# encoding: utf-8 """ @author: liaoxingyu @contact: sherlockliao01@gmail.com """ import logging import math import torch import torch.nn.functional as F from torch import nn from fastreid.layers import ( IBN, SELayer, get_norm, ) from fastreid.modeling.backbones import BACKBONE_REGISTRY from fastreid.utils import comm from fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message logger = logging.getLogger("fastreid.overhaul.backbone") model_urls = { '18x': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', '34x': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', '50x': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', '101x': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'ibn_18x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet18_ibn_a-2f571257.pth', 'ibn_34x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet34_ibn_a-94bc1577.pth', 'ibn_50x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet50_ibn_a-d9d0bb7b.pth', 'ibn_101x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet101_ibn_a-59ea0ac6.pth', 'se_ibn_101x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/se_resnet101_ibn_a-fabed4e2.pth', } class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, bn_norm, with_ibn=False, with_se=False, stride=1, downsample=None, reduction=16): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False) if with_ibn: self.bn1 = IBN(planes, bn_norm) else: self.bn1 = get_norm(bn_norm, planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = get_norm(bn_norm, planes) self.relu = nn.ReLU(inplace=True) if with_se: self.se = SELayer(planes, reduction) else: self.se = nn.Identity() self.downsample = downsample self.stride = stride def forward(self, x): x = self.relu(x) identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.se(out) if self.downsample is not None: identity = self.downsample(x) out += identity # out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, bn_norm, with_ibn=False, with_se=False, stride=1, downsample=None, reduction=16): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) if with_ibn: self.bn1 = IBN(planes, bn_norm) else: self.bn1 = get_norm(bn_norm, planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = get_norm(bn_norm, planes) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = get_norm(bn_norm, planes * self.expansion) self.relu = nn.ReLU(inplace=True) if with_se: self.se = SELayer(planes * self.expansion, reduction) else: self.se = nn.Identity() self.downsample = downsample self.stride = stride def forward(self, x): x = self.relu(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) out = self.se(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, last_stride, bn_norm, with_ibn, with_se, with_nl, block, layers, non_layers): self.channel_nums = [] self.inplanes = 64 super().__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = get_norm(bn_norm, 64) self.relu = nn.ReLU(inplace=True) # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True) self.layer1 = self._make_layer(block, 64, layers[0], 1, bn_norm, with_ibn, with_se) self.layer2 = self._make_layer(block, 128, layers[1], 2, bn_norm, with_ibn, with_se) self.layer3 = self._make_layer(block, 256, layers[2], 2, bn_norm, with_ibn, with_se) self.layer4 = self._make_layer(block, 512, layers[3], last_stride, bn_norm, with_se=with_se) self.random_init() def _make_layer(self, block, planes, blocks, stride=1, bn_norm="BN", with_ibn=False, with_se=False): 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), get_norm(bn_norm, planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, bn_norm, with_ibn, with_se, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes, bn_norm, with_ibn, with_se)) self.channel_nums.append(self.inplanes) return nn.Sequential(*layers) def forward(self, 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 = F.relu(x, inplace=True) return x def random_init(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels nn.init.normal_(m.weight, 0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def get_bn_before_relu(self): if isinstance(self.layer1[0], Bottleneck): bn1 = self.layer1[-1].bn3 bn2 = self.layer2[-1].bn3 bn3 = self.layer3[-1].bn3 bn4 = self.layer4[-1].bn3 elif isinstance(self.layer1[0], BasicBlock): bn1 = self.layer1[-1].bn2 bn2 = self.layer2[-1].bn2 bn3 = self.layer3[-1].bn2 bn4 = self.layer4[-1].bn2 else: logger.info("ResNet unknown block error!") return [bn1, bn2, bn3, bn4] def extract_feature(self, x, preReLU=False): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) feat1 = self.layer1(x) feat2 = self.layer2(feat1) feat3 = self.layer3(feat2) feat4 = self.layer4(feat3) if not preReLU: feat1 = F.relu(feat1) feat2 = F.relu(feat2) feat3 = F.relu(feat3) feat4 = F.relu(feat4) return [feat1, feat2, feat3, feat4], F.relu(feat4) def get_channel_nums(self): return self.channel_nums def init_pretrained_weights(key): """Initializes model with pretrained weights. Layers that don't match with pretrained layers in name or size are kept unchanged. """ import os import errno import gdown def _get_torch_home(): ENV_TORCH_HOME = 'TORCH_HOME' ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME' DEFAULT_CACHE_DIR = '~/.cache' torch_home = os.path.expanduser( os.getenv( ENV_TORCH_HOME, os.path.join( os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch' ) ) ) return torch_home torch_home = _get_torch_home() model_dir = os.path.join(torch_home, 'checkpoints') try: os.makedirs(model_dir) except OSError as e: if e.errno == errno.EEXIST: # Directory already exists, ignore. pass else: # Unexpected OSError, re-raise. raise filename = model_urls[key].split('/')[-1] cached_file = os.path.join(model_dir, filename) if not os.path.exists(cached_file): if comm.is_main_process(): gdown.download(model_urls[key], cached_file, quiet=False) comm.synchronize() logger.info(f"Loading pretrained model from {cached_file}") state_dict = torch.load(cached_file, map_location=torch.device('cpu')) return state_dict @BACKBONE_REGISTRY.register() def build_resnet_backbone_distill(cfg): """ Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance. """ # fmt: off pretrain = cfg.MODEL.BACKBONE.PRETRAIN pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH last_stride = cfg.MODEL.BACKBONE.LAST_STRIDE bn_norm = cfg.MODEL.BACKBONE.NORM with_ibn = cfg.MODEL.BACKBONE.WITH_IBN with_se = cfg.MODEL.BACKBONE.WITH_SE with_nl = cfg.MODEL.BACKBONE.WITH_NL depth = cfg.MODEL.BACKBONE.DEPTH # fmt: on num_blocks_per_stage = { '18x': [2, 2, 2, 2], '34x': [3, 4, 6, 3], '50x': [3, 4, 6, 3], '101x': [3, 4, 23, 3], }[depth] nl_layers_per_stage = { '18x': [0, 0, 0, 0], '34x': [0, 0, 0, 0], '50x': [0, 2, 3, 0], '101x': [0, 2, 9, 0] }[depth] block = { '18x': BasicBlock, '34x': BasicBlock, '50x': Bottleneck, '101x': Bottleneck }[depth] model = ResNet(last_stride, bn_norm, with_ibn, with_se, with_nl, block, num_blocks_per_stage, nl_layers_per_stage) if pretrain: # Load pretrain path if specifically if pretrain_path: try: state_dict = torch.load(pretrain_path, map_location=torch.device('cpu')) logger.info(f"Loading pretrained model from {pretrain_path}") except FileNotFoundError as e: logger.info(f'{pretrain_path} is not found! Please check this path.') raise e except KeyError as e: logger.info("State dict keys error! Please check the state dict.") raise e else: key = depth if with_ibn: key = 'ibn_' + key if with_se: key = 'se_' + key state_dict = init_pretrained_weights(key) incompatible = model.load_state_dict(state_dict, strict=False) if incompatible.missing_keys: logger.info( get_missing_parameters_message(incompatible.missing_keys) ) if incompatible.unexpected_keys: logger.info( get_unexpected_parameters_message(incompatible.unexpected_keys) ) return model