机器学习CV代码练习（九）之Resnet50模型实现

比眉伴天荒 2022-08-29 13:52 219阅读 0赞

### 机器学习CV代码练习（九）之Resnet50模型实现 ###

# -*- coding: utf-8 -*-
    '''ResNet50 model for Keras. # Reference: - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) '''
    
    from keras.layers import Input
    from keras import layers
    from keras.layers import Dense
    from keras.layers import Activation
    from keras.layers import Flatten
    from keras.layers import Conv2D
    from keras.layers import MaxPooling2D
    from keras.layers import GlobalMaxPooling2D
    from keras.layers import ZeroPadding2D
    from keras.layers import AveragePooling2D
    from keras.layers import GlobalAveragePooling2D
    from keras.layers import BatchNormalization
    from keras.models import Model
    import keras.backend as K
    
    #不变的block
    def identity_block(input_tensor, kernel_size, filters, stage, block):
        """The identity block is the block that has no conv layer at shortcut. # Arguments input_tensor: input tensor kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names # Returns Output tensor for the block. """
        filters1, filters2, filters3 = filters
        if K.image_data_format() == 'channels_last':
            bn_axis = 3
        else:
            bn_axis = 1
        conv_name_base = 'res' + str(stage) + block + '_branch'
        bn_name_base = 'bn' + str(stage) + block + '_branch'
        # 对input——tensor不做处理，直接做ADD
        x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
        x = Activation('relu')(x)
    
        x = Conv2D(filters2, kernel_size,
                   padding='same', name=conv_name_base + '2b')(x)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
        x = Activation('relu')(x)
    
        x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
    
        # 对input——tensor不做处理，直接做ADD
        x = layers.add([x, input_tensor])
        x = Activation('relu')(x)
        return x
    
    # conv操作的block
    def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
        """conv_block is the block that has a conv layer at shortcut # Arguments input_tensor: input tensor kernel_size: defualt 3, the kernel size of middle conv layer at main path filters: list of integers, the filterss of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names # Returns Output tensor for the block. Note that from stage 3, the first conv layer at main path is with strides=(2,2) And the shortcut should have strides=(2,2) as well """
        filters1, filters2, filters3 = filters
        if K.image_data_format() == 'channels_last':
            bn_axis = 3
        else:
            bn_axis = 1
        conv_name_base = 'res' + str(stage) + block + '_branch'
        bn_name_base = 'bn' + str(stage) + block + '_branch'
    
        x = Conv2D(filters1, (1, 1), strides=strides,
                   name=conv_name_base + '2a')(input_tensor)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
        x = Activation('relu')(x)
    
        x = Conv2D(filters2, kernel_size, padding='same',
                   name=conv_name_base + '2b')(x)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
        x = Activation('relu')(x)
    
        x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
        # 对input——tensor做处理，得到shortcut
        shortcut = Conv2D(filters3, (1, 1), strides=strides,
                          name=conv_name_base + '1')(input_tensor)
        shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
    
        x = layers.add([x, shortcut])
        x = Activation('relu')(x)
        return x
    
    # 总的Resnet
    def ResNet50(input_shape, classes=1000):
        """Instantiates the ResNet50 architecture. # Arguments input_shape: the input shape has to be `(224, 224, 3)` (with `channels_last` data format). classes: optional number of classes to classify images into # Returns A Keras model instance. """
        # 输入层
        img_input = Input(shape=input_shape)
        # 处理通道
        if K.image_data_format() == 'channels_last':
            bn_axis = 3
        else:
            bn_axis = 1
    
        x = ZeroPadding2D((3, 3))(img_input)
        x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
        x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
        x = Activation('relu')(x)
        x = MaxPooling2D((3, 3), strides=(2, 2))(x)
    
        x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
        x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
        x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
    
        x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
        x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
        x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
        x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
    
        x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
    
        x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
    
        x = AveragePooling2D((7, 7), name='avg_pool')(x)
    
        x = Flatten()(x)
        x = Dense(classes, activation='softmax', name='fc1000')(x)
    
        # Create model.
        model = Model(img_input, x, name='resnet50')
        return model