def __init__(self,
codeword_multiplier,
out_dims,
block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
super(GlobalLocalPredResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
in_dims = []
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
in_dims.append(self.inplanes)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
dilate=replace_stride_with_dilation[0])
in_dims.append(self.inplanes)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])
in_dims.append(self.inplanes)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
in_dims.append(self.inplanes)
in_dims.append(self.inplanes)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
self.sparse_layers = nn.ModuleList()
for input_dim, output_dim in zip(in_dims, out_dims):
self.sparse_layers.append(Layers.GlobalLocalPred(input_dim,
output_dim*codeword_multiplier))
def __init__(self, nb_codewords, scale, nb_class=10, return_output=False):
super(PredAllCNN, self).__init__()
self.scale = scale
in_dims = []
self.return_output = return_output
nb_conv = int(48 * scale)
self.conv1 = nn.Conv2d(3,
nb_conv,
kernel_size=7,
stride=2,
padding=3,
bias=True)
self.bn1 = nn.BatchNorm2d(nb_conv)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
in_dims.append(nb_conv)
# 56x56x48
in_conv = 48
nb_conv = int(96 * scale)
self.block1 = nn.Sequential(*self.block_(((in_conv, nb_conv, 3, 1, 1),
(nb_conv, nb_conv, 3, 1,
1), (nb_conv, nb_conv, 3, 2,
1))))
in_dims.append(nb_conv)
# 28x28x96
in_conv = nb_conv
nb_conv = int(192 * scale)
self.block2 = nn.Sequential(*self.block_(((in_conv, nb_conv, 3, 1, 1),
(nb_conv, nb_conv, 3, 1,
1), (nb_conv, nb_conv, 3, 2,
1))))
in_dims.append(nb_conv)
# 14x14x192
in_conv = nb_conv
nb_conv = int(256 * scale)
self.block3 = nn.Sequential(*self.block_(((in_conv, nb_conv, 3, 1, 1),
(nb_conv, nb_conv, 3, 1,
1), (nb_conv, nb_conv, 3, 2,
1))))
# 7x7x 256
in_conv = nb_conv
nb_conv = int(384 * scale)
self.block4 = nn.Sequential(*self.block_(((in_conv, nb_conv, 3, 1, 1),
(nb_conv, nb_conv, 3, 1,
1), (nb_conv, nb_conv, 1, 1,
0))))
in_dims.append(nb_conv)
in_dims.append(nb_conv)
self.output_layer = nn.Linear(nb_conv, nb_class)
self.sparse_layers = nn.ModuleList()
for idx, in_dim in enumerate(in_dims):
self.sparse_layers.append(
Layers.GlobalLocalPred(in_dim, nb_codewords[idx]))
self.initialize()
def __init__(self,
codeword_multiplier,
centers,
intercept,
scale,
nb_class=10,
return_output=False):
super(GlobalLocalPred, self).__init__()
self.scale = scale
self.return_output = return_output
in_dims = []
in_conv = 3
nb_conv = int(96 * scale)
self.block1 = nn.Sequential(*self.block_(((in_conv, nb_conv, 3, 1, 1),
(nb_conv, nb_conv, 3, 1,
1), (nb_conv, nb_conv, 3, 2,
1))))
in_dims.append(nb_conv)
in_conv = nb_conv
nb_conv = int(192 * scale)
self.block2 = nn.Sequential(*self.block_(((in_conv, nb_conv, 3, 1, 1),
(nb_conv, nb_conv, 3, 1,
1), (nb_conv, nb_conv, 3, 2,
1))))
in_dims.append(nb_conv)
in_conv = nb_conv
nb_conv = int(256 * scale)
self.block3 = nn.Sequential(*self.block_(((in_conv, nb_conv, 3, 1, 1),
(nb_conv, nb_conv, 3, 1,
1), (nb_conv, nb_conv, 3, 2,
1))))
in_conv = nb_conv
nb_conv = int(384 * scale)
self.block4 = nn.Sequential(*self.block_(((in_conv, nb_conv, 3, 1, 1),
(nb_conv, nb_conv, 3, 1,
1), (nb_conv, nb_conv, 1, 1,
0))))
in_dims.append(nb_conv)
self.output_layer = nn.Linear(nb_conv, nb_class)
# nbof layers
if centers is None:
centers = [
None,
] * 3
if intercept is None:
intercept = [
None,
] * 3
self.sparse_layers = nn.ModuleList()
teacher_in_dims = [128, 256, 1024]
self.sparse_layers.append(
Layers.GlobalLocalPred(in_dims[0],
teacher_in_dims[0] * codeword_multiplier,
centers=centers[0],
intercept=intercept[0]))
self.sparse_layers.append(
Layers.GlobalLocalPred(in_dims[1],
teacher_in_dims[1] * codeword_multiplier,
centers=centers[1],
intercept=intercept[1]))
self.sparse_layers.append(
Layers.GlobalLocalPred(in_dims[2],
teacher_in_dims[2] * codeword_multiplier,
centers=centers[2],
intercept=intercept[2]))
self.initialize()
