Update app.py

app.py

@@ -16,6 +16,34 @@ label=gr.outputs.Label()
 examples=["filibe-1-1.jpg",
           "ohrid-3-1.jpg",
           "varna-1-1.jpg"]
+def gradcam(img_create, learn):
+    x,= first(dls_augmented.test_dl([img]))
+    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()
 
-intf=gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
+    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)
+    gcam=cam_map[1].detach().cpu()
+    x_dec = TensorImage(dls_augmented.train.decode((x,))[0][0])
+    _,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 gcam
+gcam=gradcam(learn_inf,learn)
+demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
+
+demo=gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)
+demo.launch(inline=False)