import os import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch._utils from .ocr import SpatialOCR_Module, SpatialGather_Module from .resnetv1b import BasicBlockV1b, BottleneckV1b relu_inplace = True class HighResolutionModule(nn.Module): def __init__(self, num_branches, blocks, num_blocks, num_inchannels, num_channels, fuse_method,multi_scale_output=True, norm_layer=nn.BatchNorm2d, align_corners=True): super(HighResolutionModule, self).__init__() self._check_branches(num_branches, num_blocks, num_inchannels, num_channels) self.num_inchannels = num_inchannels self.fuse_method = fuse_method self.num_branches = num_branches self.norm_layer = norm_layer self.align_corners = align_corners self.multi_scale_output = multi_scale_output self.branches = self._make_branches( num_branches, blocks, num_blocks, num_channels) self.fuse_layers = self._make_fuse_layers() self.relu = nn.ReLU(inplace=relu_inplace) def _check_branches(self, num_branches, num_blocks, num_inchannels, num_channels): if num_branches != len(num_blocks): error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( num_branches, len(num_blocks)) raise ValueError(error_msg) if num_branches != len(num_channels): error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( num_branches, len(num_channels)) raise ValueError(error_msg) if num_branches != len(num_inchannels): error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( num_branches, len(num_inchannels)) raise ValueError(error_msg) def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1): downsample = None if stride != 1 or \ self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.num_inchannels[branch_index], num_channels[branch_index] * block.expansion, kernel_size=1, stride=stride, bias=False), self.norm_layer(num_channels[branch_index] * block.expansion), ) layers = [] layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index], stride, downsample=downsample, norm_layer=self.norm_layer)) self.num_inchannels[branch_index] = \ num_channels[branch_index] * block.expansion for i in range(1, num_blocks[branch_index]): layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index], norm_layer=self.norm_layer)) return nn.Sequential(*layers) def _make_branches(self, num_branches, block, num_blocks, num_channels): branches = [] for i in range(num_branches): branches.append( self._make_one_branch(i, block, num_blocks, num_channels)) return nn.ModuleList(branches) def _make_fuse_layers(self): if self.num_branches == 1: return None num_branches = self.num_branches num_inchannels = self.num_inchannels fuse_layers = [] for i in range(num_branches if self.multi_scale_output else 1): fuse_layer = [] for j in range(num_branches): if j > i: fuse_layer.append(nn.Sequential( nn.Conv2d(in_channels=num_inchannels[j], out_channels=num_inchannels[i], kernel_size=1, bias=False), self.norm_layer(num_inchannels[i]))) elif j == i: fuse_layer.append(None) else: conv3x3s = [] for k in range(i - j): if k == i - j - 1: num_outchannels_conv3x3 = num_inchannels[i] conv3x3s.append(nn.Sequential( nn.Conv2d(num_inchannels[j], num_outchannels_conv3x3, kernel_size=3, stride=2, padding=1, bias=False), self.norm_layer(num_outchannels_conv3x3))) else: num_outchannels_conv3x3 = num_inchannels[j] conv3x3s.append(nn.Sequential( nn.Conv2d(num_inchannels[j], num_outchannels_conv3x3, kernel_size=3, stride=2, padding=1, bias=False), self.norm_layer(num_outchannels_conv3x3), nn.ReLU(inplace=relu_inplace))) fuse_layer.append(nn.Sequential(*conv3x3s)) fuse_layers.append(nn.ModuleList(fuse_layer)) return nn.ModuleList(fuse_layers) def get_num_inchannels(self): return self.num_inchannels def forward(self, x): if self.num_branches == 1: return [self.branches[0](x[0])] for i in range(self.num_branches): x[i] = self.branches[i](x[i]) x_fuse = [] for i in range(len(self.fuse_layers)): y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) for j in range(1, self.num_branches): if i == j: y = y + x[j] elif j > i: width_output = x[i].shape[-1] height_output = x[i].shape[-2] y = y + F.interpolate( self.fuse_layers[i][j](x[j]), size=[height_output, width_output], mode='bilinear', align_corners=self.align_corners) else: y = y + self.fuse_layers[i][j](x[j]) x_fuse.append(self.relu(y)) return x_fuse class HighResolutionNet(nn.Module): def __init__(self, width, num_classes, ocr_width=256, small=False, norm_layer=nn.BatchNorm2d, align_corners=True, opt=None): super(HighResolutionNet, self).