神经网络学习小记录35——Inception ResnetV2模型的复现详解

素颜马尾好姑娘i 2023-06-25 08:18 9阅读 0赞

### 神经网络学习小记录35——Inception ResnetV2模型的复现详解 ###

*  学习前言
 *  什么是Inception ResnetV2
 *  源码下载
 *  Inception-ResNetV2的网络结构
 *   *  1、Stem的结构：
     *  2、Inception-resnet-A的结构：
     *  3、Inception-resnet-B的结构：
     *  4、Inception-resnet-C的结构：
 *  全部代码

# 学习前言 #

我死了我死了我死了！  
![在这里插入图片描述][20190723165901974.jpg_pic_center]

# 什么是Inception ResnetV2 #

Inception ResnetV2是Inception ResnetV1的一个加强版，两者的结构差距不大，如果大家想了解Inception ResnetV1可以看一下我的另一个blog。facenet的神经网络结构就是Inception ResnetV1。  
[神经网络学习小记录32——facenet详解及其keras实现][32_facenet_keras]

# 源码下载 #

[https://github.com/bubbliiiing/classic-convolution-network][https_github.com_bubbliiiing_classic-convolution-network]

# Inception-ResNetV2的网络结构 #

Inception-ResNetV2和Inception-ResNetV1采用同一个主干网络。

它的结构很有意思！

如图所示为整个网络的主干结构：  
![在这里插入图片描述][20191219190418800.png_pic_center]  
可以看到里面的结构分为几个重要的部分  
1、stem  
2、Inception-resnet-A  
3、Inception-resnet-B  
4、Inception-resnet-C

## 1、Stem的结构： ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center]  
在Inception-ResNetV2里，它的Input为299x299x3大小，输入后进行：  
三次卷积 -> 最大池化 -> 两次卷积 -> 最大池化 -> 四个分支 -> 堆叠  
python实现代码如下：

input_shape = [299,299,3]
    
    img_input = Input(shape=input_shape)
    
    # Stem block: 299,299,3 -> 35 x 35 x 192
    x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
    x = conv2d_bn(x, 32, 3, padding='valid')
    x = conv2d_bn(x, 64, 3)
    x = MaxPooling2D(3, strides=2)(x)
    x = conv2d_bn(x, 80, 1, padding='valid')
    x = conv2d_bn(x, 192, 3, padding='valid')
    x = MaxPooling2D(3, strides=2)(x)
    
    # Mixed 5b (Inception-A block):35 x 35 x 192 -> 35 x 35 x 320
    branch_0 = conv2d_bn(x, 96, 1)
    branch_1 = conv2d_bn(x, 48, 1)
    branch_1 = conv2d_bn(branch_1, 64, 5)
    branch_2 = conv2d_bn(x, 64, 1)
    branch_2 = conv2d_bn(branch_2, 96, 3)
    branch_2 = conv2d_bn(branch_2, 96, 3)
    branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    
    x = Concatenate(name='mixed_5b')(branches)

## 2、Inception-resnet-A的结构： ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 1]  
Inception-resnet-A的结构分为四个分支  
1、未经处理直接输出  
2、经过一次1x1的32通道的卷积处理  
3、经过一次1x1的32通道的卷积处理和一次3x3的32通道的卷积处理  
4、经过一次1x1的32通道的卷积处理、一次3x3的48通道和一次3x3的64通道卷积处理

234步的结果堆叠后进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 32, 1)
    branch_1 = conv2d_bn(x, 32, 1)
    branch_1 = conv2d_bn(branch_1, 32, 3)
    branch_2 = conv2d_bn(x, 32, 1)
    branch_2 = conv2d_bn(branch_2, 48, 3)
    branch_2 = conv2d_bn(branch_2, 64, 3)
    branches = [branch_0, branch_1, branch_2]
    
    block_name = block_type + '_' + str(block_idx)
    mixed = Concatenate(name=block_name + '_mixed')(branches)
    up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,se_bias=True,name=block_name + '_conv')
    
    x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
                output_shape=K.int_shape(x)[1:],
                arguments={
        'scale': scale},
                name=block_name)([x, up])
    if activation is not None:
        x = Activation(activation, name=block_name + '_ac')(x)

## 3、Inception-resnet-B的结构： ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 2]  
Inception-resnet-B的结构分为四个分支  
1、未经处理直接输出  
2、经过一次1x1的192通道的卷积处理  
3、经过一次1x1的128通道的卷积处理、一次1x7的160通道的卷积处理和一次7x1的192通道的卷积处理

