def __init__(
self,
num_classes=100,
num_channels=3,
dimensions=(32, 32), # pylint: disable=unused-argument
bias=False,
**kwargs
):
super().__init__()
self.conv1 = ai8x.FusedConv2dReLU(num_channels, 16, 3, stride=1, padding=1, bias=bias,
**kwargs)
self.conv2 = ai8x.FusedConv2dReLU(16, 20, 3, stride=1, padding=1, bias=bias, **kwargs)
self.conv3 = ai8x.FusedConv2dReLU(20, 20, 3, stride=1, padding=1, bias=bias, **kwargs)
self.conv4 = ai8x.FusedConv2dReLU(20, 20, 3, stride=1, padding=1, bias=bias, **kwargs)
self.conv5 = ai8x.FusedMaxPoolConv2dReLU(20, 20, 3, pool_size=2, pool_stride=2,
stride=1, padding=1, bias=bias, **kwargs)
self.conv6 = ai8x.FusedConv2dReLU(20, 20, 3, stride=1, padding=1, bias=bias, **kwargs)
self.conv7 = ai8x.FusedConv2dReLU(20, 44, 3, stride=1, padding=1, bias=bias, **kwargs)
self.conv8 = ai8x.FusedMaxPoolConv2dReLU(44, 48, 3, pool_size=2, pool_stride=2,
stride=1, padding=1, bias=bias, **kwargs)
self.conv9 = ai8x.FusedConv2dReLU(48, 48, 3, stride=1, padding=1, bias=bias, **kwargs)
self.conv10 = ai8x.FusedMaxPoolConv2dReLU(48, 96, 3, pool_size=2, pool_stride=2,
stride=1, padding=1, bias=bias, **kwargs)
self.conv11 = ai8x.FusedMaxPoolConv2dReLU(96, 512, 1, pool_size=2, pool_stride=2,
padding=0, bias=bias, **kwargs)
self.conv12 = ai8x.FusedConv2dReLU(512, 128, 1, stride=1, padding=0, bias=bias, **kwargs)
self.conv13 = ai8x.FusedMaxPoolConv2dReLU(128, 128, 3, pool_size=2, pool_stride=2,
stride=1, padding=1, bias=bias, **kwargs)
self.conv14 = ai8x.Conv2d(128, num_classes, 1, stride=1, padding=0, bias=bias,
wide=True, **kwargs)
def __init__(self, num_classes=10, num_channels=3, dimensions=(28, 28),
fc_inputs=8, bias=False):
super().__init__()
# AI84 Limits
assert dimensions[0] == dimensions[1] # Only square supported
# Keep track of image dimensions so one constructor works for all image sizes
dim = dimensions[0]
self.conv1 = ai8x.FusedConv2dReLU(num_channels, 8, 3,
padding=1, bias=bias)
# padding 1 -> no change in dimensions -> 8x28x28
pad = 2 if dim == 28 else 1
self.conv2 = ai8x.FusedMaxPoolConv2dReLU(8, 8, 3, pool_size=2, pool_stride=2,
padding=pad, bias=bias)
dim //= 2 # pooling, padding 0 -> 8x14x14
if pad == 2:
dim += 2 # padding 2 -> 8x16x16
self.conv3 = ai8x.FusedMaxPoolConv2dReLU(8, fc_inputs, 3,
pool_size=4, pool_stride=4, padding=1,
bias=bias)
dim //= 4 # pooling, padding 0 -> 8x4x4
# padding 1 -> 8x4x4
self.fc = ai8x.Linear(fc_inputs*dim*dim, num_classes, bias=True, wide=True)
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=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=2, num_channels=3, dimensions=(64, 64),
fc_inputs=30, bias=False, **kwargs):
super().__init__()
# AI85 Limits
assert dimensions[0] == dimensions[1] # Only square supported
# Keep track of image dimensions so one constructor works for all image sizes
dim = dimensions[0]
self.conv1 = ai8x.FusedConv2dReLU(num_channels, 15, 3,
padding=1, bias=bias, **kwargs)
