Spaces:
Running
on
Zero
Running
on
Zero
| # ------------------------------------------------------------------------------ | |
| # Reference: https://github.com/HRNet/HRNet-Image-Classification | |
| # ------------------------------------------------------------------------------ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.utils.model_zoo as model_zoo | |
| __all__ = [ 'hrnet18s', 'hrnet18', 'hrnet32' ] | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| 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 * self.expansion, kernel_size=1, | |
| bias=False) | |
| self.bn3 = nn.BatchNorm2d(planes * self.expansion) | |
| 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 HighResolutionModule(nn.Module): | |
| def __init__(self, num_branches, blocks, num_blocks, num_inchannels, | |
| num_channels, fuse_method, multi_scale_output=True): | |
| super(HighResolutionModule, self).__init__() | |
| self._check_branches( | |
| num_branches, blocks, num_blocks, num_inchannels, num_channels) | |
| self.num_inchannels = num_inchannels | |
| self.fuse_method = fuse_method | |
| self.num_branches = num_branches | |
| 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(False) | |
| def _check_branches(self, num_branches, blocks, 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), | |
| nn.BatchNorm2d(num_channels[branch_index] * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.num_inchannels[branch_index], | |
| num_channels[branch_index], stride, downsample)) | |
| 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])) | |
| 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(num_inchannels[j], | |
| num_inchannels[i], | |
| 1, | |
| 1, | |
| 0, | |
| bias=False), | |
| nn.BatchNorm2d(num_inchannels[i]), | |
| nn.Upsample(scale_factor=2**(j-i), mode='nearest'))) | |
| 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, | |
| 3, 2, 1, bias=False), | |
| nn.BatchNorm2d(num_outchannels_conv3x3))) | |
| else: | |
| num_outchannels_conv3x3 = num_inchannels[j] | |
| conv3x3s.append(nn.Sequential( | |
| nn.Conv2d(num_inchannels[j], | |
| num_outchannels_conv3x3, | |
| 3, 2, 1, bias=False), | |
| nn.BatchNorm2d(num_outchannels_conv3x3), | |
| nn.ReLU(False))) | |
| 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] | |
| 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, num_modules, num_branches, block, | |
| num_blocks, num_channels, fuse_method, **kwargs): | |
| super(HighResolutionNet, self).__init__() | |
| self.num_modules = num_modules | |
| self.num_branches = num_branches | |
| self.block = block | |
| self.num_blocks = num_blocks | |
| self.num_channels = num_channels | |
| self.fuse_method = fuse_method | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, | |
| bias=False) | |
| self.bn2 = nn.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| # layer1 | |
| num_channels, num_blocks = self.num_channels[0][0], self.num_blocks[0][0] | |
| self.layer1 = self._make_layer(self.block[0], 64, num_channels, num_blocks) | |
| stage1_out_channel = self.block[0].expansion*num_channels | |
| # layer2 | |
| num_channels, num_blocks = self.num_channels[1], self.num_blocks[1] | |
| num_channels = [ | |
| num_channels[i] * self.block[1].expansion for i in range(len(num_channels))] | |
| self.transition1 = self._make_transition_layer([stage1_out_channel], num_channels) | |
| self.stage2, pre_stage_channels = self._make_stage(1, num_channels) | |
| # layer3 | |
| num_channels, num_blocks = self.num_channels[2], self.num_blocks[2] | |
| num_channels = [ | |
| num_channels[i] * self.block[2].expansion for i in range(len(num_channels))] | |
| self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels) | |
| self.stage3, pre_stage_channels = self._make_stage(2, num_channels) | |
| # layer4 | |
| num_channels, num_blocks = self.num_channels[3], self.num_blocks[3] | |
| num_channels = [ | |
| num_channels[i] * self.block[3].expansion for i in range(len(num_channels))] | |
| self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels) | |
| self.stage4, pre_stage_channels = self._make_stage(3, num_channels, multi_scale_output=True) | |
| self._out_channels = sum(pre_stage_channels) | |
| 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], | |
| 3, | |
| 1, | |
| 1, | |
| bias=False), | |
| nn.BatchNorm2d( | |
| num_channels_cur_layer[i], ), | |
| nn.ReLU(inplace=True))) | |
| 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, 3, 2, 1, bias=False), | |
| nn.BatchNorm2d(outchannels, ), | |
| nn.ReLU(inplace=True))) | |
| 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), | |
| nn.BatchNorm2d(planes * block.expansion, ), | |
| ) | |
| layers = [] | |
| layers.append(block(inplanes, planes, stride, downsample)) | |
| inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def _make_stage(self, stage_index, in_channels, | |
| multi_scale_output=True): | |
| num_modules = self.num_modules[stage_index] | |
| num_branches = self.num_branches[stage_index] | |
| num_blocks = self.num_blocks[stage_index] | |
| num_channels = self.num_channels[stage_index] | |
| block = self.block[stage_index] | |