23步的结果堆叠后进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 192, 1)
    branch_1 = conv2d_bn(x, 128, 1)
    branch_1 = conv2d_bn(branch_1, 160, [1, 7])
    branch_1 = conv2d_bn(branch_1, 192, [7, 1])
    branches = [branch_0, branch_1]
    
    block_name = block_type + '_' + str(block_idx)
    mixed = Concatenate(name=block_name + '_mixed')(branches)
    up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,se_bias=True,name=block_name + '_conv')
    
    x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
                output_shape=K.int_shape(x)[1:],
                arguments={
        'scale': scale},
                name=block_name)([x, up])
    if activation is not None:
        x = Activation(activation, name=block_name + '_ac')(x)

## 4、Inception-resnet-C的结构： ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 3]  
Inception-resnet-B的结构分为四个分支  
1、未经处理直接输出  
2、经过一次1x1的192通道的卷积处理  
3、经过一次1x1的192通道的卷积处理、一次1x3的224通道的卷积处理和一次3x1的256通道的卷积处理

23步的结果堆叠后进行一次卷积，并与第一步的结果相加，实质上这是一个残差网络结构。

实现代码如下：

branch_0 = conv2d_bn(x, 192, 1)
    branch_1 = conv2d_bn(x, 192, 1)
    branch_1 = conv2d_bn(branch_1, 224, [1, 3])
    branch_1 = conv2d_bn(branch_1, 256, [3, 1])
    branches = [branch_0, branch_1]
    
    block_name = block_type + '_' + str(block_idx)
    mixed = Concatenate(name=block_name + '_mixed')(branches)
    up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,se_bias=True,name=block_name + '_conv')
    
    x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
                output_shape=K.int_shape(x)[1:],
                arguments={
        'scale': scale},
                name=block_name)([x, up])
    if activation is not None:
        x = Activation(activation, name=block_name + '_ac')(x)

# 全部代码 #

import warnings
    import numpy as np
    
    from keras.preprocessing import image
    from keras.models import Model
    from keras.layers import Activation,AveragePooling2D,BatchNormalization,Concatenate
    from keras.layers import Conv2D,Dense,GlobalAveragePooling2D,GlobalMaxPooling2D,Input,Lambda,MaxPooling2D
    from keras.applications.imagenet_utils import decode_predictions
    from keras.utils.data_utils import get_file
    from keras import backend as K
    BASE_WEIGHT_URL = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.7/'
    
    def conv2d_bn(x,filters,kernel_size,strides=1,padding='same',activation='relu',use_bias=False,name=None):
        x = Conv2D(filters,
                   kernel_size,
                   strides=strides,
                   padding=padding,
                   use_bias=use_bias,
                   name=name)(x)
        if not use_bias:
            bn_axis = 1 if K.image_data_format() == 'channels_first' else 3
            bn_name = None if name is None else name + '_bn'
            x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
        if activation is not None:
            ac_name = None if name is None else name + '_ac'
            x = Activation(activation, name=ac_name)(x)
        return x
    
    def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):
        if block_type == 'block35':
            branch_0 = conv2d_bn(x, 32, 1)
            branch_1 = conv2d_bn(x, 32, 1)
            branch_1 = conv2d_bn(branch_1, 32, 3)
            branch_2 = conv2d_bn(x, 32, 1)
            branch_2 = conv2d_bn(branch_2, 48, 3)
            branch_2 = conv2d_bn(branch_2, 64, 3)
            branches = [branch_0, branch_1, branch_2]
        elif block_type == 'block17':
            branch_0 = conv2d_bn(x, 192, 1)
            branch_1 = conv2d_bn(x, 128, 1)
            branch_1 = conv2d_bn(branch_1, 160, [1, 7])
            branch_1 = conv2d_bn(branch_1, 192, [7, 1])
            branches = [branch_0, branch_1]
        elif block_type == 'block8':
            branch_0 = conv2d_bn(x, 192, 1)
            branch_1 = conv2d_bn(x, 192, 1)
            branch_1 = conv2d_bn(branch_1, 224, [1, 3])
            branch_1 = conv2d_bn(branch_1, 256, [3, 1])
            branches = [branch_0, branch_1]
        else:
            raise ValueError('Unknown Inception-ResNet block type. '
                             'Expects "block35", "block17" or "block8", '
                             'but got: ' + str(block_type))
    
        block_name = block_type + '_' + str(block_idx)
        mixed = Concatenate(name=block_name + '_mixed')(branches)
        up = conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None,use_bias=True,name=block_name + '_conv')
    
        x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
                   output_shape=K.int_shape(x)[1:],
                   arguments={
        'scale': scale},
                   name=block_name)([x, up])
        if activation is not None:
            x = Activation(activation, name=block_name + '_ac')(x)
        return x
    
    
    def InceptionResNetV2(input_shape=[299,299,3],
                          classes=1000):
    
        input_shape = [299,299,3]
    
        img_input = Input(shape=input_shape)
    
