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cifar_resnet.py
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cifar_resnet.py
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import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
normalization = nn.BatchNorm2d
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class Identity(nn.Module):
def forward(self, input):
return input + 0.0
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = normalization(planes)
self.relu = nn.ReLU(inplace=False)
self.conv2 = conv3x3(planes, planes)
self.bn2 = normalization(planes)
self.shortcut = Identity()
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.shortcut(out)
out = self.relu(out)
return out
def forward_masked(self, x, mask_weight=None, mask_bias=None):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.shortcut(out)
out = self.relu(out)
if mask_weight is not None:
out = out * mask_weight[None,:,None,None]
if mask_bias is not None:
out = out + mask_bias[None,:,None,None]
return out
def forward_threshold(self, x, threshold=1e10):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu(out)
b, c, w, h = out.shape
mask = out.view(b, c, -1).mean(2) < threshold
out = mask[:, :, None, None] * out
# print(mask.sum(1).float().mean(0))
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = normalization(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = normalization(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = normalization(planes * 4)
self.relu = nn.ReLU(inplace=False)
self.shortcut = Identity()
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.shortcut(out)
out = self.relu(out)
return out
class AbstractResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
super(AbstractResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = normalization(64)
self.relu = nn.ReLU(inplace=False)
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)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
def _initial_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
normalization(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def features(self, x):
x = self.maxpool(self.relu(self.bn1(self.conv1(x))))
x = self.layer4(self.layer3(self.layer2(self.layer1(x))))
return x
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def load_state_dict(self, state_dict, strict=True):
missing_keys = []
unexpected_keys = []
error_msgs = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(self)
if strict:
error_msg = ''
if len(unexpected_keys) > 0:
error_msgs.insert(
0, 'Unexpected key(s) in state_dict: {}. '.format(
', '.join('"{}"'.format(k) for k in unexpected_keys)))
if len(missing_keys) > 0:
error_msgs.insert(
0, 'Missing key(s) in state_dict: {}. '.format(
', '.join('"{}"'.format(k) for k in missing_keys)))
if len(error_msgs) > 0:
print('Warning(s) in loading state_dict for {}:\n\t{}'.format(self.__class__.__name__, "\n\t".join(error_msgs)))
class ResNetCifar(AbstractResNet):
def __init__(self, block, layers, num_classes=10, method='', p=None, info=None):
super(ResNetCifar, self).__init__(block, layers, num_classes)
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.method = method
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.relu = nn.ReLU(inplace=False)
self.avgpool = nn.AvgPool2d(4, stride=1)
self._initial_weight()
def features(self, x):
out = self.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
return out
def forward(self, x, fc_params=None):
feat = self.features(x)
feat = self.avgpool(feat)
feat = feat.view(feat.size(0), -1)
out = self.fc(feat)
return out
def forward_features(self, x):
feat = self.features(x)
feat = self.avgpool(feat)
feat = feat.view(feat.size(0), -1)
return feat
def forward_head(self, feat):
out = self.fc(feat)
return out
def resnet18_cifar(**kwargs):
return ResNetCifar(BasicBlock, [2,2,2,2], **kwargs)