state_dict = torch.load(cfg.test_model, map_location='cpu')['model']
时间: 2024-06-05 16:08:57 浏览: 18
这段代码是使用PyTorch加载模型的state_dict参数。其中,cfg.test_model是模型文件的路径,map_location='cpu'表示将模型加载到CPU上,['model']表示从保存模型的字典中获取模型参数。加载完成后,可以将state_dict赋值给模型的对应参数,从而恢复模型的状态。
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state_dict = torch.load('pruned_model_weights.pth')
这行代码加载了剪枝后的模型的权重。state_dict是一个字典,其中包含了模型的所有权重参数。字典的键是参数的名称,而值是张量。你可以使用state_dict查看模型的权重参数,例如:
```
print(state_dict.keys())
```
这将输出模型中所有权重参数的名称。你可以使用这些名称来访问特定的参数,例如:
```
weight = state_dict['conv1.weight']
```
这将返回名为'conv1.weight'的卷积层的权重张量。你可以使用这些权重来重建模型,或者将它们加载到另一个模型中。
net.load_state_dict(torch.load(model_weight_path, map_location='cpu'))
This line of code loads the state dictionary of a pre-trained PyTorch model from a specified file path, with the option to specify the device location for the loaded model.
- `torch.load(model_weight_path)` loads the state dictionary from the file path specified by `model_weight_path`.
- `map_location='cpu'` specifies that the loaded model should be moved to the CPU if it was originally trained on a GPU. This is useful if you do not have access to a GPU or if you want to test the model on a CPU.
Assuming `net` is the PyTorch model you want to load the weights into, this line of code would load the pre-trained weights into `net`.