Batch Normalization

[ChrisAlthaus]

Hi, i would like to train the backbone with detector_pretrain_net.py. When looking at the models architecture i realized that batch normalization is disabled. Is this behaviour indended? If not how can I change this to normal batch normalization?

[Sequential(
  (body): ResNet(
    (stem): StemWithFixedBatchNorm(
      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
      (bn1): FrozenBatchNorm2d()
    )
    (layer1): Sequential(
      (0): BottleneckWithFixedBatchNorm(
        (downsample): Sequential(
          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): FrozenBatchNorm2d()
        )
        (conv1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): FrozenBatchNorm2d()
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        (bn2): FrozenBatchNorm2d()
        (conv3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): FrozenBatchNorm2d()
      )
      (1): BottleneckWithFixedBatchNorm(
        (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): FrozenBatchNorm2d()
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        (bn2): FrozenBatchNorm2d()
        (conv3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): FrozenBatchNorm2d()
      )
      (2): BottleneckWithFixedBatchNorm(
        (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): FrozenBatchNorm2d()
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        (bn2): FrozenBatchNorm2d()
        (conv3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): FrozenBatchNorm2d()
      )

Greetings, Christian