__init__() self.opt = opt self.norm_layer = norm_layer self.width = width self.ocr_width = ocr_width self.ocr_on = ocr_width > 0 self.align_corners = align_corners self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = norm_layer(64) self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False) self.bn2 = norm_layer(64) self.relu = nn.ReLU(inplace=relu_inplace) num_blocks = 2 if small else 4 stage1_num_channels = 64 self.layer1 = self._make_layer(BottleneckV1b, 64, stage1_num_channels, blocks=num_blocks) stage1_out_channel = BottleneckV1b.expansion * stage1_num_channels self.stage2_num_branches = 2 num_channels = [width, 2 * width] num_inchannels = [ num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))] self.transition1 = self._make_transition_layer( [stage1_out_channel], num_inchannels) self.stage2, pre_stage_channels = self._make_stage( BasicBlockV1b, num_inchannels=num_inchannels, num_modules=1, num_branches=self.stage2_num_branches, num_blocks=2 * [num_blocks], num_channels=num_channels) self.stage3_num_branches = 3 num_channels = [width, 2 * width, 4 * width] num_inchannels = [ num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))] self.transition2 = self._make_transition_layer( pre_stage_channels, num_inchannels) self.stage3, pre_stage_channels = self._make_stage( BasicBlockV1b, num_inchannels=num_inchannels, num_modules=3 if small else 4, num_branches=self.stage3_num_branches, num_blocks=3 * [num_blocks], num_channels=num_channels) self.stage4_num_branches = 4 num_channels = [width, 2 * width, 4 * width, 8 * width] num_inchannels = [ num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))] self.transition3 = self._make_transition_layer( pre_stage_channels, num_inchannels) self.stage4, pre_stage_channels = self._make_stage( BasicBlockV1b, num_inchannels=num_inchannels, num_modules=2 if small else 3, num_branches=self.stage4_num_branches, num_blocks=4 * [num_blocks], num_channels=num_channels) if self.ocr_on: last_inp_channels = np.int(np.sum(pre_stage_channels)) ocr_mid_channels = 2 * ocr_width ocr_key_channels = ocr_width self.conv3x3_ocr = nn.Sequential( nn.Conv2d(last_inp_channels, ocr_mid_channels, kernel_size=3, stride=1, padding=1), norm_layer(ocr_mid_channels), nn.ReLU(inplace=relu_inplace), ) self.ocr_gather_head = SpatialGather_Module(num_classes) self.ocr_distri_head = SpatialOCR_Module(in_channels=ocr_mid_channels, key_channels=ocr_key_channels, out_channels=ocr_mid_channels, scale=1, dropout=0.05, norm_layer=norm_layer, align_corners=align_corners, opt=opt) def _make_transition_layer( self, num_channels_pre_layer, num_channels_cur_layer): num_branches_cur = len(num_channels_cur_layer) num_branches_pre = len(num_channels_pre_layer) transition_layers = [] for i in range(num_branches_cur): if i < num_branches_pre: if num_channels_cur_layer[i] != num_channels_pre_layer[i]: transition_layers.append(nn.Sequential( nn.Conv2d(num_channels_pre_layer[i], num_channels_cur_layer[i], kernel_size=3, stride=1, padding=1, bias=False), self.norm_layer(num_channels_cur_layer[i]), nn.ReLU(inplace=relu_inplace))) else: transition_layers.append(None) else: conv3x3s = [] for j in range(i + 1 - num_branches_pre): inchannels = num_channels_pre_layer[-1] outchannels = num_channels_cur_layer[i] \ if j == i - num_branches_pre else inchannels conv3x3s.append(nn.Sequential( nn.Conv2d(inchannels, outchannels, kernel_size=3, stride=2, padding=1, bias=False), self.norm_layer(outchannels), nn.ReLU(inplace=relu_inplace))) transition_layers.append(nn.Sequential(*conv3x3s)) return