diff --git a/.idea/3D-MiniNet.iml b/.idea/3D-MiniNet.iml
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diff --git a/README.md b/README.md
index 7afb15d..3097c05 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
-
+
# 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation
@@ -12,12 +12,7 @@
-## Abstract
-LIDAR semantic segmentation, which assigns a semantic label to each 3D point measured by the LIDAR, is becoming an essential task for many robotic applications such as autonomous driving. Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many of these real-world applications.
-This work presents 3D-MiniNet, a novel approach for LIDAR semantic segmentation that combines 3D and 2D learning layers. It first learns a 2D representation from the raw points through a novel projection which extracts local and global information from the 3D data. This representation is fed to an efficient 2D Fully Convolutional Neural Network (FCNN) that produces a 2D semantic segmentation. These 2D semantic labels are re-projected back to the 3D space and enhanced through a post-processing module. The main novelty in our strategy relies on the projection learning module. Our detailed ablation study shows how each component contributes to the final performance of 3D-MiniNet. We validate our approach on well known public benchmarks (SemanticKITTI and KITTI), where 3D-MiniNet gets state-of-the-art results while being faster and more parameter-efficient than previous methods.
-
-
## Introduction
This repository contains the implementation of **3D-MiniNet**, a fast and efficient method for semantic segmentation of LIDAR point clouds.
@@ -25,17 +20,28 @@ The following figure shows the basic building block of our **3D-MiniNet**:
+
3D-MiniNet overview. It takes *P* groups of *N* points each and computes semantic segmentation of the *M* points of the point cloud where *PxN=M*.
It consists of two main modules: our proposed learning module (on the left) which learns a 2D tensor which is fed to the second module, an efficient FCNN backbone (on the right) which computes the 2D semantic segmentation. Each 3D point of the point cloud is given a semantic label based on the 2D segmentation.
-## Code will be soon released (Pytorch and Tensorflow implementation)
-The Pytorch implementation will use the [RangeNet++ (Milioto et al. IROS2019) repo](https://github.com/PRBonn/lidar-bonnetal) as its code base.
+## Code (Pytorch and Tensorflow implementation)
+
+Our [PyTorch code](pytorch_code/lidar-bonnetal/train/tasks/semantic/) is based on [Milioto et al. code](https://github.com/PRBonn/lidar-bonnetal) and the [Tensorflow code](tensorflow_code) is based on [Biasutti et al. code](https://github.com/pbias/lunet). For copyright license, please check both code base licenses.
+
+We took their code base and integrate our approach. Therefore, please, consider also citing or checking their work.
-The Tensorflow implementation will use the [LuNet (Biasutti et al. ICCVW2019) repo](https://github.com/pbias/lunet) as its code base.
-The code will be released around April.
-In the meantime, check those works out and give them love, they are really good works!
+## Citation
+If you find 3D-MiniNet useful, please consider citing:
+```
+@article{alonso2020MiniNet3D,
+ title={3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation},
+ author={Alonso, I{\~n}igo and Riazuelo, Luis and Montesano, Luis and Murillo, Ana C},
+ journal={arXiv preprint arXiv:2002.10893},
+ year={2020}
+}
+```
diff --git a/pytorch_code/LICENSE b/pytorch_code/LICENSE
new file mode 100644
index 0000000..8ee7768
--- /dev/null
+++ b/pytorch_code/LICENSE
@@ -0,0 +1,21 @@
+The MIT License
+
+Copyright (c) 2019 Andres Milioto, Jens Behley, Cyrill Stachniss, Photogrammetry and Robotics Lab, University of Bonn.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/pytorch_code/lidar-bonnetal/.gitignore b/pytorch_code/lidar-bonnetal/.gitignore
new file mode 100644
index 0000000..e6810c8
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/.gitignore
@@ -0,0 +1,11 @@
+*.pyc
+*.pyo
+*.o
+*.so
+*.a
+**build
+.vscode
+.catkin_tools/
+devel/
+logs/
+external.hpp
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diff --git a/pytorch_code/lidar-bonnetal/train/.idea/inspectionProfiles/profiles_settings.xml b/pytorch_code/lidar-bonnetal/train/.idea/inspectionProfiles/profiles_settings.xml
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diff --git a/pytorch_code/lidar-bonnetal/train/__init__.py b/pytorch_code/lidar-bonnetal/train/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch_code/lidar-bonnetal/train/backbones/3dmininet.py b/pytorch_code/lidar-bonnetal/train/backbones/3dmininet.py
new file mode 100644
index 0000000..2d45298
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/backbones/3dmininet.py
@@ -0,0 +1,487 @@
+# This file was modified from https://github.com/BobLiu20/YOLOv3_PyTorch
+# It needed to be modified in order to accomodate for different strides in the
+
+import torch.nn as nn
+from collections import OrderedDict
+import torch.nn.functional as F
+
+import torch
+
+from torch.nn import functional as f
+
+
+
+
+
+class moduleProyection(nn.Module):
+ def __init__(self, channels_in, channels_out, channels=[16, 32, 64, 128], conv_feature=128, neighbours=16,
+ track_running_stats=True):
+ super(moduleProyection, self).__init__()
+
+
+ # Local Feature Extractor
+
+ # 1st FC
+ self.conv_fc1 = nn.Conv2d(channels_in, channels[0], 1, 1, 0, 1, 1, bias=False)
+ self.bn1 = nn.BatchNorm2d(channels[0], eps=1e-3, track_running_stats=track_running_stats)
+ self.relu1 = nn.ReLU()
+
+ # 2nd FC
+ self.conv_fc2 = nn.Conv2d(channels[0], channels[1], 1, 1, 0, 1, 1, bias=False)
+ self.bn2 = nn.BatchNorm2d(channels[1], eps=1e-3, track_running_stats=track_running_stats)
+ self.relu2 = nn.ReLU()
+
+ # 3rd FC
+ self.conv_fc3 = nn.Conv2d(channels[1], channels[2], 1, 1, 0, 1, 1, bias=False)
+ self.bn3 = nn.BatchNorm2d(channels[2], eps=1e-3, track_running_stats=track_running_stats)
+ self.relu3 = nn.ReLU()
+
+ # 4th FC
+ self.conv_fc4 = nn.Conv2d(channels[2], channels[3], 1, 1, 0, 1, 1, bias=False)
+ self.bn4 = nn.BatchNorm2d(channels[3], eps=1e-3, track_running_stats=track_running_stats)
+ self.relu4 = nn.ReLU()
+
+ self.pool = nn.MaxPool2d(kernel_size=(neighbours, 1), stride=(1, 1), padding=0)
+
+
+
+ # Context Feature Extractor
+ self.pool_context = nn.MaxPool2d(kernel_size=(9, 1), stride=(1, 1), padding=0)
+
+ # Context with dilation 1
+ self.conv_fc_context_1 = nn.Conv2d(channels[1], channels[2], 1, 1, 0, 1, 1, bias=False)
+ self.bn_context_1 = nn.BatchNorm2d(channels[2], eps=1e-3, track_running_stats=track_running_stats)
+ self.relu_context_1 = nn.ReLU()
+
+ # Context with dilation 2
+ self.conv_fc_context_2 = nn.Conv2d(channels[1], channels[1], 1, 1, 0, 1, 1, bias=False)
+ self.bn_context_2 = nn.BatchNorm2d(channels[1], eps=1e-3, track_running_stats=track_running_stats)
+ self.relu_context_2 = nn.ReLU()
+
+ # Context with dilation 3
+ self.conv_fc_context_3 = nn.Conv2d(channels[1], channels[1], 1, 1, 0, 1, 1, bias=False)
+ self.bn_context_3 = nn.BatchNorm2d(channels[1], eps=1e-3, track_running_stats=track_running_stats)
+ self.relu_context_3 = nn.ReLU()
+
+ # Spatial Feature Extractor
+ self.conv = nn.Conv2d(channels_in, conv_feature, (neighbours, 1), 1, 0, 1, 1, bias=False)
+ self.bn_conv = nn.BatchNorm2d(conv_feature, eps=1e-3, track_running_stats=track_running_stats)
+ self.relu_conv = nn.ReLU()
+
+
+ # Feature Fusion
+ self.conv_atten = nn.Conv2d(channels[-1] + conv_feature + channels[3],
+ channels[-1] + conv_feature + channels[3], kernel_size=1, bias=True)
+ self.sigmoid_atten = nn.Sigmoid()
+
+ # Bottleneck
+ self.conv_out = nn.Conv2d(channels[-1] + conv_feature + channels[3], channels_out, 1, 1, 0, 1, 1, bias=False)
+ self.bn_out = nn.BatchNorm2d(channels_out, eps=1e-3, track_running_stats=track_running_stats)
+ self.relu_out = nn.ReLU()
+
+ def forward(self, inputs, size=[16, 512]):
+
+ # Local Feature Extractor
+
+ # 1st FC
+ x = self.conv_fc1(inputs)
+ x = self.bn1(x)
+ x = self.relu1(x)
+
+ # 2nd FC
+ x = self.conv_fc2(x)
+ x = self.bn2(x)
+ x2 = self.relu2(x)
+
+ # 3rd FC
+ x = self.conv_fc3(x2)
+ x = self.bn3(x)
+ x = self.relu3(x)
+
+ # 4th FC
+ x = self.conv_fc4(x)
+ x = self.bn4(x)
+ x = self.relu4(x)
+ x = self.pool(x)
+
+
+ # Context Feature Extractor
+ x_context = self.pool(x2)
+
+ # reshape to image-like shape
+ x_context = x_context.squeeze(2)
+ n, c, _ = x_context.size()
+ h = size[0]
+ w = size[1]
+ x_context = x_context.view(n, c, h, w)
+
+ # fast point grouping for dilation 1, 2 and 3
+ windows_size = 3
+ list_dil_1 = []
+ list_dil_2 = []
+ list_dil_3 = []
+ for b in range(int(x_context.shape[0])):
+ dil_1 = f.unfold(x_context[b, ...].unsqueeze(1), kernel_size=windows_size, stride=1, padding=1, dilation=1)
+ dil_2 = f.unfold(x_context[b, ...].unsqueeze(1), kernel_size=windows_size, stride=1, padding=2, dilation=2)
+ dil_3 = f.unfold(x_context[b, ...].unsqueeze(1), kernel_size=windows_size, stride=1, padding=3, dilation=3)
+ list_dil_1.append(dil_1.unsqueeze(0))
+ list_dil_2.append(dil_2.unsqueeze(0))
+ list_dil_3.append(dil_3.unsqueeze(0))
+
+ x_context_1 = torch.cat(list_dil_1, dim=0)
+ x_context_2 = torch.cat(list_dil_2, dim=0)
+ x_context_3 = torch.cat(list_dil_3, dim=0)
+
+
+ # Context with dilation 1
+ x_context_1 = self.conv_fc_context_1(x_context_1)
+ x_context_1 = self.bn_context_1(x_context_1)
+ x_context_1 = self.relu_context_1(x_context_1)
+ x_context_1 = self.pool_context(x_context_1)
+
+ # Context with dilation 2
+ x_context_2 = self.conv_fc_context_2(x_context_2)
+ x_context_2 = self.bn_context_2(x_context_2)
+ x_context_2 = self.relu_context_2(x_context_2)
+ x_context_2 = self.pool_context(x_context_2)
+
+ # Context with dilation 3
+ x_context_3 = self.conv_fc_context_3(x_context_3)
+ x_context_3 = self.bn_context_3(x_context_3)
+ x_context_3 = self.relu_context_3(x_context_3)
+ x_context_3 = self.pool_context(x_context_3)
+
+ x_context_1 = torch.cat((x_context_1, x_context_2, x_context_3), dim=1)
+
+ # Reshape to image-like
+ x_context_1 = x_context_1.squeeze(2)
+ n, c, _ = x_context_1.size()
+ x_context_1 = x_context_1.view(n, c, h, w)
+
+
+ # Spatial Feature Extractor
+ x_conv = self.conv(inputs)
+ x_conv = self.bn_conv(x_conv)
+ x_conv = self.relu_conv(x_conv)
+ x = torch.cat((x, x_conv), dim=1)
+
+ # reshape to image-like shape
+ x = x.squeeze(2)
+ n, c, _ = x.size()
+ h = size[0]
+ w = size[1]
+ x = x.view(n, c, h, w)
+
+ x = torch.cat((x, x_context_1), dim=1)
+
+
+ # Feature Fusion
+ atten = F.adaptive_avg_pool2d(x, 1)
+ atten = self.conv_atten(atten)
+ atten = self.sigmoid_atten(atten)
+ x = torch.mul(x, atten)
+
+ # Bottleneck
+ x = self.conv_out(x)
+ x = self.bn_out(x)
+ x = self.relu_out(x)
+
+ return x
+
+# Depthwise Separable convolution
+class moduleERS(nn.Module):
+ def __init__(self, in_channels, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, bias=False, dropprob=0., mul = 1, track_running_stats=True):
+ super(moduleERS, self).__init__()
+ self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, 1, groups=in_channels, bias=bias)
+ self.pointwise = nn.Conv2d(in_channels, out_planes*mul, 1, 1, 0, 1, 1, bias=bias)
+ self.bn1 = nn.BatchNorm2d(in_channels, eps=1e-3, track_running_stats=track_running_stats)
+ self.bn = nn.BatchNorm2d(out_planes*mul, eps=1e-3, track_running_stats=track_running_stats)
+ self.relu1 = nn.LeakyReLU(0.1)
+ self.relu2 = nn.LeakyReLU(0.1)
+
+ self.dropout = nn.Dropout2d(dropprob)
+
+
+ def forward(self, inputs):
+ # depthwise convolution
+ x = self.conv1(inputs)
+ x = self.bn1(x)
+ x = self.relu1(x)
+
+ # point-wise convolution
+ x = self.pointwise(x)
+ x = self.bn(x)
+
+ if self.dropout.p != 0:
+ x = self.dropout(x)
+
+ if x.shape[1] == inputs.shape[1]:
+ return self.relu2(x)+ inputs
+ else:
+ return self.relu2(x)
+
+# Multi-dilation Depthwise Separable convolution
+class moduleERS_muldil(nn.Module):
+ def __init__(self, in_channels, out_planes, kernel_size=3, stride=1, padding=1, dilation=[1, 8], bias=False, dropprob=0., mul=1, track_running_stats=True):
+ super(moduleERS_muldil, self).__init__()
+ self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, 1, groups=in_channels, bias=bias)
+ self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, dilation, dilation, groups=in_channels,
+ bias=bias)
+ self.pointwise = nn.Conv2d(in_channels, out_planes*mul, 1, 1, 0, 1, 1, bias=bias)
+ self.bn1 = nn.BatchNorm2d(in_channels, eps=1e-3, track_running_stats=track_running_stats)
+ self.bn2 = nn.BatchNorm2d(in_channels, eps=1e-3, track_running_stats=track_running_stats)
+ self.bn = nn.BatchNorm2d(out_planes*mul, eps=1e-3, track_running_stats=track_running_stats)
+
+ self.relu1 = nn.LeakyReLU(0.1)
+ self.relu2 = nn.LeakyReLU(0.1)
+ self.relu3 = nn.LeakyReLU(0.1)
+
+ self.dropout = nn.Dropout2d(dropprob)
+
+
+ def forward(self, inputs):
+
+ # depthwise convolution
+ x = self.conv1(inputs)
+ x = self.bn1(x)
+ x = self.relu1(x)
+
+ # dilated depthwise convolution
+ x2 = self.conv2(inputs)
+ x2 = self.bn2(x2)
+ x2 = self.relu2(x2)
+ x += x2
+
+ # point-wise convolution
+ x = self.pointwise(x)
+ x = self.bn(x)
+
+ if self.dropout.p != 0:
+ x = self.dropout(x)
+
+ if x.shape[1] == inputs.shape[1]:
+ return self.relu3(x)+ inputs
+ else:
+ return self.relu3(x)
+
+class BasicBlock_mul(nn.Module):
+ def __init__(self, inplanes, out_planes, dilation=1, dropprob=0., mul=1):
+ super(BasicBlock_mul, self).__init__()
+ self.conv1 = moduleERS_muldil(inplanes, inplanes, kernel_size=3, stride=1, padding=1, dilation=dilation, bias=False,
+ dropprob=dropprob)
+ self.conv2 = moduleERS_muldil(inplanes, out_planes, kernel_size=3, stride=1, padding=1,
+ dilation=[dilation[0]*2, dilation[1] * 2], bias=False, dropprob=dropprob, mul=mul)
+
+ def forward(self, x):
+ out = self.conv1(x)
+ out = self.conv2(out)
+
+ return out
+
+
+class BasicBlock(nn.Module):
+ def __init__(self, inplanes, out_planes, dilation=1, dropprob=0., mul=1):
+ super(BasicBlock, self).__init__()
+ self.conv1 = moduleERS(inplanes, inplanes, kernel_size=3, stride=1, padding=1, dilation=dilation, bias=False,
+ dropprob=dropprob)
+ self.conv2 = moduleERS(inplanes, out_planes, kernel_size=3, stride=1, padding=1, dilation=dilation, bias=False,
+ dropprob=dropprob, mul=mul)
+
+ def forward(self, x):
+ out = self.conv1(x)
+ out = self.conv2(out)
+
+ return out
+
+
+# ******************************************************************************
+
+
+
+class Backbone(nn.Module):
+ """
+ Class for DarknetSeg. Subclasses PyTorch's own "nn" module
+ """
+
+ def __init__(self, params):
+ super(Backbone, self).__init__()
+ self.use_range = params["input_depth"]["range"]
+ self.use_xyz = params["input_depth"]["xyz"]
+ self.use_remission = params["input_depth"]["remission"]
+ self.OS = params["OS"]
+ self.block_1 = params["block_1"]
+ self.block_2 = params["block_2"]
+ self.features_bottleneck = int(params["features_bottleneck"])
+
+ # input depth calc
+ self.input_depth = 0
+ self.input_idxs = []
+
+ # 2TIMES, FOR ABSOLUTE AND RELATIVE DATA
+ if self.use_range:
+ self.input_depth += 2
+ self.input_idxs.append(0)
+ self.input_idxs.append(5)
+ if self.use_xyz:
+ self.input_depth += 6
+ self.input_idxs.extend([1, 2, 3])
+ self.input_idxs.extend([6, 7, 8])
+ if self.use_remission:
+ self.input_depth += 2
+ self.input_idxs.append(4)
+ self.input_idxs.append(9)
+
+ self.input_depth += 1
+ self.input_idxs.append(10)
+
+ self.input_depth_absolute = int(self.input_depth / 2)
+
+ print("Depth of backbone input = ", self.input_depth)
+
+ # stride play
+ self.strides = [2, 2, 2, 2]
+ self.strides_2 = [2, 2, 2, 2]
+ # check current stride
+ current_os = 1
+ for s in self.strides:
+ current_os *= s
+ print("Original OS: ", current_os)
+
+ # make the new stride
+ if self.OS > current_os:
+ print("Can't do OS, ", self.OS,
+ " because it is bigger than original ", current_os)
+ else:
+ # redo strides according to needed stride
+ for i, stride in enumerate(reversed(self.strides), 0):
+ if int(current_os) != self.OS:
+ if stride == 2:
+ current_os /= 2
+ self.strides[-1 - i] = 1
+ if int(current_os) == self.OS:
+ break
+ print("New OS: ", int(current_os))
+ print("Strides: ", self.strides)
+
+
+ self.proj = moduleProyection(self.input_depth, self.features_bottleneck, [int(self.features_bottleneck/8),
+ int(self.features_bottleneck/4), int(self.features_bottleneck/2), self.features_bottleneck],
+ conv_feature = self.features_bottleneck, neighbours=16)
+
+ # encoder block, planes, blocks, stride, bn_d=0.1, dilation=1, dropprob=0.)
+ self.enc1 = self._make_enc_layer(BasicBlock, [self.input_depth_absolute, self.features_bottleneck, self.features_bottleneck, self.features_bottleneck], 0,
+ stride=(self.strides[0], self.strides_2[0]), dilation=1)
+
+ self.enc2 = self._make_enc_layer(BasicBlock, [self.features_bottleneck, self.features_bottleneck, self.features_bottleneck, self.features_bottleneck], self.block_1, downsampling=False,
+ stride=(self.strides[1], self.strides_2[1]), dilation=1, dropprob=0.25)
+ self.enc3 = self._make_enc_layer(BasicBlock_mul, [self.features_bottleneck, self.features_bottleneck, self.features_bottleneck, self.features_bottleneck], self.block_2,
+ stride=(self.strides[2], self.strides_2[2]),dilation=2, dropprob=0.25)
+
+ # last channels
+ self.last_channels = self.features_bottleneck
+
+
+ # make layer useful function
+ def _make_enc_layer(self, block, planes, blocks, stride, bn_d=0.1, dilation=1, dropprob=0., multiplier=1, downsampling=True):
+ #planes: inplanes, downsample planes, working planes, output planes
+ layers = []
+
+
+
+ # downsample
+ if downsampling:
+ if blocks == 0:
+ layers.append(("conv0", nn.Conv2d(planes[0], planes[0],
+ kernel_size=3,
+ stride=[stride[1], stride[0]], dilation=1,
+ padding=1, groups=planes[0], bias=False)))
+ layers.append(("bn0", nn.BatchNorm2d(planes[0], momentum=bn_d)))
+ layers.append(("relu0", nn.LeakyReLU(0.1)))
+
+ layers.append(("conv01", nn.Conv2d(planes[0], planes[1]*multiplier,
+ kernel_size=1,
+ stride=1, dilation=1,
+ padding=0, bias=False)))
+ layers.append(("bn01", nn.BatchNorm2d(planes[1]*multiplier, momentum=bn_d)))
+ layers.append(("relu01", nn.LeakyReLU(0.1)))
+ else:
+ layers.append(("conv00", nn.Conv2d(planes[0], planes[0],
+ kernel_size=3,
+ stride=[stride[1], stride[0]], dilation=1,
+ padding=1,groups= planes[0], bias=False)))
+ layers.append(("bn00", nn.BatchNorm2d(planes[0], momentum=bn_d)))
+ layers.append(("relu00", nn.LeakyReLU(0.1)))
+
+ layers.append(("conv01", nn.Conv2d(planes[0], planes[1],
+ kernel_size=1,
+ stride=1, dilation=1,
+ padding=0, bias=False)))
+ layers.append(("bn01", nn.BatchNorm2d(planes[1], momentum=bn_d)))
+ layers.append(("relu01", nn.LeakyReLU(0.1)))
+
+
+ max_dil = 8
+ i_reset = 0
+
+ if downsampling:
+ inplanes = planes[1]
+ else:
+ inplanes = planes[0]
+
+ out_planes = planes[2]
+
+ if dilation > 1:
+ dil = [int(dilation/2), int(dilation/2)]
+ else:
+ dil = dilation
+
+ for i in range(0, blocks):
+ if i == blocks - 1:
+ mul = multiplier
+ out_planes = planes[3]
+ else:
+ mul = 1
+
+ layers.append(("residual_{}".format(i),
+ block(inplanes, out_planes, dilation=dil, dropprob=dropprob, mul=mul)))
+
+ inplanes = planes[2]
+
+ if dilation > 1:
+ dil = [dil[0], dil[1] * 4]
+ if dil[1] > max_dil:
+ i_reset += 1
+ dil = [1, 1]
+
+ return nn.Sequential(OrderedDict(layers))
+
+ def run_layer(self, x, layer, skips, os):
+ y = layer(x)
+
+ return y, skips, os
+
+ def forward(self, x_in):
+ image = x_in[0]
+ points = x_in[1]
+
+ projection = self.proj(points)
+ skips = {}
+ os = 1
+
+ x, skips, os = self.run_layer(image[:,:self.input_depth_absolute], self.enc1, skips, os)
+ skips[2] = x
+
+ x, skips, os = self.run_layer(projection, self.enc2, skips, os)
+ skips[4] = x
+
+
+ x, skips, os = self.run_layer(x, self.enc3, skips, os)
+
+
+ return x, skips
+
+ def get_last_depth(self):
+ return self.last_channels
+
+ def get_input_depth(self):
+ return self.input_depth
diff --git a/pytorch_code/lidar-bonnetal/train/backbones/__init__.py b/pytorch_code/lidar-bonnetal/train/backbones/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch_code/lidar-bonnetal/train/common/__init__.py b/pytorch_code/lidar-bonnetal/train/common/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch_code/lidar-bonnetal/train/common/avgmeter.py b/pytorch_code/lidar-bonnetal/train/common/avgmeter.py
new file mode 100644
index 0000000..a9a0530
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/avgmeter.py
@@ -0,0 +1,20 @@
+# This file is covered by the LICENSE file in the root of this project.
+
+
+class AverageMeter(object):
+ """Computes and stores the average and current value"""
+
+ def __init__(self):
+ self.reset()
+
+ def reset(self):
+ self.val = 0
+ self.avg = 0
+ self.sum = 0
+ self.count = 0
+
+ def update(self, val, n=1):
+ self.val = val
+ self.sum += val * n
+ self.count += n
+ self.avg = self.sum / self.count
diff --git a/pytorch_code/lidar-bonnetal/train/common/laserscan.py b/pytorch_code/lidar-bonnetal/train/common/laserscan.py
new file mode 100644
index 0000000..1a1d6fb
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/laserscan.py
@@ -0,0 +1,332 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+
+import random
+class LaserScan:
+ """Class that contains LaserScan with x,y,z,r"""
+ EXTENSIONS_SCAN = ['.bin']
+
+ def __init__(self, project=False, H=64, W=1024, fov_up=3.0, fov_down=-25.0, training=False):
+ self.project = project
+ self.proj_H = H
+ self.proj_W = W
+ self.proj_fov_up = fov_up
+ self.proj_fov_down = fov_down
+ self.training = training
+ self.reset()
+
+ def reset(self):
+ """ Reset scan members. """
+ self.points = np.zeros((0, 3), dtype=np.float32) # [m, 3]: x, y, z
+ self.remissions = np.zeros((0, 1), dtype=np.float32) # [m ,1]: remission
+
+ # projected range image - [H,W] range (-1 is no data)
+ self.proj_range = np.full((self.proj_H, self.proj_W), -1,
+ dtype=np.float32)
+
+ # unprojected range (list of depths for each point)
+ self.unproj_range = np.zeros((0, 1), dtype=np.float32)
+
+ # projected point cloud xyz - [H,W,3] xyz coord (-1 is no data)
+ self.proj_xyz = np.full((self.proj_H, self.proj_W, 3), -1,
+ dtype=np.float32)
+
+ # projected remission - [H,W] intensity (-1 is no data)
+ self.proj_remission = np.full((self.proj_H, self.proj_W), -1,
+ dtype=np.float32)
+
+ # projected index (for each pixel, what I am in the pointcloud)
+ # [H,W] index (-1 is no data)
+ self.proj_idx = np.full((self.proj_H, self.proj_W), -1,
+ dtype=np.int32)
+
+ # for each point, where it is in the range image
+ self.proj_x = np.zeros((0, 1), dtype=np.int32) # [m, 1]: x
+ self.proj_y = np.zeros((0, 1), dtype=np.int32) # [m, 1]: y
+
+ # mask containing for each pixel, if it contains a point or not
+ self.proj_mask = np.zeros((self.proj_H, self.proj_W),
+ dtype=np.int32) # [H,W] mask
+
+ def size(self):
+ """ Return the size of the point cloud. """
+ return self.points.shape[0]
+
+ def __len__(self):
+ return self.size()
+
+ def open_scan(self, filename):
+ """ Open raw scan and fill in attributes
+ """
+ # reset just in case there was an open structure
+ self.reset()
+
+ # check filename is string
+ if not isinstance(filename, str):
+ raise TypeError("Filename should be string type, "
+ "but was {type}".format(type=str(type(filename))))
+
+ # check extension is a laserscan
+ if not any(filename.endswith(ext) for ext in self.EXTENSIONS_SCAN):
+ raise RuntimeError("Filename extension is not valid scan file.")
