text: import torch
  import logging
  import torch.nn as nn
  import math
  import numpy as np
  import torch.utils.model_zoo as model_zoo
  from utils import colorful
  
  __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
  		   'resnet152']
  
  model_urls = {
  	'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
  	'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
  	'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
  	'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
  	'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
  }
  
  
  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, stride=1, downsample=None):
  		super(BasicBlock, self).__init__()
  		self.conv1 = conv3x3(inplanes, planes, stride)
  		self.bn1 = nn.BatchNorm2d(planes)
  		self.relu = nn.ReLU(inplace=True)
  		self.conv2 = conv3x3(planes, 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
  
  
  class Bottleneck(nn.Module):
  	expansion = 4
  
  	def __init__(self, inplanes, planes, stride=1, downsample=None):
  		super(Bottleneck, self).__init__()
  		self.conv1 = nn.Conv2d(inplanes, 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, planes * 4, kernel_size=1, bias=False)
  		self.bn3 = nn.BatchNorm2d(planes * 4)
  		self.relu = nn.ReLU(inplace=True)
  		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)
  		out = self.relu(out)
  
  		out = self.conv3(out)
  		out = self.bn3(out)
  
  		if self.downsample is not None:
  			residual = self.downsample(x)
  
  		out += residual
  		out = self.relu(out)
  
  		return out
  
  
  class ResNet(nn.Module):
  
  	def __init__(self, block, layers, low_dim=128, in_channel=3, width=1, non_linear_head=False):
  		self.inplanes = 64
  		super(ResNet, self).__init__()
  		self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=7, stride=2, padding=3,
  							   bias=False)
  		self.bn1 = nn.BatchNorm2d(64)
  		self.relu = nn.ReLU(inplace=True)
  
  		self.base = int(64 * width)
  
  		self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  		self.layer1 = self._make_layer(block, self.base, layers[0])
  		self.layer2 = self._make_layer(block, self.base * 2, layers[1], stride=2)
  		self.layer3 = self._make_layer(block, self.base * 4, layers[2], stride=2)
  		self.layer4 = self._make_layer(block, self.base * 8, layers[3], stride=2)
  		self.avgpool = nn.AvgPool2d(7, stride=1)
  		print(non_linear_head)
  		if not non_linear_head:
  			self.fc = nn.Linear(self.base * 8 * block.expansion, low_dim)
  		else:
  			logging.info(colorful('Using Non Linear Head'))
  			self.fc = nn.Sequential(
  						nn.Linear(self.base * 8 * block.expansion, 2048),
  						nn.ReLU(inplace=True),
  						nn.Linear(2048, low_dim),
  			)
  
  
  		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_()
  			if isinstance(m, nn.Conv2d):
  				nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
  			elif isinstance(m, nn.Linear):
  				nn.init.xavier_uniform_(m.weight)
  				nn.init.constant_(m.bias, 0)
  			elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
  				nn.init.constant_(m.weight, 1)
  				nn.init.constant_(m.bias, 0)
  
  
  
  	def _make_layer(self, block, planes, blocks, stride=1):
  		downsample = None
  		if stride != 1 or self.inplanes != planes * block.expansion:
  			downsample = nn.Sequential(
  				nn.Conv2d(self.inplanes, planes * block.expansion,
  						  kernel_size=1, stride=stride, bias=False),
  				nn.BatchNorm2d(planes * block.expansion),
  			)
  
  		layers = []
  		layers.append(block(self.inplanes, planes, stride, downsample))
  		self.inplanes = planes * block.expansion
  		for i in range(1, blocks):
  			layers.append(block(self.inplanes, planes))
  
  		return nn.Sequential(*layers)
  
  	def forward(self, x, layer=7, all_blocks=False):
  		if all_blocks:
  			return self.forward_convs(x)
  		if layer <= 0:
  			return x
  		x = self.conv1(x)
  		x = self.bn1(x)
  		x = self.relu(x)
  		x = self.maxpool(x)
  		if layer == 1:
  			return x
  		x = self.layer1(x)
  		if layer == 2:
  			return x
  		x = self.layer2(x)
  		if layer == 3:
  			return x
  		x = self.layer3(x)
  		if layer == 4:
  			return x
  		x = self.layer4(x)
  		if layer == 5:
  			return x
  		x = self.avgpool(x)
  		x = x.view(x.size(0), -1)
  		if layer == 6:
  			return x
  		x = self.fc(x)
  
  		return x
  
  	def forward_convs(self, x):
  		feats = []
  		x = self.conv1(x)
  		x = self.bn1(x)
  		x = self.relu(x)
  		x = self.maxpool(x)
  		feats.append(x)
  
  		for block in self.layer1:
  			x = block(x)
  			feats.append(x)
  		for block in self.layer2:
  			x = block(x)
  			feats.append(x)
  		for block in self.layer3:
  			x = block(x)
  			feats.append(x)
  		for block in self.layer4:
  			x = block(x)
  			feats.append(x)
  
  		return feats
  
  def resnet18(pretrained=False, **kwargs):
  	"""Constructs a ResNet-18 model.
  	Args:
  		pretrained (bool): If True, returns a model pre-trained on ImageNet
  	"""
  	model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
  	if pretrained:
  		model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
  	return model
  
  
  def resnet34(pretrained=False, **kwargs):
  	"""Constructs a ResNet-34 model.
  	Args:
  		pretrained (bool): If True, returns a model pre-trained on ImageNet
  	"""
  	model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
  	if pretrained:
  		model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
  	return model
  
  
  def resnet50(pretrained=False, **kwargs):
  	"""Constructs a ResNet-50 model.
  	Args:
  		pretrained (bool): If True, returns a model pre-trained on ImageNet
  	"""
  	model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
  	if pretrained:
  		model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
  	return model
  
  
  def resnet101(pretrained=False, **kwargs):
  	"""Constructs a ResNet-101 model.
  	Args:
  		pretrained (bool): If True, returns a model pre-trained on ImageNet
  	"""
  	model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
  	if pretrained:
  		model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
  	return model
  
  
  def resnet152(pretrained=False, **kwargs):
  	"""Constructs a ResNet-152 model.
  	Args:
  		pretrained (bool): If True, returns a model pre-trained on ImageNet
  	"""
  	model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
  	if pretrained:
  		model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
  	return model
  
  
  
  