# padding 1 -> no change in dimensions -> 15x64x64
pad = 2 if dim == 28 else 1
self.conv2 = ai8x.FusedMaxPoolConv2dReLU(15, 30, 3, pool_size=2, pool_stride=2,
padding=pad, bias=bias, **kwargs)
dim //= 2 # pooling, padding 0 -> 30x32x32
if pad == 2:
dim += 2 # padding 2 -> 30x16x16
self.conv3 = ai8x.FusedMaxPoolConv2dReLU(30, 60, 3, pool_size=2, pool_stride=2, padding=1,
bias=bias, **kwargs)
dim //= 2 # pooling, padding 0 -> 60x16x16
self.conv4 = ai8x.FusedMaxPoolConv2dReLU(60, 30, 3, pool_size=2, pool_stride=2, padding=1,
bias=bias, **kwargs)
dim //= 2 # pooling, padding 0 -> 30x8x8
self.conv5 = ai8x.FusedMaxPoolConv2dReLU(30, 30, 3, pool_size=2, pool_stride=2, padding=1,
bias=bias, **kwargs)
dim //= 2 # pooling, padding 0 -> 30x4x4
self.conv6 = ai8x.FusedConv2dReLU(30, fc_inputs, 3, padding=1, bias=bias, **kwargs)
self.fc = ai8x.Linear(fc_inputs*dim*dim, num_classes, bias=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=10, num_channels=3, dimensions=(28, 28),
planes=60, pool=2, fc_inputs=12, bias=False):
super().__init__()
# Limits
assert planes + num_channels <= ai8x.dev.WEIGHT_INPUTS
assert planes + fc_inputs <= ai8x.dev.WEIGHT_DEPTH-1
# Keep track of image dimensions so one constructor works for all image sizes
dim = dimensions[0]
self.conv1 = ai8x.FusedConv2dReLU(num_channels, planes, 3,
padding=1, bias=bias)
# padding 1 -> no change in dimensions -> MNIST: 28x28 | CIFAR: 32x32
pad = 2 if dim == 28 else 1
self.conv2 = ai8x.FusedMaxPoolConv2dReLU(planes, planes, 3, pool_size=2, pool_stride=2,
padding=pad, bias=bias)
dim //= 2 # pooling, padding 0 -> MNIST: 14x14 | CIFAR: 16x16
if pad == 2:
dim += 2 # MNIST: padding 2 -> 16x16 | CIFAR: padding 1 -> 16x16
self.conv3 = ai8x.FusedMaxPoolConv2dReLU(planes, 128-planes-fc_inputs, 3,
pool_size=2, pool_stride=2, padding=1,
bias=bias)
dim //= 2 # pooling, padding 0 -> 8x8
# padding 1 -> no change in dimensions
self.conv4 = ai8x.FusedAvgPoolConv2dReLU(128-planes-fc_inputs,
fc_inputs, 3,
pool_size=pool, pool_stride=2, padding=1,
bias=bias)
dim //= pool # pooling, padding 0 -> 4x4
# padding 1 -> no change in dimensions
self.fc = ai8x.Linear(fc_inputs*dim*dim, num_classes, bias=True, wide=True)
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_channels=3,
num_classes=10,
dimensions=(32, 32),
bias=False,
**kwargs):
super().__init__()
dim1 = dimensions[0]
dim2 = dimensions[1]
# 3x32x32
self.conv1 = ai8x.FusedMaxPoolConv2dReLU(in_channels=num_channels,
out_channels=64,
kernel_size=3,
padding=1,
bias=bias,
**kwargs)
dim1 //= 2
dim2 //= 2
# 64x16x16
self.fire1 = ai8x_fire.Fire(in_planes=64,
squeeze_planes=16,
expand1x1_planes=64,
expand3x3_planes=64,
bias=bias,
**kwargs)
# 128x16x16
self.fire2 = ai8x_fire.Fire(in_planes=128,
squeeze_planes=16,
expand1x1_planes=64,
expand3x3_planes=64,
bias=bias,
**kwargs)
# 128x16x16
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2,
padding=0) # check if kernel size=3
dim1 //= 2
dim2 //= 2
# 128x8x8
self.fire3 = ai8x_fire.Fire(in_planes=128,
