ResNet

This is an unofficial PyTorch implementation created by Ignacio Oguiza - oguiza@timeseriesAI.co

ResNet

 ResNet (c_in, c_out)

Same as nn.Module, but no need for subclasses to call super().__init__

source

ResBlock

 ResBlock (ni, nf, kss=[7, 5, 3])

Same as nn.Module, but no need for subclasses to call super().__init__

xb = torch.rand(2, 3, 4)
test_eq(ResNet(3,2)(xb).shape, [xb.shape[0], 2])
test_eq(count_parameters(ResNet(3, 2)), 479490) # for (3,2)

ResNet(3,2)

ResNet(
  (resblock1): ResBlock(
    (convblock1): ConvBlock(
      (0): Conv1d(3, 64, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
      (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (convblock2): ConvBlock(
      (0): Conv1d(64, 64, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
      (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (convblock3): ConvBlock(
      (0): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
      (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (shortcut): ConvBlock(
      (0): Conv1d(3, 64, kernel_size=(1,), stride=(1,), bias=False)
      (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (add): Add
    (act): ReLU()
  )
  (resblock2): ResBlock(
    (convblock1): ConvBlock(
      (0): Conv1d(64, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (convblock2): ConvBlock(
      (0): Conv1d(128, 128, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (convblock3): ConvBlock(
      (0): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (shortcut): ConvBlock(
      (0): Conv1d(64, 128, kernel_size=(1,), stride=(1,), bias=False)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (add): Add
    (act): ReLU()
  )
  (resblock3): ResBlock(
    (convblock1): ConvBlock(
      (0): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (convblock2): ConvBlock(
      (0): Conv1d(128, 128, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (convblock3): ConvBlock(
      (0): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (shortcut): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (add): Add
    (act): ReLU()
  )
  (gap): AdaptiveAvgPool1d(output_size=1)
  (squeeze): Squeeze(dim=-1)
  (fc): Linear(in_features=128, out_features=2, bias=True)
)