class ResNet(nn.Module):
def __init__(self,
block,
layers,
input_channels,
num_classes=10,
zero_init_residual=False,
groups=1,
width_per_group=64,
replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
self.q_logvar_init = 0.05
self.p_logvar_init = math.log(0.05)
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 = BBBConv2d(self.q_logvar_init,
self.p_logvar_init,
input_channels,
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])
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.drop = nn.Dropout(p=.5)
self.classifier = BBBLinearFactorial(self.q_logvar_init,
self.p_logvar_init,
512 * block.expansion,
num_classes,
flow=False)
print(block.expansion)
layers2 = [
self.conv1, self.bn1, self.relu, self.maxpool, self.layer1,
self.layer2, self.layer3, self.layer4, self.avgpool
]
self.layers2 = nn.ModuleList(layers2)
for m in self.modules():
if isinstance(m, BBBConv2d):
m.reset_parameters()
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)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(
block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def probforward(self, x, dropout=False):
loss = 0
i = 0
out, kl = self.conv1.probforward(x)
out = self.relu(self.bn1(out))
loss += kl
out, kl = self.pf(out, self.layer1)
loss += kl
out, kl = self.pf(out, self.layer2)
loss += kl
out, kl = self.pf(out, self.layer3)
loss += kl
out, kl = self.pf(out, self.layer4)
loss += kl
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
if (dropout):
x = self.drop(x)
x, _kl = self.classifier.probforward(out)
kl += loss
logits = x
return logits, kl
def pf(self, x, layer):
kl = 0
for l in layer:
#print(l)
if hasattr(l, 'probforward') and callable(l.probforward):
x, _kl, = l.probforward(x)
kl += _kl
else:
print(l)
x = l.forward(x)
return x, kl
class BBBAlexNet(nn.Module):
'''The architecture of AlexNet with Bayesian Layers'''
def __init__(self, outputs, inputs):
super(BBBAlexNet, self).__init__()
flow = False
self.q_logvar_init = 0.05
self.p_logvar_init = math.log(0.05)
self.classifier = BBBLinearFactorial(self.q_logvar_init,
self.p_logvar_init,
1 * 1 * 128,
outputs,
flow=flow)
self.conv1 = BBBConv2d(self.q_logvar_init,
self.p_logvar_init,
inputs,
64,
kernel_size=11,
stride=4,
padding=5,
flow=flow)
self.soft1 = nn.Softplus()
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = BBBConv2d(self.q_logvar_init,
self.p_logvar_init,
64,
192,
kernel_size=5,
padding=2,
flow=flow)
self.soft2 = nn.Softplus()
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = BBBConv2d(self.q_logvar_init,
self.p_logvar_init,
192,
384,
kernel_size=3,
padding=1,
flow=flow)
self.soft3 = nn.Softplus()
self.conv4 = BBBConv2d(self.q_logvar_init,
self.p_logvar_init,
384,
256,
kernel_size=3,
padding=1,
flow=flow)
self.soft4 = nn.Softplus()
self.conv5 = BBBConv2d(self.q_logvar_init,
self.p_logvar_init,
256,
128,
kernel_size=3,
padding=1,
flow=flow)
self.soft5 = nn.Softplus()
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
# self.flatten = FlattenLayer(1 * 1 * 128)
# self.fc1 = BBBLinearFactorial(q_logvar_init, N, p_logvar_init, 1* 1 * 128, outputs)
layers = [
self.conv1, self.soft1, self.pool1, self.conv2, self.soft2,
self.pool2, self.conv3, self.soft3, self.conv4, self.soft4,
self.conv5, self.soft5, self.pool3
]
self.layers = nn.ModuleList(layers)
def probforward(self, x):
kl = 0
for layer in self.layers:
if hasattr(layer, 'probforward') and callable(layer.probforward):
x, _kl, = layer.probforward(x)
else:
x = layer.forward(x)
x = x.view(x.size(0), -1)
x, _kl = self.classifier.probforward(x)
kl += _kl
logits = x
return logits, kl