This is an unofficial PyTorch implementation by Ignacio Oguiza - oguiza@gmail.com based on:
class XceptionModule[source]
XceptionModule(ni,nf,ks=40,bottleneck=True) ::Module
Same as nn.Module, but no need for subclasses to call super().__init__
class XceptionBlock[source]
XceptionBlock(ni,nf,residual=True,ks=40,bottleneck=True) ::Module
Same as nn.Module, but no need for subclasses to call super().__init__
class XceptionTime[source]
XceptionTime(c_in,c_out,nf=16,nb_filters=None,adaptive_size=50,residual=True) ::Module
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(total_params(XceptionTime(3, 2))[0], 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(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(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(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(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)
)
)
)