is 'none', then (N)(N) +

                                  • Output: scalar. If reduction is 'none', then (N)(N) + .

                                  diff --git a/docs/stable/generated/torch.nn.MultiMarginLoss.html b/docs/stable/generated/torch.nn.MultiMarginLoss.html index 770d4e2ce3a0..2839b83b19a1 100644 --- a/docs/stable/generated/torch.nn.MultiMarginLoss.html +++ b/docs/stable/generated/torch.nn.MultiMarginLoss.html @@ -343,40 +343,53 @@

                                  MultiMarginLoss class torch.nn.MultiMarginLoss(p: int = 1, margin: float = 1.0, weight: Optional[torch.Tensor] = None, size_average=None, reduce=None, reduction: str = 'mean')[source]

                                  Creates a criterion that optimizes a multi-class classification hinge -loss (margin-based loss) between input xx +loss (margin-based loss) between input xx + (a 2D mini-batch Tensor) and -output yy +output yy + (which is a 1D tensor of target class indices, -0≤y≤x.size(1)−10 \leq y \leq \text{x.size}(1)-1 +0≤y≤x.size(1)−10 \leq y \leq \text{x.size}(1)-1 + ):

                                  -

                                  For each mini-batch sample, the loss in terms of the 1D input xx +

                                  For each mini-batch sample, the loss in terms of the 1D input xx + and scalar -output yy +output yy + is:

                                  -loss(x,y)=∑imax(0,margin−x[y]+x[i]))px.size(0)\text{loss}(x, y) = \frac{\sum_i \max(0, \text{margin} - x[y] + x[i]))^p}{\text{x.size}(0)} +loss(x,y)=∑imax⁡(0,margin−x[y]+x[i]))px.size(0)\text{loss}(x, y) = \frac{\sum_i \max(0, \text{margin} - x[y] + x[i]))^p}{\text{x.size}(0)} + + + +

                                  where x∈{0,  ⋯ ,  x.size(0)−1}x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\} - -

                                  where x∈{0,⋯,x.size(0)−1}x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\} -and i≠yi \neq y +and i≠yi \neq y + .

                                  Optionally, you can give non-equal weighting on the classes by passing a 1D weight tensor into the constructor.

                                  The loss function then becomes:

                                  -loss(x,y)=∑imax(0,w[y]∗(margin−x[y]+x[i]))p)x.size(0)\text{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\text{margin} - x[y] + x[i]))^p)}{\text{x.size}(0)} +loss(x,y)=∑imax⁡(0,w[y]∗(margin−x[y]+x[i]))p)x.size(0)\text{loss}(x, y) = \frac{\sum_i \max(0, w[y] * (\text{margin} - x[y] + x[i]))^p)}{\text{x.size}(0)} + + -
                                  Parameters
                                    -

                                  • p (int, optional) – Has a default value of 11 -. 11 - and 22 +

                                  • p (int, optional) – Has a default value of 11 + +. 11 + + and 22 + are the only supported values.

                                    • -

                                  • margin (float, optional) – Has a default value of 11 +

                                  • margin (float, optional) – Has a default value of 11 + .

                                  • weight (Tensor, optional) – a manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is diff --git a/docs/stable/generated/torch.nn.MultiheadAttention.html b/docs/stable/generated/torch.nn.MultiheadAttention.html index 6a5a03b3607b..fac1a085735f 100644 --- a/docs/stable/generated/torch.nn.MultiheadAttention.html +++ b/docs/stable/generated/torch.nn.MultiheadAttention.html @@ -346,6 +346,11 @@

                                    MultiheadAttention +MultiHead(Q,K,V)=Concat(head1,…,headh)WOwhereheadi=Attention(QWiQ,KWiK,VWiV)\text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O +\text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V) + + +
                                    Parameters
                                      @@ -370,7 +375,7 @@

                                      MultiheadAttention
                                      -forward(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, key_padding_mask: Optional[torch.Tensor] = None, need_weights: bool = True, attn_mask: Optional[torch.Tensor] = None) → Tuple[torch.Tensor, Optional[torch.Tensor]][source]
                                      +forward(query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None)[source]
                                      Parameters
                                        @@ -390,23 +395,29 @@

                                        MultiheadAttention
                                        Shape:
                                        diff --git a/docs/stable/generated/torch.nn.NLLLoss.html b/docs/stable/generated/torch.nn.NLLLoss.html index dd7e37c9ecde..505d91729d6a 100644 --- a/docs/stable/generated/torch.nn.NLLLoss.html +++ b/docs/stable/generated/torch.nn.NLLLoss.html @@ -349,42 +349,60 @@

                                        NLLLoss(minibatch,C)(minibatch, C) - or (minibatch,C,d1,d2,...,dK)(minibatch, C, d_1, d_2, ..., d_K) +(minibatch,C)(minibatch, C) + + or (minibatch,C,d1,d2,...,dK)(minibatch, C, d_1, d_2, ..., d_K) + -with K≥1K \geq 1 +with K≥1K \geq 1 + for the K-dimensional case (described later).

                                        Obtaining log-probabilities in a neural network is easily achieved by adding a LogSoftmax layer in the last layer of your network. You may use CrossEntropyLoss instead, if you prefer not to add an extra layer.

                                        -

                                        The target that this loss expects should be a class index in the range [0,C−1][0, C-1] +

                                        The target that this loss expects should be a class index in the range [0,C−1][0, C-1] + where C = number of classes; if ignore_index is specified, this loss also accepts this class index (this index may not necessarily be in the class range).

                                        The unreduced (i.e. with reduction set to 'none') loss can be described as:

                                        -

                                        where xx - is the input, yy - is the target, ww +ℓ(x,y)=L={l1,…,lN}⊤,ln=−wynxn,yn,wc=weight[c]⋅1{c≠ignore_index},\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad +l_n = - w_{y_n} x_{n,y_n}, \quad +w_{c} = \text{weight}[c] \cdot \mathbb{1}\{c \not= \text{ignore\_index}\}, + + + +

                                        where xx + + is the input, yy + + is the target, ww + is the weight, and -NN +NN + is the batch size. If reduction is not 'none' (default 'mean'), then

                                        -ℓ(x,y)={∑n=1N1∑n=1Nwynln,if reduction=’mean’;∑n=1Nln,if reduction=’sum’.\ell(x, y) = \begin{cases} +ℓ(x,y)={∑n=1N1∑n=1Nwynln,if reduction=’mean’;∑n=1Nln,if reduction=’sum’.\ell(x, y) = \begin{cases} \sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, & \text{if reduction} = \text{'mean';}\\ \sum_{n=1}^N l_n, & \text{if reduction} = \text{'sum'.} \end{cases} - + +

                                        Can also be used for higher dimension inputs, such as 2D images, by providing -an input of size (minibatch,C,d1,d2,...,dK)(minibatch, C, d_1, d_2, ..., d_K) - with K≥1K \geq 1 +an input of size (minibatch,C,d1,d2,...,dK)(minibatch, C, d_1, d_2, ..., d_K) + + with K≥1K \geq 1 + , -where KK +where KK + is the number of dimensions, and a target of appropriate shape (see below). In the case of images, it computes NLL loss per-pixel.

                                        @@ -417,24 +435,34 @@

                                        NLLLoss
                                        Shape:
                                          -

                                        • Input: (N,C)(N, C) +

                                        • Input: (N,C)(N, C) + where C = number of classes, or -(N,C,d1,d2,...,dK)(N, C, d_1, d_2, ..., d_K) - with K≥1K \geq 1 +(N,C,d1,d2,...,dK)(N, C, d_1, d_2, ..., d_K) + + with K≥1K \geq 1 + in the case of K-dimensional loss.

                                          • -

                                        • Target: (N)(N) - where each value is 0≤targets[i]≤C−10 \leq \text{targets}[i] \leq C-1 +

                                        • Target: (N)(N) + + where each value is 0≤targets[i]≤C−10 \leq \text{targets}[i] \leq C-1 + , or -(N,d1,d2,...,dK)(N, d_1, d_2, ..., d_K) - with K≥1K \geq 1 +(N,d1,d2,...,dK)(N, d_1, d_2, ..., d_K) + + with K≥1K \geq 1 + in the case of K-dimensional loss.

                                        • Output: scalar. -If reduction is 'none', then the same size as the target: (N)(N) +If reduction is 'none', then the same size as the target: (N)(N) + , or -(N,d1,d2,...,dK)(N, d_1, d_2, ..., d_K) - with K≥1K \geq 1 +(N,d1,d2,...,dK)(N, d_1, d_2, ..., d_K) + + with K≥1K \geq 1 + in the case of K-dimensional loss.

                                        diff --git a/docs/stable/generated/torch.nn.PReLU.html b/docs/stable/generated/torch.nn.PReLU.html index a4f62cc66861..1f06872d7013 100644 --- a/docs/stable/generated/torch.nn.PReLU.html +++ b/docs/stable/generated/torch.nn.PReLU.html @@ -344,27 +344,33 @@

                                        PReLU class torch.nn.PReLU(num_parameters: int = 1, init: float = 0.25)[source]

                                        Applies the element-wise function:

                                        -PReLU(x)=max(0,x)+a∗min(0,x)\text{PReLU}(x) = \max(0,x) + a * \min(0,x) +PReLU(x)=max⁡(0,x)+a∗min⁡(0,x)\text{PReLU}(x) = \max(0,x) + a * \min(0,x) + + -

                                        or

                                        -PReLU(x)={x, if x≥0ax, otherwise \text{PReLU}(x) = +PReLU(x)={x, if x≥0ax, otherwise \text{PReLU}(x) = \begin{cases} x, & \text{ if } x \geq 0 \\ ax, & \text{ otherwise } \end{cases} - -

                                        Here aa + + +

                                        Here aa + is a learnable parameter. When called without arguments, nn.PReLU() uses a single -parameter aa +parameter aa + across all input channels. If called with nn.PReLU(nChannels), -a separate aa +a separate aa + is used for each input channel.

                                        Note

                                        -

                                        weight decay should not be used when learning aa +

                                        weight decay should not be used when learning aa + for good performance.

                                        @@ -375,21 +381,25 @@

                                        PReLU
                                        Parameters
                                          -

                                        • num_parameters (int) – number of aa +

                                        • num_parameters (int) – number of aa + to learn. Although it takes an int as input, there is only two values are legitimate: 1, or the number of channels at input. Default: 1

                                          • -

                                        • init (float) – the initial value of aa +

                                        • init (float) – the initial value of aa + . Default: 0.25

                                        Shape:
                                          -

                                        • Input: (N,∗)(N, *) +

                                        • Input: (N,∗)(N, *) + where * means, any number of additional dimensions

                                          • -

                                        • Output: (N,∗)(N, *) +

                                        • Output: (N,∗)(N, *) + , same shape as the input

                                        diff --git a/docs/stable/generated/torch.nn.PairwiseDistance.html b/docs/stable/generated/torch.nn.PairwiseDistance.html index a66db92a95a6..9023f03cd48d 100644 --- a/docs/stable/generated/torch.nn.PairwiseDistance.html +++ b/docs/stable/generated/torch.nn.PairwiseDistance.html @@ -342,13 +342,16 @@

                                        PairwiseDistance
                                        class torch.nn.PairwiseDistance(p: float = 2.0, eps: float = 1e-06, keepdim: bool = False)[source]
                                        -

                                        Computes the batchwise pairwise distance between vectors v1v_1 -, v2v_2 +

                                        Computes the batchwise pairwise distance between vectors v1v_1 + +, v2v_2 + using the p-norm:

                                        -∥x∥p=(∑i=1n∣xi∣p)1/p.\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}. +∥x∥p=(∑i=1n∣xi∣p)1/p.\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}. + + -
                                        Parameters
                                        diff --git a/docs/stable/generated/torch.nn.PixelShuffle.html b/docs/stable/generated/torch.nn.PixelShuffle.html index 06c442c30d1d..028976d937ba 100644 --- a/docs/stable/generated/torch.nn.PixelShuffle.html +++ b/docs/stable/generated/torch.nn.PixelShuffle.html @@ -342,12 +342,15 @@

                                        PixelShuffle
                                        class torch.nn.PixelShuffle(upscale_factor: int)[source]
                                        -

                                        Rearranges elements in a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W) +

                                        Rearranges elements in a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W) + -to a tensor of shape (∗,C,H×r,W×r)(*, C, H \times r, W \times r) +to a tensor of shape (∗,C,H×r,W×r)(*, C, H \times r, W \times r) + .

                                        This is useful for implementing efficient sub-pixel convolution -with a stride of 1/r1/r +with a stride of 1/r1/r + .

                                        Look at the paper: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network @@ -359,14 +362,19 @@

                                        PixelShuffle
                                        Shape:
                                          -

                                        • Input: (N,L,Hin,Win)(N, L, H_{in}, W_{in}) - where L=C×upscale_factor2L=C \times \text{upscale\_factor}^2 +

                                        • Input: (N,L,Hin,Win)(N, L, H_{in}, W_{in}) + + where L=C×upscale_factor2L=C \times \text{upscale\_factor}^2 +

                                          • -

                                        • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) +

                                        • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) + where -Hout=Hin×upscale_factorH_{out} = H_{in} \times \text{upscale\_factor} +Hout=Hin×upscale_factorH_{out} = H_{in} \times \text{upscale\_factor} + -and Wout=Win×upscale_factorW_{out} = W_{in} \times \text{upscale\_factor} +and Wout=Win×upscale_factorW_{out} = W_{in} \times \text{upscale\_factor} +

                                        diff --git a/docs/stable/generated/torch.nn.PoissonNLLLoss.html b/docs/stable/generated/torch.nn.PoissonNLLLoss.html index bf1940acf361..ce91440c79d0 100644 --- a/docs/stable/generated/torch.nn.PoissonNLLLoss.html +++ b/docs/stable/generated/torch.nn.PoissonNLLLoss.html @@ -345,10 +345,11 @@

                                        PoissonNLLLoss -target∼Poisson(input)loss(input,target)=input−target∗log(input)+log(target!)\text{target} \sim \mathrm{Poisson}(\text{input}) +target∼Poisson(input)loss(input,target)=input−target∗log⁡(input)+log⁡(target!)\text{target} \sim \mathrm{Poisson}(\text{input}) \text{loss}(\text{input}, \text{target}) = \text{input} - \text{target} * \log(\text{input}) - + \log(\text{target!}) + + \log(\text{target!}) +

                                        The last term can be omitted or approximated with Stirling formula. The approximation is used for target values more than 1. For targets less or equal to 1 zeros are added to the loss.

                                        @@ -356,23 +357,27 @@

                                        PoissonNLLLossParameters
                                        @@ -400,23 +406,33 @@

                                        RNNCell

                                        Note

                                        -

                                        All the weights and biases are initialized from U(−k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}) +

                                        All the weights and biases are initialized from U(−k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}) + -where k=1hidden_sizek = \frac{1}{\text{hidden\_size}} +where k=1hidden_sizek = \frac{1}{\text{hidden\_size}} +

                                        Examples:

                                        diff --git a/docs/stable/generated/torch.nn.RReLU.html b/docs/stable/generated/torch.nn.RReLU.html index 708f50228efb..c4befa9f6dd4 100644 --- a/docs/stable/generated/torch.nn.RReLU.html +++ b/docs/stable/generated/torch.nn.RReLU.html @@ -347,16 +347,19 @@

                                        RReLU

                                        Empirical Evaluation of Rectified Activations in Convolutional Network.

                                        The function is defined as:

                                        -RReLU(x)={xif x≥0ax otherwise \text{RReLU}(x) = +RReLU(x)={xif x≥0ax otherwise \text{RReLU}(x) = \begin{cases} x & \text{if } x \geq 0 \\ ax & \text{ otherwise } \end{cases} - -

                                        where aa + + +

                                        where aa + is randomly sampled from uniform distribution -U(lower,upper)\mathcal{U}(\text{lower}, \text{upper}) +U(lower,upper)\mathcal{U}(\text{lower}, \text{upper}) + .

                                        See: https://arxiv.org/pdf/1505.00853.pdf

                                        @@ -364,9 +367,11 @@

                                        RReLU
                                        Parameters
                                          -

                                        • lower – lower bound of the uniform distribution. Default: 18\frac{1}{8} +

                                        • lower – lower bound of the uniform distribution. Default: 18\frac{1}{8} +

                                          • -

                                        • upper – upper bound of the uniform distribution. Default: 13\frac{1}{3} +

                                        • upper – upper bound of the uniform distribution. Default: 13\frac{1}{3} +

                                        • inplace – can optionally do the operation in-place. Default: False

                                        @@ -374,10 +379,12 @@

                                        RReLU

                                        Shape:
                                          -

                                        • Input: (N,∗)(N, *) +

                                        • Input: (N,∗)(N, *) + where * means, any number of additional dimensions

                                          • -

                                        • Output: (N,∗)(N, *) +

                                        • Output: (N,∗)(N, *) + , same shape as the input

                                        diff --git a/docs/stable/generated/torch.nn.ReLU.html b/docs/stable/generated/torch.nn.ReLU.html index 553852c94359..e32935e9b725 100644 --- a/docs/stable/generated/torch.nn.ReLU.html +++ b/docs/stable/generated/torch.nn.ReLU.html @@ -343,7 +343,8 @@

                                        ReLU
                                        class torch.nn.ReLU(inplace: bool = False)[source]

                                        Applies the rectified linear unit function element-wise:

                                        -

                                        ReLU(x)=(x)+=max(0,x)\text{ReLU}(x) = (x)^+ = \max(0, x) +

                                        ReLU(x)=(x)+=max⁡(0,x)\text{ReLU}(x) = (x)^+ = \max(0, x) +

                                        Parameters
                                        @@ -352,10 +353,12 @@

                                        ReLU

                                        Shape:
                                          -

                                        • Input: (N,∗)(N, *) +

                                        • Input: (N,∗)(N, *) + where * means, any number of additional dimensions

                                          • -

                                        • Output: (N,∗)(N, *) +

                                        • Output: (N,∗)(N, *) + , same shape as the input

                                        diff --git a/docs/stable/generated/torch.nn.ReLU6.html b/docs/stable/generated/torch.nn.ReLU6.html index b52c1b0b9a43..4290cb514582 100644 --- a/docs/stable/generated/torch.nn.ReLU6.html +++ b/docs/stable/generated/torch.nn.ReLU6.html @@ -344,9 +344,10 @@

                                        ReLU6 class torch.nn.ReLU6(inplace: bool = False)[source]

                                        Applies the element-wise function:

                                        -ReLU6(x)=min(max(0,x),6)\text{ReLU6}(x) = \min(\max(0,x), 6) +ReLU6(x)=min⁡(max⁡(0,x),6)\text{ReLU6}(x) = \min(\max(0,x), 6) + + -
                                        Parameters

                                        inplace – can optionally do the operation in-place. Default: False

                                        @@ -354,10 +355,12 @@

                                        ReLU6

                                        Shape:
                                          -

                                        • Input: (N,∗)(N, *) +

                                        • Input: (N,∗)(N, *) + where * means, any number of additional dimensions

                                          • -

                                        • Output: (N,∗)(N, *) +

                                        • Output: (N,∗)(N, *) + , same shape as the input

                                        diff --git a/docs/stable/generated/torch.nn.ReflectionPad1d.html b/docs/stable/generated/torch.nn.ReflectionPad1d.html index 219fa3107aba..2e37e06ee043 100644 --- a/docs/stable/generated/torch.nn.ReflectionPad1d.html +++ b/docs/stable/generated/torch.nn.ReflectionPad1d.html @@ -341,25 +341,30 @@

                                        ReflectionPad1d

                                        -class torch.nn.ReflectionPad1d(padding: Union[int, Tuple[int, int]])[source]
                                        +class torch.nn.ReflectionPad1d(padding: Union[T, Tuple[T, T]])[source]

                                        Pads the input tensor using the reflection of the input boundary.

                                        For N-dimensional padding, use torch.nn.functional.pad().

                                        Parameters

                                        padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 2-tuple, uses -(padding_left\text{padding\_left} -, padding_right\text{padding\_right} +(padding_left\text{padding\_left} + +, padding_right\text{padding\_right} + )

                                        Shape:
                                          -

                                        • Input: (N,C,Win)(N, C, W_{in}) +

                                        • Input: (N,C,Win)(N, C, W_{in}) +

                                          • -

                                        • Output: (N,C,Wout)(N, C, W_{out}) +

                                        • Output: (N,C,Wout)(N, C, W_{out}) + where

                                          -

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                        diff --git a/docs/stable/generated/torch.nn.ReflectionPad2d.html b/docs/stable/generated/torch.nn.ReflectionPad2d.html index ec6c2a0ecfd3..bad8b629b0b1 100644 --- a/docs/stable/generated/torch.nn.ReflectionPad2d.html +++ b/docs/stable/generated/torch.nn.ReflectionPad2d.html @@ -341,29 +341,37 @@

                                        ReflectionPad2d

                                        -class torch.nn.ReflectionPad2d(padding: Union[int, Tuple[int, int, int, int]])[source]
                                        +class torch.nn.ReflectionPad2d(padding: Union[T, Tuple[T, T, T, T]])[source]

                                        Pads the input tensor using the reflection of the input boundary.

                                        For N-dimensional padding, use torch.nn.functional.pad().

                                        Parameters

                                        padding (int, tuple) – the size of the padding. If is int, uses the same -padding in all boundaries. If a 4-tuple, uses (padding_left\text{padding\_left} +padding in all boundaries. If a 4-tuple, uses (padding_left\text{padding\_left} + , -padding_right\text{padding\_right} -, padding_top\text{padding\_top} -, padding_bottom\text{padding\_bottom} +padding_right\text{padding\_right} + +, padding_top\text{padding\_top} + +, padding_bottom\text{padding\_bottom} + )

                                        Shape:
                                          -

                                        • Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in}) +

                                        • Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in}) +

                                          • -

                                        • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) +

                                        • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) + where

                                          -

                                          Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom} +

                                          Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom} +

                                          -

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                        diff --git a/docs/stable/generated/torch.nn.ReplicationPad1d.html b/docs/stable/generated/torch.nn.ReplicationPad1d.html index cd105383c9e9..b8205f05f485 100644 --- a/docs/stable/generated/torch.nn.ReplicationPad1d.html +++ b/docs/stable/generated/torch.nn.ReplicationPad1d.html @@ -341,25 +341,30 @@

                                        ReplicationPad1d

                                        -class torch.nn.ReplicationPad1d(padding: Union[int, Tuple[int, int]])[source]
                                        +class torch.nn.ReplicationPad1d(padding: Union[T, Tuple[T, T]])[source]

                                        Pads the input tensor using replication of the input boundary.

