Tensorflow2.0 keras MobileNetV2 代码实现
目录
- 迁移学习
- 自编程序
- Block
- Depthwise Separable block
- Keras MobileNetV2复现
- 参考
网络介绍请参看：博文
keras搭建深度学习模型的若干方法：博文
直接上网络结构
迁移学习
依旧看看标准答案
import tensorflow as tf
from tensorflow import keras
base_model = keras.applications.MobileNetV2(weights='imagenet')
base_model.summary()
自编程序
Block
import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.backend as K
from tensorflow.keras import layers, models, Sequential, backend
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization, Activation, GlobalAveragePooling2D
from tensorflow.keras.layers import Concatenate, Lambda, Input, ZeroPadding2D, AveragePooling2D, DepthwiseConv2D, Reshape
def relu6(x):
return K.relu(x, max_value=6)
# 保证特征层数为8的倍数
def make_divisible(v, divisor, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v+divisor/2)//divisor*divisor) #//向下取整，除
if new_v<0.9*v:
new_v +=divisor
return new_v
def conv_block (x, nb_filter, kernel=(1,1), stride=(1,1), name=None):
x = Conv2D(nb_filter, kernel, strides=stride, padding='same', use_bias=False, name=name+'_conv1')(x)
x = BatchNormalization(axis=3, name=name+'_bn1')(x)
x = Activation(relu6, name=name+'_relu')(x)
return x
def depthwise_res_block(x, nb_filter, kernel, stride, t, alpha, resdiual=False, name=None):
input_tensor=x
exp_channels= x.shape[-1]*t #扩展维度
alpha_channels = int(nb_filter*alpha) #压缩维度
x = conv_block(x, exp_channels, (1,1), (1,1), name=name+'_expansion')
x = DepthwiseConv2D(kernel, padding='same', strides=stride, depth_multiplier=1, use_bias=False, name=name+'_dpconv')(x)
x = BatchNormalization(axis=3, name=name+'_bn1')(x)
x = Activation(relu6, name=name+'_relu1')(x)
x = Conv2D(alpha_channels, (1,1), padding='same', use_bias=False, strides=(1,1), name=name+'_conv_2')(x)
x = BatchNormalization(axis=3, name=name+'_bn2')(x)
if resdiual:
x = layers.add([x, input_tensor])
return x
def inverted_residual_layers(x,nb_filter, stride, t, alpha, n, name=None):
x = depthwise_res_block(x, nb_filter, (3,3), stride, t, alpha,False, name=name+'_dep1')
for i in range(1, n):
x = depthwise_res_block(x, nb_filter, (3,3), (1,1), t, alpha, True, name=name+'_dep'+str(i+1) )
return x
此处注意网络中的所有卷积网络use_bias=False，即神经元无偏差设置
Depthwise Separable block
对比原文和Keras训练的模型，结构有稍许变化，此版以Keras中的模型为准
def MovblieNetV2 (nb_classes, alpha=1., dropout=0):
img_input = Input(shape=(224, 224, 3))
first_filter = make_divisible(32*alpha, 8)
x = conv_block(img_input, first_filter, (3,3), (2,2), name='first_conv_block')
x = DepthwiseConv2D((3,3), padding='same', strides=(1,1), depth_multiplier=1, use_bias=False, name='expanded_dpconv')(x)
x = BatchNormalization(axis=3, name='expanded_bn1')(x)
x = Activation(relu6, name='expanded_relu1')(x)
x = Conv2D(16, (1,1), padding='same', use_bias=False, strides=(1,1), name='expanded_conv_2')(x)
x = BatchNormalization(axis=3, name='expanded_bn2')(x)
x = inverted_residual_layers(x, 24, (1,1), 6, alpha, 1, name='layer1') # base_model nb_filter=16, t=1
x = inverted_residual_layers(x, 24, (2,2), 6, alpha, 1, name='layer2') # base_model n=2
x = inverted_residual_layers(x, 32, (2,2), 6, alpha, 3, name='layer3')
x = inverted_residual_layers(x, 64, (2,2), 6, alpha, 4, name='layer4')
x = inverted_residual_layers(x, 96, (2,2), 6, alpha, 3, name='layer5')
