Gradcam with guided backprop for transfer learning in Tensorflow 2.0

Question

I get an error using gradient visualization with transfer learning in TF 2.0. The gradient visualization works on a model that does not use transfer learning.

When I run my code I get the error:

    assert str(id(x)) in tensor_dict, 'Could not compute output ' + str(x)
AssertionError: Could not compute output Tensor("block5_conv3/Identity:0", shape=(None, 14, 14, 512), dtype=float32)

When I run the code below it errors. I think there's an issue with the naming conventions or connecting inputs and outputs from the base model, vgg16, to the layers I'm adding. Really appreciate your help!

"""
Broken example when grad_model is created. 
"""
!pip uninstall tensorflow
!pip install tensorflow==2.0.0
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
import matplotlib.pyplot as plt

IMAGE_PATH = '/content/cat.3.jpg'
LAYER_NAME = 'block5_conv3'
model_layer = 'vgg16'
CAT_CLASS_INDEX = 281

imsize = (224,224,3)

img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224))
plt.figure()
plt.imshow(img)
img = tf.io.read_file(IMAGE_PATH)
img = tf.image.decode_jpeg(img)
img = tf.cast(img, dtype=tf.float32)
# img = tf.keras.preprocessing.image.img_to_array(img)
img = tf.image.resize(img, (224,224))
img = tf.reshape(img, (1, 224,224,3))

input = layers.Input(shape=(imsize[0], imsize[1], imsize[2]))
base_model = tf.keras.applications.VGG16(include_top=False, weights='imagenet',
                                          input_shape=(imsize[0], imsize[1], imsize[2]))
# base_model.trainable = False
flat = layers.Flatten()
dropped = layers.Dropout(0.5)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()

fc1 = layers.Dense(16, activation='relu', name='dense_1')
fc2 = layers.Dense(16, activation='relu', name='dense_2')
fc3 = layers.Dense(128, activation='relu', name='dense_3')
prediction = layers.Dense(2, activation='softmax', name='output')
for layr in base_model.layers:
    if ('block5' in layr.name):

        layr.trainable = True
    else:
        layr.trainable = False

x = base_model(input)
x = global_average_layer(x)
x = fc1(x)
x = fc2(x)
x = prediction(x)

model = tf.keras.models.Model(inputs = input, outputs = x)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
                  loss='binary_crossentropy',
                  metrics=['accuracy'])

This portion of the code is where the error lies. I'm not sure what is the correct way to label inputs and outputs.

# Create a graph that outputs target convolution and output
grad_model = tf.keras.models.Model(inputs = [model.input, model.get_layer(model_layer).input], 
                                   outputs=[model.get_layer(model_layer).get_layer(LAYER_NAME).output,
                                            model.output])

print(model.get_layer(model_layer).get_layer(LAYER_NAME).output)
# Get the score for target class

# Get the score for target class
with tf.GradientTape() as tape:
    conv_outputs, predictions = grad_model(img)
    loss = predictions[:, 1]

The section below is for plotting a heatmap of gradcam.

print('Prediction shape:', predictions.get_shape())
# Extract filters and gradients
output = conv_outputs[0]
grads = tape.gradient(loss, conv_outputs)[0]

# Apply guided backpropagation
gate_f = tf.cast(output > 0, 'float32')
gate_r = tf.cast(grads > 0, 'float32')
guided_grads = gate_f * gate_r * grads

# Average gradients spatially
weights = tf.reduce_mean(guided_grads, axis=(0, 1))

# Build a ponderated map of filters according to gradients importance
cam = np.ones(output.shape[0:2], dtype=np.float32)

for index, w in enumerate(weights):
    cam += w * output[:, :, index]

# Heatmap visualization
cam = cv2.resize(cam.numpy(), (224, 224))
cam = np.maximum(cam, 0)
heatmap = (cam - cam.min()) / (cam.max() - cam.min())

cam = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)

output_image = cv2.addWeighted(cv2.cvtColor(img.astype('uint8'), cv2.COLOR_RGB2BGR), 0.5, cam, 1, 0)

plt.figure()
plt.imshow(output_image)
plt.show()

I also asked this to the tensorflow team on github at https://github.com/tensorflow/tensorflow/issues/37680.

Schoonover

Answer 1

I figured it out. If you set up the model extending the vgg16 base model with your own layers, rather than inserting the base model into a new model like a layer, then it works. First set up the model and be sure to declare the input_tensor.

inp = layers.Input(shape=(imsize[0], imsize[1], imsize[2]))
base_model = tf.keras.applications.VGG16(include_top=False, weights='imagenet', input_tensor=inp,
                                          input_shape=(imsize[0], imsize[1], imsize[2]))

This way we don't have to include a line like x=base_model(inp) to show what input we want to put in. That's already included in tf.keras.applications.VGG16(...).

