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| import subprocess | |
| subprocess.check_call(['pip', 'install', 'git+https://github.com/NVIDIA/MinkowskiEngine']) | |
| import MinkowskiEngine as ME | |
| import torch.nn as nn | |
| from MinkowskiEngine.modules.resnet_block import BasicBlock | |
| class ResNetBase(nn.Module): | |
| BLOCK = None | |
| LAYERS = () | |
| INIT_DIM = 64 | |
| PLANES = (64, 128, 256, 512) | |
| def __init__(self, in_channels, out_channels, D=3): | |
| nn.Module.__init__(self) | |
| self.D = D | |
| assert self.BLOCK is not None | |
| self.network_initialization(in_channels, out_channels, D) | |
| self.weight_initialization() | |
| def network_initialization(self, in_channels, out_channels, D): | |
| self.inplanes = self.INIT_DIM | |
| self.conv1 = nn.Sequential( | |
| ME.MinkowskiConvolution( | |
| in_channels, self.inplanes, kernel_size=3, stride=2, dimension=D | |
| ), | |
| ME.MinkowskiInstanceNorm(self.inplanes), | |
| ME.MinkowskiReLU(inplace=True), | |
| ME.MinkowskiMaxPooling(kernel_size=2, stride=2, dimension=D), | |
| ) | |
| self.layer1 = self._make_layer( | |
| self.BLOCK, self.PLANES[0], self.LAYERS[0], stride=2 | |
| ) | |
| self.layer2 = self._make_layer( | |
| self.BLOCK, self.PLANES[1], self.LAYERS[1], stride=2 | |
| ) | |
| self.layer3 = self._make_layer( | |
| self.BLOCK, self.PLANES[2], self.LAYERS[2], stride=2 | |
| ) | |
| self.layer4 = self._make_layer( | |
| self.BLOCK, self.PLANES[3], self.LAYERS[3], stride=2 | |
| ) | |
| self.conv5 = nn.Sequential( | |
| ME.MinkowskiDropout(), | |
| ME.MinkowskiConvolution( | |
| self.inplanes, self.inplanes, kernel_size=3, stride=3, dimension=D | |
| ), | |
| ME.MinkowskiInstanceNorm(self.inplanes), | |
| ME.MinkowskiGELU(), | |
| ) | |
| self.glob_pool = ME.MinkowskiGlobalMaxPooling() | |
| self.final = ME.MinkowskiLinear(self.inplanes, out_channels, bias=True) | |
| def weight_initialization(self): | |
| for m in self.modules(): | |
| if isinstance(m, ME.MinkowskiConvolution): | |
| ME.utils.kaiming_normal_(m.kernel, mode="fan_out", nonlinearity="relu") | |
| if isinstance(m, ME.MinkowskiBatchNorm): | |
| nn.init.constant_(m.bn.weight, 1) | |
| nn.init.constant_(m.bn.bias, 0) | |
| def _make_layer(self, block, planes, blocks, stride=1, dilation=1, bn_momentum=0.1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| ME.MinkowskiConvolution( | |
| self.inplanes, | |
| planes * block.expansion, | |
| kernel_size=1, | |
| stride=stride, | |
| dimension=self.D, | |
| ), | |
| ME.MinkowskiBatchNorm(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append( | |
| block( | |
| self.inplanes, | |
| planes, | |
| stride=stride, | |
| dilation=dilation, | |
| downsample=downsample, | |
| dimension=self.D, | |
| ) | |
| ) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append( | |
| block( | |
| self.inplanes, planes, stride=1, dilation=dilation, dimension=self.D | |
| ) | |
| ) | |
| return nn.Sequential(*layers) | |
| def forward(self, x: ME.SparseTensor): | |
| x = self.conv1(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.conv5(x) | |
| x = self.glob_pool(x) | |
| return self.final(x) | |
| class MinkResNet(ResNetBase): | |
| BLOCK = BasicBlock | |
| DILATIONS = (1, 1, 1, 1, 1, 1, 1, 1) | |
| LAYERS = (2, 2, 2, 2, 2, 2, 2, 2) | |
| PLANES = (32, 64, 128, 256, 256, 128, 96, 96) | |
| INIT_DIM = 32 | |
| OUT_TENSOR_STRIDE = 1 | |
| # To use the model, must call initialize_coords before forward pass. | |
| # Once data is processed, call clear to reset the model before calling | |
| # initialize_coords | |
| def __init__(self, D=3): | |
| self.in_channels = 6 | |
| self.out_channels = 1280 | |
| self.embedding_channel = 1024 | |
| ResNetBase.__init__(self, self.in_channels, self.out_channels, D) | |
| def get_conv_block(self, in_channel, out_channel, kernel_size, stride): | |