                                        For N-dimensional padding, use torch.nn.functional.pad().

                                        Parameters

                                        padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 2-tuple, uses -(padding_left\text{padding\_left} -, padding_right\text{padding\_right} +(padding_left\text{padding\_left} + +, padding_right\text{padding\_right} + )

                                        Shape:
                                          -

                                        • Input: (N,C,Win)(N, C, W_{in}) +

                                        • Input: (N,C,Win)(N, C, W_{in}) +

                                          • -

                                        • Output: (N,C,Wout)(N, C, W_{out}) +

                                        • Output: (N,C,Wout)(N, C, W_{out}) + where

                                          -

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                        diff --git a/docs/stable/generated/torch.nn.ReplicationPad2d.html b/docs/stable/generated/torch.nn.ReplicationPad2d.html index 74721774af9b..7bc481550408 100644 --- a/docs/stable/generated/torch.nn.ReplicationPad2d.html +++ b/docs/stable/generated/torch.nn.ReplicationPad2d.html @@ -341,29 +341,37 @@

                                        ReplicationPad2d

                                        -class torch.nn.ReplicationPad2d(padding: Union[int, Tuple[int, int, int, int]])[source]
                                        +class torch.nn.ReplicationPad2d(padding: Union[T, Tuple[T, T, T, T]])[source]

                                        Pads the input tensor using replication of the input boundary.

                                        For N-dimensional padding, use torch.nn.functional.pad().

                                        Parameters

                                        padding (int, tuple) – the size of the padding. If is int, uses the same -padding in all boundaries. If a 4-tuple, uses (padding_left\text{padding\_left} +padding in all boundaries. If a 4-tuple, uses (padding_left\text{padding\_left} + , -padding_right\text{padding\_right} -, padding_top\text{padding\_top} -, padding_bottom\text{padding\_bottom} +padding_right\text{padding\_right} + +, padding_top\text{padding\_top} + +, padding_bottom\text{padding\_bottom} + )

                                        Shape:
                                          -

                                        • Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in}) +

                                        • Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in}) +

                                          • -

                                        • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) +

                                        • Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) + where

                                          -

                                          Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom} +

                                          Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom} +

                                          -

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                        diff --git a/docs/stable/generated/torch.nn.ReplicationPad3d.html b/docs/stable/generated/torch.nn.ReplicationPad3d.html index 13ab1c5c3ce3..b5ecf78afbcd 100644 --- a/docs/stable/generated/torch.nn.ReplicationPad3d.html +++ b/docs/stable/generated/torch.nn.ReplicationPad3d.html @@ -341,35 +341,46 @@

                                        ReplicationPad3d

                                        -class torch.nn.ReplicationPad3d(padding: Union[int, Tuple[int, int, int, int, int, int]])[source]
                                        +class torch.nn.ReplicationPad3d(padding: Union[T, Tuple[T, T, T, T, T, T]])[source]

                                        Pads the input tensor using replication of the input boundary.

                                        For N-dimensional padding, use torch.nn.functional.pad().

                                        Parameters

                                        padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 6-tuple, uses -(padding_left\text{padding\_left} -, padding_right\text{padding\_right} +(padding_left\text{padding\_left} + +, padding_right\text{padding\_right} + , -padding_top\text{padding\_top} -, padding_bottom\text{padding\_bottom} +padding_top\text{padding\_top} + +, padding_bottom\text{padding\_bottom} + , -padding_front\text{padding\_front} -, padding_back\text{padding\_back} +padding_front\text{padding\_front} + +, padding_back\text{padding\_back} + )

                                        Shape:
                                          -

                                        • Input: (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in}) +

                                        • Input: (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in}) +

                                          • -

                                        • Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) +

                                        • Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) + where

                                          -

                                          Dout=Din+padding_front+padding_backD_{out} = D_{in} + \text{padding\_front} + \text{padding\_back} +

                                          Dout=Din+padding_front+padding_backD_{out} = D_{in} + \text{padding\_front} + \text{padding\_back} +

                                          -

                                          Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom} +

                                          Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom} +

                                          -

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                          Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right} +

                                        diff --git a/docs/stable/generated/torch.nn.SELU.html b/docs/stable/generated/torch.nn.SELU.html index 661c58830061..632bf0ad2880 100644 --- a/docs/stable/generated/torch.nn.SELU.html +++ b/docs/stable/generated/torch.nn.SELU.html @@ -344,12 +344,15 @@

                                        SELUclass torch.nn.SELU(inplace: bool = False)[source]

                                        Applied element-wise, as:

                                        -SELU(x)=scale∗(max(0,x)+min(0,α∗(exp(x)−1)))\text{SELU}(x) = \text{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))) +SELU(x)=scale∗(max⁡(0,x)+min⁡(0,α∗(exp⁡(x)−1)))\text{SELU}(x) = \text{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))) + + + +

                                        with α=1.6732632423543772848170429916717\alpha = 1.6732632423543772848170429916717 - -

                                        with α=1.6732632423543772848170429916717\alpha = 1.6732632423543772848170429916717 and -scale=1.0507009873554804934193349852946\text{scale} = 1.0507009873554804934193349852946 +scale=1.0507009873554804934193349852946\text{scale} = 1.0507009873554804934193349852946 + .

                                        More details can be found in the paper Self-Normalizing Neural Networks .

                                        @@ -359,10 +362,12 @@

                                        SELU

                                        Shape:
                                          -

                                        • Input: (N,∗)(N, *) +

                                        • Input: (N,∗)(N, *) + where * means, any number of additional dimensions

                                          • -

                                        • Output: (N,∗)(N, *) +

                                        • Output: (N,∗)(N, *) + , same shape as the input

                                        diff --git a/docs/stable/generated/torch.nn.Sigmoid.html b/docs/stable/generated/torch.nn.Sigmoid.html index f765a7e8754e..1a7c09331a6a 100644 --- a/docs/stable/generated/torch.nn.Sigmoid.html +++ b/docs/stable/generated/torch.nn.Sigmoid.html @@ -344,15 +344,18 @@

                                        Sigmoidclass torch.nn.Sigmoid[source]

                                        Applies the element-wise function:

                                        -Sigmoid(x)=σ(x)=11+exp(−x)\text{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)} +Sigmoid(x)=σ(x)=11+exp⁡(−x)\text{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)} + + -
                                        Shape:
                                          -

                                        • Input: (N,∗)(N, *) +

                                        • Input: (N,∗)(N, *) + where * means, any number of additional dimensions

                                          • -

                                        • Output: (N,∗)(N, *) +

                                        • Output: (N,∗)(N, *) + , same shape as the input

                                        diff --git a/docs/stable/generated/torch.nn.SmoothL1Loss.html b/docs/stable/generated/torch.nn.SmoothL1Loss.html index 8cb1e1e28c3c..28f2798d5bdf 100644 --- a/docs/stable/generated/torch.nn.SmoothL1Loss.html +++ b/docs/stable/generated/torch.nn.SmoothL1Loss.html @@ -348,26 +348,34 @@

                                        SmoothL1Loss -loss(x,y)=1n∑izi\text{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i} +loss(x,y)=1n∑izi\text{loss}(x, y) = \frac{1}{n} \sum_{i} z_{i} + + + +

                                        where ziz_{i} - -

                                        where ziz_{i} is given by:

                                        -zi={0.5(xi−yi)2,if ∣xi−yi∣<1∣xi−yi∣−0.5,otherwise z_{i} = +zi={0.5(xi−yi)2,if ∣xi−yi∣<1∣xi−yi∣−0.5,otherwise z_{i} = \begin{cases} 0.5 (x_i - y_i)^2, & \text{if } |x_i - y_i| < 1 \\ |x_i - y_i| - 0.5, & \text{otherwise } \end{cases} - -

                                        xx - and yy - arbitrary shapes with a total of nn + + +

                                        xx + + and yy + + arbitrary shapes with a total of nn + elements each -the sum operation still operates over all the elements, and divides by nn +the sum operation still operates over all the elements, and divides by nn + .

                                        -

                                        The division by nn +

                                        The division by nn + can be avoided if sets reduction = 'sum'.

                                        Parameters
                                        @@ -392,14 +400,18 @@

                                        SmoothL1Loss
                                        Shape:
                                          -

                                        • Input: (N,∗)(N, *) - where ∗* +

                                        • Input: (N,∗)(N, *) + + where ∗* + means, any number of additional dimensions

                                          • -

                                        • Target: (N,∗)(N, *) +

                                        • Target: (N,∗)(N, *) + , same shape as the input

                                        • Output: scalar. If reduction is 'none', then -(N,∗)(N, *) +(N,∗)(N, *) + , same shape as the input

                                        diff --git a/docs/stable/generated/torch.nn.SoftMarginLoss.html b/docs/stable/generated/torch.nn.SoftMarginLoss.html index c8ecb3409e81..e638acfe4f5f 100644 --- a/docs/stable/generated/torch.nn.SoftMarginLoss.html +++ b/docs/stable/generated/torch.nn.SoftMarginLoss.html @@ -343,14 +343,17 @@

                                        SoftMarginLoss class torch.nn.SoftMarginLoss(size_average=None, reduce=None, reduction: str = 'mean')[source]

                                        Creates a criterion that optimizes a two-class classification -logistic loss between input tensor xx - and target tensor yy +logistic loss between input tensor xx + + and target tensor yy + (containing 1 or -1).

                                        -loss(x,y)=∑ilog(1+exp(−y[i]∗x[i]))x.nelement()\text{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\text{x.nelement}()} +loss(x,y)=∑ilog⁡(1+exp⁡(−y[i]∗x[i]))x.nelement()\text{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\text{x.nelement}()} + + -
                                        Parameters

                                        @@ -391,10 +400,12 @@

                                        torch.pca_lowrank
                                        Parameters
                                          -

                                        • A (Tensor) – the input tensor of size (∗,m,n)(*, m, n) +

                                        • A (Tensor) – the input tensor of size (∗,m,n)(*, m, n) +

                                        • q (int, optional) – a slightly overestimated rank of -AA +AA + . By default, q = min(6, m, n).

                                        • center (bool, optional) – if True, center the input tensor, diff --git a/docs/stable/generated/torch.pinverse.html b/docs/stable/generated/torch.pinverse.html index 93e99364723c..51da4a5251f7 100644 --- a/docs/stable/generated/torch.pinverse.html +++ b/docs/stable/generated/torch.pinverse.html @@ -359,15 +359,18 @@

                                          torch.pinverse
                                          Parameters
                                            -

                                          • input (Tensor) – The input tensor of size (∗,m,n)(*, m, n) - where ∗* +

                                          • input (Tensor) – The input tensor of size (∗,m,n)(*, m, n) + + where ∗* + is zero or more batch dimensions

                                          • rcond (float) – A floating point value to determine the cutoff for small singular values. Default: 1e-15

                                          Returns
                                          -

                                          The pseudo-inverse of input of dimensions (∗,n,m)(*, n, m) +

                                          The pseudo-inverse of input of dimensions (∗,n,m)(*, n, m) +

                                        diff --git a/docs/stable/generated/torch.poisson.html b/docs/stable/generated/torch.poisson.html index 62ac0f7949bf..242abe4ba9eb 100644 --- a/docs/stable/generated/torch.poisson.html +++ b/docs/stable/generated/torch.poisson.html @@ -346,9 +346,10 @@

                                        torch.poissoninput i.e.,

                                        -outi∼Poisson(inputi)\text{out}_i \sim \text{Poisson}(\text{input}_i) +outi∼Poisson(inputi)\text{out}_i \sim \text{Poisson}(\text{input}_i) + + -
                                        Parameters
                                          diff --git a/docs/stable/generated/torch.polygamma.html b/docs/stable/generated/torch.polygamma.html index 34ba8beeb9a5..d9248456537a 100644 --- a/docs/stable/generated/torch.polygamma.html +++ b/docs/stable/generated/torch.polygamma.html @@ -342,17 +342,21 @@

                                          torch.polygamma
                                          torch.polygamma(n, input, out=None) → Tensor
                                          -

                                          Computes the nthn^{th} +

                                          Computes the nthn^{th} + derivative of the digamma function on input. -n≥0n \geq 0 +n≥0n \geq 0 + is called the order of the polygamma function.

                                          -ψ(n)(x)=d(n)dx(n)ψ(x)\psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x) +ψ(n)(x)=d(n)dx(n)ψ(x)\psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x) + + -

                                          Note

                                          -

                                          This function is not implemented for n≥2n \geq 2 +

                                          This function is not implemented for n≥2n \geq 2 + .

                                          diff --git a/docs/stable/generated/torch.pow.html b/docs/stable/generated/torch.pow.html index 5e1021f18c22..909b5a941647 100644 --- a/docs/stable/generated/torch.pow.html +++ b/docs/stable/generated/torch.pow.html @@ -348,14 +348,16 @@

                                          torch.powinput.

                                          When exponent is a scalar value, the operation applied is:

                                          -outi=xiexponent\text{out}_i = x_i ^ \text{exponent} +outi=xiexponent\text{out}_i = x_i ^ \text{exponent} + + -

                                          When exponent is a tensor, the operation applied is:

                                          -outi=xiexponenti\text{out}_i = x_i ^ {\text{exponent}_i} +outi=xiexponenti\text{out}_i = x_i ^ {\text{exponent}_i} + + -

                                          When exponent is a tensor, the shapes of input and exponent must be broadcastable.

                                          @@ -393,9 +395,10 @@

                                          torch.powout is of the same shape as exponent

                                          The operation applied is:

                                          -outi=selfexponenti\text{out}_i = \text{self} ^ {\text{exponent}_i} +outi=selfexponenti\text{out}_i = \text{self} ^ {\text{exponent}_i} + + -
                                          Parameters
                                            diff --git a/docs/stable/generated/torch.qr.html b/docs/stable/generated/torch.qr.html index 0f6fa090dada..704a32ff194d 100644 --- a/docs/stable/generated/torch.qr.html +++ b/docs/stable/generated/torch.qr.html @@ -343,11 +343,14 @@

                                            torch.qr torch.qr(input, some=True, out=None) -> (Tensor, Tensor)

                                            Computes the QR decomposition of a matrix or a batch of matrices input, -and returns a namedtuple (Q, R) of tensors such that input=QR\text{input} = Q R +and returns a namedtuple (Q, R) of tensors such that input=QR\text{input} = Q R + -with QQ +with QQ + being an orthogonal matrix or batch of orthogonal matrices and -RR +RR + being an upper triangular matrix or batch of upper triangular matrices.

                                            If some is True, then this function returns the thin (reduced) QR factorization. Otherwise, if some is False, this function returns the complete QR factorization.

                                            @@ -365,20 +368,26 @@

                                            torch.qr
                                            Parameters
                                              -

                                            • input (Tensor) – the input tensor of size (∗,m,n)(*, m, n) +

                                            • input (Tensor) – the input tensor of size (∗,m,n)(*, m, n) + where * is zero or more -batch dimensions consisting of matrices of dimension m×nm \times n +batch dimensions consisting of matrices of dimension m×nm \times n + .

                                            • some (bool, optional) – Set to True for reduced QR decomposition and False for complete QR decomposition.

                                            • out (tuple, optional) – tuple of Q and R tensors satisfying input = torch.matmul(Q, R). -The dimensions of Q and R are (∗,m,k)(*, m, k) - and (∗,k,n)(*, k, n) +The dimensions of Q and R are (∗,m,k)(*, m, k) + + and (∗,k,n)(*, k, n) + -respectively, where k=min(m,n)k = \min(m, n) +respectively, where k=min⁡(m,n)k = \min(m, n) + if some: is True and -k=mk = m +k=mk = m + otherwise.

                                            diff --git a/docs/stable/generated/torch.quasirandom.SobolEngine.html b/docs/stable/generated/torch.quasirandom.SobolEngine.html index d19daa6e646b..a99bdcf3aea8 100644 --- a/docs/stable/generated/torch.quasirandom.SobolEngine.html +++ b/docs/stable/generated/torch.quasirandom.SobolEngine.html @@ -385,7 +385,8 @@

                                            SobolEnginedraw(n=1, out=None, dtype=torch.float32)[source]

                                            Function to draw a sequence of n points from a Sobol sequence. Note that the samples are dependent on the previous samples. The size -of the result is (n,dimension)(n, dimension) +of the result is (n,dimension)(n, dimension) + .

                                            Parameters
                                            diff --git a/docs/stable/generated/torch.rand.html b/docs/stable/generated/torch.rand.html index 32de2a293115..d8fe7b5c6b8b 100644 --- a/docs/stable/generated/torch.rand.html +++ b/docs/stable/generated/torch.rand.html @@ -343,7 +343,8 @@

                                            torch.rand torch.rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

                                            Returns a tensor filled with random numbers from a uniform distribution -on the interval [0,1)[0, 1) +on the interval [0,1)[0, 1) +

                                            The shape of the tensor is defined by the variable argument size.

                                            diff --git a/docs/stable/generated/torch.rand_like.html b/docs/stable/generated/torch.rand_like.html index 56af8f9b6ed4..5a00f1b6ae39 100644 --- a/docs/stable/generated/torch.rand_like.html +++ b/docs/stable/generated/torch.rand_like.html @@ -343,7 +343,8 @@

                                            torch.rand_like torch.rand_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) → Tensor

                                            Returns a tensor with the same size as input that is filled with -random numbers from a uniform distribution on the interval [0,1)[0, 1) +random numbers from a uniform distribution on the interval [0,1)[0, 1) + . torch.rand_like(input) is equivalent to torch.rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

                                            diff --git a/docs/stable/generated/torch.randn.html b/docs/stable/generated/torch.randn.html index e093437474d6..451edfffd54e 100644 --- a/docs/stable/generated/torch.randn.html +++ b/docs/stable/generated/torch.randn.html @@ -346,9 +346,10 @@

                                            torch.randn -outi∼N(0,1)\text{out}_{i} \sim \mathcal{N}(0, 1) +outi∼N(0,1)\text{out}_{i} \sim \mathcal{N}(0, 1) + + -

                                            The shape of the tensor is defined by the variable argument size.

                                            Parameters
                                            diff --git a/docs/stable/generated/torch.range.html b/docs/stable/generated/torch.range.html index 872f25d679aa..6be8ea942f28 100644 --- a/docs/stable/generated/torch.range.html +++ b/docs/stable/generated/torch.range.html @@ -342,14 +342,16 @@

                                            torch.range
                                            torch.range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
                                            -

                                            Returns a 1-D tensor of size ⌊end−startstep⌋+1\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1 +

                                            Returns a 1-D tensor of size ⌊end−startstep⌋+1\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1 + with values from start to end with step step. Step is the gap between two values in the tensor.

                                            -outi+1=outi+step.\text{out}_{i+1} = \text{out}_i + \text{step}. +outi+1=outi+step.\text{out}_{i+1} = \text{out}_i + \text{step}. + + -

                                            Warning

                                            This function is deprecated in favor of torch.arange().

                                            diff --git a/docs/stable/generated/torch.reciprocal.html b/docs/stable/generated/torch.reciprocal.html index 5179e2107300..40a82effeff3 100644 --- a/docs/stable/generated/torch.reciprocal.html +++ b/docs/stable/generated/torch.reciprocal.html @@ -344,9 +344,10 @@

                                            torch.reciprocaltorch.reciprocal(input, out=None) → Tensor

                                            Returns a new tensor with the reciprocal of the elements of input

                                            -outi=1inputi\text{out}_{i} = \frac{1}{\text{input}_{i}} +outi=1inputi\text{out}_{i} = \frac{1}{\text{input}_{i}} + + -
                                            Parameters
                                              diff --git a/docs/stable/generated/torch.rfft.html b/docs/stable/generated/torch.rfft.html index 73a5e84f5604..efd536c24ad6 100644 --- a/docs/stable/generated/torch.rfft.html +++ b/docs/stable/generated/torch.rfft.html @@ -350,31 +350,45 @@

                                              torch.rfftsignal_ndim. input must be a tensor with at least signal_ndim dimensions with optionally arbitrary number of leading batch dimensions. If normalized is set to True, this normalizes the result -by dividing it with ∏i=1KNi\sqrt{\prod_{i=1}^K N_i} +by dividing it with ∏i=1KNi\sqrt{\prod_{i=1}^K N_i} + so that the operator is -unitary, where NiN_i - is the size of signal dimension ii +unitary, where NiN_i + + is the size of signal dimension ii + .

                                              The real-to-complex Fourier transform results follow conjugate symmetry:

                                              +X[ω1,…,ωd]=X∗[N1−ω1,…,Nd−ωd],X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d], + + +

                                              where the index arithmetic is computed modulus the size of the corresponding -dimension,  ∗\ ^* +dimension,  ∗\ ^* + is the conjugate operator, and -dd +dd + = signal_ndim. onesided flag controls whether to avoid redundancy in the output results. If set to True (default), the output will -not be full complex result of shape (∗,2)(*, 2) -, where ∗* +not be full complex result of shape (∗,2)(*, 2) + +, where ∗* + is the shape of input, but instead the last dimension will be halfed as of size -⌊Nd2⌋+1\lfloor \frac{N_d}{2} \rfloor + 1 +⌊Nd2⌋+1\lfloor \frac{N_d}{2} \rfloor + 1 + .

                                              The inverse of this function is irfft().

                                              diff --git a/docs/stable/generated/torch.rsqrt.html b/docs/stable/generated/torch.rsqrt.html index 6148981f5353..02a1fb9a8c6d 100644 --- a/docs/stable/generated/torch.rsqrt.html +++ b/docs/stable/generated/torch.rsqrt.html @@ -345,16 +345,20 @@

                                              torch.rsqrtinput.

