[GitHub] indhub closed pull request #13196: [Example] Fixing Gradcam implementation

[GitBox <gi...@apache.org>]

indhub closed pull request #13196: [Example] Fixing Gradcam implementation
URL: https://github.com/apache/incubator-mxnet/pull/13196
 
 
   

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diff --git a/docs/tutorials/vision/cnn_visualization.md b/docs/tutorials/vision/cnn_visualization.md
index 940c261efc8..a350fffaa36 100644
--- a/docs/tutorials/vision/cnn_visualization.md
+++ b/docs/tutorials/vision/cnn_visualization.md
@@ -151,7 +151,8 @@ def show_images(pred_str, images):
     for i in range(num_images):
         fig.add_subplot(rows, cols, i+1)
         plt.xlabel(titles[i])
-        plt.imshow(images[i], cmap='gray' if i==num_images-1 else None)
+        img = images[i].astype(np.uint8)
+        plt.imshow(img, cmap='gray' if i==num_images-1 else None)
     plt.show()
 ```
 
diff --git a/example/cnn_visualization/gradcam.py b/example/cnn_visualization/gradcam.py
index a8708f78758..54cb65eef11 100644
--- a/example/cnn_visualization/gradcam.py
+++ b/example/cnn_visualization/gradcam.py
@@ -249,8 +249,8 @@ def visualize(net, preprocessed_img, orig_img, conv_layer_name):
     imggrad = get_image_grad(net, preprocessed_img)
     conv_out, conv_out_grad = get_conv_out_grad(net, preprocessed_img, conv_layer_name=conv_layer_name)
 
-    cam = get_cam(imggrad, conv_out)
-    
+    cam = get_cam(conv_out_grad, conv_out)
+    cam = cv2.resize(cam, (imggrad.shape[1], imggrad.shape[2]))
     ggcam = get_guided_grad_cam(cam, imggrad)
     img_ggcam = grad_to_image(ggcam)
     
diff --git a/example/cnn_visualization/vgg.py b/example/cnn_visualization/vgg.py
index b6215a334e3..a8a0ef6c8de 100644
--- a/example/cnn_visualization/vgg.py
+++ b/example/cnn_visualization/vgg.py
@@ -72,11 +72,17 @@ def get_vgg(num_layers, pretrained=False, ctx=mx.cpu(),
             root=os.path.join('~', '.mxnet', 'models'), **kwargs):
     layers, filters = vgg_spec[num_layers]
     net = VGG(layers, filters, **kwargs)
-    if pretrained:
-        from mxnet.gluon.model_zoo.model_store import get_model_file
-        batch_norm_suffix = '_bn' if kwargs.get('batch_norm') else ''
-        net.load_params(get_model_file('vgg%d%s'%(num_layers, batch_norm_suffix),
-                                       root=root), ctx=ctx)
+    net.initialize(ctx=ctx)
+    
+    # Get the pretrained model
+    vgg = mx.gluon.model_zoo.vision.get_vgg(num_layers, pretrained=True, ctx=ctx)
+    
+    # Set the parameters in the new network
+    params = vgg.collect_params()
+    for key in params:
+        param = params[key]
+        net.collect_params()[net.prefix+key.replace(vgg.prefix, '')].set_data(param.data())
+   
     return net
 
 def vgg16(**kwargs):


 

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