| return nn.Sequential( | |
| ME.MinkowskiConvolution( | |
| in_channel, | |
| out_channel, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| dimension=self.D, | |
| ), | |
| ME.MinkowskiBatchNorm(out_channel), | |
| ME.MinkowskiLeakyReLU(), | |
| ) | |
| def get_mlp_block(self, in_channel, out_channel): | |
| return nn.Sequential( | |
| ME.MinkowskiLinear(in_channel, out_channel, bias=False), | |
| ME.MinkowskiBatchNorm(out_channel), | |
| ME.MinkowskiLeakyReLU(), | |
| ) | |
| def network_initialization(self, in_channels, out_channels, D): | |
| # Output of the first conv concated to conv6 | |
| self.inplanes = self.INIT_DIM | |
| self.conv0p1s1 = ME.MinkowskiConvolution( | |
| in_channels, self.inplanes, kernel_size=5, dimension=D) | |
| self.bn0 = ME.MinkowskiBatchNorm(self.inplanes) | |
| self.conv1p1s2 = ME.MinkowskiConvolution( | |
| self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=D) | |
| self.bn1 = ME.MinkowskiBatchNorm(self.inplanes) | |
| self.block1 = self._make_layer(self.BLOCK, self.PLANES[0], | |
| self.LAYERS[0]) | |
| self.conv2p2s2 = ME.MinkowskiConvolution( | |
| self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=D) | |
| self.bn2 = ME.MinkowskiBatchNorm(self.inplanes) | |
| self.block2 = self._make_layer(self.BLOCK, self.PLANES[1], | |
| self.LAYERS[1]) | |
| self.conv3p4s2 = ME.MinkowskiConvolution( | |
| self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=D) | |
| self.bn3 = ME.MinkowskiBatchNorm(self.inplanes) | |
| self.block3 = self._make_layer(self.BLOCK, self.PLANES[2], | |
| self.LAYERS[2]) | |
| self.conv4p8s2 = ME.MinkowskiConvolution( | |
| self.inplanes, self.inplanes, kernel_size=2, stride=2, dimension=D) | |
| self.bn4 = ME.MinkowskiBatchNorm(self.inplanes) | |
| self.block4 = self._make_layer(self.BLOCK, self.PLANES[3], | |
| self.LAYERS[3]) | |
| self.conv5 = nn.Sequential( | |
| self.get_conv_block( | |
| self.PLANES[0] + self.PLANES[1] + self.PLANES[2] + self.PLANES[3], | |
| self.embedding_channel // 2, | |
| kernel_size=3, | |
| stride=2, | |
| ), | |
| self.get_conv_block( | |
| self.embedding_channel // 2, | |
| self.embedding_channel, | |
| kernel_size=3, | |
| stride=2, | |
| ), | |
| ) | |
| self.relu = ME.MinkowskiReLU(inplace=True) | |
| self.global_max_pool = ME.MinkowskiGlobalMaxPooling() | |
| self.global_avg_pool = ME.MinkowskiGlobalAvgPooling() | |
| self.final = nn.Sequential( | |
| self.get_mlp_block(self.embedding_channel * 2, 1024), | |
| ME.MinkowskiDropout(), | |
| self.get_mlp_block(1024, 1024), | |
| ME.MinkowskiLinear(1024, out_channels, bias=True), | |
| ) | |
| def forward(self, xyz, features, device="cuda", quantization_size=0.05): | |
| xyz[:, 1:] = xyz[:, 1:] / quantization_size | |
| #print(xyz.dtype, xyz, quantization_size) | |
| x = ME.TensorField( | |
| coordinates=xyz, | |
| features=features, | |
| device=device, | |
| ) | |
| out = self.conv0p1s1(x.sparse()) | |
| out = self.bn0(out) | |
| out_p1 = self.relu(out) | |
| out = self.conv1p1s2(out_p1) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out_b1p2 = self.block1(out) | |
| out = self.conv2p2s2(out_b1p2) | |
| out = self.bn2(out) | |
| out = self.relu(out) | |
| out_b2p4 = self.block2(out) | |
| out = self.conv3p4s2(out_b2p4) | |
| out = self.bn3(out) | |
| out = self.relu(out) | |
| out_b3p8 = self.block3(out) | |
| # tensor_stride=16 | |
| out = self.conv4p8s2(out_b3p8) | |
| out = self.bn4(out) | |
| out = self.relu(out) | |
| out = self.block4(out) | |
| x1 = out_b1p2.slice(x) | |
| x2 = out_b2p4.slice(x) | |
| x3 = out_b3p8.slice(x) | |
| x4 = out.slice(x) | |
| x = ME.cat(x1, x2, x3, x4) | |
| y = self.conv5(x.sparse()) | |
| x1 = self.global_max_pool(y) | |
| x2 = self.global_avg_pool(y) | |
| return self.final(ME.cat(x1, x2)).F | |
| class MinkResNet34(MinkResNet): | |
| LAYERS = (3, 4, 6, 3) |