                                              -outi=1inputi\text{out}_{i} = \frac{1}{\sqrt{\text{input}_{i}}} - - +outi=1inputi\text{out}_{i} = \frac{1}{\sqrt{\text{input}_{i}}} + + +
                                              Parameters
                                                diff --git a/docs/stable/generated/torch.save.html b/docs/stable/generated/torch.save.html index 406b36c39f2a..7e2376f710ab 100644 --- a/docs/stable/generated/torch.save.html +++ b/docs/stable/generated/torch.save.html @@ -341,7 +341,7 @@

                                                torch.save

                                                -torch.save(obj, f, pickle_module=<module 'pickle' from '/scratch/rzou/pt/v1.6-env/lib/python3.8/pickle.py'>, pickle_protocol=2, _use_new_zipfile_serialization=True)[source]
                                                +torch.save(obj, f, pickle_module=<module 'pickle' from '/opt/conda/lib/python3.6/pickle.py'>, pickle_protocol=2, _use_new_zipfile_serialization=True)[source]

                                                Saves an object to a disk file.

                                                See also: Recommended approach for saving a model

                                                diff --git a/docs/stable/generated/torch.sigmoid.html b/docs/stable/generated/torch.sigmoid.html index 70f4821a6d60..1e9998cce8d6 100644 --- a/docs/stable/generated/torch.sigmoid.html +++ b/docs/stable/generated/torch.sigmoid.html @@ -344,9 +344,10 @@

                                                torch.sigmoidtorch.sigmoid(input, out=None) → Tensor

                                                Returns a new tensor with the sigmoid of the elements of input.

                                                -outi=11+e−inputi\text{out}_{i} = \frac{1}{1 + e^{-\text{input}_{i}}} +outi=11+e−inputi\text{out}_{i} = \frac{1}{1 + e^{-\text{input}_{i}}} + + -
                                                Parameters
                                                  diff --git a/docs/stable/generated/torch.sign.html b/docs/stable/generated/torch.sign.html index b2ffb68d414d..b17cd1a39871 100644 --- a/docs/stable/generated/torch.sign.html +++ b/docs/stable/generated/torch.sign.html @@ -344,6 +344,10 @@

                                                  torch.signtorch.sign(input, out=None) → Tensor

                                                  Returns a new tensor with the signs of the elements of input.

                                                  +outi=sgn⁡(inputi)\text{out}_{i} = \operatorname{sgn}(\text{input}_{i}) + + +
                                                  Parameters
                                                    diff --git a/docs/stable/generated/torch.sin.html b/docs/stable/generated/torch.sin.html index d4172744f0c3..c39423fbc3d2 100644 --- a/docs/stable/generated/torch.sin.html +++ b/docs/stable/generated/torch.sin.html @@ -344,9 +344,10 @@

                                                    torch.sintorch.sin(input, out=None) → Tensor

                                                    Returns a new tensor with the sine of the elements of input.

                                                    -outi=sin(inputi)\text{out}_{i} = \sin(\text{input}_{i}) +outi=sin⁡(inputi)\text{out}_{i} = \sin(\text{input}_{i}) + + -
                                                    Parameters
                                                      diff --git a/docs/stable/generated/torch.sinh.html b/docs/stable/generated/torch.sinh.html index 47e5e0fae90c..e9073d6d8e76 100644 --- a/docs/stable/generated/torch.sinh.html +++ b/docs/stable/generated/torch.sinh.html @@ -345,9 +345,10 @@

                                                      torch.sinhinput.

                                                      -outi=sinh(inputi)\text{out}_{i} = \sinh(\text{input}_{i}) +outi=sinh⁡(inputi)\text{out}_{i} = \sinh(\text{input}_{i}) + + -
                                                      Parameters
                                                        diff --git a/docs/stable/generated/torch.solve.html b/docs/stable/generated/torch.solve.html index fbca1c96d97a..ece7980cfcbc 100644 --- a/docs/stable/generated/torch.solve.html +++ b/docs/stable/generated/torch.solve.html @@ -343,7 +343,8 @@

                                                        torch.solve torch.solve(input, A, out=None) -> (Tensor, Tensor)

                                                        This function returns the solution to the system of linear -equations represented by AX=BAX = B +equations represented by AX=BAX = B + and the LU factorization of A, in order as a namedtuple solution, LU.

                                                        LU contains L and U factors for LU factorization of A.

                                                        @@ -360,14 +361,19 @@

                                                        torch.solve
                                                        Parameters
                                                          -

                                                        • input (Tensor) – input matrix BB - of size (∗,m,k)(*, m, k) - , where ∗* +

                                                        • input (Tensor) – input matrix BB + + of size (∗,m,k)(*, m, k) + + , where ∗* + is zero or more batch dimensions.

                                                          • -

                                                        • A (Tensor) – input square matrix of size (∗,m,m)(*, m, m) +

                                                        • A (Tensor) – input square matrix of size (∗,m,m)(*, m, m) + , where -∗* +∗* + is zero or more batch dimensions.

                                                        • out ((Tensor, Tensor), optional) – optional output tuple.

                                                        diff --git a/docs/stable/generated/torch.sqrt.html b/docs/stable/generated/torch.sqrt.html index 04aaa42523ac..2266b9f94abb 100644 --- a/docs/stable/generated/torch.sqrt.html +++ b/docs/stable/generated/torch.sqrt.html @@ -344,16 +344,20 @@

                                                        torch.sqrttorch.sqrt(input, out=None) → Tensor

                                                        Returns a new tensor with the square-root of the elements of input.

                                                        -outi=inputi\text{out}_{i} = \sqrt{\text{input}_{i}} - - +outi=inputi\text{out}_{i} = \sqrt{\text{input}_{i}} + + +
                                                        Parameters
                                                          diff --git a/docs/stable/generated/torch.squeeze.html b/docs/stable/generated/torch.squeeze.html index c5471cb2081c..983970d12f23 100644 --- a/docs/stable/generated/torch.squeeze.html +++ b/docs/stable/generated/torch.squeeze.html @@ -344,15 +344,19 @@

                                                          torch.squeezetorch.squeeze(input, dim=None, out=None) → Tensor

                                                          Returns a tensor with all the dimensions of input of size 1 removed.

                                                          For example, if input is of shape: -(A×1×B×C×1×D)(A \times 1 \times B \times C \times 1 \times D) +(A×1×B×C×1×D)(A \times 1 \times B \times C \times 1 \times D) + then the out tensor -will be of shape: (A×B×C×D)(A \times B \times C \times D) +will be of shape: (A×B×C×D)(A \times B \times C \times D) + .

                                                          When dim is given, a squeeze operation is done only in the given -dimension. If input is of shape: (A×1×B)(A \times 1 \times B) +dimension. If input is of shape: (A×1×B)(A \times 1 \times B) + , squeeze(input, 0) leaves the tensor unchanged, but squeeze(input, 1) -will squeeze the tensor to the shape (A×B)(A \times B) +will squeeze the tensor to the shape (A×B)(A \times B) + .

                                                          Note

                                                          diff --git a/docs/stable/generated/torch.stft.html b/docs/stable/generated/torch.stft.html index e272b0337964..8982a854bade 100644 --- a/docs/stable/generated/torch.stft.html +++ b/docs/stable/generated/torch.stft.html @@ -341,15 +341,24 @@

                                                          torch.stft

                                                          -torch.stft(input: torch.Tensor, n_fft: int, hop_length: Optional[int] = None, win_length: Optional[int] = None, window: Optional[torch.Tensor] = None, center: bool = True, pad_mode: str = 'reflect', normalized: bool = False, onesided: bool = True) → torch.Tensor[source]
                                                          +torch.stft(input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode='reflect', normalized=False, onesided=True)[source]

                                                          Short-time Fourier transform (STFT).

                                                          Ignoring the optional batch dimension, this method computes the following expression:

                                                          -

                                                          where mm - is the index of the sliding window, and ω\omega +X[m,ω]=∑k=0win_length-1window[k] input[m×hop_length+k] exp⁡(−j2π⋅ωkwin_length),X[m, \omega] = \sum_{k = 0}^{\text{win\_length-1}}% + \text{window}[k]\ \text{input}[m \times \text{hop\_length} + k]\ % + \exp\left(- j \frac{2 \pi \cdot \omega k}{\text{win\_length}}\right), + + + +

                                                          where mm + + is the index of the sliding window, and ω\omega + is -the frequency that 0≤ω<n_fft0 \leq \omega < \text{n\_fft} +the frequency that 0≤ω<n_fft0 \leq \omega < \text{n\_fft} + . When onesided is the default value True,

                                                            @@ -361,39 +370,54 @@

                                                            torch.stftn_fft.

                                                          • window can be a 1-D tensor of size win_length, e.g., from torch.hann_window(). If window is None (default), it is -treated as if having 11 +treated as if having 11 + everywhere in the window. If -win_length<n_fft\text{win\_length} < \text{n\_fft} +win_length<n_fft\text{win\_length} < \text{n\_fft} + , window will be padded on both sides to length n_fft before being applied.

                                                          • If center is True (default), input will be padded on -both sides so that the tt +both sides so that the tt + -th frame is centered at time -t×hop_lengtht \times \text{hop\_length} -. Otherwise, the tt +t×hop_lengtht \times \text{hop\_length} + +. Otherwise, the tt + -th frame -begins at time t×hop_lengtht \times \text{hop\_length} +begins at time t×hop_lengtht \times \text{hop\_length} + .

                                                          • pad_mode determines the padding method used on input when center is True. See torch.nn.functional.pad() for all available options. Default is "reflect".

                                                            • -

                                                          • If onesided is True (default), only values for ω\omega +

                                                          • If onesided is True (default), only values for ω\omega + + +in [0,1,2,…,⌊n_fft2⌋+1]\left[0, 1, 2, \dots, \left\lfloor \frac{\text{n\_fft}}{2} \right\rfloor + 1\right] + -in are returned because the real-to-complex Fourier transform satisfies the -conjugate symmetry, i.e., X[m,ω]=X[m,n_fft−ω]∗X[m, \omega] = X[m, \text{n\_fft} - \omega]^* +conjugate symmetry, i.e., X[m,ω]=X[m,n_fft−ω]∗X[m, \omega] = X[m, \text{n\_fft} - \omega]^* + .

                                                          • If normalized is True (default is False), the function -returns the normalized STFT results, i.e., multiplied by (frame_length)−0.5(\text{frame\_length})^{-0.5} +returns the normalized STFT results, i.e., multiplied by (frame_length)−0.5(\text{frame\_length})^{-0.5} + .

                                                          Returns the real and the imaginary parts together as one tensor of size -(∗×N×T×2)(* \times N \times T \times 2) -, where ∗* +(∗×N×T×2)(* \times N \times T \times 2) + +, where ∗* + is the optional -batch size of input, NN +batch size of input, NN + is the number of frequencies where -STFT is applied, TT +STFT is applied, TT + is the total number of frames used, and each pair in the last dimension represents a complex number as the real part and the imaginary part.

                                                          @@ -412,11 +436,14 @@

                                                          torch.stftint, optional) – the size of window frame and STFT filter. Default: None (treated as equal to n_fft)

                                                        • window (Tensor, optional) – the optional window function. -Default: None (treated as window of all 11 +Default: None (treated as window of all 11 + s)

                                                        • center (bool, optional) – whether to pad input on both sides so -that the tt --th frame is centered at time t×hop_lengtht \times \text{hop\_length} +that the tt + +-th frame is centered at time t×hop_lengtht \times \text{hop\_length} + . Default: True

                                                        • pad_mode (string, optional) – controls the padding method used when diff --git a/docs/stable/generated/torch.svd.html b/docs/stable/generated/torch.svd.html index 737fad6b6b83..20ab6db1bbb5 100644 --- a/docs/stable/generated/torch.svd.html +++ b/docs/stable/generated/torch.svd.html @@ -344,15 +344,19 @@

                                                          torch.svdtorch.svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor)

                                                          This function returns a namedtuple (U, S, V) which is the singular value decomposition of a input real matrix or batches of real matrices input such that -input=U×diag(S)×VTinput = U \times diag(S) \times V^T +input=U×diag(S)×VTinput = U \times diag(S) \times V^T + .

                                                          If some is True (default), the method returns the reduced singular value decomposition i.e., if the last two dimensions of input are m and n, then the returned -U and V matrices will contain only min(n,m)min(n, m) +U and V matrices will contain only min(n,m)min(n, m) + orthonormal columns.

                                                          If compute_uv is False, the returned U and V matrices will be zero matrices -of shape (m×m)(m \times m) - and (n×n)(n \times n) +of shape (m×m)(m \times m) + + and (n×n)(n \times n) + respectively. some will be ignored here.

                                                          Note

                                                          @@ -393,9 +397,11 @@

                                                          torch.svd
                                                          Parameters
                                                            -

                                                          • input (Tensor) – the input tensor of size (∗,m,n)(*, m, n) +

                                                          • input (Tensor) – the input tensor of size (∗,m,n)(*, m, n) + where * is zero or more -batch dimensions consisting of m×nm \times n +batch dimensions consisting of m×nm \times n + matrices.

                                                          • some (bool, optional) – controls the shape of returned U and V

                                                          • compute_uv (bool, optional) – option whether to compute U and V or not

                                                            • diff --git a/docs/stable/generated/torch.svd_lowrank.html b/docs/stable/generated/torch.svd_lowrank.html index be0765a3b327..3d4687d9a836 100644 --- a/docs/stable/generated/torch.svd_lowrank.html +++ b/docs/stable/generated/torch.svd_lowrank.html @@ -341,14 +341,18 @@

                                                            torch.svd_lowrank

                                                            -torch.svd_lowrank(A: torch.Tensor, q: Optional[int] = 6, niter: Optional[int] = 2, M: Optional[torch.Tensor] = None) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor][source]
                                                            +torch.svd_lowrank(A, q=6, niter=2, M=None)[source]

                                                            Return the singular value decomposition (U, S, V) of a matrix, -batches of matrices, or a sparse matrix AA +batches of matrices, or a sparse matrix AA + such that -A≈Udiag(S)VTA \approx U diag(S) V^T -. In case MM +A≈Udiag(S)VTA \approx U diag(S) V^T + +. In case MM + is given, then -SVD is computed for the matrix A−MA - M +SVD is computed for the matrix A−MA - M + .

                                                            Note

                                                            @@ -373,14 +377,16 @@

                                                            torch.svd_lowranktorch.svd cannot handle.

                                                            -
                                                            Arguments::

                                                            A (Tensor): the input tensor of size (∗,m,n)(*, m, n) +

                                                            Arguments::

                                                            A (Tensor): the input tensor of size (∗,m,n)(*, m, n) +

                                                            q (int, optional): a slightly overestimated rank of A.

                                                            niter (int, optional): the number of subspace iterations to

                                                            conduct; niter must be a nonnegative integer, and defaults to 2

                                                            -
                                                            M (Tensor, optional): the input tensor’s mean of size

                                                            (∗,1,n)(*, 1, n) +

                                                            M (Tensor, optional): the input tensor’s mean of size

                                                            (∗,1,n)(*, 1, n) + .

                                                            diff --git a/docs/stable/generated/torch.symeig.html b/docs/stable/generated/torch.symeig.html index f14b8361c2c9..ce4644cf302c 100644 --- a/docs/stable/generated/torch.symeig.html +++ b/docs/stable/generated/torch.symeig.html @@ -346,7 +346,8 @@

                                                            torch.symeiginput or a batch of real symmetric matrices, represented by a namedtuple (eigenvalues, eigenvectors).

                                                            This function calculates all eigenvalues (and vectors) of input -such that input=Vdiag(e)VT\text{input} = V \text{diag}(e) V^T +such that input=Vdiag(e)VT\text{input} = V \text{diag}(e) V^T + .

                                                            The boolean argument eigenvectors defines computation of both eigenvectors and eigenvalues or eigenvalues only.

                                                            @@ -374,7 +375,8 @@

                                                            torch.symeig
                                                            Parameters
                                                            + -
                                                          • close() (torch.utils.tensorboard.writer.SummaryWriter method) -
                                                          • coalesce() (torch.sparse.FloatTensor method)
                                                          • CocoCaptions (class in torchvision.datasets) @@ -1513,7 +1479,7 @@

                                                            C

                                                            D

                                                            - + @@ -8094,21 +8094,6 @@ b:int)->intmath.isfinite(a:float)->bool - -math.remainder(a:int, - b:int)->float - -math.remainder(a:float, - b:float)->float - -math.remainder(a:int, - b:float)->float - -math.remainder(a:float, - b:int)->float - -math.remainder(a:number, - b:number)->float diff --git a/docs/stable/nn.functional.html b/docs/stable/nn.functional.html index 74fbbdd21dd3..25167c54f742 100644 --- a/docs/stable/nn.functional.html +++ b/docs/stable/nn.functional.html @@ -359,11 +359,14 @@

                                                            conv1d
                                                            Parameters
                                                            @@ -406,11 +410,14 @@

                                                            conv2d
                                                            Parameters
                                                            @@ -454,11 +462,14 @@

                                                            conv3d
                                                            Parameters
                                                            @@ -501,11 +513,14 @@

                                                            conv_transpose1d
                                                            Parameters

                                                            - @@ -660,8 +661,10 @@

                                                            Recurrent Layers

                                                            - @@ -712,11 +715,13 @@

                                                            Linear Layers

                                                            A placeholder identity operator that is argument-insensitive.

                                                            - - @@ -730,15 +735,21 @@

                                                            Dropout Layers

                                                            During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution.

                                                            - - @@ -765,13 +776,17 @@

                                                            Distance Functions

                                                            - - @@ -782,13 +797,17 @@

                                                            Loss Functions

                                                            - - @@ -813,52 +832,74 @@

                                                            Loss Functions

                                                            This loss combines a Sigmoid layer and the BCELoss in one single class.

                                                            - - - - - - - - @@ -869,8 +910,10 @@

                                                            Vision Layers

                                                            - diff --git a/docs/stable/nn.init.html b/docs/stable/nn.init.html index 54f53d2fc9f3..a194a15d1a26 100644 --- a/docs/stable/nn.init.html +++ b/docs/stable/nn.init.html @@ -350,41 +350,53 @@ - - - - - - @@ -405,9 +417,10 @@
                                                            -torch.nn.init.uniform_(tensor: torch.Tensor, a: float = 0.0, b: float = 1.0) → torch.Tensor[source]
                                                            +torch.nn.init.uniform_(tensor, a=0.0, b=1.0)[source]

                                                            Fills the input Tensor with values drawn from the uniform -distribution U(a,b)\mathcal{U}(a, b) +distribution U(a,b)\mathcal{U}(a, b) + .

                                                            Parameters
                                                            @@ -427,9 +440,10 @@
                                                            -torch.nn.init.normal_(tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0) → torch.Tensor[source]
                                                            +torch.nn.init.normal_(tensor, mean=0.0, std=1.0)[source]

                                                            Fills the input Tensor with values drawn from the normal -distribution N(mean,std2)\mathcal{N}(\text{mean}, \text{std}^2) +distribution N(mean,std2)\mathcal{N}(\text{mean}, \text{std}^2) + .

                                                            Parameters
                                                            @@ -449,8 +463,9 @@
                                                            -torch.nn.init.constant_(tensor: torch.Tensor, val: float) → torch.Tensor[source]
                                                            -

                                                            Fills the input Tensor with the value val\text{val} +torch.nn.init.constant_(tensor, val)[source] +

                                                            Fills the input Tensor with the value val\text{val} + .

                                                            Parameters
                                                            @@ -469,7 +484,7 @@
                                                            -torch.nn.init.ones_(tensor: torch.Tensor) → torch.Tensor[source]
                                                            +torch.nn.init.ones_(tensor)[source]

                                                            Fills the input Tensor with the scalar value 1.

                                                            Parameters
                                                            @@ -485,7 +500,7 @@
                                                            -torch.nn.init.zeros_(tensor: torch.Tensor) → torch.Tensor[source]
                                                            +torch.nn.init.zeros_(tensor)[source]

                                                            Fills the input Tensor with the scalar value 0.

                                                            Parameters
                                                            @@ -543,24 +558,26 @@
                                                            -torch.nn.init.xavier_uniform_(tensor: torch.Tensor, gain: float = 1.0) → torch.Tensor[source]
                                                            +torch.nn.init.xavier_uniform_(tensor, gain=1.0)[source]

                                                            Fills the input Tensor with values according to the method described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using a uniform distribution. The resulting tensor will have values sampled from -U(−a,a)\mathcal{U}(-a, a) +U(−a,a)\mathcal{U}(-a, a) + where

                                                            -a=gain×6fan_in+fan_outa = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}} - - +a=gain×6fan_in+fan_outa = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}} + + +

                                                            Also known as Glorot initialization.

                                                            Parameters
                                                            @@ -579,24 +596,26 @@
                                                            -torch.nn.init.xavier_normal_(tensor: torch.Tensor, gain: float = 1.0) → torch.Tensor[source]
                                                            +torch.nn.init.xavier_normal_(tensor, gain=1.0)[source]

                                                            Fills the input Tensor with values according to the method described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using a normal distribution. The resulting tensor will have values sampled from -N(0,std2)\mathcal{N}(0, \text{std}^2) +N(0,std2)\mathcal{N}(0, \text{std}^2) + where

                                                            -std=gain×2fan_in+fan_out\text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}} - - +std=gain×2fan_in+fan_out\text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}} + + +

                                                            Also known as Glorot initialization.

                                                            Parameters
                                                            @@ -620,19 +639,21 @@ described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a uniform distribution. The resulting tensor will have values sampled from -U(−bound,bound)\mathcal{U}(-\text{bound}, \text{bound}) +U(−bound,bound)\mathcal{U}(-\text{bound}, \text{bound}) + where

                                                            -bound=gain×3fan_mode\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}} - - +bound=gain×3fan_mode\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}} + + +

                                                            Also known as He initialization.

                                                            Parameters
                                                            @@ -663,19 +684,24 @@ described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a normal distribution. The resulting tensor will have values sampled from -N(0,std2)\mathcal{N}(0, \text{std}^2) +N(0,std2)\mathcal{N}(0, \text{std}^2) + where

                                                            -std=gainfan_mode\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}} - - +std=gainfan_mode\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}} + + +

                                                            Also known as He initialization.

                                                            Parameters
                                                            @@ -710,7 +736,8 @@
                                                            Parameters
                                                              -

                                                            • tensor – an n-dimensional torch.Tensor, where n≥2n \geq 2 +

                                                            • tensor – an n-dimensional torch.Tensor, where n≥2n \geq 2 +

                                                            • gain – optional scaling factor

                                                            @@ -728,7 +755,8 @@ torch.nn.init.sparse_(tensor, sparsity, std=0.01)[source]

                                                            Fills the 2D input Tensor as a sparse matrix, where the non-zero elements will be drawn from the normal distribution -N(0,0.01)\mathcal{N}(0, 0.01) +N(0,0.01)\mathcal{N}(0, 0.01) + , as described in Deep learning via Hessian-free optimization - Martens, J. (2010).

