def test_JesterDataset_with_padding(self):
number_of_frames = 15
self.assertGreater(number_of_frames, self.min_number_of_frames)
dataset = JesterDataset(self.train_csv_file,
self.video_dir,
number_of_frames=number_of_frames,
video_transform=None)
self.assertEqual(len(dataset), self.train_test_validation_size[0])
for frames, label in dataset:
self.assertEqual(len(frames), number_of_frames)
self.assertIn(label, self.testDataset.labels)
def test_JesterDataset_with_validation_set(self):
number_of_frames = 10
self.assertLessEqual(number_of_frames, self.min_number_of_frames)
dataset = JesterDataset(self.validation_csv_file,
self.video_dir,
number_of_frames=number_of_frames,
video_transform=None)
self.assertEqual(len(dataset), self.train_test_validation_size[2])
for frames, label in dataset:
self.assertEqual(len(frames), number_of_frames)
self.assertIsNone(label)
def test_JesterDataset_with_frame_select_from_end(self):
number_of_frames = 5
self.assertLess(number_of_frames, self.min_number_of_frames)
dataset = JesterDataset(
self.train_csv_file,
self.video_dir,
number_of_frames=number_of_frames,
frame_select_strategy=JesterDataset.FrameSelectStrategy.FROM_END,
video_transform=None)
self.assertEqual(len(dataset), self.train_test_validation_size[0])
for frames, label in dataset:
self.assertEqual(len(frames), number_of_frames)
self.assertIn(label, self.testDataset.labels)
def data_processing(
batch_size=4,
channel_nb=3,
frame_nb=16,
frame_size=(112, 112),
csv_file='./annotation_jester/annotation_train.csv',
video_dir='./jester_data/20bn-jester-v1',
frame_select_strategy=JesterDataset.FrameSelectStrategy.RANDOM,
frame_padding=JesterDataset.FramePadding.REPEAT_END,
shuffle=True,
num_workers=4):
"""
Process the video data. 1. transform data and convert them to tensor; 2. read the csv file, such as 'train', 'label'
:param batch_size: the size of batch
:param channel_nb: the number of channels
:param frame_nb: the number of frames
:param frame_size: the size of frame in video, e.g., (100, 120)
:param csv_file: read the csv file, such as train, validation, test, and labels
:param video_dir: the directory of the video
:param frame_select_strategy: FROM_BEGINNING = 0, FROM_END = 1, RANDOM = 2
:param frame_padding: REPEAT_END = 0, REPEAT_BEGINNING = 2
:param shuffle: whether to shuffle the data
:param num_workers: the number of workers
:return: data loader, the batch in it is (batch_size, channel_nb, frame_nb, height, width), e.g., (4,3,16,100,120)
"""
video_transform_list = [
Resize(frame_size),
ClipToTensor(channel_nb=channel_nb),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
video_transform = Compose(video_transform_list)
dataset = JesterDataset(csv_file=csv_file,
video_dir=video_dir,
number_of_frames=frame_nb,
video_transform=video_transform,
frame_select_strategy=frame_select_strategy,
frame_padding=frame_padding)
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
return dataloader
def test_JesterDataset_with_dataloader(self):
dataset = JesterDataset(self.train_csv_file,
self.video_dir,
video_transform=ClipToTensor())
preferred_batch_size = 4
dataloader = DataLoader(dataset,
batch_size=preferred_batch_size,
shuffle=True,
num_workers=1)
batch_sizes = []
for i_batch, sample_batched in enumerate(dataloader):
batch, label = sample_batched
actual_batch_size = len(batch)
batch_sizes.append(actual_batch_size)
self.assertLessEqual(actual_batch_size, preferred_batch_size)
number_of_batches_with_batch_size = batch_sizes.count(
preferred_batch_size)
self.assertGreaterEqual(number_of_batches_with_batch_size, 1)
from torch.utils.data.dataloader import DataLoader
from jesterdataset import JesterDataset
from torchvideotransforms.volume_transforms import ClipToTensor
dataset = JesterDataset("./jester_data/jester-v1-train.csv",
"./jester_data/20bn-jester-v1",
video_transform=ClipToTensor())
dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4)
for i_batch, sample_batched in enumerate(dataloader):
batch, label = sample_batched
assert (len(batch) <= 4)
print(f"Batch number {i_batch} has a batch size of {len(batch)}")