#!/usr/bin/python # -*- encoding: utf-8 -*- import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.model_zoo as modelzoo resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.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): def __init__(self, in_chan, out_chan, stride=1): super(BasicBlock, self).__init__() self.conv1 = conv3x3(in_chan, out_chan, stride) self.bn1 = nn.BatchNorm2d(out_chan) self.conv2 = conv3x3(out_chan, out_chan) self.bn2 = nn.BatchNorm2d(out_chan) self.relu = nn.ReLU(inplace=True) self.downsample = None if in_chan != out_chan or stride != 1: self.downsample = nn.Sequential( nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_chan), ) def forward(self, x): residual = self.conv1(x) residual = F.relu(self.bn1(residual)) residual = self.conv2(residual) residual = self.bn2(residual) shortcut = x if self.downsample is not None: shortcut = self.downsample(x) out = shortcut + residual out = self.relu(out) return out def create_layer_basic(in_chan, out_chan, bnum, stride=1): layers = [BasicBlock(in_chan, out_chan, stride=stride)] for i in range(bnum-1): layers.append(BasicBlock(out_chan, out_chan, stride=1)) return nn.Sequential(*layers) class Resnet18(nn.Module): def __init__(self): super(Resnet18, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) self.init_weight() def forward(self, x): x = self.conv1(x) x = F.relu(self.bn1(x)) x = self.maxpool(x) x = self.layer1(x) feat8 = self.layer2(x) # 1/8 feat16 = self.layer3(feat8) # 1/16 feat32 = self.layer4(feat16) # 1/32 return feat8, feat16, feat32 def init_weight(self): state_dict = modelzoo.load_url(resnet18_url) self_state_dict = self.state_dict() for k, v in state_dict.items(): if 'fc' in k: continue self_state_dict.update({k: v}) self.load_state_dict(self_state_dict) def get_params(self): wd_params, nowd_params = [], [] for _, module in self.named_modules(): if isinstance(module, (nn.Linear, nn.Conv2d)): wd_params.append(module.weight) if not module.bias is None: nowd_params.append(module.bias) elif isinstance(module, nn.BatchNorm2d): nowd_params += list(module.parameters()) return wd_params, nowd_params