def vis_grad(model, class_index, layer, image_path, size=[224, 224]):
original_image = cv2.imread(image_path, 1)
#plt.imshow(original_image)
#plt.show()
prep_img = preprocess_image(original_image, size)
file_name_to_export = 'model' + '_classindex_' + str(
class_index) + '-layer_' + str(layer)
# Grad cam
gcv2 = GradCam(model, target_layer=layer)
# Generate cam mask
cam = gcv2.generate_cam(prep_img, class_index, size)
print('Grad cam completed')
# Guided backprop
GBP = GuidedBackprop(model)
# Get gradients
guided_grads = GBP.generate_gradients(prep_img, class_index)
print('Guided backpropagation completed')
# Guided Grad cam
cam_gb = guided_grad_cam(cam, guided_grads)
#save_gradient_images(cam_gb, file_name_to_export + '_GGrad_Cam')
grayscale_cam_gb = convert_to_grayscale(cam_gb)
#save_gradient_images(grayscale_cam_gb, file_name_to_export + '_GGrad_Cam_gray')
print('Guided grad cam completed')
cam_gb = trans(cam_gb)
grayscale_cam_gb = trans(grayscale_cam_gb)
return cam_gb, grayscale_cam_gb
def vis_gradcam(model, class_index, layer, image_path, size=[224, 224]):
original_image = cv2.imread(image_path, 1)
#plt.imshow(original_image)
#plt.show()
prep_img = preprocess_image(original_image, size)
file_name_to_export = 'model' + '_classindex_' + str(
class_index) + '-layer_' + str(layer)
# Grad cam
gcv2 = GradCam(model, target_layer=layer)
# Generate cam mask
cam = gcv2.generate_cam(prep_img, class_index, size)
print('Grad cam completed')
#save_class_activation_on_image(original_image, cam, file_name_to_export)
img_with_heatmap, activation_heatmap = act_on_img(original_image, cam,
size)
return cam, activation_heatmap, img_with_heatmap
"""
cam_gb = np.multiply(grad_cam_mask, guided_backprop_mask)
return cam_gb
if __name__ == '__main__':
# Get params
target_example = 0 # Snake
(original_image, prep_img, target_class, file_name_to_export, pretrained_model) =\
get_example_params(target_example)
# Grad cam
# gcv2 = GradCam(pretrained_model, target_layer=11)
gcv2 = GradCam(pretrained_model, target_layer=7)
# Generate cam mask
cam = gcv2.generate_cam(prep_img, target_class)
print('Grad cam completed')
# Guided backprop
GBP = GuidedBackprop(pretrained_model)
# Get gradients
guided_grads = GBP.generate_gradients(prep_img, target_class)
print('Guided backpropagation completed')
# Guided Grad cam
cam_gb = guided_grad_cam(cam, guided_grads)
save_gradient_images(cam_gb, file_name_to_export + '_GGrad_Cam')
grayscale_cam_gb = convert_to_grayscale(cam_gb)
save_gradient_images(grayscale_cam_gb,
file_name_to_export + '_GGrad_Cam_gray')
print('Guided grad cam completed')
# print(pretrained_model)
probs, label = predict2(image_path, pretrained_model.to(device))
print(os.path.basename(image_path))
print(probs)
# print(*label, sep=", ")
print([idx_to_class[idx] for idx in label], sep=", ")
print("-" * 60 + "\n")
# gradcam
original_image = PIL.Image.open(image_path).convert('RGB')
prep_img = process_image(original_image).unsqueeze(0)
crop_image = only_crop_image(original_image)
target_class = label[0]
file_name_to_export = "butterfly_gradcam"
# Grad cam
grad_cam = GradCam(pretrained_model, target_layer=target_layer)
# Generate cam mask
cam = grad_cam.generate_cam(prep_img, target_class)
# Save mask
save_class_activation_images(
crop_image, cam,
os.path.join('heatmaps',
os.path.splitext(os.path.basename(model_dir))[0]),
"{}_{}_{:.2f}".format(
os.path.splitext(os.path.basename(image_path))[0],
idx_to_class[label[0]], probs[0]))
guided_backprop_mask (np_arr):Guided backprop mask
"""
cam_gb = np.multiply(grad_cam_mask, guided_backprop_mask)
return cam_gb
if __name__ == '__main__':
# Get params
target_example = 0 # Snake
(original_image, prep_img, target_class, file_name_to_export, pretrained_model) =\
get_params(target_example)
# Grad cam
gcv2 = GradCam(pretrained_model, target_layer=11)
# Generate cam mask
cam = gcv2.generate_cam(prep_img, target_class)
print('Grad cam completed')
# Guided backprop
GBP = GuidedBackprop(pretrained_model)
# Get gradients
guided_grads = GBP.generate_gradients(prep_img, target_class)
print('Guided backpropagation completed')
# Guided Grad cam
cam_gb = guided_grad_cam(cam, guided_grads)
save_gradient_images(cam_gb, file_name_to_export + '_GGrad_Cam')
grayscale_cam_gb = convert_to_grayscale(cam_gb)
save_gradient_images(grayscale_cam_gb, file_name_to_export + '_GGrad_Cam_gray')
print('Guided grad cam completed')