                                                            diff --git a/docs/stable/notes/amp_examples.html b/docs/stable/notes/amp_examples.html index ccbab90a7b65..4c006d2bdee1 100644 --- a/docs/stable/notes/amp_examples.html +++ b/docs/stable/notes/amp_examples.html @@ -416,7 +416,8 @@

                                                            Typical Mixed Precision Training.grad attributes between backward() and scaler.step(optimizer), you should unscale them first. For example, gradient clipping manipulates a set of gradients such that their global norm (see torch.nn.utils.clip_grad_norm_()) or maximum magnitude (see torch.nn.utils.clip_grad_value_()) -is <=<= +is <=<= + some user-imposed threshold. If you attempted to clip without unscaling, the gradients’ norm/maximum magnitude would also be scaled, so your requested threshold (which was meant to be the threshold for unscaled gradients) would be invalid.

                                                            diff --git a/docs/stable/notes/autograd.html b/docs/stable/notes/autograd.html index 92b302cca425..389f19ec8c73 100644 --- a/docs/stable/notes/autograd.html +++ b/docs/stable/notes/autograd.html @@ -520,7 +520,9 @@

                                                            No thread safety on C++ hooks

                                                            PyTorch follows JAX’s convention for autograd for Complex Numbers.

                                                            -

                                                            Suppose we have a function which we can decompose into functions u and v +

                                                            Suppose we have a function F:C→CF: ℂ → ℂ + + which we can decompose into functions u and v which compute the real and imaginary parts of the function:

                                                            def F(z):
@@ -529,40 +531,61 @@ 
                                                            What notion of complex derivative does PyTorch use?
                                                            -

                                                            where 1j1j +

                                                            where 1j1j + is a unit imaginary number.

                                                            -

                                                            We define the JVPJVP - for function FF - at (x,y)(x, y) +

                                                            We define the JVPJVP + + for function FF + + at (x,y)(x, y) + applied to a tangent -vector c+dj∈Cc+dj \in C +vector c+dj∈Cc+dj \in C + as:

                                                            -[11j]∗J∗[cd]\begin{bmatrix} 1 & 1j \end{bmatrix} * J * \begin{bmatrix} c \\ d \end{bmatrix} +[11j]∗J∗[cd]\begin{bmatrix} 1 & 1j \end{bmatrix} * J * \begin{bmatrix} c \\ d \end{bmatrix} + + -

                                                            where

                                                            +J=[∂u(x,y)∂x∂u(x,y)∂y∂v(x,y)∂x∂v(x,y)∂y]J = \begin{bmatrix} + \frac{\partial u(x, y)}{\partial x} & \frac{\partial u(x, y)}{\partial y}\\ + \frac{\partial v(x, y)}{\partial x} & \frac{\partial v(x, y)}{\partial y} \end{bmatrix} \\ + + +
                                                            -

                                                            This is similar to the definition of the JVP for a function defined from , and the multiplication -with [1,1j]T[1, 1j]^T +

                                                            This is similar to the definition of the JVP for a function defined from R2→R2R^2 → R^2 + +, and the multiplication +with [1,1j]T[1, 1j]^T + is used to identify the result as a complex number.

                                                            -

                                                            We define the VJPVJP - of FF - at (x,y)(x, y) - for a cotangent vector c+dj∈Cc+dj \in C +

                                                            We define the VJPVJP + + of FF + + at (x,y)(x, y) + + for a cotangent vector c+dj∈Cc+dj \in C + as:

                                                            -[c−d]∗J∗[1−1j]\begin{bmatrix} c & -d \end{bmatrix} * J * \begin{bmatrix} 1 \\ -1j \end{bmatrix} +[c−d]∗J∗[1−1j]\begin{bmatrix} c & -d \end{bmatrix} * J * \begin{bmatrix} 1 \\ -1j \end{bmatrix} + + -

                                                            In PyTorch, the VJP is mostly what we care about, as it is the computation performed when we do backward -mode automatic differentiation. Notice that d and 1j1j +mode automatic differentiation. Notice that d and 1j1j + are negated in the formula above. Please look at the JAX docs to get explanation for the negative signs in the formula.

                                                            @@ -570,44 +593,64 @@

                                                            What notion of complex derivative does PyTorch use?

                                                            What happens if I call backward() on a complex scalar?

                                                            The gradient for a complex function is computed assuming the input function is a holomorphic function. -This is because for general functions, the Jacobian has 4 real-valued degrees of freedom +This is because for general C→Cℂ → ℂ + + functions, the Jacobian has 4 real-valued degrees of freedom (as in the 2x2 Jacobian matrix above), so we can’t hope to represent all of them with in a complex number. However, for holomorphic functions, the gradient can be fully represented with complex numbers due to the Cauchy-Riemann equations that ensure that 2x2 Jacobians have the special form of a scale-and-rotate matrix in the complex plane, i.e. the action of a single complex number under multiplication. And so, we can obtain that gradient using backward which is just a call to vjp with covector 1.0.

                                                            The net effect of this assumption is that the partial derivatives of the imaginary part of the function -(v(x,y)v(x, y) +(v(x,y)v(x, y) + above) are discarded for torch.autograd.backward() on a complex scalar (e.g., this is equivalent to dropping the imaginary part of the loss before performing a backwards).

                                                            For any other desired behavior, you can specify the covector grad_output in torch.autograd.backward() call accordingly.

                                                            How are the JVP and VJP defined for cross-domain functions?

                                                            -

                                                            Based on formulas above and the behavior we expect to see (going from should be an identity), +

                                                            Based on formulas above and the behavior we expect to see (going from C→R2→Cℂ → ℝ^2 → ℂ + + should be an identity), we use the formula given below for cross-domain functions.

                                                            -

                                                            The JVPJVP - and VJPVJP - for a are defined as:

                                                            +

                                                            The JVPJVP + + and VJPVJP + + for a f1:C→R2f1: ℂ → ℝ^2 + + are defined as:

                                                            -JVP=J∗[cd]JVP = J * \begin{bmatrix} c \\ d \end{bmatrix} +JVP=J∗[cd]JVP = J * \begin{bmatrix} c \\ d \end{bmatrix} + + -
                                                            -VJP=[cd]∗J∗[1−1j]VJP = \begin{bmatrix} c & d \end{bmatrix} * J * \begin{bmatrix} 1 \\ -1j \end{bmatrix} +VJP=[cd]∗J∗[1−1j]VJP = \begin{bmatrix} c & d \end{bmatrix} * J * \begin{bmatrix} 1 \\ -1j \end{bmatrix} + + -
                                                            -

                                                            The JVPJVP - and VJPVJP - for a are defined as:

                                                            +

                                                            The JVPJVP + + and VJPVJP + + for a f1:R2→Cf1: ℝ^2 → ℂ + + are defined as:

                                                            +JVP=[11j]∗J∗[cd]JVP = \begin{bmatrix} 1 & 1j \end{bmatrix} * J * \begin{bmatrix} c \\ d \end{bmatrix} \\ \\ + + +
                                                            -VJP=[c−d]∗JVJP = \begin{bmatrix} c & -d \end{bmatrix} * J +VJP=[c−d]∗JVJP = \begin{bmatrix} c & -d \end{bmatrix} * J + + -
                                                            diff --git a/docs/stable/notes/faq.html b/docs/stable/notes/faq.html index 4595f6068540..dc58263a1309 100644 --- a/docs/stable/notes/faq.html +++ b/docs/stable/notes/faq.html @@ -396,7 +396,8 @@

                                                            My model reports “cuda runtime error(2): out of memory”repackage function as described in this forum post.

                                                            Don’t use linear layers that are too large. -A linear layer nn.Linear(m, n) uses O(nm)O(nm) +A linear layer nn.Linear(m, n) uses O(nm)O(nm) + memory: that is to say, the memory requirements of the weights scales quadratically with the number of features. It is very easy diff --git a/docs/stable/objects.inv b/docs/stable/objects.inv index eff8372f9df8b55996d26fe3fd0b27c94055e0e5..06689dafc92c47fe8ef905e04861cc5cbb6e7832 100644 GIT binary patch delta 43164 zcmV)jK%u|I-U5)_0+2fyZDDhCWpW@WF)lVPFkvt&G&4CeWi>J&DUp0ke`Qyy+qXM0 zn2Rcvt6Y6lWwj(9_Z<{KGs(zITHK0DN-_rbx8GRF*cS%N1DxYh2H)p--U!CdScay{ zPM2?+rhEW(-*gx4%PuR^zQ1^VG2oE?@-$TCtG``rz8fUt%d{*JpaR3G$^8)Zix1*` z*`&ig{5w4Y-ld)d*~~BZe+TsI^^by%7ZAD`4tg0tFep2vT{x(0>IG6w)t2Bn$(wOs zE{EeZU4U+1l5kWzhGWJ&4F=`ukS6UA{wi{CE;5{$=`eJ~euR0z6S?d)A-hoUUQU*EEx-_0%y~u?A1U7?>48*k z7I0N*J+4Hx4o6fPe|h&1X}Q4QJ~l%)BW_Ddv8VAepS`1I|Zs2bym~?L++uwLgX0hY0M`?C~k>#GI6lLv!rX+=vNnX7=Ij z%pP=gGnQqs1QR5ro6K@RiaKk?dI*9HPel)sw9c2Ik_E838J3}zh&A0J&=RTVMOx2+ zVqrO4p4oJmf4>BEl(TfqPC@Nf28uG!JcwSNUUiu_CFmbt7C@4sK7%f(Q!qi{=;9m( z9P7sr`!cQCQ2U@{Rwkq!_D%gqnE6l~$7bwb2oThLgMhYc@-Rd{hSZbV4+0JdIBsEQelif*b>1#}pQaKS2;^ z2(wPJQ%FyXAd)bMZo1FmSdkt>;sMPQQ0;@p9|&|P(;*Pj)M*(6Ii|3bgd>t5NKnR- 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                                                            Notes

                                                              @@ -247,7 +252,7 @@
                                                              • torchaudio
                                                              • torchtext
                                                                • -
                                                              • torchvision
                                                                • +
                                                              • torchvision
                                                              • TorchElastic
                                                              • TorchServe
                                                              • PyTorch on XLA Devices
                                                                • @@ -585,9 +590,7 @@

                                                                optimizer.step(
                                                                class torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)[source]

                                                                Implements Adam algorithm.

                                                                -

                                                                It has been proposed in Adam: A Method for Stochastic Optimization. -The implementation of the L2 penalty follows changes proposed in -Decoupled Weight Decay Regularization.

                                                                +

                                                                It has been proposed in Adam: A Method for Stochastic Optimization.

                                                                Parameters
                                                                  @@ -726,7 +729,7 @@

                                                                  optimizer.step(
                                                                  class torch.optim.ASGD(params, lr=0.01, lambd=0.0001, alpha=0.75, t0=1000000.0, weight_decay=0)[source]

                                                                  Implements Averaged Stochastic Gradient Descent.

                                                                  -

                                                                  It has been proposed in Acceleration of stochastic approximation by +

                                                                  It has been proposed in Acceleration of stochastic approximation by averaging.

                                                                  Parameters
                                                                  @@ -812,12 +815,12 @@

                                                                  optimizer.step( class torch.optim.RMSprop(params, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)[source]

                                                                  Implements RMSprop algorithm.

                                                                  Proposed by G. Hinton in his -course.

                                                                  +course.

                                                                  The centered version first appears in Generating Sequences With Recurrent Neural Networks.

                                                                  The implementation here takes the square root of the gradient average before adding epsilon (note that TensorFlow interchanges these two operations). The effective -learning rate is thus α/(v+ϵ)\alpha/(\sqrt{v} + \epsilon) +M834 80h400000v40h-400000z'/>​+ϵ) - where α\alpha + where α\alpha -is the scheduled learning rate and vv +is the scheduled learning rate and vv is the weighted moving average of the squared gradient.

                                                                  @@ -931,32 +934,32 @@

                                                                  optimizer.step( Sutskever et. al. and implementations in some other frameworks.

                                                                  Considering the specific case of Momentum, the update can be written as

                                                                  -vt+1=μ∗vt+gt+1,pt+1=pt−lr∗vt+1,\begin{aligned} +vt+1=μ∗vt+gt+1,pt+1=pt−lr∗vt+1,\begin{aligned} v_{t+1} & = \mu * v_{t} + g_{t+1}, \\ p_{t+1} & = p_{t} - \text{lr} * v_{t+1}, \end{aligned} - + -

                                                                  where pp +

                                                                  where pp -, gg +, gg -, vv +, vv - and μ\mu + and μ\mu denote the parameters, gradient, velocity, and momentum respectively.

                                                                  This is in contrast to Sutskever et. al. and other frameworks which employ an update of the form

                                                                  -vt+1=μ∗vt+lr∗gt+1,pt+1=pt−vt+1.\begin{aligned} +vt+1=μ∗vt+lr∗gt+1,pt+1=pt−vt+1.\begin{aligned} v_{t+1} & = \mu * v_{t} + \text{lr} * g_{t+1}, \\ p_{t+1} & = p_{t} - v_{t+1}. \end{aligned} - +

                                                                  The Nesterov version is analogously modified.

                                                                  @@ -1000,7 +1003,7 @@

                                                                  How to adjust learning rate
                                                                  -class torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1, verbose=False)[source]
                                                                  +class torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1)[source]

                                                                  Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.

                                                                  @@ -1011,8 +1014,6 @@

                                                                  How to adjust learning rateint) – The index of last epoch. Default: -1.

                                                                  -

                                                                • verbose (bool) – If True, prints a message to stdout for -each update. Default: False.

                                                                @@ -1053,7 +1054,7 @@

                                                                How to adjust learning rate
                                                                -class torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_lambda, last_epoch=-1, verbose=False)[source]
                                                                +class torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_lambda, last_epoch=-1)[source]

                                                                Multiply the learning rate of each parameter group by the factor given in the specified function. When last_epoch=-1, sets initial lr as lr.

                                                                @@ -1064,8 +1065,6 @@

                                                                How to adjust learning rateint) – The index of last epoch. Default: -1.

                                                                -

                                                              • verbose (bool) – If True, prints a message to stdout for -each update. Default: False.

                                                            @@ -1104,7 +1103,7 @@

                                                            How to adjust learning rate
                                                            -class torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1, verbose=False)[source]
                                                            +class torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)[source]

                                                            Decays the learning rate of each parameter group by gamma every step_size epochs. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler. When @@ -1117,8 +1116,6 @@

                                                            How to adjust learning ratefloat) – Multiplicative factor of learning rate decay. Default: 0.1.

                                                          • last_epoch (int) – The index of last epoch. Default: -1.

                                                            • -

                                                          • verbose (bool) – If True, prints a message to stdout for -each update. Default: False.

                                                            • @@ -1139,7 +1136,7 @@

                                                            How to adjust learning rate
                                                            -class torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1, verbose=False)[source]
                                                            +class torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=0.1, last_epoch=-1)[source]

                                                            Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones. Notice that such decay can happen simultaneously with other changes to the learning rate from outside @@ -1152,8 +1149,6 @@

                                                            How to adjust learning ratefloat) – Multiplicative factor of learning rate decay. Default: 0.1.

                                                          • last_epoch (int) – The index of last epoch. Default: -1.

                                                            • -

                                                          • verbose (bool) – If True, prints a message to stdout for -each update. Default: False.

                                                            • @@ -1173,7 +1168,7 @@

                                                            How to adjust learning rate
                                                            -class torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma, last_epoch=-1, verbose=False)[source]
                                                            +class torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma, last_epoch=-1)[source]

                                                            Decays the learning rate of each parameter group by gamma every epoch. When last_epoch=-1, sets initial lr as lr.

                                                            @@ -1182,8 +1177,6 @@

                                                            How to adjust learning rateOptimizer) – Wrapped optimizer.

                                                          • gamma (float) – Multiplicative factor of learning rate decay.

                                                          • last_epoch (int) – The index of last epoch. Default: -1.

                                                            • -

                                                          • verbose (bool) – If True, prints a message to stdout for -each update. Default: False.

                                                            • @@ -1191,16 +1184,16 @@

                                                            How to adjust learning rate
                                                            -class torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max, eta_min=0, last_epoch=-1, verbose=False)[source]
                                                            +class torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max, eta_min=0, last_epoch=-1)[source]

                                                            Set the learning rate of each parameter group using a cosine annealing -schedule, where ηmax\eta_{max} +schedule, where ηmax\eta_{max} is set to the initial lr and -TcurT_{cur} +TcurT_{cur} is the number of epochs since the last restart in SGDR:

                                                            -ηt=ηmin+12(ηmax−ηmin)(1+cos⁡(TcurTmaxπ)),Tcur≠(2k+1)Tmax;ηt+1=ηt+12(ηmax−ηmin)(1−cos⁡(1Tmaxπ)),Tcur=(2k+1)Tmax.\begin{aligned} +ηt=ηmin+12(ηmax−ηmin)(1+cos⁡(TcurTmaxπ)),Tcur≠(2k+1)Tmax;ηt+1=ηt+12(ηmax−ηmin)(1−cos⁡(1Tmaxπ)),Tcur=(2k+1)Tmax.\begin{aligned} \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right), & T_{cur} \neq (2k+1)T_{max}; \\ @@ -1209,17 +1202,17 @@

                                                            How to adjust learning rate

                                                            SGDR: Stochastic Gradient Descent with Warm Restarts. Note that this only @@ -1231,8 +1224,6 @@

                                                            How to adjust learning rateint) – Maximum number of iterations.

                                                          • eta_min (float) – Minimum learning rate. Default: 0.

                                                          • last_epoch (int) – The index of last epoch. Default: -1.

                                                            • -

                                                          • verbose (bool) – If True, prints a message to stdout for -each update. Default: False.

                                                            • @@ -1240,7 +1231,7 @@

                                                            How to adjust learning rate
                                                            -class torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-08, verbose=False)[source]
                                                            +class torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=False, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-08)[source]

                                                            Reduce learning rate when a metric has stopped improving. Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This scheduler reads a metrics @@ -1262,6 +1253,8 @@

                                                            How to adjust learning ratebool) – If True, prints a message to stdout for +each update. Default: False.

                                                          • threshold (float) – Threshold for measuring the new optimum, to only focus on significant changes. Default: 1e-4.

                                                          • threshold_mode (str) – One of rel, abs. In rel mode, @@ -1277,8 +1270,6 @@

                                                            How to adjust learning ratefloat) – Minimal decay applied to lr. If the difference between new and old lr is smaller than eps, the update is ignored. Default: 1e-8.

                                                            • -

                                                          • verbose (bool) – If True, prints a message to stdout for -each update. Default: False.

                                                            • @@ -1296,7 +1287,7 @@

                                                            How to adjust learning rate
                                                            -class torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr, max_lr, step_size_up=2000, step_size_down=None, mode='triangular', gamma=1.0, scale_fn=None, scale_mode='cycle', cycle_momentum=True, base_momentum=0.8, max_momentum=0.9, last_epoch=-1, verbose=False)[source]
                                                            +class torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr, max_lr, step_size_up=2000, step_size_down=None, mode='triangular', gamma=1.0, scale_fn=None, scale_mode='cycle', cycle_momentum=True, base_momentum=0.8, max_momentum=0.9, last_epoch=-1)[source]

                                                            Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). The policy cycles the learning rate between two boundaries with a constant frequency, as detailed in @@ -1374,8 +1365,6 @@

                                                            How to adjust learning ratebool) – If True, prints a message to stdout for -each update. Default: False.

                                                            @@ -1402,7 +1391,7 @@

                                                            How to adjust learning rate
                                                            -class torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr, total_steps=None, epochs=None, steps_per_epoch=None, pct_start=0.3, anneal_strategy='cos', cycle_momentum=True, base_momentum=0.85, max_momentum=0.95, div_factor=25.0, final_div_factor=10000.0, last_epoch=-1, verbose=False)[source]
                                                            +class torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr, total_steps=None, epochs=None, steps_per_epoch=None, pct_start=0.3, anneal_strategy='cos', cycle_momentum=True, base_momentum=0.85, max_momentum=0.95, div_factor=25.0, final_div_factor=10000.0, last_epoch=-1)[source]

                                                            Sets the learning rate of each parameter group according to the 1cycle learning rate policy. The 1cycle policy anneals the learning rate from an initial learning rate to some maximum learning rate and then @@ -1476,8 +1465,6 @@

                                                            How to adjust learning ratebool) – If True, prints a message to stdout for -each update. Default: False.

                                                            @@ -1495,31 +1482,31 @@

                                                            How to adjust learning rate
                                                            -class torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0, T_mult=1, eta_min=0, last_epoch=-1, verbose=False)[source]
                                                            +class torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0, T_mult=1, eta_min=0, last_epoch=-1)[source]

                                                            Set the learning rate of each parameter group using a cosine annealing -schedule, where ηmax\eta_{max} +schedule, where ηmax\eta_{max} - is set to the initial lr, TcurT_{cur} + is set to the initial lr, TcurT_{cur} -is the number of epochs since the last restart and TiT_{i} +is the number of epochs since the last restart and TiT_{i} is the number of epochs between two warm restarts in SGDR:

                                                            -ηt=ηmin+12(ηmax−ηmin)(1+cos⁡(TcurTiπ))\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + +ηt=ηmin+12(ηmax−ηmin)(1+cos⁡(TcurTiπ))\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) - + -

                                                            When Tcur=TiT_{cur}=T_{i} +

                                                            When Tcur=TiT_{cur}=T_{i} -, set ηt=ηmin\eta_t = \eta_{min} +, set ηt=ηmin\eta_t = \eta_{min} . -When Tcur=0T_{cur}=0 +When Tcur=0T_{cur}=0 - after restart, set ηt=ηmax\eta_t=\eta_{max} + after restart, set ηt=ηmax\eta_t=\eta_{max} .

                                                            It has been proposed in @@ -1529,13 +1516,11 @@

                                                            How to adjust learning rate

                                                            • optimizer (Optimizer) – Wrapped optimizer.

