用theano库在ResNet中添加CBAM

CBAM是一种用于卷积神经网络的注意力机制，可以帮助网络更好地关注重要的特征，提高网络性能。在ResNet中添加CBAM需要进行以下步骤：

1. 在导入库的部分，添加以下代码：

from theano import tensor as T
from theano import function

2. 定义CBAM的注意力模块，代码如下：

def cbam(inputs, reduction_ratio=0.5, name=None):
    # Channel attention module
    pool = T.mean(inputs, axis=[2, 3], keepdims=True)
    pool = T.flatten(pool, outdim=2)
    shared_layer_one = T.nnet.relu(T.dot(pool, T.alloc(1.0, inputs.shape[1], int(inputs.shape[1]*reduction_ratio))))
    shared_layer_two = T.dot(shared_layer_one, T.alloc(1.0, int(inputs.shape[1]*reduction_ratio), inputs.shape[1]))
    attention = T.nnet.sigmoid(shared_layer_two)
    attention = T.reshape(attention, (inputs.shape[0], inputs.shape[1], 1, 1))
    weighted_input = inputs * attention

    # Spatial attention module
    filters = inputs.shape[1]
    conv_output = T.nnet.relu(T.nnet.conv2d(inputs, T.alloc(1.0, filters, 1, 1), border_mode='same'))
    pool_output = T.nnet.relu(T.nnet.max_pool_2d(conv_output, (1, 1), ignore_border=True))
    shared_layer_one = T.nnet.relu(T.nnet.conv2d(pool_output, T.alloc(1.0, filters, 1, 1), border_mode='same'))
    shared_layer_two = T.nnet.conv2d(shared_layer_one, T.alloc(1.0, filters, 1, 1), border_mode='same')
    attention = T.nnet.sigmoid(shared_layer_two)
    attention = T.addbroadcast(attention, 1, 2)
    attention = T.addbroadcast(attention, 1, 3)
    weighted_input = inputs * attention

    return weighted_input

3. 在ResNet的每个残差块中，添加CBAM模块，代码如下：

def residual_block(input, filters, reduction_ratio=0.5, stride=1, name=None):
    shortcut = input

    if stride != 1 or input.shape[1] != filters * 4:
        shortcut = T.nnet.relu(T.nnet.conv2d(input, filters * 4, (1, 1), subsample=(stride, stride), border_mode='same', name=name + '_shortcut_conv'))

    conv1 = T.nnet.relu(T.nnet.conv2d(input, filters, (1, 1), subsample=(stride, stride), border_mode='same', name=name + '_conv1'))
    conv2 = T.nnet.relu(cbam(T.nnet.conv2d(conv1, filters, (3, 3), border_mode='same', name=name + '_conv2'), reduction_ratio=reduction_ratio, name=name + '_cbam'))
    conv3 = T.nnet.conv2d(conv2, filters * 4, (1, 1), border_mode='same', name=name + '_conv3')

    output = T.nnet.relu(conv3 + shortcut)

    return output

4. 在ResNet的网络结构中，调用上面定义的残差块函数，代码如下：

def resnet(input_shape=(3, 224, 224), num_classes=1000, block_sizes=[64, 128, 256, 512], reduction_ratio=0.5):
    input = T.tensor4('input')
    output = input - T.alloc(0.5, input.shape[0], input.shape[1], input.shape[2], input.shape[3])
    output = output * T.alloc(2.0, input.shape[0], input.shape[1], input.shape[2], input.shape[3])

    output = T.nnet.relu(T.nnet.conv2d(output, block_sizes[0], (7, 7), subsample=(2, 2), border_mode='same', name='conv1'))
    output = T.nnet.max_pool_2d(output, (3, 3), subsample=(2, 2), ignore_border=False)

    output = residual_block(output, block_sizes[0], reduction_ratio=reduction_ratio, name='2a')
    output = residual_block(output, block_sizes[0], reduction_ratio=reduction_ratio, name='2b')
    output = residual_block(output, block_sizes[0], reduction_ratio=reduction_ratio, name='2c')

    output = residual_block(output, block_sizes[1], stride=2, reduction_ratio=reduction_ratio, name='3a')
    output = residual_block(output, block_sizes[1], reduction_ratio=reduction_ratio, name='3b')
    output = residual_block(output, block_sizes[1], reduction_ratio=reduction_ratio, name='3c')
    output = residual_block(output, block_sizes[1], reduction_ratio=reduction_ratio, name='3d')

    output = residual_block(output, block_sizes[2], stride=2, reduction_ratio=reduction_ratio, name='4a')
    output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4b')
    output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4c')
    output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4d')
    output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4e')
    output = residual_block(output, block_sizes[2], reduction_ratio=reduction_ratio, name='4f')

    output = residual_block(output, block_sizes[3], stride=2, reduction_ratio=reduction_ratio, name='5a')
    output = residual_block(output, block_sizes[3], reduction_ratio=reduction_ratio, name='5b')
    output = residual_block(output, block_sizes[3], reduction_ratio=reduction_ratio, name='5c')

    output = T.nnet.relu(T.nnet.mean(T.nnet.relu(output), axis=(2, 3)))
    output = T.nnet.softmax(T.dot(output, T.alloc(0.0, num_classes, block_sizes[3]*4)), axis=-1)

    f = function([input], output)

    return f

以上就是用theano库在ResNet中添加CBAM的步骤。