model.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from thop import profile


class BasicBlock(nn.Module):
	def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
		super(BasicBlock, self).__init__()
		self.bn1 = nn.BatchNorm2d(in_planes)
		self.relu1 = nn.ReLU(inplace=True)
		self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
		                       padding=1, bias=False)
		self.bn2 = nn.BatchNorm2d(out_planes)
		self.relu2 = nn.ReLU(inplace=True)
		self.conv2 = nn.Conv2d(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 nn.Conv2d(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(nn.Module):
	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(nn.Module):
	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 = nn.Conv2d(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 = nn.BatchNorm2d(nChannels[3])
		self.relu = nn.ReLU(inplace=True)
		self.fc = nn.Linear(nChannels[3], num_classes)
		self.nChannels = nChannels[3]
		
		self._initialize_weights()
		
	def _initialize_weights(self):
		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))
				if m.bias is not None:
					m.bias.data.zero_()
			elif isinstance(m, nn.BatchNorm2d):
				m.weight.data.fill_(1.0)
				m.bias.data.zero_()
			elif isinstance(m, nn.Linear):
				n = m.weight.size(0)  # fan-out
				init_range = 1.0 / math.sqrt(n)
				m.weight.data.uniform_(-init_range, init_range)
				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)


if __name__ == '__main__':
	model = WideResNet(depth=40, num_classes=10, widen_factor=2, dropRate=0.3)
	flops, params = profile(model, inputs=(torch.randn(1, 3, 32, 32),), verbose=False)
	print('Params: %.2fM, Flops:%.2fM' % ((params / 1e6), (flops / 1e6)))