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resnet.py
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resnet.py
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'''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import get_gaussian_filter
class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
in_planes,
planes,
stride=1,
):
super(BasicBlock, self).__init__()
self.planes = planes
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_kernel = True
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def get_new_kernels(self, kernel_size, std):
self.kernel1 = get_gaussian_filter(
kernel_size=kernel_size,
sigma=std,
channels=self.planes,
)
self.kernel2 = get_gaussian_filter(
kernel_size=kernel_size,
sigma=std,
channels=self.planes,
)
def forward(self, x):
out = self.conv1(x)
out = F.relu(self.bn1(self.kernel1(out)))
out = self.conv2(out)
out = self.bn2(self.kernel2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, args):
super(ResNet, self).__init__()
self.in_planes = 64
self.std = args.std
self.factor = args.std_factor
self.epoch = args.epoch
self.kernel_size = args.kernel_size
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.linear = nn.Linear(512*block.expansion, args.num_classes)
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
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):
out = self.conv1(x)
out = F.relu(self.bn1(self.kernel1(out)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def get_new_kernels(self, epoch_count):
if epoch_count % self.epoch == 0 and epoch_count is not 0:
self.std *= self.factor
self.kernel1 = get_gaussian_filter(
kernel_size=self.kernel_size,
sigma=self.std,
channels=64,
)
for child in self.layer1.children():
child.get_new_kernels(self.kernel_size, self.std)
for child in self.layer2.children():
child.get_new_kernels(self.kernel_size, self.std)
for child in self.layer3.children():
child.get_new_kernels(self.kernel_size, self.std)
for child in self.layer4.children():
child.get_new_kernels(self.kernel_size, self.std)
def ResNet18(args):
return ResNet(BasicBlock, [2,2,2,2], args)
def ResNet34(args):
return ResNet(BasicBlock, [3,4,6,3], args)
def ResNet50():
return ResNet(Bottleneck, [3,4,6,3], args)
def ResNet101():
return ResNet(Bottleneck, [3,4,23,3], args)
def test():
net = ResNet18()
y = net(torch.randn(1,3,32,32))
print(y.size())