用tensorflow写将ECA加入到Resnet50中

可以参考以下步骤：

1. 在TensorFlow中加载ResNet50模型。

2. 创建一个自定义层，实现ECA（Efficient Channel Attention）操作。可以参考以下代码：

import tensorflow as tf

class EcaLayer(tf.keras.layers.Layer):
    def __init__(self, gamma=2, b=1, **kwargs):
        super(EcaLayer, self).__init__(**kwargs)
        self.gamma = gamma
        self.b = b

    def call(self, inputs):
        shape = tf.shape(inputs)
        batch_size, h, w, c = shape[0], shape[1], shape[2], shape[3]
        t = int(abs((math.log(c, 2) + self.b) / self.gamma))
        k_size = max(3, t if t % 2 else t + 1)
        padding = (k_size - 1) // 2
        avg_pool = tf.keras.layers.AveragePooling2D(
            pool_size=(k_size, k_size),
            strides=1,
            padding='same'
        )(inputs)
        conv = tf.keras.layers.Conv2D(
            filters=1,
            kernel_size=(1, 1),
            strides=1,
            padding='same'
        )(avg_pool)
        sigmoid = tf.keras.activations.sigmoid(conv)
        outputs = inputs * sigmoid
        return outputs

    def get_config(self):
        config = super(EcaLayer, self).get_config()
        config.update({'gamma': self.gamma, 'b': self.b})
        return config

3. 修改ResNet50模型，将ECA层插入到Conv2D层之后。可以参考以下代码：

def resnet50(include_top=True,
             weights=None,
             input_tensor=None,
             input_shape=None,
             pooling=None,
             classes=1000):
    inputs = keras_utils.get_input(
        input_tensor, shape=input_shape, name='input'
    )

    # Stem convolutional layers
    x = layers.Conv2D(
        filters=64,
        kernel_size=(7, 7),
        strides=2,
        padding='same',
        name='conv1_conv',
    )(inputs)
    x = layers.BatchNormalization(name='conv1_bn')(x)
    x = layers.Activation('relu', name='conv1_relu')(x)
    x = layers.MaxPooling2D(
        pool_size=(3, 3), strides=2, padding='same', name='pool1'
    )(x)

    # Stage 2
    x = conv_block(x, 3, filters=[64, 64, 256], stage=2, block='a', eca=True)
    x = identity_block(x, 3, filters=[64, 64, 256], stage=2, block='b', eca=True)
    x = identity_block(x, 3, filters=[64, 64, 256], stage=2, block='c', eca=True)

    # Stage 3
    x = conv_block(x, 3, filters=[128, 128, 512], stage=3, block='a', eca=True)
    x = identity_block(x, 3, filters=[128, 128, 512], stage=3, block='b', eca=True)
    x = identity_block(x, 3, filters=[128, 128, 512], stage=3, block='c', eca=True)
    x = identity_block(x, 3, filters=[128, 128, 512], stage=3, block='d', eca=True)

    # Stage 4
    x = conv_block(x, 3, filters=[256, 256, 1024], stage=4, block='a', eca=True)
    x = identity_block(x, 3, filters=[256, 256, 1024], stage=4, block='b', eca=True)
    x = identity_block(x, 3, filters=[256, 256, 1024], stage=4, block='c', eca=True)
    x = identity_block(x, 3, filters=[256, 256, 1024], stage=4, block='d', eca=True)
    x = identity_block(x, 3, filters=[256, 256, 1024], stage=4, block='e', eca=True)
    x = identity_block(x, 3, filters=[256, 256, 1024], stage=4, block='f', eca=True)

    # Stage 5
    x = conv_block(x, 3, filters=[512, 512, 2048], stage=5, block='a', eca=True)
    x = identity_block(x, 3, filters=[512, 512, 2048], stage=5, block='b', eca=True)
    x = identity_block(x, 3, filters=[512, 512, 2048], stage=5, block='c', eca=True)

    if include_top:
        # Classification block
        x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
        x = layers.Dense(classes, activation='softmax', name='output')(x)
    else:
        if pooling == 'avg':
            x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
        elif pooling == 'max':
            x = layers.GlobalMaxPooling2D(name='max_pool')(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = keras_utils.get_source_inputs(input_tensor)
    else:
        inputs = inputs

    # Create model.
    model = training.Model(inputs, x, name='resnet50')

    return model

其中，eca=True表示这个块会使用ECA层。

4. 将模型编译并训练。可以参考以下代码：

model = resnet50()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=20, batch_size=32)

以上就是用TensorFlow将ECA加入到ResNet50中的方法。