+
+ # if all goes well, open pointcloud
+ scan = np.fromfile(filename, dtype=np.float32)
+ scan = scan.reshape((-1, 4))
+
+ # put in attribute
+ points = scan[:, 0:3] # get xyz
+ remissions = scan[:, 3] # get remission
+
+ self.with_augmentation = self.training and random.random() > 0.5
+
+ # Augmentation
+ if self.with_augmentation:
+ # remove random points
+ MAX_PERCENT_REMOVE = 0.075
+ n_points = remissions.shape[0]
+ n_delete = int(random.random() * n_points * MAX_PERCENT_REMOVE)
+ self.indices_del = np.random.randint(0, n_points, n_delete)
+ remissions = np.delete(remissions, self.indices_del, axis=0)
+ points = np.delete(points, self.indices_del, axis=0)
+
+ # rotate the point cloud
+ euler_angle = np.random.normal(0.0, 40, 1)[0] #40
+
+ r = np.array(R.from_euler('zyx', [[euler_angle, 0, 0]], degrees=True).as_matrix())
+ r_t = r.transpose()
+ points = points.dot(r_t)
+ points = np.squeeze(points)
+
+ # shit the point cloud cloud
+ shift_x = np.random.normal(0.0, 0.35, 1)[0]
+ shift_y = np.random.normal(0.0, 0.35, 1)[0]
+ shift_z = np.random.normal(0.0, 0.0035, 1)[0]
+ points[:,0]= points[:,0] + shift_x
+ points[:,1]= points[:,1] + shift_y
+ points[:,2]= points[:,2] + shift_z
+
+ if random.random() > 0.5:
+ points[:, 0] = -points[:, 0]
+
+ if random.random() > 0.5:
+ points[:, 1] = -points[:, 1]
+
+
+ self.set_points(points, remissions)
+
+ def set_points(self, points, remissions=None):
+ """ Set scan attributes (instead of opening from file)
+ """
+ # reset just in case there was an open structure
+ self.reset()
+
+ # check scan makes sense
+ if not isinstance(points, np.ndarray):
+ raise TypeError("Scan should be numpy array")
+
+ # check remission makes sense
+ if remissions is not None and not isinstance(remissions, np.ndarray):
+ raise TypeError("Remissions should be numpy array")
+
+ # put in attribute
+ self.points = points # get xyz
+ if remissions is not None:
+ self.remissions = remissions # get remission
+ else:
+ self.remissions = np.zeros((points.shape[0]), dtype=np.float32)
+
+ # if projection is wanted, then do it and fill in the structure
+ if self.project:
+ self.do_range_projection()
+
+ def do_range_projection(self):
+ """ Project a pointcloud into a spherical projection image.projection.
+ Function takes no arguments because it can be also called externally
+ if the value of the constructor was not set (in case you change your
+ mind about wanting the projection)
+ """
+ # laser parameters
+ fov_up = self.proj_fov_up / 180.0 * np.pi # field of view up in rad
+ fov_down = self.proj_fov_down / 180.0 * np.pi # field of view down in rad
+ fov = abs(fov_down) + abs(fov_up) # get field of view total in rad
+
+ # get depth of all points
+ depth = np.linalg.norm(self.points, 2, axis=1)
+
+ # get scan components
+ scan_x = self.points[:, 0]
+ scan_y = self.points[:, 1]
+ scan_z = self.points[:, 2]
+
+ # get angles of all points
+ yaw = -np.arctan2(scan_y, scan_x)
+ pitch = np.arcsin(scan_z / depth)
+
+ # get projections in image coords
+ proj_x = 0.5 * (yaw / np.pi + 1.0) # in [0.0, 1.0]
+ proj_y = 1.0 - (pitch + abs(fov_down)) / fov # in [0.0, 1.0]
+
+ # scale to image size using angular resolution
+ proj_x *= self.proj_W # in [0.0, W]
+ proj_y *= self.proj_H # in [0.0, H]
+
+ # round and clamp for use as index
+ proj_x = np.floor(proj_x)
+ proj_x = np.minimum(self.proj_W - 1, proj_x)
+ proj_x = np.maximum(0, proj_x).astype(np.int32) # in [0,W-1]
+ self.proj_x = np.copy(proj_x) # store a copy in orig order
+
+ proj_y = np.floor(proj_y)
+ proj_y = np.minimum(self.proj_H - 1, proj_y)
+ proj_y = np.maximum(0, proj_y).astype(np.int32) # in [0,H-1]
+ self.proj_y = np.copy(proj_y) # stope a copy in original order
+
+ # copy of depth in original order
+ self.unproj_range = np.copy(depth)
+
+ # order in decreasing depth
+ indices = np.arange(depth.shape[0])
+ order = np.argsort(depth)[::-1]
+ depth = depth[order]
+ indices = indices[order]
+ points = self.points[order]
+ remission = self.remissions[order]
+ proj_y = proj_y[order]
+ proj_x = proj_x[order]
+
+ # assing to images
+ self.proj_range[proj_y, proj_x] = depth
+ self.proj_xyz[proj_y, proj_x] = points
+ self.proj_remission[proj_y, proj_x] = remission
+ self.proj_idx[proj_y, proj_x] = indices
+ self.proj_mask = (self.proj_idx > 0).astype(np.int32)
+
+
+class SemLaserScan(LaserScan):
+ """Class that contains LaserScan with x,y,z,r,sem_label,sem_color_label,inst_label,inst_color_label"""
+ EXTENSIONS_LABEL = ['.label']
+
+ def __init__(self, sem_color_dict=None, project=False, H=64, W=1024, fov_up=3.0, fov_down=-25.0, max_classes=300, training=False):
+ super(SemLaserScan, self).__init__(project, H, W, fov_up, fov_down, training)
+ self.reset()
+
+ # make semantic colors
+ if sem_color_dict:
+ # if I have a dict, make it
+ max_sem_key = 0
+ for key, data in sem_color_dict.items():
+ if key + 1 > max_sem_key:
+ max_sem_key = key + 1
+ self.sem_color_lut = np.zeros((max_sem_key + 100, 3), dtype=np.float32)
+ for key, value in sem_color_dict.items():
+ self.sem_color_lut[key] = np.array(value, np.float32) / 255.0
+ else:
+ # otherwise make random
+ max_sem_key = max_classes
+ self.sem_color_lut = np.random.uniform(low=0.0,
+ high=1.0,
+ size=(max_sem_key, 3))
+ # force zero to a gray-ish color
+ self.sem_color_lut[0] = np.full((3), 0.1)
+
+ # make instance colors
+ max_inst_id = 100000
+ self.inst_color_lut = np.random.uniform(low=0.0,
+ high=1.0,
+ size=(max_inst_id, 3))
+ # force zero to a gray-ish color
+ self.inst_color_lut[0] = np.full((3), 0.1)
+
+ def reset(self):
+ """ Reset scan members. """
+ super(SemLaserScan, self).reset()
+
+ # semantic labels
+ self.sem_label = np.zeros((0, 1), dtype=np.int32) # [m, 1]: label
+ self.sem_label_color = np.zeros((0, 3), dtype=np.float32) # [m ,3]: color
+
+ # instance labels
+ self.inst_label = np.zeros((0, 1), dtype=np.int32) # [m, 1]: label
+ self.inst_label_color = np.zeros((0, 3), dtype=np.float32) # [m ,3]: color
+
+ # projection color with semantic labels
+ self.proj_sem_label = np.zeros((self.proj_H, self.proj_W),
+ dtype=np.int32) # [H,W] label
+ self.proj_sem_color = np.zeros((self.proj_H, self.proj_W, 3),
+ dtype=np.float) # [H,W,3] color
+
+ # projection color with instance labels
+ self.proj_inst_label = np.zeros((self.proj_H, self.proj_W),
+ dtype=np.int32) # [H,W] label
+ self.proj_inst_color = np.zeros((self.proj_H, self.proj_W, 3),
+ dtype=np.float) # [H,W,3] color
+
+ def open_label(self, filename):
+ """ Open raw scan and fill in attributes
+ """
+ # check filename is string
+ if not isinstance(filename, str):
+ raise TypeError("Filename should be string type, "
+ "but was {type}".format(type=str(type(filename))))
+
+ # check extension is a laserscan
+ if not any(filename.endswith(ext) for ext in self.EXTENSIONS_LABEL):
+ raise RuntimeError("Filename extension is not valid label file.")
+
+ # if all goes well, open label
+ label = np.fromfile(filename, dtype=np.int32)
+ label = label.reshape((-1))
+
+ # if augmentation, remove the labels form the removed points
+ if self.with_augmentation:
+ label = np.delete(label, self.indices_del, axis=0)
+
+ # set it
+ self.set_label(label)
+
+ def set_label(self, label):
+ """ Set points for label not from file but from np
+ """
+ # check label makes sense
+ if not isinstance(label, np.ndarray):
+ raise TypeError("Label should be numpy array")
+
+ # only fill in attribute if the right size
+ if label.shape[0] == self.points.shape[0]:
+ self.sem_label = label & 0xFFFF # semantic label in lower half
+ self.inst_label = label >> 16 # instance id in upper half
+ else:
+ print("Points shape: ", self.points.shape)
+ print("Label shape: ", label.shape)
+ raise ValueError("Scan and Label don't contain same number of points")
+
+ # sanity check
+ assert((self.sem_label + (self.inst_label << 16) == label).all())
+
+ if self.project:
+ self.do_label_projection()
+
+ def colorize(self):
+ """ Colorize pointcloud with the color of each semantic label
+ """
+ self.sem_label_color = self.sem_color_lut[self.sem_label]
+ self.sem_label_color = self.sem_label_color.reshape((-1, 3))
+
+ self.inst_label_color = self.inst_color_lut[self.inst_label]
+ self.inst_label_color = self.inst_label_color.reshape((-1, 3))
+
+ def do_label_projection(self):
+ # only map colors to labels that exist
+ mask = self.proj_idx >= 0
+
+ # semantics
+ self.proj_sem_label[mask] = self.sem_label[self.proj_idx[mask]]
+ self.proj_sem_color[mask] = self.sem_color_lut[self.sem_label[self.proj_idx[mask]]]
+
+ # instances
+ self.proj_inst_label[mask] = self.inst_label[self.proj_idx[mask]]
+ self.proj_inst_color[mask] = self.inst_color_lut[self.inst_label[self.proj_idx[mask]]]
+
diff --git a/pytorch_code/lidar-bonnetal/train/common/laserscanvis.py b/pytorch_code/lidar-bonnetal/train/common/laserscanvis.py
new file mode 100644
index 0000000..c8309a3
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/laserscanvis.py
@@ -0,0 +1,219 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import vispy
+from vispy.scene import visuals, SceneCanvas
+import numpy as np
+from matplotlib import pyplot as plt
+from common.laserscan import LaserScan, SemLaserScan
+
+
+class LaserScanVis:
+ """Class that creates and handles a visualizer for a pointcloud"""
+
+ def __init__(self, scan, scan_names, label_names, offset=0,
+ semantics=True, instances=False):
+ self.scan = scan
+ self.scan_names = scan_names
+ self.label_names = label_names
+ self.offset = offset
+ self.semantics = semantics
+ self.instances = instances
+ # sanity check
+ if not self.semantics and self.instances:
+ print("Instances are only allowed in when semantics=True")
+ raise ValueError
+
+ self.reset()
+ self.update_scan()
+
+ def reset(self):
+ """ Reset. """
+ # last key press (it should have a mutex, but visualization is not
+ # safety critical, so let's do things wrong)
+ self.action = "no" # no, next, back, quit are the possibilities
+
+ # new canvas prepared for visualizing data
+ self.canvas = SceneCanvas(keys='interactive', show=True)
+ # interface (n next, b back, q quit, very simple)
+ self.canvas.events.key_press.connect(self.key_press)
+ self.canvas.events.draw.connect(self.draw)
+ # grid
+ self.grid = self.canvas.central_widget.add_grid()
+
+ # laserscan part
+ self.scan_view = vispy.scene.widgets.ViewBox(
+ border_color='white', parent=self.canvas.scene)
+ self.grid.add_widget(self.scan_view, 0, 0)
+ self.scan_vis = visuals.Markers()
+ self.scan_view.camera = 'turntable'
+ self.scan_view.add(self.scan_vis)
+ visuals.XYZAxis(parent=self.scan_view.scene)
+ # add semantics
+ if self.semantics:
+ print("Using semantics in visualizer")
+ self.sem_view = vispy.scene.widgets.ViewBox(
+ border_color='white', parent=self.canvas.scene)
+ self.grid.add_widget(self.sem_view, 0, 1)
+ self.sem_vis = visuals.Markers()
+ self.sem_view.camera = 'turntable'
+ self.sem_view.add(self.sem_vis)
+ visuals.XYZAxis(parent=self.sem_view.scene)
+ # self.sem_view.camera.link(self.scan_view.camera)
+
+ if self.instances:
+ print("Using instances in visualizer")
+ self.inst_view = vispy.scene.widgets.ViewBox(
+ border_color='white', parent=self.canvas.scene)
+ self.grid.add_widget(self.inst_view, 0, 2)
+ self.inst_vis = visuals.Markers()
+ self.inst_view.camera = 'turntable'
+ self.inst_view.add(self.inst_vis)
+ visuals.XYZAxis(parent=self.inst_view.scene)
+ # self.inst_view.camera.link(self.scan_view.camera)
+
+ # img canvas size
+ self.multiplier = 1
+ self.canvas_W = 1024
+ self.canvas_H = 64
+ if self.semantics:
+ self.multiplier += 1
+ if self.instances:
+ self.multiplier += 1
+
+ # new canvas for img
+ self.img_canvas = SceneCanvas(keys='interactive', show=True,
+ size=(self.canvas_W, self.canvas_H * self.multiplier))
+ # grid
+ self.img_grid = self.img_canvas.central_widget.add_grid()
+ # interface (n next, b back, q quit, very simple)
+ self.img_canvas.events.key_press.connect(self.key_press)
+ self.img_canvas.events.draw.connect(self.draw)
+
+ # add a view for the depth
+ self.img_view = vispy.scene.widgets.ViewBox(
+ border_color='white', parent=self.img_canvas.scene)
+ self.img_grid.add_widget(self.img_view, 0, 0)
+ self.img_vis = visuals.Image(cmap='viridis')
+ self.img_view.add(self.img_vis)
+
+ # add semantics
+ if self.semantics:
+ self.sem_img_view = vispy.scene.widgets.ViewBox(
+ border_color='white', parent=self.img_canvas.scene)
+ self.img_grid.add_widget(self.sem_img_view, 1, 0)
+ self.sem_img_vis = visuals.Image(cmap='viridis')
+ self.sem_img_view.add(self.sem_img_vis)
+
+ # add instances
+ if self.instances:
+ self.inst_img_view = vispy.scene.widgets.ViewBox(
+ border_color='white', parent=self.img_canvas.scene)
+ self.img_grid.add_widget(self.inst_img_view, 2, 0)
+ self.inst_img_vis = visuals.Image(cmap='viridis')
+ self.inst_img_view.add(self.inst_img_vis)
+
+ def get_mpl_colormap(self, cmap_name):
+ cmap = plt.get_cmap(cmap_name)
+
+ # Initialize the matplotlib color map
+ sm = plt.cm.ScalarMappable(cmap=cmap)
+
+ # Obtain linear color range
+ color_range = sm.to_rgba(np.linspace(0, 1, 256), bytes=True)[:, 2::-1]
+
+ return color_range.reshape(256, 3).astype(np.float32) / 255.0
+
+ def update_scan(self):
+ # first open data
+ self.scan.open_scan(self.scan_names[self.offset])
+ if self.semantics:
+ self.scan.open_label(self.label_names[self.offset])
+ self.scan.colorize()
+
+ # then change names
+ title = "scan " + str(self.offset) + " of " + str(len(self.scan_names))
+ self.canvas.title = title
+ self.img_canvas.title = title
+
+ # then do all the point cloud stuff
+
+ # plot scan
+ power = 16
+ # print()
+ range_data = np.copy(self.scan.unproj_range)
+ # print(range_data.max(), range_data.min())
+ range_data = range_data**(1 / power)
+ # print(range_data.max(), range_data.min())
+ viridis_range = ((range_data - range_data.min()) /
+ (range_data.max() - range_data.min()) *
+ 255).astype(np.uint8)
+ viridis_map = self.get_mpl_colormap("viridis")
+ viridis_colors = viridis_map[viridis_range]
+ self.scan_vis.set_data(self.scan.points,
+ face_color=viridis_colors[..., ::-1],
+ edge_color=viridis_colors[..., ::-1],
+ size=1)
+
+ # plot semantics
+ if self.semantics:
+ self.sem_vis.set_data(self.scan.points,
+ face_color=self.scan.sem_label_color[..., ::-1],
+ edge_color=self.scan.sem_label_color[..., ::-1],
+ size=1)
+
+ # plot instances
+ if self.instances:
+ self.inst_vis.set_data(self.scan.points,
+ face_color=self.scan.inst_label_color[..., ::-1],
+ edge_color=self.scan.inst_label_color[..., ::-1],
+ size=1)
+
+ # now do all the range image stuff
+ # plot range image
+ data = np.copy(self.scan.proj_range)
+ # print(data[data > 0].max(), data[data > 0].min())
+ data[data > 0] = data[data > 0]**(1 / power)
+ data[data < 0] = data[data > 0].min()
+ # print(data.max(), data.min())
+ data = (data - data[data > 0].min()) / \
+ (data.max() - data[data > 0].min())
+ # print(data.max(), data.min())
+ self.img_vis.set_data(data)
+ self.img_vis.update()
+
+ if self.semantics:
+ self.sem_img_vis.set_data(self.scan.proj_sem_color[..., ::-1])
+ self.sem_img_vis.update()
+
+ if self.instances:
+ self.inst_img_vis.set_data(self.scan.proj_inst_color[..., ::-1])
+ self.inst_img_vis.update()
+
+ # interface
+ def key_press(self, event):
+ self.canvas.events.key_press.block()
+ self.img_canvas.events.key_press.block()
+ if event.key == 'N':
+ self.offset += 1
+ self.update_scan()
+ elif event.key == 'B':
+ self.offset -= 1
+ self.update_scan()
+ elif event.key == 'Q' or event.key == 'Escape':
+ self.destroy()
+
+ def draw(self, event):
+ if self.canvas.events.key_press.blocked():
+ self.canvas.events.key_press.unblock()
+ if self.img_canvas.events.key_press.blocked():
+ self.img_canvas.events.key_press.unblock()
+
+ def destroy(self):
+ # destroy the visualization
+ self.canvas.close()
+ self.img_canvas.close()
+ vispy.app.quit()
+
+ def run(self):
+ vispy.app.run()
diff --git a/pytorch_code/lidar-bonnetal/train/common/logger.py b/pytorch_code/lidar-bonnetal/train/common/logger.py
new file mode 100644
index 0000000..4fd7563
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/logger.py
@@ -0,0 +1,76 @@
+# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
+
+import tensorflow as tf
+import numpy as np
+import scipy.misc
+try:
+ from StringIO import StringIO # Python 2.7
+except ImportError:
+ from io import BytesIO # Python 3.x
+
+
+class Logger(object):
+
+ def __init__(self, log_dir):
+ """Create a summary writer logging to log_dir."""
+ self.writer = tf.summary.FileWriter(log_dir)
+
+ def scalar_summary(self, tag, value, step):
+ """Log a scalar variable."""
+ summary = tf.Summary(
+ value=[tf.Summary.Value(tag=tag, simple_value=value)])
+ self.writer.add_summary(summary, step)
+ self.writer.flush()
+
+ def image_summary(self, tag, images, step):
+ """Log a list of images."""
+
+ img_summaries = []
+ for i, img in enumerate(images):
+ # Write the image to a string
+ try:
+ s = StringIO()
+ except:
+ s = BytesIO()
+ scipy.misc.toimage(img).save(s, format="png")
+
+ # Create an Image object
+ img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
+ height=img.shape[0],
+ width=img.shape[1])
+ # Create a Summary value
+ img_summaries.append(tf.Summary.Value(
+ tag='%s/%d' % (tag, i), image=img_sum))
+
+ # Create and write Summary
+ summary = tf.Summary(value=img_summaries)
+ self.writer.add_summary(summary, step)
+ self.writer.flush()
+
+ def histo_summary(self, tag, values, step, bins=1000):
+ """Log a histogram of the tensor of values."""
+
+ # Create a histogram using numpy
+ counts, bin_edges = np.histogram(values, bins=bins)
+
+ # Fill the fields of the histogram proto
+ hist = tf.HistogramProto()
+ hist.min = float(np.min(values))
+ hist.max = float(np.max(values))
+ hist.num = int(np.prod(values.shape))
+ hist.sum = float(np.sum(values))
+ hist.sum_squares = float(np.sum(values**2))
+
+ # Drop the start of the first bin
+ bin_edges = bin_edges[1:]
+
+ # Add bin edges and counts
+ for edge in bin_edges:
+ hist.bucket_limit.append(edge)
+ for c in counts:
+ hist.bucket.append(c)
+
+ # Create and write Summary
+ summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
+ self.writer.add_summary(summary, step)
+ self.writer.flush()
diff --git a/pytorch_code/lidar-bonnetal/train/common/onehot.py b/pytorch_code/lidar-bonnetal/train/common/onehot.py
new file mode 100644
index 0000000..5748e49
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/onehot.py
@@ -0,0 +1,137 @@
+# This file is covered by the LICENSE file in the root of this project.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import __init__ as booger
+
+
+class oneHot(nn.Module):
+ def __init__(self, device, nclasses, spatial_dim=2):
+ super().__init__()
+ self.device = device
+ self.nclasses = nclasses
+ self.spatial_dim = spatial_dim
+
+ def onehot1dspatial(self, x):
+ # we only do tensors that 1d tensors that are batched or not, so check
+ assert(len(x.shape) == 1 or len(x.shape) == 2)
+ # if not batched, batch
+ remove_dim = False # flag to unbatch
+ if len(x.shape) == 1:
+ # add batch dimension
+ x = x[None, ...]
+ remove_dim = True
+
+ # get tensor shape
+ n, b = x.shape
+
+ # scatter to onehot
+ one_hot = torch.zeros((n, self.nclasses, b),
+ device=self.device).scatter_(1, x.unsqueeze(1), 1)
+
+ # x is now [n,classes,b]
+
+ # if it used to be unbatched, then unbatch it
+ if remove_dim:
+ one_hot = one_hot[0]
+
+ return one_hot
+
+ def onehot2dspatial(self, x):
+ # we only do tensors that 2d tensors that are batched or not, so check
+ assert(len(x.shape) == 2 or len(x.shape) == 3)
+ # if not batched, batch
+ remove_dim = False # flag to unbatch
+ if len(x.shape) == 2:
+ # add batch dimension
+ x = x[None, ...]
+ remove_dim = True
+
+ # get tensor shape
+ n, h, w = x.shape
+
+ # scatter to onehot
+ one_hot = torch.zeros((n, self.nclasses, h, w),
+ device=self.device).scatter_(1, x.unsqueeze(1), 1)
+
+ # x is now [n,classes,b]
+
+ # if it used to be unbatched, then unbatch it
+ if remove_dim:
+ one_hot = one_hot[0]
+
+ return one_hot
+
+ def forward(self, x):
+ # do onehot here
+ if self.spatial_dim == 1:
+ return self.onehot1dspatial(x)
+ elif self.spatial_dim == 2:
+ return self.onehot2dspatial(x)
+
+
+if __name__ == "__main__":
+ # get device
+ if torch.cuda.is_available():
+ device = torch.device('cuda')
+ else:
+ device = torch.device('cpu')
+
+ # define number of classes
+ nclasses = 6
+ print("*"*80)
+ print("Num classes 1d =", nclasses)
+ print("*"*80)
+
+ # test 1d unbatched case
+ print("Tensor 1d spat dim, unbatched")
+ tensor = torch.arange(0, nclasses).to(device) # [0,1,2,3,4,5]
+ print("in:", tensor)
+ module = oneHot(device, nclasses, spatial_dim=1)
+ print("out:", module(tensor))
+ print("*"*80)
+
+ # test 1d batched case
+ print("*"*80)
+ print("Tensor 1d spat dim, batched")
+ tensor = torch.arange(0, nclasses).to(device) # [0,1,2,3,4,5]
+ tensor = torch.cat([tensor.unsqueeze(0),
+ tensor.unsqueeze(0)]) # [[0,1,2,3,4,5], [0,1,2,3,4,5]]
+ print("in:", tensor)
+ module = oneHot(device, nclasses, spatial_dim=1)
+ print("out:", module(tensor))
+ print("*"*80)
+
+ # for 2 use less classes
+ nclasses = 3
+ print("*"*80)
+ print("Num classes 2d =", nclasses)
+ print("*"*80)
+
+ # test 2d unbatched case
+ print("*"*80)
+ print("Tensor 2d spat dim, unbatched")
+ tensor = torch.arange(0, nclasses).to(device) # [0,1,2]
+ tensor = torch.cat([tensor.unsqueeze(0), # [[0,1,2],
+ tensor.unsqueeze(0), # [0,1,2],
+ tensor.unsqueeze(0), # [0,1,2],
+ tensor.unsqueeze(0)]) # [0,1,2]]
+ print("in:", tensor)
+ module = oneHot(device, nclasses, spatial_dim=2)
+ print("out:", module(tensor))
+ print("*"*80)
+
+ # test 2d batched case
+ print("*"*80)
+ print("Tensor 2d spat dim, unbatched")
+ tensor = torch.arange(0, nclasses).to(device) # [0,1,2]
+ tensor = torch.cat([tensor.unsqueeze(0), # [[0,1,2],
+ tensor.unsqueeze(0), # [0,1,2],
+ tensor.unsqueeze(0), # [0,1,2],
+ tensor.unsqueeze(0)]) # [0,1,2]]
+ tensor = torch.cat([tensor.unsqueeze(0),
+ tensor.unsqueeze(0)]) # 2 of the same 2d tensor
+ print("in:", tensor)
+ module = oneHot(device, nclasses, spatial_dim=2)
+ print("out:", module(tensor))
+ print("*"*80)
diff --git a/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/__init__.py b/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/batchnorm.py b/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/batchnorm.py
new file mode 100644
index 0000000..3521697
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/batchnorm.py
@@ -0,0 +1,369 @@
+# -*- coding: utf-8 -*-
+# File : batchnorm.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import collections
+
+import torch
+import torch.nn.functional as F
+
+from torch.nn.modules.batchnorm import _BatchNorm
+from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
+
+from .comm import SyncMaster
+from .replicate import DataParallelWithCallback
+
+__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d',
+ 'SynchronizedBatchNorm3d', 'convert_model']
+
+
+def _sum_ft(tensor):
+ """sum over the first and last dimention"""
+ return tensor.sum(dim=0).sum(dim=-1)
+
+
+def _unsqueeze_ft(tensor):
+ """add new dementions at the front and the tail"""
+ return tensor.unsqueeze(0).unsqueeze(-1)
+
+
+_ChildMessage = collections.namedtuple(
+ '_ChildMessage', ['sum', 'ssum', 'sum_size'])
+_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
+
+
+class _SynchronizedBatchNorm(_BatchNorm):
+ def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
+ super(_SynchronizedBatchNorm, self).__init__(
+ num_features, eps=eps, momentum=momentum, affine=affine)
+
+ self._sync_master = SyncMaster(self._data_parallel_master)
+
+ self._is_parallel = False
+ self._parallel_id = None
+ self._slave_pipe = None
+
+ def forward(self, input):
+ # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
+ if not (self._is_parallel and self.training):
+ return F.batch_norm(
+ input, self.running_mean, self.running_var, self.weight, self.bias,
+ self.training, self.momentum, self.eps)
+
+ # Resize the input to (B, C, -1).
+ input_shape = input.size()
+ input = input.view(input.size(0), self.num_features, -1)
+
+ # Compute the sum and square-sum.
+ sum_size = input.size(0) * input.size(2)
+ input_sum = _sum_ft(input)
+ input_ssum = _sum_ft(input ** 2)
+
+ # Reduce-and-broadcast the statistics.
+ if self._parallel_id == 0:
+ mean, inv_std = self._sync_master.run_master(
+ _ChildMessage(input_sum, input_ssum, sum_size))
+ else:
+ mean, inv_std = self._slave_pipe.run_slave(
+ _ChildMessage(input_sum, input_ssum, sum_size))
+
+ # Compute the output.
+ if self.affine:
+ # MJY:: Fuse the multiplication for speed.
+ output = (input - _unsqueeze_ft(mean)) * \
+ _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
+ else:
+ output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
+
+ # Reshape it.
