size mismatch for model.fc.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([5]).
时间: 2024-02-02 08:30:18 浏览: 20
根据你的描述,这个错误是由于模型中的`model.fc.bias`参数的形状不匹配引起的。从错误信息来看,你正在尝试从一个checkpoint中加载参数,但是加载的参数形状为`torch.Size([10])`,而当前模型中`model.fc.bias`的形状为`torch.Size([5])`。
解决这个问题的方法取决于你的具体需求。如果你确定加载的checkpoint参数是正确的,你可以尝试调整当前模型中`model.fc.bias`的形状,使其与checkpoint参数的形状相匹配。你可以使用`torch.nn.functional.pad()`函数或者手动调整参数形状。
另一种解决办法是重新训练模型,确保模型的结构和参数形状与加载的checkpoint相匹配。
请注意,在调整参数形状之前,确保你已经仔细检查了模型结构和参数,以免出现其他错误。
相关问题
RuntimeError: Error(s) in loading state_dict for Wav2vec2Model: size mismatch for decoder.model.0.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]). size mismatch for decoder.model.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for decoder.model.3.weight: copying a param with shape torch.Size([64, 256]) from checkpoint, the shape in current model is torch.Size([7, 128]). size mismatch for decoder.model.3.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([7]).如何解决报错请举例
这个错误是由于在加载预训练模型的参数时,模型的某些层的维度和预训练模型的参数维度不匹配导致的。解决这个问题的方法是调整模型的参数维度,使其与预训练模型的参数维度相匹配。下面是一个简单的示例:
```python
import torch
class MyModel(torch.nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.encoder = torch.nn.Linear(512, 256)
self.decoder = torch.nn.Linear(256, 512)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x
model = MyModel()
pretrained_model = torch.load("pretrained_model.pth")
# 遍历预训练模型的参数,调整模型参数维度
for name, param in pretrained_model.items():
if name.startswith("decoder"):
if "weight" in name:
param = param[:256, :]
if "bias" in name:
param = param[:256]
model_dict = model.state_dict()
model_dict[name].copy_(param)
# 在此处使用模型进行推理或训练
```
在上面的示例中,我们首先定义了一个自定义模型MyModel,其中包含一个512维的输入层和一个512维的输出层。然后我们加载了一个名为“pretrained_model.pth”的预训练模型,并遍历了其所有的参数。对于decoder层的参数,我们将其维度调整为与自定义模型相匹配。最后,我们使用调整后的模型进行推理或训练。
Error(s) in loading state_dict for LeNet: size mismatch for conv1.weight: copying a param with shape torch.Size([10, 1, 5, 5]) from checkpoint, the shape in current model is torch.Size([6, 1, 5, 5]). size mismatch for conv1.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([6]). size mismatch for conv2.weight: copying a param with shape torch.Size([20, 10, 5, 5]) from checkpoint, the shape in current model is torch.Size([16, 6, 5, 5]). size mismatch for conv2.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]). size mismatch for fc1.weight: copying a param with shape torch.Size([50, 320]) from checkpoint, the shape in current model is torch.Size([120, 256]). size mismatch for fc1.bias: copying a param with shape torch.Size([50]) from checkpoint, the shape in current model is torch.Size([120]). size mismatch for fc2.weight: copying a param with shape torch.Size([10, 50]) from checkpoint, the shape in current model is torch.Size([84, 120]). size mismatch for fc2.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([84]).
这个错误信息提示我们在加载预训练模型的权重时,发现模型中的部分参数形状与当前创建的模型不匹配,具体来说,就是:
- `conv1.weight`的形状应该为`(6, 1, 5, 5)`,但是预训练模型中的形状为`(10, 1, 5, 5)`;
- `conv1.bias`的形状应该为`(6,)`,但是预训练模型中的形状为`(10,)`;
- `conv2.weight`的形状应该为`(16, 6, 5, 5)`,但是预训练模型中的形状为`(20, 10, 5, 5)`;
- `conv2.bias`的形状应该为`(16,)`,但是预训练模型中的形状为`(20,)`;
- `fc1.weight`的形状应该为`(120, 256)`,但是预训练模型中的形状为`(50, 320)`;
- `fc1.bias`的形状应该为`(120,)`,但是预训练模型中的形状为`(50,)`;
- `fc2.weight`的形状应该为`(84, 120)`,但是预训练模型中的形状为`(10, 50)`;
- `fc2.bias`的形状应该为`(84,)`,但是预训练模型中的形状为`(10,)`。
这个问题通常是由于预训练模型的结构与当前创建的模型结构不一致导致的。解决方法是在当前模型中手动修改这些参数的形状,使其与预训练模型的结构一致。