squeeze_planes=32,
expand1x1_planes=128,
expand3x3_planes=128,
bias=bias,
**kwargs)
# 256x8x8
self.fire4 = ai8x_fire.Fire(in_planes=256,
squeeze_planes=32,
expand1x1_planes=128,
expand3x3_planes=128,
bias=bias,
**kwargs)
# 256x8x8
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2,
padding=0) # check if kernel size=3
dim1 //= 2
dim2 //= 2
# 256x4x4
self.fire5 = ai8x_fire.Fire(in_planes=256,
squeeze_planes=48,
expand1x1_planes=192,
expand3x3_planes=192,
bias=bias,
**kwargs)
# 384x4x4
self.fire6 = ai8x_fire.Fire(in_planes=384,
squeeze_planes=48,
expand1x1_planes=192,
expand3x3_planes=192,
bias=bias,
**kwargs)
# 384x4x4
self.fire7 = ai8x_fire.Fire(in_planes=384,
squeeze_planes=48,
expand1x1_planes=256,
expand3x3_planes=256,
bias=bias,
**kwargs)
# 512x4x4
self.fire8 = ai8x_fire.Fire(in_planes=512,
squeeze_planes=64,
expand1x1_planes=256,
expand3x3_planes=256,
bias=bias,
**kwargs)
# 512x4x4
# self.conv2 = ai8x.FusedAvgPoolConv2dReLU(in_channels=512, out_channels=num_classes,
# kernel_size=1, pool_size=4, pool_stride=4)
self.fc = ai8x.SoftwareLinear(512 * dim1 * dim2,
num_classes,
bias=bias)
# num_classesx1x1
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=2,
num_channels=1,
dimensions=(80, 80),
planes=8,
pool=2,
fc_inputs=2,
bias=False,
**kwargs):
super().__init__()
# Limits
assert planes + num_channels <= ai8x.dev.WEIGHT_INPUTS
assert planes + fc_inputs <= ai8x.dev.WEIGHT_DEPTH - 1
# 1x80x80 --> 8x80x80 (padding by 1 so same dimension)
self.conv1 = ai8x.FusedConv2dReLU(1,
8,
3,
padding=1,
bias=False,
**kwargs)
self.conv2 = ai8x.FusedConv2dReLU(8,
8,
3,
padding=1,
bias=False,
**kwargs)
# 8x80x80 --> 16x40x40 (padding by 1 so same dimension)
self.conv3 = ai8x.FusedMaxPoolConv2dReLU(8,
16,
3,
padding=1,
bias=False,
pool_size=2,
pool_stride=2,
**kwargs)
self.conv4 = ai8x.FusedConv2dReLU(16,
16,
3,
padding=1,
bias=False,
**kwargs)
# 16x40x40 --> 32x20x20 (padding by 1 so increase dimension)
self.conv5 = ai8x.FusedMaxPoolConv2dReLU(16,
32,
3,
padding=1,
bias=False,
pool_size=2,
pool_stride=2,
**kwargs)
self.conv6 = ai8x.FusedConv2dReLU(32,
32,
3,
padding=1,
bias=False,
**kwargs)
# 32x20x20 --> 64x12x12 (padding by 2 so increase dimension)
self.conv7 = ai8x.FusedMaxPoolConv2dReLU(32,
64,
3,
padding=2,
bias=False,
pool_size=2,
pool_stride=2,
**kwargs)
self.conv8 = ai8x.FusedConv2dReLU(64,
64,
3,
padding=1,
bias=False,
**kwargs)
# 64x12x12 --> 64x6x6 (padding by 1 so same dimension)
self.conv9 = ai8x.FusedMaxPoolConv2dReLU(64,
64,
3,
padding=1,
bias=False,
pool_size=2,
pool_stride=2,
**kwargs)
# 64x6x6 --> 64x3x3 (passing by 1 so same dimension)
self.conv10 = ai8x.FusedMaxPoolConv2dReLU(64,
64,
3,
padding=1,
bias=False,
pool_size=2,
pool_stride=2,
**kwargs)
# flatten to fully connected layer
self.fc1 = ai8x.FusedLinearReLU(64 * 3 * 3, 10, bias=True, **kwargs)
self.fc2 = ai8x.Linear(10, 2, bias=True, wide=True, **kwargs)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
