数据集下载地址：
链接：https://pan.baidu.com/s/1l1AnBgkAAEhh0vI5_loWKw
提取码：2xq4
创建数据集：https://www.cnblogs.com/xiximayou/p/12398285.html
读取数据集：https://www.cnblogs.com/xiximayou/p/12422827.html
进行训练：https://www.cnblogs.com/xiximayou/p/12448300.html
保存模型并继续进行训练：https://www.cnblogs.com/xiximayou/p/12452624.html
加载保存的模型并测试：https://www.cnblogs.com/xiximayou/p/12459499.html
划分验证集并边训练边验证：https://www.cnblogs.com/xiximayou/p/12464738.html
使用学习率衰减策略并边训练边测试：https://www.cnblogs.com/xiximayou/p/12468010.html
利用tensorboard可视化训练和测试过程：https://www.cnblogs.com/xiximayou/p/12482573.html
从命令行接收参数：https://www.cnblogs.com/xiximayou/p/12488662.html
使用top1和top5准确率来衡量模型：https://www.cnblogs.com/xiximayou/p/12489069.html
epoch、batchsize、step之间的关系：https://www.cnblogs.com/xiximayou/p/12405485.html
之前都是从头开始训练模型，本节我们要使用预训练的模型来进行训练。
只需要在train.py中加上：
if baseline:
model =torchvision.models.resnet18(pretrained=False)
model.fc = nn.Linear(model.fc.in_features,2,bias=False)
else:
print("使用预训练的resnet18模型")
model=torchvision.models.resnet18(pretrained=True)
for i in model.state_dict():
print(i)
model.fc = nn.Linear(model.fc.in_features,2,bias=False)
print(model)
使用预训练的resnet18模型
conv1.weight
bn1.weight
bn1.bias
bn1.running_mean
bn1.running_var
bn1.num_batches_tracked
layer1.0.conv1.weight
layer1.0.bn1.weight
layer1.0.bn1.bias
layer1.0.bn1.running_mean
layer1.0.bn1.running_var
layer1.0.bn1.num_batches_tracked
layer1.0.conv2.weight
layer1.0.bn2.weight
layer1.0.bn2.bias
layer1.0.bn2.running_mean
layer1.0.bn2.running_var
layer1.0.bn2.num_batches_tracked
layer1.1.conv1.weight
layer1.1.bn1.weight
layer1.1.bn1.bias
layer1.1.bn1.running_mean
layer1.1.bn1.running_var
layer1.1.bn1.num_batches_tracked
layer1.1.conv2.weight
layer1.1.bn2.weight
layer1.1.bn2.bias
layer1.1.bn2.running_mean
layer1.1.bn2.running_var
layer1.1.bn2.num_batches_tracked
layer2.0.conv1.weight
layer2.0.bn1.weight
layer2.0.bn1.bias
layer2.0.bn1.running_mean
layer2.0.bn1.running_var
layer2.0.bn1.num_batches_tracked
layer2.0.conv2.weight
layer2.0.bn2.weight
layer2.0.bn2.bias
layer2.0.bn2.running_mean
layer2.0.bn2.running_var
layer2.0.bn2.num_batches_tracked
layer2.0.downsample.0.weight
layer2.0.downsample.1.weight
layer2.0.downsample.1.bias
layer2.0.downsample.1.running_mean
layer2.0.downsample.1.running_var
layer2.0.downsample.1.num_batches_tracked
layer2.1.conv1.weight
layer2.1.bn1.weight
layer2.1.bn1.bias
layer2.1.bn1.running_mean
layer2.1.bn1.running_var
layer2.1.bn1.num_batches_tracked
layer2.1.conv2.weight
layer2.1.bn2.weight
layer2.1.bn2.bias
layer2.1.bn2.running_mean
layer2.1.bn2.running_var
layer2.1.bn2.num_batches_tracked
layer3.0.conv1.weight
layer3.0.bn1.weight
layer3.0.bn1.bias
layer3.0.bn1.running_mean
layer3.0.bn1.running_var
layer3.0.bn1.num_batches_tracked
layer3.0.conv2.weight
layer3.0.bn2.weight
layer3.0.bn2.bias
layer3.0.bn2.running_mean
layer3.0.bn2.running_var
layer3.0.bn2.num_batches_tracked
layer3.0.downsample.0.weight
layer3.0.downsample.1.weight
layer3.0.downsample.1.bias
layer3.0.downsample.1.running_mean
layer3.0.downsample.1.running_var
layer3.0.downsample.1.num_batches_tracked
layer3.1.conv1.weight
layer3.1.bn1.weight
layer3.1.bn1.bias
layer3.1.bn1.running_mean
layer3.1.bn1.running_var
layer3.1.bn1.num_batches_tracked
layer3.1.conv2.weight
layer3.1.bn2.weight
layer3.1.bn2.bias
layer3.1.bn2.running_mean
layer3.1.bn2.running_var
layer3.1.bn2.num_batches_tracked
