tensorflow2.6+mobileNet

tensorflow2.6+mobileNet

 

import warnings
warnings.filterwarnings("ignore")
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'


from tensorflow.keras.layers import (DepthwiseConv2D,BatchNormalization,
                                     Activation,Conv2D,Input,GlobalAveragePooling2D,
                                     Reshape,Dropout,)
import tensorflow.keras.backend as K
from tensorflow.keras.models import Model

def relu6(x):
    return K.relu(x, max_value=6)

def _depthwise_conv_block(inputs,pointwise_conv_filters,alpha,
                          depth_multiplier=1,strides=(1,1),block_id=1):
    pointwise_conv_filters = int(pointwise_conv_filters * alpha)

    x = DepthwiseConv2D((3,3),strides=strides,padding='same',
                        depth_multiplier=depth_multiplier,use_bias=False,
                        name=f'conv_dw_{block_id}')(inputs)
    x = BatchNormalization(name=f'conv_dw_{block_id}_bn')(x)
    x = Activation(relu6,name=f'conv_dw_{block_id}_relu')(x)

    x = Conv2D(pointwise_conv_filters,(1,1),padding='same',
               use_bias=False,strides=(1,1),name=f'conv_pw_{block_id}')(x)
    x = BatchNormalization(name=f'conv_pw_{block_id}_bn')(x)
    return Activation(relu6,name=f'conv_pw_{block_id}_relu')(x)

def _conv_block(inputs,filters,alpha,kernel=(3,3),strides=(1,1)):
    filters = int(filters * alpha)
    x = Conv2D(filters,kernel,padding='same',use_bias=False,
               strides=strides,name='conv1')(inputs)
    x = BatchNormalization(name='conv1_bn')(x)
    return Activation(relu6,name='conv1_relu')(x)

def MobileNet(input_shape=None,alpha=1.0,depth_multiplier=1,dropout=1e-3,
              classes=1000):
    img_input = Input(shape=input_shape)

    # 224,224,3 -> 112,112,32 [224/2]向上取整
    x = _conv_block(img_input,32,alpha,strides=(2,2))

    # 112,112,32 -> 112,112,64
    x = _depthwise_conv_block(x,64,alpha,depth_multiplier,block_id=1)

    # 112,112,64 -> 56,56,128
    x = _depthwise_conv_block(x,128,alpha,depth_multiplier,strides=(2,2),block_id=2)
    x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3)

    # 56,56,128 -> 28,28,256
    x = _depthwise_conv_block(x,256,alpha,depth_multiplier,strides=(2,2),
                              block_id=4)
    x = _depthwise_conv_block(x,256,alpha,depth_multiplier,block_id=5)

    # 28,28,256 -> 14,14,512
    x = _depthwise_conv_block(x,512,alpha,depth_multiplier,strides=(2,2),
                              block_id=6)

    # 14,14,512 -> 14,14,512 (5*)
    for i in range(1,6):
        x = _depthwise_conv_block(x, 512, alpha, depth_multiplier,
                              block_id=(6+i))

    # 14,14,512 -> 7,7,1024
    x = _depthwise_conv_block(x,1024,alpha,depth_multiplier,strides=(2,2),
                              block_id=12)
    # 7,7,1024 -> 7,7,1024
    x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier,
                              block_id=13)

    # 7,7,1024 -> 1,1,1024
    x = GlobalAveragePooling2D()(x)
    print('x.shape = ',x.shape[-1])
    shape = (1,1,x.shape[-1])

    x = Reshape(shape,name='reshape_1')(x)
    x = Dropout(dropout,name='dropout')(x)

    x = Conv2D(classes,(1,1),padding='same',name='conv_preds')(x)
    x = Activation('softmax',name='act_softmax')(x)
    x = Reshape((classes,),name='reshape_2')(x)

    model = Model(img_input,x,name=f'mobilenet_{alpha:.2f}')
    return model

if __name__ == '__main__':
    model = MobileNet(input_shape=(224,224,3))
    model.summary()