+ return output.view(input_shape)
+
+ def __data_parallel_replicate__(self, ctx, copy_id):
+ self._is_parallel = True
+ self._parallel_id = copy_id
+
+ # parallel_id == 0 means master device.
+ if self._parallel_id == 0:
+ ctx.sync_master = self._sync_master
+ else:
+ self._slave_pipe = ctx.sync_master.register_slave(copy_id)
+
+ def _data_parallel_master(self, intermediates):
+ """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
+
+ # Always using same "device order" makes the ReduceAdd operation faster.
+ # Thanks to:: Tete Xiao (http://tetexiao.com/)
+ intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
+
+ to_reduce = [i[1][:2] for i in intermediates]
+ to_reduce = [j for i in to_reduce for j in i] # flatten
+ target_gpus = [i[1].sum.get_device() for i in intermediates]
+
+ sum_size = sum([i[1].sum_size for i in intermediates])
+ sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
+ mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
+
+ broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
+
+ outputs = []
+ for i, rec in enumerate(intermediates):
+ outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
+
+ return outputs
+
+ def _compute_mean_std(self, sum_, ssum, size):
+ """Compute the mean and standard-deviation with sum and square-sum. This method
+ also maintains the moving average on the master device."""
+ assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
+ mean = sum_ / size
+ sumvar = ssum - sum_ * mean
+ unbias_var = sumvar / (size - 1)
+ bias_var = sumvar / size
+
+ self.running_mean = (1 - self.momentum) * \
+ self.running_mean + self.momentum * mean.data
+ self.running_var = (1 - self.momentum) * \
+ self.running_var + self.momentum * unbias_var.data
+
+ return mean, bias_var.clamp(self.eps) ** -0.5
+
+
+class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
+ r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
+ mini-batch.
+
+ .. math::
+
+ y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+ This module differs from the built-in PyTorch BatchNorm1d as the mean and
+ standard-deviation are reduced across all devices during training.
+
+ For example, when one uses `nn.DataParallel` to wrap the network during
+ training, PyTorch's implementation normalize the tensor on each device using
+ the statistics only on that device, which accelerated the computation and
+ is also easy to implement, but the statistics might be inaccurate.
+ Instead, in this synchronized version, the statistics will be computed
+ over all training samples distributed on multiple devices.
+
+ Note that, for one-GPU or CPU-only case, this module behaves exactly same
+ as the built-in PyTorch implementation.
+
+ The mean and standard-deviation are calculated per-dimension over
+ the mini-batches and gamma and beta are learnable parameter vectors
+ of size C (where C is the input size).
+
+ During training, this layer keeps a running estimate of its computed mean
+ and variance. The running sum is kept with a default momentum of 0.1.
+
+ During evaluation, this running mean/variance is used for normalization.
+
+ Because the BatchNorm is done over the `C` dimension, computing statistics
+ on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
+
+ Args:
+ num_features: num_features from an expected input of size
+ `batch_size x num_features [x width]`
+ eps: a value added to the denominator for numerical stability.
+ Default: 1e-5
+ momentum: the value used for the running_mean and running_var
+ computation. Default: 0.1
+ affine: a boolean value that when set to ``True``, gives the layer learnable
+ affine parameters. Default: ``True``
+
+ Shape:
+ - Input: :math:`(N, C)` or :math:`(N, C, L)`
+ - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
+
+ Examples:
+ >>> # With Learnable Parameters
+ >>> m = SynchronizedBatchNorm1d(100)
+ >>> # Without Learnable Parameters
+ >>> m = SynchronizedBatchNorm1d(100, affine=False)
+ >>> input = torch.autograd.Variable(torch.randn(20, 100))
+ >>> output = m(input)
+ """
+
+ def _check_input_dim(self, input):
+ if input.dim() != 2 and input.dim() != 3:
+ raise ValueError('expected 2D or 3D input (got {}D input)'
+ .format(input.dim()))
+ super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
+
+
+class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
+ r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
+ of 3d inputs
+
+ .. math::
+
+ y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+ This module differs from the built-in PyTorch BatchNorm2d as the mean and
+ standard-deviation are reduced across all devices during training.
+
+ For example, when one uses `nn.DataParallel` to wrap the network during
+ training, PyTorch's implementation normalize the tensor on each device using
+ the statistics only on that device, which accelerated the computation and
+ is also easy to implement, but the statistics might be inaccurate.
+ Instead, in this synchronized version, the statistics will be computed
+ over all training samples distributed on multiple devices.
+
+ Note that, for one-GPU or CPU-only case, this module behaves exactly same
+ as the built-in PyTorch implementation.
+
+ The mean and standard-deviation are calculated per-dimension over
+ the mini-batches and gamma and beta are learnable parameter vectors
+ of size C (where C is the input size).
+
+ During training, this layer keeps a running estimate of its computed mean
+ and variance. The running sum is kept with a default momentum of 0.1.
+
+ During evaluation, this running mean/variance is used for normalization.
+
+ Because the BatchNorm is done over the `C` dimension, computing statistics
+ on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
+
+ Args:
+ num_features: num_features from an expected input of
+ size batch_size x num_features x height x width
+ eps: a value added to the denominator for numerical stability.
+ Default: 1e-5
+ momentum: the value used for the running_mean and running_var
+ computation. Default: 0.1
+ affine: a boolean value that when set to ``True``, gives the layer learnable
+ affine parameters. Default: ``True``
+
+ Shape:
+ - Input: :math:`(N, C, H, W)`
+ - Output: :math:`(N, C, H, W)` (same shape as input)
+
+ Examples:
+ >>> # With Learnable Parameters
+ >>> m = SynchronizedBatchNorm2d(100)
+ >>> # Without Learnable Parameters
+ >>> m = SynchronizedBatchNorm2d(100, affine=False)
+ >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
+ >>> output = m(input)
+ """
+
+ def _check_input_dim(self, input):
+ if input.dim() != 4:
+ raise ValueError('expected 4D input (got {}D input)'
+ .format(input.dim()))
+ super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
+
+
+class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
+ r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
+ of 4d inputs
+
+ .. math::
+
+ y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+ This module differs from the built-in PyTorch BatchNorm3d as the mean and
+ standard-deviation are reduced across all devices during training.
+
+ For example, when one uses `nn.DataParallel` to wrap the network during
+ training, PyTorch's implementation normalize the tensor on each device using
+ the statistics only on that device, which accelerated the computation and
+ is also easy to implement, but the statistics might be inaccurate.
+ Instead, in this synchronized version, the statistics will be computed
+ over all training samples distributed on multiple devices.
+
+ Note that, for one-GPU or CPU-only case, this module behaves exactly same
+ as the built-in PyTorch implementation.
+
+ The mean and standard-deviation are calculated per-dimension over
+ the mini-batches and gamma and beta are learnable parameter vectors
+ of size C (where C is the input size).
+
+ During training, this layer keeps a running estimate of its computed mean
+ and variance. The running sum is kept with a default momentum of 0.1.
+
+ During evaluation, this running mean/variance is used for normalization.
+
+ Because the BatchNorm is done over the `C` dimension, computing statistics
+ on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
+ or Spatio-temporal BatchNorm
+
+ Args:
+ num_features: num_features from an expected input of
+ size batch_size x num_features x depth x height x width
+ eps: a value added to the denominator for numerical stability.
+ Default: 1e-5
+ momentum: the value used for the running_mean and running_var
+ computation. Default: 0.1
+ affine: a boolean value that when set to ``True``, gives the layer learnable
+ affine parameters. Default: ``True``
+
+ Shape:
+ - Input: :math:`(N, C, D, H, W)`
+ - Output: :math:`(N, C, D, H, W)` (same shape as input)
+
+ Examples:
+ >>> # With Learnable Parameters
+ >>> m = SynchronizedBatchNorm3d(100)
+ >>> # Without Learnable Parameters
+ >>> m = SynchronizedBatchNorm3d(100, affine=False)
+ >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
+ >>> output = m(input)
+ """
+
+ def _check_input_dim(self, input):
+ if input.dim() != 5:
+ raise ValueError('expected 5D input (got {}D input)'
+ .format(input.dim()))
+ super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
+
+
+def convert_model(module):
+ """Traverse the input module and its child recursively
+ and replace all instance of torch.nn.modules.batchnorm.BatchNorm*N*d
+ to SynchronizedBatchNorm*N*d
+
+ Args:
+ module: the input module needs to be convert to SyncBN model
+
+ Examples:
+ >>> import torch.nn as nn
+ >>> import torchvision
+ >>> # m is a standard pytorch model
+ >>> m = torchvision.models.resnet18(True)
+ >>> m = nn.DataParallel(m)
+ >>> # after convert, m is using SyncBN
+ >>> m = convert_model(m)
+ """
+ if isinstance(module, torch.nn.DataParallel):
+ mod = module.module
+ mod = convert_model(mod)
+ mod = DataParallelWithCallback(mod)
+ return mod
+
+ mod = module
+ for pth_module, sync_module in zip([torch.nn.modules.batchnorm.BatchNorm1d,
+ torch.nn.modules.batchnorm.BatchNorm2d,
+ torch.nn.modules.batchnorm.BatchNorm3d],
+ [SynchronizedBatchNorm1d,
+ SynchronizedBatchNorm2d,
+ SynchronizedBatchNorm3d]):
+ if isinstance(module, pth_module):
+ mod = sync_module(module.num_features, module.eps,
+ module.momentum, module.affine)
+ mod.running_mean = module.running_mean
+ mod.running_var = module.running_var
+ if module.affine:
+ mod.weight.data = module.weight.data.clone().detach()
+ mod.bias.data = module.bias.data.clone().detach()
+
+ for name, child in module.named_children():
+ mod.add_module(name, convert_model(child))
+
+ return mod
diff --git a/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/comm.py b/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/comm.py
new file mode 100644
index 0000000..7dfb9fb
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/comm.py
@@ -0,0 +1,138 @@
+# -*- coding: utf-8 -*-
+# File : comm.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import queue
+import collections
+import threading
+
+__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster']
+
+
+class FutureResult(object):
+ """A thread-safe future implementation. Used only as one-to-one pipe."""
+
+ def __init__(self):
+ self._result = None
+ self._lock = threading.Lock()
+ self._cond = threading.Condition(self._lock)
+
+ def put(self, result):
+ with self._lock:
+ assert self._result is None, 'Previous result has\'t been fetched.'
+ self._result = result
+ self._cond.notify()
+
+ def get(self):
+ with self._lock:
+ if self._result is None:
+ self._cond.wait()
+
+ res = self._result
+ self._result = None
+ return res
+
+
+_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])
+_SlavePipeBase = collections.namedtuple(
+ '_SlavePipeBase', ['identifier', 'queue', 'result'])
+
+
+class SlavePipe(_SlavePipeBase):
+ """Pipe for master-slave communication."""
+
+ def run_slave(self, msg):
+ self.queue.put((self.identifier, msg))
+ ret = self.result.get()
+ self.queue.put(True)
+ return ret
+
+
+class SyncMaster(object):
+ """An abstract `SyncMaster` object.
+
+ - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
+ call `register(id)` and obtain an `SlavePipe` to communicate with the master.
+ - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
+ and passed to a registered callback.
+ - After receiving the messages, the master device should gather the information and determine to message passed
+ back to each slave devices.
+ """
+
+ def __init__(self, master_callback):
+ """
+
+ Args:
+ master_callback: a callback to be invoked after having collected messages from slave devices.
+ """
+ self._master_callback = master_callback
+ self._queue = queue.Queue()
+ self._registry = collections.OrderedDict()
+ self._activated = False
+
+ def __getstate__(self):
+ return {'master_callback': self._master_callback}
+
+ def __setstate__(self, state):
+ self.__init__(state['master_callback'])
+
+ def register_slave(self, identifier):
+ """
+ Register an slave device.
+
+ Args:
+ identifier: an identifier, usually is the device id.
+
+ Returns: a `SlavePipe` object which can be used to communicate with the master device.
+
+ """
+ if self._activated:
+ assert self._queue.empty(), 'Queue is not clean before next initialization.'
+ self._activated = False
+ self._registry.clear()
+ future = FutureResult()
+ self._registry[identifier] = _MasterRegistry(future)
+ return SlavePipe(identifier, self._queue, future)
+
+ def run_master(self, master_msg):
+ """
+ Main entry for the master device in each forward pass.
+ The messages were first collected from each devices (including the master device), and then
+ an callback will be invoked to compute the message to be sent back to each devices
+ (including the master device).
+
+ Args:
+ master_msg: the message that the master want to send to itself. This will be placed as the first
+ message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.
+
+ Returns: the message to be sent back to the master device.
+
+ """
+ self._activated = True
+
+ intermediates = [(0, master_msg)]
+ for i in range(self.nr_slaves):
+ intermediates.append(self._queue.get())
+
+ results = self._master_callback(intermediates)
+ assert results[0][0] == 0, 'The first result should belongs to the master.'
+
+ for i, res in results:
+ if i == 0:
+ continue
+ self._registry[i].result.put(res)
+
+ for i in range(self.nr_slaves):
+ assert self._queue.get() is True
+
+ return results[0][1]
+
+ @property
+ def nr_slaves(self):
+ return len(self._registry)
diff --git a/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/replicate.py b/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/replicate.py
new file mode 100644
index 0000000..a17b489
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/sync_batchnorm/replicate.py
@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+# File : replicate.py
+# Author : Jiayuan Mao
+# Email : maojiayuan@gmail.com
+# Date : 27/01/2018
+#
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import functools
+
+from torch.nn.parallel.data_parallel import DataParallel
+
+__all__ = [
+ 'CallbackContext',
+ 'execute_replication_callbacks',
+ 'DataParallelWithCallback',
+ 'patch_replication_callback'
+]
+
+
+class CallbackContext(object):
+ pass
+
+
+def execute_replication_callbacks(modules):
+ """
+ Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
+
+ The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+ Note that, as all modules are isomorphism, we assign each sub-module with a context
+ (shared among multiple copies of this module on different devices).
+ Through this context, different copies can share some information.
+
+ We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback
+ of any slave copies.
+ """
+ master_copy = modules[0]
+ nr_modules = len(list(master_copy.modules()))
+ ctxs = [CallbackContext() for _ in range(nr_modules)]
+
+ for i, module in enumerate(modules):
+ for j, m in enumerate(module.modules()):
+ if hasattr(m, '__data_parallel_replicate__'):
+ m.__data_parallel_replicate__(ctxs[j], i)
+
+
+class DataParallelWithCallback(DataParallel):
+ """
+ Data Parallel with a replication callback.
+
+ An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by
+ original `replicate` function.
+ The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+ Examples:
+ > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+ # sync_bn.__data_parallel_replicate__ will be invoked.
+ """
+
+ def replicate(self, module, device_ids):
+ modules = super(DataParallelWithCallback,
+ self).replicate(module, device_ids)
+ execute_replication_callbacks(modules)
+ return modules
+
+
+def patch_replication_callback(data_parallel):
+ """
+ Monkey-patch an existing `DataParallel` object. Add the replication callback.
+ Useful when you have customized `DataParallel` implementation.
+
+ Examples:
+ > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
+ > patch_replication_callback(sync_bn)
+ # this is equivalent to
+ > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+ > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+ """
+
+ assert isinstance(data_parallel, DataParallel)
+
+ old_replicate = data_parallel.replicate
+
+ @functools.wraps(old_replicate)
+ def new_replicate(module, device_ids):
+ modules = old_replicate(module, device_ids)
+ execute_replication_callbacks(modules)
+ return modules
+
+ data_parallel.replicate = new_replicate
diff --git a/pytorch_code/lidar-bonnetal/train/common/warmupLR.py b/pytorch_code/lidar-bonnetal/train/common/warmupLR.py
new file mode 100644
index 0000000..26d09d0
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/common/warmupLR.py
@@ -0,0 +1,50 @@
+# This file is covered by the LICENSE file in the root of this project.
+
+import torch.optim.lr_scheduler as toptim
+
+
+class warmupLR(toptim._LRScheduler):
+ """ Warmup learning rate scheduler.
+ Initially, increases the learning rate from 0 to the final value, in a
+ certain number of steps. After this number of steps, each step decreases
+ LR exponentially.
+ """
+
+ def __init__(self, optimizer, lr, warmup_steps, momentum, decay):
+ # cyclic params
+ self.optimizer = optimizer
+ self.lr = lr
+ self.warmup_steps = warmup_steps
+ self.momentum = momentum
+ self.decay = decay
+
+ # cap to one
+ if self.warmup_steps < 1:
+ self.warmup_steps = 1
+
+ # cyclic lr
+ self.initial_scheduler = toptim.CyclicLR(self.optimizer,
+ base_lr=0,
+ max_lr=self.lr,
+ step_size_up=self.warmup_steps,
+ step_size_down=self.warmup_steps,
+ cycle_momentum=False,
+ base_momentum=self.momentum,
+ max_momentum=self.momentum)
+
+ # our params
+ self.last_epoch = -1 # fix for pytorch 1.1 and below
+ self.finished = False # am i done
+ super().__init__(optimizer)
+
+ def get_lr(self):
+ return [self.lr * (self.decay ** self.last_epoch) for lr in self.base_lrs]
+
+ def step(self, epoch=None):
+ if self.finished or self.initial_scheduler.last_epoch >= self.warmup_steps:
+ if not self.finished:
+ self.base_lrs = [self.lr for lr in self.base_lrs]
+ self.finished = True
+ return super(warmupLR, self).step(epoch)
+ else:
+ return self.initial_scheduler.step(epoch)
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/__init__.py b/pytorch_code/lidar-bonnetal/train/tasks/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/README.md b/pytorch_code/lidar-bonnetal/train/tasks/semantic/README.md
new file mode 100644
index 0000000..3e30c43
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/README.md
@@ -0,0 +1,117 @@
+# 3D-MiniNet: Pytorch Implementation
+
+This code allows to reproduce the experiments from the 3D-MiniNet paper.
+
+## Dependencies
+
+First you need to install the nvidia driver and CUDA, so have fun!
+
+- CUDA Installation guide: [link](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
+
+- System dependencies:
+
+ ```sh
+ $ sudo apt-get update
+ $ sudo apt-get install -yqq build-essential ninja-build \
+ python3-dev python3-pip apt-utils curl git cmake unzip autoconf autogen \
+ libtool mlocate zlib1g-dev python3-numpy python3-wheel wget \
+ software-properties-common openjdk-8-jdk libpng-dev \
+ libxft-dev ffmpeg python3-pyqt5.qtopengl
+ $ sudo updatedb
+ ```
+
+- Python dependencies
+
+ ```sh
+ $ sudo pip3 install -r requirements.txt
+ ```
+
+
+
+## Dataset
+
+Download the SemanticKitti dataset [here](http://semantic-kitti.org/dataset.html#download)
+
+## Configuration files
+
+Architecture configuration files are located at [config/arch](config/arch/)
+Dataset configuration files are located at [config/labels](config/labels/)
+
+## Apps
+
+`ALL SCRIPTS CAN BE INVOKED WITH -h TO GET EXTRA HELP ON HOW TO RUN THEM`
+
+### Visualization
+
+To visualize the data (in this example sequence 00):
+
+```sh
+$ ./visualize.py -d /path/to/dataset/ -s 00
+```
+
+To visualize the predictions (in this example sequence 00):
+
+```sh
+$ ./visualize.py -d /path/to/dataset/ -p /path/to/predictions/ -s 00
+```
+
+### Training
+
+To train a network (from scratch):
+
+```sh
+$ ./train.py -d /path/to/dataset -ac config/arch/CHOICE.yaml -l /path/to/log
+```
+
+To train a network (from pretrained model):
+
+```
+$ ./train.py -d /path/to/dataset -ac config/arch/CHOICE.yaml -dc config/labels/CHOICE.yaml -l /path/to/log -p /path/to/pretrained
+```
+
+Change any training parameters like the number of training epochs in the config/labels/CHOICE.yaml configuration file.
+
+Besides, for a minor boost of performance you can:
+- Play with the hyperparameters
+- Train more epochs or doing several trainings lowering the learning rate
+- Change the [data augmentation](https://github.com/Shathe/3D-MiniNet-private/blob/master/pytorch_code/lidar-bonnetal/train/common/laserscan.py#L85)
+- Add the validation set to the training: line 56 of the (modules/trainer.py): train_sequences=self.DATA["split"]["train"] + self.DATA["split"]["valid"],
+
+
+### Inference
+
+To infer the predictions for the entire dataset:
+
+```sh
+$ ./infer.py -d /path/to/dataset/ -l /path/for/predictions -m /path/to/model
+````
+
+### Evaluation
+
+Upload test inferences on the [evaluation platform](https://competitions.codalab.org/competitions/20331)
+
+For validating against the validation set:
+```sh
+$ ./evaluate_iou.py -d /path/to/dataset/ -p /path/for/predictions --split valid
+````
+
+
+
+## Pre-trained Models
+
+- [3D-MiniNet](models/3D-MiniNet)
+- [3D-MiniNet-small](models/3D-MiniNet-small)
+- [3D-MiniNet-tiny](models/3D-MiniNet-tiny)
+
+These models have been trained both on train and validation data.
+
+To enable kNN post-processing, just change the boolean value to `True` in the `arch_cfg.yaml` file parameter, inside the model directory.
+
+### Troubleshooting
+- Please, **if you have a CUDA out memory error **lower the batch size, although performance can be compromised.**
+
+- If you have some problems with the GPU (like you have only 1 gpu and it is not founding it correctly) you may add at the beginning of the training file:
+```
+n_gpu = 0
+os.environ["CUDA_VISIBLE_DEVICES"] = str(n_gpu)
+```
\ No newline at end of file
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/__init__.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/__init__.py
new file mode 100644
index 0000000..8979f75
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/__init__.py
@@ -0,0 +1,4 @@
+import sys
+TRAIN_PATH = "../../"
+DEPLOY_PATH = "../../../deploy"
+sys.path.insert(0, TRAIN_PATH)
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet-small.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet-small.yaml
new file mode 100644
index 0000000..ec5731d
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet-small.yaml
@@ -0,0 +1,98 @@
+################################################################################
+# training parameters
+################################################################################
+train:
+ loss: "xentropy" # must be either xentropy or iou
+ max_epochs: 500
+ lr: 0.005 # sgd learning rate
+ wup_epochs: 0 # warmup during first XX epochs (can be float)
+ momentum: 0.9 # sgd momentum
+ lr_decay: 0.99 # learning rate decay per epoch after initial cycle (from min lr)
+ w_decay: 0.0001 # weight decay
+ batch_size: 16 # batch size
+ report_batch: 1 # every x batches, report loss
+ report_epoch: 1 # every x epochs, report validation set
+ epsilon_w: 0.001 # class weight w = 1 / (content + epsilon_w)
+ save_summary: False # Summary of weight histograms for tensorboard
+ save_scans: False # False doesn't save anything, True saves some
+ # sample images (one per batch of the last calculated batch)
+ # in log folder
+ show_scans: False # show scans during training
+ workers: 6 # number of threads to get data
+
+################################################################################
+# backbone parameters
+################################################################################
+backbone:
+ name: "3dmininet"
+ input_depth:
+ range: True
+ xyz: True
+ remission: True
+ block_1: 12 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 10 # number of blocks of the second resolution (w/8, h/8)
+ features_bottleneck: 128 # number of blocks of the second resolution (w/8, h/8)
+ OS: 16 # output stride
+ train: True # train backbone?
+
+################################################################################
+# decoder parameters
+################################################################################
+decoder:
+ name: "3dmininet"
+ block_1: 2 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 1 # number of blocks of the second resolution (w/2, h/2)
+ features_bottleneck: 128 # number of blocks of the second resolution (w/8, h/8)
+ train: True # train decoder?
+
+################################################################################
+# classification head parameters
+################################################################################
+head:
+ name: "segmentation"
+ train: True
+
+################################################################################
+# postproc parameters
+################################################################################
+post:
+ CRF:
+ use: False
+ train: True
+ params: False # this should be a dict when in use
+ KNN:
+ use: True
+ params:
+ knn: 7
+ search: 7
+ sigma: 1.0
+ cutoff: 2.0
+
+################################################################################
+# classification head parameters
+################################################################################
+# dataset (to find parser)
+dataset:
+ labels: "kitti"
+ scans: "kitti"
+ max_points: 150000 # max of any scan in dataset
+ sensor:
+ name: "HDL64"
+ type: "spherical" # projective
+ fov_up: 3
+ fov_down: -25
+ img_prop:
+ width: 2048
+ height: 64
+ img_means: #range,x,y,z,signal
+ - 12.12
+ - 10.88
+ - 0.23
+ - -1.04
+ - 0.21
+ img_stds: #range,x,y,z,signal
+ - 12.32
+ - 11.47
+ - 6.91
+ - 0.86
+ - 0.16
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet-tiny.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet-tiny.yaml
new file mode 100644
index 0000000..5e3ba82
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet-tiny.yaml
@@ -0,0 +1,98 @@
+################################################################################
+# training parameters
+################################################################################
+train:
+ loss: "xentropy" # must be either xentropy or iou
+ max_epochs: 500
+ lr: 0.005 # sgd learning rate
+ wup_epochs: 0 # warmup during first XX epochs (can be float)
+ momentum: 0.9 # sgd momentum
+ lr_decay: 0.99 # learning rate decay per epoch after initial cycle (from min lr)
+ w_decay: 0.0001 # weight decay
+ batch_size: 24 # batch size
+ report_batch: 1 # every x batches, report loss
+ report_epoch: 1 # every x epochs, report validation set
+ epsilon_w: 0.001 # class weight w = 1 / (content + epsilon_w)
+ save_summary: False # Summary of weight histograms for tensorboard
+ save_scans: False # False doesn't save anything, True saves some
+ # sample images (one per batch of the last calculated batch)
+ # in log folder
+ show_scans: False # show scans during training
+ workers: 6 # number of threads to get data
+
+################################################################################
+# backbone parameters
+################################################################################
+backbone:
+ name: "3dmininet"
+ input_depth:
+ range: True
+ xyz: True
+ remission: True
+ block_1: 7 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 5 # number of blocks of the second resolution (w/8, h/8)
+ features_bottleneck: 96 # number of blocks of the second resolution (w/8, h/8)
+ OS: 16 # output stride
+ train: True # train backbone?
+
+################################################################################
+# decoder parameters
+################################################################################
+decoder:
+ name: "3dmininet"
+ block_1: 2 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 1 # number of blocks of the second resolution (w/2, h/2)
+ features_bottleneck: 96 # number of blocks of the second resolution (w/8, h/8)
+ train: True # train decoder?
+
+################################################################################
+# classification head parameters
+################################################################################
+head:
+ name: "segmentation"
+ train: True
+
+################################################################################
+# postproc parameters
+################################################################################
+post:
+ CRF:
+ use: False
+ train: True
+ params: False # this should be a dict when in use
+ KNN:
+ use: True
+ params:
+ knn: 7
+ search: 7
+ sigma: 1.0
+ cutoff: 2.0
+
+################################################################################
+# classification head parameters
+################################################################################
+# dataset (to find parser)
+dataset:
+ labels: "kitti"
+ scans: "kitti"
+ max_points: 150000 # max of any scan in dataset
+ sensor:
+ name: "HDL64"
+ type: "spherical" # projective
+ fov_up: 3
+ fov_down: -25
+ img_prop:
+ width: 2048
+ height: 64
+ img_means: #range,x,y,z,signal
+ - 12.12
+ - 10.88
+ - 0.23
+ - -1.04
+ - 0.21
+ img_stds: #range,x,y,z,signal
+ - 12.32
+ - 11.47
+ - 6.91
+ - 0.86
+ - 0.16
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet.yaml
new file mode 100644
index 0000000..75ed49d
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/arch/3dmininet.yaml
@@ -0,0 +1,98 @@
+################################################################################
+# training parameters
+################################################################################
+train:
+ loss: "xentropy" # must be either xentropy or iou
+ max_epochs: 500
+ lr: 0.005 # sgd learning rate
+ wup_epochs: 0 # warmup during first XX epochs (can be float)
+ momentum: 0.9 # sgd momentum
+ lr_decay: 0.99 # learning rate decay per epoch after initial cycle (from min lr)
+ w_decay: 0.0001 # weight decay
+ batch_size: 8 # batch size
+ report_batch: 1 # every x batches, report loss
+ report_epoch: 1 # every x epochs, report validation set
+ epsilon_w: 0.001 # class weight w = 1 / (content + epsilon_w)
+ save_summary: False # Summary of weight histograms for tensorboard
+ save_scans: False # False doesn't save anything, True saves some
+ # sample images (one per batch of the last calculated batch)
+ # in log folder
+ show_scans: False # show scans during training
+ workers: 6 # number of threads to get data
+
+################################################################################
+# backbone parameters
+################################################################################
+backbone:
+ name: "3dmininet"
+ input_depth:
+ range: True
+ xyz: True
+ remission: True
+ block_1: 25 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 15 # number of blocks of the second resolution (w/8, h/8)
+ features_bottleneck: 192 # number of blocks of the second resolution (w/8, h/8)
+ OS: 16 # output stride
+ train: True # train backbone?
