最后通过调用model的load_state_dict方法用预训练的模型参数来初始化你构建的网络结构,这个方法就是PyTorch中通用的用一个模型的参数初始化另一个模型的层的操作。load_state_dict方法还有一个重要的参数是strict,该参数默认是True,表示预训练模型的层和你的网络结构层严格对应相等(比如层名和维度)。 defresnet50(pr...
load_state_dict(state_dict, strict=False) return model def sa_resnet50(pretrained=False): model = _sanet('SANet-50', SABottleneck, [3, 4, 6, 3], pretrained=pretrained) return model def sa_resnet101(pretrained=False): model = _sanet('SANet-101', SABottleneck, [3, 4, 23, 3], ...
model_weight_path = "./resnet34-333f7ec4.pth" missing_keys, unexpected_keys = net.load_state_dict(torch.load(model_weight_path), strict=False) inchannel = net.fc.in_features net.fc = nn.Linear(inchannel, 5) net.to(device)注意...
通过调用model的load_state_dict方法用预训练的模型参数来初始化你构建的网络结构,这个方法就是PyTorch中通用的用一个模型的参数初始化另一个模型的层的操作。load_state_dict方法还有一个重要的参数是strict,该参数默认是True,表示预训练模型的层和你的网络结构层严格对应相等(比如层名和维度)。 def resnet50(pretr...
missing_keys, unexpected_keys = net.load_state_dict(pre_dict, strict=False) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 3 冻结特征提取层权重进行训练 简单训练一下时,需要冻结前面特征提取层的权重,让它们不参与训练,实现代码如下: ...
model1.load_state_dict(model_dict1)missing, unspected = model2.load_state_dict(model_dict2)image = cv.imread('zhn1.jpg')image = letter_box(image, 224)image = image[:, :, ::-1].transpose(2, 0, 1)print('Network loading complete.')model1.eval()model2.eval()with torch.no_grad(...
res18.load_state_dict(model_state_dict, strict=False) ### data normalization for both training set data_transforms = transforms.Compose([ transforms.ToPILImage(), transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], ...
} 2.读取预训练模型,将全连接层的输出改为想要的类别数 model =resnet34() model.load_state_dict(torch.load("resnet34-b627a593.pth"),strict=False) inchannel=model.fc.in_features model.fc= nn.Linear(inchannel,2)
model = create_model(f./models/{ model_name}.yaml).cpu() model.load_state_dict(load_state_dict(./models/v1-5-pruned.ckpt, location=cuda), strict=False) model.load_state_dict(load_state_dict(f./models/{ model_name}.pth, location=cuda), strict=False)模型命名...
load_fc is False: if "classifier" in key: del state_dict[key] res = pattern.match(key) if res: new_key = res.group(1) + res.group(2) state_dict[new_key] = state_dict[key] del state_dict[key] model.load_state_dict(state_dict, strict=load_fc) print("successfully load pre...