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| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| import math | |
| from backbone.repvgg import get_RepVGG_func_by_name | |
| import utils | |
| class SixDRepNet(nn.Module): | |
| def __init__(self, | |
| backbone_name, backbone_file, deploy, | |
| bins=(1, 2, 3, 6), | |
| droBatchNorm=nn.BatchNorm2d, | |
| pretrained=True): | |
| super(SixDRepNet, self).__init__() | |
| repvgg_fn = get_RepVGG_func_by_name(backbone_name) | |
| backbone = repvgg_fn(deploy) | |
| if pretrained: | |
| checkpoint = torch.load(backbone_file) | |
| if 'state_dict' in checkpoint: | |
| checkpoint = checkpoint['state_dict'] | |
| ckpt = {k.replace('module.', ''): v for k, | |
| v in checkpoint.items()} # strip the names | |
| backbone.load_state_dict(ckpt) | |
| self.layer0, self.layer1, self.layer2, self.layer3, self.layer4 = backbone.stage0, backbone.stage1, backbone.stage2, backbone.stage3, backbone.stage4 | |
| self.gap = nn.AdaptiveAvgPool2d(output_size=1) | |
| last_channel = 0 | |
| for n, m in self.layer4.named_modules(): | |
| if ('rbr_dense' in n or 'rbr_reparam' in n) and isinstance(m, nn.Conv2d): | |
| last_channel = m.out_channels | |
| fea_dim = last_channel | |
| self.linear_reg = nn.Linear(fea_dim, 6) | |
| def forward(self, x): | |
| x = self.layer0(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x= self.gap(x) | |
| x = torch.flatten(x, 1) | |
| x = self.linear_reg(x) | |
| return utils.compute_rotation_matrix_from_ortho6d(x,use_gpu=False) | |
| class SixDRepNet2(nn.Module): | |
| def __init__(self, block, layers, fc_layers=1): | |
| self.inplanes = 64 | |
| super(SixDRepNet2, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| 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.avgpool = nn.AvgPool2d(7) | |
| self.linear_reg = nn.Linear(512*block.expansion,6) | |
| # Vestigial layer from previous experiments | |
| self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| 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), | |
| ) | |
| 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): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.maxpool(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| x = x.view(x.size(0), -1) | |
| x = self.linear_reg(x) | |
| out = utils.compute_rotation_matrix_from_ortho6d(x) | |
| return out |