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1.ResNet18
ResNet18
from tensorflow import keras
from tensorflow.keras import layers
INPUT_SIZE = 224
CLASS_NUM = 1000
# stage_name=2,3,4,5; block_name=a,b,c
def ConvBlock(input_tensor, num_output, stride, stage_name, block_name):
filter1, filter2 = num_output
x = layers.Conv2D(filter1, 3, strides=stride, padding='same', name='res'+stage_name+block_name+'_branch2a')(input_tensor)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2a')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2a_relu')(x)
x = layers.Conv2D(filter2, 3, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2b')(x)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2b')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2b_relu')(x)
shortcut = layers.Conv2D(filter2, 1, strides=stride, padding='same', name='res'+stage_name+block_name+'_branch1')(input_tensor)
shortcut = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch1')(shortcut)
x = layers.add([x, shortcut], name='res'+stage_name+block_name)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_relu')(x)
return x
def IdentityBlock(input_tensor, num_output, stage_name, block_name):
filter1, filter2 = num_output
x = layers.Conv2D(filter1, 3, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2a')(input_tensor)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2a')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2a_relu')(x)
x = layers.Conv2D(filter2, 3, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2b')(x)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2b')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2b_relu')(x)
shortcut = input_tensor
x = layers.add([x, shortcut], name='res'+stage_name+block_name)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_relu')(x)
return x
def ResNet18(input_shape, class_num):
input = keras.Input(shape=input_shape, name='input')
# conv1
x = layers.Conv2D(64, 7, strides=(2, 2), padding='same', name='conv1')(input) # 7×7, 64, stride 2
x = layers.BatchNormalization(name='bn_conv1')(x)
x = layers.Activation('relu', name='conv1_relu')(x)
x = layers.MaxPooling2D((3, 3), strides=2, padding='same', name='pool1')(x) # 3×3 max pool, stride 2
# conv2_x
x = ConvBlock(input_tensor=x, num_output=(64, 64), stride=(1, 1), stage_name='2', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(64, 64), stage_name='2', block_name='b')
# conv3_x
x = ConvBlock(input_tensor=x, num_output=(128, 128), stride=(2, 2), stage_name='3', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(128, 128), stage_name='3', block_name='b')
# conv4_x
x = ConvBlock(input_tensor=x, num_output=(256, 256), stride=(2, 2), stage_name='4', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(256, 256), stage_name='4', block_name='b')
# conv5_x
x = ConvBlock(input_tensor=x, num_output=(512, 512), stride=(2, 2), stage_name='5', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(512, 512), stage_name='5', block_name='b')
# average pool, 1000-d fc, softmax
x = layers.AveragePooling2D((7, 7), strides=(1, 1), name='pool5')(x)
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(class_num, activation='softmax', name='fc1000')(x)
model = keras.Model(input, x, name='resnet18')
model.summary()
return model
if __name__ == '__main__':
model = ResNet18((INPUT_SIZE, INPUT_SIZE, 3), CLASS_NUM)
print('Done.')
train_resnet18.py
from tensorflow import keras
from tensorflow.keras import layers
INPUT_SIZE = 224
CLASS_NUM = 2
# stage_name=2,3,4,5; block_name=a,b,c
def ConvBlock(input_tensor, num_output, stride, stage_name, block_name):
filter1, filter2 = num_output
x = layers.Conv2D(filter1, 3, strides=stride, padding='same', name='res'+stage_name+block_name+'_branch2a')(input_tensor)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2a')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2a_relu')(x)
x = layers.Conv2D(filter2, 3, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2b')(x)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2b')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2b_relu')(x)
shortcut = layers.Conv2D(filter2, 1, strides=stride, padding='same', name='res'+stage_name+block_name+'_branch1')(input_tensor)
shortcut = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch1')(shortcut)
x = layers.add([x, shortcut], name='res'+stage_name+block_name)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_relu')(x)
return x
def IdentityBlock(input_tensor, num_output, stage_name, block_name):
