def init_dataset(opt):
'''
Initialize the datasets, samplers and dataloaders
'''
if opt.dataset == 'omniglot':
train_dataset = OmniglotDataset(mode='train')
val_dataset = OmniglotDataset(mode='val')
trainval_dataset = OmniglotDataset(mode='trainval')
test_dataset = OmniglotDataset(mode='test')
elif opt.dataset == 'mini_imagenet':
train_dataset = MiniImagenetDataset(mode='train')
val_dataset = MiniImagenetDataset(mode='val')
trainval_dataset = MiniImagenetDataset(mode='val')
test_dataset = MiniImagenetDataset(mode='test')
train_bs_class = BatchSampler
eval_bs_class = BatchSampler
if opt.task_shuffling == 'non_overlapping':
train_bs_class = NonOverlappingTasksBatchSampler
elif opt.task_shuffling == 'intratask':
train_bs_class = IntraTaskBatchSampler
# Opt for mini_imagenet:
# Namespace(batch_size=32, cuda=True, dataset='mini_imagenet', epochs=100,
# exp='mini_imagenet_5way_1shot', iterations=10000, lr=0.0001, num_cls=5, num_samples=1)
tr_sampler = train_bs_class(labels=train_dataset.y,
classes_per_it=opt.num_cls,
num_samples=opt.num_samples,
iterations=opt.iterations,
batch_size=opt.batch_size)
val_sampler = eval_bs_class(labels=val_dataset.y,
classes_per_it=opt.num_cls,
num_samples=opt.num_samples,
iterations=opt.iterations,
batch_size=opt.batch_size)
trainval_sampler = eval_bs_class(labels=trainval_dataset.y,
classes_per_it=opt.num_cls,
num_samples=opt.num_samples,
iterations=opt.iterations,
batch_size=opt.batch_size)
test_sampler = eval_bs_class(labels=test_dataset.y,
classes_per_it=opt.num_cls,
num_samples=opt.num_samples,
iterations=opt.iterations,
batch_size=opt.batch_size)
tr_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_sampler=tr_sampler)
val_dataloader = torch.utils.data.DataLoader(val_dataset,
batch_sampler=val_sampler)
trainval_dataloader = torch.utils.data.DataLoader(
trainval_dataset, batch_sampler=trainval_sampler)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_sampler=test_sampler)
return tr_dataloader, val_dataloader, trainval_dataloader, test_dataloader
def init_dataset(opt):
'''
Initialize the datasets, samplers and dataloaders
'''
if opt.dataset == 'omniglot':
train_dataset = OmniglotDataset(mode='train')
val_dataset = OmniglotDataset(mode='val')
trainval_dataset = OmniglotDataset(mode='trainval')
test_dataset = OmniglotDataset(mode='test')
elif opt.dataset == 'mini_imagenet':
train_dataset = MiniImagenetDataset(mode='train')
val_dataset = MiniImagenetDataset(mode='val')
trainval_dataset = MiniImagenetDataset(mode='val')
test_dataset = MiniImagenetDataset(mode='test')
tr_sampler = PrototypicalBatchSampler(labels=train_dataset.y,
classes_per_it=opt.classes_per_it_tr,
num_samples=opt.num_support_tr +
opt.num_query_tr,
iterations=opt.iterations)
val_sampler = PrototypicalBatchSampler(
labels=val_dataset.y,
classes_per_it=opt.classes_per_it_val,
num_samples=opt.num_support_val + opt.num_query_val,
iterations=opt.iterations)
trainval_sampler = PrototypicalBatchSampler(
labels=trainval_dataset.y,
classes_per_it=opt.classes_per_it_tr,
num_samples=opt.num_support_tr + opt.num_query_tr,
iterations=opt.iterations)
test_sampler = PrototypicalBatchSampler(
labels=test_dataset.y,
classes_per_it=opt.classes_per_it_val,
num_samples=opt.num_support_val + opt.num_query_val,
iterations=opt.iterations)
tr_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_sampler=tr_sampler)
val_dataloader = torch.utils.data.DataLoader(val_dataset,
batch_sampler=val_sampler)
trainval_dataloader = torch.utils.data.DataLoader(
trainval_dataset, batch_sampler=trainval_sampler)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_sampler=test_sampler)
return tr_dataloader, val_dataloader, trainval_dataloader, test_dataloader
def init_dataset(opt):
'''
Initialize the datasets, samplers and dataloaders
'''
if opt.dataset == 'omniglot':
train_dataset = OmniglotDataset(mode='train')
val_dataset = OmniglotDataset(mode='val')
trainval_dataset = OmniglotDataset(mode='trainval')
test_dataset = OmniglotDataset(mode='test')
elif opt.dataset == 'mini_imagenet':
train_dataset = MiniImagenetDataset(mode='train')
val_dataset = MiniImagenetDataset(mode='val')
trainval_dataset = MiniImagenetDataset(mode='val')
test_dataset = MiniImagenetDataset(mode='test')
tr_sampler = BatchSampler(labels=train_dataset.y,
classes_per_it=opt.num_cls,
num_samples=opt.num_samples,
iterations=opt.iterations,
batch_size=opt.batch_size)
val_sampler = BatchSampler(labels=val_dataset.y,
classes_per_it=opt.num_cls,
num_samples=opt.num_samples,
iterations=opt.iterations,
batch_size=opt.batch_size)
trainval_sampler = BatchSampler(labels=trainval_dataset.y,
classes_per_it=opt.num_cls,
num_samples=opt.num_samples,
iterations=opt.iterations,
batch_size=opt.batch_size)
test_sampler = BatchSampler(labels=test_dataset.y,
classes_per_it=opt.num_cls,
num_samples=opt.num_samples,
iterations=opt.iterations,
batch_size=opt.batch_size)
tr_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_sampler=tr_sampler)
val_dataloader = torch.utils.data.DataLoader(val_dataset,
batch_sampler=val_sampler)
trainval_dataloader = torch.utils.data.DataLoader(
trainval_dataset, batch_sampler=trainval_sampler)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_sampler=test_sampler)
return tr_dataloader, val_dataloader, trainval_dataloader, test_dataloader
def init_dataset(opt):
'''
Initialize the datasets, samplers and dataloaders
'''
if opt.dataset == 'omniglot':
test_dataset = OmniglotDataset(mode='test')
elif opt.dataset == 'mini_imagenet':
test_dataset = MiniImagenetDataset(mode='val')
else:
print('Dataset is not valid')
test_sampler = PrototypicalBatchSampler(labels=test_dataset.y,
classes_per_it=opt.classes_per_it_val,
num_samples=opt.num_support_val + opt.num_query_val,
iterations=opt.iterations)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_sampler=test_sampler)
return test_dataloader