| fuse_method = self.fuse_method[stage_index] | |
| 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, | |
| in_channels, | |
| num_channels, | |
| fuse_method, | |
| reset_multi_scale_output) | |
| ) | |
| in_channels = modules[-1].get_num_inchannels() | |
| return nn.Sequential(*modules), in_channels | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.conv2(x) | |
| x = self.bn2(x) | |
| x = self.relu(x) | |
| x = self.layer1(x) | |
| x_list = [] | |
| for i in range(self.num_branches[1]): | |
| 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.num_branches[2]): | |
| if self.transition2[i] is not None: | |
| 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.num_branches[3]): | |
| if self.transition3[i] is not None: | |
| x_list.append(self.transition3[i](y_list[-1])) | |
| else: | |
| x_list.append(y_list[i]) | |
| y_list = self.stage4(x_list) | |
| kwargs = { | |
| 'size': tuple(y_list[0].shape[-2:]), | |
| 'mode': 'bilinear', 'align_corners': False, | |
| } | |
| return torch.cat([F.interpolate(y,**kwargs) for y in y_list], 1) | |
| def hrnet18s(pretrained=True, **kwargs): | |
| model = HighResolutionNet( | |
| num_modules = [1, 1, 3, 2], | |
| num_branches = [1, 2, 3, 4], | |
| block = [Bottleneck, BasicBlock, BasicBlock, BasicBlock], | |
| num_blocks = [(2,), (2,2), (2,2,2), (2,2,2,2)], | |
| num_channels = [(64,), (18,36), (18,36,72), (18,36,72,144)], | |
| fuse_method = ['SUM', 'SUM', 'SUM', 'SUM'], | |
| **kwargs | |
| ) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['hrnet_w18s']), strict=False) | |
| return model | |
| def hrnet18(pretrained=False, **kwargs): | |
| model = HighResolutionNet( | |
| num_modules = [1, 1, 4, 3], | |
| num_branches = [1, 2, 3, 4], | |
| block = [Bottleneck, BasicBlock, BasicBlock, BasicBlock], | |
| num_blocks = [(4,), (4,4), (4,4,4), (4,4,4,4)], | |
| num_channels = [(64,), (18,36), (18,36,72), (18,36,72,144)], | |
| fuse_method = ['SUM', 'SUM', 'SUM', 'SUM'], | |
| **kwargs | |
| ) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['hrnet18']), strict=False) | |
| return model | |
| def hrnet32(pretrained=False, **kwargs): | |
| model = HighResolutionNet( | |
| num_modules = [1, 1, 4, 3], | |
| num_branches = [1, 2, 3, 4], | |
| block = [Bottleneck, BasicBlock, BasicBlock, BasicBlock], | |
| num_blocks = [(4,), (4,4), (4,4,4), (4,4,4,4)], | |
| num_channels = [(64,), (32,64), (32,64,128), (32,64,128,256)], | |
| fuse_method = ['SUM', 'SUM', 'SUM', 'SUM'], | |
| **kwargs | |
| ) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['hrnet32']), strict=False) | |
| return model | |
| class BinaryHeadBlock(nn.Module): | |
| """BinaryHeadBlock | |
| """ | |
| def __init__(self, in_channels, proj_channels, out_channels, **kwargs): | |
| super(BinaryHeadBlock, self).__init__() | |
| self.layers = nn.Sequential( | |
| nn.Conv2d(in_channels, proj_channels, 1, bias=False), | |
| nn.BatchNorm2d(proj_channels), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(proj_channels, out_channels*2, 1, bias=False), | |
| ) | |
| def forward(self, input): | |
| N, C, H, W = input.shape | |
| return self.layers(input).view(N, 2, -1, H, W) | |
| def heatmap2coord(heatmap, topk=9): | |
| N, C, H, W = heatmap.shape | |
| score, index = heatmap.view(N,C,1,-1).topk(topk, dim=-1) | |
| coord = torch.cat([index%W, index//W], dim=2) | |
| return (coord*F.softmax(score, dim=-1)).sum(-1) | |
| class BinaryHeatmap2Coordinate(nn.Module): | |
| """BinaryHeatmap2Coordinate | |
| """ | |
| def __init__(self, stride=4.0, topk=5, **kwargs): | |
| super(BinaryHeatmap2Coordinate, self).__init__() | |
| self.topk = topk | |
| self.stride = stride | |
| def forward(self, input): | |
| return self.stride * heatmap2coord(input[:,1,...], self.topk) | |
| def __repr__(self): | |
| format_string = self.__class__.__name__ + '(' | |
| format_string += 'topk={}, '.format(self.topk) | |
| format_string += 'stride={}'.format(self.stride) | |
| format_string += ')' | |
| return format_string | |
| class HeatmapHead(nn.Module): | |
| """HeatmapHead | |
| """ | |
| def __init__(self): | |
| super(HeatmapHead, self).__init__() | |
| self.decoder = BinaryHeatmap2Coordinate( | |
| topk=9, | |
| stride=4.0, | |
| ) | |
| self.head = BinaryHeadBlock( | |
| in_channels=270, | |
| proj_channels=270, | |
| out_channels=98, | |
| ) | |
| def forward(self, input): | |
| heatmap = self.head(input) | |
| ldmk = self.decoder(heatmap) | |
| return heatmap[:,1,...], ldmk | |
| class LandmarkDetector(nn.Module): | |
| def __init__(self, model_path): | |
| super(LandmarkDetector, self).__init__() | |
| self.backbone = HighResolutionNet( | |
| num_modules = [1, 1, 4, 3], | |
| num_branches = [1, 2, 3, 4], | |
| block = [Bottleneck, BasicBlock, BasicBlock, BasicBlock], | |
| num_blocks = [(4,), (4,4), (4,4,4), (4,4,4,4)], | |
| num_channels = [(64,), (18,36), (18,36,72), (18,36,72,144)], | |
| fuse_method = ['SUM', 'SUM', 'SUM', 'SUM'] | |
| ) | |
| self.heatmap_head = HeatmapHead() | |
| self.load_state_dict(torch.load(model_path)) | |
| def forward(self, img): | |
| heatmap, landmark = self.heatmap_head(self.backbone(img)) | |
| return heatmap, landmark |