def main():
# os.makedirs('sample', exist_ok=True)
opt = TestOptions().parse()
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('# Inference images = %d' % dataset_size)
model = create_model(opt)
print(dataset)
for i, data in enumerate(dataset):
# add gaussian noise channel
# wash the label
t_mask = torch.FloatTensor(
(data['label'].cpu().numpy() == 7).astype(np.float))
#
# data['label'] = data['label'] * (1 - t_mask) + t_mask * 4
mask_clothes = torch.FloatTensor(
(data['label'].cpu().numpy() == 4).astype(np.int))
mask_fore = torch.FloatTensor(
(data['label'].cpu().numpy() > 0).astype(np.int))
img_fore = data['image'] * mask_fore
img_fore_wc = img_fore * mask_fore
all_clothes_label = changearm(data['label'])
############## Forward Pass ######################
fake_image, warped_cloth, refined_cloth = model(
Variable(data['label'].cuda()), Variable(data['edge'].cuda()),
Variable(img_fore.cuda()), Variable(mask_clothes.cuda()),
Variable(data['color'].cuda()), Variable(all_clothes_label.cuda()),
Variable(data['image'].cuda()), Variable(data['pose'].cuda()),
Variable(data['image'].cuda()), Variable(mask_fore.cuda()))
# Variable(im_data.cuda(), volatile=True) 처럼 volatile 옵션을 넣어서 기울기 계산을 안할 수 도 있음
# 그러면 메모리가 덜 사용되겠지?
# make output folders
output_dir = os.path.join(opt.results_dir, opt.phase)
fake_image_dir = os.path.join(output_dir, 'try_on')
os.makedirs(fake_image_dir, exist_ok=True)
warped_cloth_dir = os.path.join(output_dir, 'warped_cloth')
os.makedirs(warped_cloth_dir, exist_ok=True)
refined_cloth_dir = os.path.join(output_dir, 'refined_cloth')
os.makedirs(refined_cloth_dir, exist_ok=True)
# save output
for j in range(opt.batchSize):
print("Saving", data['name'][j])
util.save_tensor_as_image(
fake_image[j], os.path.join(fake_image_dir, data['name'][j]))
util.save_tensor_as_image(
warped_cloth[j], os.path.join(warped_cloth_dir,
data['name'][j]))
util.save_tensor_as_image(
refined_cloth[j],
os.path.join(refined_cloth_dir, data['name'][j]))
util.save_tensor_as_image(
fake_image[j],
os.path.join("../static/images/results", data['name'][j]))
def main():
opt = Options().parse()
dataset = AlignedDataset(opt)
dataloader = DataLoader(dataset,
batch_size=opt.batchSize,
shuffle=not opt.serial_batches,
num_workers=int(opt.nThreads))
dataset_size = len(dataset)
print(f'training images = {dataset_size}')
model = Inference(opt)
model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)
for _, data in enumerate(dataloader):
mask_clothes = torch.FloatTensor(
(data['label'].cpu().numpy() == 4).astype(np.int))
mask_fore = torch.FloatTensor(
(data['label'].cpu().numpy() > 0).astype(np.int))
img_fore = data['image'] * mask_fore
all_clothes_label = changearm(data['label'])
fake_image, warped_cloth, refined_cloth = model(
data['label'].cuda(), data['edge'].cuda(), img_fore.cuda(),
mask_clothes.cuda(), data['color'].cuda(),
all_clothes_label.cuda(), data['image'].cuda(),
data['pose'].cuda(), data['image'].cuda(), mask_fore.cuda())
# make output folders
output_dir = os.path.join(opt.results_dir, opt.phase)
fake_image_dir = os.path.join(output_dir, 'try-on')
os.makedirs(fake_image_dir, exist_ok=True)
warped_cloth_dir = os.path.join(output_dir, 'warped_cloth')
os.makedirs(warped_cloth_dir, exist_ok=True)
refined_cloth_dir = os.path.join(output_dir, 'refined_cloth')
os.makedirs(refined_cloth_dir, exist_ok=True)
# save output
for j in range(opt.batchSize):
name = data['path'][j].split('/')[-1]
print("Saving", name)
util.save_tensor_as_image(fake_image[j],
os.path.join(fake_image_dir, name))
util.save_tensor_as_image(warped_cloth[j],
os.path.join(warped_cloth_dir, name))
util.save_tensor_as_image(refined_cloth[j],
os.path.join(refined_cloth_dir, name))
def main():
opt = Options().parse()
dataset = AlignedDataset(opt)
data = dataset.transform(
'/home/dung/Project/AI/DeepFashion_Try_On/Data_preprocessing/ACGPN_traindata/train_img/000024_0.jpg',
'/home/dung/Project/AI/DeepFashion_Try_On/Data_preprocessing/ACGPN_traindata/train_color/000052_1.jpg'
)
model = Inference(opt)
model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)
mask_clothes = torch.FloatTensor(
(data['label'].cpu().numpy() == 4).astype(np.int))
mask_fore = torch.FloatTensor(
(data['label'].cpu().numpy() > 0).astype(np.int))
img_fore = data['image'] * mask_fore
all_clothes_label = changearm(data['label'])
fake_image, warped_cloth, refined_cloth = model(
data['label'].cuda(), data['edge'].cuda(), img_fore.cuda(),
mask_clothes.cuda(), data['color'].cuda(), all_clothes_label.cuda(),
data['image'].cuda(), data['pose'].cuda(), data['image'].cuda(),
mask_fore.cuda())
# make output folders
output_dir = os.path.join(opt.results_dir, opt.phase)
fake_image_dir = os.path.join(output_dir, 'try-on')
os.makedirs(fake_image_dir, exist_ok=True)
warped_cloth_dir = os.path.join(output_dir, 'warped_cloth')
os.makedirs(warped_cloth_dir, exist_ok=True)
refined_cloth_dir = os.path.join(output_dir, 'refined_cloth')
os.makedirs(refined_cloth_dir, exist_ok=True)
# save output
for j in range(opt.batchSize):
name = data['path'].split('/')[-1]
print("Saving", name)
util.save_tensor_as_image(fake_image[j],
os.path.join(fake_image_dir, name))
util.save_tensor_as_image(warped_cloth[j],
os.path.join(warped_cloth_dir, name))
util.save_tensor_as_image(refined_cloth[j],
os.path.join(refined_cloth_dir, name))