filter1, filter2 = num_output
x = layers.Conv2D(filter1, 3, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2a')(input_tensor)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2a')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2a_relu')(x)
x = layers.Conv2D(filter2, 3, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2b')(x)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2b')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2b_relu')(x)
shortcut = input_tensor
x = layers.add([x, shortcut], name='res'+stage_name+block_name)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_relu')(x)
return x
def ResNet18(input_shape, class_num):
input = keras.Input(shape=input_shape, name='input')
# conv1
x = layers.Conv2D(64, 7, strides=(2, 2), padding='same', name='conv1')(input) # 7×7, 64, stride 2
x = layers.BatchNormalization(name='bn_conv1')(x)
x = layers.Activation('relu', name='conv1_relu')(x)
x = layers.MaxPooling2D((3, 3), strides=2, padding='same', name='pool1')(x) # 3×3 max pool, stride 2
# conv2_x
x = ConvBlock(input_tensor=x, num_output=(64, 64), stride=(1, 1), stage_name='2', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(64, 64), stage_name='2', block_name='b')
# conv3_x
x = ConvBlock(input_tensor=x, num_output=(128, 128), stride=(2, 2), stage_name='3', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(128, 128), stage_name='3', block_name='b')
# conv4_x
x = ConvBlock(input_tensor=x, num_output=(256, 256), stride=(2, 2), stage_name='4', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(256, 256), stage_name='4', block_name='b')
# conv5_x
x = ConvBlock(input_tensor=x, num_output=(512, 512), stride=(2, 2), stage_name='5', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(512, 512), stage_name='5', block_name='b')
# average pool, 1000-d fc, softmax
x = layers.AveragePooling2D((7, 7), strides=(1, 1), name='pool5')(x)
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(class_num, activation='softmax', name='fc1000')(x)
model = keras.Model(input, x, name='resnet18')
model.summary()
return model
if __name__ == '__main__':
model = ResNet18((INPUT_SIZE, INPUT_SIZE, 3), CLASS_NUM)
print('Done.')
predict_resnet18.py
import matplotlib.pyplot as plt
from ResNet18 import ResNet18
import cv2
import numpy as np
from tensorflow.keras import backend as K # K.set_image_dim_ordering('tf')
from tensorflow.keras.utils import to_categorical
INPUT_IMG_SIZE = 224
NUM_CLASSES = 2
label_dict = {
0:'CAT', 1:'DOG'}
def show_predict_probability(y_gts, predictions, x_imgs, predict_probabilitys, idx):
for i in range(len(label_dict)):
print(label_dict[i]+', Probability:%1.9f'%(predict_probabilitys[idx][i]))
print('label: ', label_dict[int(y_gts[idx])], ', predict: ', label_dict[predictions[idx]])
plt.figure(figsize=(2, 2))
plt.imshow(np.reshape(x_imgs[idx], (INPUT_IMG_SIZE, INPUT_IMG_SIZE, 3)))
plt.show()
def plot_images_labels_prediction(images, labels, prediction, idx, num):
fig = plt.gcf()
fig.set_size_inches(12, 14)
if num>25: num=25
for i in range(0, num):
ax = plt.subplot(2, 5, 1+i)
ax.imshow(images[idx], cmap='binary')
title = 'labels='+str(labels[idx])
if len(prediction) > 0:
title += "prediction="+str(prediction[idx])
ax.set_title(title, fontsize=10)
idx += 1
plt.show()
if __name__ == '__main__':
log_path = r"D:\02.Work\00.LearnML\003.Net\ResNet\log\\"
model = ResNet18((224, 224, 3), NUM_CLASSES)
model.load_weights(log_path+"resnet18.h5")
### cat dog dataset
lines = []
root_path = r"D:\03.Data\01.CatDog"
with open(root_path + "\\test.txt") as f:
lines = f.readlines()
x_images_normalize = []
y_labels_onehot = []
y_labels = []
for i in range(len(lines)):
img_path = lines[i].split(";")[0]
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (INPUT_IMG_SIZE, INPUT_IMG_SIZE))
img = img / 255
x_images_normalize.append(img)
label = to_categorical(lines[i].split(";")[1], num_classes=NUM_CLASSES)
y_labels_onehot.append(label)
y_labels.append(lines[i].split(";")[1])
x_images_normalize = np.array(x_images_normalize)
# x_images_normalize = x_images_normalize.reshape(-1, INPUT_IMG_SIZE, INPUT_IMG_SIZE, 3)
y_labels_onehot = np.array(y_labels_onehot)
predict_probability = model.predict(x_images_normalize, verbose=1)
predict = np.argmax(predict_probability, axis=1)
plot_images_labels_prediction(x_images_normalize, y_labels, predict, 0, 10)
show_predict_probability(y_labels, predict, x_images_normalize, predict_probability, 0)
print('done')
2.ResNet50
ResNet50
from tensorflow import keras
