def dataset_setup(self):
"""Sets up the datasets for the application."""
settings = self.settings
if settings.crowd_dataset == CrowdDataset.ucf_qnrf:
self.dataset_class = UcfQnrfFullImageDataset
self.train_dataset = UcfQnrfTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_examples=settings.labeled_dataset_size)
self.train_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.validation_dataset = UcfQnrfTransformedDataset(dataset='test',
seed=101)
elif settings.crowd_dataset == CrowdDataset.shanghai_tech:
self.dataset_class = ShanghaiTechFullImageDataset
self.train_dataset = ShanghaiTechTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_examples=settings.labeled_dataset_size,
map_directory_name=settings.map_directory_name,
image_patch_size=self.settings.image_patch_size,
label_patch_size=self.settings.label_patch_size)
self.train_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.validation_dataset = ShanghaiTechTransformedDataset(
dataset='test',
seed=101,
map_directory_name=settings.map_directory_name,
image_patch_size=self.settings.image_patch_size,
label_patch_size=self.settings.label_patch_size)
elif settings.crowd_dataset == CrowdDataset.ucf_cc_50:
seed = 0
self.dataset_class = UcfCc50FullImageDataset
self.train_dataset = UcfCc50TransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=seed,
test_start=settings.labeled_dataset_seed * 10,
inverse_map=settings.inverse_map,
map_directory_name=settings.map_directory_name)
self.train_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.validation_dataset = UcfCc50TransformedDataset(
dataset='test',
seed=seed,
test_start=settings.labeled_dataset_seed * 10,
inverse_map=settings.inverse_map,
map_directory_name=settings.map_directory_name)
else:
raise ValueError('{} is not an understood crowd dataset.'.format(
settings.crowd_dataset))
def dataset_setup(self):
"""Sets up the datasets for the application."""
settings = self.settings
if settings.crowd_dataset == 'World Expo':
train_transform = torchvision.transforms.Compose([data.RandomlySelectPathWithNoPerspectiveRescale(),
data.RandomHorizontalFlip(),
data.NegativeOneToOneNormalizeImage(),
data.NumpyArraysToTorchTensors()])
validation_transform = torchvision.transforms.Compose([data.RandomlySelectPathWithNoPerspectiveRescale(),
data.NegativeOneToOneNormalizeImage(),
data.NumpyArraysToTorchTensors()])
dataset_path = '../World Expo/'
with open(os.path.join(dataset_path, 'viable_with_validation_and_random_test.json')) as json_file:
cameras_dict = json.load(json_file)
self.train_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['train'],
number_of_cameras=settings.number_of_cameras,
number_of_images_per_camera=settings.number_of_images_per_camera,
transform=train_transform, seed=settings.labeled_dataset_seed)
self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size, shuffle=True,
pin_memory=True, num_workers=settings.number_of_data_workers)
# self.unlabeled_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['validation'],
# transform=train_transform, unlabeled=True,
# seed=100)
self.unlabeled_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['train'],
number_of_cameras=settings.number_of_cameras,
transform=train_transform, unlabeled=True,
seed=settings.labeled_dataset_seed)
self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size, shuffle=True,
pin_memory=True, num_workers=settings.number_of_data_workers)
self.validation_dataset = CrowdDataset(dataset_path, camera_names=cameras_dict['validation'],
transform=validation_transform, seed=101)
elif settings.crowd_dataset == 'ShanghaiTech':
train_transform = torchvision.transforms.Compose([data.ExtractPatchForRandomPosition(),
data.RandomHorizontalFlip(),
data.NegativeOneToOneNormalizeImage(),
data.NumpyArraysToTorchTensors()])
validation_transform = torchvision.transforms.Compose([data.ExtractPatchForRandomPosition(),
data.NegativeOneToOneNormalizeImage(),
data.NumpyArraysToTorchTensors()])
self.train_dataset = ShanghaiTechDataset(transform=train_transform, seed=settings.labeled_dataset_seed)
self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size, shuffle=True,
pin_memory=True, num_workers=settings.number_of_data_workers)
self.unlabeled_dataset = ShanghaiTechDataset(transform=train_transform, seed=settings.labeled_dataset_seed,
unlabeled=True)
self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size, shuffle=True,
pin_memory=True, num_workers=settings.number_of_data_workers)
self.validation_dataset = ShanghaiTechDataset(dataset='test', transform=validation_transform, seed=101)
else:
raise ValueError('{} is not an understood crowd dataset.'.format(settings.crowd_dataset))
def dataset_setup(self):
"""Sets up the datasets for the application."""