        # Stem block: 299,299,3 -> 35 x 35 x 192
        x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid')
        x = conv2d_bn(x, 32, 3, padding='valid')
        x = conv2d_bn(x, 64, 3)
        x = MaxPooling2D(3, strides=2)(x)
        x = conv2d_bn(x, 80, 1, padding='valid')
        x = conv2d_bn(x, 192, 3, padding='valid')
        x = MaxPooling2D(3, strides=2)(x)
    
        # Mixed 5b (Inception-A block):35 x 35 x 192 -> 35 x 35 x 320
        branch_0 = conv2d_bn(x, 96, 1)
        branch_1 = conv2d_bn(x, 48, 1)
        branch_1 = conv2d_bn(branch_1, 64, 5)
        branch_2 = conv2d_bn(x, 64, 1)
        branch_2 = conv2d_bn(branch_2, 96, 3)
        branch_2 = conv2d_bn(branch_2, 96, 3)
        branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
        branch_pool = conv2d_bn(branch_pool, 64, 1)
        branches = [branch_0, branch_1, branch_2, branch_pool]
    
        x = Concatenate(name='mixed_5b')(branches)
    
        # 10次Inception-ResNet-A block:35 x 35 x 320 -> 35 x 35 x 320
        for block_idx in range(1, 11):
            x = inception_resnet_block(x,
                                       scale=0.17,
                                       block_type='block35',
                                       block_idx=block_idx)
    
        # Reduction-A block:35 x 35 x 320 -> 17 x 17 x 1088
        branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid')
        branch_1 = conv2d_bn(x, 256, 1)
        branch_1 = conv2d_bn(branch_1, 256, 3)
        branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid')
        branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
        branches = [branch_0, branch_1, branch_pool]
        x = Concatenate(name='mixed_6a')(branches)
    
        # 20次Inception-ResNet-B block: 17 x 17 x 1088 -> 17 x 17 x 1088 
        for block_idx in range(1, 21):
            x = inception_resnet_block(x,
                                       scale=0.1,
                                       block_type='block17',
                                       block_idx=block_idx)
    
    
        # Reduction-B block: 17 x 17 x 1088 -> 8 x 8 x 2080
        branch_0 = conv2d_bn(x, 256, 1)
        branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid')
        branch_1 = conv2d_bn(x, 256, 1)
        branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid')
        branch_2 = conv2d_bn(x, 256, 1)
        branch_2 = conv2d_bn(branch_2, 288, 3)
        branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid')
        branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x)
        branches = [branch_0, branch_1, branch_2, branch_pool]
        x = Concatenate(name='mixed_7a')(branches)
    
        # 10次Inception-ResNet-C block: 8 x 8 x 2080 -> 8 x 8 x 2080
        for block_idx in range(1, 10):
            x = inception_resnet_block(x,
                                       scale=0.2,
                                       block_type='block8',
                                       block_idx=block_idx)
        x = inception_resnet_block(x,
                                   scale=1.,
                                   activation=None,
                                   block_type='block8',
                                   block_idx=10)
    
        # 8 x 8 x 2080 -> 8 x 8 x 1536
        x = conv2d_bn(x, 1536, 1, name='conv_7b')
    
        x = GlobalAveragePooling2D(name='avg_pool')(x)
        x = Dense(classes, activation='softmax', name='predictions')(x)
    
        inputs = img_input
    
        # 创建模型
        model = Model(inputs, x, name='inception_resnet_v2')
    
        return model
    
    def preprocess_input(x):
        x /= 255.
        x -= 0.5
        x *= 2.
        return x
    
    if __name__ == '__main__':
        model = InceptionResNetV2()
        fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5'
        weights_path = get_file(fname,BASE_WEIGHT_URL + fname,cache_subdir='models',file_hash='e693bd0210a403b3192acc6073ad2e96')
        model.load_weights(fname)
        img_path = 'elephant.jpg'
        img = image.load_img(img_path, target_size=(299, 299))
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
    
        x = preprocess_input(x)
    
        preds = model.predict(x)
        print('Predicted:', decode_predictions(preds))

[20190723165901974.jpg_pic_center]: https://img-blog.csdnimg.cn/20190723165901974.jpg#pic_center
[32_facenet_keras]: https://blog.csdn.net/weixin_44791964/article/details/103618595
[https_github.com_bubbliiiing_classic-convolution-network]: https://github.com/bubbliiiing/classic-convolution-network
[20191219190418800.png_pic_center]: https://img-blog.csdnimg.cn/20191219190418800.png#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20191227155901305.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 1]: https://img-blog.csdnimg.cn/20191227160223715.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 2]: https://img-blog.csdnimg.cn/20191227160545359.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center 3]: https://img-blog.csdnimg.cn/20191227160736855.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center

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