Update app.py

app.py

@@ -7,6 +7,52 @@ class Hook():
 
 learn = load_learner("resnet152_fit_one_cycle_freeze_91acc.pkl", cpu=True)
 
+#@title DataLoader
+path = "/content/drive/My Drive/arc_project/dataset/train_val_cropped"
+dblock = DataBlock(blocks = (ImageBlock, CategoryBlock),
+                   get_items = get_image_files,
+                   splitter = GrandparentSplitter(train_name="train",
+                                                  valid_name="valid"),
+                   get_y=parent_label,
+                   item_tfms=RandomResizedCrop(128, min_scale=0.7),
+                   batch_tfms=[*aug_transforms(),
+                   Normalize.from_stats(*imagenet_stats)])
+dls_augmented = dblock.dataloaders(path, shuffle=True)
+dls_augmented.train.show_batch(max_n=8, nrows=2, figsize=(28,10))
+
+class Hook():
+  def hook_func(self, m, i, o): self.stored = o.detach().clone()
+
+def gradcam(img_create):
+
+  pred,idx,probs=learn.predict(img_create)
+  x,= first(dls_augmented.test_dl([img_create]))
+  hook_output = Hook()
+  hook = learn.model[0].register_forward_hook(hook_output.hook_func)
+  with torch.no_grad(): output = learn.model.eval()(x)
+  act = hook_output.stored[0]
+  hook.remove()
+
+  input_size=act.shape[0]
+  out_size=learn.model[1][-1].in_features
+  kernel_size=act.shape[1]
+  a=act
+  new_act=tensor(np.zeros((out_size,kernel_size,kernel_size)))
+  sum=tensor(np.zeros((1,kernel_size,kernel_size)))
+  for i in range(0,input_size,4):
+    sum=tensor(np.zeros((1,kernel_size,kernel_size)))
+    for j in range(i,i+4):
+      sum=sum+act[j,:,:]
+    new_act[int(i/4),:,:]=sum/4
+  cam_map = torch.einsum('ck,kij->cij', learn.model[1][-1].weight, new_act)
+
+  x_dec = TensorImage(dls_augmented.train.decode((x,))[0][0])
+  fig,ax = plt.subplots()
+  x_dec.show(ctx=ax)
+  ax.imshow(cam_map[1].detach().cpu(), alpha=0.6, extent=(0,128,128,0), interpolation='bilinear', cmap='magma');
+
+  return dict(zip(categories, map(float, probs))), fig
+
 categories = ('arbanasi', 'filibe', 'gjirokoster', 'iskodra', 'kula', 'kuzguncuk', 'larissa_ampelakia', 'mardin', 'ohrid', 'pristina', 'safranbolu', 'selanik', 'sozopol_suzebolu', 'tiran', 'varna')
 def classify_img(img):
   pred,idx,probs=learn.predict(img)
@@ -23,6 +69,6 @@ examples=["filibe-1-1.jpg",
           "varna-1-1.jpg"]
 
 
-demo = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)
+demo = gr.Interface(fn=gradcam, inputs=image, outputs=[label, image], examples=examples)
 
 demo.launch(inline=False)