x.shape =  1024
Model: "mobilenet_1.00"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
conv1 (Conv2D)               (None, 112, 112, 32)      864       
_________________________________________________________________
conv1_bn (BatchNormalization (None, 112, 112, 32)      128       
_________________________________________________________________
conv1_relu (Activation)      (None, 112, 112, 32)      0         
_________________________________________________________________
conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       
_________________________________________________________________
conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       
_________________________________________________________________
conv_dw_1_relu (Activation)  (None, 112, 112, 32)      0         
_________________________________________________________________
conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      
_________________________________________________________________
conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       
_________________________________________________________________
conv_pw_1_relu (Activation)  (None, 112, 112, 64)      0         
_________________________________________________________________
conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       
_________________________________________________________________
conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       
_________________________________________________________________
conv_dw_2_relu (Activation)  (None, 56, 56, 64)        0         
_________________________________________________________________
conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      
_________________________________________________________________
conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       
_________________________________________________________________
conv_pw_2_relu (Activation)  (None, 56, 56, 128)       0         
_________________________________________________________________
conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      
_________________________________________________________________
conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       
_________________________________________________________________
conv_dw_3_relu (Activation)  (None, 56, 56, 128)       0         
_________________________________________________________________
conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     
_________________________________________________________________
conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       
_________________________________________________________________
conv_pw_3_relu (Activation)  (None, 56, 56, 128)       0         
_________________________________________________________________
conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      
_________________________________________________________________
conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       
_________________________________________________________________
conv_dw_4_relu (Activation)  (None, 28, 28, 128)       0         
_________________________________________________________________
conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     
_________________________________________________________________
conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      
_________________________________________________________________
conv_pw_4_relu (Activation)  (None, 28, 28, 256)       0         
_________________________________________________________________
conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      
_________________________________________________________________
conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      
_________________________________________________________________
conv_dw_5_relu (Activation)  (None, 28, 28, 256)       0         
_________________________________________________________________
conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     
_________________________________________________________________
conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      
_________________________________________________________________
conv_pw_5_relu (Activation)  (None, 28, 28, 256)       0         
_________________________________________________________________
conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      
_________________________________________________________________
conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      
_________________________________________________________________
conv_dw_6_relu (Activation)  (None, 14, 14, 256)       0         
_________________________________________________________________
conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    
_________________________________________________________________
conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_6_relu (Activation)  (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_7_relu (Activation)  (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_7_relu (Activation)  (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_8_relu (Activation)  (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_8_relu (Activation)  (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_9_relu (Activation)  (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_9_relu (Activation)  (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_10_relu (Activation) (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_10_relu (Activation) (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      
_________________________________________________________________
conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_dw_11_relu (Activation) (None, 14, 14, 512)       0         
_________________________________________________________________
conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    
_________________________________________________________________
conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      
_________________________________________________________________
conv_pw_11_relu (Activation) (None, 14, 14, 512)       0         
_________________________________________________________________
conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      
_________________________________________________________________
conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      
_________________________________________________________________
conv_dw_12_relu (Activation) (None, 7, 7, 512)         0         
_________________________________________________________________
conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    
_________________________________________________________________
conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      
_________________________________________________________________
conv_pw_12_relu (Activation) (None, 7, 7, 1024)        0         
_________________________________________________________________
conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      
_________________________________________________________________
conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      
_________________________________________________________________
conv_dw_13_relu (Activation) (None, 7, 7, 1024)        0         
_________________________________________________________________
conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   
_________________________________________________________________
conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      
_________________________________________________________________
conv_pw_13_relu (Activation) (None, 7, 7, 1024)        0         
_________________________________________________________________
global_average_pooling2d (Gl (None, 1024)              0         
_________________________________________________________________
reshape_1 (Reshape)          (None, 1, 1, 1024)        0         
_________________________________________________________________
dropout (Dropout)            (None, 1, 1, 1024)        0         
_________________________________________________________________
conv_preds (Conv2D)          (None, 1, 1, 1000)        1025000   
_________________________________________________________________
act_softmax (Activation)     (None, 1, 1, 1000)        0         
_________________________________________________________________
reshape_2 (Reshape)          (None, 1000)              0         
=================================================================
Total params: 4,253,864
Trainable params: 4,231,976
Non-trainable params: 21,888
_________________________________________________________________

Process finished with exit code 0