def main():
# parse options
parser = TestOptions()
opts = parser.parse()
# data loader
train_loader, input_data_par = get_loader(1)
# model
print('\n--- load model ---')
model = DRIT(opts)
model.setgpu(opts.gpu)
model.resume(opts.resume, train=False)
model.eval()
# directory
result_dir = os.path.join(opts.result_dir, opts.name)
if not os.path.exists(result_dir):
os.mkdir(result_dir)
# test
print('\n--- testing ---')
for it, (images_a, images_b, labels) in enumerate(train_loader['test']):
images_a = images_a.cuda(opts.gpu).detach()
images_b = images_b.cuda(opts.gpu).detach()
with torch.no_grad():
loss = model.test_model(images_a, images_b)
print('it:{}, loss:{}'.format(it, loss))
return
def main():
# parse options
parser = TestOptions()
opts = parser.parse()
# data loader
print('\n--- load dataset ---')
datasetA = dataset_single(opts, 'A', opts.input_dim_a)
datasetB = dataset_single(opts, 'B', opts.input_dim_b)
if opts.a2b:
loader = torch.utils.data.DataLoader(datasetA,
batch_size=1,
num_workers=opts.nThreads)
loader_attr = torch.utils.data.DataLoader(datasetB,
batch_size=1,
num_workers=opts.nThreads,
shuffle=True)
else:
loader = torch.utils.data.DataLoader(datasetB,
batch_size=1,
num_workers=opts.nThreads)
loader_attr = torch.utils.data.DataLoader(datasetA,
batch_size=1,
num_workers=opts.nThreads,
shuffle=True)
# model
print('\n--- load model ---')
model = DRIT(opts)
model.setgpu(opts.gpu)
model.resume(opts.resume, train=False)
model.eval()
# directory
result_dir = os.path.join(opts.result_dir, opts.name)
if not os.path.exists(result_dir):
os.mkdir(result_dir)
# test
print('\n--- testing ---')
for idx1, img1 in enumerate(loader):
print('{}/{}'.format(idx1, len(loader)))
img1 = img1.cuda(opts.gpu)
imgs = [img1]
names = ['input']
for idx2, img2 in enumerate(loader_attr):
if idx2 == opts.num:
break
img2 = img2.cuda(opts.gpu)
with torch.no_grad():
if opts.a2b:
img = model.test_forward_transfer(img1, img2, a2b=True)
else:
img = model.test_forward_transfer(img2, img1, a2b=False)
imgs.append(img)
names.append('output_{}'.format(idx2))
save_imgs(imgs, names, os.path.join(result_dir, '{}'.format(idx1)))
return
def main():
# parse options
parser = TestOptions()
opts = parser.parse()
# data loader
print('\n--- load dataset ---')
if opts.a2b:
dataset = dataset_single(opts, 'A', opts.input_dim_a)
subdir = "a2b"
else:
dataset = dataset_single(opts, 'B', opts.input_dim_b)
subdir = "b2a"
loader = torch.utils.data.DataLoader(dataset, batch_size=1, num_workers=opts.nThreads)
# model
print('\n--- load model ---')
model = DRIT(opts)
model.setgpu(opts.gpu)
model.resume(opts.resume, train=False)
model.eval()
# directory
result_dir = os.path.join(opts.result_dir, opts.name, subdir)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
# test
print('\n--- testing ---')
for idx1, img1 in enumerate(loader):
print('{}/{}'.format(idx1, len(loader)))
img1 = img1.cuda()
imgs = [img1]
names = ['input']
for idx2 in range(opts.num):
with torch.no_grad():
img = model.test_forward(img1, a2b=opts.a2b)
imgs.append(img)
names.append('output_{}'.format(idx2))
save_imgs(imgs, names, os.path.join(result_dir, '{}'.format(idx1)))
return
def main():
# parse options
parser = TestOptions()
opts = parser.parse()
# data loader
print('\n--- load dataset ---')
datasetA = dataset_single(opts, 'A', opts.input_dim_a)
datasetB = dataset_single(opts, 'B', opts.input_dim_b)
if opts.a2b:
loader = torch.utils.data.DataLoader(datasetA,
batch_size=1,
num_workers=opts.nThreads)
loader_attr = torch.utils.data.DataLoader(datasetB,
batch_size=1,
num_workers=opts.nThreads,
shuffle=True)
else:
loader = torch.utils.data.DataLoader(datasetB,
batch_size=1,
num_workers=opts.nThreads)
loader_attr = torch.utils.data.DataLoader(datasetA,
batch_size=1,
num_workers=opts.nThreads,
shuffle=True)
# model
print('\n--- load model ---')
model = DRIT(opts)
model.setgpu(opts.gpu)
model.resume(opts.resume, train=False)
model.eval()
# directory
result_dir = os.path.join(opts.result_dir, opts.name)
if not os.path.exists(result_dir):
os.mkdir(result_dir)
# test
print('\n--- testing ---')
for idx1, (img1, img1_path) in enumerate(loader):
print('{}/{}'.format(idx1, len(loader)))
img1_path = img1_path[0]
img1_prefix = os.path.basename(img1_path).split('.')[0]
# print('img1_prefix:', img1_prefix)
img1 = img1.cuda()
imgs = [img1]
# print('img1 type:', type(img1))
names = [f'{img1_prefix}_input']
for idx2, (img2, img2_path) in enumerate(loader_attr):
img2_path = img2_path[0]
img2_prefix = os.path.basename(img2_path).split('.')[0]
# print('img2_prefix:', img2_prefix)
if img1_prefix == img2_prefix:
img2 = img2.cuda()
imgs.append(img2)
names.append(f'{img2_prefix}_real')
# print('img2 type:', type(img2))
with torch.no_grad():
if opts.a2b:
img = model.test_forward_transfer(img1, img2, a2b=True)
else:
img = model.test_forward_transfer(img2,
img1,
a2b=False)
imgs.append(img)
names.append(f'{img2_prefix}_fake')
break
save_imgs(imgs, names, result_dir)
return
test_loader = data.get_test_loader('test', opts.data_name, vocab,
opts.crop_size, opts.batch_size,
opts.workers, opts)
subspace = model_2.VSE(opts)
subspace.setgpu()
subspace.load_state_dict(torch.load(opts.resume2))
subspace.val_start()
# model
print('\n--- load model ---')
model = DRIT(opts)
model.setgpu(opts.gpu)
model.resume(opts.resume, train=False)
model.eval()
a = None
b = None
c = None
d = None
for it, (images, captions, lengths, ids) in enumerate(test_loader):
if it >= opts.test_iter:
break
images = images.cuda(opts.gpu).detach()
captions = captions.cuda(opts.gpu).detach()
img_emb, cap_emb = subspace.forward_emb(images,
captions,
lengths,
volatile=True)