Pytorch 基于编码器的转移学习视频合成

我在Pytork中遇到了一个非常奇怪的错误，在这个错误中，我使用以下模型合成视频。我试图将转移学习仅应用于编码器。为此，我用

requires\u grad=False

冻结了生成器的权重，并对编码器做了相反的操作

这是我的模型：

autoencoder(
  (generator): VideoGenerator(
    (recurrent): GRUCell(10, 10)
    (main): Sequential(
      (0): ConvTranspose2d(64, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace)
      (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): ReLU(inplace)
      (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (8): ReLU(inplace)
      (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (11): ReLU(inplace)
      (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
      (13): Tanh()
    )
  )
  (encoder): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): ReLU(inplace)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (3): ReLU(inplace)
    (4): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (5): ReLU(inplace)
    (6): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (7): ReLU(inplace)
    (8): Conv2d(512, 64, kernel_size=(4, 4), stride=(1, 1), bias=False)
  )
)

这是我在params.requires_grad中循环时的输出

*** encoder ***
1 False
2 False
3 False
4 False
5 False
6 False
7 False
8 False
9 False
10 False
11 False
12 False
13 False
14 False
15 False
16 False
17 False
*** generator ***
18 True
19 True
20 True
21 True
22 True

但这会产生如下运行时错误：

RuntimeError                              Traceback (most recent call last)
<ipython-input-7-73fe2d39b929> in on_wl_clicked(b)
     87 #         print(model)
     88 
---> 89         loss.backward()
     90 
     91 #         show_state("AFTER BACKWARD STEP:", model)

/usr/lib/python3.7/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
    100                 products. Defaults to ``False``.
    101         """
--> 102         torch.autograd.backward(self, gradient, retain_graph, create_graph)
    103 
    104     def register_hook(self, hook):

/usr/lib/python3.7/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
     88     Variable._execution_engine.run_backward(
     89         tensors, grad_tensors, retain_graph, create_graph,
---> 90         allow_unreachable=True)  # allow_unreachable flag
     91 
     92 

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

---

关于这个错误是从哪里产生的，或者如何纠正它的，有什么想法吗？

问题是，损失张量从链式规则的顶部继承了它的自动梯度，并且由于第一个张量具有

requires\u grad=False

，所以损失张量也是False

修复很简单，添加行

loss.requires_grad = True 
loss.backward()

另一种解决方案就是这样修改优化器

# optimizer = optim.Adam(param = model.parameters(), lr = lr)
optimizer = optim.Adam(param = model.encoder.parameters(), lr = lr)

# optimizer = optim.Adam(param = model.parameters(), lr = lr)
optimizer = optim.Adam(param = model.encoder.parameters(), lr = lr)