#!/usr/bin/python3
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from torchsummary import summary
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=False)
self.conv2 = nn.Conv2d(out_channels, out_channels,
kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.shortcut = nn.Sequential()
if stride != 1 or in_channels != out_channels:
self.shortcut = nn.Sequential(nn.Conv2d(
in_channels, out_channels, stride=stride, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels))
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += self.shortcut(identity)
out = self.relu(out)
return out
class ResNet18(nn.Module):
def __init__(self, num_classes=1000):
super(ResNet18, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
stride=2, padding=3, bias=True)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(64, 64, 2, stride=1)
self.layer2 = self._make_layer(64, 128, 2, stride=2)
self.layer3 = self._make_layer(128, 256, 2, stride=2)
self.layer4 = self._make_layer(256, 512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_classes)
def _make_layer(self, in_channels, out_channels, blocks, stride=1):
layer = []
layer.append(ResidualBlock(in_channels, out_channels, stride))
for _ in range(1, blocks):
layer.append(ResidualBlock(out_channels, out_channels))
return nn.Sequential(*layer)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.maxpool(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avgpool(out)
out = torch.flatten(out, 1)
out = self.fc(out)
return out
model = ResNet18()
print(model)
torch.save(model, "my-resnet18.pth")
# summary(model, (3, 224, 224))
###############################################################
# model = torch.load('my-resnet18.pth', map_location=torch.device('cpu'))
# # 定义输入张量的大小
# input_shape = (1, 3, 32, 32)
# dummy_input = torch.randn(input_shape)
# # 将模型导出为 ONNX 格式
# torch.onnx.export(model, dummy_input, 'my-resnet18.onnx', input_names=['input'], output_names=['output'])