Pytorch模型迁移和迁移学习,导入部分模型参数的操作

 import torch from torchvision import models if __name__ == "__main__": # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = 'cpu' print("-----device:{}".format(device)) print("-----Pytorch version:{}".format(torch.__version__)) input_tensor = torch.zeros(1, 3, 100, 100) print('input_tensor:', input_tensor.shape) pretrained_file = "model/resnet18-5c106cde.pth" model = models.resnet18() model.load_state_dict(torch.load(pretrained_file)) model.eval() out = model(input_tensor) print("out:", out.shape, out[0, 0:10])

 class ResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, zero_init_residual=False): super(ResNet, self).__init__() self.inplanes = 64 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.layer44 = self._make_layer(block, 512, layers[3], stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ 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.layer44(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x 

 model = models.resnet18() model.load_state_dict(torch.load(pretrained_file))

 def transfer_model(pretrained_file, model): ''' 只导入pretrained_file部分模型参数 tensor([-0.7119, 0.0688, -1.7247, -1.7182, -1.2161, -0.7323, -2.1065, -0.5433,-1.5893, -0.5562] update: D.update([E, ]**F) -> None. Update D from dict/iterable E and F. If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] :param pretrained_file: :param model: :return: ''' pretrained_dict = torch.load(pretrained_file) # get pretrained dict model_dict = model.state_dict() # get model dict # 在合并前(update),需要去除pretrained_dict一些不需要的参数 pretrained_dict = transfer_state_dict(pretrained_dict, model_dict) model_dict.update(pretrained_dict) # 更新(合并)模型的参数 model.load_state_dict(model_dict) return model def transfer_state_dict(pretrained_dict, model_dict): ''' 根据model_dict,去除pretrained_dict一些不需要的参数,以便迁移到新的网络 url: https://blog.csdn.net/qq_34914551/article/details/87871134 :param pretrained_dict: :param model_dict: :return: ''' # state_dict2 = {k: v for k, v in save_model.items() if k in model_dict.keys()} state_dict = {} for k, v in pretrained_dict.items(): if k in model_dict.keys(): # state_dict.setdefault(k, v) state_dict[k] = v else: print("Missing key(s) in state_dict :{}".format(k)) return state_dict if __name__ == "__main__": input_tensor = torch.zeros(1, 3, 100, 100) print('input_tensor:', input_tensor.shape) pretrained_file = "model/resnet18-5c106cde.pth" # model = resnet18() # model.load_state_dict(torch.load(pretrained_file)) # model.eval() # out = model(input_tensor) # print("out:", out.shape, out[0, 0:10]) model1 = resnet18() model1 = transfer_model(pretrained_file, model1) out1 = model1(input_tensor) print("out1:", out1.shape, out1[0, 0:10])

 modify_state_dict(pretrained_dict, model_dict, old_prefix, new_prefix)

 def string_rename(old_string, new_string, start, end): new_string = old_string[:start] + new_string + old_string[end:] return new_string def modify_model(pretrained_file, model, old_prefix, new_prefix): ''' :param pretrained_file: :param model: :param old_prefix: :param new_prefix: :return: ''' pretrained_dict = torch.load(pretrained_file) model_dict = model.state_dict() state_dict = modify_state_dict(pretrained_dict, model_dict, old_prefix, new_prefix) model.load_state_dict(state_dict) return model def modify_state_dict(pretrained_dict, model_dict, old_prefix, new_prefix): ''' 修改model dict :param pretrained_dict: :param model_dict: :param old_prefix: :param new_prefix: :return: ''' state_dict = {} for k, v in pretrained_dict.items(): if k in model_dict.keys(): # state_dict.setdefault(k, v) state_dict[k] = v else: for o, n in zip(old_prefix, new_prefix): prefix = k[:len(o)] if prefix == o: kk = string_rename(old_string=k, new_string=n, start=0, end=len(o)) print("rename layer modules:{}-->{}".format(k, kk)) state_dict[kk] = v return state_dict

 if __name__ == "__main__": input_tensor = torch.zeros(1, 3, 100, 100) print('input_tensor:', input_tensor.shape) pretrained_file = "model/resnet18-5c106cde.pth" # model = models.resnet18() # model.load_state_dict(torch.load(pretrained_file)) # model.eval() # out = model(input_tensor) # print("out:", out.shape, out[0, 0:10]) # # model1 = resnet18() # model1 = transfer_model(pretrained_file, model1) # out1 = model1(input_tensor) # print("out1:", out1.shape, out1[0, 0:10]) # new_file = "new_model.pth" model = resnet18() new_model = modify_model(pretrained_file, model, old_prefix=["layer4"], new_prefix=["layer44"]) torch.save(new_model.state_dict(), new_file) model2 = resnet18() model2.load_state_dict(torch.load(new_file)) model2.eval() out2 = model2(input_tensor) print("out2:", out2.shape, out2[0, 0:10])

 import torch import torchvision.models as models from collections import OrderedDict if __name__=="__main__": resnet18 = models.resnet18(False) print("resnet18",resnet18) # use named_children() resnet18_v1 = OrderedDict(resnet18.named_children()) # remove avgpool,fc resnet18_v1.pop("avgpool") resnet18_v1.pop("fc") resnet18_v1 = torch.nn.Sequential(resnet18_v1) print("resnet18_v1",resnet18_v1) # use children resnet18_v2 = torch.nn.Sequential(*list(resnet18.children())[:-2]) print(resnet18_v2,resnet18_v2)

 checkpoint = torch.load(config.pretrained_model) # change name and load parameters model_dict = model.net1.state_dict() checkpoint = {k.replace('features.features', 'featureExtract1'): v for k, v in checkpoint.items()} checkpoint = {k:v for k,v in checkpoint.items() if k in model_dict.keys()} model_dict.update(checkpoint) model.net1.load_state_dict(model_dict)