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resnet_cifar.py
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resnet_cifar.py
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from __future__ import absolute_import
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from torch.autograd import Variable
__all__ = ['resnet']
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 BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, cfg, stride=1, downsample=None):
# cfg should be a number in this case
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, cfg, stride)
self.bn1 = nn.BatchNorm2d(cfg)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(cfg, planes)
self.bn2 = nn.BatchNorm2d(planes)
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 += residual
out = self.relu(out)
return out
def downsample_basic_block(x, planes):
x = nn.AvgPool2d(2,2)(x)
zero_pads = torch.Tensor(
x.size(0), planes - x.size(1), x.size(2), x.size(3)).zero_()
if isinstance(x.data, torch.cuda.FloatTensor):
zero_pads = zero_pads.cuda()
out = Variable(torch.cat([x.data, zero_pads], dim=1))
return out
class ResNet(nn.Module):
def __init__(self, depth, dataset='cifar10', cfg=None):
super(ResNet, self).__init__()
# Model type specifies number of layers for CIFAR-10 model
assert (depth - 2) % 6 == 0, 'depth should be 6n+2'
n = (depth - 2) // 6
block = BasicBlock
if cfg == None:
cfg = [[16]*n, [32]*n, [64]*n]
cfg = [item for sub_list in cfg for item in sub_list]
self.cfg = cfg
self.inplanes = 16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 16, n, cfg=cfg[0:n])
self.layer2 = self._make_layer(block, 32, n, cfg=cfg[n:2*n], stride=2)
self.layer3 = self._make_layer(block, 64, n, cfg=cfg[2*n:3*n], stride=2)
self.avgpool = nn.AvgPool2d(8)
if dataset == 'cifar10':
num_classes = 10
elif dataset == 'cifar100':
num_classes = 100
self.fc = nn.Linear(64 * block.expansion, num_classes)
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, cfg, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = partial(downsample_basic_block, planes=planes*block.expansion)
layers = []
layers.append(block(self.inplanes, planes, cfg[0], stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, cfg[i]))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x) # 32x32
x = self.layer1(x) # 32x32
x = self.layer2(x) # 16x16
x = self.layer3(x) # 8x8
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet(**kwargs):
"""
Constructs a ResNet model.
"""
return ResNet(**kwargs)
# if __name__ == '__main__':
# net = resnet(depth=56)
# x=Variable(torch.FloatTensor(16, 3, 32, 32))
# y = net(x)
# print(y.data.shape)