CNN: visualize a model using its description

Question

CNN: visualize a model using its description

lxg95

2021年9月25日 11:18

i created a Resnet model, which i want to show in a presentation, but i don't know how to visualize what i have done? Is there a tool or something to get a graphic from the description of my model.

Here you can see how my description looks (had to cut a part out of it as i were using too many characters):

    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)
)

I hope someone is able to help me with this

Best regards, Alex

Topic inceptionresnetv2 pytorch keras tensorflow python

Category Data Science

serali · Accepted Answer · 2021年9月25日 11:18

1

serali answered at 2021年9月25日 11:18

Using TensorFlow(Keras), you can get a simple graph of the model by running model.summary(). Or, you can use TensorBoard to get more "better looking" graphs which, to be honest I never used but looks good.

prashant0598 · Accepted Answer · 2021年9月25日 11:05

1

prashant0598 answered at 2021年9月25日 11:05

This repo has a list of tools for visualizing which might be useful. Though I would recommend using monitoring tools like wandb with which you can visualize how each layer is behaving.

CNN: visualize a model using its description

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