def __init__(
self,
num_classes=21,
num_channels=128,
dimensions=(128, 1), # pylint: disable=unused-argument
bias=False,
**kwargs
):
super().__init__()
# T: 128 F :128
self.conv1 = ai8x.FusedConv1dReLU(num_channels, 100, 1, stride=1, padding=0,
bias=bias, **kwargs)
# T: 128 F: 100
self.conv2 = ai8x.FusedConv1dReLU(100, 48, 3, stride=1, padding=0,
bias=bias, **kwargs)
# T: 126 F : 48
self.conv3 = ai8x.FusedMaxPoolConv1dReLU(48, 96, 3, stride=1, padding=1,
bias=bias, **kwargs)
# T: 62 F : 96
self.conv4 = ai8x.FusedConv1dReLU(96, 128, 3, stride=1, padding=0,
bias=bias, **kwargs)
# T : 60 F : 128
self.conv5 = ai8x.FusedMaxPoolConv1dReLU(128, 160, 3, stride=1, padding=1,
bias=bias, **kwargs)
# T: 30 F : 160
self.conv6 = ai8x.FusedConv1dReLU(160, 192, 3, stride=1, padding=0,
bias=bias, **kwargs)
# T: 28 F : 192
self.conv7 = ai8x.FusedAvgPoolConv1dReLU(192, 192, 3, stride=1, padding=1,
bias=bias, **kwargs)
# T : 14 F: 256
self.conv8 = ai8x.FusedConv1dReLU(192, 32, 3, stride=1, padding=0,
bias=bias, **kwargs)
# T: 12 F : 32
self.fc = ai8x.Linear(32 * 12, num_classes, bias=bias, wide=True, **kwargs)
def __init__(
self,
num_classes=21,
num_channels=128,
dimensions=(128, 1), # pylint: disable=unused-argument
bias=False,
**kwargs):
super().__init__()
self.drop = nn.Dropout(p=0.2)
# Time: 128 Feature :128
self.voice_conv1 = ai8x.FusedConv1dReLU(num_channels,
100,
1,
stride=1,
padding=0,
bias=bias,
**kwargs)
# T: 128 F: 100
self.voice_conv2 = ai8x.FusedConv1dReLU(100,
96,
3,
stride=1,
padding=0,
bias=bias,
**kwargs)
# T: 126 F : 96
self.voice_conv3 = ai8x.FusedMaxPoolConv1dReLU(96,
64,
3,
stride=1,
padding=1,
bias=bias,
**kwargs)
# T: 62 F : 64
self.voice_conv4 = ai8x.FusedConv1dReLU(64,
48,
3,
stride=1,
padding=0,
bias=bias,
**kwargs)
# T : 60 F : 48
self.kws_conv1 = ai8x.FusedMaxPoolConv1dReLU(48,
64,
3,
stride=1,
padding=1,
bias=bias,
**kwargs)
# T: 30 F : 64
self.kws_conv2 = ai8x.FusedConv1dReLU(64,
96,
3,
stride=1,
padding=0,
bias=bias,
**kwargs)
# T: 28 F : 96
self.kws_conv3 = ai8x.FusedAvgPoolConv1dReLU(96,
100,
3,
stride=1,
padding=1,
bias=bias,
**kwargs)
# T : 14 F: 100
self.kws_conv4 = ai8x.FusedMaxPoolConv1dReLU(100,
64,
6,
stride=1,
padding=1,
bias=bias,
**kwargs)
# T : 2 F: 128
self.fc = ai8x.Linear(256, num_classes, bias=bias, wide=True, **kwargs)