如何从状态字典创建模型？

Question

我正在尝试从当前不在 PyTorch 模型动物园中的 faster_rcnn_resnet101 模型加载检查点 pth 文件。 这会导致 PyTorch 抛出一个 KeyError 说我状态字典中的层与我从模型动物园加载的 faster_rcnn_fpn_resnet50 的模型架构不匹配。

注意：我尝试发布 fast_rcnn_fpn_resnet50 模型的架构，但我超过了 30.000 的字符限制

打印（状态字典[模型].keys（））：

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roi_heads.res5.0.conv1.norm.running_var
roi_heads.res5.0.conv2.weight
roi_heads.res5.0.conv2.norm.weight
roi_heads.res5.0.conv2.norm.bias
roi_heads.res5.0.conv2.norm.running_mean
roi_heads.res5.0.conv2.norm.running_var
roi_heads.res5.0.conv3.weight
roi_heads.res5.0.conv3.norm.weight
roi_heads.res5.0.conv3.norm.bias
roi_heads.res5.0.conv3.norm.running_mean
roi_heads.res5.0.conv3.norm.running_var
roi_heads.res5.1.conv1.weight
roi_heads.res5.1.conv1.norm.weight
roi_heads.res5.1.conv1.norm.bias
roi_heads.res5.1.conv1.norm.running_mean
roi_heads.res5.1.conv1.norm.running_var
roi_heads.res5.1.conv2.weight
roi_heads.res5.1.conv2.norm.weight
roi_heads.res5.1.conv2.norm.bias
roi_heads.res5.1.conv2.norm.running_mean
roi_heads.res5.1.conv2.norm.running_var
roi_heads.res5.1.conv3.weight
roi_heads.res5.1.conv3.norm.weight
roi_heads.res5.1.conv3.norm.bias
roi_heads.res5.1.conv3.norm.running_mean
roi_heads.res5.1.conv3.norm.running_var
roi_heads.res5.2.conv1.weight
roi_heads.res5.2.conv1.norm.weight
roi_heads.res5.2.conv1.norm.bias
roi_heads.res5.2.conv1.norm.running_mean
roi_heads.res5.2.conv1.norm.running_var
roi_heads.res5.2.conv2.weight
roi_heads.res5.2.conv2.norm.weight
roi_heads.res5.2.conv2.norm.bias
roi_heads.res5.2.conv2.norm.running_mean
roi_heads.res5.2.conv2.norm.running_var
roi_heads.res5.2.conv3.weight
roi_heads.res5.2.conv3.norm.weight
roi_heads.res5.2.conv3.norm.bias
roi_heads.res5.2.conv3.norm.running_mean
roi_heads.res5.2.conv3.norm.running_var
roi_heads.box_predictor.cls_score.weight
roi_heads.box_predictor.cls_score.bias
roi_heads.box_predictor.bbox_pred.weight
roi_heads.box_predictor.bbox_pred.bias

有没有办法通过迭代模型字典在torch中动态创建faster_rcnn_resnet101模型的模型架构，还是有其他方法？

我曾尝试使用 hubconf.py 为 torch.hub 搜索 GitHub 存储库，但无济于事。

Answer 1

TLDR； 简短的回答是你不能。

state_dict的nn.Module包含模块的状态，但不包含其功能。 仅使用状态字典来实例化模型是不可能的。

您可以根据状态字典键和权重形状设法初始化一些子模块，但这不能保证。 在任何情况下，您都无法知道模型的行为，因为此信息根本不包含在其状态字典中。 因此，您需要访问模型的forward定义以了解前向逻辑，即哪些函数应用于输入和中间输出以及这些子模块的使用顺序。

---

看看这个最小的例子，展示了两个具有相同状态字典的模型。 然而，他们的forward却各不相同：

class A(nn.Linear):
    def forward(self, x):
        return super().forward(x)

class B(nn.Linear):
    def forward(self, x):
        return super().forward(x)**2

在这里， a和b被初始化，状态字典从一个复制到另一个：

>>> a = A(2,1)
>>> b = B(2,1)
>>> a.load_state_dict(b.state_dict())

a和b都具有完全相同的状态......但实际上实现了两个不同的功能！