PSMNet:Pyramid Stereo Matching Network学习测试笔记02-网络结构图

写在前面的话：恕我直言，看到计算图的第一眼我的内心是崩溃的。
2019年09月28日18:02:55补充说明：CSDN博客发布版权更新，如果您看了博客并且用到PSMNet相关东西，请注明引用原作者的文章：

@inproceedings{chang2018pyramid,
title={Pyramid Stereo Matching Network},
author={Chang, Jia-Ren and Chen, Yong-Sheng},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={5410–5418},
year={2018}
}

图如下：

看不清楚是不是。在我分享的资源里面有原图，先放下属性：

我的机子还可以，但是打开卡了。推荐用ImageGlass打开。可以方便看大图。

插：2019年09月28日17:59:55后来发现还是文章里给的图直观，后续（这个系列的后续更新）看参数结构通道变化可以直接对照这张图看：

还真有终端直接打印出来的：

终端文本图

DataParallel(
  (module): PSMNet(
    (feature_extraction): feature_extraction(
      (firstconv): Sequential(
        (0): Sequential(
          (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
        (2): Sequential(
          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (3): ReLU(inplace)
        (4): Sequential(
          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (5): ReLU(inplace)
      )
      (layer1): Sequential(
        (0): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (2): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (layer2): Sequential(
        (0): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
          (downsample): Sequential(
            (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (2): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (3): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (4): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (5): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (6): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (7): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (8): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (9): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (10): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (11): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (12): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (13): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (14): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (15): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (layer3): Sequential(
        (0): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
          (downsample): Sequential(
            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (2): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (layer4): Sequential(
        (0): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (2): BasicBlock(
          (conv1): Sequential(
            (0): Sequential(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
            (1): ReLU(inplace)
          )
          (conv2): Sequential(
            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (branch1): Sequential(
        (0): AvgPool2d(kernel_size=(64, 64), stride=(64, 64), padding=0)
        (1): Sequential(
          (0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (2): ReLU(inplace)
      )
      (branch2): Sequential(
        (0): AvgPool2d(kernel_size=(32, 32), stride=(32, 32), padding=0)
        (1): Sequential(
          (0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (2): ReLU(inplace)
      )
      (branch3): Sequential(
        (0): AvgPool2d(kernel_size=(16, 16), stride=(16, 16), padding=0)
        (1): Sequential(
          (0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (2): ReLU(inplace)
      )
      (branch4): Sequential(
        (0): AvgPool2d(kernel_size=(8, 8), stride=(8, 8), padding=0)
        (1): Sequential(
          (0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (2): ReLU(inplace)
      )
      (lastconv): Sequential(
        (0): Sequential(
          (0): Conv2d(320, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
        (2): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (dres0): Sequential(
      (0): Sequential(
        (0): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): ReLU(inplace)
      (2): Sequential(
        (0): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (3): ReLU(inplace)
    )
    (dres1): Sequential(
      (0): Sequential(
        (0): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): ReLU(inplace)
      (2): Sequential(
        (0): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (dres2): hourglass(
      (conv1): Sequential(
        (0): Sequential(
          (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv2): Sequential(
        (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (conv3): Sequential(
        (0): Sequential(
          (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv4): Sequential(
        (0): Sequential(
          (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv5): Sequential(
        (0): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (conv6): Sequential(
        (0): ConvTranspose3d(64, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (dres3): hourglass(
      (conv1): Sequential(
        (0): Sequential(
          (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv2): Sequential(
        (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (conv3): Sequential(
        (0): Sequential(
          (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv4): Sequential(
        (0): Sequential(
          (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv5): Sequential(
        (0): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (conv6): Sequential(
        (0): ConvTranspose3d(64, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (dres4): hourglass(
      (conv1): Sequential(
        (0): Sequential(
          (0): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv2): Sequential(
        (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (conv3): Sequential(
        (0): Sequential(
          (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv4): Sequential(
        (0): Sequential(
          (0): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): ReLU(inplace)
      )
      (conv5): Sequential(
        (0): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (conv6): Sequential(
        (0): ConvTranspose3d(64, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), output_padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (classif1): Sequential(
      (0): Sequential(
        (0): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): ReLU(inplace)
      (2): Conv3d(32, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
    )
    (classif2): Sequential(
      (0): Sequential(
        (0): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): ReLU(inplace)
      (2): Conv3d(32, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
    )
    (classif3): Sequential(
      (0): Sequential(
        (0): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (1): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): ReLU(inplace)
      (2): Conv3d(32, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
    )
  )
)

不着急，慢慢学。