174 lines
5.7 KiB
Python
174 lines
5.7 KiB
Python
'''
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Date: 2021-10-25 11:51:29 am
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Author: dihuangdh
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Descriptions:
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-----
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LastEditTime: 2021-10-25 11:51:58 am
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LastEditors: dihuangdh
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'''
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import torch
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import torch.nn as nn
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import torchvision.models.resnet as resnet
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import numpy as np
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import math
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class Bottleneck(nn.Module):
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""" Redefinition of Bottleneck residual block
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Adapted from the official PyTorch implementation
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"""
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class HMR(nn.Module):
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""" SMPL Iterative Regressor with ResNet50 backbone
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"""
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def __init__(self, block, layers):
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self.inplanes = 64
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super(HMR, self).__init__()
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npose = 3 + 45
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = nn.AvgPool2d(7, stride=1)
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self.fc1 = nn.Linear(512 * block.expansion + npose + 13, 1024)
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self.drop1 = nn.Dropout()
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self.fc2 = nn.Linear(1024, 1024)
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self.drop2 = nn.Dropout()
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self.decpose = nn.Linear(1024, npose)
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self.decshape = nn.Linear(1024, 10)
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self.deccam = nn.Linear(1024, 3)
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nn.init.xavier_uniform_(self.decpose.weight, gain=0.01)
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nn.init.xavier_uniform_(self.decshape.weight, gain=0.01)
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nn.init.xavier_uniform_(self.deccam.weight, gain=0.01)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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init_pose = torch.zeros(npose).unsqueeze(0)
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init_shape = torch.zeros(10).unsqueeze(0)
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init_cam = torch.zeros(3).unsqueeze(0)
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self.register_buffer('init_pose', init_pose)
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self.register_buffer('init_shape', init_shape)
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self.register_buffer('init_cam', init_cam)
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x, init_pose=None, init_shape=None, init_cam=None, n_iter=3):
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batch_size = x.shape[0]
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if init_pose is None:
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init_pose = self.init_pose.expand(batch_size, -1)
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if init_shape is None:
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init_shape = self.init_shape.expand(batch_size, -1)
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if init_cam is None:
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init_cam = self.init_cam.expand(batch_size, -1)
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x1 = self.layer1(x)
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x2 = self.layer2(x1)
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x3 = self.layer3(x2)
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x4 = self.layer4(x3)
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xf = self.avgpool(x4)
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xf = xf.view(xf.size(0), -1)
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pred_pose = init_pose
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pred_shape = init_shape
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pred_cam = init_cam
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for i in range(n_iter):
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xc = torch.cat([xf, pred_pose, pred_shape, pred_cam],1)
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xc = self.fc1(xc)
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xc = self.drop1(xc)
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xc = self.fc2(xc)
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xc = self.drop2(xc)
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pred_pose = self.decpose(xc) + pred_pose
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pred_shape = self.decshape(xc) + pred_shape
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pred_cam = self.deccam(xc) + pred_cam
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# pred_rotmat = rot6d_to_rotmat(pred_pose).view(batch_size, 24, 3, 3)
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return {
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'poses': pred_pose,
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'shapes': pred_shape,
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'cam': pred_cam
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}
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def hmr(pretrained=True, **kwargs):
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""" Constructs an HMR model with ResNet50 backbone.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = HMR(Bottleneck, [3, 4, 6, 3], **kwargs)
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if pretrained:
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resnet_imagenet = resnet.resnet50(pretrained=True)
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model.load_state_dict(resnet_imagenet.state_dict(),strict=False)
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return model |