Pulled optimizer.py

1508.06576/optimizer.py

+from loss import Loss
+from feature_maps import FeatureMaps
+import torch.optim as optim
+from torchvision.utils import save_image
+
+class Optimizer:
+    @staticmethod
+    def gradient_descent(content_img, style_img, content_img_clone):
+        '''
+        Fuction to apply gradient descent on the content image
+        @params
+        content_img: Original Image
+        style_img: Styling Image
+        content_img_clone: Copy of Original Image
+        Author: @gaurangathavale
+        '''
+        epoch = 1000
+        learning_rate = 0.001
+        alpha = 10
+        beta = 100
+
+        optimizer=optim.Adam([content_img_clone],lr=learning_rate)
+        print(optimizer)
+
+        for e in range(epoch):
+    
+            feature_maps = FeatureMaps()
+            content_fmaps = feature_maps.get_fmaps(content_img)
+            style_fmaps = feature_maps.get_fmaps(style_img)
+            # content_clone_fmaps = feature_maps.get_fmaps(content_img_clone)
+            content_white_noise_fmaps = feature_maps.get_fmaps(content_img, [21])
+            style_white_noise_fmaps = feature_maps.get_fmaps(style_img, [21])
+            
+            total_loss = Loss.total_loss(alpha, beta, content_fmaps, content_white_noise_fmaps, style_fmaps, style_white_noise_fmaps)
+            
+            # clears x.grad for every parameter x in the optimizer. 
+            # It’s important to call this before total_loss.backward(), otherwise it will accumulate the gradients from multiple passes.
+            optimizer.zero_grad() 
+            
+            # total_loss.backward() computes dtotal_loss/dx for every parameter x which has requires_grad=True
+            total_loss.backward()
+            
+            # Optimization Step / Update Rule
+            optimizer.step()
+            
+            if(not (e%100)):
+                print(total_loss)
+                
+                save_image(content_img_clone,"styled.png")
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