def _create_mapping_loader(config, dataset_class, tf3, partition,
truncate=False, truncate_pc=None,
tencrop=False,
shuffle=False):
if truncate:
print("Note: creating mapping loader with truncate == True")
if tencrop:
assert (tf3 is None)
imgs_list = []
if config.test_on_all_frame and partition == "test":
for i in xrange(10):
imgs_curr = dataset_class(root=config.dataset_root,
transform=tf3,
frame=i,
crop=config.crop_by_bb,
partition=partition)
if truncate:
print("shrinking dataset from %d" % len(imgs_curr))
imgs_curr = TruncatedDataset(imgs_curr, pc=truncate_pc)
print("... to %d" % len(imgs_curr))
if tencrop:
imgs_curr = TenCropAndFinish(imgs_curr, input_sz=config.input_sz,
include_rgb=config.include_rgb)
imgs_list.append(imgs_curr)
else:
for i in xrange(config.base_num):
imgs_curr = dataset_class(root=config.dataset_root,
transform=tf3,
frame=config.base_frame + config.base_interval * i,
crop=config.crop_by_bb,
partition=partition)
if truncate:
print("shrinking dataset from %d" % len(imgs_curr))
imgs_curr = TruncatedDataset(imgs_curr, pc=truncate_pc)
print("... to %d" % len(imgs_curr))
if tencrop:
imgs_curr = TenCropAndFinish(imgs_curr, input_sz=config.input_sz,
include_rgb=config.include_rgb)
imgs_list.append(imgs_curr)
imgs = ConcatDataset(imgs_list)
dataloader = torch.utils.data.DataLoader(imgs,
batch_size=config.batch_sz,
# full batch
shuffle=shuffle,
num_workers=0,
drop_last=False)
if not shuffle:
assert (isinstance(dataloader.sampler,
torch.utils.data.sampler.SequentialSampler))
return dataloader
def _create_mapping_loader(config,
dataset_class,
tf3,
partitions,
target_transform=None,
truncate=False,
truncate_pc=None,
tencrop=False,
shuffle=False):
if truncate:
print("Note: creating mapping loader with truncate == True")
if tencrop:
assert (tf3 is None)
imgs_list = []
for partition in partitions:
if "STL10" == config.dataset:
imgs_curr = dataset_class(root=config.dataset_root,
transform=tf3,
split=partition,
target_transform=target_transform)
elif config.dataset == "MNIST-adv":
imgs_curr = dataset_class(
root=config.dataset_root,
transform=tf3,
train=partition,
target_transform=target_transform) + AdversarialDataset(
config.adv_path, config.adv_n)
else:
imgs_curr = dataset_class(root=config.dataset_root,
transform=tf3,
train=partition,
target_transform=target_transform)
if truncate:
print("shrinking dataset from %d" % len(imgs_curr))
imgs_curr = TruncatedDataset(imgs_curr, pc=truncate_pc)
print("... to %d" % len(imgs_curr))
if tencrop:
imgs_curr = TenCropAndFinish(imgs_curr,
input_sz=config.input_sz,
include_rgb=config.include_rgb)
imgs_list.append(imgs_curr)
imgs = ConcatDataset(imgs_list)
dataloader = torch.utils.data.DataLoader(
imgs,
batch_size=config.batch_sz,
# full batch
shuffle=shuffle,
num_workers=0,
drop_last=False)
if not shuffle:
assert (isinstance(dataloader.sampler,
torch.utils.data.sampler.SequentialSampler))
return dataloader