class SampaddingConv1D_BN[source]
SampaddingConv1D_BN(in_channels,out_channels,kernel_size) ::Module
Same as nn.Module, but no need for subclasses to call super().__init__
class build_layer_with_layer_parameter[source]
build_layer_with_layer_parameter(layer_parameters) ::Module
formerly build_layer_with_layer_parameter
class OmniScaleCNN[source]
OmniScaleCNN(c_in,c_out,seq_len,paramenter_number_of_layer_list=[1024, 229376],few_shot=False) ::Module
Same as nn.Module, but no need for subclasses to call super().__init__
get_Prime_number_in_a_range[source]
get_Prime_number_in_a_range(start,end)
get_out_channel_number[source]
get_out_channel_number(paramenter_layer,in_channel,prime_list)
generate_layer_parameter_list[source]
generate_layer_parameter_list(start,end,paramenter_number_of_layer_list,in_channel=1)
bs = 16
c_in = 3
seq_len = 12
c_out = 2
xb = torch.rand(bs, c_in, seq_len)
m = create_model(OmniScaleCNN, c_in, c_out, seq_len)
test_eq(OmniScaleCNN(c_in, c_out, seq_len)(xb).shape, [bs, c_out])
m
OmniScaleCNN(
(net): Sequential(
(0): build_layer_with_layer_parameter(
(conv_list): ModuleList(
(0): SampaddingConv1D_BN(
(padding): ConstantPad1d(padding=(0, 0), value=0)
(conv1d): Conv1d(3, 56, kernel_size=(1,), stride=(1,))
(bn): BatchNorm1d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): SampaddingConv1D_BN(
(padding): ConstantPad1d(padding=(0, 1), value=0)
(conv1d): Conv1d(3, 56, kernel_size=(2,), stride=(1,))
(bn): BatchNorm1d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): SampaddingConv1D_BN(
(padding): ConstantPad1d(padding=(1, 1), value=0)
(conv1d): Conv1d(3, 56, kernel_size=(3,), stride=(1,))
(bn): BatchNorm1d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(1): build_layer_with_layer_parameter(
(conv_list): ModuleList(
(0): SampaddingConv1D_BN(
(padding): ConstantPad1d(padding=(0, 0), value=0)
(conv1d): Conv1d(168, 227, kernel_size=(1,), stride=(1,))
(bn): BatchNorm1d(227, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): SampaddingConv1D_BN(
(padding): ConstantPad1d(padding=(0, 1), value=0)
(conv1d): Conv1d(168, 227, kernel_size=(2,), stride=(1,))
(bn): BatchNorm1d(227, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): SampaddingConv1D_BN(
(padding): ConstantPad1d(padding=(1, 1), value=0)
(conv1d): Conv1d(168, 227, kernel_size=(3,), stride=(1,))
(bn): BatchNorm1d(227, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(2): build_layer_with_layer_parameter(
(conv_list): ModuleList(
(0): SampaddingConv1D_BN(
(padding): ConstantPad1d(padding=(0, 0), value=0)
(conv1d): Conv1d(681, 510, kernel_size=(1,), stride=(1,))
(bn): BatchNorm1d(510, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): SampaddingConv1D_BN(
(padding): ConstantPad1d(padding=(0, 1), value=0)
(conv1d): Conv1d(681, 510, kernel_size=(2,), stride=(1,))
(bn): BatchNorm1d(510, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
)
(gap): GAP1d(
(gap): AdaptiveAvgPool1d(output_size=1)
(flatten): Flatten(full=False)
)
(hidden): Linear(in_features=1020, out_features=2, bias=True)
)