model.py

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)




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