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resnet.py
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resnet.py
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import torch
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 ResNet(nn.Module):
def __init__(self, criterion, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 48
num_blocks = [2, 2, 2, 2]
self._criterion = criterion
block = BasicBlock
self.conv1 = nn.Conv2d(3, 48, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(48)
self.layer1 = self._make_layer(block, 48, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 96, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 192, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 384, num_blocks[3], stride=2)
self.linear = nn.Linear(384*block.expansion, num_classes)
# self.in_planes = 64
# num_blocks = [2, 2, 2, 2]
# self._criterion = criterion
# block = BasicBlock
# 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, num_classes)
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, alphas):
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
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 new(self):
model_new = ResNet(self._criterion).cuda()
return model_new
# def _loss(self, input, alphas ,target):
# logits = self(input, alphas)
def _loss(self, input ,target):
logits = self(input)
return self._criterion(logits, target)
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# import torch.nn.init as init
# from torch.autograd import Variable
# # __all__ = ['ResNet', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet1202']
# def _weights_init(m):
# classname = m.__class__.__name__
# #print(classname)
# if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
# init.kaiming_normal_(m.weight)
# class LambdaLayer(nn.Module):
# def __init__(self, lambd):
# super(LambdaLayer, self).__init__()
# self.lambd = lambd
# def forward(self, x):
# return self.lambd(x)
# class BasicBlock(nn.Module):
# expansion = 1
# def __init__(self, in_planes, planes, stride=1, option='A'):
# 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 != planes:
# if option == 'A':
# """
# For CIFAR10 ResNet paper uses option A.
# """
# self.shortcut = LambdaLayer(lambda x:
# F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0))
# elif option == 'B':
# 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 ResNet(nn.Module):
# def __init__(self, block = BasicBlock, num_blocks = [2, 2, 2, 2], num_classes=10): #18
# # def __init__(self, block = BasicBlock, num_blocks = [9, 9, 9, 9], num_classes=10): #56
# # def __init__(self, block=Bottleneck, num_blocks=[3, 4, 6, 3], num_classes=10): #50
# super(ResNet, self).__init__()
# self.in_planes = 16
# self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
# self.bn1 = nn.BatchNorm2d(16)
# self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
# self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
# self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
# self.linear = nn.Linear(64, num_classes)
# self.apply(_weights_init)
# 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 = F.relu(self.bn1(self.conv1(x)))
# out = self.layer1(out)
# out = self.layer2(out)
# out = self.layer3(out)
# out = F.avg_pool2d(out, out.size()[3])
# out = out.view(out.size(0), -1)
# out = self.linear(out)
# return out