CNN：使用其描述可视化模型

数据挖掘 Python 喀拉斯 张量流 火炬 inceptionresnetv2
2022-02-15 01:06:35

我创建了一个 Resnet 模型，我想在演示文稿中展示它，但我不知道如何可视化我所做的事情？是否有工具或其他东西可以从我的模型描述中获取图形。

在这里你可以看到我的描述看起来如何（因为我使用了太多字符，所以不得不删掉一部分）：

    model is :  ResNet(
 (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
 (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 (relu): ReLU(inplace=True)
 (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
 (layer1): Sequential(
   (0): Bottleneck(
     (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
     (downsample): Sequential(
       (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
       (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
   (1): Bottleneck(
     (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (2): Bottleneck(
     (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
  )
 (layer2): Sequential(
   (0): Bottleneck(
     (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
     (downsample): Sequential(
       (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
       (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
   (1): Bottleneck(
     (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (2): Bottleneck(
     (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (3): Bottleneck(
     (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (4): Bottleneck(
     (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (5): Bottleneck(
     (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (6): Bottleneck(
     (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (7): Bottleneck(
     (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
  )
 (layer3): Sequential(
   (0): Bottleneck(
     (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
     (downsample): Sequential(
       (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
       (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
   (1): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (2): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (3): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (4): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (5): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (6): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (7): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (8): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (9): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (10): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (11): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (12): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (13): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (14): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (15): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (16): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (17): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   [...]
   (30): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (31): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (32): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (33): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (34): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (35): Bottleneck(
     (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
  )
 (layer4): Sequential(
   (0): Bottleneck(
     (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
     (downsample): Sequential(
       (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
       (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
   (1): Bottleneck(
     (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
   (2): Bottleneck(
     (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
     (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
    )
  )
 (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
 (fc): Linear(in_features=2048, out_features=2, bias=True)
)

我希望有人能够帮助我解决这个问题

最好的问候，亚历克斯

2个回答

这个repo有一个用于可视化的工具列表，这些工具可能有用。虽然我建议使用wandb 之类的监控工具，您可以使用它来可视化每个层的行为方式。

使用 TensorFlow(Keras)，您可以通过运行model.summary()获得模型的简单图。或者，您可以使用TensorBoard获得更多“更好看”的图表，老实说，我从未使用过但看起来不错。

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