RuntimeError: Error(s) in loading state_dict for Tacotron: size mismatch for encoder_proj.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([128, 1024]). size mismatch for decoder.attn_rnn.weight_ih: copying a param with shape torch.Size([384, 768]) from checkpoint, the shape in current model is torch.Size([384, 1280]). size mismatch for decoder.rnn_input.weight: copying a param with shape torch.Size([1024, 640]) from checkpoint, the shape in current model is torch.Size([1024, 1152]). size mismatch for decoder.stop_proj.weight: copying a param with shape torch.Size([1, 1536]) from checkpoint, the shape in current model is torch.Size([1, 2048]).
时间: 2023-02-09 19:19:22 浏览: 744
这个错误表明在加载模型参数时发生了问题。具体来说,在尝试将参数从检查点加载到当前模型时,发现了形状不匹配的问题。在这种情况下,参数的大小在检查点和当前模型中不同。需要修复代码或检查点来解决这个问题。
相关问题
RuntimeError: Error(s) in loading state_dict for Network_bn: size mismatch for fc1.weight: copying a param with shape torch.Size([4, 21168]) from checkpoint, the shape in current model is torch.Size([5, 21168]). size mismatch for fc1.bias: copying a param with shape torch.Size([4]) from checkpoint, the shape in current model is torch.Size([5]).
这个错误是由于加载模型参数时,发现模型的参数形状与checkpoint中的形状不匹配导致的。具体来说,出错的是网络中的fc1层的权重和偏置。
在checkpoint中,fc1权重的形状为torch.Size([4, 21168]),而当前模型中的形状为torch.Size([5, 21168])。同样,fc1偏置的形状在checkpoint中为torch.Size([4]),而当前模型中的形状为torch.Size([5])。
要解决这个问题,你需要确保当前模型的fc1层与checkpoint中的fc1层具有相同的形状。可能的解决方法包括:
- 修改当前模型的fc1层的权重和偏置的形状,使其与checkpoint中的形状匹配。
- 使用checkpoint中的fc1层的权重和偏置,替换当前模型中对应的参数。
具体哪种方法适合取决于你的需求和具体情况。请注意,在修改模型参数之前,最好先备份当前模型,以防止意外损坏模型。
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。
阅读全文