from tensorflow.keras import layers
INPUT_SIZE = 224
CLASS_NUM = 1000
# stage_name=2,3,4,5; block_name=a,b,c
def ConvBlock(input_tensor, num_output, stride, stage_name, block_name):
filter1, filter2, filter3 = num_output
x = layers.Conv2D(filter1, 1, strides=stride, padding='same', name='res'+stage_name+block_name+'_branch2a')(input_tensor)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2a')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2a_relu')(x)
x = layers.Conv2D(filter2, 3, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2b')(x)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2b')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2b_relu')(x)
x = layers.Conv2D(filter3, 1, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2c')(x)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2c')(x)
shortcut = layers.Conv2D(filter3, 1, strides=stride, padding='same', name='res'+stage_name+block_name+'_branch1')(input_tensor)
shortcut = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch1')(shortcut)
x = layers.add([x, shortcut], name='res'+stage_name+block_name)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_relu')(x)
return x
def IdentityBlock(input_tensor, num_output, stage_name, block_name):
filter1, filter2, filter3 = num_output
x = layers.Conv2D(filter1, 1, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2a')(input_tensor)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2a')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2a_relu')(x)
x = layers.Conv2D(filter2, 3, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2b')(x)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2b')(x)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_branch2b_relu')(x)
x = layers.Conv2D(filter3, 1, strides=(1, 1), padding='same', name='res'+stage_name+block_name+'_branch2c')(x)
x = layers.BatchNormalization(name='bn'+stage_name+block_name+'_branch2c')(x)
shortcut = input_tensor
x = layers.add([x, shortcut], name='res'+stage_name+block_name)
x = layers.Activation('relu', name='res'+stage_name+block_name+'_relu')(x)
return x
def ResNet50(input_shape, class_num):
input = keras.Input(shape=input_shape, name='input')
# conv1
x = layers.Conv2D(64, 7, strides=(2, 2), padding='same', name='conv1')(input) # 7×7, 64, stride 2
x = layers.BatchNormalization(name='bn_conv1')(x)
x = layers.Activation('relu', name='conv1_relu')(x)
x = layers.MaxPooling2D((3, 3), strides=2, padding='same', name='pool1')(x) # 3×3 max pool, stride 2
# conv2_x
x = ConvBlock(input_tensor=x, num_output=(64, 64, 256), stride=(1, 1), stage_name='2', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(64, 64, 256), stage_name='2', block_name='b')
x = IdentityBlock(input_tensor=x, num_output=(64, 64, 256), stage_name='2', block_name='c')
# conv3_x
x = ConvBlock(input_tensor=x, num_output=(128, 128, 512), stride=(2, 2), stage_name='3', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(128, 128, 512), stage_name='3', block_name='b')
x = IdentityBlock(input_tensor=x, num_output=(128, 128, 512), stage_name='3', block_name='c')
x = IdentityBlock(input_tensor=x, num_output=(128, 128, 512), stage_name='3', block_name='d')
# conv4_x
x = ConvBlock(input_tensor=x, num_output=(256, 256, 1024), stride=(2, 2), stage_name='4', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(256, 256, 1024), stage_name='4', block_name='b')
x = IdentityBlock(input_tensor=x, num_output=(256, 256, 1024), stage_name='4', block_name='c')
x = IdentityBlock(input_tensor=x, num_output=(256, 256, 1024), stage_name='4', block_name='d')
x = IdentityBlock(input_tensor=x, num_output=(256, 256, 1024), stage_name='4', block_name='e')
x = IdentityBlock(input_tensor=x, num_output=(256, 256, 1024), stage_name='4', block_name='f')
# conv5_x
x = ConvBlock(input_tensor=x, num_output=(512, 512, 2048), stride=(2, 2), stage_name='5', block_name='a')
x = IdentityBlock(input_tensor=x, num_output=(512, 512, 2048), stage_name='5', block_name='b')
x = IdentityBlock(input_tensor=x, num_output=(512, 512, 2048), stage_name='5', block_name='c')
# average pool, 1000-d fc, softmax
x = layers.AveragePooling2D((7, 7), strides=(1, 1), name='pool5')(x)
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(class_num, activation='softmax', name='fc1000')(x)
model = keras.Model(input, x, name='resnet50')
model.summary()
return model
if __name__ == '__main__':
model = ResNet50((INPUT_SIZE, INPUT_SIZE, 3), CLASS_NUM)
print('Done.')
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