new

app.py

@@ -1,37 +1 @@
-from torch.nn.functional import mse_loss
-import torch
-import dataprepare
-from model import VGG16
-def white_noise_img(model, original_image, content_image, epochs = 5):
-  #FIRST OF ALL REMOVE THE BATCH_SIZE FROM THE IMAGE
-    original_image = original_image.squeeze(0)
-  #GENERATE A NEW IMAGE AND TRACK THE PROCESS SO WE CAN PARAMETRIZE COMPUTING GRADIENTS
-    generated_image = torch.rand_like(original_image,requires_grad=True)
-    optimizer = torch.optim.Adam([generated_image], lr= 0.01)
-
-    for i in range(epochs):
-        optimizer.zero_grad()
-
-        content_result = model(content_image)
-        #style_result = model(original_image)
-        generated_result = model(generated_image)
-
-
-        content_loss = sum(mse_loss(generated_result["content"][l], content_result["content"][l]) for l in content_result["content"])
-        #style_loss = sum(#mse(gram(generated_image[style][l]), gram(style_image[l])) #for l in style_layers)
-        result_list = model(generated_image)
-        print(f"Loss at content type: {content_loss}")
-
-
-image = dataprepare.image_process('/content/drive/MyDrive/16546923557574.jpg')
-content_image = dataprepare.image_process('/content/drive/MyDrive/imagen2.png')
-print(image.shape)
-print(content_image.shape)
-image = image.view(1, image.shape[0],image.shape[1],image.shape[2])
-model = VGG16(num_features=5,num_classes=5)
-
-model = model.to("cpu")
-#result_list = model(image)
-#style_computing(result_list, model, image)
-#view_activations()
-white_noise_img(model,image,content_image)
+import train

main.py

@@ -1 +0,0 @@
-import app

train.py

@@ -0,0 +1,38 @@
+from torch.nn.functional import mse_loss
+import torch
+import dataprepare
+from model import VGG16
+
+def white_noise_img(model, original_image, content_image, epochs = 5):
+  #FIRST OF ALL REMOVE THE BATCH_SIZE FROM THE IMAGE
+    original_image = original_image.squeeze(0)
+  #GENERATE A NEW IMAGE AND TRACK THE PROCESS SO WE CAN PARAMETRIZE COMPUTING GRADIENTS
+    generated_image = torch.rand_like(original_image,requires_grad=True)
+    optimizer = torch.optim.Adam([generated_image], lr= 0.01)
+
+    for i in range(epochs):
+        optimizer.zero_grad()
+
+        content_result = model(content_image)
+        #style_result = model(original_image)
+        generated_result = model(generated_image)
+
+
+        content_loss = sum(mse_loss(generated_result["content"][l], content_result["content"][l]) for l in content_result["content"])
+        #style_loss = sum(#mse(gram(generated_image[style][l]), gram(style_image[l])) #for l in style_layers)
+        result_list = model(generated_image)
+        print(f"Loss at content type: {content_loss}")
+
+
+image = dataprepare.image_process('/content/drive/MyDrive/16546923557574.jpg')
+content_image = dataprepare.image_process('/content/drive/MyDrive/imagen2.png')
+print(image.shape)
+print(content_image.shape)
+image = image.view(1, image.shape[0],image.shape[1],image.shape[2])
+model = VGG16(num_features=5,num_classes=5)
+
+model = model.to("cpu")
+#result_list = model(image)
+#style_computing(result_list, model, image)
+#view_activations()
+white_noise_img(model,image,content_image)