Compare Models about pytorch-retinanet

[JoshVarty]

Network Stem

Detectron

INFO net.py: 259: data                        : (2, 3, 512, 896)     => conv1                       : (2, 64, 256, 448)    ------- (op: Conv)
                Args:[name: "kernel"i: 7, name: "order"s: "NCHW", name: "pad"i: 3, name: "stride"i: 2, name: "exhaustive_search"i: 0]
INFO net.py: 259: conv1                       : (2, 64, 256, 448)    => conv1                       : (2, 64, 256, 448)    ------- (op: AffineChannel)
INFO net.py: 259: conv1                       : (2, 64, 256, 448)    => conv1                       : (2, 64, 256, 448)    ------- (op: Relu)
                Args:[name: "order"s: "NCHW", name: "cudnn_exhaustive_search"i: 0]
INFO net.py: 259: conv1                       : (2, 64, 256, 448)    => pool1                       : (2, 64, 128, 224)    ------- (op: MaxPool)
                Args:[name: "order"s: "NCHW", name: "cudnn_exhaustive_search"i: 0, name: "kernel"i: 3, name: "pad"i: 1, name: "stride"i: 2]

PyTorch

✓(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
✓(affineChannel): AffineChannel()
✓(relu): ReLU()
✓(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

Differences:

• PyTorch doesn't seem to have a ReLU
• What is AffineChannel? Is it BatchNorm?
  • Op source
  • math::AffineChannel source
  • I think it's just saying "Define a learnable scale and bias parameter for each channel. Multiply each input channel by the corresponding scale and add bias.