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resnet18_32x32.py
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import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False), nn.BatchNorm2d(self.expansion * planes))
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes,
self.expansion * planes,
kernel_size=1,
bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False), nn.BatchNorm2d(self.expansion * planes))
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet18_32x32(nn.Module):
def __init__(self, block=BasicBlock, num_blocks=None, num_classes=10):
super(ResNet18_32x32, self).__init__()
if num_blocks is None:
num_blocks = [2, 2, 2, 2]
self.in_planes = 64
self.conv1 = nn.Conv2d(3,
64,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
# self.avgpool = nn.AvgPool2d(4)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.feature_size = 512 * block.expansion
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x, return_feature=False, return_feature_list=False):
feature1 = F.relu(self.bn1(self.conv1(x)))
feature2 = self.layer1(feature1)
feature3 = self.layer2(feature2)
feature4 = self.layer3(feature3)
feature5 = self.layer4(feature4)
feature5 = self.avgpool(feature5)
feature = feature5.view(feature5.size(0), -1)
logits_cls = self.fc(feature)
feature_list = [feature1, feature2, feature3, feature4, feature5]
if return_feature:
return logits_cls, feature
elif return_feature_list:
return logits_cls, feature_list
else:
return logits_cls
def forward_threshold(self, x, threshold):
feature1 = F.relu(self.bn1(self.conv1(x)))
feature2 = self.layer1(feature1)
feature3 = self.layer2(feature2)
feature4 = self.layer3(feature3)
feature5 = self.layer4(feature4)
feature5 = self.avgpool(feature5)
feature = feature5.clip(max=threshold)
feature = feature.view(feature.size(0), -1)
logits_cls = self.fc(feature)
return logits_cls
def get_fc(self):
fc = self.fc
return fc.weight.cpu().detach().numpy(), fc.bias.cpu().detach().numpy()