                                                            • T_0 (int) – Number of iterations for the first restart.

                                                              • -

                                                            • T_mult (int, optional) – A factor increases TiT_{i} +

                                                            • T_mult (int, optional) – A factor increases TiT_{i} after a restart. Default: 1.

                                                            • eta_min (float, optional) – Minimum learning rate. Default: 0.

                                                            • last_epoch (int, optional) – The index of last epoch. Default: -1.

                                                              • -

                                                            • verbose (bool) – If True, prints a message to stdout for -each update. Default: False.

                                                            @@ -1570,100 +1555,6 @@

                                                            How to adjust learning rate -

                                                            Stochastic Weight Averaging

                                                            -

                                                            torch.optim.swa_utils implements Stochastic Weight Averaging (SWA). In particular, -torch.optim.swa_utils.AveragedModel class implements SWA models, -torch.optim.swa_utils.SWALR implements the SWA learning rate scheduler and -torch.optim.swa_utils.update_bn() is a utility function used to update SWA batch -normalization statistics at the end of training.

                                                            -

                                                            SWA has been proposed in Averaging Weights Leads to Wider Optima and Better Generalization.

                                                            -
                                                            -

                                                            Constructing averaged models

                                                            -

                                                            AveragedModel class serves to compute the weights of the SWA model. You can create an -averaged model by running:

                                                            -
                                                            >>> swa_model = AveragedModel(model)
-
                                                            -
                                                            -

                                                            Here the model model can be an arbitrary torch.nn.Module object. swa_model -will keep track of the running averages of the parameters of the model. To update these -averages, you can use the update_parameters() function:

                                                            -
                                                            >>> swa_model.update_parameters(model)
-
                                                            -
                                                            -
                                                            -
                                                            -

                                                            SWA learning rate schedules

                                                            -

                                                            Typically, in SWA the learning rate is set to a high constant value. SWALR is a -learning rate scheduler that anneals the learning rate to a fixed value, and then keeps it -constant. For example, the following code creates a scheduler that linearly anneals the -learning rate from its initial value to 0.05 in 5 epochs within each parameter group:

                                                            -
                                                            >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, \
->>>         anneal_strategy="linear", anneal_epochs=5, swa_lr=0.05)
-
                                                            -
                                                            -

                                                            You can also use cosine annealing to a fixed value instead of linear annealing by setting -anneal_strategy="cos".

                                                            -
                                                            -
                                                            -

                                                            Taking care of batch normalization

                                                            -

                                                            update_bn() is a utility function that allows to compute the batchnorm statistics for the SWA model -on a given dataloader loader at the end of training:

                                                            -
                                                            >>> torch.optim.swa_utils.update_bn(loader, swa_model)
-
                                                            -
                                                            -

                                                            update_bn() applies the swa_model to every element in the dataloader and computes the activation -statistics for each batch normalization layer in the model.

                                                            -
                                                            -

                                                            Warning

                                                            -

                                                            update_bn() assumes that each batch in the dataloader loader is either a tensors or a list of -tensors where the first element is the tensor that the network swa_model should be applied to. -If your dataloader has a different structure, you can update the batch normalization statistics of the -swa_model by doing a forward pass with the swa_model on each element of the dataset.

                                                            -
                                                            -
                                                            -
                                                            -

                                                            Custom averaging strategies

                                                            -

                                                            By default, torch.optim.swa_utils.AveragedModel computes a running equal average of -the parameters that you provide, but you can also use custom averaging functions with the -avg_fn parameter. In the following example ema_model computes an exponential moving average.

                                                            -

                                                            Example:

                                                            -
                                                            >>> ema_avg = lambda averaged_model_parameter, model_parameter, num_averaged:\
->>>         0.1 * averaged_model_parameter + 0.9 * model_parameter
->>> ema_model = torch.optim.swa_utils.AveragedModel(model, avg_fn=ema_avg)
-
                                                            -
                                                            -
                                                            -
                                                            -

                                                            Putting it all together

                                                            -

                                                            In the example below, swa_model is the SWA model that accumulates the averages of the weights. -We train the model for a total of 300 epochs and we switch to the SWA learning rate schedule -and start to collect SWA averages of the parameters at epoch 160:

                                                            -
                                                            >>> loader, optimizer, model, loss_fn = ...
->>> swa_model = torch.optim.swa_utils.AveragedModel(model)
->>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=300)
->>> swa_start = 160
->>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
->>>
->>> for epoch in range(300):
->>>       for input, target in loader:
->>>           optimizer.zero_grad()
->>>           loss_fn(model(input), target).backward()
->>>           optimizer.step()
->>>       if i > swa_start:
->>>           swa_model.update_parameters(model)
->>>           swa_scheduler.step()
->>>       else:
->>>           scheduler.step()
->>>
->>> # Update bn statistics for the swa_model at the end
->>> torch.optim.swa_utils.update_bn(loader, swa_model)
->>> # Use swa_model to make predictions on test data
->>> preds = swa_model(test_input)
-
                                                            -
                                                            -
                                                            @@ -1723,14 +1614,6 @@

                                                            Putting it all together
                                                          • Algorithms
                                                          • How to adjust learning rate
                                                            • -
                                                          • Stochastic Weight Averaging -
                                                            • @@ -1983,4 +1866,4 @@

                                                            Resources

                                                            }) - + \ No newline at end of file diff --git a/docs/stable/quantization.html b/docs/stable/quantization.html index ff717cc61a94..038edb0de7c6 100644 --- a/docs/stable/quantization.html +++ b/docs/stable/quantization.html @@ -957,7 +957,7 @@

                                                            Top-level quantization APIs
                                                            -torch.quantization.prepare(model, inplace=False, white_list={<class 'torch.nn.modules.activation.ReLU6'>, <class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d'>, <class 'torch.nn.modules.instancenorm.InstanceNorm2d'>, <class 'torch.nn.intrinsic.modules.fused.ConvReLU3d'>, <class 'torch.nn.modules.instancenorm.InstanceNorm3d'>, <class 'torch.nn.modules.conv.Conv3d'>, <class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvReLU2d'>, <class 'torch.nn.modules.normalization.GroupNorm'>, <class 'torch.nn.qat.modules.linear.Linear'>, <class 'torch.nn.modules.batchnorm.BatchNorm3d'>, <class 'torch.nn.intrinsic.modules.fused.BNReLU3d'>, <class 'torch.nn.modules.rnn.RNNCell'>, <class 'torch.nn.modules.activation.ELU'>, <class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d'>, <class 'torch.nn.intrinsic.modules.fused.ConvBnReLU2d'>, <class 'torch.nn.modules.rnn.LSTM'>, <class 'torch.nn.intrinsic.modules.fused.ConvBn2d'>, <class 'torch.nn.modules.normalization.LayerNorm'>, <class 'torch.nn.modules.conv.Conv1d'>, <class 'torch.nn.modules.rnn.GRUCell'>, <class 'torch.nn.modules.linear.Linear'>, <class 'torch.nn.intrinsic.modules.fused.LinearReLU'>, <class 'torch.nn.modules.activation.ReLU'>, <class 'torch.nn.modules.conv.Conv2d'>, <class 'torch.nn.intrinsic.modules.fused.ConvReLU1d'>, <class 'torch.nn.intrinsic.modules.fused.ConvReLU2d'>, <class 'torch.nn.intrinsic.qat.modules.linear_relu.LinearReLU'>, <class 'torch.quantization.stubs.QuantStub'>, <class 'torch.nn.modules.batchnorm.BatchNorm2d'>, <class 'torch.nn.intrinsic.modules.fused.BNReLU2d'>, <class 'torch.nn.quantized.modules.functional_modules.FloatFunctional'>, <class 'torch.nn.modules.activation.Hardswish'>, <class 'torch.nn.modules.rnn.LSTMCell'>, <class 'torch.nn.modules.container.Sequential'>, <class 'torch.nn.qat.modules.conv.Conv2d'>, <class 'torch.nn.modules.instancenorm.InstanceNorm1d'>}, observer_non_leaf_module_list=None)[source]
                                                            +torch.quantization.prepare(model, inplace=False, white_list={<class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvReLU2d'>, <class 'torch.nn.intrinsic.modules.fused.BNReLU2d'>, <class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d'>, <class 'torch.nn.modules.rnn.LSTM'>, <class 'torch.nn.modules.activation.ReLU'>, <class 'torch.nn.modules.conv.Conv2d'>, <class 'torch.nn.modules.instancenorm.InstanceNorm2d'>, <class 'torch.nn.intrinsic.qat.modules.linear_relu.LinearReLU'>, <class 'torch.nn.modules.rnn.LSTMCell'>, <class 'torch.nn.qat.modules.linear.Linear'>, <class 'torch.nn.quantized.modules.functional_modules.FloatFunctional'>, <class 'torch.nn.intrinsic.modules.fused.ConvReLU3d'>, <class 'torch.nn.modules.activation.ELU'>, <class 'torch.nn.modules.batchnorm.BatchNorm3d'>, <class 'torch.nn.modules.container.Sequential'>, <class 'torch.nn.modules.activation.ReLU6'>, <class 'torch.quantization.stubs.QuantStub'>, <class 'torch.nn.modules.linear.Linear'>, <class 'torch.nn.modules.conv.Conv1d'>, <class 'torch.nn.modules.normalization.GroupNorm'>, <class 'torch.nn.modules.activation.Hardswish'>, <class 'torch.nn.modules.instancenorm.InstanceNorm3d'>, <class 'torch.nn.modules.rnn.RNNCell'>, <class 'torch.nn.intrinsic.modules.fused.ConvBnReLU2d'>, <class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d'>, <class 'torch.nn.intrinsic.modules.fused.ConvBn2d'>, <class 'torch.nn.modules.instancenorm.InstanceNorm1d'>, <class 'torch.nn.intrinsic.modules.fused.BNReLU3d'>, <class 'torch.nn.intrinsic.modules.fused.LinearReLU'>, <class 'torch.nn.modules.conv.Conv3d'>, <class 'torch.nn.qat.modules.conv.Conv2d'>, <class 'torch.nn.modules.normalization.LayerNorm'>, <class 'torch.nn.modules.rnn.GRUCell'>, <class 'torch.nn.intrinsic.modules.fused.ConvReLU1d'>, <class 'torch.nn.intrinsic.modules.fused.ConvReLU2d'>, <class 'torch.nn.modules.batchnorm.BatchNorm2d'>}, observer_non_leaf_module_list=None)[source]

                                                            Prepares a copy of the model for quantization calibration or quantization-aware training.

                                                            Quantization configuration should be assigned preemptively to individual submodules in .qconfig attribute.

                                                            @@ -1274,16 +1274,21 @@

                                                            Observersxminx_\text{min} - and xmaxx_\text{max} +

                                                            Given running min/max as xminx_\text{min} + + and xmaxx_\text{max} + , -scale ss - and zero point zz +scale ss + + and zero point zz + are computed as:

                                                            -

                                                            The running minimum/maximum xmin/maxx_\text{min/max} +

                                                            The running minimum/maximum xmin/maxx_\text{min/max} + is computed as:

                                                            -xmin={min(X)if xmin=Nonemin(xmin,min(X))otherwisexmax={max(X)if xmax=Nonemax(xmax,max(X))otherwise\begin{array}{ll} +xmin={min⁡(X)if xmin=Nonemin⁡(xmin,min⁡(X))otherwisexmax={max⁡(X)if xmax=Nonemax⁡(xmax,max⁡(X))otherwise\begin{array}{ll} x_\text{min} &= \begin{cases} \min(X) & \text{if~}x_\text{min} = \text{None} \\ \min\left(x_\text{min}, \min(X)\right) & \text{otherwise} @@ -1292,14 +1297,18 @@

                                                            Observers

                                                            -xmin={min(X)if xmin=None(1−c)xmin+cmin(X)otherwisexmax={max(X)if xmax=None(1−c)xmax+cmax(X)otherwise\begin{array}{ll} +xmin={min⁡(X)if xmin=None(1−c)xmin+cmin⁡(X)otherwisexmax={max⁡(X)if xmax=None(1−c)xmax+cmax⁡(X)otherwise\begin{array}{ll} x_\text{min} = \begin{cases} \min(X) & \text{if~}x_\text{min} = \text{None} \\ (1 - c) x_\text{min} + c \min(X) & \text{otherwise} @@ -1361,11 +1373,15 @@

                                                            Observers

                                                            -torch.nn.quantized.functional.linear(input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, scale: Optional[float] = None, zero_point: Optional[int] = None) → torch.Tensor[source]
                                                            +torch.nn.quantized.functional.linear(input, weight, bias=None, scale=None, zero_point=None)[source]

                                                            Applies a linear transformation to the incoming quantized data: -y=xAT+by = xA^T + b +y=xAT+by = xA^T + b + . See Linear

                                                            @@ -1921,14 +1938,18 @@

                                                            torch.nn.quantized
                                                            Shape:
                                                              -

                                                            • Input: (N,∗,in_features)(N, *, in\_features) +

                                                            • Input: (N,∗,in_features)(N, *, in\_features) + where * means any number of additional dimensions

                                                              • -

                                                            • Weight: (out_features,in_features)(out\_features, in\_features) +

                                                            • Weight: (out_features,in_features)(out\_features, in\_features) +

                                                              • -

                                                            • Bias: (out_features)(out\_features) +

                                                            • Bias: (out_features)(out\_features) +

                                                              • -

                                                            • Output: (N,∗,out_features)(N, *, out\_features) +

                                                            • Output: (N,∗,out_features)(N, *, out\_features) +

                                                            @@ -1944,11 +1965,14 @@

                                                            torch.nn.quantized
                                                            Parameters
                                                              -

                                                            • input – quantized input tensor of shape (minibatch,in_channels,iW)(\text{minibatch} , \text{in\_channels} , iW) +

                                                            • input – quantized input tensor of shape (minibatch,in_channels,iW)(\text{minibatch} , \text{in\_channels} , iW) +

                                                              • -

                                                            • weight – quantized filters of shape (out_channels,in_channelsgroups,iW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , iW) +

                                                            • weight – quantized filters of shape (out_channels,in_channelsgroups,iW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , iW) +

                                                              • -

                                                            • bias – non-quantized bias tensor of shape (out_channels)(\text{out\_channels}) +

                                                            • bias – non-quantized bias tensor of shape (out_channels)(\text{out\_channels}) + . The tensor type must be torch.float.

                                                            • stride – the stride of the convolving kernel. Can be a single number or a tuple (sW,). Default: 1

                                                              • @@ -1956,7 +1980,8 @@

                                                              torch.nn.quantizedin_channels\text{in\_channels} +

                                                            • groups – split input into groups, in_channels\text{in\_channels} + should be divisible by the number of groups. Default: 1

                                                            • padding_mode – the padding mode to use. Only “zeros” is supported for quantized convolution at the moment. Default: “zeros”

                                                              • @@ -1992,11 +2017,14 @@

                                                              torch.nn.quantized
                                                              Parameters
                                                                -

                                                              • input – quantized input tensor of shape (minibatch,in_channels,iH,iW)(\text{minibatch} , \text{in\_channels} , iH , iW) +

                                                              • input – quantized input tensor of shape (minibatch,in_channels,iH,iW)(\text{minibatch} , \text{in\_channels} , iH , iW) +

                                                                • -

                                                              • weight – quantized filters of shape (out_channels,in_channelsgroups,kH,kW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW) +

                                                              • weight – quantized filters of shape (out_channels,in_channelsgroups,kH,kW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW) +

                                                                • -

                                                              • bias – non-quantized bias tensor of shape (out_channels)(\text{out\_channels}) +

                                                              • bias – non-quantized bias tensor of shape (out_channels)(\text{out\_channels}) + . The tensor type must be torch.float.

                                                              • stride – the stride of the convolving kernel. Can be a single number or a tuple (sH, sW). Default: 1

                                                                • @@ -2004,7 +2032,8 @@

                                                                torch.nn.quantizedin_channels\text{in\_channels} +

                                                              • groups – split input into groups, in_channels\text{in\_channels} + should be divisible by the number of groups. Default: 1

                                                              • padding_mode – the padding mode to use. Only “zeros” is supported for quantized convolution at the moment. Default: “zeros”

                                                                • @@ -2041,13 +2070,16 @@

                                                                torch.nn.quantizedParameters

                                                                • input – quantized input tensor of shape -(minibatch,in_channels,iD,iH,iW)(\text{minibatch} , \text{in\_channels} , iD , iH , iW) +(minibatch,in_channels,iD,iH,iW)(\text{minibatch} , \text{in\_channels} , iD , iH , iW) +

                                                                • weight – quantized filters of shape -(out_channels,in_channelsgroups,kD,kH,kW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kD , kH , kW) +(out_channels,in_channelsgroups,kD,kH,kW)(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kD , kH , kW) +

                                                                • bias – non-quantized bias tensor of shape -(out_channels)(\text{out\_channels}) +(out_channels)(\text{out\_channels}) + . The tensor type must be torch.float.

                                                                • stride – the stride of the convolving kernel. Can be a single number or a tuple (sD, sH, sW). Default: 1

                                                                  • @@ -2055,7 +2087,8 @@

                                                                  torch.nn.quantizedin_channels\text{in\_channels} +

                                                                • groups – split input into groups, in_channels\text{in\_channels} + should be divisible by the number of groups. Default: 1

                                                                • padding_mode – the padding mode to use. Only “zeros” is supported for @@ -2097,7 +2130,7 @@

                                                                  torch.nn.quantized
                                                                  -torch.nn.quantized.functional.adaptive_avg_pool2d(input: Tensor, output_size: BroadcastingList2[int]) → Tensor[source]
                                                                  +torch.nn.quantized.functional.adaptive_avg_pool2d(input, output_size)[source]

                                                                  Applies a 2D adaptive average pooling over a quantized input signal composed of several quantized input planes.

                                                                  @@ -2116,9 +2149,11 @@

                                                                  torch.nn.quantized
                                                                  torch.nn.quantized.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)[source]
                                                                  -

                                                                  Applies 2D average-pooling operation in kH×kWkH \times kW +

                                                                  Applies 2D average-pooling operation in kH×kWkH \times kW + regions by step size -sH×sWsH \times sW +sH×sWsH \times sW + steps. The number of output features is equal to the number of input planes.

                                                                  @@ -2129,7 +2164,8 @@

                                                                  torch.nn.quantized
                                                                  Parameters
                                                                    -

                                                                  • input – quantized input tensor (minibatch,in_channels,iH,iW)(\text{minibatch} , \text{in\_channels} , iH , iW) +

                                                                  • input – quantized input tensor (minibatch,in_channels,iH,iW)(\text{minibatch} , \text{in\_channels} , iH , iW) +

                                                                  • kernel_size – size of the pooling region. Can be a single number or a tuple (kH, kW)

                                                                    • @@ -2197,7 +2233,7 @@

                                                                    torch.nn.quantized
                                                                    -torch.nn.quantized.functional.hardswish(input: torch.Tensor, scale: float, zero_point: int) → torch.Tensor[source]
                                                                    +torch.nn.quantized.functional.hardswish(input, scale, zero_point)[source]

                                                                    This is the quantized version of hardswish().

                                                                    Parameters
                                                                    @@ -2340,8 +2376,10 @@

                                                                    ReLU
                                                                    class torch.nn.quantized.ReLU(inplace=False)[source]

                                                                    Applies quantized rectified linear unit function element-wise:

                                                                    -

                                                                    ReLU(x)=max(x0,x)\text{ReLU}(x)= \max(x_0, x) -, where x0x_0 +

                                                                    ReLU(x)=max⁡(x0,x)\text{ReLU}(x)= \max(x_0, x) + +, where x0x_0 + is the zero point.

                                                                    Please see https://pytorch.org/docs/stable/nn.html#torch.nn.ReLU for more documentation on ReLU.

                                                                    @@ -2352,10 +2390,12 @@

                                                                    ReLU

                                                                    Shape:
                                                                      -

                                                                    • Input: (N,∗)(N, *) +

                                                                    • Input: (N,∗)(N, *) + where * means, any number of additional dimensions

                                                                      • -

                                                                    • Output: (N,∗)(N, *) +

                                                                    • Output: (N,∗)(N, *) + , same shape as the input

                                                                    @@ -2376,10 +2416,13 @@

                                                                    ReLU6
                                                                    class torch.nn.quantized.ReLU6(inplace=False)[source]

                                                                    Applies the element-wise function:

                                                                    -

                                                                    ReLU6(x)=min(max(x0,x),q(6))\text{ReLU6}(x) = \min(\max(x_0, x), q(6)) -, where x0x_0 +

                                                                    ReLU6(x)=min⁡(max⁡(x0,x),q(6))\text{ReLU6}(x) = \min(\max(x_0, x), q(6)) + +, where x0x_0 + is the -zero_point, and q(6)q(6) +zero_point, and q(6)q(6) + is the quantized representation of number 6.

                                                                    Parameters
                                                                    @@ -2388,10 +2431,12 @@

                                                                    ReLU6

                                                                    Shape:
                                                                      -

                                                                    • Input: (N,∗)(N, *) +

                                                                    • Input: (N,∗)(N, *) + where * means, any number of additional dimensions

                                                                      • -

                                                                    • Output: (N,∗)(N, *) +

                                                                    • Output: (N,∗)(N, *) + , same shape as the input

                                                                    @@ -2744,9 +2789,11 @@

                                                                    Linear
                                                                    Variables

                                                                    • ~Linear.weight (Tensor) – the non-learnable quantized weights of the module of -shape (out_features,in_features)(\text{out\_features}, \text{in\_features}) +shape (out_features,in_features)(\text{out\_features}, \text{in\_features}) + .

                                                                      • -

                                                                    • ~Linear.bias (Tensor) – the non-learnable bias of the module of shape (out_features)(\text{out\_features}) +

                                                                    • ~Linear.bias (Tensor) – the non-learnable bias of the module of shape (out_features)(\text{out\_features}) + . If bias is True, the values are initialized to zero.

                                                                    • ~Linear.scale – scale parameter of output Quantized Tensor, type: double

                                                                      • @@ -2893,10 +2940,12 @@

                                                                      Linear
                                                                      Variables

                                                                      • ~Linear.weight (Tensor) – the non-learnable quantized weights of the module which are of -shape (out_features,in_features)(\text{out\_features}, \text{in\_features}) +shape (out_features,in_features)(\text{out\_features}, \text{in\_features}) + .

