pytorch - LibTorch，将 deeplabv3_resnet101 转换为 c++

Question

我正在尝试使用 PyTorch website 中的示例代码转换 python 模型以在 PyTorch c++ api (LibTorch) 中使用。

Converting to Torch Script via Tracing
To convert a PyTorch model to Torch Script via tracing, you must pass an instance of your model along with an example input to the torch.jit.trace function. This will produce a torch.jit.ScriptModule object with the trace of your model evaluation embedded in the module’s forward method:

import torch
import torchvision

# An instance of your model.
model = torchvision.models.resnet18()

# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 224, 224)

# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(model, example)
traced_script_module.save("model.pt")

这个示例工作正常，并按预期保存了文件。 当我切换到这个模型时:

model = models.segmentation.deeplabv3_resnet101(pretrained=True)

它给了我以下错误:

File "convert.py", line 14, in <module>
    traced_script_module = torch.jit.trace(model, example)
  File "C:\Python37\lib\site-packages\torch\jit\__init__.py", line 636, in trace
          raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 256, 1, 1])

我认为这是因为 example 格式错误，但是我怎样才能得到正确的格式呢？

根据下面的评论，我的新代码是:

import torch
import torchvision
from torchvision import models


model = models.segmentation.deeplabv3_resnet101(pretrained=True)
model.eval()


# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 224, 224)

# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(model, example)

traced_script_module.save("model.pt")

我现在收到错误:

File "convert.py", line 15, in <module>
    traced_script_module = torch.jit.trace(model, example)
  File "C:\Python37\lib\site-packages\torch\jit\__init__.py", line 636, in trace
    var_lookup_fn, _force_outplace)
RuntimeError: Only tensors and (possibly nested) tuples of tensors are supported as inputs or outputs of traced functions (toIValue at C:\a\w\1\s\windows\pytorch\torch/csrc/jit/pybind_utils.h:91)
(no backtrace available)

Answer 1

(来自 pytorch 论坛)

trace 仅支持以张量或张量元组作为输出的模块。 根据deeplabv3的实现，它的输出是OrderedDict。这是一个问题。 为了解决这个问题，制作一个包装模块

class wrapper(torch.nn.Module):
    def __init__(self, model):
        super(wrapper, self).__init__()
        self.model = model

    def forward(self, input):
        results = []
        output = self.model(input)
        for k, v in output.items():
            results.append(v)
        return tuple(results)

model = wrapper(deeplap_model)
#trace...

我的模型已保存。