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()
(fc): Linear(in_features=128, out_features=2, bias=True)
)