Added MPS for Mac Users

Author: edwin-yan

api.py

@@ -3,10 +3,25 @@
 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.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
-    if not torch.cuda.is_available():
-        print('\nSetting device to "cpu", since torch is not built with "cuda" support...')
+
+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)
@@ -17,43 +32,43 @@ def remove_watermark(image_path, mask_path, max_dim, reg_noise, input_depth, lr,
         num_channels_down = [128] * 5,
         num_channels_up = [128] * 5,
         num_channels_skip = [128] * 5
-    ).type(DTYPE)
+    ).type(DTYPE).to(device)
 
-    objective = torch.nn.MSELoss().type(DTYPE)
+    objective = torch.nn.MSELoss().type(DTYPE).to(device)
     optimizer = optim.Adam(generator.parameters(), lr)
 
-    image_var = np_to_torch_array(image_np).type(DTYPE)
-    mask_var = np_to_torch_array(mask_np).type(DTYPE)
+    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)
+    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)
+    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)
 
@@ -62,4 +77,4 @@ def remove_watermark(image_path, mask_path, max_dim, reg_noise, input_depth, lr,
     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)
+    pil_image.save(output_path)