pytorch 預訓練模型修改
阿新 • • 發佈:2018-11-13
# 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=9): 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) #新增一個反捲積層 self.convtranspose1 = nn.ConvTranspose2d(2048, 2048, kernel_size=3, stride=1, padding=1, output_padding=0, groups=1, bias=False, dilation=1) #新增一個最大池化層 self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) #去掉原來的fc層,新增一個fclass層 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) #新加層的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 #載入model resnet50 = models.resnet50(pretrained=True) cnn = CNN(Bottleneck, [3, 4, 6, 3]) #讀取引數 pretrained_dict = resnet50.state_dict() model_dict = cnn.state_dict() # 將pretrained_dict裡不屬於model_dict的鍵剔除掉 pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} # 更新現有的model_dict model_dict.update(pretrained_dict) # 載入我們真正需要的state_dict cnn.load_state_dict(model_dict) # print(resnet50) print(cnn) --------------------- 作者:whut_ldz 來源:CSDN 原文:https://blog.csdn.net/whut_ldz/article/details/78845947 版權宣告:本文為博主原創文章,轉載請附上博文連結!