我遵循Keras文档中的GradCam示例https://keras.io/examples/vision/grad_cam/并希望对其进行修改，以便它可以处理一批图像，而不是一次只处理一个图像。
我已经能够做到这一点，但不得不使用对tf.map_fn的调用，我希望去掉它以提高性能。
到目前为止我的进展(Google Coolab的完整代码(：
#https://keras.io/examples/vision/grad_cam/
def make_gradcam_heatmap(grad_model, images, pred_index=None):
images = tf.cast(images, tf.float32)
# Then, we compute the gradient of the top predicted class for our input image
# with respect to the activations of the last conv layer
with tf.GradientTape() as tape:
tape.watch(images)
last_conv_layer_output, preds = grad_model(images)
if pred_index is None:
pred_index = tf.argmax(preds[0])
class_channel = preds[:, pred_index]
# This is the gradient of the output neuron (top predicted or chosen)
# with regard to the output feature map of the last conv layer
grads = tape.gradient(class_channel, last_conv_layer_output)
assert grads is not None, "GradientTape returned gradients=None"
# This is a vector where each entry is the mean intensity of the gradient
# over a specific feature map channel
pooled_grads = tf.reduce_mean(grads, axis=(1, 2))
# We multiply each channel in the feature map array
# by "how important this channel is" with regard to the top predicted class
# then sum all the channels to obtain the heatmap class activation
def single_image(index):
return last_conv_layer_output[index] @ pooled_grads[index][tf.newaxis, ..., tf.newaxis]
heatmaps = tf.map_fn(single_image, tf.range(tf.shape(grads)[0]), dtype=tf.float32)
# normalize the whole batch to [0, 1]
heatmaps -= tf.math.reduce_min(heatmaps, axis=(0,1,2,3))
heatmaps /= tf.math.reduce_max(heatmaps, axis=(0,1,2,3))
return heatmaps
有没有办法重写这个代码，使其不使用tf.map_fn？
def single_image(index):
return last_conv_layer_output[index] @ pooled_grads[index][tf.newaxis, ..., tf.newaxis]
heatmaps = tf.map_fn(single_image, tf.range(tf.shape(grads)[0]), dtype=tf.float32)
您可以使用matmul、
heatmaps = tf.matmul(last_conv_layer_output, pooled_grads[:,tf.newaxis, :, tf.newaxis])