Making Class Activation Map (CAM) for EfficientNetB3 architecture - tensorflow

Question

I would like to draw a class activation map for a model built upon EfficeintNet B3. But when I follow different tutorials and codes from different sources, it simply fails....
#load images
img = tf.keras.preprocessing.image.load_img(
base, target_size=(img_height, img_width))
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
last_conv = model.layers[2].layers[-3]
grad_model = tf.keras.models.Model(
[model.inputs], [last_conv.output, model.output])
Can't build a grad_model
ValueError: Graph disconnected: cannot obtain value for tensor
KerasTensor(type_spec=TensorSpec(shape=(None, 300, 300, 3),
dtype=tf.float32, name='input_1'), name='input_1',
description="created by layer 'input_1'") at layer "stem_conv". The
following previous layers were accessed without issue: []
This is the model:
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
sequential (Sequential) (None, 300, 300, 3) 0
_________________________________________________________________
rescaling (Rescaling) (None, 300, 300, 3) 0
_________________________________________________________________
efficientnet-b3 (Functional) (None, 10, 10, 1536) 10783528
_________________________________________________________________
global_average_pooling2d (Gl (None, 1536) 0
_________________________________________________________________
dropout (Dropout) (None, 1536) 0
_________________________________________________________________
dense (Dense) (None, 128) 196736
_________________________________________________________________
dense_1 (Dense) (None, 5) 645
=================================================================

Answer 1

To address the graph disconnected value error, you need to build the grad cam model properly. Here is one of the ways to build a model for grad-cam.
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
inputs = tf.keras.Input(shape=(300, 300, 3))
x = keras.applications.EfficientNetB3(
input_tensor=inputs, # pass input to input_tensor
include_top=False,
weights=None
)
# flat the base model with x.output
x = layers.GlobalAveragePooling2D()(x.output)
# others
x = layers.Dense(128)(x)
x = layers.Dropout(0.5)(x)
x = layers.Dense(5)(x)
model = keras.Model(inputs, x)
for i, layer in enumerate(model.layers[-10:]):
print(i, layer.name, layer.output_shape, layer.trainable)
0 block7b_project_bn (None, 10, 10, 384) True
1 block7b_drop (None, 10, 10, 384) True
2 block7b_add (None, 10, 10, 384) True
3 top_conv (None, 10, 10, 1536) True
4 top_bn (None, 10, 10, 1536) True
5 top_activation (None, 10, 10, 1536) True # < - We will pick this 2D maps
6 global_average_pooling2d_2 (None, 1536) True
7 dense_2 (None, 128) True
8 dropout (None, 128) True
9 dense_3 (None, 5) True
Build Grad-CAM Model
grad_model = keras.models.Model(
[model.inputs],
[
model.get_layer('top_activation').output,
model.output
]
)
Check
With your setup, you would get a disconnected error with the following code. But now, it wouldn't happen.
import numpy as np
image = np.random.rand(1, 300, 300, 3).astype(np.float32)
with tf.GradientTape() as tape:
convOutputs, predictions = grad_model(tf.cast(image, tf.float32))
loss = predictions[:, tf.argmax(predictions[0])]
grads = tape.gradient(loss, convOutputs)
print(grads.shape)
(1, 10, 10, 1536) # NO DISCONNECTED ERROR
To get the heatmaps from your grad-cam model, check the following answers and sources as references.
Grad-CAM
Guided-GradCAM
Grad-CAM mult-output Model
GradCAM-Callback
Swin-Transformer : GradCAM