                                                                      • ~Linear.bias (Tensor) – the non-learnable floating point bias of the module of shape -(out_features)(\text{out\_features}) +(out_features)(\text{out\_features}) + . If bias is True, the values are initialized to zero.

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\ No newline at end of file diff --git a/docs/stable/sparse.html b/docs/stable/sparse.html index 0491b9d2bc71..171c71c98963 100644 --- a/docs/stable/sparse.html +++ b/docs/stable/sparse.html @@ -593,7 +593,7 @@

                                                                      Functions

                                                                      -torch.sparse.addmm(mat: torch.Tensor, mat1: torch.Tensor, mat2: torch.Tensor, beta: float = 1, alpha: float = 1) → torch.Tensor[source]
                                                                      +torch.sparse.addmm(mat, mat1, mat2, beta=1, alpha=1)[source]

                                                                      This function does exact same thing as torch.addmm() in the forward, except that it supports backward for sparse matrix mat1. mat1 need to have sparse_dim = 2. Note that the gradients of mat1 is a @@ -604,10 +604,13 @@

                                                                      FunctionsTensor) – a dense matrix to be added

                                                                    • mat1 (SparseTensor) – a sparse matrix to be multiplied

                                                                    • mat2 (Tensor) – a dense matrix be multiplied

                                                                      • -

                                                                    • beta (Number, optional) – multiplier for mat (β\beta +

                                                                    • beta (Number, optional) – multiplier for mat (β\beta + )

                                                                      • -

                                                                    • alpha (Number, optional) – multiplier for mat1@mat2mat1 @ mat2 - (α\alpha +

                                                                    • alpha (Number, optional) – multiplier for mat1@mat2mat1 @ mat2 + + (α\alpha + )

                                                                    @@ -619,10 +622,13 @@

                                                                    Functionstorch.sparse.mm(mat1, mat2)[source]

                                                                    Performs a matrix multiplication of the sparse matrix mat1 and dense matrix mat2. Similar to torch.mm(), If mat1 is a -(n×m)(n \times m) - tensor, mat2 is a (m×p)(m \times p) +(n×m)(n \times m) + + tensor, mat2 is a (m×p)(m \times p) + tensor, out will be a -(n×p)(n \times p) +(n×p)(n \times p) + dense tensor. mat1 need to have sparse_dim = 2. This function also supports backward for both matrices. Note that the gradients of mat1 is a coalesced sparse tensor.

                                                                    @@ -664,7 +670,7 @@

                                                                    Functions
                                                                    -torch.sparse.sum(input: torch.Tensor, dim: Optional[Tuple[int]] = None, dtype: Optional[int] = None) → torch.Tensor[source]
                                                                    +torch.sparse.sum(input, dim=None, dtype=None)[source]

                                                                    Returns the sum of each row of SparseTensor input in the given dimensions dim. If dim is a list of dimensions, reduce over all of them. When sum over all sparse_dim, this method diff --git a/docs/stable/tensorboard.html b/docs/stable/tensorboard.html index b9e4d388d3e7..a95701ed6e96 100644 --- a/docs/stable/tensorboard.html +++ b/docs/stable/tensorboard.html @@ -396,563 +396,6 @@

                                                                    torch.utils.tensorboard


                                                                  -
                                                                  -
                                                                  -class torch.utils.tensorboard.writer.SummaryWriter(log_dir=None, comment='', purge_step=None, max_queue=10, flush_secs=120, filename_suffix='')[source]
                                                                  -

                                                                  Writes entries directly to event files in the log_dir to be -consumed by TensorBoard.

                                                                  -

                                                                  The SummaryWriter class provides a high-level API to create an event file -in a given directory and add summaries and events to it. The class updates the -file contents asynchronously. This allows a training program to call methods -to add data to the file directly from the training loop, without slowing down -training.

                                                                  -
                                                                  -
                                                                  -__init__(log_dir=None, comment='', purge_step=None, max_queue=10, flush_secs=120, filename_suffix='')[source]
                                                                  -

                                                                  Creates a SummaryWriter that will write out events and summaries -to the event file.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • log_dir (string) – Save directory location. Default is -runs/CURRENT_DATETIME_HOSTNAME, which changes after each run. -Use hierarchical folder structure to compare -between runs easily. e.g. pass in ‘runs/exp1’, ‘runs/exp2’, etc. -for each new experiment to compare across them.

                                                                    • -

                                                                  • comment (string) – Comment log_dir suffix appended to the default -log_dir. If log_dir is assigned, this argument has no effect.

                                                                    • -

                                                                  • purge_step (int) – When logging crashes at step T+XT+X - and restarts at step TT -, -any events whose global_step larger or equal to TT - will be -purged and hidden from TensorBoard. -Note that crashed and resumed experiments should have the same log_dir.

                                                                    • -

                                                                  • max_queue (int) – Size of the queue for pending events and -summaries before one of the ‘add’ calls forces a flush to disk. -Default is ten items.

                                                                    • -

                                                                  • flush_secs (int) – How often, in seconds, to flush the -pending events and summaries to disk. Default is every two minutes.

                                                                    • -

                                                                  • filename_suffix (string) – Suffix added to all event filenames in -the log_dir directory. More details on filename construction in -tensorboard.summary.writer.event_file_writer.EventFileWriter.

                                                                    • -
                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-
-# create a summary writer with automatically generated folder name.
-writer = SummaryWriter()
-# folder location: runs/May04_22-14-54_s-MacBook-Pro.local/
-
-# create a summary writer using the specified folder name.
-writer = SummaryWriter("my_experiment")
-# folder location: my_experiment
-
-# create a summary writer with comment appended.
-writer = SummaryWriter(comment="LR_0.1_BATCH_16")
-# folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/
-
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_scalar(tag, scalar_value, global_step=None, walltime=None)[source]
                                                                  -

                                                                  Add scalar data to summary.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • scalar_value (float or string/blobname) – Value to save

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -with seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-writer = SummaryWriter()
-x = range(100)
-for i in x:
-    writer.add_scalar('y=2x', i * 2, i)
-writer.close()
-
                                                                  -
                                                                  -

                                                                  Expected result:

                                                                  -_images/add_scalar.png -
                                                                  - -
                                                                  -
                                                                  -add_scalars(main_tag, tag_scalar_dict, global_step=None, walltime=None)[source]
                                                                  -

                                                                  Adds many scalar data to summary.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • main_tag (string) – The parent name for the tags

                                                                    • -

                                                                  • tag_scalar_dict (dict) – Key-value pair storing the tag and corresponding values

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-writer = SummaryWriter()
-r = 5
-for i in range(100):
-    writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r),
-                                    'xcosx':i*np.cos(i/r),
-                                    'tanx': np.tan(i/r)}, i)
-writer.close()
-# This call adds three values to the same scalar plot with the tag
-# 'run_14h' in TensorBoard's scalar section.
-
                                                                  -
                                                                  -

                                                                  Expected result:

                                                                  -_images/add_scalars.png -
                                                                  - -
                                                                  -
                                                                  -add_histogram(tag, values, global_step=None, bins='tensorflow', walltime=None, max_bins=None)[source]
                                                                  -

                                                                  Add histogram to summary.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • values (torch.Tensor, numpy.array, or string/blobname) – Values to build histogram

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • bins (string) – One of {‘tensorflow’,’auto’, ‘fd’, …}. This determines how the bins are made. You can find -other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-import numpy as np
-writer = SummaryWriter()
-for i in range(10):
-    x = np.random.random(1000)
-    writer.add_histogram('distribution centers', x + i, i)
-writer.close()
-
                                                                  -
                                                                  -

                                                                  Expected result:

                                                                  -_images/add_histogram.png -
                                                                  - -
                                                                  -
                                                                  -add_image(tag, img_tensor, global_step=None, walltime=None, dataformats='CHW')[source]
                                                                  -

                                                                  Add image data to summary.

                                                                  -

                                                                  Note that this requires the pillow package.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • img_tensor (torch.Tensor, numpy.array, or string/blobname) – Image data

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -
                                                                  -
                                                                  Shape:

                                                                  img_tensor: Default is (3,H,W)(3, H, W) -. You can use torchvision.utils.make_grid() to -convert a batch of tensor into 3xHxW format or call add_images and let us do the job. -Tensor with (1,H,W)(1, H, W) -, (H,W)(H, W) -, (H,W,3)(H, W, 3) - is also suitable as long as -corresponding dataformats argument is passed, e.g. CHW, HWC, HW.

                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-import numpy as np
-img = np.zeros((3, 100, 100))
-img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
-img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
-
-img_HWC = np.zeros((100, 100, 3))
-img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
-img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
-
-writer = SummaryWriter()
-writer.add_image('my_image', img, 0)
-
-# If you have non-default dimension setting, set the dataformats argument.
-writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
-writer.close()
-
                                                                  -
                                                                  -

                                                                  Expected result:

                                                                  -_images/add_image.png -
                                                                  - -
                                                                  -
                                                                  -add_images(tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW')[source]
                                                                  -

                                                                  Add batched image data to summary.

                                                                  -

                                                                  Note that this requires the pillow package.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • img_tensor (torch.Tensor, numpy.array, or string/blobname) – Image data

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -

                                                                  • dataformats (string) – Image data format specification of the form -NCHW, NHWC, CHW, HWC, HW, WH, etc.

                                                                    • -
                                                                  -
                                                                  -
                                                                  -
                                                                  -
                                                                  Shape:

                                                                  img_tensor: Default is (N,3,H,W)(N, 3, H, W) -. If dataformats is specified, other shape will be -accepted. e.g. NCHW or NHWC.

                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-import numpy as np
-
-img_batch = np.zeros((16, 3, 100, 100))
-for i in range(16):
-    img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
-    img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i
-
-writer = SummaryWriter()
-writer.add_images('my_image_batch', img_batch, 0)
-writer.close()
-
                                                                  -
                                                                  -

                                                                  Expected result:

                                                                  -_images/add_images.png -
                                                                  - -
                                                                  -
                                                                  -add_figure(tag, figure, global_step=None, close=True, walltime=None)[source]
                                                                  -

                                                                  Render matplotlib figure into an image and add it to summary.

                                                                  -

                                                                  Note that this requires the matplotlib package.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • figure (matplotlib.pyplot.figure) – Figure or a list of figures

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • close (bool) – Flag to automatically close the figure

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_video(tag, vid_tensor, global_step=None, fps=4, walltime=None)[source]
                                                                  -

                                                                  Add video data to summary.

                                                                  -

                                                                  Note that this requires the moviepy package.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • vid_tensor (torch.Tensor) – Video data

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • fps (float or int) – Frames per second

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -
                                                                  -
                                                                  Shape:

                                                                  vid_tensor: (N,T,C,H,W)(N, T, C, H, W) -. The values should lie in [0, 255] for type uint8 or [0, 1] for type float.

                                                                  -
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_audio(tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None)[source]
                                                                  -

                                                                  Add audio data to summary.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • snd_tensor (torch.Tensor) – Sound data

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • sample_rate (int) – sample rate in Hz

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -
                                                                  -
                                                                  Shape:

                                                                  snd_tensor: (1,L)(1, L) -. The values should lie between [-1, 1].

                                                                  -
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_text(tag, text_string, global_step=None, walltime=None)[source]
                                                                  -

                                                                  Add text data to summary.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • text_string (string) – String to save

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  writer.add_text('lstm', 'This is an lstm', 0)
-writer.add_text('rnn', 'This is an rnn', 10)
-
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_graph(model, input_to_model=None, verbose=False)[source]
                                                                  -

                                                                  Add graph data to summary.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • model (torch.nn.Module) – Model to draw.

                                                                    • -

                                                                  • input_to_model (torch.Tensor or list of torch.Tensor) – A variable or a tuple of -variables to be fed.

                                                                    • -

                                                                  • verbose (bool) – Whether to print graph structure in console.

                                                                    • -
                                                                  -
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_embedding(mat, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None)[source]
                                                                  -

                                                                  Add embedding projector data to summary.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • mat (torch.Tensor or numpy.array) – A matrix which each row is the feature vector of the data point

                                                                    • -

                                                                  • metadata (list) – A list of labels, each element will be convert to string

                                                                    • -

                                                                  • label_img (torch.Tensor) – Images correspond to each data point

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • tag (string) – Name for the embedding

                                                                    • -
                                                                  -
                                                                  -
                                                                  -
                                                                  -
                                                                  Shape:

                                                                  mat: (N,D)(N, D) -, where N is number of data and D is feature dimension

                                                                  -

                                                                  label_img: (N,C,H,W)(N, C, H, W) -

                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  import keyword
-import torch
-meta = []
-while len(meta)<100:
-    meta = meta+keyword.kwlist # get some strings
-meta = meta[:100]
-
-for i, v in enumerate(meta):
-    meta[i] = v+str(i)
-
-label_img = torch.rand(100, 3, 10, 32)
-for i in range(100):
-    label_img[i]*=i/100.0
-
-writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
-writer.add_embedding(torch.randn(100, 5), label_img=label_img)
-writer.add_embedding(torch.randn(100, 5), metadata=meta)
-
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_pr_curve(tag, labels, predictions, global_step=None, num_thresholds=127, weights=None, walltime=None)[source]
                                                                  -

                                                                  Adds precision recall curve. -Plotting a precision-recall curve lets you understand your model’s -performance under different threshold settings. With this function, -you provide the ground truth labeling (T/F) and prediction confidence -(usually the output of your model) for each target. The TensorBoard UI -will let you choose the threshold interactively.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • labels (torch.Tensor, numpy.array, or string/blobname) – Ground truth data. Binary label for each element.

                                                                    • -

                                                                  • predictions (torch.Tensor, numpy.array, or string/blobname) – The probability that an element be classified as true. -Value should in [0, 1]

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • num_thresholds (int) – Number of thresholds used to draw the curve.

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-import numpy as np
-labels = np.random.randint(2, size=100)  # binary label
-predictions = np.random.rand(100)
-writer = SummaryWriter()
-writer.add_pr_curve('pr_curve', labels, predictions, 0)
-writer.close()
-
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_custom_scalars(layout)[source]
                                                                  -

                                                                  Create special chart by collecting charts tags in ‘scalars’. Note that this function can only be called once -for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called -before or after the training loop.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -

                                                                  layout (dict) – {categoryName: charts}, where charts is also a dictionary -{chartName: ListOfProperties}. The first element in ListOfProperties is the chart’s type -(one of Multiline or Margin) and the second element should be a list containing the tags -you have used in add_scalar function, which will be collected into the new chart.

                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]},
-             'USA':{ 'dow':['Margin',   ['dow/aaa', 'dow/bbb', 'dow/ccc']],
-                  'nasdaq':['Margin',   ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
-
-writer.add_custom_scalars(layout)
-
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_mesh(tag, vertices, colors=None, faces=None, config_dict=None, global_step=None, walltime=None)[source]
                                                                  -

                                                                  Add meshes or 3D point clouds to TensorBoard. The visualization is based on Three.js, -so it allows users to interact with the rendered object. Besides the basic definitions -such as vertices, faces, users can further provide camera parameter, lighting condition, etc. -Please check https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene for -advanced usage.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • tag (string) – Data identifier

                                                                    • -

                                                                  • vertices (torch.Tensor) – List of the 3D coordinates of vertices.

                                                                    • -

                                                                  • colors (torch.Tensor) – Colors for each vertex

                                                                    • -

                                                                  • faces (torch.Tensor) – Indices of vertices within each triangle. (Optional)

                                                                    • -

                                                                  • config_dict – Dictionary with ThreeJS classes names and configuration.

                                                                    • -

                                                                  • global_step (int) – Global step value to record

                                                                    • -

                                                                  • walltime (float) – Optional override default walltime (time.time()) -seconds after epoch of event

                                                                    • -
                                                                  -
                                                                  -
                                                                  -
                                                                  -
                                                                  Shape:

                                                                  vertices: (B,N,3)(B, N, 3) -. (batch, number_of_vertices, channels)

                                                                  -

                                                                  colors: (B,N,3)(B, N, 3) -. The values should lie in [0, 255] for type uint8 or [0, 1] for type float.

                                                                  -

                                                                  faces: (B,N,3)(B, N, 3) -. The values should lie in [0, number_of_vertices] for type uint8.

                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-vertices_tensor = torch.as_tensor([
-    [1, 1, 1],
-    [-1, -1, 1],
-    [1, -1, -1],
-    [-1, 1, -1],
-], dtype=torch.float).unsqueeze(0)
-colors_tensor = torch.as_tensor([
-    [255, 0, 0],
-    [0, 255, 0],
-    [0, 0, 255],
-    [255, 0, 255],
-], dtype=torch.int).unsqueeze(0)
-faces_tensor = torch.as_tensor([
-    [0, 2, 3],
-    [0, 3, 1],
-    [0, 1, 2],
-    [1, 3, 2],
-], dtype=torch.int).unsqueeze(0)
-
-writer = SummaryWriter()
-writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor)
-
-writer.close()
-
                                                                  -
                                                                  -
                                                                  - -
                                                                  -
                                                                  -add_hparams(hparam_dict, metric_dict)[source]
                                                                  -

                                                                  Add a set of hyperparameters to be compared in TensorBoard.

                                                                  -
                                                                  -
                                                                  Parameters
                                                                  -
                                                                    -

                                                                  • hparam_dict (dict) – Each key-value pair in the dictionary is the -name of the hyper parameter and it’s corresponding value. -The type of the value can be one of bool, string, float, -int, or None.

                                                                    • -

                                                                  • metric_dict (dict) – Each key-value pair in the dictionary is the -name of the metric and it’s corresponding value. Note that the key used -here should be unique in the tensorboard record. Otherwise the value -you added by add_scalar will be displayed in hparam plugin. In most -cases, this is unwanted.

                                                                    • -
                                                                  -
                                                                  -
                                                                  -

                                                                  Examples:

                                                                  -
                                                                  from torch.utils.tensorboard import SummaryWriter
-with SummaryWriter() as w:
-    for i in range(5):
-        w.add_hparams({'lr': 0.1*i, 'bsize': i},
-                      {'hparam/accuracy': 10*i, 'hparam/loss': 10*i})
-
                                                                  -
                                                                  -

                                                                  Expected result:

                                                                  -_images/add_hparam.png -
                                                                  - -
                                                                  -
                                                                  -flush()[source]
                                                                  -

                                                                  Flushes the event file to disk. -Call this method to make sure that all pending events have been written to -disk.

                                                                  -
                                                                  - -
                                                                  -
                                                                  -close()[source]
                                                                  -
                                                                  - -
                                                                  -

                                                            diff --git a/docs/stable/tensors.html b/docs/stable/tensors.html index a736f3530b43..d84a58fab950 100644 --- a/docs/stable/tensors.html +++ b/docs/stable/tensors.html @@ -1048,9 +1048,11 @@
                                                            bernoulli(*, generator=None) → Tensor
                                                            -

                                                            Returns a result tensor where each result[i]\texttt{result[i]} +

                                                            Returns a result tensor where each result[i]\texttt{result[i]} + is independently -sampled from Bernoulli(self[i])\text{Bernoulli}(\texttt{self[i]}) +sampled from Bernoulli(self[i])\text{Bernoulli}(\texttt{self[i]}) + . self must have floating point dtype, and the result will have the same dtype.

                                                            See torch.bernoulli()

                                                            @@ -1063,7 +1065,8 @@
                                                            bernoulli_(p=0.5, *, generator=None) → Tensor

                                                            Fills each location of self with an independent sample from -Bernoulli(p)\text{Bernoulli}(\texttt{p}) +Bernoulli(p)\text{Bernoulli}(\texttt{p}) + . self can have integral dtype.

                                                            @@ -1073,9 +1076,11 @@ bernoulli_(p_tensor, *, generator=None) → Tensor

                                                            p_tensor should be a tensor containing probabilities to be used for drawing the binary random number.

                                                            -

                                                            The ith\text{i}^{th} +

                                                            The ith\text{i}^{th} + element of self tensor will be set to a -value sampled from Bernoulli(p_tensor[i])\text{Bernoulli}(\texttt{p\_tensor[i]}) +value sampled from Bernoulli(p_tensor[i])\text{Bernoulli}(\texttt{p\_tensor[i]}) + .

                                                            self can have integral dtype, but p_tensor must have floating point dtype.

                                                            @@ -1185,7 +1190,8 @@ cauchy_(median=0, sigma=1, *, generator=None) → Tensor

                                                            Fills the tensor with numbers drawn from the Cauchy distribution:

                                                            -f(x)=1πσ(x−median)2+σ2f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2} +f(x)=1πσ(x−median)2+σ2f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2} +

                                                            @@ -1752,7 +1758,8 @@ exponential_(lambd=1, *, generator=None) → Tensor

                                                            Fills self tensor with elements drawn from the exponential distribution:

                                                            -f(x)=λe−λxf(x) = \lambda e^{-\lambda x} +f(x)=λe−λxf(x) = \lambda e^{-\lambda x} +
                                                            @@ -1874,7 +1881,8 @@ geometric_(p, *, generator=None) → Tensor

                                                            Fills self tensor with elements drawn from the geometric distribution:

                                                            -f(X=k)=pk−1(1−p)f(X=k) = p^{k - 1} (1 - p) +f(X=k)=pk−1(1−p)f(X=k) = p^{k - 1} (1 - p) +
                                                            @@ -2385,21 +2393,28 @@
                                                            log_normal_(mean=1, std=2, *, generator=None)

                                                            Fills self tensor with numbers samples from the log-normal distribution -parameterized by the given mean μ\mu +parameterized by the given mean μ\mu + and standard deviation -σ\sigma +σ\sigma + . Note that mean and std are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:

                                                            -f(x)=1xσ2π e−(lnx−μ)22σ2f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}} +f(x)=1xσ2π e−(ln⁡x−μ)22σ2f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}} +
                                                            @@ -4053,9 +4068,10 @@

                                                            Fills self tensor with numbers sampled from the continuous uniform distribution:

                                                            -P(x)=1to−fromP(x) = \dfrac{1}{\text{to} - \text{from}} +P(x)=1to−fromP(x) = \dfrac{1}{\text{to} - \text{from}} + + -
                                                            @@ -4113,10 +4129,15 @@ of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span -across original dimensions that satisfy the following -contiguity-like condition that ,

                                                            +across original dimensions d,d+1,…,d+kd, d+1, \dots, d+k + + that satisfy the following +contiguity-like condition that ∀i=d,…,d+k−1\forall i = d, \dots, d+k-1 + +,

                                                            -stride[i]=stride[i+1]×size[i+1]\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1] +stride[i]=stride[i+1]×size[i+1]\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1] +

                                                            Otherwise, it will not be possible to view self tensor as shape without copying it (e.g., via contiguous()). When it is unclear whether a view() can be performed, it is advisable to use reshape(), which diff --git a/docs/stable/torch.html b/docs/stable/torch.html index ae7b37442814..0530ddd03f6f 100644 --- a/docs/stable/torch.html +++ b/docs/stable/torch.html @@ -429,19 +429,23 @@

                                                            Tensorsinput.