x = inverted_residual_layers(x, 160, (2,2), 6, alpha, 3, name='layer6')
x = inverted_residual_layers(x, 320, (2,2), 6, alpha, 1, name='layer7')
if alpha >1.0:
last_filter = make_divisible(1280*alpha,8)
else:
last_filter=1280
x = conv_block(x, last_filter, (1,1), (2,2), name='last_conv_block')
x = GlobalAveragePooling2D()(x)
x = Reshape((1,1,last_filter), name='reshape_1')(x)
x = Dropout(dropout, name='dropout')(x)
x = Conv2D(nb_classes, (1,1), padding='same', name='conv_preds')(x)
x = Reshape((nb_classes,), name='reshape_2')(x)
x = Activation('softmax', name='act_softmax')(x)
model = models.Model(img_input, x, name='MobileNetV2')
return model
def main():
model = MovblieNetV2(1000, 1.0, 0.2)
model.summary()
if __name__=='__main__':
main()
网络总参数与迁移学习的数目相同，说明重建成功
Keras MobileNetV2复现
后期在load_weights时报错发现不匹配Keras.applications已训练网络得权重文件mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224.h5。对比后发现在局部结构与原文有出入。所以按照Keras Keras MobileNetV2得结构进行复现
import tensorflow as tf
from tensorflow import keras
import tensorflow.keras.backend as K
from tensorflow.keras import layers, models, Sequential, backend
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization, Activation, GlobalAveragePooling2D
from tensorflow.keras.layers import Concatenate, Lambda, Input, ZeroPadding2D, AveragePooling2D, DepthwiseConv2D, Reshape
def relu6(x):
return K.relu(x, max_value=6)
# 保证特征层数为8的倍数
def make_divisible(v, divisor, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v+divisor/2)//divisor*divisor) #//向下取整，除
if new_v<0.9*v:
new_v +=divisor
return new_v
def pad_size(inputs, kernel_size):
input_size = inputs.shape[1:3]
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size)
if input_size[0] is None:
adjust = (1,1)
else:
adjust = (1- input_size[0]%2, 1-input_size[1]%2)
correct = (kernel_size[0]//2, kernel_size[1]//2)
return ((correct[0] - adjust[0], correct[0]),
(correct[1] - adjust[1], correct[1]))
def conv_block (x, nb_filter, kernel=(1,1), stride=(1,1), name=None):
x = Conv2D(nb_filter, kernel, strides=stride, padding='same', use_bias=False, name=name+'_expand')(x)
x = BatchNormalization(axis=3, name=name+'_expand_BN')(x)
x = Activation(relu6, name=name+'_expand_relu')(x)
return x
def depthwise_res_block(x, nb_filter, kernel, stride, t, alpha, resdiual=False, name=None):
input_tensor=x
exp_channels= x.shape[-1]*t #扩展维度
alpha_channels = int(nb_filter*alpha) #压缩维度
x = conv_block(x, exp_channels, (1,1), (1,1), name=name)
if stride[0]==2:
x = ZeroPadding2D(padding=pad_size(x, 3), name=name+'_pad')(x)
x = DepthwiseConv2D(kernel, padding='same' if stride[0]==1 else 'valid', strides=stride, depth_multiplier=1, use_bias=False, name=name+'_depthwise')(x)
x = BatchNormalization(axis=3, name=name+'_depthwise_BN')(x)
x = Activation(relu6, name=name+'_depthwise_relu')(x)
x = Conv2D(alpha_channels, (1,1), padding='same', use_bias=False, strides=(1,1), name=name+'_project')(x)
x = BatchNormalization(axis=3, name=name+'_project_BN')(x)
if resdiual:
x = layers.add([x, input_tensor], name=name+'_add')
return x
def MovblieNetV2 (nb_classes, alpha=1., dropout=0):
img_input = Input(shape=(224, 224, 3))
first_filter = make_divisible(32*alpha, 8)
x = ZeroPadding2D(padding=pad_size(img_input, 3), name='Conv1_pad')(img_input)
