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| # -*- coding: utf-8 -*- | |
| # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is | |
| # holder of all proprietary rights on this computer program. | |
| # You can only use this computer program if you have closed | |
| # a license agreement with MPG or you get the right to use the computer | |
| # program from someone who is authorized to grant you that right. | |
| # Any use of the computer program without a valid license is prohibited and | |
| # liable to prosecution. | |
| # | |
| # Copyright©2019 Max-Planck-Gesellschaft zur Förderung | |
| # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute | |
| # for Intelligent Systems. All rights reserved. | |
| # | |
| # Contact: ps-license@tuebingen.mpg.de | |
| from lib.net.net_util import * | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class HourGlass(nn.Module): | |
| def __init__(self, num_modules, depth, num_features, opt): | |
| super(HourGlass, self).__init__() | |
| self.num_modules = num_modules | |
| self.depth = depth | |
| self.features = num_features | |
| self.opt = opt | |
| self._generate_network(self.depth) | |
| def _generate_network(self, level): | |
| self.add_module('b1_' + str(level), | |
| ConvBlock(self.features, self.features, self.opt)) | |
| self.add_module('b2_' + str(level), | |
| ConvBlock(self.features, self.features, self.opt)) | |
| if level > 1: | |
| self._generate_network(level - 1) | |
| else: | |
| self.add_module('b2_plus_' + str(level), | |
| ConvBlock(self.features, self.features, self.opt)) | |
| self.add_module('b3_' + str(level), | |
| ConvBlock(self.features, self.features, self.opt)) | |
| def _forward(self, level, inp): | |
| # Upper branch | |
| up1 = inp | |
| up1 = self._modules['b1_' + str(level)](up1) | |
| # Lower branch | |
| low1 = F.avg_pool2d(inp, 2, stride=2) | |
| low1 = self._modules['b2_' + str(level)](low1) | |
| if level > 1: | |
| low2 = self._forward(level - 1, low1) | |
| else: | |
| low2 = low1 | |
| low2 = self._modules['b2_plus_' + str(level)](low2) | |
| low3 = low2 | |
| low3 = self._modules['b3_' + str(level)](low3) | |
| # NOTE: for newer PyTorch (1.3~), it seems that training results are degraded due to implementation diff in F.grid_sample | |
| # if the pretrained model behaves weirdly, switch with the commented line. | |
| # NOTE: I also found that "bicubic" works better. | |
| up2 = F.interpolate(low3, | |
| scale_factor=2, | |
| mode='bicubic', | |
| align_corners=True) | |
| # up2 = F.interpolate(low3, scale_factor=2, mode='nearest) | |
| return up1 + up2 | |
| def forward(self, x): | |
| return self._forward(self.depth, x) | |
| class HGFilter(nn.Module): | |
| def __init__(self, opt, num_modules, in_dim): | |
| super(HGFilter, self).__init__() | |
| self.num_modules = num_modules | |
| self.opt = opt | |
| [k, s, d, p] = self.opt.conv1 | |
| # self.conv1 = nn.Conv2d(in_dim, 64, kernel_size=7, stride=2, padding=3) | |
| self.conv1 = nn.Conv2d(in_dim, | |
| 64, | |
| kernel_size=k, | |
| stride=s, | |
| dilation=d, | |
| padding=p) | |
| if self.opt.norm == 'batch': | |
| self.bn1 = nn.BatchNorm2d(64) | |
| elif self.opt.norm == 'group': | |
| self.bn1 = nn.GroupNorm(32, 64) | |
| if self.opt.hg_down == 'conv64': | |
| self.conv2 = ConvBlock(64, 64, self.opt) | |
| self.down_conv2 = nn.Conv2d(64, | |
| 128, | |
| kernel_size=3, | |
| stride=2, | |
| padding=1) | |
| elif self.opt.hg_down == 'conv128': | |
| self.conv2 = ConvBlock(64, 128, self.opt) | |
| self.down_conv2 = nn.Conv2d(128, | |
| 128, | |
| kernel_size=3, | |
| stride=2, | |
| padding=1) | |
| elif self.opt.hg_down == 'ave_pool': | |
| self.conv2 = ConvBlock(64, 128, self.opt) | |
| else: | |
| raise NameError('Unknown Fan Filter setting!') | |
| self.conv3 = ConvBlock(128, 128, self.opt) | |
| self.conv4 = ConvBlock(128, 256, self.opt) | |
| # Stacking part | |
| for hg_module in range(self.num_modules): | |
| self.add_module('m' + str(hg_module), | |
| HourGlass(1, opt.num_hourglass, 256, self.opt)) | |
| self.add_module('top_m_' + str(hg_module), | |
| ConvBlock(256, 256, self.opt)) | |
| self.add_module( | |
| 'conv_last' + str(hg_module), | |
| nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) | |
| if self.opt.norm == 'batch': | |
| self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) | |
| elif self.opt.norm == 'group': | |
| self.add_module('bn_end' + str(hg_module), | |
| nn.GroupNorm(32, 256)) | |
| self.add_module( | |
| 'l' + str(hg_module), | |