                                                            - - - @@ -584,11 +588,13 @@

                                                            Indexing, Slicing, Joining, Mutating Opsinput with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input i.e.,

                                                            - - @@ -753,7 +759,8 @@

                                                            Pointwise Opsinput.

                                                            - @@ -862,14 +869,16 @@

                                                            Pointwise Opsinput with the scalar other and returns a new resulting tensor.

                                                            - - @@ -998,11 +1007,13 @@

                                                            Comparison OpsTrue if two tensors have the same size and elements, False otherwise.

                                                            - - @@ -1021,11 +1032,13 @@

                                                            Comparison Ops(values, indices) where values is the k th smallest element of each row of the input tensor in the given dimension dim.

                                                            - - @@ -1035,7 +1048,8 @@

                                                            Comparison Opsinput tensor.

                                                            - @@ -1107,7 +1121,8 @@

                                                            Other Operations

                                                            - @@ -1165,10 +1180,14 @@

                                                            Other Operations

                                                            - @@ -1239,20 +1258,25 @@

                                                            BLAS and LAPACK Operationsinput and mat2.

                                                            - - - - @@ -1280,17 +1304,22 @@

                                                            BLAS and LAPACK Operations

                                                            - - @@ -1321,22 +1350,29 @@

                                                            BLAS and LAPACK Operations

                                                            - - - - @@ -1349,12 +1385,15 @@

                                                            BLAS and LAPACK Operations

                                                            - - diff --git a/docs/stable/torchvision/ops.html b/docs/stable/torchvision/ops.html index 6e0e55564707..28143bebd2bc 100644 --- a/docs/stable/torchvision/ops.html +++ b/docs/stable/torchvision/ops.html @@ -346,7 +346,7 @@

                                                            torchvision.ops
                                                            -torchvision.ops.nms(boxes: torch.Tensor, scores: torch.Tensor, iou_threshold: float) → torch.Tensor[source]
                                                            +torchvision.ops.nms(boxes, scores, iou_threshold)[source]

                                                            Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU).

                                                            NMS iteratively removes lower scoring boxes which have an @@ -379,7 +379,7 @@

                                                            torchvision.ops
                                                            -torchvision.ops.roi_align(input: torch.Tensor, boxes: torch.Tensor, output_size: None, spatial_scale: float = 1.0, sampling_ratio: int = -1, aligned: bool = False) → torch.Tensor[source]
                                                            +torchvision.ops.roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1, aligned=False)[source]

                                                            Performs Region of Interest (RoI) Align operator described in Mask R-CNN

                                                            Parameters
                                                            @@ -412,7 +412,7 @@

                                                            torchvision.ops
                                                            -torchvision.ops.ps_roi_align(input: torch.Tensor, boxes: torch.Tensor, output_size: int, spatial_scale: float = 1.0, sampling_ratio: int = -1) → torch.Tensor[source]
                                                            +torchvision.ops.ps_roi_align(input, boxes, output_size, spatial_scale=1.0, sampling_ratio=-1)[source]

                                                            Performs Position-Sensitive Region of Interest (RoI) Align operator mentioned in Light-Head R-CNN.

                                                            @@ -443,7 +443,7 @@

                                                            torchvision.ops
                                                            -torchvision.ops.roi_pool(input: torch.Tensor, boxes: torch.Tensor, output_size: None, spatial_scale: float = 1.0) → torch.Tensor[source]
                                                            +torchvision.ops.roi_pool(input, boxes, output_size, spatial_scale=1.0)[source]

                                                            Performs Region of Interest (RoI) Pool operator described in Fast R-CNN

                                                            Parameters
                                                            @@ -468,7 +468,7 @@

                                                            torchvision.ops
                                                            -torchvision.ops.ps_roi_pool(input: torch.Tensor, boxes: torch.Tensor, output_size: int, spatial_scale: float = 1.0) → torch.Tensor[source]
                                                            +torchvision.ops.ps_roi_pool(input, boxes, output_size, spatial_scale=1.0)[source]

                                                            Performs Position-Sensitive Region of Interest (RoI) Pool operator described in R-FCN

                                                            @@ -494,7 +494,7 @@

                                                            torchvision.ops
                                                            -torchvision.ops.deform_conv2d(input: torch.Tensor, offset: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1), padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1)) → torch.Tensor[source]
                                                            +torchvision.ops.deform_conv2d(input, offset, weight, bias=None, stride=(1, 1), padding=(0, 0), dilation=(1, 1))[source]

                                                            Performs Deformable Convolution, described in Deformable Convolutional Networks

                                                            Parameters
                                                            @@ -537,37 +537,37 @@

                                                            torchvision.ops
                                                            -class torchvision.ops.RoIAlign(output_size: None, spatial_scale: float, sampling_ratio: int, aligned: bool = False)[source]
                                                            +class torchvision.ops.RoIAlign(output_size, spatial_scale, sampling_ratio, aligned=False)[source]

                                                            See roi_align

                                                            -class torchvision.ops.PSRoIAlign(output_size: int, spatial_scale: float, sampling_ratio: int)[source]
                                                            +class torchvision.ops.PSRoIAlign(output_size, spatial_scale, sampling_ratio)[source]

                                                            See ps_roi_align

                                                            -class torchvision.ops.RoIPool(output_size: None, spatial_scale: float)[source]
                                                            +class torchvision.ops.RoIPool(output_size, spatial_scale)[source]

                                                            See roi_pool

                                                            -class torchvision.ops.PSRoIPool(output_size: int, spatial_scale: float)[source]
                                                            +class torchvision.ops.PSRoIPool(output_size, spatial_scale)[source]

                                                            See ps_roi_pool

                                                            -class torchvision.ops.DeformConv2d(in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = True)[source]
                                                            +class torchvision.ops.DeformConv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)[source]

                                                            See deform_conv2d

                                                            -class torchvision.ops.MultiScaleRoIAlign(featmap_names: List[str], output_size: List[int], sampling_ratio: int)[source]
                                                            +class torchvision.ops.MultiScaleRoIAlign(featmap_names, output_size, sampling_ratio)[source]

                                                            Multi-scale RoIAlign pooling, which is useful for detection with or without FPN.

                                                            It infers the scale of the pooling via the heuristics present in the FPN paper.

                                                            @@ -599,7 +599,7 @@

                                                            torchvision.ops
                                                            -class torchvision.ops.FeaturePyramidNetwork(in_channels_list: List[int], out_channels: int, extra_blocks: Optional[torchvision.ops.feature_pyramid_network.ExtraFPNBlock] = None)[source]
                                                            +class torchvision.ops.FeaturePyramidNetwork(in_channels_list, out_channels, extra_blocks=None)[source]

                                                            Module that adds a FPN from on top of a set of feature maps. This is based on “Feature Pyramid Network for Object Detection”.

                                                            The feature maps are currently supposed to be in increasing depth diff --git a/docs/stable/torchvision/transforms.html b/docs/stable/torchvision/transforms.html index 84808663e602..82cc9108dc74 100644 --- a/docs/stable/torchvision/transforms.html +++ b/docs/stable/torchvision/transforms.html @@ -367,14 +367,12 @@

                                                            Transforms on PIL Image
                                                            class torchvision.transforms.CenterCrop(size)[source]
                                                            -

                                                            Crops the given image at the center. -The image can be a PIL Image or a torch Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +

                                                            Crops the given PIL Image at the center.

                                                            Parameters

                                                            size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is -made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).

                                                            +made.

                                                            @@ -406,10 +404,7 @@

                                                            Transforms on PIL Image
                                                            class torchvision.transforms.FiveCrop(size)[source]
                                                            -

                                                            Crop the given image into four corners and the central crop. -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading -dimensions

                                                            +

                                                            Crop the given PIL Image into four corners and the central crop

                                                            Note

                                                            This transform returns a tuple of images and there may be a mismatch in the number of @@ -419,8 +414,7 @@

                                                            Transforms on PIL Image
                                                            Parameters

                                                            size (sequence or int) – Desired output size of the crop. If size is an int -instead of sequence like (h, w), a square crop of size (size, size) is made. -If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).

                                                            +instead of sequence like (h, w), a square crop of size (size, size) is made.

                                                            Example

                                                            @@ -464,23 +458,20 @@

                                                            Transforms on PIL Image
                                                            class torchvision.transforms.Pad(padding, fill=0, padding_mode='constant')[source]
                                                            -

                                                            Pad the given image on all sides with the given “pad” value. -The image can be a PIL Image or a torch Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +

                                                            Pad the given PIL Image on all sides with the given “pad” value.

                                                            Parameters
                                                              -

                                                            • padding (int or tuple or list) – Padding on each border. If a single int is provided this +

                                                            • padding (int or tuple) – Padding on each border. If a single int is provided this is used to pad all borders. If tuple of length 2 is provided this is the padding on left/right and top/bottom respectively. If a tuple of length 4 is provided -this is the padding for the left, top, right and bottom borders respectively. -In torchscript mode padding as single int is not supported, use a tuple or -list of length 1: [padding, ].

                                                              • +this is the padding for the left, top, right and bottom borders +respectively.

                                                            • fill (int or tuple) – Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant

                                                            • padding_mode (str) –

                                                              Type of padding. Should be: constant, edge, reflect or symmetric. -Default is constant. Mode symmetric is not yet supported for Tensor inputs.

                                                              +Default is constant.

                                                              • constant: pads with a constant value, this value is specified with fill

                                                              • edge: pads with the last value at the edge of the image

                                                                • @@ -557,33 +548,26 @@

                                                                Transforms on PIL Image
                                                                class torchvision.transforms.RandomCrop(size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant')[source]
                                                                -

                                                                Crop the given image at a random location. -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading -dimensions

                                                                +

                                                                Crop the given PIL Image at a random location.

                                                                Parameters

                                                                • size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is -made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).

                                                                  • +made.

                                                                • padding (int or sequence, optional) – Optional padding on each border -of the image. Default is None. If a single int is provided this -is used to pad all borders. If tuple of length 2 is provided this is the padding -on left/right and top/bottom respectively. If a tuple of length 4 is provided -this is the padding for the left, top, right and bottom borders respectively. -In torchscript mode padding as single int is not supported, use a tuple or -list of length 1: [padding, ].

                                                                  • +of the image. Default is None, i.e no padding. If a sequence of length +4 is provided, it is used to pad left, top, right, bottom borders +respectively. If a sequence of length 2 is provided, it is used to +pad left/right, top/bottom borders, respectively.

                                                                • pad_if_needed (boolean) – It will pad the image if smaller than the desired size to avoid raising an exception. Since cropping is done after padding, the padding seems to be done at a random offset.

                                                                  • -

                                                                • fill (int or tuple) – Pixel fill value for constant fill. Default is 0. If a tuple of +

                                                                • fill – Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant

                                                                  • -

                                                                • padding_mode (str) –

                                                                  Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. -Mode symmetric is not yet supported for Tensor inputs.

                                                                  -
                                                                  -
                                                                    +

                                                                  • padding_mode –

                                                                    Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.

                                                                    +

                                                                    • constant: pads with a constant value, this value is specified with fill

                                                                    • edge: pads with the last value on the edge of the image

                                                                    • reflect: pads with reflection of image (without repeating the last value on the edge)

                                                                      @@ -599,7 +583,6 @@

                                                                      Transforms on PIL Image

                                                                -

                                                            @@ -666,9 +649,7 @@

                                                            Transforms on PIL Image
                                                            class torchvision.transforms.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=2)[source]
                                                            -

                                                            Crop the given image to random size and aspect ratio. -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +

                                                            Crop the given PIL Image to random size and aspect ratio.

                                                            A crop of random size (default: of 0.08 to 1.0) of the original size and a random aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop is finally resized to given size. @@ -676,14 +657,10 @@

                                                            Transforms on PIL Image
                                                            Parameters
                                                              -

                                                            • size (int or sequence) – expected output size of each edge. If size is an -int instead of sequence like (h, w), a square output size (size, size) is -made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).

                                                              • -

                                                            • scale (tuple of python:float) – range of size of the origin size cropped

                                                              • -

                                                            • ratio (tuple of python:float) – range of aspect ratio of the origin aspect ratio cropped.

                                                              • -

                                                            • interpolation (int) – Desired interpolation enum defined by filters. -Default is PIL.Image.BILINEAR. If input is Tensor, only PIL.Image.NEAREST, PIL.Image.BILINEAR -and PIL.Image.BICUBIC are supported.

                                                              • +

                                                            • size – expected output size of each edge

                                                              • +

                                                            • scale – range of size of the origin size cropped

                                                              • +

                                                            • ratio – range of aspect ratio of the origin aspect ratio cropped

                                                              • +

                                                            • interpolation – Default: PIL.Image.BILINEAR

                                                            @@ -725,7 +702,7 @@

                                                            Transforms on PIL Image
                                                            class torchvision.transforms.RandomVerticalFlip(p=0.5)[source]
                                                            -

                                                            Vertically flip the given image randomly with a given probability. +

                                                            Vertically flip the given PIL Image randomly with a given probability. The image can be a PIL Image or a torch Tensor, in which case it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            @@ -739,9 +716,7 @@

                                                            Transforms on PIL Image
                                                            class torchvision.transforms.Resize(size, interpolation=2)[source]
                                                            -

                                                            Resize the input image to the given size. -The image can be a PIL Image or a torch Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +

                                                            Resize the input PIL Image to the given size.

                                                            Parameters
                                                              @@ -749,12 +724,9 @@

                                                              Transforms on PIL Image[size, ].

                                                              -

                                                            • interpolation (int, optional) – Desired interpolation enum defined by filters. -Default is PIL.Image.BILINEAR. If input is Tensor, only PIL.Image.NEAREST, PIL.Image.BILINEAR -and PIL.Image.BICUBIC are supported.

                                                              • +(size * height / width, size)

                                                              +

                                                            • interpolation (int, optional) – Desired interpolation. Default is +PIL.Image.BILINEAR

                                                            @@ -769,11 +741,8 @@

                                                            Transforms on PIL Image
                                                            class torchvision.transforms.TenCrop(size, vertical_flip=False)[source]
                                                            -

                                                            Crop the given image into four corners and the central crop plus the flipped version of -these (horizontal flipping is used by default). -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading -dimensions

                                                            +

                                                            Crop the given PIL Image into four corners and the central crop plus the flipped version of +these (horizontal flipping is used by default)

                                                            Note

                                                            This transform returns a tuple of images and there may be a mismatch in the number of @@ -785,7 +754,7 @@

                                                            Transforms on PIL Image

                                                            • size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is -made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).

                                                              • +made.

                                                            • vertical_flip (bool) – Use vertical flipping instead of horizontal

                                                            @@ -1036,7 +1005,7 @@

                                                            Functional Transforms
                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be adjusted.

                                                              • +

                                                            • img (PIL Image or Torch Tensor) – Image to be adjusted.

                                                            • brightness_factor (float) – How much to adjust the brightness. Can be any non negative number. 0 gives a black image, 1 gives the original image while 2 increases the brightness by a factor of 2.

                                                              • @@ -1046,7 +1015,7 @@

                                                              Functional Transforms

                                                              Brightness adjusted image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image or Torch Tensor

                                                            @@ -1058,7 +1027,7 @@

                                                            Functional Transforms
                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be adjusted.

                                                              • +

                                                            • img (PIL Image or Torch Tensor) – Image to be adjusted.

                                                            • contrast_factor (float) – How much to adjust the contrast. Can be any non negative number. 0 gives a solid gray image, 1 gives the original image while 2 increases the contrast by a factor of 2.

                                                              • @@ -1068,39 +1037,35 @@

                                                              Functional Transforms

                                                              Contrast adjusted image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image or Torch Tensor

                                                            -torchvision.transforms.functional.adjust_gamma(img: torch.Tensor, gamma: float, gain: float = 1) → torch.Tensor[source]
                                                            +torchvision.transforms.functional.adjust_gamma(img, gamma, gain=1)[source]

                                                            Perform gamma correction on an image.

                                                            Also known as Power Law Transform. Intensities in RGB mode are adjusted based on the following equation:

                                                            -Iout=255×gain×(Iin255)γI_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma} +Iout=255×gain×(Iin255)γI_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma} + + -

                                                            See Gamma Correction for more details.

                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – PIL Image to be adjusted.

                                                              • -

                                                            • gamma (float) – Non negative real number, same as γ\gamma +

                                                            • img (PIL Image) – PIL Image to be adjusted.

                                                              • +

                                                            • gamma (float) – Non negative real number, same as γ\gamma + in the equation. gamma larger than 1 make the shadows darker, while gamma smaller than 1 make dark regions lighter.

                                                            • gain (float) – The constant multiplier.

                                                            -
                                                            Returns
                                                            -

                                                            Gamma correction adjusted image.

                                                            -
                                                            -
                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            -
                                                            @@ -1141,7 +1106,7 @@

                                                            Functional Transforms
                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be adjusted.

                                                              • +

                                                            • img (PIL Image or Torch Tensor) – Image to be adjusted.

                                                            • saturation_factor (float) – How much to adjust the saturation. 0 will give a black and white image, 1 will give the original image while 2 will enhance the saturation by a factor of 2.

                                                              • @@ -1151,61 +1116,51 @@

                                                              Functional Transforms

                                                              Saturation adjusted image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image or Torch Tensor

                                                            -torchvision.transforms.functional.affine(img: torch.Tensor, angle: float, translate: List[int], scale: float, shear: List[float], resample: int = 0, fillcolor: Optional[int] = None) → torch.Tensor[source]
                                                            -

                                                            Apply affine transformation on the image keeping image center invariant. -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions.

                                                            +torchvision.transforms.functional.affine(img, angle, translate, scale, shear, resample=0, fillcolor=None)[source] +

                                                            Apply affine transformation on the image keeping image center invariant

                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – image to be rotated.

                                                              • +

                                                            • img (PIL Image) – PIL Image to be rotated.

                                                            • angle (float or int) – rotation angle in degrees between -180 and 180, clockwise direction.

                                                            • translate (list or tuple of python:integers) – horizontal and vertical translations (post-rotation translation)

                                                            • scale (float) – overall scale

                                                              • -

                                                            • shear (float or tuple or list) – shear angle value in degrees between -180 to 180, clockwise direction. -If a tuple of list is specified, the first value corresponds to a shear parallel to the x axis, while -the second value corresponds to a shear parallel to the y axis.

                                                              • -

                                                            • resample (PIL.Image.NEAREST or PIL.Image.BILINEAR or PIL.Image.BICUBIC, optional) – An optional resampling filter. See filters for more information. -If omitted, or if the image is PIL Image and has mode “1” or “P”, it is set to PIL.Image.NEAREST. -If input is Tensor, only PIL.Image.NEAREST and PIL.Image.BILINEAR are supported.

                                                              • +

                                                            • shear (float or tuple or list) – shear angle value in degrees between -180 to 180, clockwise direction.

                                                              • +

                                                            • a tuple of list is specified, the first value corresponds to a shear parallel to the x axis, while (If) –

                                                              • +

                                                            • second value corresponds to a shear parallel to the y axis. (the) –

                                                              • +

                                                            • resample (PIL.Image.NEAREST or PIL.Image.BILINEAR or PIL.Image.BICUBIC, optional) – An optional resampling filter. +See filters for more information. +If omitted, or if the image has mode “1” or “P”, it is set to PIL.Image.NEAREST.

                                                            • fillcolor (int) – Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)

                                                            -
                                                            Returns
                                                            -

                                                            Transformed image.

                                                            -
                                                            -
                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            -
                                                            -torchvision.transforms.functional.center_crop(img: torch.Tensor, output_size: List[int]) → torch.Tensor[source]
                                                            -

                                                            Crops the given image at the center. -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +torchvision.transforms.functional.center_crop(img, output_size)[source] +

                                                            Crop the given PIL Image and resize it to desired size.

                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be cropped.

                                                              • -

                                                            • output_size (sequence or int) – (height, width) of the crop box. If int or sequence with single int -it is used for both directions.

                                                              • +

                                                            • img (PIL Image) – Image to be cropped. (0,0) denotes the top left corner of the image.

                                                              • +

                                                            • output_size (sequence or int) – (height, width) of the crop box. If int, +it is used for both directions

                                                            Returns

                                                            Cropped image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image

                                                            @@ -1245,15 +1200,12 @@

                                                            Functional Transforms
                                                            -torchvision.transforms.functional.crop(img: torch.Tensor, top: int, left: int, height: int, width: int) → torch.Tensor[source]
                                                            -

                                                            Crop the given image at specified location and output size. -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading -dimensions

                                                            +torchvision.transforms.functional.crop(img, top, left, height, width)[source] +

                                                            Crop the given PIL Image.

                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be cropped. (0,0) denotes the top left corner of the image.

                                                              • +

                                                            • img (PIL Image) – Image to be cropped. (0,0) denotes the top left corner of the image.

                                                            • top (int) – Vertical component of the top left corner of the crop box.

                                                            • left (int) – Horizontal component of the top left corner of the crop box.

                                                            • height (int) – Height of the crop box.

                                                              • @@ -1264,14 +1216,14 @@

                                                              Functional Transforms

                                                              Cropped image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image

                                                            -torchvision.transforms.functional.erase(img: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False) → torch.Tensor[source]
                                                            +torchvision.transforms.functional.erase(img, i, j, h, w, v, inplace=False)[source]

                                                            Erase the input Tensor Image with given value.

                                                            Parameters
                                                            @@ -1296,10 +1248,8 @@

                                                            Functional Transforms
                                                            -torchvision.transforms.functional.five_crop(img: torch.Tensor, size: List[int]) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor][source]
                                                            -

                                                            Crop the given image into four corners and the central crop. -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +torchvision.transforms.functional.five_crop(img, size)[source] +

                                                            Crop the given PIL Image into four corners and the central crop.