settings = self.settings
# self.dataset_class = UcfQnrfFullImageDataset
# self.train_dataset = UcfQnrfTransformedDataset(middle_transform=data.RandomHorizontalFlip(),
# seed=settings.labeled_dataset_seed,
# number_of_examples=settings.labeled_dataset_size)
# self.train_dataset_loader = DataLoader(self.train_dataset, batch_size=settings.batch_size,
# pin_memory=self.settings.pin_memory,
# num_workers=settings.number_of_data_workers)
# self.unlabeled_dataset = UcfQnrfTransformedDataset(middle_transform=data.RandomHorizontalFlip(),
# seed=100,
# number_of_examples=settings.unlabeled_dataset_size)
# self.unlabeled_dataset_loader = DataLoader(self.unlabeled_dataset, batch_size=settings.batch_size,
# pin_memory=self.settings.pin_memory,
# num_workers=settings.number_of_data_workers)
# self.validation_dataset = UcfQnrfTransformedDataset(dataset='test', seed=101)
self.dataset_class = ShanghaiTechFullImageDataset
self.train_dataset = ShanghaiTechTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_examples=settings.labeled_dataset_size,
inverse_map=settings.inverse_map,
map_directory_name=settings.map_directory_name)
self.train_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.unlabeled_dataset = ShanghaiTechTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=100,
number_of_examples=settings.unlabeled_dataset_size,
inverse_map=settings.inverse_map,
map_directory_name=settings.map_directory_name)
self.unlabeled_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.validation_dataset = ShanghaiTechTransformedDataset(
dataset='test',
seed=101,
inverse_map=settings.inverse_map,
map_directory_name=settings.map_directory_name)
def dataset_setup(self):
"""Sets up the datasets for the application."""
settings = self.settings
self.dataset_class = UcfQnrfFullImageDataset
self.train_dataset = UcfQnrfTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_examples=settings.labeled_dataset_size)
self.train_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.unlabeled_dataset = UcfQnrfTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=100,
number_of_examples=settings.unlabeled_dataset_size)
self.unlabeled_dataset_loader = DataLoader(
self.unlabeled_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.validation_dataset = UcfQnrfTransformedDataset(dataset='test',
seed=101)
def dataset_setup(self):
"""Sets up the datasets for the application."""
settings = self.settings
if settings.crowd_dataset == CrowdDataset.ucf_qnrf:
self.dataset_class = UcfQnrfFullImageDataset
self.train_dataset = UcfQnrfTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_examples=settings.labeled_dataset_size,
map_directory_name=settings.map_directory_name)
self.train_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.unlabeled_dataset = UcfQnrfTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_examples=settings.unlabeled_dataset_size,
map_directory_name=settings.map_directory_name,
examples_start=settings.labeled_dataset_size)
self.unlabeled_dataset_loader = DataLoader(
self.unlabeled_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.validation_dataset = UcfQnrfTransformedDataset(
dataset='test',
seed=101,
map_directory_name=settings.map_directory_name)
elif settings.crowd_dataset == CrowdDataset.shanghai_tech:
self.dataset_class = ShanghaiTechFullImageDataset
self.train_dataset = ShanghaiTechTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_examples=settings.labeled_dataset_size,
map_directory_name=settings.map_directory_name)
self.train_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.unlabeled_dataset = ShanghaiTechTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=100,
number_of_examples=settings.unlabeled_dataset_size,
map_directory_name=settings.map_directory_name)
self.unlabeled_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.validation_dataset = ShanghaiTechTransformedDataset(
dataset='test',
seed=101,
map_directory_name=settings.map_directory_name)
elif settings.crowd_dataset == CrowdDataset.world_expo:
self.dataset_class = WorldExpoFullImageDataset
self.train_dataset = WorldExpoTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_cameras=settings.number_of_cameras,
number_of_images_per_camera=settings.
number_of_images_per_camera)
self.train_dataset_loader = DataLoader(
self.train_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.unlabeled_dataset = WorldExpoTransformedDataset(
middle_transform=data.RandomHorizontalFlip(),
seed=settings.labeled_dataset_seed,
number_of_cameras=settings.number_of_cameras,
number_of_images_per_camera=settings.
number_of_images_per_camera)
self.unlabeled_dataset_loader = DataLoader(
self.unlabeled_dataset,
batch_size=settings.batch_size,
pin_memory=self.settings.pin_memory,
num_workers=settings.number_of_data_workers)
self.validation_dataset = WorldExpoTransformedDataset(
dataset='validation', seed=101)
if self.settings.batch_size > self.train_dataset.length:
self.settings.batch_size = self.train_dataset.length