Instead of putting this vgg16 base model inside another model, it's easier to do gradcam by adding layers to the base model itself. I grab the output of the last layer of VGG16 (with the top removed), which is the pooling layer.

block5_pool = base_model.get_layer('block5_pool')
x = global_average_layer(block5_pool.output)
x = fc1(x)
x = prediction(x)

model = tf.keras.models.Model(inputs = inp, outputs = x)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
                  loss='binary_crossentropy',
                  metrics=['accuracy'])

Now, I grab the layer for visualization, LAYER_NAME='block5_conv3'.

# Create a graph that outputs target convolution and output
grad_model = tf.keras.models.Model(inputs = [model.input], 
                                   outputs=[model.output, model.get_layer(LAYER_NAME).output])

print(model.get_layer(LAYER_NAME).output)
# Get the score for target class

# Get the score for target class
with tf.GradientTape() as tape:
    predictions, conv_outputs = grad_model(img)
    loss = predictions[:, 1]
print('Prediction shape:', predictions.get_shape())
# Extract filters and gradients
output = conv_outputs[0]
grads = tape.gradient(loss, conv_outputs)[0]

Schoonover

Answer 2

We (I plus a number of team members developing a project) found a similar problem with a code implementing Grad-CAM that we found in a tutorial.

That code didn't work with a model consisting of the base model of VGG19 plus a few extra layers added on top of it. The problem was that the VGG19 base model was inserted as a "layer" inside our model, and apparently the GradCAM code didn't know how to deal with it - we were getting a "Graph disconnected..." error. Then after some debugging (carried out by another team member, not me) we managed to modify the original code to make it work for this kind of model that contains another model inside it. The idea is to add the inner model as an extra argument of the class GradCAM. Since this may be helpful to others I am including the modified code below (we also renamed the GradCAM class as My_GradCAM).

class My_GradCAM:
    def __init__(self, model, classIdx, inner_model=None, layerName=None):
        self.model = model
        self.classIdx = classIdx
        self.inner_model = inner_model
        if self.inner_model == None:
            self.inner_model = model
        self.layerName = layerName 

[...]

        gradModel = tensorflow.keras.models.Model(inputs=[self.inner_model.inputs],
                  outputs=[self.inner_model.get_layer(self.layerName).output,
                  self.inner_model.output])                                   

Then the class can be instantiated by adding the inner model as the extra argument, e.g.:

cam = My_GradCAM(model, None, inner_model=model.get_layer("vgg19"), layerName="block5_pool")

I hope this helps.

Edit: Credit to Mirtha Lucas for doing the debugging and finding the solution.

Thebault

I had the same problem as mentioned in this solution ("Graph disconnected..."), and this worked perfectly! I was fine-tuning an Xception model btw. Thank you! – Dyun

Answer 3

After a lot of struggle, I condense the way to draw the heat map when you are using transfer learning. Here is the keras official tutorial

The issue I encounter is that when I'm trying to draw the heat map from my model, the densenet can be only seen as functional layer in my model. So the make_gradcam_heatmap can not figure out the layer that inside functional layer. As the 5th layer shows.

Therefore, to simulate the Keras official document, I need to only use the densenet as the model for visualization. Here is the step

1. Only Take out the model from your model

   dense_model = dense_model.get_layer('densenet121')
   

2. Copy the weight from dense model to your new initiated model

   inputs = tf.keras.Input(shape=(224, 224, 3))
   model = model_builder(weights="imagenet", include_top=True, input_tensor=inputs)
   for layer, dense_layer in zip(model.layers[1:], dense_model.layers[1:]):
       layer.set_weights(dense_layer.get_weights())
   
   relu = model.get_layer('relu')
   x = tf.keras.layers.GlobalAveragePooling2D()(relu.output)
   outputs = tf.keras.layers.Dense(5)(x)
   model = tf.keras.models.Model(inputs = inputs, outputs = outputs)
   

3. Draw the heat map

   preprocess_input = keras.applications.densenet.preprocess_input
   img_array = preprocess_input(get_img_array(img_path, size=(224, 224)))
   heatmap = make_gradcam_heatmap(img_array, model, 'bn')
   plt.matshow(heatmap)
   plt.show()
   

4. get_img_array, make_gradcam_heatmap and save_and_display_gradcam are kept in still. Follow the keras tutorial then you are good to go.

Equestrienne

how do I implement your solution in my code? IMG_SHAPE = IMG_SIZE + (3,) base_model = tf.keras.applications.ResNet50(input_shape=IMG_SHAPE, include_top=False, weights='imagenet') inputs = tf.keras.Input(shape=(224, 224, 3)) x = data_augmentation(inputs) x = preprocess_input(x) x = base_model(x, training=True) x = global_average_layer(x) x = tf.keras.layers.Dropout(0.2)(x) outputs = prediction_layer(x) model = tf.keras.Model(inputs, outputs) – Mesoblast