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プログラムを全文記載しました。
test
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@@ -28,13 +28,97 @@
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```python3
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def target_category_loss(x, category_index, nb_classes):
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return tf.multiply(x, K.one_hot([category_index], nb_classes))
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def target_category_loss_output_shape(input_shape):
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return input_shape
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def normalize(x):
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return x / (K.sqrt(K.mean(K.square(x))) + 1e-5)
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def load_image(path):
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img_path = path
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img = image.load_img(img_path, target_size=(224, 224))
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x = image.img_to_array(img)
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x = np.expand_dims(x, axis=0)
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x = preprocess_input(x)
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return x
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def register_gradient():
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if "GuidedBackProp" not in ops._gradient_registry._registry:
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@ops.RegisterGradient("GuidedBackProp")
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def _GuidedBackProp(op, grad):
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dtype = op.inputs[0].dtype
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return grad * tf.cast(grad > 0., dtype) * \
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tf.cast(op.inputs[0] > 0., dtype)
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def compile_saliency_function(model, activation_layer='block5_conv3'):
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input_img = model.input
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layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])
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layer_output = layer_dict[activation_layer].output
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max_output = K.max(layer_output, axis=3)
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saliency = K.gradients(K.sum(max_output), input_img)[0]
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return K.function([input_img, K.learning_phase()], [saliency])
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def modify_backprop(model, name):
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g = tf.get_default_graph()
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with g.gradient_override_map({'Relu': name}):
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new_model = VGG16(weights='imagenet')
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return new_model
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def deprocess_image(x):
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if np.ndim(x) > 3:
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x = np.squeeze(x)
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x = np.squeeze(x)
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# normalize tensor: center on 0., ensure std is 0.1
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x -= x.mean()
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# clip to [0, 1]
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x += 0.5
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x = np.clip(x, 0, 1)
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# convert to RGB array
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x *= 255
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if K.image_dim_ordering() == 'tf':
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def grad_cam(input_model, image, category_index, layer_name):
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nb_classes = 1000
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target_layer = lambda x: target_category_loss(x, category_index, nb_classes)
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x = input_model.layers[-1].output
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x = Lambda(target_layer, output_shape=target_category_loss_output_shape)(x)
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model = keras.models.Model(input_model.layers[0].input, x)
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loss = K.sum(model.layers[-1].output)
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conv_output = [l for l in model.layers if l.name is layer_name][0].output
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grads = normalize(K.gradients(loss, conv_output)[0])
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gradient_function = K.function([model.layers[0].input], [conv_output, grads])
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output, grads_val = gradient_function([image])
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output, grads_val = output[0, :], grads_val[0, :, :, :]
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weights = np.mean(grads_val, axis = (0, 1))
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cam = np.ones(output.shape[0 : 2], dtype = np.float32)
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for i, w in enumerate(weights):
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cam += w * output[:, :, i]
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cam = cv2.resize(cam, (224, 224))
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cam = np.maximum(cam, 0)
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heatmap = cam / np.max(cam)
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image = image[0, :]
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image -= np.min(image)
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image = np.minimum(image, 255)
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cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
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cam = np.float32(cam) + np.float32(image)
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cam = 255 * cam / np.max(cam)
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return np.uint8(cam), heatmap
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preprocessed_input = load_image("./dog_cat.jpg")
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model = VGG16(weights='imagenet')
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predictions = model.predict(preprocessed_input)
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top_1 = decode_predictions(predictions)[0][0]
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print('Predicted class:')
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print('%s (%s) with probability %.2f' % (top_1[1], top_1[0], top_1[2]))
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predicted_class = np.argmax(predictions)
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print(predicted_class)
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cam, heatmap = grad_cam(model, preprocessed_input, predicted_class, "block5_conv3")
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cv2.imwrite("gradcam.jpg", cam)
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register_gradient()
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guided_model = modify_backprop(model, 'GuidedBackProp')
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gradcam = saliency[0] * heatmap[..., np.newaxis]
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cv2.imwrite("guied_gradcam.jpg", deprocess_image(gradcam))
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cv2.imwrite("guided_gradcam.jpg", deprocess_image(gradcam))
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