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=2,
num_channels=1,
dimensions=(22, 1),
fc_inputs=16,
bias=False):
super().__init__()
dim1 = dimensions[0]
self.mfcc_conv1 = ai8x.FusedConv1dReLU(num_channels,
64,
5,
stride=1,
padding=2,
bias=bias)
self.dropout1 = nn.Dropout(0.2)
self.mfcc_conv2 = ai8x.FusedConv1dReLU(64,
32,
5,
stride=1,
padding=2,
bias=bias)
self.mfcc_conv4 = ai8x.FusedConv1dReLU(32,
fc_inputs,
5,
stride=1,
padding=2,
bias=bias)
self.fc = ai8x.Linear(fc_inputs * dim1, num_classes, bias=bias)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
def __init__(
self,
num_classes=21,
num_channels=128,
dimensions=(128, 1), # pylint: disable=unused-argument
fc_inputs=7,
bias=False,
**kwargs
):
super().__init__()
self.voice_conv1 = ai8x.FusedConv1dReLU(num_channels, 100, 1, stride=1, padding=0,
bias=bias, **kwargs)
self.voice_conv2 = ai8x.FusedConv1dReLU(100, 100, 1, stride=1, padding=0,
bias=bias, **kwargs)
self.voice_conv3 = ai8x.FusedConv1dReLU(100, 50, 1, stride=1, padding=0,
bias=bias, **kwargs)
self.voice_conv4 = ai8x.FusedConv1dReLU(50, 16, 1, stride=1, padding=0,
bias=bias, **kwargs)
self.kws_conv1 = ai8x.FusedConv2dReLU(16, 32, 3, stride=1, padding=1,
bias=bias, **kwargs)
self.kws_conv2 = ai8x.FusedConv2dReLU(32, 64, 3, stride=1, padding=1,
bias=bias, **kwargs)
self.kws_conv3 = ai8x.FusedConv2dReLU(64, 64, 3, stride=1, padding=1,
bias=bias, **kwargs)
self.kws_conv4 = ai8x.FusedConv2dReLU(64, 30, 3, stride=1, padding=1,
bias=bias, **kwargs)
self.kws_conv5 = ai8x.FusedConv2dReLU(30, fc_inputs, 3, stride=1, padding=1,
bias=bias, **kwargs)
self.fc = ai8x.Linear(fc_inputs * 128, num_classes, bias=bias, wide=True, **kwargs)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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)