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 configuration used when building these files. When it is not found, a full rebuild will be done. -config: 93625c989914b71802289037a0f16437 +config: 892a2ec27a03fe01b7f360c8920a2882 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/stable/_images/add_histogram.png b/docs/stable/_images/add_histogram.png deleted file mode 100644 index d9185e4b10b5..000000000000 Binary files a/docs/stable/_images/add_histogram.png and /dev/null differ diff --git a/docs/stable/_images/add_hparam.png b/docs/stable/_images/add_hparam.png deleted file mode 100644 index 5cebef5b5889..000000000000 Binary files a/docs/stable/_images/add_hparam.png and /dev/null differ diff --git a/docs/stable/_images/add_image.png b/docs/stable/_images/add_image.png deleted file mode 100644 index 0b675524b459..000000000000 Binary files a/docs/stable/_images/add_image.png and /dev/null differ diff --git a/docs/stable/_images/add_images.png b/docs/stable/_images/add_images.png deleted file mode 100644 index 5fcbf36580b7..000000000000 Binary files a/docs/stable/_images/add_images.png and /dev/null differ diff --git a/docs/stable/_images/add_scalar.png b/docs/stable/_images/add_scalar.png deleted file mode 100644 index a872b93eca32..000000000000 Binary files a/docs/stable/_images/add_scalar.png and /dev/null differ diff --git a/docs/stable/_images/add_scalars.png b/docs/stable/_images/add_scalars.png deleted file mode 100644 index 2a31a4b76cf9..000000000000 Binary files a/docs/stable/_images/add_scalars.png and /dev/null differ diff --git a/docs/stable/_modules/index.html b/docs/stable/_modules/index.html index dbd0a4b2c340..24ba1abb638e 100644 --- a/docs/stable/_modules/index.html +++ b/docs/stable/_modules/index.html @@ -494,7 +494,6 @@

All modules for which code is available

  • torch.utils.data.distributed
  • torch.utils.data.sampler
  • torch.utils.mobile_optimizer
    • -
  • torch.utils.tensorboard.writer
  • torchvision
    • 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 eff8372f9df8..06689dafc92c 100644 Binary files a/docs/stable/objects.inv and b/docs/stable/objects.inv differ diff --git a/docs/stable/optim.html b/docs/stable/optim.html index 2faee72a0330..f8bfd6caf69b 100644 --- a/docs/stable/optim.html +++ b/docs/stable/optim.html @@ -182,7 +182,12 @@ - + + + + + +

                                                            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.

                                                                      diff --git a/docs/stable/searchindex.js b/docs/stable/searchindex.js index 061f3364c8b6..bae7f18819fd 100644 --- a/docs/stable/searchindex.js +++ b/docs/stable/searchindex.js @@ -1 +1 @@ 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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 + .