def plot_examples(low_res_folder, gen):
files = os.listdir(low_res_folder)
gen.eval()
for file in files:
image = Image.open("Data/LR/" + file)
with torch.no_grad():
upscaled_img = gen(
config.test_transform(
image=np.asarray(image))["image"].unsqueeze(0).to(
config.DEVICE))
save_image(upscaled_img * 0.5 + 0.5, f"Data/saved/{file}")
gen.train()
def plot_examples(low_res_folder, gen):
files = os.listdir(low_res_folder)
gen.eval() # 测试模式
for file in files:
print(file)
image = Image.open(os.path.join(low_res_folder, file))
with torch.no_grad():
upscaled_img = gen(
config.test_transform(
image=np.asarray(image))["image"].unsqueeze(0).to(
config.DEVICE))
save_image(upscaled_img, f"saved_images/{file}")
gen.train() # 训练模式
def save_examples(low_res_dir, gen):
files = os.listdir(low_res_dir)
gen.eval()
for file in files:
image = Image.open(os.path.join(low_res_dir, file))
with torch.no_grad():
upscaled_img = gen(
config.test_transform(
image=np.asarray(image))["image"].unsqueeze(0).to(
config.DEVICE))
save_image(upscaled_img * 0.5 + 0.5, f"saved_images/{file}")
gen.train()