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grad_cam_tf2.py
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grad_cam_tf2.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jan 12 23:56:12 2022
@author: SUN Qinggang
E-mail: [email protected]
It works with tensorflow 2.1.0, not works with tensorflow 1.12.0 and keras 2.2.4,
you may creat a new enviroment and only run this script, and run others with tensorflow 1.12.0 and keras 2.2.4.
References:
https://github.com/keras-team/keras-io/blob/5307440674625f9201e7faa9a060984d9ac44a87/examples/vision/grad_cam.py#L65
Title: Grad-CAM class activation visualization
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2020/04/26
Last modified: 2021/03/07
Description: How to obtain a class activation heatmap for an image classification model.
Adapted from Deep Learning with Python (2017).
"""
import keras_resnet
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from prepare_data_shipsear_recognition_mix_s0tos3 import read_data
import tensorflow as tf
def prepare_input(img_array):
if img_array.ndim == 2:
img_array = np.expand_dims(img_array, axis=0) # (1, height, wide)
img_array = np.expand_dims(img_array, axis=-1) # (1, height, wide, 1)
return img_array
def build_model_gradcam(num_model, id1, id2, od, fname_weights):
inputs = tf.keras.layers.Input(shape=(id1, id2, 1))
if num_model in (120, 12, 13, 10): # ResNet
block = keras_resnet.blocks.basic_2d
parameters = {"kernel_initializer": "he_normal"}
classes = od
assert classes > 0
freeze_bn = False
dropout_fc = None
numerical_names = None
# output_activation = 'sigmoid'
output_activation = 'linear'
if num_model == 12: # ResNet 18
blocks = [2, 2, 2, 2]
axis = 3 if tf.keras.backend.image_data_format() == "channels_last" else 1
if numerical_names is None:
numerical_names = [True] * len(blocks)
x = tf.keras.layers.Conv2D(64, (7, 7), strides=(2, 2), use_bias=False,
name="conv1", padding="same", **parameters)(inputs)
x = keras_resnet.layers.BatchNormalization(axis=axis, epsilon=1e-5, freeze=freeze_bn, name="bn_conv1")(x)
x = tf.keras.layers.Activation("relu", name="conv1_relu")(x)
x = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same", name="pool1")(x)
features = 64
for stage_id, iterations in enumerate(blocks):
for block_id in range(iterations):
x = block(
features,
stage_id,
block_id,
numerical_name=(block_id > 0 and numerical_names[stage_id]),
freeze_bn=freeze_bn,
parameters=parameters
)(x)
features *= 2
last_conv_layer = x
x_ = tf.keras.layers.GlobalAveragePooling2D(name="pool5")(x)
x_ = tf.keras.layers.Dense(classes, activation=output_activation, name=f'fc{classes}')(x_)
if dropout_fc:
x_ = tf.keras.layers.Dropout(dropout_fc)(x_)
# of the last conv layer as well as the output predictions
grad_model = tf.keras.models.Model(inputs=inputs, outputs=[last_conv_layer, x_])
print(grad_model.summary())
if fname_weights:
grad_model.load_weights(fname_weights)
return grad_model
def make_gradcam_heatmap(inputs, grad_model, pred_index=None):
# 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:
tensor_inputs = tf.convert_to_tensor(inputs)
tape.watch(tensor_inputs)
last_conv_layer_output, preds = grad_model(tensor_inputs)
print(f'last_conv_layer_output {last_conv_layer_output}')
print(f'preds {preds}')
tape.watch(last_conv_layer_output)
if pred_index is None:
pred_index = tf.argmax(preds[0])
class_channel = preds[:, pred_index] # loss
tape.watch(class_channel)
print(f'class_channel {class_channel}')
# 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)
print(f'grads {grads}')
# 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=(0, 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
last_conv_layer_output = last_conv_layer_output[0]
heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
heatmap = tf.squeeze(heatmap)
# For visualization purpose, we will also normalize the heatmap between 0 & 1
heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
return heatmap.numpy()
def save_and_display_gradcam(img, heatmap, fname_heatmap, fname_superimposed, alpha=0.4):
img = np.squeeze(img)
heatmap = np.uint8(255 * heatmap)
# Use jet colormap to colorize heatmap
jet = cm.get_cmap("jet")
# Use RGB values of the colormap
jet_colors = jet(np.arange(256))[:, :3]
