test_transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize
])
dataset_train = opts.dataset(opts.data, train=True, transform=train_transform, download=True)
dataset_train_eval = opts.dataset(opts.data, train=True, transform=test_transform, download=True)
dataset_eval = opts.dataset(opts.data, train=False, transform=test_transform, download=True)
print("Number of images in Training Set: %d" % len(dataset_train))
print("Number of images in Test set: %d" % len(dataset_eval))
loader_train_sample = DataLoader(dataset_train, batch_sampler=NPairs(dataset_train,
opts.batch,
m=opts.num_image_per_class,
iter_per_epoch=opts.iter_per_epoch),
pin_memory=True, num_workers=opts.num_workers)
loader_train_eval = DataLoader(dataset_train_eval, shuffle=False, batch_size=opts.batch, drop_last=False,
pin_memory=False, num_workers=opts.num_workers)
loader_eval = DataLoader(dataset_eval, shuffle=False, batch_size=opts.batch, drop_last=False,
pin_memory=True, num_workers=opts.num_workers)
model = LinearEmbedding(base_model,
output_size=base_model.output_size,
embedding_size=opts.embedding_size,
normalize=not opts.no_normalize).cuda()
if opts.load is not None:
model.load_state_dict(torch.load(opts.load))
dataset_train_eval = opts.dataset(opts.data,
train=True,
transform=test_transform,
download=True)
dataset_eval = opts.dataset(opts.data,
train=False,
transform=test_transform,
download=True)
print(len(dataset_train))
print(len(dataset_eval))
loader_train_sample = DataLoader(dataset_train,
batch_sampler=NPairs(
dataset_train,
opts.batch,
m=5,
iter_per_epoch=opts.iter_per_epoch),
pin_memory=True,
num_workers=opts.num_workers)
loader_train = DataLoader(dataset_train_eval,
shuffle=False,
batch_size=opts.batch,
drop_last=False,
pin_memory=False,
num_workers=opts.num_workers)
loader_eval_sample = DataLoader(dataset_eval,
batch_sampler=NPairs(
dataset_train,
opts.batch,
])
dataset_train = opts.dataset(opts.data,
train=True,
transform=train_transform,
download=True)
dataset_eval = opts.dataset(opts.data,
train=False,
transform=test_transform,
download=True)
print(len(dataset_train))
print(len(dataset_eval))
loader_train = DataLoader(dataset_train,
batch_sampler=NPairs(dataset_train, opts.batch, m=5),
num_workers=1,
pin_memory=True)
loader_eval = DataLoader(dataset_eval,
shuffle=False,
batch_size=opts.batch,
drop_last=False,
num_workers=1,
pin_memory=True)
if opts.no_embedding:
model = NoEmbedding(base_model, normalize=not opts.no_normalize).cuda()
else:
model = LinearEmbedding(base_model,
output_size=base_model.output_size,
embedding_size=opts.embedding_size,