x = Conv2D(first_filter, (3,3), strides=(2,2), padding='valid', use_bias=False, name='Conv1')(x)
x = BatchNormalization(axis=3, name='bn_Conv1')(x)
x = Activation(relu6, name='Conv1_relu')(x)
x = DepthwiseConv2D((3,3), padding='same', strides=(1,1), depth_multiplier=1, use_bias=False, name='expanded_conv_depthwise')(x)
x = BatchNormalization(axis=3, name='expanded_conv_depthwise_BN')(x)
x = Activation(relu6, name='expanded_conv_depthwise_relu')(x)
x = Conv2D(16, (1,1), padding='same', use_bias=False, strides=(1,1), name='expanded_conv_project')(x)
x = BatchNormalization(axis=3, name='expanded_conv_project_BN')(x)
x = depthwise_res_block(x, 24, (3,3), (2,2), 6, alpha, resdiual=False, name='block_1')
x = depthwise_res_block(x, 24, (3,3), (1,1), 6, alpha, resdiual=True, name='block_2')
x = depthwise_res_block(x, 32, (3,3), (2,2), 6, alpha, resdiual=False, name='block_3')
x = depthwise_res_block(x, 32, (3,3), (1,1), 6, alpha, resdiual=True, name='block_4')
x = depthwise_res_block(x, 32, (3,3), (1,1), 6, alpha, resdiual=True, name='block_5')
x = depthwise_res_block(x, 64, (3,3), (2,2), 6, alpha, resdiual=False, name='block_6')
x = depthwise_res_block(x, 64, (3,3), (1,1), 6, alpha, resdiual=True, name='block_7')
x = depthwise_res_block(x, 64, (3,3), (1,1), 6, alpha, resdiual=True, name='block_8')
x = depthwise_res_block(x, 64, (3,3), (1,1), 6, alpha, resdiual=True, name='block_9')
x = depthwise_res_block(x, 96, (3,3), (1,1), 6, alpha, resdiual=False, name='block_10')
x = depthwise_res_block(x, 96, (3,3), (1,1), 6, alpha, resdiual=True, name='block_11')
x = depthwise_res_block(x, 96, (3,3), (1,1), 6, alpha, resdiual=True, name='block_12')
x = depthwise_res_block(x, 160, (3,3), (2,2), 6, alpha, resdiual=False, name='block_13')
x = depthwise_res_block(x, 160, (3,3), (1,1), 6, alpha, resdiual=True, name='block_14')
x = depthwise_res_block(x, 160, (3,3), (1,1), 6, alpha, resdiual=True, name='block_15')
x = depthwise_res_block(x, 320, (3,3), (1,1), 6, alpha, resdiual=False, name='block_16')
if alpha >1.0:
last_filter = make_divisible(1280*alpha,8)
else:
last_filter=1280
x = Conv2D(last_filter, (1,1), strides=(1,1), use_bias=False, name='Conv_1')(x)
x = BatchNormalization(axis=3, name='Conv_1_bn')(x)
x = Activation(relu6, name='out_relu')(x)
x = GlobalAveragePooling2D()(x)
x = Dense(nb_classes, activation='softmax',use_bias=True, name='Logits')(x)
model = models.Model(img_input, x, name='MobileNetV2')
return model
def prepocess(x):
x = tf.io.read_file(x)
x = tf.image.decode_jpeg(x, channels=3)
x = tf.image.resize(x, [224,224])
x =tf.expand_dims(x, 0) # 扩维
x = preprocess_input(x)
return x
#preprocess_input 等效于/255 -0.5 *2（缩放到-1 ，1）
import os
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions
def main():
model = MovblieNetV2(1000, 1.0, 0.2)
model.summary()
weight_path=r'C:\Users\jackey\.keras\models\mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224.h5'
if os.path.exists(weight_path):
model.load_weights(weight_path, )
img=prepocess('Car.jpg')
Predictions = model.predict(img)
print('Predicted:', decode_predictions(Predictions, top=3)[0])
if __name__=='__main__':
main()
参考
https://github.com/xiaochus/MobileNetV2/blob/master/mobilenet_v2.py