| nn.Conv2d(256, | |
| opt.hourglass_dim, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0)) | |
| if hg_module < self.num_modules - 1: | |
| self.add_module( | |
| 'bl' + str(hg_module), | |
| nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) | |
| self.add_module( | |
| 'al' + str(hg_module), | |
| nn.Conv2d(opt.hourglass_dim, | |
| 256, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0)) | |
| def forward(self, x): | |
| x = F.relu(self.bn1(self.conv1(x)), True) | |
| tmpx = x | |
| if self.opt.hg_down == 'ave_pool': | |
| x = F.avg_pool2d(self.conv2(x), 2, stride=2) | |
| elif self.opt.hg_down in ['conv64', 'conv128']: | |
| x = self.conv2(x) | |
| x = self.down_conv2(x) | |
| else: | |
| raise NameError('Unknown Fan Filter setting!') | |
| x = self.conv3(x) | |
| x = self.conv4(x) | |
| previous = x | |
| outputs = [] | |
| for i in range(self.num_modules): | |
| hg = self._modules['m' + str(i)](previous) | |
| ll = hg | |
| ll = self._modules['top_m_' + str(i)](ll) | |
| ll = F.relu( | |
| self._modules['bn_end' + str(i)]( | |
| self._modules['conv_last' + str(i)](ll)), True) | |
| # Predict heatmaps | |
| tmp_out = self._modules['l' + str(i)](ll) | |
| outputs.append(tmp_out) | |
| if i < self.num_modules - 1: | |
| ll = self._modules['bl' + str(i)](ll) | |
| tmp_out_ = self._modules['al' + str(i)](tmp_out) | |
| previous = previous + ll + tmp_out_ | |
| return outputs | |
| class FuseHGFilter(nn.Module): | |
| def __init__(self, opt, num_modules, in_dim): | |
| super(FuseHGFilter, self).__init__() | |
| self.num_modules = num_modules | |
| self.opt = opt | |
| [k, s, d, p] = self.opt.conv1 | |
| # self.conv1 = nn.Conv2d(in_dim, 64, kernel_size=7, stride=2, padding=3) | |
| self.conv1 = nn.Conv2d(in_dim, | |
| 64, | |
| kernel_size=k, | |
| stride=s, | |
| dilation=d, | |
| padding=p) | |
| if self.opt.norm == 'batch': | |
| self.bn1 = nn.BatchNorm2d(64) | |
| elif self.opt.norm == 'group': | |
| self.bn1 = nn.GroupNorm(32, 64) | |
| self.conv2 = ConvBlock(64, 128, self.opt) | |
| self.down_conv2 = nn.Conv2d(128, | |
| 96, | |
| kernel_size=3, | |
| stride=2, | |
| padding=1) | |
| # elif self.opt.hg_down == 'conv128': | |
| # self.conv2 = ConvBlock(64, 128, self.opt) | |
| # self.down_conv2 = nn.Conv2d(128, | |
| # 128, | |
| # kernel_size=3, | |
| # stride=2, | |
| # padding=1) | |
| dim=96+32 | |
| self.conv3 = ConvBlock(dim, dim, self.opt) | |
| self.conv4 = ConvBlock(dim, 256, self.opt) | |
| # Stacking part | |
| for hg_module in range(self.num_modules): | |
| self.add_module('m' + str(hg_module), | |
| HourGlass(1, opt.num_hourglass, 256, self.opt)) | |
| self.add_module('top_m_' + str(hg_module), | |
| ConvBlock(256, 256, self.opt)) | |
| self.add_module( | |
| 'conv_last' + str(hg_module), | |
| nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) | |
| if self.opt.norm == 'batch': | |
| self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) | |
| elif self.opt.norm == 'group': | |
| self.add_module('bn_end' + str(hg_module), | |
| nn.GroupNorm(32, 256)) | |
| hourglass_dim=256 | |
| self.add_module( | |
| 'l' + str(hg_module), | |
| nn.Conv2d(256, | |
| hourglass_dim, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0)) | |
| if hg_module < self.num_modules - 1: | |
| self.add_module( | |
| 'bl' + str(hg_module), | |
| nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) | |
| self.add_module( | |
| 'al' + str(hg_module), | |
| nn.Conv2d(hourglass_dim, | |
| 256, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0)) | |
| self.up_conv=nn.ConvTranspose2d(hourglass_dim,64,kernel_size=2,stride=2) | |
| def forward(self, x,plane): | |
| x = F.relu(self.bn1(self.conv1(x)), True) # 64*256*256 | |
| tmpx = x | |
| x = self.conv2(x) | |
| x = self.down_conv2(x) | |
| x=torch.cat([x,plane],1) # 128*128*128 | |
| x = self.conv3(x) | |
| x = self.conv4(x) | |
| previous = x | |
| outputs = [] | |
| for i in range(self.num_modules): | |
| hg = self._modules['m' + str(i)](previous) | |
| ll = hg | |
| ll = self._modules['top_m_' + str(i)](ll) | |
| ll = F.relu( | |
| self._modules['bn_end' + str(i)]( | |
| self._modules['conv_last' + str(i)](ll)), True) | |
| # Predict heatmaps | |
| tmp_out = self._modules['l' + str(i)](ll) | |
| outputs.append(tmp_out) | |
| if i < self.num_modules - 1: | |
| ll = self._modules['bl' + str(i)](ll) | |
| tmp_out_ = self._modules['al' + str(i)](tmp_out) | |
| previous = previous + ll + tmp_out_ | |
| out=self.up_conv(outputs[-1]) | |
| return out |