+
+################################################################################
+# decoder parameters
+################################################################################
+decoder:
+ name: "3dmininet" # ['3dmininet', '3dmininetsmall', '3dmininettiny']
+ block_1: 4 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 2 # number of blocks of the second resolution (w/2, h/2)
+ features_bottleneck: 192 # number of blocks of the second resolution (w/8, h/8)
+ train: True # train decoder?
+
+################################################################################
+# classification head parameters
+################################################################################
+head:
+ name: "segmentation"
+ train: True
+
+################################################################################
+# postproc parameters
+################################################################################
+post:
+ CRF:
+ use: False
+ train: True
+ params: False # this should be a dict when in use
+ KNN:
+ use: True
+ params:
+ knn: 7
+ search: 7
+ sigma: 1.0
+ cutoff: 2.0
+
+################################################################################
+# classification head parameters
+################################################################################
+# dataset (to find parser)
+dataset:
+ labels: "kitti"
+ scans: "kitti"
+ max_points: 150000 # max of any scan in dataset
+ sensor:
+ name: "HDL64"
+ type: "spherical" # projective
+ fov_up: 3
+ fov_down: -25
+ img_prop:
+ width: 2048
+ height: 64
+ img_means: #range,x,y,z,signal
+ - 12.12
+ - 10.88
+ - 0.23
+ - -1.04
+ - 0.21
+ img_stds: #range,x,y,z,signal
+ - 12.32
+ - 11.47
+ - 6.91
+ - 0.86
+ - 0.16
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/labels/semantic-kitti-all.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/labels/semantic-kitti-all.yaml
new file mode 100644
index 0000000..74d815d
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/labels/semantic-kitti-all.yaml
@@ -0,0 +1,224 @@
+# This file is covered by the LICENSE file in the root of this project.
+name: "kitti"
+labels:
+ 0 : "unlabeled"
+ 1 : "outlier"
+ 10: "car"
+ 11: "bicycle"
+ 13: "bus"
+ 15: "motorcycle"
+ 16: "on-rails"
+ 18: "truck"
+ 20: "other-vehicle"
+ 30: "person"
+ 31: "bicyclist"
+ 32: "motorcyclist"
+ 40: "road"
+ 44: "parking"
+ 48: "sidewalk"
+ 49: "other-ground"
+ 50: "building"
+ 51: "fence"
+ 52: "other-structure"
+ 60: "lane-marking"
+ 70: "vegetation"
+ 71: "trunk"
+ 72: "terrain"
+ 80: "pole"
+ 81: "traffic-sign"
+ 99: "other-object"
+ 252: "moving-car"
+ 253: "moving-bicyclist"
+ 254: "moving-person"
+ 255: "moving-motorcyclist"
+ 256: "moving-on-rails"
+ 257: "moving-bus"
+ 258: "moving-truck"
+ 259: "moving-other-vehicle"
+color_map: # bgr
+ 0 : [0, 0, 0]
+ 1 : [0, 0, 255]
+ 10: [245, 150, 100]
+ 11: [245, 230, 100]
+ 13: [250, 80, 100]
+ 15: [150, 60, 30]
+ 16: [255, 0, 0]
+ 18: [180, 30, 80]
+ 20: [255, 0, 0]
+ 30: [30, 30, 255]
+ 31: [200, 40, 255]
+ 32: [90, 30, 150]
+ 40: [255, 0, 255]
+ 44: [255, 150, 255]
+ 48: [75, 0, 75]
+ 49: [75, 0, 175]
+ 50: [0, 200, 255]
+ 51: [50, 120, 255]
+ 52: [0, 150, 255]
+ 60: [170, 255, 150]
+ 70: [0, 175, 0]
+ 71: [0, 60, 135]
+ 72: [80, 240, 150]
+ 80: [150, 240, 255]
+ 81: [0, 0, 255]
+ 99: [255, 255, 50]
+ 252: [245, 150, 100]
+ 256: [255, 0, 0]
+ 253: [200, 40, 255]
+ 254: [30, 30, 255]
+ 255: [90, 30, 150]
+ 257: [250, 80, 100]
+ 258: [180, 30, 80]
+ 259: [255, 0, 0]
+content: # as a ratio with the total number of points
+ 0: 0.018889854628292943
+ 1: 0.0002937197336781505
+ 10: 0.040818519255974316
+ 11: 0.00016609538710764618
+ 13: 2.7879693665067774e-05
+ 15: 0.00039838616015114444
+ 16: 0.0
+ 18: 0.0020633612104619787
+ 20: 0.0016218197275284021
+ 30: 0.00017698551338515307
+ 31: 1.1065903904919655e-08
+ 32: 5.532951952459828e-09
+ 40: 0.1987493871255525
+ 44: 0.014717169549888214
+ 48: 0.14392298360372
+ 49: 0.0039048553037472045
+ 50: 0.1326861944777486
+ 51: 0.0723592229456223
+ 52: 0.002395131480328884
+ 60: 4.7084144280367186e-05
+ 70: 0.26681502148037506
+ 71: 0.006035012012626033
+ 72: 0.07814222006271769
+ 80: 0.002855498193863172
+ 81: 0.0006155958086189918
+ 99: 0.009923127583046915
+ 252: 0.001789309418528068
+ 253: 0.00012709999297008662
+ 254: 0.00016059776092534436
+ 255: 3.745553104802113e-05
+ 256: 0.0
+ 257: 0.00011351574470342043
+ 258: 0.00010157861367183268
+ 259: 4.3840131989471124e-05
+# classes that are indistinguishable from single scan or inconsistent in
+# ground truth are mapped to their closest equivalent
+learning_map:
+ 0 : 0 # "unlabeled"
+ 1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
+ 10: 1 # "car"
+ 11: 2 # "bicycle"
+ 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
+ 15: 3 # "motorcycle"
+ 16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
+ 18: 4 # "truck"
+ 20: 5 # "other-vehicle"
+ 30: 6 # "person"
+ 31: 7 # "bicyclist"
+ 32: 8 # "motorcyclist"
+ 40: 9 # "road"
+ 44: 10 # "parking"
+ 48: 11 # "sidewalk"
+ 49: 12 # "other-ground"
+ 50: 13 # "building"
+ 51: 14 # "fence"
+ 52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped
+ 60: 9 # "lane-marking" to "road" ---------------------------------mapped
+ 70: 15 # "vegetation"
+ 71: 16 # "trunk"
+ 72: 17 # "terrain"
+ 80: 18 # "pole"
+ 81: 19 # "traffic-sign"
+ 99: 0 # "other-object" to "unlabeled" ----------------------------mapped
+ 252: 20 # "moving-car"
+ 253: 21 # "moving-bicyclist"
+ 254: 22 # "moving-person"
+ 255: 23 # "moving-motorcyclist"
+ 256: 24 # "moving-on-rails" mapped to "moving-other-vehicle" ------mapped
+ 257: 24 # "moving-bus" mapped to "moving-other-vehicle" -----------mapped
+ 258: 25 # "moving-truck"
+ 259: 24 # "moving-other-vehicle"
+learning_map_inv: # inverse of previous map
+ 0: 0 # "unlabeled", and others ignored
+ 1: 10 # "car"
+ 2: 11 # "bicycle"
+ 3: 15 # "motorcycle"
+ 4: 18 # "truck"
+ 5: 20 # "other-vehicle"
+ 6: 30 # "person"
+ 7: 31 # "bicyclist"
+ 8: 32 # "motorcyclist"
+ 9: 40 # "road"
+ 10: 44 # "parking"
+ 11: 48 # "sidewalk"
+ 12: 49 # "other-ground"
+ 13: 50 # "building"
+ 14: 51 # "fence"
+ 15: 70 # "vegetation"
+ 16: 71 # "trunk"
+ 17: 72 # "terrain"
+ 18: 80 # "pole"
+ 19: 81 # "traffic-sign"
+ 20: 252 # "moving-car"
+ 21: 253 # "moving-bicyclist"
+ 22: 254 # "moving-person"
+ 23: 255 # "moving-motorcyclist"
+ 24: 259 # "moving-other-vehicle"
+ 25: 258 # "moving-truck"
+learning_ignore: # Ignore classes
+ 0: True # "unlabeled", and others ignored
+ 1: False # "car"
+ 2: False # "bicycle"
+ 3: False # "motorcycle"
+ 4: False # "truck"
+ 5: False # "other-vehicle"
+ 6: False # "person"
+ 7: False # "bicyclist"
+ 8: False # "motorcyclist"
+ 9: False # "road"
+ 10: False # "parking"
+ 11: False # "sidewalk"
+ 12: False # "other-ground"
+ 13: False # "building"
+ 14: False # "fence"
+ 15: False # "vegetation"
+ 16: False # "trunk"
+ 17: False # "terrain"
+ 18: False # "pole"
+ 19: False # "traffic-sign"
+ 20: False # "moving-car"
+ 21: False # "moving-bicyclist"
+ 22: False # "moving-person"
+ 23: False # "moving-motorcyclist"
+ 24: False # "moving-other-vehicle"
+ 25: False # "moving-truck"
+split: # sequence numbers
+ train:
+ - 0
+ - 1
+ - 2
+ - 3
+ - 4
+ - 5
+ - 6
+ - 7
+ - 9
+ - 10
+ valid:
+ - 8
+ test:
+ - 11
+ - 12
+ - 13
+ - 14
+ - 15
+ - 16
+ - 17
+ - 18
+ - 19
+ - 20
+ - 21
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/labels/semantic-kitti.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/labels/semantic-kitti.yaml
new file mode 100644
index 0000000..a4af922
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/config/labels/semantic-kitti.yaml
@@ -0,0 +1,212 @@
+# This file is covered by the LICENSE file in the root of this project.
+name: "kitti"
+labels:
+ 0 : "unlabeled"
+ 1 : "outlier"
+ 10: "car"
+ 11: "bicycle"
+ 13: "bus"
+ 15: "motorcycle"
+ 16: "on-rails"
+ 18: "truck"
+ 20: "other-vehicle"
+ 30: "person"
+ 31: "bicyclist"
+ 32: "motorcyclist"
+ 40: "road"
+ 44: "parking"
+ 48: "sidewalk"
+ 49: "other-ground"
+ 50: "building"
+ 51: "fence"
+ 52: "other-structure"
+ 60: "lane-marking"
+ 70: "vegetation"
+ 71: "trunk"
+ 72: "terrain"
+ 80: "pole"
+ 81: "traffic-sign"
+ 99: "other-object"
+ 252: "moving-car"
+ 253: "moving-bicyclist"
+ 254: "moving-person"
+ 255: "moving-motorcyclist"
+ 256: "moving-on-rails"
+ 257: "moving-bus"
+ 258: "moving-truck"
+ 259: "moving-other-vehicle"
+color_map: # bgr
+ 0 : [0, 0, 0]
+ 1 : [0, 0, 255]
+ 10: [245, 150, 100]
+ 11: [245, 230, 100]
+ 13: [250, 80, 100]
+ 15: [150, 60, 30]
+ 16: [255, 0, 0]
+ 18: [180, 30, 80]
+ 20: [255, 0, 0]
+ 30: [30, 30, 255]
+ 31: [200, 40, 255]
+ 32: [90, 30, 150]
+ 40: [255, 0, 255]
+ 44: [255, 150, 255]
+ 48: [75, 0, 75]
+ 49: [75, 0, 175]
+ 50: [0, 200, 255]
+ 51: [50, 120, 255]
+ 52: [0, 150, 255]
+ 60: [170, 255, 150]
+ 70: [0, 175, 0]
+ 71: [0, 60, 135]
+ 72: [80, 240, 150]
+ 80: [150, 240, 255]
+ 81: [0, 0, 255]
+ 99: [255, 255, 50]
+ 252: [245, 150, 100]
+ 256: [255, 0, 0]
+ 253: [200, 40, 255]
+ 254: [30, 30, 255]
+ 255: [90, 30, 150]
+ 257: [250, 80, 100]
+ 258: [180, 30, 80]
+ 259: [255, 0, 0]
+content: # as a ratio with the total number of points
+ 0: 0.018889854628292943
+ 1: 0.0002937197336781505
+ 10: 0.040818519255974316
+ 11: 0.00016609538710764618
+ 13: 2.7879693665067774e-05
+ 15: 0.00039838616015114444
+ 16: 0.0
+ 18: 0.0020633612104619787
+ 20: 0.0016218197275284021
+ 30: 0.00017698551338515307
+ 31: 1.1065903904919655e-08
+ 32: 5.532951952459828e-09
+ 40: 0.1987493871255525
+ 44: 0.014717169549888214
+ 48: 0.14392298360372
+ 49: 0.0039048553037472045
+ 50: 0.1326861944777486
+ 51: 0.0723592229456223
+ 52: 0.002395131480328884
+ 60: 4.7084144280367186e-05
+ 70: 0.26681502148037506
+ 71: 0.006035012012626033
+ 72: 0.07814222006271769
+ 80: 0.002855498193863172
+ 81: 0.0006155958086189918
+ 99: 0.009923127583046915
+ 252: 0.001789309418528068
+ 253: 0.00012709999297008662
+ 254: 0.00016059776092534436
+ 255: 3.745553104802113e-05
+ 256: 0.0
+ 257: 0.00011351574470342043
+ 258: 0.00010157861367183268
+ 259: 4.3840131989471124e-05
+# classes that are indistinguishable from single scan or inconsistent in
+# ground truth are mapped to their closest equivalent
+learning_map:
+ 0 : 0 # "unlabeled"
+ 1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
+ 10: 1 # "car"
+ 11: 2 # "bicycle"
+ 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
+ 15: 3 # "motorcycle"
+ 16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
+ 18: 4 # "truck"
+ 20: 5 # "other-vehicle"
+ 30: 6 # "person"
+ 31: 7 # "bicyclist"
+ 32: 8 # "motorcyclist"
+ 40: 9 # "road"
+ 44: 10 # "parking"
+ 48: 11 # "sidewalk"
+ 49: 12 # "other-ground"
+ 50: 13 # "building"
+ 51: 14 # "fence"
+ 52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped
+ 60: 9 # "lane-marking" to "road" ---------------------------------mapped
+ 70: 15 # "vegetation"
+ 71: 16 # "trunk"
+ 72: 17 # "terrain"
+ 80: 18 # "pole"
+ 81: 19 # "traffic-sign"
+ 99: 0 # "other-object" to "unlabeled" ----------------------------mapped
+ 252: 1 # "moving-car" to "car" ------------------------------------mapped
+ 253: 7 # "moving-bicyclist" to "bicyclist" ------------------------mapped
+ 254: 6 # "moving-person" to "person" ------------------------------mapped
+ 255: 8 # "moving-motorcyclist" to "motorcyclist" ------------------mapped
+ 256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped
+ 257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped
+ 258: 4 # "moving-truck" to "truck" --------------------------------mapped
+ 259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped
+learning_map_inv: # inverse of previous map
+ 0: 0 # "unlabeled", and others ignored
+ 1: 10 # "car"
+ 2: 11 # "bicycle"
+ 3: 15 # "motorcycle"
+ 4: 18 # "truck"
+ 5: 20 # "other-vehicle"
+ 6: 30 # "person"
+ 7: 31 # "bicyclist"
+ 8: 32 # "motorcyclist"
+ 9: 40 # "road"
+ 10: 44 # "parking"
+ 11: 48 # "sidewalk"
+ 12: 49 # "other-ground"
+ 13: 50 # "building"
+ 14: 51 # "fence"
+ 15: 70 # "vegetation"
+ 16: 71 # "trunk"
+ 17: 72 # "terrain"
+ 18: 80 # "pole"
+ 19: 81 # "traffic-sign"
+learning_ignore: # Ignore classes
+ 0: True # "unlabeled", and others ignored
+ 1: False # "car"
+ 2: False # "bicycle"
+ 3: False # "motorcycle"
+ 4: False # "truck"
+ 5: False # "other-vehicle"
+ 6: False # "person"
+ 7: False # "bicyclist"
+ 8: False # "motorcyclist"
+ 9: False # "road"
+ 10: False # "parking"
+ 11: False # "sidewalk"
+ 12: False # "other-ground"
+ 13: False # "building"
+ 14: False # "fence"
+ 15: False # "vegetation"
+ 16: False # "trunk"
+ 17: False # "terrain"
+ 18: False # "pole"
+ 19: False # "traffic-sign"
+split: # sequence numbers
+ train:
+ - 0
+ - 1
+ - 2
+ - 3
+ - 4
+ - 5
+ - 6
+ - 7
+ - 9
+ - 10
+ valid:
+ - 8
+ test:
+ - 11
+ - 12
+ - 13
+ - 14
+ - 15
+ - 16
+ - 17
+ - 18
+ - 19
+ - 20
+ - 21
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/dataset/kitti/__init__.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/dataset/kitti/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/dataset/kitti/parser.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/dataset/kitti/parser.py
new file mode 100644
index 0000000..4ce89d5
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/dataset/kitti/parser.py
@@ -0,0 +1,417 @@
+import os
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from torch.nn import functional as f
+from common.laserscan import LaserScan, SemLaserScan
+
+EXTENSIONS_SCAN = ['.bin']
+EXTENSIONS_LABEL = ['.label']
+
+
+def is_scan(filename):
+ return any(filename.endswith(ext) for ext in EXTENSIONS_SCAN)
+
+
+def is_label(filename):
+ return any(filename.endswith(ext) for ext in EXTENSIONS_LABEL)
+
+
+class SemanticKitti(Dataset):
+
+ def __init__(self, root, # directory where data is
+ sequences, # sequences for this data (e.g. [1,3,4,6])
+ labels, # label dict: (e.g 10: "car")
+ color_map, # colors dict bgr (e.g 10: [255, 0, 0])
+ learning_map, # classes to learn (0 to N-1 for xentropy)
+ learning_map_inv, # inverse of previous (recover labels)
+ sensor, # sensor to parse scans from
+ max_points=150000, # max number of points present in dataset
+ gt=True, training=True): # send ground truth?
+ # save deats
+ self.root = os.path.join(root, "sequences")
+ self.sequences = sequences
+ self.labels = labels
+ self.color_map = color_map
+ self.learning_map = learning_map
+ self.learning_map_inv = learning_map_inv
+ self.sensor = sensor
+ self.sensor_img_H = sensor["img_prop"]["height"]
+ self.sensor_img_W = sensor["img_prop"]["width"]
+ self.sensor_img_means = torch.tensor(sensor["img_means"],
+ dtype=torch.float)
+ self.sensor_img_stds = torch.tensor(sensor["img_stds"],
+ dtype=torch.float)
+ self.sensor_fov_up = sensor["fov_up"]
+ self.sensor_fov_down = sensor["fov_down"]
+ self.max_points = max_points
+ self.gt = gt
+ self.training = training
+ # get number of classes (can't be len(self.learning_map) because there
+ # are multiple repeated entries, so the number that matters is how many
+ # there are for the xentropy)
+ self.nclasses = len(self.learning_map_inv)
+
+ # sanity checks
+
+ # make sure directory exists
+ if os.path.isdir(self.root):
+ print("Sequences folder exists! Using sequences from %s" % self.root)
+ else:
+ raise ValueError("Sequences folder doesn't exist! Exiting...")
+
+ # make sure labels is a dict
+ assert(isinstance(self.labels, dict))
+
+ # make sure color_map is a dict
+ assert(isinstance(self.color_map, dict))
+
+ # make sure learning_map is a dict
+ assert(isinstance(self.learning_map, dict))
+
+ # make sure sequences is a list
+ assert(isinstance(self.sequences, list))
+
+ # placeholder for filenames
+ self.scan_files = []
+ self.label_files = []
+
+ # fill in with names, checking that all sequences are complete
+ for seq in self.sequences:
+ # to string
+ seq = '{0:02d}'.format(int(seq))
+
+ print("parsing seq {}".format(seq))
+
+ # get paths for each
+ scan_path = os.path.join(self.root, seq, "velodyne")
+ label_path = os.path.join(self.root, seq, "labels")
+
+ # get files
+ scan_files = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(scan_path)) for f in fn if is_scan(f)]
+ label_files = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(label_path)) for f in fn if is_label(f)]
+
+ # check all scans have labels
+ if self.gt:
+ assert(len(scan_files) == len(label_files))
+
+ # extend list
+ self.scan_files.extend(scan_files)
+ self.label_files.extend(label_files)
+
+ # sort for correspondance
+ self.scan_files.sort()
+ self.label_files.sort()
+
+ print("Using {} scans from sequences {}".format(len(self.scan_files),
+ self.sequences))
+
+ def __getitem__(self, index):
+ # get item in tensor shape
+
+ scan_file = self.scan_files[index]
+ if self.gt:
+ label_file = self.label_files[index]
+
+ # open a semantic laserscan
+ if self.gt:
+ scan = SemLaserScan(self.color_map,
+ project=True,
+ H=self.sensor_img_H,
+ W=self.sensor_img_W,
+ fov_up=self.sensor_fov_up,
+ fov_down=self.sensor_fov_down, training=self.training)
+ else:
+ scan = LaserScan(project=True,
+ H=self.sensor_img_H,
+ W=self.sensor_img_W,
+ fov_up=self.sensor_fov_up,
+ fov_down=self.sensor_fov_down, training=self.training)
+
+ # open and obtain scan
+ scan.open_scan(scan_file)
+ if self.gt:
+ scan.open_label(label_file)
+ # map unused classes to used classes (also for projection)
+ scan.sem_label = self.map(scan.sem_label, self.learning_map)
+ scan.proj_sem_label = self.map(scan.proj_sem_label, self.learning_map)
+
+ # make a tensor of the uncompressed data (with the max num points)
+ unproj_n_points = scan.points.shape[0]
+ unproj_xyz = torch.full((self.max_points, 3), -1.0, dtype=torch.float)
+ unproj_xyz[:unproj_n_points] = torch.from_numpy(scan.points)
+ unproj_range = torch.full([self.max_points], -1.0, dtype=torch.float)
+ unproj_range[:unproj_n_points] = torch.from_numpy(scan.unproj_range)
+ unproj_remissions = torch.full([self.max_points], -1.0, dtype=torch.float)
+ unproj_remissions[:unproj_n_points] = torch.from_numpy(scan.remissions)
+ if self.gt:
+ unproj_labels = torch.full([self.max_points], -1.0, dtype=torch.int32)
+ unproj_labels[:unproj_n_points] = torch.from_numpy(scan.sem_label)
+ else:
+ unproj_labels = []
+
+ # get points and labels
+ proj_range = torch.from_numpy(scan.proj_range).clone()
+ proj_xyz = torch.from_numpy(scan.proj_xyz).clone()
+ proj_remission = torch.from_numpy(scan.proj_remission).clone()
+ proj_mask = torch.from_numpy(scan.proj_mask)
+ if self.gt:
+ proj_labels = torch.from_numpy(scan.proj_sem_label).clone()
+ proj_labels = proj_labels * proj_mask
+ else:
+ proj_labels = []
+ proj_x = torch.full([self.max_points], -1, dtype=torch.long)
+ proj_x[:unproj_n_points] = torch.from_numpy(scan.proj_x)
+ proj_y = torch.full([self.max_points], -1, dtype=torch.long)
+ proj_y[:unproj_n_points] = torch.from_numpy(scan.proj_y)
+ proj = torch.cat([proj_range.unsqueeze(0).clone(),
+ proj_xyz.clone().permute(2, 0, 1),
+ proj_remission.unsqueeze(0).clone()])
+
+
+ proj = proj * proj_mask.float()
+
+ proj_blocked = proj.unsqueeze(1) # Swap Batch and channel dimensions
+
+ proj = (proj - self.sensor_img_means[:, None, None]
+ ) / self.sensor_img_stds[:, None, None]
+
+ proj = proj * proj_mask.float()
+
+ # get name and sequence
+ path_norm = os.path.normpath(scan_file)
+ path_split = path_norm.split(os.sep)
+ path_seq = path_split[-3]
+ path_name = path_split[-1].replace(".bin", ".label")
+ # print("path_norm: ", path_norm)
+ # print("path_seq", path_seq)
+ # print("path_name", path_name)
+
+ n, c, h, w = proj_blocked.size()
+
+ windows_size = 4 # windows size
+ proj_chan_group_points = f.unfold(proj_blocked, kernel_size=windows_size, stride=windows_size)
+
+ projmask_chan_group_points = f.unfold(proj_mask.unsqueeze(0).unsqueeze(0).float(), kernel_size=windows_size,
+ stride=windows_size)
+
+ # Get the mean point (taking apart non-valid points
+ proj_chan_group_points_sum = torch.sum(proj_chan_group_points, dim=1)
+ projmask_chan_group_points_sum = torch.sum(projmask_chan_group_points, dim=1)
+ proj_chan_group_points_mean = proj_chan_group_points_sum / projmask_chan_group_points_sum
+
+ # tile it for being able to substract it to the other points
+ tiled_proj_chan_group_points_mean = proj_chan_group_points_mean.unsqueeze(1).repeat(1, windows_size * windows_size,
+ 1)
+
+ # remove nans due to empty blocks
+ is_nan = tiled_proj_chan_group_points_mean != tiled_proj_chan_group_points_mean
+ tiled_proj_chan_group_points_mean[is_nan] = 0.
+
+ # compute valid mask per point
+ tiled_projmask_chan_group_points = (1 - projmask_chan_group_points.repeat(n, 1, 1)).byte()
+
+ # substract mean point to points
+ proj_chan_group_points_relative = proj_chan_group_points - tiled_proj_chan_group_points_mean
+
+
+ # set to zero points which where non valid at the beginning
+ proj_chan_group_points_relative[tiled_projmask_chan_group_points] = 0.
+
+ # NOW proj_chan_group_points_relative HAS Xr, Yr, Zr, Rr, Dr relative to the mean point
+ proj_norm_chan_group_points = f.unfold(proj.unsqueeze(1), kernel_size=windows_size, stride=windows_size)
+ xyz_relative = proj_chan_group_points_relative[1:4, ...]
+ relative_distance = torch.tensor(np.linalg.norm(xyz_relative.numpy(), ord=2, axis=0)).float().unsqueeze(0)
+
+ # NOW proj_norm_chan_group_points HAS X, Y, Z, R, D. Now we have to concat them both
+ proj_chan_group_points_combined = torch.cat(
+ [proj_norm_chan_group_points, proj_chan_group_points_relative, relative_distance], dim=0)
+
+ # convert back to image for image-convolution-branch
+ proj_out = f.fold(proj_chan_group_points_combined, proj_blocked.shape[-2:], kernel_size=windows_size,
+ stride=windows_size)
+ proj_out = proj_out.squeeze(1)
+
+ return proj_out, proj_mask, proj_labels, unproj_labels, path_seq, path_name, proj_x, proj_y, proj_range, unproj_range, proj_xyz, unproj_xyz, proj_remission, unproj_remissions, unproj_n_points, proj_chan_group_points_combined
+
+ def __len__(self):
+ return len(self.scan_files)
+
+ @staticmethod
+ def map(label, mapdict):
+ # put label from original values to xentropy
+ # or vice-versa, depending on dictionary values
+ # make learning map a lookup table
+ maxkey = 0
+ for key, data in mapdict.items():
+ if isinstance(data, list):
+ nel = len(data)
+ else:
+ nel = 1
+ if key > maxkey:
+ maxkey = key
+ # +100 hack making lut bigger just in case there are unknown labels
+ if nel > 1:
+ lut = np.zeros((maxkey + 100, nel), dtype=np.int32)
+ else:
+ lut = np.zeros((maxkey + 100), dtype=np.int32)
+ for key, data in mapdict.items():
+ try:
+ lut[key] = data
+ except IndexError:
+ print("Wrong key ", key)
+ # do the mapping
+ return lut[label]
+
+
+class Parser():
+ # standard conv, BN, relu
+ def __init__(self,
+ root, # directory for data
+ train_sequences, # sequences to train
+ valid_sequences, # sequences to validate.
