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data_loader.py
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from base import MultiDistBaseDataLoaderExplicitSplit, BaseDataLoaderExplicitSplit
from data_loader.transforms import init_transform_dict
from data_loader.ConceptualCaptions_dataset import ConceptualCaptions3M
from data_loader.MSRVTT_dataset import MSRVTT
from data_loader.WebVid_dataset import WebVid
def dataset_loader(dataset_name,
text_params,
video_params,
data_dir,
question,
metadata_dir=None,
split='train',
tsfms=None,
cut=None,
subsample=1,
sliding_window_stride=-1,
reader='cv2'):
kwargs = dict(
dataset_name=dataset_name,
text_params=text_params,
video_params=video_params,
data_dir=data_dir,
question=question,
metadata_dir=metadata_dir,
split=split,
tsfms=tsfms,
cut=cut,
subsample=subsample,
sliding_window_stride=sliding_window_stride,
reader=reader
)
# TODO: change to...
# dataset = globals()[dataset_name]
# ...is this safe / or just lazy?
if dataset_name == "MSRVTT":
dataset = MSRVTT(**kwargs)
elif dataset_name == "WebVid":
dataset = WebVid(**kwargs)
elif dataset_name == "ConceptualCaptions3M":
dataset = ConceptualCaptions3M(**kwargs)
else:
raise NotImplementedError(f"Dataset: {dataset_name} not found.")
return dataset
class MultiDistTextVideoDataLoader(MultiDistBaseDataLoaderExplicitSplit):
def __init__(self,
args,
dataset_name,
text_params,
video_params,
data_dir,
question,
metadata_dir=None,
split='train',
tsfm_params=None,
cut=None,
subsample=1,
sliding_window_stride=-1,
reader='cv2',
batch_size=1,
num_workers=1,
shuffle=True):
if tsfm_params is None:
tsfm_params = {}
tsfm_dict = init_transform_dict(**tsfm_params)
tsfm = tsfm_dict[split]
dataset = dataset_loader(dataset_name, text_params, video_params, data_dir, question,
metadata_dir, split, tsfm, cut, subsample, sliding_window_stride, reader)
super().__init__(args, dataset, batch_size, shuffle, num_workers)
self.dataset_name = dataset_name
class TextVideoDataLoader(BaseDataLoaderExplicitSplit):
def __init__(self,
dataset_name,
text_params,
video_params,
data_dir,
question,
metadata_dir=None,
split='train',
tsfm_params=None,
cut=None,
subsample=1,
sliding_window_stride=-1,
reader='cv2',
batch_size=1,
num_workers=1,
shuffle=True):
if tsfm_params is None:
tsfm_params = {}
tsfm_dict = init_transform_dict(**tsfm_params)
tsfm = tsfm_dict[split]
dataset = dataset_loader(dataset_name, text_params, video_params, data_dir, question, metadata_dir, split, tsfm, cut,
subsample, sliding_window_stride, reader)
super().__init__(dataset, batch_size, shuffle, num_workers)
self.dataset_name = dataset_name
class TextVideoDataLoader_CLIP(BaseDataLoaderExplicitSplit):
def __init__(self,
dataset_name,
text_params,
video_params,
data_dir,
question,
metadata_dir=None,
split='train',
tsfm_params=None,
cut=None,
subsample=1,
sliding_window_stride=-1,
reader='cv2',
batch_size=1,
num_workers=1,
shuffle=True):
if tsfm_params is None:
tsfm_params = {}
tsfm_dict = init_transform_dict_clip(**tsfm_params)
tsfm = tsfm_dict[split]
dataset = dataset_loader(dataset_name, text_params, video_params, data_dir, question, metadata_dir, split, tsfm, cut,
subsample, sliding_window_stride, reader)
super().__init__(dataset, batch_size, shuffle, num_workers)
self.dataset_name = dataset_name