'''
导入库
'''
import torch
import torch.nn as nn
import torchvision
import torch.utils.data as Data
from torch.utils import model_zoo
import math
from torch.autograd import Variable
from torchvision.transforms import Compose, ToTensor, Resize
import gc
gc.collect()
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
# 对输入图像进行处理，转换为（224，224）,因为resnet18要求输入为（224，224），并转化为tensor
def input_transform():
return Compose([
Resize(224), # 改变尺寸
ToTensor(), # 变成tensor
])
# Mnist 手写数字，数据导入
train_data = torchvision.datasets.MNIST(
root='mnist/', # 保存或者提取位置
train=True, # this is training data
transform=input_transform(), # 转换 PIL.Image or numpy.ndarray 成
# torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
download=True, # 没下载就下载, 下载了就不用再下了
)
test_data = torchvision.datasets.MNIST(
root='mnist/', # 保存或者提取位置
train=False, # this is training data
transform=input_transform(), # 转换 PIL.Image or numpy.ndarray 成
# torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
download=True, # 没下载就下载, 下载了就不用再下了
)
BATCH_SIZE = 32
'''
进行批处理
'''
loader = Data.DataLoader(dataset=train_data,
batch_size=BATCH_SIZE,
shuffle=True,
)
'''
定义resnet18
'''
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
# inplanes其实就是channel,叫法不同
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
# 把shortcut那的channel的维度统一
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=10):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, # 因为mnist为（1，28，28）灰度图，因此输入通道数为1
bias=False)
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(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
# downsample 主要用来处理H(x)=F(x)+x中F(x)和xchannel维度不匹配问题
downsample = None
# self.inplanes为上个box_block的输出channel,planes为当前box_block块的输入channel
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
# [2, 2, 2, 2]和结构图[]X2是对应的
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained: # 加载模型权重
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# print(device)
net = resnet18().to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=0.01)
loss_func = torch.nn.CrossEntropyLoss()
for epoch in range(3):
for step, (batch_x, batch_y) in enumerate(loader):
b_x = Variable(batch_x).to(device)
b_y = Variable(batch_y).to(device)
predict = net(b_x)
loss = loss_func(predict, b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 5 == 0:
print('epoch:{}, step:{}, loss:{}'.format(epoch, step, loss))