+ test_sequences, # sequences to test (if none, don't get)
+ labels, # labels in data
+ color_map, # color for each label
+ learning_map, # mapping for training labels
+ learning_map_inv, # recover labels from xentropy
+ sensor, # sensor to use
+ max_points, # max points in each scan in entire dataset
+ batch_size, # batch size for train and val
+ workers, # threads to load data
+ gt=True, # get gt?
+ shuffle_train=True): # shuffle training set?
+ super(Parser, self).__init__()
+
+ # if I am training, get the dataset
+ self.root = root
+ self.train_sequences = train_sequences
+ self.valid_sequences = valid_sequences
+ self.test_sequences = test_sequences
+ self.labels = labels
+ self.color_map = color_map
+ self.learning_map = learning_map
+ self.learning_map_inv = learning_map_inv
+ self.sensor = sensor
+ self.max_points = max_points
+ self.batch_size = batch_size
+ self.workers = workers
+ self.gt = gt
+ self.shuffle_train = shuffle_train
+
+ # number of classes that matters is the one for xentropy
+ self.nclasses = len(self.learning_map_inv)
+
+ # Data loading code
+ self.train_dataset = SemanticKitti(root=self.root,
+ sequences=self.train_sequences,
+ labels=self.labels,
+ color_map=self.color_map,
+ learning_map=self.learning_map,
+ learning_map_inv=self.learning_map_inv,
+ sensor=self.sensor,
+ max_points=max_points,
+ gt=self.gt, training=True)
+
+ self.trainloader = torch.utils.data.DataLoader(self.train_dataset,
+ batch_size=self.batch_size,
+ shuffle=self.shuffle_train,
+ num_workers=self.workers,
+ pin_memory=True,
+ drop_last=True)
+ assert len(self.trainloader) > 0
+ self.trainiter = iter(self.trainloader)
+
+ self.valid_dataset = SemanticKitti(root=self.root,
+ sequences=self.valid_sequences,
+ labels=self.labels,
+ color_map=self.color_map,
+ learning_map=self.learning_map,
+ learning_map_inv=self.learning_map_inv,
+ sensor=self.sensor,
+ max_points=max_points,
+ gt=self.gt, training=False)
+
+ self.validloader = torch.utils.data.DataLoader(self.valid_dataset,
+ batch_size=self.batch_size,
+ shuffle=False,
+ num_workers=self.workers,
+ pin_memory=True,
+ drop_last=True)
+ assert len(self.validloader) > 0
+ self.validiter = iter(self.validloader)
+
+ if self.test_sequences:
+ self.test_dataset = SemanticKitti(root=self.root,
+ sequences=self.test_sequences,
+ labels=self.labels,
+ color_map=self.color_map,
+ learning_map=self.learning_map,
+ learning_map_inv=self.learning_map_inv,
+ sensor=self.sensor,
+ max_points=max_points,
+ gt=False, training=False)
+
+ self.testloader = torch.utils.data.DataLoader(self.test_dataset,
+ batch_size=self.batch_size,
+ shuffle=False,
+ num_workers=self.workers,
+ pin_memory=True,
+ drop_last=True)
+ assert len(self.testloader) > 0
+ self.testiter = iter(self.testloader)
+
+ def get_train_batch(self):
+ scans = self.trainiter.next()
+ return scans
+
+ def get_train_set(self):
+ return self.trainloader
+
+ def get_valid_batch(self):
+ scans = self.validiter.next()
+ return scans
+
+ def get_valid_set(self):
+ return self.validloader
+
+ def get_test_batch(self):
+ scans = self.testiter.next()
+ return scans
+
+ def get_test_set(self):
+ return self.testloader
+
+ def get_train_size(self):
+ return len(self.trainloader)
+
+ def get_valid_size(self):
+ return len(self.validloader)
+
+ def get_test_size(self):
+ return len(self.testloader)
+
+ def get_n_classes(self):
+ return self.nclasses
+
+ def get_original_class_string(self, idx):
+ return self.labels[idx]
+
+ def get_xentropy_class_string(self, idx):
+ return self.labels[self.learning_map_inv[idx]]
+
+ def to_original(self, label):
+ # put label in original values
+ return SemanticKitti.map(label, self.learning_map_inv)
+
+ def to_xentropy(self, label):
+ # put label in xentropy values
+ return SemanticKitti.map(label, self.learning_map)
+
+ def to_color(self, label):
+ # put label in original values
+ label = SemanticKitti.map(label, self.learning_map_inv)
+ # put label in color
+ return SemanticKitti.map(label, self.color_map)
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/decoders/3dmininet.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/decoders/3dmininet.py
new file mode 100644
index 0000000..a7672bf
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/decoders/3dmininet.py
@@ -0,0 +1,148 @@
+# This file was modified from https://github.com/BobLiu20/YOLOv3_PyTorch
+# It needed to be modified in order to accomodate for different strides in the
+
+import torch.nn as nn
+from collections import OrderedDict
+import torch.nn.functional as F
+import torch
+
+
+# Depthwise Separable convolution
+class moduleERS(nn.Module):
+ def __init__(self, in_channels, out_channles, kernel_size=3, stride=1, padding=1, dilation=1, bias=False, dropprob=0.,track_running_stats=True):
+ super(moduleERS, self).__init__()
+ self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, 1, groups=in_channels, bias=bias)
+ self.pointwise = nn.Conv2d(in_channels, out_channles, 1, 1, 0, 1, 1, bias=bias)
+ self.bn1 = nn.BatchNorm2d(in_channels, eps=1e-3, track_running_stats=track_running_stats)
+ self.bn = nn.BatchNorm2d(out_channles, eps=1e-3, track_running_stats=track_running_stats)
+
+ self.relu1 = nn.LeakyReLU(0.1)
+ self.relu2 = nn.LeakyReLU(0.1)
+ self.dropout = nn.Dropout2d(dropprob)
+
+ def forward(self, inputs):
+
+ # depthwise convolution
+ x = self.conv1(inputs)
+ x = self.bn1(x)
+ x = self.relu1(x)
+
+ # point-wise convolution
+ x = self.pointwise(x)
+ x = self.bn(x)
+
+
+ if self.dropout.p != 0:
+ x = self.dropout(x)
+
+
+ if x.shape[1] == inputs.shape[1]:
+ return self.relu2(x)+ inputs
+ else:
+ return self.relu2(x)
+
+
+class BasicBlock_half(nn.Module):
+ def __init__(self, inplanes, out_planes, dropprob=0):
+ super(BasicBlock_half, self).__init__()
+ self.conv1 = moduleERS(inplanes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, bias=False, dropprob=dropprob)
+
+ def forward(self, x):
+ out = self.conv1(x)
+
+ return out
+
+
+# ******************************************************************************
+
+class Decoder(nn.Module):
+ """
+ Class for DarknetSeg. Subclasses PyTorch's own "nn" module
+ """
+
+ def __init__(self, params, stub_skips, OS=16, feature_depth=1024):
+ super(Decoder, self).__init__()
+ self.backbone_OS = OS
+ self.backbone_feature_depth = feature_depth
+ print(params)
+ self.block_1 = params["block_1"]
+ self.block_2 = params["block_2"]
+ self.features_bottleneck = int(params["features_bottleneck"])
+
+
+ self.strides = [2, 2, 2, 2]
+ self.strides_2 = [2, 2, 1, 1]
+
+ # check current stride
+ current_os = 1
+ for s in self.strides:
+ current_os *= s
+ print("Decoder original OS: ", int(current_os))
+ # redo strides according to needed stride
+ for i, stride in enumerate(self.strides):
+ if int(current_os) != self.backbone_OS:
+ if stride == 2:
+ current_os /= 2
+ self.strides[i] = 1
+ if int(current_os) == self.backbone_OS:
+ break
+ print("Decoder new OS: ", int(current_os))
+ print("Decoder strides: ", self.strides)
+
+ # decoder
+
+ self.dec4 = self._make_dec_layer(BasicBlock_half, [self.backbone_feature_depth, self.features_bottleneck], n_blocks=self.block_1)
+ self.dec3 = self._make_dec_layer(BasicBlock_half, [self.features_bottleneck, int(self.features_bottleneck/2)], n_blocks=self.block_2)
+
+ # layer list to execute with skips
+ self.layers = [self.dec4, self.dec3]
+
+
+ # last channels
+ self.last_channels = int(self.features_bottleneck/2)
+
+ def _make_dec_layer(self, block, planes, n_blocks=1, dropprob=0):
+ layers = []
+
+
+ # blocks
+ layers.append(("residual", block(planes[0], planes[1], dropprob=dropprob)))
+ for i in range(n_blocks - 1):
+ layers.append(("residual" + str(i), block(planes[1], planes[1], dropprob=dropprob)))
+
+
+
+
+ return nn.Sequential(OrderedDict(layers))
+
+ def run_layer(self, x, layer, skips, os, detach_skip = True):
+ if detach_skip:
+ x = x + skips[os].detach() # add skip (detach is for non-gradient)
+ else:
+ x = x + skips[os]
+
+
+ feats = layer(x) # up
+
+ x = feats
+ return x, skips, int(os/2)
+
+ def forward(self, x, skips):
+ os = self.backbone_OS
+ os /= 4
+
+ x = nn.functional.interpolate(x, size=(x.shape[2]*2, x.shape[3] * 2), mode='bilinear', align_corners=True)
+
+ x, skips, os = self.run_layer(x, self.dec4, skips, os, detach_skip = False) # detach false porque es aun encoder
+
+ x = nn.functional.interpolate(x, size=(x.shape[2] * 2, x.shape[3] * 2), mode='bilinear', align_corners=True)
+
+
+ x, skips, os = self.run_layer(x, self.dec3, skips, os, detach_skip = False) # detach false porque es la otra branch
+ x = nn.functional.interpolate(x, size=(x.shape[2] * 2, x.shape[3] * 2), mode='bilinear', align_corners=True)
+
+
+ return x
+
+ def get_last_depth(self):
+ return self.last_channels
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/decoders/__init__.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/decoders/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/evaluate_biou.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/evaluate_biou.py
new file mode 100755
index 0000000..c98097d
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/evaluate_biou.py
@@ -0,0 +1,225 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import argparse
+import os
+import yaml
+import sys
+import numpy as np
+import torch
+import __init__ as booger
+
+from tasks.semantic.modules.ioueval import biouEval
+from common.laserscan import SemLaserScan
+
+# possible splits
+splits = ["train", "valid", "test"]
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser("./evaluate_biou.py")
+ parser.add_argument(
+ '--dataset', '-d',
+ type=str,
+ required=True,
+ help='Dataset dir. No Default',
+ )
+ parser.add_argument(
+ '--predictions', '-p',
+ type=str,
+ required=None,
+ help='Prediction dir. Same organization as dataset, but predictions in'
+ 'each sequences "prediction" directory. No Default. If no option is set'
+ ' we look for the labels in the same directory as dataset'
+ )
+ parser.add_argument(
+ '--split', '-s',
+ type=str,
+ required=False,
+ choices=["train", "valid", "test"],
+ default="valid",
+ help='Split to evaluate on. One of ' +
+ str(splits) + '. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--data_cfg', '-dc',
+ type=str,
+ required=False,
+ default="config/labels/semantic-kitti.yaml",
+ help='Dataset config file. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--border', '-bs',
+ type=int,
+ required=False,
+ default=1,
+ help='Border size. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--conn', '-c',
+ type=int,
+ required=False,
+ default=4,
+ help='Kernel connectivity. Defaults to %(default)s',
+ )
+ FLAGS, unparsed = parser.parse_known_args()
+
+ # fill in real predictions dir
+ if FLAGS.predictions is None:
+ FLAGS.predictions = FLAGS.dataset
+
+ # print summary of what we will do
+ print("*" * 80)
+ print("INTERFACE:")
+ print("Data: ", FLAGS.dataset)
+ print("Predictions: ", FLAGS.predictions)
+ print("Split: ", FLAGS.split)
+ print("Config: ", FLAGS.data_cfg)
+ print("Border Mask Size", FLAGS.border)
+ print("Border Mask Connectivity", FLAGS.conn)
+ print("*" * 80)
+
+ # assert split
+ assert(FLAGS.split in splits)
+
+ # open data config file
+ try:
+ print("Opening data config file %s" % FLAGS.data_cfg)
+ DATA = yaml.safe_load(open(FLAGS.data_cfg, 'r'))
+ except Exception as e:
+ print(e)
+ print("Error opening data yaml file.")
+ quit()
+
+ # get number of interest classes, and the label mappings
+ class_strings = DATA["labels"]
+ class_remap = DATA["learning_map"]
+ class_inv_remap = DATA["learning_map_inv"]
+ class_ignore = DATA["learning_ignore"]
+ nr_classes = len(class_inv_remap)
+
+ # make lookup table for mapping
+ maxkey = 0
+ for key, data in class_remap.items():
+ if key > maxkey:
+ maxkey = key
+ # +100 hack making lut bigger just in case there are unknown labels
+ remap_lut = np.zeros((maxkey + 100), dtype=np.int32)
+ for key, data in class_remap.items():
+ try:
+ remap_lut[key] = data
+ except IndexError:
+ print("Wrong key ", key)
+ # print(remap_lut)
+
+ # create evaluator
+ ignore = []
+ for cl, ign in class_ignore.items():
+ if ign:
+ x_cl = int(cl)
+ ignore.append(x_cl)
+ print("Ignoring xentropy class ", x_cl, " in IoU evaluation")
+
+ # create evaluator
+ device = torch.device(
+ "cuda") if torch.cuda.is_available() else torch.device('cpu')
+ evaluator = biouEval(nr_classes, device, ignore,
+ FLAGS.border, FLAGS.conn)
+ evaluator.reset()
+
+ # get test set
+ test_sequences = DATA["split"][FLAGS.split]
+
+ # get scan paths
+ scan_names = []
+ for sequence in test_sequences:
+ sequence = '{0:02d}'.format(int(sequence))
+ scan_paths = os.path.join(FLAGS.dataset, "sequences",
+ str(sequence), "velodyne")
+ # populate the scan names
+ seq_scan_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(scan_paths)) for f in fn if ".bin" in f]
+ seq_scan_names.sort()
+ scan_names.extend(seq_scan_names)
+ # print(scan_names)
+
+ # get label paths
+ label_names = []
+ for sequence in test_sequences:
+ sequence = '{0:02d}'.format(int(sequence))
+ label_paths = os.path.join(FLAGS.dataset, "sequences",
+ str(sequence), "labels")
+ # populate the label names
+ seq_label_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(label_paths)) for f in fn if ".label" in f]
+ seq_label_names.sort()
+ label_names.extend(seq_label_names)
+ # print(label_names)
+
+ # get predictions paths
+ pred_names = []
+ for sequence in test_sequences:
+ sequence = '{0:02d}'.format(int(sequence))
+ pred_paths = os.path.join(FLAGS.predictions, "sequences",
+ sequence, "predictions")
+ # populate the label names
+ seq_pred_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(pred_paths)) for f in fn if ".label" in f]
+ seq_pred_names.sort()
+ pred_names.extend(seq_pred_names)
+ # print(pred_names)
+
+ # check that I have the same number of files
+ # print("labels: ", len(label_names))
+ # print("predictions: ", len(pred_names))
+ assert(len(label_names) == len(scan_names) and
+ len(label_names) == len(pred_names))
+
+ print("Evaluating sequences: ")
+ # open each file, get the tensor, and make the iou comparison
+ for scan_file, label_file, pred_file in zip(scan_names, label_names, pred_names):
+ print("evaluating label ", label_file, "with", pred_file)
+ # open label
+ label = SemLaserScan(project=True)
+ label.open_scan(scan_file)
+ label.open_label(label_file)
+ u_label_sem = remap_lut[label.sem_label] # remap to xentropy format
+ p_label_sem = remap_lut[label.proj_sem_label] # remap to xentropy format
+ u_scan_px = label.proj_x
+ u_scan_py = label.proj_y
+
+ # open prediction
+ pred = SemLaserScan(project=True)
+ pred.open_scan(scan_file)
+ pred.open_label(pred_file)
+ u_pred_sem = remap_lut[pred.sem_label] # remap to xentropy format
+
+ # add single scan to evaluation
+ evaluator.addBorderBatch1d(p_label_sem, u_pred_sem, u_label_sem,
+ u_scan_px, u_scan_py)
+
+ # when I am done, print the evaluation
+ m_accuracy = evaluator.getacc()
+ m_jaccard, class_jaccard = evaluator.getIoU()
+
+ print('Validation set:\n'
+ 'bAcc avg {m_accuracy:.3f}\n'
+ 'bIoU avg {m_jaccard:.3f}'.format(m_accuracy=m_accuracy,
+ m_jaccard=m_jaccard))
+ # print also classwise
+ for i, jacc in enumerate(class_jaccard):
+ if i not in ignore:
+ print('bIoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
+ i=i, class_str=class_strings[class_inv_remap[i]], jacc=jacc))
+
+ # print for spreadsheet
+ print("*" * 80)
+ print("below can be copied straight for paper table")
+ for i, jacc in enumerate(class_jaccard):
+ if i not in ignore:
+ sys.stdout.write('{jacc:.3f}'.format(jacc=jacc.item()))
+ sys.stdout.write(",")
+ sys.stdout.write('{jacc:.3f}'.format(jacc=m_jaccard.item()))
+ sys.stdout.write(",")
+ sys.stdout.write('{acc:.3f}'.format(acc=m_accuracy.item()))
+ sys.stdout.write('\n')
+ sys.stdout.flush()
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/evaluate_iou.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/evaluate_iou.py
new file mode 100755
index 0000000..7f4e038
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/evaluate_iou.py
@@ -0,0 +1,218 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import argparse
+import os
+import yaml
+import sys
+import numpy as np
+import torch
+import __init__ as booger
+
+from tasks.semantic.modules.ioueval import iouEval
+from common.laserscan import SemLaserScan
+
+# possible splits
+splits = ["train", "valid", "test"]
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser("./evaluate_iou.py")
+ parser.add_argument(
+ '--dataset', '-d',
+ type=str,
+ required=True,
+ help='Dataset dir. No Default',
+ )
+ parser.add_argument(
+ '--predictions', '-p',
+ type=str,
+ required=None,
+ help='Prediction dir. Same organization as dataset, but predictions in'
+ 'each sequences "prediction" directory. No Default. If no option is set'
+ ' we look for the labels in the same directory as dataset'
+ )
+ parser.add_argument(
+ '--split', '-s',
+ type=str,
+ required=False,
+ choices=["train", "valid", "test"],
+ default="valid",
+ help='Split to evaluate on. One of ' +
+ str(splits) + '. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--data_cfg', '-dc',
+ type=str,
+ required=False,
+ default="config/labels/semantic-kitti.yaml",
+ help='Dataset config file. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--limit', '-l',
+ type=int,
+ required=False,
+ default=None,
+ help='Limit to the first "--limit" points of each scan. Useful for'
+ ' evaluating single scan from aggregated pointcloud.'
+ ' Defaults to %(default)s',
+ )
+
+ FLAGS, unparsed = parser.parse_known_args()
+
+ # fill in real predictions dir
+ if FLAGS.predictions is None:
+ FLAGS.predictions = FLAGS.dataset
+
+ # print summary of what we will do
+ print("*" * 80)
+ print("INTERFACE:")
+ print("Data: ", FLAGS.dataset)
+ print("Predictions: ", FLAGS.predictions)
+ print("Split: ", FLAGS.split)
+ print("Config: ", FLAGS.data_cfg)
+ print("Limit: ", FLAGS.limit)
+ print("*" * 80)
+
+ # assert split
+ assert(FLAGS.split in splits)
+
+ # open data config file
+ try:
+ print("Opening data config file %s" % FLAGS.data_cfg)
+ DATA = yaml.safe_load(open(FLAGS.data_cfg, 'r'))
+ except Exception as e:
+ print(e)
+ print("Error opening data yaml file.")
+ quit()
+
+ # get number of interest classes, and the label mappings
+ class_strings = DATA["labels"]
+ class_remap = DATA["learning_map"]
+ class_inv_remap = DATA["learning_map_inv"]
+ class_ignore = DATA["learning_ignore"]
+ nr_classes = len(class_inv_remap)
+
+ # make lookup table for mapping
+ maxkey = 0
+ for key, data in class_remap.items():
+ if key > maxkey:
+ maxkey = key
+ # +100 hack making lut bigger just in case there are unknown labels
+ remap_lut = np.zeros((maxkey + 100), dtype=np.int32)
+ for key, data in class_remap.items():
+ try:
+ remap_lut[key] = data
+ except IndexError:
+ print("Wrong key ", key)
+ # print(remap_lut)
+
+ # create evaluator
+ ignore = []
+ for cl, ign in class_ignore.items():
+ if ign:
+ x_cl = int(cl)
+ ignore.append(x_cl)
+ print("Ignoring xentropy class ", x_cl, " in IoU evaluation")
+
+ # create evaluator
+ device = torch.device("cpu")
+ evaluator = iouEval(nr_classes, device, ignore)
+ evaluator.reset()
+
+ # get test set
+ test_sequences = DATA["split"][FLAGS.split]
+
+ # get scan paths
+ scan_names = []
+ for sequence in test_sequences:
+ sequence = '{0:02d}'.format(int(sequence))
+ scan_paths = os.path.join(FLAGS.dataset, "sequences",
+ str(sequence), "velodyne")
+ # populate the scan names
+ seq_scan_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(scan_paths)) for f in fn if ".bin" in f]
+ seq_scan_names.sort()
+ scan_names.extend(seq_scan_names)
+ # print(scan_names)
+
+ # get label paths
+ label_names = []
+ for sequence in test_sequences:
+ sequence = '{0:02d}'.format(int(sequence))
+ label_paths = os.path.join(FLAGS.dataset, "sequences",
+ str(sequence), "labels")
+ # populate the label names
+ seq_label_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(label_paths)) for f in fn if ".label" in f]
+ seq_label_names.sort()
+ label_names.extend(seq_label_names)
+ # print(label_names)
+
+ # get predictions paths
+ pred_names = []
+ for sequence in test_sequences:
+ sequence = '{0:02d}'.format(int(sequence))
+ pred_paths = os.path.join(FLAGS.predictions, "sequences",
+ sequence, "predictions")
+ # populate the label names
+ seq_pred_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(pred_paths)) for f in fn if ".label" in f]
+ seq_pred_names.sort()
+ pred_names.extend(seq_pred_names)
+ # print(pred_names)
+
+ # check that I have the same number of files
+ # print("labels: ", len(label_names))
+ # print("predictions: ", len(pred_names))
+ assert(len(label_names) == len(scan_names) and
+ len(label_names) == len(pred_names))
+
+ print("Evaluating sequences: ")
+ # open each file, get the tensor, and make the iou comparison
+ for scan_file, label_file, pred_file in zip(scan_names, label_names, pred_names):
+ print("evaluating label ", label_file, "with", pred_file)
+ # open label
+ label = SemLaserScan(project=False)
+ label.open_scan(scan_file)
+ label.open_label(label_file)
+ u_label_sem = remap_lut[label.sem_label] # remap to xentropy format
+ if FLAGS.limit is not None:
+ u_label_sem = u_label_sem[:FLAGS.limit]
+
+ # open prediction
+ pred = SemLaserScan(project=False)
+ pred.open_scan(scan_file)
+ pred.open_label(pred_file)
+ u_pred_sem = remap_lut[pred.sem_label] # remap to xentropy format
+ if FLAGS.limit is not None:
+ u_pred_sem = u_pred_sem[:FLAGS.limit]
+
+ # add single scan to evaluation
+ evaluator.addBatch(u_pred_sem, u_label_sem)
+
+ # when I am done, print the evaluation
+ m_accuracy = evaluator.getacc()
+ m_jaccard, class_jaccard = evaluator.getIoU()
+
+ print('Validation set:\n'
+ 'Acc avg {m_accuracy:.3f}\n'
+ 'IoU avg {m_jaccard:.3f}'.format(m_accuracy=m_accuracy,
+ m_jaccard=m_jaccard))
+ # print also classwise
+ for i, jacc in enumerate(class_jaccard):
+ if i not in ignore:
+ print('IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
+ i=i, class_str=class_strings[class_inv_remap[i]], jacc=jacc))
+
+ # print for spreadsheet
+ print("*" * 80)
+ print("below can be copied straight for paper table")
+ for i, jacc in enumerate(class_jaccard):
+ if i not in ignore:
+ sys.stdout.write('{jacc:.3f}'.format(jacc=jacc.item()))
+ sys.stdout.write(",")
+ sys.stdout.write('{jacc:.3f}'.format(jacc=m_jaccard.item()))
+ sys.stdout.write(",")
+ sys.stdout.write('{acc:.3f}'.format(acc=m_accuracy.item()))
+ sys.stdout.write('\n')
+ sys.stdout.flush()
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/infer.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/infer.py
new file mode 100755
index 0000000..b42fb84
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/infer.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import argparse
+import subprocess
+import datetime
+import yaml
+from shutil import copyfile
+import os
+import shutil
+import __init__ as booger
+
+from tasks.semantic.modules.user import *
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser("./infer.py")
+ parser.add_argument(
+ '--dataset', '-d',
+ type=str,
+ required=True,
+ help='Dataset to train with. No Default',
+ )
+ parser.add_argument(
+ '--log', '-l',
+ type=str,
+ default=os.path.expanduser("~") + '/logs/' +
+ datetime.datetime.now().strftime("%Y-%-m-%d-%H:%M") + '/',
+ help='Directory to put the predictions. Default: ~/logs/date+time'
+ )
+ parser.add_argument(
+ '--model', '-m',
+ type=str,
+ required=True,
+ default=None,
+ help='Directory to get the trained model.'
+ )
+ FLAGS, unparsed = parser.parse_known_args()
+
+ # print summary of what we will do
+ print("----------")
+ print("INTERFACE:")
+ print("dataset", FLAGS.dataset)
+ print("log", FLAGS.log)
+ print("model", FLAGS.model)
+ print("----------\n")
+ print("Commit hash (training version): ", str(
+ subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']).strip()))
+ print("----------\n")
+
+ # open arch config file
+ try:
+ print("Opening arch config file from %s" % FLAGS.model)
+ ARCH = yaml.safe_load(open(FLAGS.model + "/arch_cfg.yaml", 'r'))
+ except Exception as e:
+ print(e)
+ print("Error opening arch yaml file.")
+ quit()
+
+ # open data config file
+ try:
+ print("Opening data config file from %s" % FLAGS.model)
+ DATA = yaml.safe_load(open(FLAGS.model + "/data_cfg.yaml", 'r'))
+ except Exception as e:
+ print(e)
+ print("Error opening data yaml file.")
+ quit()
+
+ # create log folder
+ try:
+ if os.path.isdir(FLAGS.log):
+ shutil.rmtree(FLAGS.log)
+ os.makedirs(FLAGS.log)
+ os.makedirs(os.path.join(FLAGS.log, "sequences"))
+ for seq in DATA["split"]["train"]:
+ seq = '{0:02d}'.format(int(seq))
+ print("train", seq)
+ os.makedirs(os.path.join(FLAGS.log, "sequences", seq))
+ os.makedirs(os.path.join(FLAGS.log, "sequences", seq, "predictions"))
+ for seq in DATA["split"]["valid"]:
+ seq = '{0:02d}'.format(int(seq))
+ print("valid", seq)
+ os.makedirs(os.path.join(FLAGS.log, "sequences", seq))
+ os.makedirs(os.path.join(FLAGS.log, "sequences", seq, "predictions"))
+ for seq in DATA["split"]["test"]:
+ seq = '{0:02d}'.format(int(seq))
+ print("test", seq)
+ os.makedirs(os.path.join(FLAGS.log, "sequences", seq))
+ os.makedirs(os.path.join(FLAGS.log, "sequences", seq, "predictions"))
+ except Exception as e:
+ print(e)
+ print("Error creating log directory. Check permissions!")
+ raise
+
+ except Exception as e:
+ print(e)
+ print("Error creating log directory. Check permissions!")