                                                            Note

                                                            This transform returns a tuple of images and there may be a @@ -1307,12 +1257,9 @@

                                                            Functional Transforms
                                                            Parameters
                                                            -
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be cropped.

                                                              • -

                                                            • size (sequence or int) – Desired output size of the crop. If size is an +

                                                              size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is -made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).

                                                              • -
                                                            +made.

                                                            Returns

                                                            @@ -1330,10 +1277,10 @@

                                                            Functional Transforms
                                                            torchvision.transforms.functional.hflip(img: torch.Tensor) → torch.Tensor[source]
                                                            -

                                                            Horizontally flip the given PIL Image or Tensor.

                                                            +

                                                            Horizontally flip the given PIL Image or torch Tensor.

                                                            Parameters
                                                            -

                                                            img (PIL Image or Tensor) – Image to be flipped. If img +

                                                            img (PIL Image or Torch Tensor) – Image to be flipped. If img is a Tensor, it is expected to be in […, H, W] format, where … means it can have an arbitrary number of trailing dimensions.

                                                            @@ -1342,7 +1289,7 @@

                                                            Functional Transforms

                                                            Horizontally flipped image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image

                                                            @@ -1376,25 +1323,21 @@

                                                            Functional Transforms
                                                            -torchvision.transforms.functional.pad(img: torch.Tensor, padding: List[int], fill: int = 0, padding_mode: str = 'constant') → torch.Tensor[source]
                                                            -

                                                            Pad the given image on all sides with the given “pad” value. -The image can be a PIL Image or a torch Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +torchvision.transforms.functional.pad(img, padding, fill=0, padding_mode='constant')[source] +

                                                            Pad the given PIL Image on all sides with specified padding mode and fill value.

                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be padded.

                                                              • -

                                                            • padding (int or tuple or list) – Padding on each border. If a single int is provided this +

                                                            • img (PIL Image) – Image to be padded.

                                                              • +

                                                            • padding (int or tuple) – Padding on each border. If a single int is provided this is used to pad all borders. If tuple of length 2 is provided this is the padding on left/right and top/bottom respectively. If a tuple of length 4 is provided -this is the padding for the left, top, right and bottom borders respectively. -In torchscript mode padding as single int is not supported, use a tuple or -list of length 1: [padding, ].

                                                              • -

                                                            • fill (int or str or tuple) – Pixel fill value for constant fill. Default is 0. If a tuple of +this is the padding for the left, top, right and bottom borders +respectively.

                                                              • +

                                                            • fill – Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. -This value is only used when the padding_mode is constant. Only int value is supported for Tensors.

                                                              • -

                                                            • padding_mode –

                                                              Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. -Mode symmetric is not yet supported for Tensor inputs.

                                                              +This value is only used when the padding_mode is constant

                                                              • +

                                                            • padding_mode –

                                                              Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.

                                                              • constant: pads with a constant value, this value is specified with fill

                                                              • edge: pads with the last value on the edge of the image

                                                                • @@ -1418,7 +1361,7 @@

                                                                Functional Transforms

                                                                Padded image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image

                                                            @@ -1468,62 +1411,55 @@

                                                            Functional Transforms
                                                            -torchvision.transforms.functional.resize(img: torch.Tensor, size: List[int], interpolation: int = 2) → torch.Tensor[source]
                                                            -

                                                            Resize the input image to the given size. -The image can be a PIL Image or a torch Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +torchvision.transforms.functional.resize(img, size, interpolation=2)[source] +

                                                            Resize the input PIL Image to the given size.

                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be resized.

                                                              • +

                                                            • img (PIL Image) – Image to be resized.

                                                            • size (sequence or int) – Desired output size. If size is a sequence like (h, w), the output size will be matched to this. If size is an int, the smaller edge of the image will be matched to this number maintaining the aspect ratio. i.e, if height > width, then image will be rescaled to -(size×heightwidth,size)\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right) -. -In torchscript mode padding as single int is not supported, use a tuple or -list of length 1: [size, ].

                                                              • -

                                                            • interpolation (int, optional) – Desired interpolation enum defined by filters. -Default is PIL.Image.BILINEAR. If input is Tensor, only PIL.Image.NEAREST, PIL.Image.BILINEAR -and PIL.Image.BICUBIC are supported.

                                                              • +(size×heightwidth,size)\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right) + +

                                                              +

                                                            • interpolation (int, optional) – Desired interpolation. Default is +PIL.Image.BILINEAR

                                                            Returns

                                                            Resized image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image

                                                            -torchvision.transforms.functional.resized_crop(img: torch.Tensor, top: int, left: int, height: int, width: int, size: List[int], interpolation: int = 2) → torch.Tensor[source]
                                                            -

                                                            Crop the given image and resize it to desired size. -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +torchvision.transforms.functional.resized_crop(img, top, left, height, width, size, interpolation=2)[source] +

                                                            Crop the given PIL Image and resize it to desired size.

                                                            Notably used in RandomResizedCrop.

                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be cropped. (0,0) denotes the top left corner of the image.

                                                              • +

                                                            • img (PIL Image) – Image to be cropped. (0,0) denotes the top left corner of the image.

                                                            • top (int) – Vertical component of the top left corner of the crop box.

                                                            • left (int) – Horizontal component of the top left corner of the crop box.

                                                            • height (int) – Height of the crop box.

                                                            • width (int) – Width of the crop box.

                                                            • size (sequence or int) – Desired output size. Same semantics as resize.

                                                              • -

                                                            • interpolation (int, optional) – Desired interpolation enum defined by filters. -Default is PIL.Image.BILINEAR. If input is Tensor, only PIL.Image.NEAREST, PIL.Image.BILINEAR -and PIL.Image.BICUBIC are supported.

                                                              • +

                                                            • interpolation (int, optional) – Desired interpolation. Default is +PIL.Image.BILINEAR.

                                                            Returns

                                                            Cropped image.

                                                            Return type
                                                            -

                                                            PIL Image or Tensor

                                                            +

                                                            PIL Image

                                                            @@ -1556,12 +1492,10 @@

                                                            Functional Transforms
                                                            -torchvision.transforms.functional.ten_crop(img: torch.Tensor, size: List[int], vertical_flip: bool = False) → List[torch.Tensor][source]
                                                            -

                                                            Generate ten cropped images from the given image. -Crop the given image into four corners and the central crop plus the -flipped version of these (horizontal flipping is used by default). -The image can be a PIL Image or a Tensor, in which case it is expected -to have […, H, W] shape, where … means an arbitrary number of leading dimensions

                                                            +torchvision.transforms.functional.ten_crop(img, size, vertical_flip=False)[source] +

                                                            Generate ten cropped images from the given PIL Image. +Crop the given PIL Image into four corners and the central crop plus the +flipped version of these (horizontal flipping is used by default).

                                                            Note

                                                            This transform returns a tuple of images and there may be a @@ -1570,10 +1504,9 @@

                                                            Functional Transforms
                                                            Parameters
                                                              -

                                                            • img (PIL Image or Tensor) – Image to be cropped.

                                                            • size (sequence or int) – Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is -made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).

                                                              • +made.

                                                            • vertical_flip (bool) – Use vertical flipping instead of horizontal

                                                            @@ -1660,7 +1593,7 @@

                                                            Functional Transforms

                                                            Vertically flip the given PIL Image or torch Tensor.

                                                            Parameters
                                                            -

                                                            img (PIL Image or Tensor) – Image to be flipped. If img +

                                                            img (PIL Image or Torch Tensor) – Image to be flipped. If img is a Tensor, it is expected to be in […, H, W] format, where … means it can have an arbitrary number of trailing dimensions.

                                                          • floor_divide_() (torch.Tensor method) -
                                                            • -
                                                          • flush() (torch.utils.tensorboard.writer.SummaryWriter method)
                                                          • fmod() (in module torch) @@ -4842,7 +4806,7 @@

                                                            S

                                                          • (torch.Tensor method)
                                                            • -
                                                          • sorted_indices (torch.nn.utils.rnn.PackedSequence attribute) +
                                                          • sorted_indices() (torch.nn.utils.rnn.PackedSequence property)
                                                          • spadd() (torch.sparse.FloatTensor method)
                                                            • @@ -5031,8 +4995,6 @@

                                                            S

                                                          • sum_to_size() (torch.Tensor method) -
                                                            • -
                                                          • SummaryWriter (class in torch.utils.tensorboard.writer)
                                                          • support (torch.distributions.bernoulli.Bernoulli attribute) @@ -5506,7 +5468,7 @@

                                                            U

                                                          • unscale_() (torch.cuda.amp.GradScaler method)
                                                            • -
                                                          • unsorted_indices (torch.nn.utils.rnn.PackedSequence attribute) +
                                                          • unsorted_indices() (torch.nn.utils.rnn.PackedSequence property)
                                                              • diff --git a/docs/stable/jit_builtin_functions.html b/docs/stable/jit_builtin_functions.html index d6449b988eb2..a5c877476e56 100644 --- a/docs/stable/jit_builtin_functions.html +++ b/docs/stable/jit_builtin_functions.html @@ -7640,7 +7640,7 @@

                                                            float

                                                            float

                                                            __float__

                                                            int

                                                            nn.LogSoftmax

                                                            Applies the log(Softmax(x))\log(\text{Softmax}(x)) +

                                                            Applies the log⁡(Softmax(x))\log(\text{Softmax}(x)) + function to an n-dimensional input Tensor.

                                                            nn.AdaptiveLogSoftmaxWithLoss

                                                            nn.RNN

                                                            Applies a multi-layer Elman RNN with tanh\tanh - or ReLU\text{ReLU} +

                                                            Applies a multi-layer Elman RNN with tanh⁡\tanh + + or ReLU\text{ReLU} + non-linearity to an input sequence.

                                                            nn.LSTM

                                                            nn.Linear

                                                            Applies a linear transformation to the incoming data: y=xAT+by = xA^T + b +

                                                            Applies a linear transformation to the incoming data: y=xAT+by = xA^T + b +

                                                            nn.Bilinear

                                                            Applies a bilinear transformation to the incoming data: y=x1TAx2+by = x_1^T A x_2 + b +

                                                            Applies a bilinear transformation to the incoming data: y=x1TAx2+by = x_1^T A x_2 + b +

                                                            nn.Dropout2d

                                                            Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj --th channel of the ii --th sample in the batched input is a 2D tensor input[i,j]\text{input}[i, j] +

                                                            Randomly zero out entire channels (a channel is a 2D feature map, e.g., the jj + +-th channel of the ii + +-th sample in the batched input is a 2D tensor input[i,j]\text{input}[i, j] + ).

                                                            nn.Dropout3d

                                                            Randomly zero out entire channels (a channel is a 3D feature map, e.g., the jj --th channel of the ii --th sample in the batched input is a 3D tensor input[i,j]\text{input}[i, j] +

                                                            Randomly zero out entire channels (a channel is a 3D feature map, e.g., the jj + +-th channel of the ii + +-th sample in the batched input is a 3D tensor input[i,j]\text{input}[i, j] + ).

                                                            nn.AlphaDropout

                                                            nn.CosineSimilarity

                                                            Returns cosine similarity between x1x_1 - and x2x_2 +

                                                            Returns cosine similarity between x1x_1 + + and x2x_2 + , computed along dim.

                                                            nn.PairwiseDistance

                                                            Computes the batchwise pairwise distance between vectors v1v_1 -, v2v_2 +

                                                            Computes the batchwise pairwise distance between vectors v1v_1 + +, v2v_2 + using the p-norm:

                                                            nn.L1Loss

                                                            Creates a criterion that measures the mean absolute error (MAE) between each element in the input xx - and target yy +

                                                            Creates a criterion that measures the mean absolute error (MAE) between each element in the input xx + + and target yy + .

                                                            nn.MSELoss

                                                            Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input xx - and target yy +

                                                            Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input xx + + and target yy + .

                                                            nn.CrossEntropyLoss

                                                            nn.MarginRankingLoss

                                                            Creates a criterion that measures the loss given inputs x1x1 -, x2x2 -, two 1D mini-batch Tensors, and a label 1D mini-batch tensor yy +

                                                            Creates a criterion that measures the loss given inputs x1x1 + +, x2x2 + +, two 1D mini-batch Tensors, and a label 1D mini-batch tensor yy + (containing 1 or -1).

                                                            nn.HingeEmbeddingLoss

                                                            Measures the loss given an input tensor xx - and a labels tensor yy +

                                                            Measures the loss given an input tensor xx + + and a labels tensor yy + (containing 1 or -1).

                                                            nn.MultiLabelMarginLoss

                                                            Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input xx - (a 2D mini-batch Tensor) and output yy +

                                                            Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input xx + + (a 2D mini-batch Tensor) and output yy + (which is a 2D Tensor of target class indices).

                                                            nn.SmoothL1Loss

                                                            Creates a criterion that uses a squared term if the absolute element-wise error falls below 1 and an L1 term otherwise.

                                                            nn.SoftMarginLoss

                                                            Creates a criterion that optimizes a two-class classification logistic loss between input tensor xx - and target tensor yy +

                                                            Creates a criterion that optimizes a two-class classification logistic loss between input tensor xx + + and target tensor yy + (containing 1 or -1).

                                                            nn.MultiLabelSoftMarginLoss

                                                            Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input xx - and target yy - of size (N,C)(N, C) +

                                                            Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input xx + + and target yy + + of size (N,C)(N, C) + .

                                                            nn.CosineEmbeddingLoss

                                                            Creates a criterion that measures the loss given input tensors x1x_1 -, x2x_2 - and a Tensor label yy +

                                                            Creates a criterion that measures the loss given input tensors x1x_1 + +, x2x_2 + + and a Tensor label yy + with values 1 or -1.

                                                            nn.MultiMarginLoss

                                                            Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input xx - (a 2D mini-batch Tensor) and output yy - (which is a 1D tensor of target class indices, 0≤y≤x.size(1)−10 \leq y \leq \text{x.size}(1)-1 +

                                                            Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input xx + + (a 2D mini-batch Tensor) and output yy + + (which is a 1D tensor of target class indices, 0≤y≤x.size(1)−10 \leq y \leq \text{x.size}(1)-1 + ):

                                                            nn.TripletMarginLoss

                                                            Creates a criterion that measures the triplet loss given an input tensors x1x1 -, x2x2 -, x3x3 - and a margin with a value greater than 00 +

                                                            Creates a criterion that measures the triplet loss given an input tensors x1x1 + +, x2x2 + +, x3x3 + + and a margin with a value greater than 00 + .

                                                            nn.PixelShuffle

                                                            Rearranges elements in a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W) - to a tensor of shape (∗,C,H×r,W×r)(*, C, H \times r, W \times r) +

                                                            Rearranges elements in a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W) + + to a tensor of shape (∗,C,H×r,W×r)(*, C, H \times r, W \times r) + .

                                                            nn.Upsample

                                                            Linear / Identity

                                                            11 +

                                                            11 +

                                                            Conv{1,2,3}D

                                                            11 +

                                                            11 +

                                                            Sigmoid

                                                            11 +

                                                            11 +

                                                            Tanh

                                                            53\frac{5}{3} +

                                                            53\frac{5}{3} +

                                                            ReLU

                                                            2\sqrt{2} +

                                                            2\sqrt{2} +

                                                            Leaky Relu

                                                            21+negative_slope2\sqrt{\frac{2}{1 + \text{negative\_slope}^2}} +

                                                            21+negative_slope2\sqrt{\frac{2}{1 + \text{negative\_slope}^2}} +

                                                            arange

                                                            Returns a 1-D tensor of size ⌈end−startstep⌉\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil +

                                                            Returns a 1-D tensor of size ⌈end−startstep⌉\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil + with values from the interval [start, end) taken with common difference step beginning from start.

                                                            range

                                                            Returns a 1-D tensor of size ⌊end−startstep⌋+1\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1 +

                                                            Returns a 1-D tensor of size ⌊end−startstep⌋+1\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1 + with values from start to end with step step.

                                                            linspace

                                                            Returns a one-dimensional tensor of steps equally spaced points between start and end.

                                                            logspace

                                                            Returns a one-dimensional tensor of steps points logarithmically spaced with base base between basestart{\text{base}}^{\text{start}} - and baseend{\text{base}}^{\text{end}} +

                                                            Returns a one-dimensional tensor of steps points logarithmically spaced with base base between basestart{\text{base}}^{\text{start}} + + and baseend{\text{base}}^{\text{end}} + .

                                                            eye

                                                            rand

                                                            Returns a tensor filled with random numbers from a uniform distribution on the interval [0,1)[0, 1) +

                                                            Returns a tensor filled with random numbers from a uniform distribution on the interval [0,1)[0, 1) +

                                                            rand_like

                                                            Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval [0,1)[0, 1) +

                                                            Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval [0,1)[0, 1) + .

                                                            randint

                                                            atan2

                                                            Element-wise arctangent of inputi/otheri\text{input}_{i} / \text{other}_{i} +

                                                            Element-wise arctangent of inputi/otheri\text{input}_{i} / \text{other}_{i} + with consideration of the quadrant.

                                                            bitwise_not

                                                            mvlgamma

                                                            Computes the multivariate log-gamma function) with dimension pp +

                                                            Computes the multivariate log-gamma function) with dimension pp + element-wise, given by

                                                            neg

                                                            Returns a new tensor with the negative of the elements of input.

                                                            polygamma

                                                            Computes the nthn^{th} +

                                                            Computes the nthn^{th} + derivative of the digamma function on input.

                                                            pow

                                                            ge

                                                            Computes input≥other\text{input} \geq \text{other} +

                                                            Computes input≥other\text{input} \geq \text{other} + element-wise.

                                                            gt

                                                            Computes input>other\text{input} > \text{other} +

                                                            Computes input>other\text{input} > \text{other} + element-wise.

                                                            isclose

                                                            le

                                                            Computes input≤other\text{input} \leq \text{other} +

                                                            Computes input≤other\text{input} \leq \text{other} + element-wise.

                                                            lt

                                                            Computes input<other\text{input} < \text{other} +

                                                            Computes input<other\text{input} < \text{other} + element-wise.

                                                            max

                                                            ne

                                                            Computes input≠otherinput \neq other +

                                                            Computes input≠otherinput \neq other + element-wise.

                                                            sort

                                                            combinations

                                                            Compute combinations of length rr +

                                                            Compute combinations of length rr + of the given tensor.

                                                            cross

                                                            meshgrid

                                                            Take NN - tensors, each of which can be either scalar or 1-dimensional vector, and create NN - N-dimensional grids, where the ii - th grid is defined by expanding the ii +

                                                            Take NN + + tensors, each of which can be either scalar or 1-dimensional vector, and create NN + + N-dimensional grids, where the ii + + th grid is defined by expanding the ii + th input over dimensions defined by other inputs.

                                                            logcumsumexp

                                                            chain_matmul

                                                            Returns the matrix product of the NN +

                                                            Returns the matrix product of the NN + 2-D tensors.

                                                            cholesky

                                                            Computes the Cholesky decomposition of a symmetric positive-definite matrix AA +

                                                            Computes the Cholesky decomposition of a symmetric positive-definite matrix AA + or for batches of symmetric positive-definite matrices.

                                                            cholesky_inverse

                                                            Computes the inverse of a symmetric positive-definite matrix AA - using its Cholesky factor uu +

                                                            Computes the inverse of a symmetric positive-definite matrix AA + + using its Cholesky factor uu + : returns matrix inv.

                                                            cholesky_solve

                                                            Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix uu +

                                                            Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix uu + .

                                                            dot

                                                            lstsq

                                                            Computes the solution to the least squares and least norm problems for a full rank matrix AA - of size (m×n)(m \times n) - and a matrix BB - of size (m×k)(m \times k) +

                                                            Computes the solution to the least squares and least norm problems for a full rank matrix AA + + of size (m×n)(m \times n) + + and a matrix BB + + of size (m×k)(m \times k) + .

                                                            lu

                                                            Computes the LU factorization of a matrix or batches of matrices A.

                                                            lu_solve

                                                            Returns the LU solve of the linear system Ax=bAx = b +

                                                            Returns the LU solve of the linear system Ax=bAx = b + using the partially pivoted LU factorization of A from torch.lu().

                                                            lu_unpack

                                                            qr

                                                            Computes the QR decomposition of a matrix or a batch of matrices input, and returns a namedtuple (Q, R) of tensors such that input=QR\text{input} = Q R - with QQ - being an orthogonal matrix or batch of orthogonal matrices and RR +

                                                            Computes the QR decomposition of a matrix or a batch of matrices input, and returns a namedtuple (Q, R) of tensors such that input=QR\text{input} = Q R + + with QQ + + being an orthogonal matrix or batch of orthogonal matrices and RR + being an upper triangular matrix or batch of upper triangular matrices.

                                                            solve

                                                            This function returns the solution to the system of linear equations represented by AX=BAX = B +

                                                            This function returns the solution to the system of linear equations represented by AX=BAX = B + and the LU factorization of A, in order as a namedtuple solution, LU.

                                                            svd

                                                            This function returns a namedtuple (U, S, V) which is the singular value decomposition of a input real matrix or batches of real matrices input such that input=U×diag(S)×VTinput = U \times diag(S) \times V^T +

                                                            This function returns a namedtuple (U, S, V) which is the singular value decomposition of a input real matrix or batches of real matrices input such that input=U×diag(S)×VTinput = U \times diag(S) \times V^T + .

                                                            svd_lowrank

                                                            Return the singular value decomposition (U, S, V) of a matrix, batches of matrices, or a sparse matrix AA - such that A≈Udiag(S)VTA \approx U diag(S) V^T +

                                                            Return the singular value decomposition (U, S, V) of a matrix, batches of matrices, or a sparse matrix AA + + such that A≈Udiag(S)VTA \approx U diag(S) V^T + .

                                                            pca_lowrank

                                                            trapz

                                                            Estimate ∫ydx\int y\,dx +

                                                            Estimate ∫y dx\int y\,dx + along dim, using the trapezoid rule.

                                                            triangular_solve

                                                            Solves a system of equations with a triangular coefficient matrix AA - and multiple right-hand sides bb +

                                                            Solves a system of equations with a triangular coefficient matrix AA + + and multiple right-hand sides bb + .

  • torch.utils.data.distributed