layer4.0.conv1.weight
layer4.0.bn1.weight
layer4.0.bn1.bias
layer4.0.bn1.running_mean
layer4.0.bn1.running_var
layer4.0.bn1.num_batches_tracked
layer4.0.conv2.weight
layer4.0.bn2.weight
layer4.0.bn2.bias
layer4.0.bn2.running_mean
layer4.0.bn2.running_var
layer4.0.bn2.num_batches_tracked
layer4.0.downsample.0.weight
layer4.0.downsample.1.weight
layer4.0.downsample.1.bias
layer4.0.downsample.1.running_mean
layer4.0.downsample.1.running_var
layer4.0.downsample.1.num_batches_tracked
layer4.1.conv1.weight
layer4.1.bn1.weight
layer4.1.bn1.bias
layer4.1.bn1.running_mean
layer4.1.bn1.running_var
layer4.1.bn1.num_batches_tracked
layer4.1.conv2.weight
layer4.1.bn2.weight
layer4.1.bn2.bias
layer4.1.bn2.running_mean
layer4.1.bn2.running_var
layer4.1.bn2.num_batches_tracked
fc.weight
fc.bias
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=2, bias=False)
)
接下来来看看如何冻结某些层，不让其在训练的时候进行梯度更新。
首先我们输出下信息看看结构:
i=0for child in model.children(): i+=1 print("第{}个child".format(str(i)))
print(child)
第1个child
Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
第2个child
BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
第3个child
ReLU(inplace=True)
第4个child
MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
第5个child
Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
第6个child
Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
第7个child
Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
第8个child
Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
第9个child
AdaptiveAvgPool2d(output_size=(1, 1))
第10个child
Linear(in_features=512, out_features=2, bias=False)
我们冻结前面的7个child，只更新第8、9、10个child的参数。可这么定义：
print("使用预训练的resnet18模型")
model=torchvision.models.resnet18(pretrained=True)
model.fc = nn.Linear(model.fc.in_features,2,bias=False)
i=0
for child in model.children():
i+=1
#print("第{}个child".format(str(i)))
#print(child)
if i<=7:
for param in child.parameters():
param.requires_grad=False
#我们打印下是否是设置成功
for name, param in model.named_parameters():
if param.requires_grad:
print("需要梯度：", name)
else:
print("不需要梯度：", name)
接下来我们还要在优化器中过滤掉不需要更新参数的层：
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=0.1, momentum=0.9,
weight_decay=1*1e-4)
结果：
使用预训练的resnet18模型 不需要梯度： conv1.weight 不需要梯度： bn1.weight 不需要梯度： bn1.bias 不需要梯度： layer1.0.conv1.weight 不需要梯度： layer1.0.bn1.weight 不需要梯度： layer1.0.bn1.bias 不需要梯度： layer1.0.conv2.weight 不需要梯度： layer1.0.bn2.weight 不需要梯度： layer1.0.bn2.bias 不需要梯度： layer1.1.conv1.weight 不需要梯度： layer1.1.bn1.weight 不需要梯度： layer1.1.bn1.bias 不需要梯度： layer1.1.conv2.weight 不需要梯度： layer1.1.bn2.weight 不需要梯度： layer1.1.bn2.bias 不需要梯度： layer2.0.conv1.weight 不需要梯度： layer2.0.bn1.weight 不需要梯度： layer2.0.bn1.bias 不需要梯度： layer2.0.conv2.weight 不需要梯度： layer2.0.bn2.weight 不需要梯度： layer2.0.bn2.bias 不需要梯度： layer2.0.downsample.0.weight 不需要梯度： layer2.0.downsample.1.weight 不需要梯度： layer2.0.downsample.1.bias 不需要梯度： layer2.1.conv1.weight 不需要梯度： layer2.1.bn1.weight 不需要梯度： layer2.1.bn1.bias 不需要梯度： layer2.1.conv2.weight 不需要梯度： layer2.1.bn2.weight 不需要梯度： layer2.1.bn2.bias 不需要梯度： layer3.0.conv1.weight 不需要梯度： layer3.0.bn1.weight 不需要梯度： layer3.0.bn1.bias 不需要梯度： layer3.0.conv2.weight 不需要梯度： layer3.0.bn2.weight 不需要梯度： layer3.0.bn2.bias 不需要梯度： layer3.0.downsample.0.weight 不需要梯度： layer3.0.downsample.1.weight 不需要梯度： layer3.0.downsample.1.bias 不需要梯度： layer3.1.conv1.weight 不需要梯度： layer3.1.bn1.weight 不需要梯度： layer3.1.bn1.bias 