+ quit()
+
+ # does model folder exist?
+ if os.path.isdir(FLAGS.model):
+ print("model folder exists! Using model from %s" % (FLAGS.model))
+ else:
+ print("model folder doesnt exist! Can't infer...")
+ quit()
+
+ #n_gpu = 0
+ #os.environ["CUDA_VISIBLE_DEVICES"] = str(n_gpu)
+
+ # create user and infer dataset
+ user = User(ARCH, DATA, FLAGS.dataset, FLAGS.log, FLAGS.model)
+ user.infer()
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/arch_cfg.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/arch_cfg.yaml
new file mode 100644
index 0000000..e643ab3
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/arch_cfg.yaml
@@ -0,0 +1,99 @@
+################################################################################
+# training parameters
+################################################################################
+train:
+ loss: "xentropy" # must be either xentropy or iou
+ max_epochs: 500
+ lr: 0.004 # sgd learning rate
+ wup_epochs: 0 # warmup during first XX epochs (can be float)
+ momentum: 0.9 # sgd momentum
+ lr_decay: 0.999 # learning rate decay per epoch after initial cycle (from min lr)
+ w_decay: 0.0001 # weight decay
+ batch_size: 3 # batch size
+ report_batch: 1 # every x batches, report loss
+ report_epoch: 1 # every x epochs, report validation set
+ epsilon_w: 0.001 # class weight w = 1 / (content + epsilon_w)
+ save_summary: False # Summary of weight histograms for tensorboard
+ save_scans: False # False doesn't save anything, True saves some
+ # sample images (one per batch of the last calculated batch)
+ # in log folder
+ show_scans: False # show scans during training
+ workers: 6 # number of threads to get data
+
+################################################################################
+# backbone parameters
+################################################################################
+backbone:
+ name: "3dmininet" # ['3dmininet', '3dmininetsmall', '3dmininettiny']
+ input_depth:
+ range: True
+ xyz: True
+ remission: True
+ block_1: 12 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 10 # number of blocks of the second resolution (w/8, h/8)
+ features_bottleneck: 128 # number of blocks of the second resolution (w/8, h/8)
+ OS: 16 # output stride (only horizontally)
+ train: True # train backbone?
+
+################################################################################
+# decoder parameters
+################################################################################
+decoder:
+ name: "3dmininet" # ['3dmininet', '3dmininetsmall', '3dmininettiny']
+ block_1: 4 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 2 # number of blocks of the second resolution (w/2, h/2)
+ features_bottleneck: 128 # number of blocks of the second resolution (w/8, h/8)
+ train: True # train decoder?
+
+################################################################################
+# classification head parameters
+################################################################################
+head:
+ name: "segmentation"
+ train: True
+
+################################################################################
+# postproc parameters
+################################################################################
+post:
+ CRF:
+ use: False
+ train: True
+ params: False # this should be a dict when in use
+ KNN:
+ use: True
+ params:
+ knn: 7
+ search: 7
+ sigma: 1.0
+ cutoff: 2.0
+
+################################################################################
+# classification head parameters
+################################################################################
+# dataset (to find parser)
+dataset:
+ labels: "kitti"
+ scans: "kitti"
+ max_points: 150000 # max of any scan in dataset
+ sensor:
+ name: "HDL64"
+ type: "spherical" # projective
+ fov_up: 3
+ fov_down: -25
+ img_prop:
+ width: 2048
+ height: 64
+ img_means: #range,x,y,z,signal
+ - 12.12
+ - 10.88
+ - 0.23
+ - -1.04
+ - 0.21
+ img_stds: #range,x,y,z,signal
+ - 12.32
+ - 11.47
+ - 6.91
+ - 0.86
+ - 0.16
+
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/backbone b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/backbone
new file mode 100644
index 0000000..3f29c00
Binary files /dev/null and b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/backbone differ
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/data_cfg.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/data_cfg.yaml
new file mode 100644
index 0000000..a4af922
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/data_cfg.yaml
@@ -0,0 +1,212 @@
+# This file is covered by the LICENSE file in the root of this project.
+name: "kitti"
+labels:
+ 0 : "unlabeled"
+ 1 : "outlier"
+ 10: "car"
+ 11: "bicycle"
+ 13: "bus"
+ 15: "motorcycle"
+ 16: "on-rails"
+ 18: "truck"
+ 20: "other-vehicle"
+ 30: "person"
+ 31: "bicyclist"
+ 32: "motorcyclist"
+ 40: "road"
+ 44: "parking"
+ 48: "sidewalk"
+ 49: "other-ground"
+ 50: "building"
+ 51: "fence"
+ 52: "other-structure"
+ 60: "lane-marking"
+ 70: "vegetation"
+ 71: "trunk"
+ 72: "terrain"
+ 80: "pole"
+ 81: "traffic-sign"
+ 99: "other-object"
+ 252: "moving-car"
+ 253: "moving-bicyclist"
+ 254: "moving-person"
+ 255: "moving-motorcyclist"
+ 256: "moving-on-rails"
+ 257: "moving-bus"
+ 258: "moving-truck"
+ 259: "moving-other-vehicle"
+color_map: # bgr
+ 0 : [0, 0, 0]
+ 1 : [0, 0, 255]
+ 10: [245, 150, 100]
+ 11: [245, 230, 100]
+ 13: [250, 80, 100]
+ 15: [150, 60, 30]
+ 16: [255, 0, 0]
+ 18: [180, 30, 80]
+ 20: [255, 0, 0]
+ 30: [30, 30, 255]
+ 31: [200, 40, 255]
+ 32: [90, 30, 150]
+ 40: [255, 0, 255]
+ 44: [255, 150, 255]
+ 48: [75, 0, 75]
+ 49: [75, 0, 175]
+ 50: [0, 200, 255]
+ 51: [50, 120, 255]
+ 52: [0, 150, 255]
+ 60: [170, 255, 150]
+ 70: [0, 175, 0]
+ 71: [0, 60, 135]
+ 72: [80, 240, 150]
+ 80: [150, 240, 255]
+ 81: [0, 0, 255]
+ 99: [255, 255, 50]
+ 252: [245, 150, 100]
+ 256: [255, 0, 0]
+ 253: [200, 40, 255]
+ 254: [30, 30, 255]
+ 255: [90, 30, 150]
+ 257: [250, 80, 100]
+ 258: [180, 30, 80]
+ 259: [255, 0, 0]
+content: # as a ratio with the total number of points
+ 0: 0.018889854628292943
+ 1: 0.0002937197336781505
+ 10: 0.040818519255974316
+ 11: 0.00016609538710764618
+ 13: 2.7879693665067774e-05
+ 15: 0.00039838616015114444
+ 16: 0.0
+ 18: 0.0020633612104619787
+ 20: 0.0016218197275284021
+ 30: 0.00017698551338515307
+ 31: 1.1065903904919655e-08
+ 32: 5.532951952459828e-09
+ 40: 0.1987493871255525
+ 44: 0.014717169549888214
+ 48: 0.14392298360372
+ 49: 0.0039048553037472045
+ 50: 0.1326861944777486
+ 51: 0.0723592229456223
+ 52: 0.002395131480328884
+ 60: 4.7084144280367186e-05
+ 70: 0.26681502148037506
+ 71: 0.006035012012626033
+ 72: 0.07814222006271769
+ 80: 0.002855498193863172
+ 81: 0.0006155958086189918
+ 99: 0.009923127583046915
+ 252: 0.001789309418528068
+ 253: 0.00012709999297008662
+ 254: 0.00016059776092534436
+ 255: 3.745553104802113e-05
+ 256: 0.0
+ 257: 0.00011351574470342043
+ 258: 0.00010157861367183268
+ 259: 4.3840131989471124e-05
+# classes that are indistinguishable from single scan or inconsistent in
+# ground truth are mapped to their closest equivalent
+learning_map:
+ 0 : 0 # "unlabeled"
+ 1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
+ 10: 1 # "car"
+ 11: 2 # "bicycle"
+ 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
+ 15: 3 # "motorcycle"
+ 16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
+ 18: 4 # "truck"
+ 20: 5 # "other-vehicle"
+ 30: 6 # "person"
+ 31: 7 # "bicyclist"
+ 32: 8 # "motorcyclist"
+ 40: 9 # "road"
+ 44: 10 # "parking"
+ 48: 11 # "sidewalk"
+ 49: 12 # "other-ground"
+ 50: 13 # "building"
+ 51: 14 # "fence"
+ 52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped
+ 60: 9 # "lane-marking" to "road" ---------------------------------mapped
+ 70: 15 # "vegetation"
+ 71: 16 # "trunk"
+ 72: 17 # "terrain"
+ 80: 18 # "pole"
+ 81: 19 # "traffic-sign"
+ 99: 0 # "other-object" to "unlabeled" ----------------------------mapped
+ 252: 1 # "moving-car" to "car" ------------------------------------mapped
+ 253: 7 # "moving-bicyclist" to "bicyclist" ------------------------mapped
+ 254: 6 # "moving-person" to "person" ------------------------------mapped
+ 255: 8 # "moving-motorcyclist" to "motorcyclist" ------------------mapped
+ 256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped
+ 257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped
+ 258: 4 # "moving-truck" to "truck" --------------------------------mapped
+ 259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped
+learning_map_inv: # inverse of previous map
+ 0: 0 # "unlabeled", and others ignored
+ 1: 10 # "car"
+ 2: 11 # "bicycle"
+ 3: 15 # "motorcycle"
+ 4: 18 # "truck"
+ 5: 20 # "other-vehicle"
+ 6: 30 # "person"
+ 7: 31 # "bicyclist"
+ 8: 32 # "motorcyclist"
+ 9: 40 # "road"
+ 10: 44 # "parking"
+ 11: 48 # "sidewalk"
+ 12: 49 # "other-ground"
+ 13: 50 # "building"
+ 14: 51 # "fence"
+ 15: 70 # "vegetation"
+ 16: 71 # "trunk"
+ 17: 72 # "terrain"
+ 18: 80 # "pole"
+ 19: 81 # "traffic-sign"
+learning_ignore: # Ignore classes
+ 0: True # "unlabeled", and others ignored
+ 1: False # "car"
+ 2: False # "bicycle"
+ 3: False # "motorcycle"
+ 4: False # "truck"
+ 5: False # "other-vehicle"
+ 6: False # "person"
+ 7: False # "bicyclist"
+ 8: False # "motorcyclist"
+ 9: False # "road"
+ 10: False # "parking"
+ 11: False # "sidewalk"
+ 12: False # "other-ground"
+ 13: False # "building"
+ 14: False # "fence"
+ 15: False # "vegetation"
+ 16: False # "trunk"
+ 17: False # "terrain"
+ 18: False # "pole"
+ 19: False # "traffic-sign"
+split: # sequence numbers
+ train:
+ - 0
+ - 1
+ - 2
+ - 3
+ - 4
+ - 5
+ - 6
+ - 7
+ - 9
+ - 10
+ valid:
+ - 8
+ test:
+ - 11
+ - 12
+ - 13
+ - 14
+ - 15
+ - 16
+ - 17
+ - 18
+ - 19
+ - 20
+ - 21
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/segmentation_decoder b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/segmentation_decoder
new file mode 100644
index 0000000..877c68b
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diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/segmentation_head b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-small/segmentation_head
new file mode 100644
index 0000000..4575988
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diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/arch_cfg.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/arch_cfg.yaml
new file mode 100644
index 0000000..bf90b96
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/arch_cfg.yaml
@@ -0,0 +1,99 @@
+################################################################################
+# training parameters
+################################################################################
+train:
+ loss: "xentropy" # must be either xentropy or iou
+ max_epochs: 500
+ lr: 0.004 # sgd learning rate
+ wup_epochs: 0 # warmup during first XX epochs (can be float)
+ momentum: 0.9 # sgd momentum
+ lr_decay: 0.999 # learning rate decay per epoch after initial cycle (from min lr)
+ w_decay: 0.0001 # weight decay
+ batch_size: 3 # batch size
+ report_batch: 1 # every x batches, report loss
+ report_epoch: 1 # every x epochs, report validation set
+ epsilon_w: 0.001 # class weight w = 1 / (content + epsilon_w)
+ save_summary: False # Summary of weight histograms for tensorboard
+ save_scans: False # False doesn't save anything, True saves some
+ # sample images (one per batch of the last calculated batch)
+ # in log folder
+ show_scans: False # show scans during training
+ workers: 6 # number of threads to get data
+
+################################################################################
+# backbone parameters
+################################################################################
+backbone:
+ name: "3dmininet" # ['3dmininet', '3dmininetsmall', '3dmininettiny']
+ input_depth:
+ range: True
+ xyz: True
+ remission: True
+ block_1: 6 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 5 # number of blocks of the second resolution (w/8, h/8)
+ features_bottleneck: 96 # number of blocks of the second resolution (w/8, h/8)
+ OS: 16 # output stride (only horizontally)
+ train: True # train backbone?
+
+################################################################################
+# decoder parameters
+################################################################################
+decoder:
+ name: "3dmininet" # ['3dmininet', '3dmininetsmall', '3dmininettiny']
+ block_1: 2 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 1 # number of blocks of the second resolution (w/2, h/2)
+ features_bottleneck: 96 # number of blocks of the second resolution (w/8, h/8)
+ train: True # train decoder?
+
+################################################################################
+# classification head parameters
+################################################################################
+head:
+ name: "segmentation"
+ train: True
+
+################################################################################
+# postproc parameters
+################################################################################
+post:
+ CRF:
+ use: False
+ train: True
+ params: False # this should be a dict when in use
+ KNN:
+ use: True
+ params:
+ knn: 7
+ search: 7
+ sigma: 1.0
+ cutoff: 2.0
+
+################################################################################
+# classification head parameters
+################################################################################
+# dataset (to find parser)
+dataset:
+ labels: "kitti"
+ scans: "kitti"
+ max_points: 150000 # max of any scan in dataset
+ sensor:
+ name: "HDL64"
+ type: "spherical" # projective
+ fov_up: 3
+ fov_down: -25
+ img_prop:
+ width: 2048
+ height: 64
+ img_means: #range,x,y,z,signal
+ - 12.12
+ - 10.88
+ - 0.23
+ - -1.04
+ - 0.21
+ img_stds: #range,x,y,z,signal
+ - 12.32
+ - 11.47
+ - 6.91
+ - 0.86
+ - 0.16
+
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/backbone b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/backbone
new file mode 100644
index 0000000..8f7d139
Binary files /dev/null and b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/backbone differ
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/data_cfg.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/data_cfg.yaml
new file mode 100644
index 0000000..a4af922
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/data_cfg.yaml
@@ -0,0 +1,212 @@
+# This file is covered by the LICENSE file in the root of this project.
+name: "kitti"
+labels:
+ 0 : "unlabeled"
+ 1 : "outlier"
+ 10: "car"
+ 11: "bicycle"
+ 13: "bus"
+ 15: "motorcycle"
+ 16: "on-rails"
+ 18: "truck"
+ 20: "other-vehicle"
+ 30: "person"
+ 31: "bicyclist"
+ 32: "motorcyclist"
+ 40: "road"
+ 44: "parking"
+ 48: "sidewalk"
+ 49: "other-ground"
+ 50: "building"
+ 51: "fence"
+ 52: "other-structure"
+ 60: "lane-marking"
+ 70: "vegetation"
+ 71: "trunk"
+ 72: "terrain"
+ 80: "pole"
+ 81: "traffic-sign"
+ 99: "other-object"
+ 252: "moving-car"
+ 253: "moving-bicyclist"
+ 254: "moving-person"
+ 255: "moving-motorcyclist"
+ 256: "moving-on-rails"
+ 257: "moving-bus"
+ 258: "moving-truck"
+ 259: "moving-other-vehicle"
+color_map: # bgr
+ 0 : [0, 0, 0]
+ 1 : [0, 0, 255]
+ 10: [245, 150, 100]
+ 11: [245, 230, 100]
+ 13: [250, 80, 100]
+ 15: [150, 60, 30]
+ 16: [255, 0, 0]
+ 18: [180, 30, 80]
+ 20: [255, 0, 0]
+ 30: [30, 30, 255]
+ 31: [200, 40, 255]
+ 32: [90, 30, 150]
+ 40: [255, 0, 255]
+ 44: [255, 150, 255]
+ 48: [75, 0, 75]
+ 49: [75, 0, 175]
+ 50: [0, 200, 255]
+ 51: [50, 120, 255]
+ 52: [0, 150, 255]
+ 60: [170, 255, 150]
+ 70: [0, 175, 0]
+ 71: [0, 60, 135]
+ 72: [80, 240, 150]
+ 80: [150, 240, 255]
+ 81: [0, 0, 255]
+ 99: [255, 255, 50]
+ 252: [245, 150, 100]
+ 256: [255, 0, 0]
+ 253: [200, 40, 255]
+ 254: [30, 30, 255]
+ 255: [90, 30, 150]
+ 257: [250, 80, 100]
+ 258: [180, 30, 80]
+ 259: [255, 0, 0]
+content: # as a ratio with the total number of points
+ 0: 0.018889854628292943
+ 1: 0.0002937197336781505
+ 10: 0.040818519255974316
+ 11: 0.00016609538710764618
+ 13: 2.7879693665067774e-05
+ 15: 0.00039838616015114444
+ 16: 0.0
+ 18: 0.0020633612104619787
+ 20: 0.0016218197275284021
+ 30: 0.00017698551338515307
+ 31: 1.1065903904919655e-08
+ 32: 5.532951952459828e-09
+ 40: 0.1987493871255525
+ 44: 0.014717169549888214
+ 48: 0.14392298360372
+ 49: 0.0039048553037472045
+ 50: 0.1326861944777486
+ 51: 0.0723592229456223
+ 52: 0.002395131480328884
+ 60: 4.7084144280367186e-05
+ 70: 0.26681502148037506
+ 71: 0.006035012012626033
+ 72: 0.07814222006271769
+ 80: 0.002855498193863172
+ 81: 0.0006155958086189918
+ 99: 0.009923127583046915
+ 252: 0.001789309418528068
+ 253: 0.00012709999297008662
+ 254: 0.00016059776092534436
+ 255: 3.745553104802113e-05
+ 256: 0.0
+ 257: 0.00011351574470342043
+ 258: 0.00010157861367183268
+ 259: 4.3840131989471124e-05
+# classes that are indistinguishable from single scan or inconsistent in
+# ground truth are mapped to their closest equivalent
+learning_map:
+ 0 : 0 # "unlabeled"
+ 1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
+ 10: 1 # "car"
+ 11: 2 # "bicycle"
+ 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
+ 15: 3 # "motorcycle"
+ 16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
+ 18: 4 # "truck"
+ 20: 5 # "other-vehicle"
+ 30: 6 # "person"
+ 31: 7 # "bicyclist"
+ 32: 8 # "motorcyclist"
+ 40: 9 # "road"
+ 44: 10 # "parking"
+ 48: 11 # "sidewalk"
+ 49: 12 # "other-ground"
+ 50: 13 # "building"
+ 51: 14 # "fence"
+ 52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped
+ 60: 9 # "lane-marking" to "road" ---------------------------------mapped
+ 70: 15 # "vegetation"
+ 71: 16 # "trunk"
+ 72: 17 # "terrain"
+ 80: 18 # "pole"
+ 81: 19 # "traffic-sign"
+ 99: 0 # "other-object" to "unlabeled" ----------------------------mapped
+ 252: 1 # "moving-car" to "car" ------------------------------------mapped
+ 253: 7 # "moving-bicyclist" to "bicyclist" ------------------------mapped
+ 254: 6 # "moving-person" to "person" ------------------------------mapped
+ 255: 8 # "moving-motorcyclist" to "motorcyclist" ------------------mapped
+ 256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped
+ 257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped
+ 258: 4 # "moving-truck" to "truck" --------------------------------mapped
+ 259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped
+learning_map_inv: # inverse of previous map
+ 0: 0 # "unlabeled", and others ignored
+ 1: 10 # "car"
+ 2: 11 # "bicycle"
+ 3: 15 # "motorcycle"
+ 4: 18 # "truck"
+ 5: 20 # "other-vehicle"
+ 6: 30 # "person"
+ 7: 31 # "bicyclist"
+ 8: 32 # "motorcyclist"
+ 9: 40 # "road"
+ 10: 44 # "parking"
+ 11: 48 # "sidewalk"
+ 12: 49 # "other-ground"
+ 13: 50 # "building"
+ 14: 51 # "fence"
+ 15: 70 # "vegetation"
+ 16: 71 # "trunk"
+ 17: 72 # "terrain"
+ 18: 80 # "pole"
+ 19: 81 # "traffic-sign"
+learning_ignore: # Ignore classes
+ 0: True # "unlabeled", and others ignored
+ 1: False # "car"
+ 2: False # "bicycle"
+ 3: False # "motorcycle"
+ 4: False # "truck"
+ 5: False # "other-vehicle"
+ 6: False # "person"
+ 7: False # "bicyclist"
+ 8: False # "motorcyclist"
+ 9: False # "road"
+ 10: False # "parking"
+ 11: False # "sidewalk"
+ 12: False # "other-ground"
+ 13: False # "building"
+ 14: False # "fence"
+ 15: False # "vegetation"
+ 16: False # "trunk"
+ 17: False # "terrain"
+ 18: False # "pole"
+ 19: False # "traffic-sign"
+split: # sequence numbers
+ train:
+ - 0
+ - 1
+ - 2
+ - 3
+ - 4
+ - 5
+ - 6
+ - 7
+ - 9
+ - 10
+ valid:
+ - 8
+ test:
+ - 11
+ - 12
+ - 13
+ - 14
+ - 15
+ - 16
+ - 17
+ - 18
+ - 19
+ - 20
+ - 21
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/segmentation_decoder b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/segmentation_decoder
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index 0000000..35649f3
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diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/segmentation_head b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/segmentation_head
new file mode 100644
index 0000000..9d6dce0
Binary files /dev/null and b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet-tiny/segmentation_head differ
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/arch_cfg.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/arch_cfg.yaml
new file mode 100644
index 0000000..8218e38
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/arch_cfg.yaml
@@ -0,0 +1,99 @@
+################################################################################
+# training parameters
+################################################################################
+train:
+ loss: "xentropy" # must be either xentropy or iou
+ max_epochs: 500
+ lr: 0.004 # sgd learning rate
+ wup_epochs: 0 # warmup during first XX epochs (can be float)
+ momentum: 0.9 # sgd momentum
+ lr_decay: 0.999 # learning rate decay per epoch after initial cycle (from min lr)
+ w_decay: 0.0001 # weight decay
+ batch_size: 3 # batch size
+ report_batch: 1 # every x batches, report loss
+ report_epoch: 1 # every x epochs, report validation set
+ epsilon_w: 0.001 # class weight w = 1 / (content + epsilon_w)
+ save_summary: False # Summary of weight histograms for tensorboard
+ save_scans: False # False doesn't save anything, True saves some
+ # sample images (one per batch of the last calculated batch)
+ # in log folder
+ show_scans: False # show scans during training
+ workers: 6 # number of threads to get data
+
+################################################################################
+# backbone parameters
+################################################################################
+backbone:
+ name: "3dmininet" # ['3dmininet', '3dmininetsmall', '3dmininettiny']
+ input_depth:
+ range: True
+ xyz: True
+ remission: True
+ block_1: 25 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 15 # number of blocks of the second resolution (w/8, h/8)
+ features_bottleneck: 192 # number of blocks of the second resolution (w/8, h/8)
+ OS: 16 # output stride (only horizontally)
+ train: True # train backbone?
+
+################################################################################
+# decoder parameters
+################################################################################
+decoder:
+ name: "3dmininet" # ['3dmininet', '3dmininetsmall', '3dmininettiny']
+ block_1: 4 # number of blocks of the first resolution (w/4, h/4)
+ block_2: 2 # number of blocks of the second resolution (w/2, h/2)
+ features_bottleneck: 192 # number of blocks of the second resolution (w/8, h/8)
+ train: True # train decoder?
+
+################################################################################
+# classification head parameters
+################################################################################
+head:
+ name: "segmentation"
+ train: True
+
+################################################################################
+# postproc parameters
+################################################################################
+post:
+ CRF:
+ use: False
+ train: True
+ params: False # this should be a dict when in use
+ KNN:
+ use: True
+ params:
+ knn: 7
+ search: 7
+ sigma: 1.0
+ cutoff: 2.0
+
+################################################################################
+# classification head parameters
+################################################################################
+# dataset (to find parser)
+dataset:
+ labels: "kitti"
+ scans: "kitti"
+ max_points: 150000 # max of any scan in dataset
+ sensor:
+ name: "HDL64"
+ type: "spherical" # projective
+ fov_up: 3
+ fov_down: -25
+ img_prop:
+ width: 2048
+ height: 64
+ img_means: #range,x,y,z,signal
+ - 12.12
+ - 10.88
+ - 0.23
+ - -1.04
+ - 0.21
+ img_stds: #range,x,y,z,signal
+ - 12.32
+ - 11.47
+ - 6.91
+ - 0.86
+ - 0.16
+
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/backbone b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/backbone
new file mode 100644
index 0000000..95ad7fe
Binary files /dev/null and b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/backbone differ
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/data_cfg.yaml b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/data_cfg.yaml
new file mode 100644
index 0000000..a4af922
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/data_cfg.yaml
@@ -0,0 +1,212 @@
+# This file is covered by the LICENSE file in the root of this project.
