Upload 2 files

api.py

@@ -0,0 +1,80 @@
+from torch import optim
+from tqdm.auto import tqdm
+from helper import *
+from model.generator import SkipEncoderDecoder, input_noise
+
+
+def remove_watermark(image_path, mask_path, max_dim, reg_noise, input_depth, lr, show_step, training_steps, tqdm_length=100):
+    DTYPE = torch.FloatTensor
+    has_set_device = False
+    if torch.cuda.is_available():
+        device = 'cuda'
+        has_set_device = True
+        print("Setting Device to CUDA...")
+    try:
+        if torch.backends.mps.is_available():
+            device = 'mps'
+            has_set_device = True
+            print("Setting Device to MPS...")
+    except Exception as e:
+        print(f"Your version of pytorch might be too old, which does not support MPS. Error: \n{e}")
+        pass
+    if not has_set_device:
+        device = 'cpu'
+        print('\nSetting device to "cpu", since torch is not built with "cuda" or "mps" support...')
+        print('It is recommended to use GPU if possible...')
+
+    image_np, mask_np = preprocess_images(image_path, mask_path, max_dim)
+
+    print('Building the model...')
+    generator = SkipEncoderDecoder(
+        input_depth,
+        num_channels_down = [128] * 5,
+        num_channels_up = [128] * 5,
+        num_channels_skip = [128] * 5
+    ).type(DTYPE).to(device)
+
+    objective = torch.nn.MSELoss().type(DTYPE).to(device)
+    optimizer = optim.Adam(generator.parameters(), lr)
+
+    image_var = np_to_torch_array(image_np).type(DTYPE).to(device)
+    mask_var = np_to_torch_array(mask_np).type(DTYPE).to(device)
+
+    generator_input = input_noise(input_depth, image_np.shape[1:]).type(DTYPE).to(device)
+
+    generator_input_saved = generator_input.detach().clone()
+    noise = generator_input.detach().clone()
+
+    print('\nStarting training...\n')
+
+    progress_bar = tqdm(range(training_steps), desc='Completed', ncols=tqdm_length)
+
+    for step in progress_bar:
+        optimizer.zero_grad()
+        generator_input = generator_input_saved
+
+        if reg_noise > 0:
+            generator_input = generator_input_saved + (noise.normal_() * reg_noise)
+
+        output = generator(generator_input)
+
+        loss = objective(output * mask_var, image_var * mask_var)
+        loss.backward()
+
+        if step % show_step == 0:
+            output_image = torch_to_np_array(output)
+            visualize_sample(image_np, output_image, nrow = 2, size_factor = 10)
+
+        progress_bar.set_postfix(Loss = loss.item())
+
+        optimizer.step()
+
+    output_image = torch_to_np_array(output)
+    visualize_sample(output_image, nrow = 1, size_factor = 10)
+
+    pil_image = Image.fromarray((output_image.transpose(1, 2, 0) * 255.0).astype('uint8'))
+
+    output_path = image_path.split('/')[-1].split('.')[-2] + '-output.jpg'
+    print(f'\nSaving final output image to: "{output_path}"\n')
+
+    pil_image.save(output_path)

test.py

@@ -0,0 +1,13 @@
+from api import remove_watermark
+
+remove_watermark(
+    image_path = IMAGE_NAME,
+    mask_path = MASK_NAME,
+    max_dim = MAX_DIM,
+    show_step = SHOW_STEPS,
+    reg_noise = REG_NOISE,
+    input_depth = INPUT_DEPTH,
+    lr = LR,
+    training_steps = TRAINING_STEPS,
+    tqdm_length = 900
+)