原文：https://makeoptim.com/deep-learning/yiai-resnet-implementation
- 前言
- 网络结构
- 实现
- 测试
- 小结
- 参考
前言
上一篇笔者使用如何阅读深度学习论文的方法阅读了 ResNet。为了加深理解，本文带大家使用 TensorFlow 2 Keras 实现 ResNet 网络。
网络结构
从论文了解到，可以根据网络的深度开发不同类型的 ResNet，如：ResNet-34、ResNet-50、ResNet-152，甚至可以自定义网络的深度，来满足需求。因此，ResNet 更像是一种范式，可能叫 ResNets 会更合适些。
ResNet 可以看做是 VGG 的升级版，区别在于 ResNet 中使用的快捷连接(shortcuts)。在下图中，我们可以看到 VGG 的架构以及 34 层 ResNet。
image
图 3.
ImageNet的网络架构例子。左：作为参考的VGG-19模型[41]。中：具有34个参数层的简单网络（36亿FLOPs）。右：具有34个参数层的残差网络（36亿FLOPs）。带点的快捷连接增加了维度。表 1 显示了更多细节和其它变种。
而对于不同的网络类型，其中的构建块（building block）也不一样，如下图所示：
image
图 5.
ImageNet的深度残差函数 F。左：ResNet-34的构建块（在56×56的特征图上），如图 3。右：ResNet-50/101/152的 “bottleneck”构建块。
论文也提供了不同深度的 ResNet 的架构图，如下所示：
image
表 1.
ImageNet架构。构建块显示在括号中（也可看图 5），以及构建块的堆叠数量。下采样通过步长为2的conv3_1,conv4_1和conv5_1执行。
实现
下面以 ResNet-50 为例，其他的类型都是类似的。
注：源码已经上传 https://github.com/CatchZeng/YiAI-examples/blob/master/papers/ResNet/ResNet.py，需要的同学可以参考。
image
首先，先实现下堆叠的残差结构（上图红色框部分）。
def ResNet50(include_top=True,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the ResNet50 architecture."""
# 堆叠的残差结构
def stack_fn(x):
x = stack(x, 64, 3, stride1=1, name='conv2')
x = stack(x, 128, 4, name='conv3')
x = stack(x, 256, 6, name='conv4')
return stack(x, 512, 3, name='conv5')
return ResNet(stack_fn, 'resnet50', include_top, input_shape, pooling, classes)
def stack(x, filters, blocks, stride1=2, name=None):
"""A set of stacked residual blocks.
Args:
x: input tensor.
filters: integer, filters of the bottleneck layer in a block.
blocks: integer, blocks in the stacked blocks.
stride1: default 2, stride of the first layer in the first block.
name: string, stack label.
Returns:
Output tensor for the stacked blocks.
"""
x = block(x, filters, stride=stride1, name=name + '_block1')
# 没有增加维度的时候可以做恒等快捷连接，不需要 conv_shortcut，可以参考图 3。
for i in range(2, blocks + 1):
x = block(x, filters, conv_shortcut=False,
name=name + '_block' + str(i))
return x
def block(x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None):
"""A residual block.
Args:
x: input tensor.
filters: integer, filters of the bottleneck layer.
kernel_size: default 3, kernel size of the bottleneck layer.
stride: default 1, stride of the first layer.
conv_shortcut: default True, use convolution shortcut if True,
otherwise identity shortcut.
name: string, block label.
Returns:
Output tensor for the residual block.
"""
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
# 当维度增加（图 3 中的虚线快捷连接）时，我们考虑两个选项：（A）快捷连接仍然执行恒等映射，额外填充零输入以增加维度。此选项不会引入额外的参数；（B）方程（2）中的投影快捷连接用于匹配维度（由 1×1 卷积完成）。对于这两个选项，当快捷连接跨越两种尺寸的特征图时，它们执行时步长为 2。
if conv_shortcut:
shortcut = layers.Conv2D(
4 * filters, 1, strides=stride, name=name + '_0_conv')(x)
shortcut = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(shortcut)
# 当输入和输出具有相同的维度时（图 3 中的实线快捷连接）时，可以直接使用恒等快捷连接
else:
shortcut = x
# 1x1xfilters
x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x)
x = layers.Activation('relu', name=name + '_1_relu')(x)
# 3x3xfilters
x = layers.Conv2D(
filters, kernel_size, padding='SAME', name=name + '_2_conv')(x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(x)
x = layers.Activation('relu', name=name + '_2_relu')(x)
# 1x1x(4倍filters)
x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name=name + '_3_bn')(x)
x = layers.Add(name=name + '_add')([shortcut, x])
x = layers.Activation('relu', name=name + '_out')(x)
return x
接着再实现输入和输出部分（上图绿色和蓝色框部分），代码如下：
def ResNet(stack_fn,
model_name='resnet',
include_top=True,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation='softmax'):
"""Instantiates the ResNet, ResNetV2, and ResNeXt architecture.
Args:
stack_fn: a function that returns output tensor for the
stacked residual blocks.
model_name: string, model name.
include_top: whether to include the fully-connected
layer at the top of the network.
input_shape: optional shape tuple, `(224, 224, 3)` (with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 inputs channels.
pooling: optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
classifier_activation: A `str` or callable. The activation function to use
on the "top" layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the "top" layer.
When loading pretrained weights, `classifier_activation` can only
be `None` or `"softmax"`.
Returns:
A `keras.Model` instance.
"""
img_input = layers.Input(shape=input_shape)
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
x = layers.ZeroPadding2D(
padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)
# conv 1 7x7, 64, stride 2
x = layers.Conv2D(64, 7, strides=2, use_bias=True,
name='conv1_conv')(x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name='conv1_bn')(x)
x = layers.Activation('relu', name='conv1_relu')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)
# 3x3 max pool, stride 2
x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)
x = stack_fn(x)
if include_top:
# average pool, 1000-d fc, softmax
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = layers.Dense(classes, activation=classifier_activation,
name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D(name='max_pool')(x)
inputs = img_input
model = Model(inputs, x, name=model_name)
return model
测试
通过与 tensorflow 官方的代码对比，可以验证模型编写的准确性。
if __name__ == '__main__':
model = ResNet50(include_top=True, input_shape=(224, 224, 3), classes=10)
model.summary()
print("----------------------------------------")
from tensorflow.keras.applications import resnet
model2 = resnet.ResNet50(
include_top=True, weights=None, input_shape=(224, 224, 3), classes=10)
model2.summary()
小结
实践出真知，从阅读到实践，是一个提升的过程。在实践中，不但可以了解到实现的细节，而且还能熟悉 TensorFlow 的生态。强烈推荐大家，多看论文，并实践。
延伸阅读
- 使用 TensorFlow 2 Keras 实现 AlexNet CNN 网络
参考
- https://www.analyticsvidhya.com/blog/2021/08/how-to-code-your-resnet-from-scratch-in-tensorflow/