不需要梯度： layer3.1.conv2.weight 不需要梯度： layer3.1.bn2.weight 不需要梯度： layer3.1.bn2.bias 需要梯度： layer4.0.conv1.weight 需要梯度： layer4.0.bn1.weight 需要梯度： layer4.0.bn1.bias 需要梯度： layer4.0.conv2.weight 需要梯度： layer4.0.bn2.weight 需要梯度： layer4.0.bn2.bias 需要梯度： layer4.0.downsample.0.weight 需要梯度： layer4.0.downsample.1.weight 需要梯度： layer4.0.downsample.1.bias 需要梯度： layer4.1.conv1.weight 需要梯度： layer4.1.bn1.weight 需要梯度： layer4.1.bn1.bias 需要梯度： layer4.1.conv2.weight 需要梯度： layer4.1.bn2.weight 需要梯度： layer4.1.bn2.bias 需要梯度： fc.weight
拓展：如果是我们自己定义的模型和预训练的模型不一致应该怎么加载参数呢？
这里以以resnet50为例，这里我们再新定义一个卷积神经网络：
# coding=UTF-8
import torchvision.models as models
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
class CNN(nn.Module):
def __init__(self, block, layers, num_classes=2):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
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)
#新增一个反卷积层
self.convtranspose1 = nn.ConvTranspose2d(2048, 2048, kernel_size=3, stride=1, padding=1, output_padding=0, groups=1, bias=False, dilation=1)
#新增一个最大池化层
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
#去掉原来的fc层，新增一个fclass层
self.fclass = nn.Linear(2048, 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 = None
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)
#新加层的forward
x = x.view(x.size(0), -1)
x = self.convtranspose1(x)
x = self.maxpool2(x)
x = x.view(x.size(0), -1)
x = self.fclass(x)
return x
#加载model
resnet50 = models.resnet50(pretrained=True)
cnn = CNN(Bottleneck, [3, 4, 6, 3])
#读取参数#取出预训练模型的参数
pretrained_dict = resnet50.state_dict()#取出本模型的参数
model_dict = cnn.state_dict()
# 将pretrained_dict里不属于model_dict的键剔除掉
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 更新现有的model_dict
model_dict.update(pretrained_dict)
# 加载我们真正需要的state_dict
cnn.load_state_dict(model_dict)
# print(resnet50)
print(cnn)
下面也摘取了一些使用部分预训练模型初始化网络的方法：
方式一： 自己网络和预训练网络结构一致的层，使用预训练网络对应层的参数批量初始化
model_dict = model.state_dict() # 取出自己网络的参数字典
pretrained_dict = torch.load("I:/迅雷下载/alexnet-owt-4df8aa71.pth")# 加载预训练网络的参数字典
# 取出预训练网络的参数字典
keys = []
for k, v in pretrained_dict.items():
keys.append(k)
i = 0
# 自己网络和预训练网络结构一致的层，使用预训练网络对应层的参数初始化
for k, v in model_dict.items():
if v.size() == pretrained_dict[keys[i]].size():
model_dict[k] = pretrained_dict[keys[i]]
#print(model_dict[k])
i = i + 1
model.load_state_dict(model_dict)
方式二：自己网络和预训练网络结构一致的层，按层初始化
# 加粗自己定义一个网络叫CNN
model = CNN（）
model_dict = model.state_dict() # 取出自己网络的参数
for k, v in model_dict.items(): # 查看自己网络参数各层叫什么名称
print(k)
pretrained_dict = torch.load("I:/迅雷下载/alexnet-owt-4df8aa71.pth")# 加载预训练网络的参数
for k, v in pretrained_dict.items(): # 查看预训练网络参数各层叫什么名称
print(k)
# 对应层赋值初始化
model_dict[‘conv1.0.weight‘] = pretrained_dict[‘features.0.weight‘] # 将自己网络的conv1.0层的权重初始化为预训练网络features.0层的权重
model_dict[‘conv1.0.bias‘] = pretrained_dict[‘features.0.bias‘] # 将自己网络的conv1.0层的偏置项初始化为预训练网络features.0层的偏置项
model_dict[‘conv2.1.weight‘] = pretrained_dict[‘features.3.weight‘]
model_dict[‘conv1.1.bias‘] = pretrained_dict[‘features.3.bias‘]
model_dict[‘conv2.1.weight‘] = pretrained_dict[‘features.6.weight‘]
model_dict[‘conv2.1.bias‘] = pretrained_dict[‘features.6.bias‘]
... ...
下一节补充下计算数据集的标准差和方差，在数据增强时对数据进行标准化的时候用。
参考：
https://blog.csdn.net/feizai1208917009/article/details/103598233
https://blog.csdn.net/whut_ldz/article/details/78845947
原文地址：https://www.cnblogs.com/xiximayou/p/12504579.html
时间： 2024-08-11 17:07:07