+name: "kitti"
+labels:
+ 0 : "unlabeled"
+ 1 : "outlier"
+ 10: "car"
+ 11: "bicycle"
+ 13: "bus"
+ 15: "motorcycle"
+ 16: "on-rails"
+ 18: "truck"
+ 20: "other-vehicle"
+ 30: "person"
+ 31: "bicyclist"
+ 32: "motorcyclist"
+ 40: "road"
+ 44: "parking"
+ 48: "sidewalk"
+ 49: "other-ground"
+ 50: "building"
+ 51: "fence"
+ 52: "other-structure"
+ 60: "lane-marking"
+ 70: "vegetation"
+ 71: "trunk"
+ 72: "terrain"
+ 80: "pole"
+ 81: "traffic-sign"
+ 99: "other-object"
+ 252: "moving-car"
+ 253: "moving-bicyclist"
+ 254: "moving-person"
+ 255: "moving-motorcyclist"
+ 256: "moving-on-rails"
+ 257: "moving-bus"
+ 258: "moving-truck"
+ 259: "moving-other-vehicle"
+color_map: # bgr
+ 0 : [0, 0, 0]
+ 1 : [0, 0, 255]
+ 10: [245, 150, 100]
+ 11: [245, 230, 100]
+ 13: [250, 80, 100]
+ 15: [150, 60, 30]
+ 16: [255, 0, 0]
+ 18: [180, 30, 80]
+ 20: [255, 0, 0]
+ 30: [30, 30, 255]
+ 31: [200, 40, 255]
+ 32: [90, 30, 150]
+ 40: [255, 0, 255]
+ 44: [255, 150, 255]
+ 48: [75, 0, 75]
+ 49: [75, 0, 175]
+ 50: [0, 200, 255]
+ 51: [50, 120, 255]
+ 52: [0, 150, 255]
+ 60: [170, 255, 150]
+ 70: [0, 175, 0]
+ 71: [0, 60, 135]
+ 72: [80, 240, 150]
+ 80: [150, 240, 255]
+ 81: [0, 0, 255]
+ 99: [255, 255, 50]
+ 252: [245, 150, 100]
+ 256: [255, 0, 0]
+ 253: [200, 40, 255]
+ 254: [30, 30, 255]
+ 255: [90, 30, 150]
+ 257: [250, 80, 100]
+ 258: [180, 30, 80]
+ 259: [255, 0, 0]
+content: # as a ratio with the total number of points
+ 0: 0.018889854628292943
+ 1: 0.0002937197336781505
+ 10: 0.040818519255974316
+ 11: 0.00016609538710764618
+ 13: 2.7879693665067774e-05
+ 15: 0.00039838616015114444
+ 16: 0.0
+ 18: 0.0020633612104619787
+ 20: 0.0016218197275284021
+ 30: 0.00017698551338515307
+ 31: 1.1065903904919655e-08
+ 32: 5.532951952459828e-09
+ 40: 0.1987493871255525
+ 44: 0.014717169549888214
+ 48: 0.14392298360372
+ 49: 0.0039048553037472045
+ 50: 0.1326861944777486
+ 51: 0.0723592229456223
+ 52: 0.002395131480328884
+ 60: 4.7084144280367186e-05
+ 70: 0.26681502148037506
+ 71: 0.006035012012626033
+ 72: 0.07814222006271769
+ 80: 0.002855498193863172
+ 81: 0.0006155958086189918
+ 99: 0.009923127583046915
+ 252: 0.001789309418528068
+ 253: 0.00012709999297008662
+ 254: 0.00016059776092534436
+ 255: 3.745553104802113e-05
+ 256: 0.0
+ 257: 0.00011351574470342043
+ 258: 0.00010157861367183268
+ 259: 4.3840131989471124e-05
+# classes that are indistinguishable from single scan or inconsistent in
+# ground truth are mapped to their closest equivalent
+learning_map:
+ 0 : 0 # "unlabeled"
+ 1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped
+ 10: 1 # "car"
+ 11: 2 # "bicycle"
+ 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped
+ 15: 3 # "motorcycle"
+ 16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped
+ 18: 4 # "truck"
+ 20: 5 # "other-vehicle"
+ 30: 6 # "person"
+ 31: 7 # "bicyclist"
+ 32: 8 # "motorcyclist"
+ 40: 9 # "road"
+ 44: 10 # "parking"
+ 48: 11 # "sidewalk"
+ 49: 12 # "other-ground"
+ 50: 13 # "building"
+ 51: 14 # "fence"
+ 52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped
+ 60: 9 # "lane-marking" to "road" ---------------------------------mapped
+ 70: 15 # "vegetation"
+ 71: 16 # "trunk"
+ 72: 17 # "terrain"
+ 80: 18 # "pole"
+ 81: 19 # "traffic-sign"
+ 99: 0 # "other-object" to "unlabeled" ----------------------------mapped
+ 252: 1 # "moving-car" to "car" ------------------------------------mapped
+ 253: 7 # "moving-bicyclist" to "bicyclist" ------------------------mapped
+ 254: 6 # "moving-person" to "person" ------------------------------mapped
+ 255: 8 # "moving-motorcyclist" to "motorcyclist" ------------------mapped
+ 256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped
+ 257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped
+ 258: 4 # "moving-truck" to "truck" --------------------------------mapped
+ 259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped
+learning_map_inv: # inverse of previous map
+ 0: 0 # "unlabeled", and others ignored
+ 1: 10 # "car"
+ 2: 11 # "bicycle"
+ 3: 15 # "motorcycle"
+ 4: 18 # "truck"
+ 5: 20 # "other-vehicle"
+ 6: 30 # "person"
+ 7: 31 # "bicyclist"
+ 8: 32 # "motorcyclist"
+ 9: 40 # "road"
+ 10: 44 # "parking"
+ 11: 48 # "sidewalk"
+ 12: 49 # "other-ground"
+ 13: 50 # "building"
+ 14: 51 # "fence"
+ 15: 70 # "vegetation"
+ 16: 71 # "trunk"
+ 17: 72 # "terrain"
+ 18: 80 # "pole"
+ 19: 81 # "traffic-sign"
+learning_ignore: # Ignore classes
+ 0: True # "unlabeled", and others ignored
+ 1: False # "car"
+ 2: False # "bicycle"
+ 3: False # "motorcycle"
+ 4: False # "truck"
+ 5: False # "other-vehicle"
+ 6: False # "person"
+ 7: False # "bicyclist"
+ 8: False # "motorcyclist"
+ 9: False # "road"
+ 10: False # "parking"
+ 11: False # "sidewalk"
+ 12: False # "other-ground"
+ 13: False # "building"
+ 14: False # "fence"
+ 15: False # "vegetation"
+ 16: False # "trunk"
+ 17: False # "terrain"
+ 18: False # "pole"
+ 19: False # "traffic-sign"
+split: # sequence numbers
+ train:
+ - 0
+ - 1
+ - 2
+ - 3
+ - 4
+ - 5
+ - 6
+ - 7
+ - 9
+ - 10
+ valid:
+ - 8
+ test:
+ - 11
+ - 12
+ - 13
+ - 14
+ - 15
+ - 16
+ - 17
+ - 18
+ - 19
+ - 20
+ - 21
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/segmentation_decoder b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/segmentation_decoder
new file mode 100644
index 0000000..a341bb9
Binary files /dev/null and b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/segmentation_decoder differ
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/segmentation_head b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/segmentation_head
new file mode 100644
index 0000000..581e9f7
Binary files /dev/null and b/pytorch_code/lidar-bonnetal/train/tasks/semantic/models/3D-MiniNet/segmentation_head differ
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/__init__.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/ioueval.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/ioueval.py
new file mode 100644
index 0000000..6b2b949
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/ioueval.py
@@ -0,0 +1,169 @@
+#!/usr/bin/env python3
+
+# This file is covered by the LICENSE file in the root of this project.
+
+import torch
+import numpy as np
+import time
+
+from tasks.semantic.postproc.borderMask import borderMask
+
+
+class iouEval:
+ def __init__(self, n_classes, device, ignore=None):
+ self.n_classes = n_classes
+ self.device = device
+ # if ignore is larger than n_classes, consider no ignoreIndex
+ self.ignore = torch.tensor(ignore).long()
+ self.include = torch.tensor(
+ [n for n in range(self.n_classes) if n not in self.ignore]).long()
+ print("[IOU EVAL] IGNORE: ", self.ignore)
+ print("[IOU EVAL] INCLUDE: ", self.include)
+ self.reset()
+
+ def num_classes(self):
+ return self.n_classes
+
+ def reset(self):
+ self.conf_matrix = torch.zeros(
+ (self.n_classes, self.n_classes), device=self.device).long()
+ self.ones = None
+ self.last_scan_size = None # for when variable scan size is used
+
+ def addBatch(self, x, y): # x=preds, y=targets
+ # if numpy, pass to pytorch
+ # to tensor
+ if isinstance(x, np.ndarray):
+ x = torch.from_numpy(np.array(x)).long().to(self.device)
+ if isinstance(y, np.ndarray):
+ y = torch.from_numpy(np.array(y)).long().to(self.device)
+
+ # sizes should be "batch_size x H x W"
+ x_row = x.reshape(-1) # de-batchify
+ y_row = y.reshape(-1) # de-batchify
+
+ # idxs are labels and predictions
+ idxs = torch.stack([x_row, y_row], dim=0)
+
+ # ones is what I want to add to conf when I
+ if self.ones is None or self.last_scan_size != idxs.shape[-1]:
+ self.ones = torch.ones((idxs.shape[-1]), device=self.device).long()
+ self.last_scan_size = idxs.shape[-1]
+
+ # make confusion matrix (cols = gt, rows = pred)
+ self.conf_matrix = self.conf_matrix.index_put_(
+ tuple(idxs), self.ones, accumulate=True)
+
+ # print(self.tp.shape)
+ # print(self.fp.shape)
+ # print(self.fn.shape)
+
+ def getStats(self):
+ # remove fp and fn from confusion on the ignore classes cols and rows
+ conf = self.conf_matrix.clone().double()
+ conf[self.ignore] = 0
+ conf[:, self.ignore] = 0
+
+ # get the clean stats
+ tp = conf.diag()
+ fp = conf.sum(dim=1) - tp
+ fn = conf.sum(dim=0) - tp
+ return tp, fp, fn
+
+ def getIoU(self):
+ tp, fp, fn = self.getStats()
+ intersection = tp
+ union = tp + fp + fn + 1e-15
+ iou = intersection / union
+ iou_mean = (intersection[self.include] / union[self.include]).mean()
+ return iou_mean, iou # returns "iou mean", "iou per class" ALL CLASSES
+
+ def getacc(self):
+ tp, fp, fn = self.getStats()
+ total_tp = tp.sum()
+ total = tp[self.include].sum() + fp[self.include].sum() + 1e-15
+ acc_mean = total_tp / total
+ return acc_mean # returns "acc mean"
+
+
+class biouEval(iouEval):
+ def __init__(self, n_classes, device, ignore=None, border_size=1, kern_conn=4):
+ super().__init__(n_classes, device, ignore)
+ self.border_size = border_size
+ self.kern_conn = kern_conn
+
+ # check that I am only ignoring one class
+ if len(ignore) > 1:
+ raise ValueError("Length of ignored class list should be 1 or 0")
+ elif len(ignore) == 0:
+ ignore = None
+ else:
+ ignore = ignore[0]
+
+ self.borderer = borderMask(self.n_classes, self.device,
+ self.border_size, self.kern_conn,
+ background_class=ignore)
+ self.reset()
+
+ def reset(self):
+ super().reset()
+ return
+
+ def addBorderBatch1d(self, range_y, x, y, px, py):
+ '''range_y=target as img, x=preds, y=targets, px,py=idxs of points of
+ pointcloud in range img
+ WARNING: Only batch size 1 works for now
+ '''
+ # if numpy, pass to pytorch
+ # to tensor
+ if isinstance(range_y, np.ndarray):
+ range_y = torch.from_numpy(np.array(range_y)).long().to(self.device)
+ if isinstance(x, np.ndarray):
+ x = torch.from_numpy(np.array(x)).long().to(self.device)
+ if isinstance(y, np.ndarray):
+ y = torch.from_numpy(np.array(y)).long().to(self.device)
+ if isinstance(px, np.ndarray):
+ px = torch.from_numpy(np.array(px)).long().to(self.device)
+ if isinstance(py, np.ndarray):
+ py = torch.from_numpy(np.array(py)).long().to(self.device)
+
+ # get border mask of range_y
+ border_mask_2d = self.borderer(range_y)
+
+ # filter px, py according to if they are on border mask or not
+ border_mask_1d = border_mask_2d[0, py, px].byte()
+
+ # get proper points from filtered x and y
+ x_in_mask = torch.masked_select(x, border_mask_1d)
+ y_in_mask = torch.masked_select(y, border_mask_1d)
+
+ # add batch
+ self.addBatch(x_in_mask, y_in_mask)
+
+
+if __name__ == "__main__":
+ # mock problem
+ nclasses = 2
+ ignore = []
+
+ # test with 2 squares and a known IOU
+ lbl = torch.zeros((7, 7)).long()
+ argmax = torch.zeros((7, 7)).long()
+
+ # put squares
+ lbl[2:4, 2:4] = 1
+ argmax[3:5, 3:5] = 1
+
+ # make evaluator
+ eval = iouEval(nclasses, torch.device('cpu'), ignore)
+
+ # run
+ eval.addBatch(argmax, lbl)
+ m_iou, iou = eval.getIoU()
+ print("*"*80)
+ print("Small iou mock problem")
+ print("IoU: ", m_iou)
+ print("IoU class: ", iou)
+ m_acc = eval.getacc()
+ print("Acc: ", m_acc)
+ print("*"*80)
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/segmentator.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/segmentator.py
new file mode 100644
index 0000000..4fa53db
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/segmentator.py
@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import imp
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tasks.semantic.postproc.CRF import CRF
+import __init__ as booger
+
+
+class Segmentator(nn.Module):
+ def __init__(self, ARCH, nclasses, path=None, path_append="", strict=False):
+ super().__init__()
+ self.ARCH = ARCH
+ self.nclasses = nclasses
+ self.path = path
+ self.path_append = path_append
+ self.strict = False
+
+ # get the model
+ bboneModule = imp.load_source("bboneModule",
+ booger.TRAIN_PATH + '/backbones/' +
+ self.ARCH["backbone"]["name"] + '.py')
+ self.backbone = bboneModule.Backbone(params=self.ARCH["backbone"])
+
+ # do a pass of the backbone to initialize the skip connections
+ stub = torch.zeros((1,
+ self.backbone.get_input_depth(),
+ self.ARCH["dataset"]["sensor"]["img_prop"]["height"],
+ self.ARCH["dataset"]["sensor"]["img_prop"]["width"]))
+ w_size = 4
+ stub_points = torch.zeros((1,
+ self.backbone.get_input_depth(),
+ w_size * w_size,
+ int(self.ARCH["dataset"]["sensor"]["img_prop"]["width"] * self.ARCH["dataset"]["sensor"]["img_prop"]["height"] / w_size / w_size)))
+
+
+
+ if torch.cuda.is_available():
+ stub = stub.cuda()
+ stub_points = stub_points.cuda()
+ self.backbone.cuda()
+ _, stub_skips = self.backbone([stub, stub_points])
+
+ decoderModule = imp.load_source("decoderModule",
+ booger.TRAIN_PATH + '/tasks/semantic/decoders/' +
+ self.ARCH["decoder"]["name"] + '.py')
+ self.decoder = decoderModule.Decoder(params=self.ARCH["decoder"],
+ stub_skips=stub_skips,
+ OS=self.ARCH["backbone"]["OS"],
+ feature_depth=self.backbone.get_last_depth())
+
+ self.head = nn.Sequential(nn.Conv2d(self.decoder.get_last_depth(),
+ self.nclasses, kernel_size=3,
+ stride=1, padding=1))
+
+ if self.ARCH["post"]["CRF"]["use"]:
+ self.CRF = CRF(self.ARCH["post"]["CRF"]["params"], self.nclasses)
+ else:
+ self.CRF = None
+
+ # train backbone?
+ if not self.ARCH["backbone"]["train"]:
+ for w in self.backbone.parameters():
+ w.requires_grad = False
+
+ # train decoder?
+ if not self.ARCH["decoder"]["train"]:
+ for w in self.decoder.parameters():
+ w.requires_grad = False
+
+ # train head?
+ if not self.ARCH["head"]["train"]:
+ for w in self.head.parameters():
+ w.requires_grad = False
+
+ # train CRF?
+ if self.CRF and not self.ARCH["post"]["CRF"]["train"]:
+ for w in self.CRF.parameters():
+ w.requires_grad = False
+
+ # print number of parameters and the ones requiring gradients
+ # print number of parameters and the ones requiring gradients
+ weights_total = sum(p.numel() for p in self.parameters())
+ weights_grad = sum(p.numel() for p in self.parameters() if p.requires_grad)
+ print("Total number of parameters (M): ", weights_total/1000000.)
+ print("Total number of parameters requires_grad: ", weights_grad)
+
+
+ # breakdown by layer
+ weights_enc = sum(p.numel() for p in self.backbone.parameters())
+ weights_dec = sum(p.numel() for p in self.decoder.parameters())
+ weights_head = sum(p.numel() for p in self.head.parameters())
+ print("Param encoder ", weights_enc)
+ print("Param decoder ", weights_dec)
+ print("Param head ", weights_head)
+ if self.CRF:
+ weights_crf = sum(p.numel() for p in self.CRF.parameters())
+ print("Param CRF ", weights_crf)
+
+ # get weights
+ if path is not None:
+ # try backbone
+ try:
+ w_dict = torch.load(path + "/backbone" + path_append,
+ map_location=lambda storage, loc: storage)
+ # w_dict.pop('proj.conv_atten.weight')
+ # w_dict.pop('proj.conv_atten.bias')
+ # w_dict.pop('proj.conv_out.weight')
+ self.backbone.load_state_dict(w_dict, strict=False)
+ print("Successfully loaded model backbone weights")
+ except Exception as e:
+ print()
+ print("Couldn't load backbone, using random weights. Error: ", e)
+ if strict:
+ print("I'm in strict mode and failure to load weights blows me up :)")
+ raise e
+
+ # try decoder
+ try:
+ w_dict = torch.load(path + "/segmentation_decoder" + path_append,
+ map_location=lambda storage, loc: storage)
+ self.decoder.load_state_dict(w_dict, strict=False)
+ print("Successfully loaded model decoder weights")
+ except Exception as e:
+ print("Couldn't load decoder, using random weights. Error: ", e)
+ if strict:
+ print("I'm in strict mode and failure to load weights blows me up :)")
+ raise e
+
+ # try head
+ try:
+ w_dict = torch.load(path + "/segmentation_head" + path_append,
+ map_location=lambda storage, loc: storage)
+ self.head.load_state_dict(w_dict, strict=False)
+ print("Successfully loaded model head weights")
+ except Exception as e:
+ print("Couldn't load head, using random weights. Error: ", e)
+ if strict:
+ print("I'm in strict mode and failure to load weights blows me up :)")
+ raise e
+
+ # try CRF
+ if self.CRF:
+ try:
+ w_dict = torch.load(path + "/segmentation_CRF" + path_append,
+ map_location=lambda storage, loc: storage)
+ self.CRF.load_state_dict(w_dict, strict=True)
+ print("Successfully loaded model CRF weights")
+ except Exception as e:
+ print("Couldn't load CRF, using random weights. Error: ", e)
+ if strict:
+ print("I'm in strict mode and failure to load weights blows me up :)")
+ raise e
+ else:
+ print("No path to pretrained, using random init.")
+
+ def forward(self, x, mask=None):
+ y, skips = self.backbone(x)
+ y = self.decoder(y, skips)
+ y = self.head(y)
+ y = F.softmax(y, dim=1)
+ if self.CRF:
+ assert(mask is not None)
+ y = self.CRF(x, y, mask)
+ return y
+
+ def save_checkpoint(self, logdir, suffix=""):
+ # Save the weights
+ torch.save(self.backbone.state_dict(), logdir +
+ "/backbone" + suffix)
+ torch.save(self.decoder.state_dict(), logdir +
+ "/segmentation_decoder" + suffix)
+ torch.save(self.head.state_dict(), logdir +
+ "/segmentation_head" + suffix)
+ if self.CRF:
+ torch.save(self.CRF.state_dict(), logdir +
+ "/segmentation_CRF" + suffix)
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/trainer.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/trainer.py
new file mode 100644
index 0000000..d030054
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/trainer.py
@@ -0,0 +1,495 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.backends.cudnn as cudnn
+import torchvision.transforms as transforms
+import imp
+import yaml
+import time
+from PIL import Image
+import __init__ as booger
+import collections
+import copy
+import cv2
+import os
+import numpy as np
+from matplotlib import pyplot as plt
+
+from common.logger import Logger
+from common.avgmeter import *
+from common.sync_batchnorm.batchnorm import convert_model
+from common.warmupLR import *
+from tasks.semantic.modules.segmentator import *
+from tasks.semantic.modules.ioueval import *
+
+
+class Trainer():
+ def __init__(self, ARCH, DATA, datadir, logdir, path=None):
+ # parameters
+ self.ARCH = ARCH
+ self.DATA = DATA
+ self.datadir = datadir
+ self.log = logdir
+ self.path = path
+
+ # put logger where it belongs
+ self.tb_logger = Logger(self.log + "/tb")
+ self.info = {"train_update": 0,
+ "train_loss": 0,
+ "train_acc": 0,
+ "train_iou": 0,
+ "valid_loss": 0,
+ "valid_acc": 0,
+ "valid_iou": 0,
+ "backbone_lr": 0,
+ "decoder_lr": 0,
+ "head_lr": 0,
+ "post_lr": 0}
+
+ # get the data
+ parserModule = imp.load_source("parserModule",
+ booger.TRAIN_PATH + '/tasks/semantic/dataset/' +
+ self.DATA["name"] + '/parser.py')
+ self.parser = parserModule.Parser(root=self.datadir,
+ train_sequences=self.DATA["split"]["train"],
+ valid_sequences=self.DATA["split"]["valid"],
+ test_sequences=None,
+ labels=self.DATA["labels"],
+ color_map=self.DATA["color_map"],
+ learning_map=self.DATA["learning_map"],
+ learning_map_inv=self.DATA["learning_map_inv"],
+ sensor=self.ARCH["dataset"]["sensor"],
+ max_points=self.ARCH["dataset"]["max_points"],
+ batch_size=self.ARCH["train"]["batch_size"],
+ workers=self.ARCH["train"]["workers"],
+ gt=True,
+ shuffle_train=True)
+
+ # weights for loss (and bias)
+ # weights for loss (and bias)
+ epsilon_w = self.ARCH["train"]["epsilon_w"]
+ content = torch.zeros(self.parser.get_n_classes(), dtype=torch.float)
+ for cl, freq in DATA["content"].items():
+ x_cl = self.parser.to_xentropy(cl) # map actual class to xentropy class
+ content[x_cl] += freq
+ self.loss_w = 1 / (content + epsilon_w) # get weights
+ power_value = 0.25
+ self.loss_w = np.power(self.loss_w, power_value) * np.power(10, 1 - power_value)
+ for x_cl, w in enumerate(self.loss_w): # ignore the ones necessary to ignore
+ if DATA["learning_ignore"][x_cl]:
+ # don't weigh
+ self.loss_w[x_cl] = 0
+ print("Loss weights from content: ", self.loss_w.data)
+
+ # concatenate the encoder and the head
+ with torch.no_grad():
+ self.model = Segmentator(self.ARCH,
+ self.parser.get_n_classes(),
+ self.path)
+
+ # GPU?
+ self.gpu = False
+ self.multi_gpu = False
+ self.n_gpus = 0
+ self.model_single = self.model
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ print("Training in device: ", self.device)
+ if torch.cuda.is_available() and torch.cuda.device_count() > 0:
+ cudnn.benchmark = True
+ cudnn.fastest = True
+ self.gpu = True
+ self.n_gpus = 1
+ self.model.cuda()
+ if torch.cuda.is_available() and torch.cuda.device_count() > 1:
+ print("Let's use", torch.cuda.device_count(), "GPUs!")
+ self.model = nn.DataParallel(self.model) # spread in gpus
+ self.model = convert_model(self.model).cuda() # sync batchnorm
+ self.model_single = self.model.module # single model to get weight names
+ self.multi_gpu = True
+ self.n_gpus = torch.cuda.device_count()
+
+ # loss
+ if "loss" in self.ARCH["train"].keys() and self.ARCH["train"]["loss"] == "xentropy":
+ self.criterion = nn.NLLLoss(weight=self.loss_w).to(self.device)
+ else:
+ raise Exception('Loss not defined in config file')
+ # loss as dataparallel too (more images in batch)
+ if self.n_gpus > 1:
+ self.criterion = nn.DataParallel(self.criterion).cuda() # spread in gpus
+
+ # optimizer
+ if self.ARCH["post"]["CRF"]["use"] and self.ARCH["post"]["CRF"]["train"]:
+ self.lr_group_names = ["post_lr"]
+ self.train_dicts = [{'params': self.model_single.CRF.parameters()}]
+ else:
+ self.lr_group_names = []
+ self.train_dicts = []
+ if self.ARCH["backbone"]["train"]:
+ self.lr_group_names.append("backbone_lr")
+ self.train_dicts.append(
+ {'params': self.model_single.backbone.parameters()})
+ if self.ARCH["decoder"]["train"]:
+ self.lr_group_names.append("decoder_lr")
+ self.train_dicts.append(
+ {'params': self.model_single.decoder.parameters()})
+ if self.ARCH["head"]["train"]:
+ self.lr_group_names.append("head_lr")
+ self.train_dicts.append({'params': self.model_single.head.parameters()})
+
+ # Use SGD optimizer to train
+ self.optimizer = optim.SGD(self.train_dicts,
+ lr=self.ARCH["train"]["lr"],
+ momentum=self.ARCH["train"]["momentum"],
+ weight_decay=self.ARCH["train"]["w_decay"])
+
+ # Use warmup learning rate
+ # post decay and step sizes come in epochs and we want it in steps
+ steps_per_epoch = self.parser.get_train_size()
+ up_steps = int(self.ARCH["train"]["wup_epochs"] * steps_per_epoch)
+ final_decay = self.ARCH["train"]["lr_decay"] ** (1/steps_per_epoch)
+ self.scheduler = warmupLR(optimizer=self.optimizer,
+ lr=self.ARCH["train"]["lr"],
+ warmup_steps=up_steps,
+ momentum=self.ARCH["train"]["momentum"],
+ decay=final_decay)
+
+
+ # show execution time
+ from thop import profile
+
+ inputs = torch.randn(1, 11, 64, 2048).cuda()
+ inputs_points = torch.randn(1, 11, 16, 8192).cuda()
+
+ flops, params = profile(self.model, inputs=([inputs, inputs_points],), verbose=False)
+ time_train = []
+ outputs = self.model([inputs, inputs_points])
+ outputs = self.model([inputs, inputs_points])
+
+ for i in range(20):
+ inputs = torch.randn(1, 11, 64, 2048).cuda()
+ inputs_points = torch.randn(1, 11, 16, 8192).cuda()
+
+ self.model.eval()
+ with torch.no_grad():
+ start_time = time.time()
+ outputs = self.model([inputs, inputs_points])
+
+ torch.cuda.synchronize() # wait for cuda to finish (cuda is asynchronous!)
+ fwt = time.time() - start_time
+ time_train.append(fwt)
+ print ("Forward time per img: %.3f (Mean: %.3f)" % (
+ fwt / 1, sum(time_train) / len(time_train) / 1))
+ print("Total number of flops (G): ", flops / 1000000000.)
+ time.sleep(0.15)
+
+ @staticmethod
+ def get_mpl_colormap(cmap_name):
+ cmap = plt.get_cmap(cmap_name)
+ # Initialize the matplotlib color map
+ sm = plt.cm.ScalarMappable(cmap=cmap)
+ # Obtain linear color range
+ color_range = sm.to_rgba(np.linspace(0, 1, 256), bytes=True)[:, 2::-1]
+ return color_range.reshape(256, 1, 3)
+
+ @staticmethod
+ def make_log_img(depth, mask, pred, gt, color_fn):
+ # input should be [depth, pred, gt]
+ # make range image (normalized to 0,1 for saving)
+ depth = (cv2.normalize(depth, None, alpha=0, beta=1,
+ norm_type=cv2.NORM_MINMAX,
+ dtype=cv2.CV_32F) * 255.0).astype(np.uint8)
+ out_img = cv2.applyColorMap(
+ depth, Trainer.get_mpl_colormap('viridis')) * mask[..., None]
+ # make label prediction
+ pred_color = color_fn((pred * mask).astype(np.int32))
+ out_img = np.concatenate([out_img, pred_color], axis=0)
+ # make label gt
+ gt_color = color_fn(gt)
+ out_img = np.concatenate([out_img, gt_color], axis=0)
+ return (out_img).astype(np.uint8)
+
+ @staticmethod
+ def save_to_log(logdir, logger, info, epoch, w_summary=False, model=None, img_summary=False, imgs=[]):
+ # save scalars
+ for tag, value in info.items():
+ logger.scalar_summary(tag, value, epoch)
+
+ # save summaries of weights and biases
+ if w_summary and model:
+ for tag, value in model.named_parameters():
+ tag = tag.replace('.', '/')
+ logger.histo_summary(tag, value.data.cpu().numpy(), epoch)
+ if value.grad is not None:
+ logger.histo_summary(
+ tag + '/grad', value.grad.data.cpu().numpy(), epoch)
+
+ if img_summary and len(imgs) > 0:
+ directory = os.path.join(logdir, "predictions")
+ if not os.path.isdir(directory):
+ os.makedirs(directory)
+ for i, img in enumerate(imgs):
+ name = os.path.join(directory, str(i) + ".png")
+ cv2.imwrite(name, img)
+
+ def train(self):
+ # accuracy and IoU stuff
+ best_train_iou = 0.0
+ best_val_iou = 0.0
+
+ self.ignore_class = []
+ for i, w in enumerate(self.loss_w):
+ if w < 1e-10:
+ self.ignore_class.append(i)
+ print("Ignoring class ", i, " in IoU evaluation")
+ self.evaluator = iouEval(self.parser.get_n_classes(),
+ self.device, self.ignore_class)
+
+
+ # train for n epochs
+ for epoch in range(self.ARCH["train"]["max_epochs"]):
+ # get info for learn rate currently
+ groups = self.optimizer.param_groups
+ for name, g in zip(self.lr_group_names, groups):
+ self.info[name] = g['lr']
+
+
+ # train for 1 epoch
+ acc, iou, loss, update_mean = self.train_epoch(train_loader=self.parser.get_train_set(),
+ model=self.model,
+ criterion=self.criterion,
+ optimizer=self.optimizer,
+ epoch=epoch,
+ evaluator=self.evaluator,
+ scheduler=self.scheduler,
+ color_fn=self.parser.to_color,
+ report=self.ARCH["train"]["report_batch"],
+ show_scans=self.ARCH["train"]["show_scans"])
+
+ # update info
+ self.info["train_update"] = update_mean
+ self.info["train_loss"] = loss
+ self.info["train_acc"] = acc
+ self.info["train_iou"] = iou
+
+ # remember best iou and save checkpoint
+ if iou > best_train_iou:
+ print("Best mean iou in training set so far, save model!")
