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- """
- # ---------------------------------------------------------------------------------
- # -*- coding: utf-8 -*-
- -----------------------------------------------------------------------------------
- # Copyright (c) Microsoft
- # Licensed under the MIT License.
- # Written by Bin Xiao (Bin.Xiao@microsoft.com)
- # Modified by Xingyi Zhou
- # Refer from: https://github.com/xingyizhou/CenterNet
-
- # Modifier: Nguyen Mau Dung (2020.08.09)
- # ------------------------------------------------------------------------------
- """
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import os
-
- import torch
- import torch.nn as nn
- import torch.utils.model_zoo as model_zoo
- import torch.nn.functional as F
-
- BN_MOMENTUM = 0.1
-
- model_urls = {
- 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
- 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
- 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
- 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
- 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.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):
- 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, momentum=BN_MOMENTUM)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
- 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, momentum=BN_MOMENTUM)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM)
- 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 PoseResNet(nn.Module):
-
- def __init__(self, block, layers, heads, head_conv, **kwargs):
- self.inplanes = 64
- self.deconv_with_bias = False
- self.heads = heads
-
- super(PoseResNet, self).__init__()
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
- self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
- self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
- self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-
- self.conv_up_level1 = nn.Conv2d(768, 256, kernel_size=1, stride=1, padding=0)
- self.conv_up_level2 = nn.Conv2d(384, 128, kernel_size=1, stride=1, padding=0)
- self.conv_up_level3 = nn.Conv2d(192, 64, kernel_size=1, stride=1, padding=0)
-
- fpn_channels = [256, 128, 64]
- for fpn_idx, fpn_c in enumerate(fpn_channels):
- for head in sorted(self.heads):
- num_output = self.heads[head]
- if head_conv > 0:
- fc = nn.Sequential(
- nn.Conv2d(fpn_c, head_conv, kernel_size=3, padding=1, bias=True),
- nn.ReLU(inplace=True),
- nn.Conv2d(head_conv, num_output, kernel_size=1, stride=1, padding=0))
- else:
- fc = nn.Conv2d(in_channels=fpn_c, out_channels=num_output, kernel_size=1, stride=1, padding=0)
-
- self.__setattr__('fpn{}_{}'.format(fpn_idx, head), fc)
-
- def _make_layer(self, block, planes, blocks, stride=1):
- 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),
- nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- _, _, input_h, input_w = x.size()
- hm_h, hm_w = input_h // 4, input_w // 4
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- out_layer1 = self.layer1(x)
- out_layer2 = self.layer2(out_layer1)
-
- out_layer3 = self.layer3(out_layer2)
-
- out_layer4 = self.layer4(out_layer3)
-
- # up_level1: torch.Size([b, 512, 14, 14])
- up_level1 = F.interpolate(out_layer4, scale_factor=2, mode='bilinear', align_corners=True)
-
- concat_level1 = torch.cat((up_level1, out_layer3), dim=1)
- # up_level2: torch.Size([b, 256, 28, 28])
- up_level2 = F.interpolate(self.conv_up_level1(concat_level1), scale_factor=2, mode='bilinear',
- align_corners=True)
-
- concat_level2 = torch.cat((up_level2, out_layer2), dim=1)
- # up_level3: torch.Size([b, 128, 56, 56]),
- up_level3 = F.interpolate(self.conv_up_level2(concat_level2), scale_factor=2, mode='bilinear',
- align_corners=True)
- # up_level4: torch.Size([b, 64, 56, 56])
- up_level4 = self.conv_up_level3(torch.cat((up_level3, out_layer1), dim=1))
-
- ret = {}
- for head in self.heads:
- temp_outs = []
- for fpn_idx, fdn_input in enumerate([up_level2, up_level3, up_level4]):
- fpn_out = self.__getattr__('fpn{}_{}'.format(fpn_idx, head))(fdn_input)
- _, _, fpn_out_h, fpn_out_w = fpn_out.size()
- # Make sure the added features having same size of heatmap output
- if (fpn_out_w != hm_w) or (fpn_out_h != hm_h):
- fpn_out = F.interpolate(fpn_out, size=(hm_h, hm_w))
- temp_outs.append(fpn_out)
- # Take the softmax in the keypoint feature pyramid network
- final_out = self.apply_kfpn(temp_outs)
-
- ret[head] = final_out
-
- return ret
-
- def apply_kfpn(self, outs):
- outs = torch.cat([out.unsqueeze(-1) for out in outs], dim=-1)
- softmax_outs = F.softmax(outs, dim=-1)
- ret_outs = (outs * softmax_outs).sum(dim=-1)
- return ret_outs
-
- def init_weights(self, num_layers, pretrained=True):
- if pretrained:
- # TODO: Check initial weights for head later
- for fpn_idx in [0, 1, 2]: # 3 FPN layers
- for head in self.heads:
- final_layer = self.__getattr__('fpn{}_{}'.format(fpn_idx, head))
- for i, m in enumerate(final_layer.modules()):
- if isinstance(m, nn.Conv2d):
- # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- # print('=> init {}.weight as normal(0, 0.001)'.format(name))
- # print('=> init {}.bias as 0'.format(name))
- if m.weight.shape[0] == self.heads[head]:
- if 'hm' in head:
- nn.init.constant_(m.bias, -2.19)
- else:
- nn.init.normal_(m.weight, std=0.001)
- nn.init.constant_(m.bias, 0)
- # pretrained_state_dict = torch.load(pretrained)
- url = model_urls['resnet{}'.format(num_layers)]
- pretrained_state_dict = model_zoo.load_url(url)
- print('=> loading pretrained model {}'.format(url))
- self.load_state_dict(pretrained_state_dict, strict=False)
-
-
- resnet_spec = {18: (BasicBlock, [2, 2, 2, 2]),
- 34: (BasicBlock, [3, 4, 6, 3]),
- 50: (Bottleneck, [3, 4, 6, 3]),
- 101: (Bottleneck, [3, 4, 23, 3]),
- 152: (Bottleneck, [3, 8, 36, 3])}
-
-
- def get_pose_net(num_layers, heads, head_conv, imagenet_pretrained):
- block_class, layers = resnet_spec[num_layers]
-
- model = PoseResNet(block_class, layers, heads, head_conv=head_conv)
- model.init_weights(num_layers, pretrained=imagenet_pretrained)
- return model
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