XceptionTime

This is an unofficial PyTorch implementation by Ignacio Oguiza - oguiza@timeseriesAI.co modified on:

Fawaz, H. I., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J. & Petitjean, F. (2019). InceptionTime: Finding AlexNet for Time Series Classification. arXiv preprint arXiv:1909.04939.

Official InceptionTime tensorflow implementation: https://github.com/hfawaz/InceptionTime

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XceptionTime

 XceptionTime (c_in, c_out, nf=16, nb_filters=None, adaptive_size=50,
               residual=True)

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

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XceptionBlock

 XceptionBlock (ni, nf, residual=True, ks=40, bottleneck=True)

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

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XceptionModule

 XceptionModule (ni, nf, ks=40, bottleneck=True)

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

bs = 16
vars = 3
seq_len = 12
c_out = 6
xb = torch.rand(bs, vars, seq_len)
test_eq(XceptionTime(vars,c_out)(xb).shape, [bs, c_out])
test_eq(XceptionTime(vars,c_out, bottleneck=False)(xb).shape, [bs, c_out])
test_eq(XceptionTime(vars,c_out, residual=False)(xb).shape, [bs, c_out])
test_eq(count_parameters(XceptionTime(3, 2)), 399540)

m = XceptionTime(2,3)
test_eq(check_weight(m, is_bn)[0].sum(), 5) # 2 shortcut + 3 bn
test_eq(len(check_bias(m, is_conv)[0]), 0)
test_eq(len(check_bias(m)[0]), 5) # 2 shortcut + 3 bn

XceptionTime(3, 2)

XceptionTime(
  (block): XceptionBlock(
    (xception): ModuleList(
      (0): XceptionModule(
        (bottleneck): Conv1d(3, 16, kernel_size=(1,), stride=(1,), bias=False)
        (convs): ModuleList(
          (0): SeparableConv1d(
            (depthwise_conv): Conv1d(16, 16, kernel_size=(39,), stride=(1,), padding=(19,), groups=16, bias=False)
            (pointwise_conv): Conv1d(16, 16, kernel_size=(1,), stride=(1,), bias=False)
          )
          (1): SeparableConv1d(
            (depthwise_conv): Conv1d(16, 16, kernel_size=(19,), stride=(1,), padding=(9,), groups=16, bias=False)
            (pointwise_conv): Conv1d(16, 16, kernel_size=(1,), stride=(1,), bias=False)
          )
          (2): SeparableConv1d(
            (depthwise_conv): Conv1d(16, 16, kernel_size=(9,), stride=(1,), padding=(4,), groups=16, bias=False)
            (pointwise_conv): Conv1d(16, 16, kernel_size=(1,), stride=(1,), bias=False)
          )
        )
        (maxconvpool): Sequential(
          (0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
          (1): Conv1d(3, 16, kernel_size=(1,), stride=(1,), bias=False)
        )
        (concat): Concat(dim=1)
      )
      (1): XceptionModule(
        (bottleneck): Conv1d(64, 32, kernel_size=(1,), stride=(1,), bias=False)
        (convs): ModuleList(
          (0): SeparableConv1d(
            (depthwise_conv): Conv1d(32, 32, kernel_size=(39,), stride=(1,), padding=(19,), groups=32, bias=False)
            (pointwise_conv): Conv1d(32, 32, kernel_size=(1,), stride=(1,), bias=False)
          )
          (1): SeparableConv1d(
            (depthwise_conv): Conv1d(32, 32, kernel_size=(19,), stride=(1,), padding=(9,), groups=32, bias=False)
            (pointwise_conv): Conv1d(32, 32, kernel_size=(1,), stride=(1,), bias=False)
          )
          (2): SeparableConv1d(
            (depthwise_conv): Conv1d(32, 32, kernel_size=(9,), stride=(1,), padding=(4,), groups=32, bias=False)
            (pointwise_conv): Conv1d(32, 32, kernel_size=(1,), stride=(1,), bias=False)
          )
        )
        (maxconvpool): Sequential(
          (0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
          (1): Conv1d(64, 32, kernel_size=(1,), stride=(1,), bias=False)
        )
        (concat): Concat(dim=1)
      )
      (2): XceptionModule(
        (bottleneck): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
        (convs): ModuleList(
          (0): SeparableConv1d(
            (depthwise_conv): Conv1d(64, 64, kernel_size=(39,), stride=(1,), padding=(19,), groups=64, bias=False)
            (pointwise_conv): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
          )
          (1): SeparableConv1d(
            (depthwise_conv): Conv1d(64, 64, kernel_size=(19,), stride=(1,), padding=(9,), groups=64, bias=False)
            (pointwise_conv): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
          )
          (2): SeparableConv1d(
            (depthwise_conv): Conv1d(64, 64, kernel_size=(9,), stride=(1,), padding=(4,), groups=64, bias=False)
            (pointwise_conv): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
          )
        )
        (maxconvpool): Sequential(
          (0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
          (1): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
        )
        (concat): Concat(dim=1)
      )
      (3): XceptionModule(
        (bottleneck): Conv1d(256, 128, kernel_size=(1,), stride=(1,), bias=False)
        (convs): ModuleList(
          (0): SeparableConv1d(
            (depthwise_conv): Conv1d(128, 128, kernel_size=(39,), stride=(1,), padding=(19,), groups=128, bias=False)
            (pointwise_conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
          )
          (1): SeparableConv1d(
            (depthwise_conv): Conv1d(128, 128, kernel_size=(19,), stride=(1,), padding=(9,), groups=128, bias=False)
            (pointwise_conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
          )
          (2): SeparableConv1d(
            (depthwise_conv): Conv1d(128, 128, kernel_size=(9,), stride=(1,), padding=(4,), groups=128, bias=False)
            (pointwise_conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
          )
        )
        (maxconvpool): Sequential(
          (0): MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
          (1): Conv1d(256, 128, kernel_size=(1,), stride=(1,), bias=False)
        )
        (concat): Concat(dim=1)
      )
    )
    (shortcut): ModuleList(
      (0): ConvBlock(
        (0): Conv1d(3, 128, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): ConvBlock(
        (0): Conv1d(128, 512, kernel_size=(1,), stride=(1,), bias=False)
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (add): Add
    (act): ReLU()
  )
  (head): Sequential(
    (0): AdaptiveAvgPool1d(output_size=50)
    (1): ConvBlock(
      (0): Conv1d(512, 256, kernel_size=(1,), stride=(1,), bias=False)
      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (2): ConvBlock(
      (0): Conv1d(256, 128, kernel_size=(1,), stride=(1,), bias=False)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (3): ConvBlock(
      (0): Conv1d(128, 2, kernel_size=(1,), stride=(1,), bias=False)
      (1): BatchNorm1d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU()
    )
    (4): GAP1d(
      (gap): AdaptiveAvgPool1d(output_size=1)
      (flatten): Flatten(full=False)
    )
  )
)