def _verify_dataset_integrity(dataset_folder, disable_dataset_integrity,
enable_deep_dataset_integrity):
"""
Verifies dataset integrity by looking at the footprint.json in the dataset folder.
In case the deep check is enable, the program will be stopped in case the check
is not passed.
Parameters
----------
dataset_folder : string
Path string that points to the three folder train/val/test. Example: ~/../../data/svhn
disable_dataset_integrity : boolean
Flag to enable or disable verifying the dataset integrity
enable_deep_dataset_integrity : boolean
Flag to enable or disable verifying the dataset integrity in a deep fashion (check the hashes of all files)
Returns
-------
None
"""
if not disable_dataset_integrity:
if enable_deep_dataset_integrity:
if not verify_integrity_deep(dataset_folder):
sys.exit(-1)
else:
verify_integrity_quick(dataset_folder)
def set_up_dataloaders(model_expected_input_size,
dataset_folder,
batch_size,
workers,
disable_dataset_integrity,
enable_deep_dataset_integrity,
inmem=False,
**kwargs):
"""
Set up the dataloaders for the specified datasets.
Parameters
----------
model_expected_input_size : tuple
Specify the height and width that the model expects.
dataset_folder : string
Path string that points to the three folder train/val/test. Example: ~/../../data/svhn
batch_size : int
Number of datapoints to process at once
workers : int
Number of workers to use for the dataloaders
inmem : boolean
Flag: if False, the dataset is loaded in an online fashion i.e. only file names are stored and images are loaded
on demand. This is slower than storing everything in memory.
Returns
-------
train_loader : torch.utils.data.DataLoader
val_loader : torch.utils.data.DataLoader
test_loader : torch.utils.data.DataLoader
Dataloaders for train, val and test.
int
Number of classes for the model.
"""
# Recover dataset name
dataset = os.path.basename(os.path.normpath(dataset_folder))
logging.info('Loading {} from:{}'.format(dataset, dataset_folder))
###############################################################################################
# Verify dataset integrity
if not disable_dataset_integrity:
if enable_deep_dataset_integrity:
if not verify_integrity_deep(dataset_folder):
sys.exit(-1)
else:
if not verify_integrity_quick(dataset_folder):
sys.exit(-1)
###############################################################################################
# Load the dataset splits as images
try:
logging.debug("Try to load dataset as images")
train_ds, val_ds, test_ds = image_folder_dataset.load_dataset(
dataset_folder, inmem, workers)
# Loads the analytics csv and extract mean and std
mean, std = _load_mean_std_from_file(dataset_folder, inmem, workers)
# Set up dataset transforms
logging.debug('Setting up dataset transforms')
transform = transforms.Compose([
transforms.Resize(model_expected_input_size),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
train_ds.transform = transform
val_ds.transform = transform
test_ds.transform = transform
train_loader, val_loader, test_loader = _dataloaders_from_datasets(
batch_size, train_ds, val_ds, test_ds, workers)
logging.info("Dataset loaded as images")
return train_loader, val_loader, test_loader, len(train_ds.classes)
except RuntimeError:
logging.debug("No images found in dataset folder provided")
###############################################################################################
# Load the dataset splits as bidimensional
try:
logging.debug("Try to load dataset as bidimensional")
train_ds, val_ds, test_ds = bidimensional_dataset.load_dataset(
dataset_folder)
# Loads the analytics csv and extract mean and std
# TODO: update bidimensional to work with new load_mean_std functions
mean, std = _load_mean_std_from_file(dataset_folder, inmem, workers)
# Bring mean and std into range [0:1] from original domain
mean = np.divide((mean - train_ds.min_coords),
np.subtract(train_ds.max_coords, train_ds.min_coords))
std = np.divide((std - train_ds.min_coords),
np.subtract(train_ds.max_coords, train_ds.min_coords))
# Set up dataset transforms
logging.debug('Setting up dataset transforms')
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)])
train_ds.transform = transform
val_ds.transform = transform
test_ds.transform = transform
train_loader, val_loader, test_loader = _dataloaders_from_datasets(
batch_size, train_ds, val_ds, test_ds, workers)
logging.info("Dataset loaded as bidimensional data")
return train_loader, val_loader, test_loader, len(train_ds.classes)
except RuntimeError:
logging.debug("No bidimensional found in dataset folder provided")
###############################################################################################
# Verify that eventually a dataset has been correctly loaded
logging.error(
"No datasets have been loaded. Verify dataset folder location or dataset folder structure"
)
sys.exit(-1)
