def get_test_data(files,
pklpath,
vocab,
glove,
batch_size=1,
num_workers=0,
pretrained=False,
data_parallel=True,
frame_trunc_length=45):
if pretrained:
pixel = Pixel(files, pklpath)
if not pklexist:
pixel.create()
pixel.save()
else:
pixel.load()
dataset = Dataset(files)
dataset.set_flags(mode='test',
data_parallel=data_parallel,
frame_trunc_length=frame_trunc_length,
pretrained=pretrained)
dataset.set_pad_indices(vocab)
dataset.create(vocab)
dataset.add_glove_vecs(glove)
if pretrained: dataset.add_video_vecs(pixel)
dataloader = data.DataLoader(dataset,
batch_size=batch_size,
collate_fn=dataset.collate_fn)
return dataloader
def get_test_data(files, vocab, glove, batch_size=1):
dataset = Dataset(files)
dataset.set_pad_indices(vocab)
dataset.create(vocab)
dataset.add_glove_vecs(glove)
dataloader = data.DataLoader(dataset,
batch_size=batch_size,
collate_fn=dataset.collate_fn)
return dataloader
def get_train_data(files,
pklpath,
glove_file,
glove_embdim,
batch_size=1,
shuffle=True,
num_workers=0,
pretrained=False,
pklexist=False,
data_parallel=True,
frame_trunc_length=45,
spatial=False):
start_time = time.time()
vocab = Vocab(files)
vocab.add_begend_vocab()
vocab.create()
vocab_time = time.time()
glove = Glove(glove_file, glove_embdim)
glove.create(vocab)
glove_time = time.time()
if pretrained:
pixel = Pixel(files, pklpath)
if not pklexist:
pixel.create()
pixel.save()
else:
pixel.load()
pixel_time = time.time()
dataset = Dataset(files)
dataset.set_flags(mode='train',
data_parallel=data_parallel,
frame_trunc_length=frame_trunc_length,
pretrained=pretrained,
spatial=spatial)
dataset.set_pad_indices(vocab)
dataset.create(vocab)
dataset.add_glove_vecs(glove)
if pretrained: dataset.add_video_vecs(pixel)
dataset_time = time.time()
print('Vocab : {0}, Glove : {1}, Pixel : {2}, Dataset : {3}'.format(
vocab_time - start_time, glove_time - vocab_time,
pixel_time - glove_time, dataset_time - pixel_time))
dataloader = data.DataLoader(dataset,
batch_size=batch_size,
collate_fn=dataset.collate_fn,
shuffle=shuffle,
num_workers=num_workers)
return dataloader, vocab, glove, dataset.__len__()
def get_train_data(files, glove_file, batch_size=1):
vocab = Vocab(files)
vocab.add_padunk_vocab()
vocab.create()
glove = Glove(glove_file)
glove.create(vocab)
dataset = Dataset(files)
dataset.set_pad_indices(vocab)
dataset.create(vocab)
dataset.add_glove_vecs(glove)
dataloader = data.DataLoader(dataset,
batch_size=batch_size,
collate_fn=dataset.collate_fn)
return dataloader, vocab, glove
