D:\software\anaconda\envs\test\lib\site-packages\torch\functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:3610.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] ---final upsample expand_first--- D:\code\Swin-Unet-main\Swin-Unet-main\test.py:133: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. msg = net.load_state_dict(torch.load(snapshot),strict=False) Traceback (most recent call last): File "D:\code\Swin-Unet-main\Swin-Unet-main\test.py", line 133, in <module> msg = net.load_state_dict(torch.load(snapshot),strict=False) File "D:\software\anaconda\envs\test\lib\site-packages\torch\nn\modules\module.py", line 2215, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for SwinUnet: size mismatch for swin_unet.output.weight: copying a param with shape torch.Size([9, 96, 1, 1]) from checkpoint, the shape in current model is torch.Size([4, 96, 1, 1]
时间: 2025-03-11 09:27:50 浏览: 169
### 解决 PyTorch 加载 Swin-Unet 模型时状态字典大小不匹配问题
当尝试加载预训练的 Swin-Unet 模型并遇到 `size mismatch for swin_unet.output.weight` 错误时,这通常意味着保存的检查点中的权重与当前模型架构之间的参数数量或维度存在差异。这种情况下可以采取几种方法来解决问题。
#### 方法一:调整模型结构以匹配预训练权重
如果可能的话,应确保使用的模型配置完全相同于用于创建预训练权重的那个版本。这意味着要仔细核对所有的超参数设置以及任何特定模块的设计细节,比如卷积层数量、通道数等。对于Swin-UMamba而言,在最后一个补丁扩展块采用了4×上采样的操作,并且移除了某些下采样特征及其对应的解码路径[^1]。因此,确认这些改动是否已经反映在所构建的新模型中非常重要。
#### 方法二:手动初始化缺失层或者裁剪多余层
另一种解决方案是在加载过程中忽略那些尺寸不符的部分而只保留相容性的部分。可以通过自定义一个函数来进行更灵活的状态字典处理:
```python
def load_partial_state_dict(model, state_dict):
own_state = model.state_dict()
loaded_layers = []
for name, param in state_dict.items():
if name not in own_state or (param.size() != own_state[name].size()):
continue
own_state[name].copy_(param)
loaded_layers.append(name)
print(f"Loaded layers: {loaded_layers}")
```
此代码片段会遍历给定的状态字典(`state_dict`)并将其中能够成功匹配上的键值对赋值给目标模型(`model`)内部相应位置处的数据成员;而对于无法直接对应起来的地方则简单跳过而不报错。
#### 方法三:重新训练最后一层或多层
考虑到Swin-UMamba减少了网络参数的数量从40M减少到了27M,并且FLOPs也显著降低,可能是由于改变了输出层或其他关键组件的结果。在这种情形下,可以选择冻结大部分原有网络结构不变的情况下单独微调最后几层甚至是整个头部结构,从而适应新的任务需求或是数据分布变化带来的影响。
通过上述三种方式之一应该可以帮助克服因模型结构调整而导致的状态字典大小不匹配的问题。当然最理想的情况还是能找到与现有项目环境最为契合的那一版官方发布的预训练模型文件。
阅读全文
相关推荐



















