adjust_classifier/ResNet.py at master · reveLATONG/adjust_classifier · GitHub

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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import shutil
from block import BasicBlock, Bottleneck
from hyper_parameters import *
#import data_division.py as didata


__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',
}


class ResNet(nn.Module):

	def __init__(self, block, layers, num_classes=1000):
		self.inplanes = 64
		super(ResNet, self).__init__()
		self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
							   bias=False)
		self.bn1 = nn.BatchNorm2d(64)
		self.relu = nn.ReLU(inplace=False)
		self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
		self.layer1 = self._make_layer(block, 64, layers[0])
		self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
		self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
		self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
		self.avgpool = nn.AvgPool2d(7, stride=1)
		self.fc = nn.Linear(512 * block.expansion, num_classes)
		for m in self.modules():
			if isinstance(m, nn.Conv2d):
				nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
			elif isinstance(m, nn.BatchNorm2d):
				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):
		x = self.relu(x)
		x = self.conv1(x)
		x = self.bn1(x)
		x = self.relu(x)
		x = self.maxpool(x)
		x = self.layer1(x)
		x = self.layer2(x)
		x = self.layer3(x)
		x = self.layer4(x)
		x = self.avgpool(x)
		x = x.view(x.size(0), -1)
		x = self.fc(x)
		return x


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'], model_dir=model_dir))
	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'], model_dir=model_dir))
	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'], model_dir=model_dir))
	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'], model_dir=model_dir))
	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'], model_dir=model_dir))
	return model