jet_heatmap = jet_colors[heatmap]
# Create an image with RGB colorized heatmap
jet_heatmap = tf.keras.preprocessing.image.array_to_img(jet_heatmap)
jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
jet_heatmap.save(fname_heatmap)
jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap)
# # Superimpose the heatmap on original image
# img = tf.keras.preprocessing.image.img_to_array(img)
# superimposed_img = jet_heatmap * alpha + img
# superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img)
# # Save the superimposed image
# superimposed_img.save(fname_superimposed)
if __name__ == '__main__':
from file_operation import mkdir
path_data_root = 'C:/data/shipsEar/multiple_class/10547_10547/s0tos3/mix_1to3'
path_data = path_data_root + '/magspectrum_264_66/s_hdf5'
path_result = 'E:/BaiduYunTongbu/save_result/shipsEar/result_recognition_mix'
path_model = path_result + '/magspectrum_264_66_or_rand/model_12_1_3_bs256'
path_save = os.path.join(path_model, 'save_heatmap')
mkdir(path_save)
def compute_and_save_heatmap(path_data, file_name, num_data, path_model, num_model, pred_index, path_save):
data = np.asarray(read_data(path_data, file_name)[num_data])
img_array = prepare_input(data)
img_size = data.shape # (158, 133)
fname_weights = os.path.join(path_model, 'weights_1_n3_100_86_0.95.hdf5')
# First, we create a model that maps the input image to the activations
# of the last conv layer as well as the output predictions
grad_model = build_model_gradcam(num_model, img_size[0], img_size[1], 3, fname_weights)
# Generate class activation heatmap
heatmap = make_gradcam_heatmap(img_array, grad_model, pred_index=pred_index)
# Display heatmap
plt.matshow(heatmap)
if path_save:
path_save_num = os.path.join(path_save, str(num_data))
mkdir(path_save_num)
fname_heatmap_class = os.path.join(path_save_num,
f'{file_name[:-5]}_num_{num_data}_class_{pred_index}_heatmap_class')
plt.savefig(f'{fname_heatmap_class}.eps')
plt.savefig(f'{fname_heatmap_class}.svg')
fname_heatmap = os.path.join(path_save_num,
f'{file_name[:-5]}_num_{num_data}_class_{pred_index}_grad_cam_heatmap.eps')
fname_superimposed = os.path.join(path_save_num,
f'{file_name[:-5]}_num_{num_data}_class_{pred_index}_grad_cam_superimposed.eps')
save_and_display_gradcam(img_array, heatmap, fname_heatmap, fname_superimposed, alpha=0.4)
compute_and_save_heatmap(path_data, 's_1.hdf5', 4999, path_model, 12, 0, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1.hdf5', 4999, path_model, 12, 1, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1.hdf5', 4999, path_model, 12, 2, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_2.hdf5', 4999, path_model, 12, 0, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_2.hdf5', 4999, path_model, 12, 1, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_2.hdf5', 4999, path_model, 12, 2, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_3.hdf5', 4999, path_model, 12, 0, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_3.hdf5', 4999, path_model, 12, 1, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_3.hdf5', 4999, path_model, 12, 2, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_2.hdf5', 4999, path_model, 12, 0, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_2.hdf5', 4999, path_model, 12, 1, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_2.hdf5', 4999, path_model, 12, 2, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_3.hdf5', 4999, path_model, 12, 0, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_3.hdf5', 4999, path_model, 12, 1, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_3.hdf5', 4999, path_model, 12, 2, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_2_3.hdf5', 4999, path_model, 12, 0, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_2_3.hdf5', 4999, path_model, 12, 1, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_2_3.hdf5', 4999, path_model, 12, 2, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_2_3.hdf5', 4999, path_model, 12, 0, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_2_3.hdf5', 4999, path_model, 12, 1, 'res5b1_relu', path_save)
compute_and_save_heatmap(path_data, 's_1_2_3.hdf5', 4999, path_model, 12, 2, 'res5b1_relu', path_save)