nn.ModuleList(transition_layers) def _make_layer(self, block, inplanes, planes, blocks, stride=1): downsample = None if stride != 1 or inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), self.norm_layer(planes * block.expansion), ) layers = [] layers.append(block(inplanes, planes, stride, downsample=downsample, norm_layer=self.norm_layer)) inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(inplanes, planes, norm_layer=self.norm_layer)) return nn.Sequential(*layers) def _make_stage(self, block, num_inchannels, num_modules, num_branches, num_blocks, num_channels, fuse_method='SUM', multi_scale_output=True): modules = [] for i in range(num_modules): # multi_scale_output is only used last module if not multi_scale_output and i == num_modules - 1: reset_multi_scale_output = False else: reset_multi_scale_output = True modules.append( HighResolutionModule(num_branches, block, num_blocks, num_inchannels, num_channels, fuse_method, reset_multi_scale_output, norm_layer=self.norm_layer, align_corners=self.align_corners) ) num_inchannels = modules[-1].get_num_inchannels() return nn.Sequential(*modules), num_inchannels def forward(self, x, mask=None, additional_features=None): hrnet_feats = self.compute_hrnet_feats(x, additional_features) if not self.ocr_on: return hrnet_feats, ocr_feats = self.conv3x3_ocr(hrnet_feats) mask = nn.functional.interpolate(mask, size=ocr_feats.size()[2:], mode='bilinear', align_corners=True) context = self.ocr_gather_head(ocr_feats, mask) ocr_feats = self.ocr_distri_head(ocr_feats, context) return ocr_feats, def compute_hrnet_feats(self, x, additional_features, return_list=False): x = self.compute_pre_stage_features(x, additional_features) x = self.layer1(x) x_list = [] for i in range(self.stage2_num_branches): if self.transition1[i] is not None: x_list.append(self.transition1[i](x)) else: x_list.append(x) y_list = self.stage2(x_list) x_list = [] for i in range(self.stage3_num_branches): if self.transition2[i] is not None: if i < self.stage2_num_branches: x_list.append(self.transition2[i](y_list[i])) else: x_list.append(self.transition2[i](y_list[-1])) else: x_list.append(y_list[i]) y_list = self.stage3(x_list) x_list = [] for i in range(self.stage4_num_branches): if self.transition3[i] is not None: if i < self.stage3_num_branches: x_list.append(self.transition3[i](y_list[i])) else: x_list.append(self.transition3[i](y_list[-1])) else: x_list.append(y_list[i]) x = self.stage4(x_list) if return_list: return x # Upsampling x0_h, x0_w = x[0].size(2), x[0].size(3) x1 = F.interpolate(x[1], size=(x0_h, x0_w), mode='bilinear', align_corners=self.align_corners) x2 = F.interpolate(x[2], size=(x0_h, x0_w), mode='bilinear', align_corners=self.align_corners) x3 = F.interpolate(x[3], size=(x0_h, x0_w), mode='bilinear', align_corners=self.align_corners) return torch.cat([x[0], x1, x2, x3], 1) def compute_pre_stage_features(self, x, additional_features): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) if additional_features is not None: x = x + additional_features x = self.conv2(x) x = self.bn2(x) return self.relu(x) def load_pretrained_weights(self, pretrained_path=''): model_dict = self.state_dict() if not os.path.exists(pretrained_path): print(f'\nFile "{pretrained_path}" does not exist.') print('You need to specify the correct path to the pre-trained weights.\n' 'You can download the weights for HRNet from the repository:\n' 'https://github.com/HRNet/HRNet-Image-Classification') exit(1) pretrained_dict = torch.load(pretrained_path, map_location={'cuda:0': 'cpu'}) pretrained_dict = {k.replace('last_layer', 'aux_head').replace('model.', ''): v for k, v in pretrained_dict.items()} params_count = len(pretrained_dict) pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict.keys()} print(f'Loaded {len(pretrained_dict)} of {params_count} pretrained parameters for HRNet') model_dict.update(pretrained_dict) self.load_state_dict(model_dict)