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wideresnet.py
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wideresnet.py
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from layers import (MetaModule, MetaConv2d, MetaBatchNorm2d, MetaLinear)
class BasicBlock(MetaModule):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = MetaBatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
self.conv1 = MetaConv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = MetaBatchNorm2d(out_planes)
self.relu2 = nn.ReLU(inplace=True)
self.conv2 = MetaConv2d(out_planes,
out_planes,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.droprate = dropRate
self.equalInOut = (in_planes == out_planes)
self.convShortcut = (not self.equalInOut) and MetaConv2d(
in_planes,
out_planes,
kernel_size=1,
stride=stride,
padding=0,
bias=False) or None
def forward(self, x):
if not self.equalInOut:
x = self.relu1(self.bn1(x))
else:
out = self.relu1(self.bn1(x))
out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, training=self.training)
out = self.conv2(out)
return torch.add(x if self.equalInOut else self.convShortcut(x), out)
class NetworkBlock(MetaModule):
def __init__(self,
nb_layers,
in_planes,
out_planes,
block,
stride,
dropRate=0.0):
super(NetworkBlock, self).__init__()
self.layer = self._make_layer(block, in_planes, out_planes, nb_layers,
stride, dropRate)
def _make_layer(self, block, in_planes, out_planes, nb_layers, stride,
dropRate):
layers = []
for i in range(int(nb_layers)):
layers.append(
block(i == 0 and in_planes or out_planes, out_planes,
i == 0 and stride or 1, dropRate))
return nn.Sequential(*layers)
def forward(self, x):
return self.layer(x)
class WideResNet(MetaModule):
def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
super(WideResNet, self).__init__()
nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
assert ((depth - 4) % 6 == 0)
n = (depth - 4) / 6
block = BasicBlock
# 1st conv before any network block
self.conv1 = MetaConv2d(3,
nChannels[0],
kernel_size=3,
stride=1,
padding=1,
bias=False)
# 1st block
self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1,
dropRate)
# 2nd block
self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2,
dropRate)
# 3rd block
self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2,
dropRate)
# global average pooling and classifier
self.bn1 = MetaBatchNorm2d(nChannels[3])
self.relu = nn.ReLU(inplace=True)
self.fc = MetaLinear(nChannels[3], num_classes)
self.nChannels = nChannels[3]
for m in self.modules():
if isinstance(m, MetaConv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, MetaBatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, MetaLinear):
m.bias.data.zero_()
def forward(self, x):
out = self.conv1(x)
out = self.block1(out)
out = self.block2(out)
out = self.block3(out)
out = self.relu(self.bn1(out))
out = F.avg_pool2d(out, 8)
out = out.view(-1, self.nChannels)
return self.fc(out)