Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
from model import MyModel
def parse_args():
parser = argparse.ArgumentParser(description='Test')
parser.add_argument('--save', action='store_true', help='save images')
parser.add_argument('--data_out_root', type=str)
parser.add_argument('--data_lq_root', type=str, required=True)
parser.add_argument('--ckpt_path', type=str, default='./checkpoint/best.pt', help='path to save checkpoints')
parser.add_argument('--ckpt_path2', type=str, default='./checkpoint/best.pt', help='path to save checkpoints')
args = parser.parse_args()
return args
def test(model, img):
_, _, h_old, w_old = img.size()
padding = 16*2
h_pad = (h_old // padding + 1) * padding - h_old
w_pad = (w_old // padding + 1) * padding - w_old
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, :h_old + h_pad, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, :w_old + w_pad]
img = tile_eval(model, img, tile=384, tile_overlap=96)
img = img[..., :h_old, :w_old]
return img
def tile_eval(model,input_,tile=128,tile_overlap=32):
b, c, h, w = input_.shape
tile = min(tile, h, w)
assert tile % 8 == 0, "tile size should be multiple of 8"
stride = tile - tile_overlap
h_idx_list = list(range(0, h-tile, stride)) + [h-tile]
w_idx_list = list(range(0, w-tile, stride)) + [w-tile]
E = torch.zeros(b, c, h, w).type_as(input_)
W = torch.zeros_like(E)
for h_idx in h_idx_list:
for w_idx in w_idx_list:
in_patch = input_[..., h_idx:h_idx+tile, w_idx:w_idx+tile]
out_patch = model(in_patch)
out_patch_mask = torch.ones_like(out_patch)
E[..., h_idx:(h_idx+tile), w_idx:(w_idx+tile)].add_(out_patch)
W[..., h_idx:(h_idx+tile), w_idx:(w_idx+tile)].add_(out_patch_mask)
restored = E.div_(W)
restored = torch.clamp(restored, 0, 1)
return restored
args = parse_args()
weight = torch.load(args.ckpt_path, map_location=lambda storage, loc: storage)
model = MyModel(decoder=True)
model.load_state_dict(weight)
model = model.cuda()
weight = torch.load(args.ckpt_path2, map_location=lambda storage, loc: storage)
model2 = MyModel(decoder=True)
model2.load_state_dict(weight)
model2 = model2.cuda()
test_path = args.data_lq_root
if args.save:
output_path = args.data_out_root
if not os.path.exists(output_path):
os.makedirs(output_path)
model.eval()
with torch.no_grad():
for img_n in sorted(os.listdir(test_path)):
lr = cv2.imread(os.path.join(test_path, img_n))
lr = cv2.cvtColor(lr, cv2.COLOR_BGR2RGB)
img = np.ascontiguousarray(lr.transpose((2, 0, 1)))
img = torch.from_numpy(img).float()
img /= 255.
img = img.unsqueeze(0).cuda()
# img = model(img)
E0 = test(model, img)
E1 = test(model2, img)
L_t = img.transpose(-2, -1)
E2 = test(model, L_t).transpose(-2, -1)
E3 = test(model2, L_t).transpose(-2, -1)
sr = (E0.clamp_(0, 1) + E1.clamp_(0, 1) + E2.clamp_(0, 1) + E3.clamp_(0, 1)) / 4.0
sr = sr.detach().cpu().squeeze(0).numpy().transpose(1, 2, 0)
sr = sr * 255.
sr = np.clip(sr.round(), 0, 255).astype(np.uint8)