ResNet实战
2020-12-17 20:33
class BasicBlock(layers.Layer): class ResNet(keras.Model): def resnet18(): def resnet34():# Resnet.py
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, Sequential
def init(self, filter_num, stride=1):
super(BasicBlock, self).init() self.conv1 = layers.Conv2D(filter_num, (3, 3), strides=stride, padding=‘same‘)
self.bn1 = layers.BatchNormalization()
self.relu = layers.Activation(‘relu‘)
self.conv2 = layers.Conv2D(filter_num, (3, 3), strides=1, padding=‘same‘)
self.bn2 = layers.BatchNormalization()
if stride != 1:
self.downsample = Sequential()
self.downsample.add(layers.Conv2D(filter_num, (1, 1), strides=stride))
else:
self.downsample = lambda x: x
def call(self, inputs, training=None):
# [b,h,w,c]
out = self.conv1(inputs)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
identity = self.downsample(inputs)
output = layers.add([out, identity])
output = tf.nn.relu(output)
return out
def init(self, layer_dims, num_classes=100): # [2,2,2,2]
super(ResNet, self).init() # 根部
self.stem = Sequential([layers.Conv2D(64, (3, 3), strides=(1, 1,)),
layers.BatchNormalization(),
layers.Activation(‘relu‘),
layers.MaxPool2D(pool_size=(2, 2), strides=(1, 1), padding=‘same‘)
])
# 64,128,256,512是通道数
self.layer1 = self.build_resblock(64, layer_dims[0])
self.layer2 = self.build_resblock(128, layer_dims[1], stride=2)
self.layer3 = self.build_resblock(256, layer_dims[2], stride=2)
self.layer4 = self.build_resblock(512, layer_dims[3], stride=2)
# output: [b, 512, h, w]
self.avgpool = layers.GlobalAveragePooling2D()
self.fc = layers.Dense(num_classes) # 分类
def call(self, inputs, training=None):
x = self.stem(inputs)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
# [b, c]
x = self.avgpool(x)
# [b]
x = self.fc(x)
return x
def build_resblock(self, filter_num, blocks, stride=1):
res_blocks = Sequential()
# may down sample
res_blocks.add(BasicBlock(filter_num, stride))
for _ in range(1, blocks):
res_blocks.add(BasicBlock(filter_num, stride=1))
return res_blocks
return ResNet([2, 2, 2, 2])
return ResNet([3, 4, 6, 3])
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