def __init__(
self,
num_classes=None, # pylint: disable=unused-argument
num_channels=3,
dimensions=(160, 120), # pylint: disable=unused-argument
bias=True,
**kwargs
):
super().__init__()
self.conv1 = ai8x.FusedConv2dReLU(num_channels, 16, 3, padding=1,
bias=False, **kwargs)
self.conv2 = ai8x.FusedMaxPoolConv2dReLU(16, 32, 3, pool_size=2, pool_stride=2,
padding=1, bias=False, **kwargs)
self.conv3 = ai8x.FusedMaxPoolConv2dReLU(32, 32, 3, pool_size=2, pool_stride=2,
padding=1, bias=bias, **kwargs)
self.conv4 = ai8x.FusedMaxPoolConv2dReLU(32, 64, 3, pool_size=2, pool_stride=2,
padding=1, bias=bias, **kwargs)
self.conv5 = ai8x.FusedMaxPoolConv2dReLU(64, 64, 3, pool_size=2, pool_stride=2,
padding=1, bias=bias, **kwargs)
self.conv6 = ai8x.FusedConv2dReLU(64, 64, 3, padding=1, bias=bias, **kwargs)
self.conv7 = ai8x.FusedConv2dReLU(64, 64, 3, padding=1, bias=bias, **kwargs)
self.conv8 = ai8x.FusedMaxPoolConv2d(64, 512, 1, pool_size=2, pool_stride=2,
padding=0, bias=False, **kwargs)
self.avgpool = ai8x.AvgPool2d((5, 3))
def __init__(
self,
num_classes=100,
num_channels=3,
dimensions=(32, 32), # pylint: disable=unused-argument
bias=False,
**kwargs):
super().__init__()
# Stem Layer
self.conv_stem = ai8x.FusedMaxPoolConv2dBNReLU(num_channels,
32,
3,
pool_size=2,
pool_stride=2,
stride=1,
batchnorm='Affine',
padding=1,
bias=bias,
eps=1e-03,
momentum=0.01,
**kwargs)
# Series of MBConv blocks
self.mb_conv1 = ai8x_blocks.MBConvBlock(32,
16,
3,
bias=bias,
se_ratio=None,
expand_ratio=1,
fused=True,
**kwargs)
self.mb_conv2 = ai8x_blocks.MBConvBlock(16,
32,
3,
bias=bias,
se_ratio=None,
expand_ratio=4,
fused=True,
**kwargs)
self.mb_conv3 = ai8x_blocks.MBConvBlock(32,
32,
3,
bias=bias,
se_ratio=None,
expand_ratio=4,
fused=True,
**kwargs)
self.mb_conv4 = ai8x_blocks.MBConvBlock(32,
48,
3,
bias=bias,
se_ratio=None,
expand_ratio=4,
fused=True,
**kwargs)
self.mb_conv5 = ai8x_blocks.MBConvBlock(48,
48,
3,
bias=bias,
se_ratio=None,
expand_ratio=4,
fused=True,
**kwargs)
self.mb_conv6 = ai8x_blocks.MBConvBlock(48,
96,
3,
bias=bias,
se_ratio=None,
expand_ratio=4,
fused=False,
**kwargs)
self.mb_conv7 = ai8x_blocks.MBConvBlock(96,
96,
3,
bias=bias,
se_ratio=None,
expand_ratio=4,
fused=False,
**kwargs)
self.mb_conv8 = ai8x_blocks.MBConvBlock(96,
128,
3,
bias=bias,
se_ratio=None,
expand_ratio=4,
fused=False,
**kwargs)
self.mb_conv9 = ai8x_blocks.MBConvBlock(128,
128,
3,
bias=bias,
se_ratio=None,
expand_ratio=4,
fused=False,
**kwargs)
# Head Layer
self.conv_head = ai8x.FusedConv2dBNReLU(128,
1024,
1,
stride=1,
batchnorm='Affine',
padding=0,
bias=bias,
eps=1e-03,
momentum=0.01,
**kwargs)
# Final linear layer
self.avg_pooling = ai8x.AvgPool2d((16, 16))
# self.dropout = nn.Dropout(0.2)
self.fc = ai8x.Linear(1024,
num_classes,
bias=bias,
wide=True,
**kwargs)