+ best_train_iou = iou
+ self.model_single.save_checkpoint(self.log, suffix="_train")
+
+ if epoch % self.ARCH["train"]["report_epoch"] == 0:
+ # evaluate on validation set
+ print("*" * 80)
+ acc, iou, loss, rand_img = self.validate(val_loader=self.parser.get_valid_set(),
+ model=self.model,
+ criterion=self.criterion,
+ evaluator=self.evaluator,
+ class_func=self.parser.get_xentropy_class_string,
+ color_fn=self.parser.to_color,
+ save_scans=self.ARCH["train"]["save_scans"])
+
+ # update info
+ self.info["valid_loss"] = loss
+ self.info["valid_acc"] = acc
+ self.info["valid_iou"] = iou
+
+ # remember best iou and save checkpoint
+ if iou > best_val_iou:
+ print("Best mean iou in validation so far, save model!")
+ print("*" * 80)
+ best_val_iou = iou
+
+ # save the weights!
+ self.model_single.save_checkpoint(self.log, suffix="")
+
+ print("*" * 80)
+
+ # save to log
+ Trainer.save_to_log(logdir=self.log,
+ logger=self.tb_logger,
+ info=self.info,
+ epoch=epoch,
+ w_summary=self.ARCH["train"]["save_summary"],
+ model=self.model_single,
+ img_summary=self.ARCH["train"]["save_scans"],
+ imgs=rand_img)
+
+ print('Finished Training')
+
+ return
+
+ def train_epoch(self, train_loader, model, criterion, optimizer, epoch, evaluator, scheduler, color_fn, report=10, show_scans=False):
+ batch_time = AverageMeter()
+ data_time = AverageMeter()
+ losses = AverageMeter()
+ acc = AverageMeter()
+ iou = AverageMeter()
+ update_ratio_meter = AverageMeter()
+
+ # empty the cache to train now
+ if self.gpu:
+ torch.cuda.empty_cache()
+
+ # switch to train mode
+ model.train()
+ end = time.time()
+ for i, (in_vol, proj_mask, proj_labels, _, path_seq, path_name, _, _, _, _, _, _, _, _, _, proj_chan_group_points) in enumerate(train_loader):
+ # measure data loading time
+ data_time.update(time.time() - end)
+ if not self.multi_gpu and self.gpu:
+ in_vol = in_vol.cuda()
+ proj_chan_group_points = proj_chan_group_points.cuda()
+ proj_mask = proj_mask.cuda()
+ if self.gpu:
+ proj_labels = proj_labels.cuda(non_blocking=True).long()
+
+
+
+ # compute output
+ output = model([in_vol, proj_chan_group_points], proj_mask)
+
+ loss = criterion(torch.log(output.clamp(min=1e-8)), proj_labels)
+
+ # compute gradient and do SGD step
+ optimizer.zero_grad()
+ if self.n_gpus > 1:
+ idx = torch.ones(self.n_gpus).cuda()
+ loss.backward(idx)
+ else:
+ loss.backward()
+ optimizer.step()
+
+ # measure accuracy and record loss
+ loss = loss.mean()
+ with torch.no_grad():
+ evaluator.reset()
+ argmax = output.argmax(dim=1)
+ evaluator.addBatch(argmax, proj_labels)
+ accuracy = evaluator.getacc()
+ jaccard, class_jaccard = evaluator.getIoU()
+
+ losses.update(loss.item(), in_vol.size(0))
+ acc.update(accuracy.item(), in_vol.size(0))
+ iou.update(jaccard.item(), in_vol.size(0))
+
+ # measure elapsed time
+ batch_time.update(time.time() - end)
+ end = time.time()
+
+ # get gradient updates and weights, so I can print the relationship of
+ # their norms
+ update_ratios = []
+ for g in self.optimizer.param_groups:
+ lr = g["lr"]
+ for value in g["params"]:
+ if value.grad is not None:
+ w = np.linalg.norm(value.data.cpu().numpy().reshape((-1)))
+ update = np.linalg.norm(-max(lr, 1e-10) *
+ value.grad.cpu().numpy().reshape((-1)))
+ update_ratios.append(update / max(w, 1e-10))
+ update_ratios = np.array(update_ratios)
+ update_mean = update_ratios.mean()
+ update_std = update_ratios.std()
+ update_ratio_meter.update(update_mean) # over the epoch
+
+ if show_scans:
+ # get the first scan in batch and project points
+ mask_np = proj_mask[0].cpu().numpy()
+ depth_np = in_vol[0][0].cpu().numpy()
+ pred_np = argmax[0].cpu().numpy()
+ gt_np = proj_labels[0].cpu().numpy()
+ out = Trainer.make_log_img(depth_np, mask_np, pred_np, gt_np, color_fn)
+ cv2.imshow("sample_training", out)
+ cv2.waitKey(1)
+
+ if i % self.ARCH["train"]["report_batch"] == 0:
+ print('Lr: {lr:.3e} | '
+ 'Update: {umean:.3e} mean,{ustd:.3e} std | '
+ 'Epoch: [{0}][{1}/{2}] | '
+ 'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) | '
+ 'Data {data_time.val:.3f} ({data_time.avg:.3f}) | '
+ 'Loss {loss.val:.4f} ({loss.avg:.4f}) | '
+ 'acc {acc.val:.3f} ({acc.avg:.3f}) | '
+ 'IoU {iou.val:.3f} ({iou.avg:.3f})'.format(
+ epoch, i, len(train_loader), batch_time=batch_time,
+ data_time=data_time, loss=losses, acc=acc, iou=iou, lr=lr,
+ umean=update_mean, ustd=update_std))
+
+ # step scheduler
+ scheduler.step()
+
+ return acc.avg, iou.avg, losses.avg, update_ratio_meter.avg
+
+ def validate(self, val_loader, model, criterion, evaluator, class_func, color_fn, save_scans):
+ batch_time = AverageMeter()
+ losses = AverageMeter()
+ acc = AverageMeter()
+ iou = AverageMeter()
+ rand_imgs = []
+
+ # switch to evaluate mode
+ model.eval()
+ evaluator.reset()
+
+
+ # empty the cache to infer in high res
+ if self.gpu:
+ torch.cuda.empty_cache()
+
+ with torch.no_grad():
+ end = time.time()
+ for i, (in_vol, proj_mask, proj_labels, _, path_seq, path_name, _, _, _, _, _, _, _, _, _, proj_chan_group_points) in enumerate(val_loader):
+ if not self.multi_gpu and self.gpu:
+ in_vol = in_vol.cuda()
+ proj_chan_group_points = proj_chan_group_points.cuda()
+ proj_mask = proj_mask.cuda()
+ if self.gpu:
+ proj_labels = proj_labels.cuda(non_blocking=True).long()
+
+ # compute output
+ output = model([in_vol, proj_chan_group_points], proj_mask)
+ loss = criterion(torch.log(output.clamp(min=1e-8)), proj_labels)
+
+ # measure accuracy and record loss
+ argmax = output.argmax(dim=1)
+ evaluator.addBatch(argmax, proj_labels)
+ losses.update(loss.mean().item(), in_vol.size(0))
+
+ if save_scans:
+ # get the first scan in batch and project points
+ mask_np = proj_mask[0].cpu().numpy()
+ depth_np = in_vol[0][0].cpu().numpy()
+ pred_np = argmax[0].cpu().numpy()
+ gt_np = proj_labels[0].cpu().numpy()
+ out = Trainer.make_log_img(depth_np,
+ mask_np,
+ pred_np,
+ gt_np,
+ color_fn)
+ rand_imgs.append(out)
+
+ # measure elapsed time
+ batch_time.update(time.time() - end)
+ end = time.time()
+
+ accuracy = evaluator.getacc()
+ jaccard, class_jaccard = evaluator.getIoU()
+ acc.update(accuracy.item(), in_vol.size(0))
+ iou.update(jaccard.item(), in_vol.size(0))
+
+ print('Validation set:\n'
+ 'Time avg per batch {batch_time.avg:.3f}\n'
+ 'Loss avg {loss.avg:.4f}\n'
+ 'Acc avg {acc.avg:.3f}\n'
+ 'IoU avg {iou.avg:.3f}'.format(batch_time=batch_time,
+ loss=losses,
+ acc=acc, iou=iou))
+ # print also classwise
+ for i, jacc in enumerate(class_jaccard):
+ print('IoU class {i:} [{class_str:}] = {jacc:.3f}'.format(
+ i=i, class_str=class_func(i), jacc=jacc))
+
+ return acc.avg, iou.avg, losses.avg, rand_imgs
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/user.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/user.py
new file mode 100644
index 0000000..edfdc80
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/modules/user.py
@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.backends.cudnn as cudnn
+import torchvision.transforms as transforms
+import imp
+import yaml
+import time
+from PIL import Image
+import __init__ as booger
+import collections
+import copy
+import cv2
+import os
+import numpy as np
+
+from tasks.semantic.modules.segmentator import *
+from tasks.semantic.postproc.KNN import KNN
+
+
+class User():
+ def __init__(self, ARCH, DATA, datadir, logdir, modeldir):
+ # parameters
+ self.ARCH = ARCH
+ self.DATA = DATA
+ self.datadir = datadir
+ self.logdir = logdir
+ self.modeldir = modeldir
+
+ # get the data
+ parserModule = imp.load_source("parserModule",
+ booger.TRAIN_PATH + '/tasks/semantic/dataset/' +
+ self.DATA["name"] + '/parser.py')
+ self.parser = parserModule.Parser(root=self.datadir,
+ train_sequences=self.DATA["split"]["train"],
+ valid_sequences=self.DATA["split"]["valid"],
+ test_sequences=self.DATA["split"]["test"],
+ labels=self.DATA["labels"],
+ color_map=self.DATA["color_map"],
+ learning_map=self.DATA["learning_map"],
+ learning_map_inv=self.DATA["learning_map_inv"],
+ sensor=self.ARCH["dataset"]["sensor"],
+ max_points=self.ARCH["dataset"]["max_points"],
+ batch_size=1,
+ workers=self.ARCH["train"]["workers"],
+ gt=True,
+ shuffle_train=False)
+
+ # concatenate the encoder and the head
+ with torch.no_grad():
+ self.model = Segmentator(self.ARCH,
+ self.parser.get_n_classes(),
+ self.modeldir)
+
+ # use knn post processing?
+ self.post = None
+ if self.ARCH["post"]["KNN"]["use"]:
+ self.post = KNN(self.ARCH["post"]["KNN"]["params"],
+ self.parser.get_n_classes())
+
+ # GPU?
+ self.gpu = False
+ self.model_single = self.model
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ print("Infering in device: ", self.device)
+ if torch.cuda.is_available() and torch.cuda.device_count() > 0:
+ cudnn.benchmark = True
+ cudnn.fastest = True
+ self.gpu = True
+ self.model.cuda()
+
+ def infer(self):
+ # # do train set
+ self.infer_subset(loader=self.parser.get_train_set(),
+ to_orig_fn=self.parser.to_original)
+ #
+ # do valid set
+ self.infer_subset(loader=self.parser.get_valid_set(),
+ to_orig_fn=self.parser.to_original)
+ # do test set
+ self.infer_subset(loader=self.parser.get_test_set(),
+ to_orig_fn=self.parser.to_original)
+
+ print('Finished Infering')
+
+ return
+
+ def infer_subset(self, loader, to_orig_fn):
+ # switch to evaluate mode
+ self.model.eval()
+
+ # empty the cache to infer in high res
+ if self.gpu:
+ torch.cuda.empty_cache()
+
+ with torch.no_grad():
+ end = time.time()
+
+ for i, (proj_in, proj_mask, _, _, path_seq, path_name, p_x, p_y, proj_range, unproj_range, _, _, _, _, npoints, proj_chan_group_points) in enumerate(loader):
+
+ p_x = p_x[0, :npoints]
+ p_y = p_y[0, :npoints]
+ proj_range = proj_range[0, :npoints]
+ unproj_range = unproj_range[0, :npoints]
+ path_seq = path_seq[0]
+ path_name = path_name[0]
+
+ if self.gpu:
+ proj_in = proj_in.cuda()
+ proj_chan_group_points = proj_chan_group_points.cuda()
+ proj_mask = proj_mask.cuda()
+ p_x = p_x.cuda()
+ p_y = p_y.cuda()
+ if self.post:
+ proj_range = proj_range.cuda()
+ unproj_range = unproj_range.cuda()
+
+ # compute output
+ proj_output = self.model([proj_in, proj_chan_group_points], proj_mask)
+
+ proj_argmax = proj_output[0].argmax(dim=0)
+
+ if self.post:
+ # knn postproc
+ unproj_argmax = self.post(proj_range,
+ unproj_range,
+ proj_argmax,
+ p_x,
+ p_y)
+ else:
+ # put in original pointcloud using indexes
+ unproj_argmax = proj_argmax[p_y, p_x]
+
+ # measure elapsed time
+ if torch.cuda.is_available():
+ torch.cuda.synchronize()
+
+ print("Infered seq", path_seq, "scan", path_name,
+ "in", time.time() - end, "sec")
+ end = time.time()
+
+ # save scan
+ # get the first scan in batch and project scan
+ pred_np = unproj_argmax.cpu().numpy()
+ pred_np = pred_np.reshape((-1)).astype(np.int32)
+
+ # map to original label
+ pred_np = to_orig_fn(pred_np)
+
+ # save scan
+ path = os.path.join(self.logdir, "sequences",
+ path_seq, "predictions", path_name)
+ pred_np.tofile(path)
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/CRF.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/CRF.py
new file mode 100644
index 0000000..24f52d1
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/CRF.py
@@ -0,0 +1,130 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import numpy as np
+from scipy import signal
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import __init__ as booger
+
+
+class LocallyConnectedXYZLayer(nn.Module):
+ def __init__(self, h, w, sigma, nclasses):
+ super().__init__()
+ # size of window
+ self.h = h
+ self.padh = h//2
+ self.w = w
+ self.padw = w//2
+ assert(self.h % 2 == 1 and self.w % 2 == 1) # window must be odd
+ self.sigma = sigma
+ self.gauss_den = 2 * self.sigma**2
+ self.nclasses = nclasses
+
+ def forward(self, xyz, softmax, mask):
+ # softmax size
+ N, C, H, W = softmax.shape
+
+ # make sofmax zero everywhere input is invalid
+ softmax = softmax * mask.unsqueeze(1).float()
+
+ # get x,y,z for distance (shape N,1,H,W)
+ x = xyz[:, 0].unsqueeze(1)
+ y = xyz[:, 1].unsqueeze(1)
+ z = xyz[:, 2].unsqueeze(1)
+
+ # im2col in size of window of input (x,y,z separately)
+ window_x = F.unfold(x, kernel_size=(self.h, self.w),
+ padding=(self.padh, self.padw))
+ center_x = F.unfold(x, kernel_size=(1, 1),
+ padding=(0, 0))
+ window_y = F.unfold(y, kernel_size=(self.h, self.w),
+ padding=(self.padh, self.padw))
+ center_y = F.unfold(y, kernel_size=(1, 1),
+ padding=(0, 0))
+ window_z = F.unfold(z, kernel_size=(self.h, self.w),
+ padding=(self.padh, self.padw))
+ center_z = F.unfold(z, kernel_size=(1, 1),
+ padding=(0, 0))
+
+ # sq distance to center (center distance is zero)
+ unravel_dist2 = (window_x - center_x)**2 + \
+ (window_y - center_y)**2 + \
+ (window_z - center_z)**2
+
+ # weight input distance by gaussian weights
+ unravel_gaussian = torch.exp(- unravel_dist2 / self.gauss_den)
+
+ # im2col in size of window of softmax to reweight by gaussian weights from input
+ cloned_softmax = softmax.clone()
+ for i in range(self.nclasses):
+ # get the softmax for this class
+ c_softmax = softmax[:, i].unsqueeze(1)
+ # unfold this class to weigh it by the proper gaussian weights
+ unravel_softmax = F.unfold(c_softmax,
+ kernel_size=(self.h, self.w),
+ padding=(self.padh, self.padw))
+ unravel_w_softmax = unravel_softmax * unravel_gaussian
+ # add dimenssion 1 to obtain the new softmax for this class
+ unravel_added_softmax = unravel_w_softmax.sum(dim=1).unsqueeze(1)
+ # fold it and put it in new tensor
+ added_softmax = unravel_added_softmax.view(N, H, W)
+ cloned_softmax[:, i] = added_softmax
+
+ return cloned_softmax
+
+
+class CRF(nn.Module):
+ def __init__(self, params, nclasses):
+ super().__init__()
+ self.params = params
+ self.iter = torch.nn.Parameter(torch.tensor(params["iter"]),
+ requires_grad=False)
+ self.lcn_size = torch.nn.Parameter(torch.tensor([params["lcn_size"]["h"],
+ params["lcn_size"]["w"]]),
+ requires_grad=False)
+ self.xyz_coef = torch.nn.Parameter(torch.tensor(params["xyz_coef"]),
+ requires_grad=False).float()
+ self.xyz_sigma = torch.nn.Parameter(torch.tensor(params["xyz_sigma"]),
+ requires_grad=False).float()
+
+ self.nclasses = nclasses
+ print("Using CRF!")
+
+ # define layers here
+ # compat init
+ self.compat_kernel_init = np.reshape(np.ones((self.nclasses, self.nclasses)) -
+ np.identity(self.nclasses),
+ [self.nclasses, self.nclasses, 1, 1])
+
+ # bilateral compatibility matrixes
+ self.compat_conv = nn.Conv2d(self.nclasses, self.nclasses, 1)
+ self.compat_conv.weight = torch.nn.Parameter(torch.from_numpy(
+ self.compat_kernel_init).float() * self.xyz_coef, requires_grad=True)
+
+ # locally connected layer for message passing
+ self.local_conn_xyz = LocallyConnectedXYZLayer(params["lcn_size"]["h"],
+ params["lcn_size"]["w"],
+ params["xyz_coef"],
+ self.nclasses)
+
+ def forward(self, input, softmax, mask):
+ # use xyz
+ xyz = input[:, 1:4]
+
+ # iteratively
+ for iter in range(self.iter):
+ # message passing as locally connected layer
+ locally_connected = self.local_conn_xyz(xyz, softmax, mask)
+
+ # reweigh with the 1x1 convolution
+ reweight_softmax = self.compat_conv(locally_connected)
+
+ # add the new values to the original softmax
+ reweight_softmax = reweight_softmax + softmax
+
+ # lastly, renormalize
+ softmax = F.softmax(reweight_softmax, dim=1)
+
+ return softmax
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/KNN.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/KNN.py
new file mode 100644
index 0000000..cf0ac41
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/KNN.py
@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import __init__ as booger
+
+
+def get_gaussian_kernel(kernel_size=3, sigma=2, channels=1):
+ # Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
+ x_coord = torch.arange(kernel_size)
+ x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
+ y_grid = x_grid.t()
+ xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
+
+ mean = (kernel_size - 1) / 2.
+ variance = sigma**2.
+
+ # Calculate the 2-dimensional gaussian kernel which is
+ # the product of two gaussian distributions for two different
+ # variables (in this case called x and y)
+ gaussian_kernel = (1. / (2. * math.pi * variance)) *\
+ torch.exp(-torch.sum((xy_grid - mean)**2., dim=-1) / (2 * variance))
+
+ # Make sure sum of values in gaussian kernel equals 1.
+ gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
+
+ # Reshape to 2d depthwise convolutional weight
+ gaussian_kernel = gaussian_kernel.view(kernel_size, kernel_size)
+
+ return gaussian_kernel
+
+
+class KNN(nn.Module):
+ def __init__(self, params, nclasses):
+ super().__init__()
+ print("*"*80)
+ print("Cleaning point-clouds with kNN post-processing")
+ self.knn = params["knn"]
+ self.search = params["search"]
+ self.sigma = params["sigma"]
+ self.cutoff = params["cutoff"]
+ self.nclasses = nclasses
+ print("kNN parameters:")
+ print("knn:", self.knn)
+ print("search:", self.search)
+ print("sigma:", self.sigma)
+ print("cutoff:", self.cutoff)
+ print("nclasses:", self.nclasses)
+ print("*"*80)
+
+ def forward(self, proj_range, unproj_range, proj_argmax, px, py):
+ ''' Warning! Only works for un-batched pointclouds.
+ If they come batched we need to iterate over the batch dimension or do
+ something REALLY smart to handle unaligned number of points in memory
+ '''
+ # get device
+ if proj_range.is_cuda:
+ device = torch.device("cuda")
+ else:
+ device = torch.device("cpu")
+
+ # sizes of projection scan
+ H, W = proj_range.shape
+
+ # number of points
+ P = unproj_range.shape
+
+ # check if size of kernel is odd and complain
+ if (self.search % 2 == 0):
+ raise ValueError("Nearest neighbor kernel must be odd number")
+
+ # calculate padding
+ pad = int((self.search - 1) / 2)
+ pad = int((self.search - 1) / 2)
+
+ # unfold neighborhood to get nearest neighbors for each pixel (range image)
+ proj_unfold_k_rang = F.unfold(proj_range[None, None, ...],
+ kernel_size=(self.search, self.search),
+ padding=(pad, pad))
+ # index with px, py to get ALL the pcld points
+ idx_list = py * W + px
+ unproj_unfold_k_rang = proj_unfold_k_rang[:, :, idx_list]
+
+ # WARNING, THIS IS A HACK
+ # Make non valid (<0) range points extremely big so that there is no screwing
+ # up the nn self.search
+ unproj_unfold_k_rang[unproj_unfold_k_rang < 0] = float("inf")
+
+ # now the matrix is unfolded TOTALLY, replace the middle points with the actual range points
+ center = int(((self.search * self.search) - 1) / 2)
+ unproj_unfold_k_rang[:, center, :] = unproj_range
+
+ # now compare range
+ k2_distances = torch.abs(unproj_unfold_k_rang - unproj_range)
+
+ # k2_distances_x = torch.abs(proj_unfold_k_x - un_proj_x) * torch.abs(proj_unfold_k_x - un_proj_x)
+ # k2_distances_y = torch.abs(proj_unfold_k_y - un_proj_y) * torch.abs(proj_unfold_k_y - un_proj_y)
+ # k2_distances_z = torch.abs(proj_unfold_k_z - un_proj_z) * torch.abs(proj_unfold_k_z - un_proj_z)
+ #
+ # k2_distances = torch.min(k2_distances_x, k2_distances_y)
+ # k2_distances = torch.min(k2_distances, k2_distances_z)
+ #
+ #
+ # k2_distances = k2_distances_x + k2_distances_y + k2_distances_z
+
+
+
+ # make a kernel to weigh the ranges according to distance in (x,y)
+ # I make this 1 - kernel because I want distances that are close in (x,y)
+ # to matter more
+ inv_gauss_k = (
+ 1 - get_gaussian_kernel(self.search, self.sigma, 1)).view(1, -1, 1)
+ inv_gauss_k = inv_gauss_k.to(device).type(proj_range.type())
+
+ # apply weighing
+ k2_distances = k2_distances * inv_gauss_k
+
+ # find nearest neighbors
+ _, knn_idx = k2_distances.topk(
+ self.knn, dim=1, largest=False, sorted=False)
+
+ # do the same unfolding with the argmax
+ proj_unfold_1_argmax = F.unfold(proj_argmax[None, None, ...].float(),
+ kernel_size=(self.search, self.search),
+ padding=(pad, pad)).long()
+ unproj_unfold_1_argmax = proj_unfold_1_argmax[:, :, idx_list]
+
+ # get the top k predictions from the knn at each pixel
+ knn_argmax = torch.gather(
+ input=unproj_unfold_1_argmax, dim=1, index=knn_idx)
+
+ # fake an invalid argmax of classes + 1 for all cutoff items
+ if self.cutoff > 0:
+ knn_distances = torch.gather(input=k2_distances, dim=1, index=knn_idx)
+ knn_invalid_idx = knn_distances > self.cutoff
+ knn_argmax[knn_invalid_idx] = self.nclasses
+
+ # now vote
+ # argmax onehot has an extra class for objects after cutoff
+ knn_argmax_onehot = torch.zeros(
+ (1, self.nclasses + 1, P[0]), device=device).type(proj_range.type())
+ ones = torch.ones_like(knn_argmax).type(proj_range.type())
+ knn_argmax_onehot = knn_argmax_onehot.scatter_add_(1, knn_argmax, ones)
+
+ # now vote (as a sum over the onehot shit) (don't let it choose unlabeled OR invalid)
+ knn_argmax_out = knn_argmax_onehot[:, 1:-1].argmax(dim=1) + 1
+
+ # reshape again
+ knn_argmax_out = knn_argmax_out.view(P)
+
+ return knn_argmax_out
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/__init__.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/__init__.py
new file mode 100644
index 0000000..24fc240
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/__init__.py
@@ -0,0 +1,4 @@
+import sys
+TRAIN_PATH = "../"
+DEPLOY_PATH = "../../deploy"
+sys.path.insert(0, TRAIN_PATH)
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/borderMask.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/borderMask.py
new file mode 100644
index 0000000..3e62d4f
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/postproc/borderMask.py
@@ -0,0 +1,304 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+"""Border Mask for 2D labeled range images.
+
+Simple module to obtain the border mask of a given range image.
+
+The border mask is defined as the zone where are intersections between
+differrent classes for the given range image.
+
+In this case we will violate a little bit the definition and will augment it. We
+define the border mask as the zone where are intersections between differnet
+classes for the given range image in determined neighborhood. To obtain this
+border mask we will need to apply de binary erosion algorithm multiple times to
+the same range image.
+
+Example:
+ Suppose we have 3 classes and this given range image(labeled):
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+ The output of the bordermask would like:
+ # 1 erode iteration with a connectivity kernel of 4:
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
+ [1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+ [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],
+ [1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1],
+ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1],
+ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1],
+ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+
+ # 2 erode iterations with a connectivity kernel of 8:
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1],
+ [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from common.onehot import oneHot
+import __init__ as booger
+
+
+class borderMask(nn.Module):
+ def __init__(self, nclasses, device, border_size, kern_conn=4, background_class=None):
+ """Get the binary border mask of a labeled 2d range image.
+
+ Args:
+ nclasses(int) : The number of classes labeled in the input image
+ device(torch.device) : Process in host or cuda?
+ border_size(int) : How many erode iterations to perform for the mask
+ kern_conn(int) : The connectivity kernel number (4 or 8)
+ background_class(int) : "unlabeled" class in dataset (to avoid double borders)
+
+ Returns:
+ eroded_output(tensor) : The 2d binary border mask, 1 where a intersection
+ between classes occurs, 0 everywhere else
+
+ """
+ super().__init__()
+ self.nclasses = nclasses
+ self.device = device
+ self.border_size = border_size
+ self.kern_conn = kern_conn
+ self.background_class = background_class
+ if self.background_class is not None:
+ self.include_idx = list(range(self.nclasses))
+ self.exclude_idx = self.include_idx.pop(self.background_class)
+
+ # check connectivity
+ # For obtaining the border mask we will be eroding the input image, for this
+ # reason we only support erode_kernels with connectivity 4 or 8
+ assert self.kern_conn in (4, 8), ("The specified kernel connectivity(kern_conn= %r) is "
+ "not supported" % self.kern_conn)
+
+ # make the onehot inferer
+ self.onehot = oneHot(self.device,
+ self.nclasses,
+ spatial_dim=2) # range labels
+
+ def forward(self, range_label):
+ # length of shape of range_label must be 3 (N, H, W)
+ must_unbatch = False # remove batch dimension after operation?
+ if len(range_label.shape) != 3:
+ range_label = range_label[None, ...]
+ must_unbatch = True
+
+ # The range_label comes labeled, we need to create one tensor per class, thus:
+ input_tensor = self.onehot(range_label) # (N, C, H, W)
+
+ # Because we are using GT range_labels, there is a lot of pixels that end up
+ # unlabeled(thus, in the background). If we feed the erosion algorithm with
+ # this "raw" gt_labels we will detect intersection between the other classes
+ # and the backgorund, and we will end with the incorrect border mask. To solve
+ # this issue we need to pre process the input gt_label. The artifact in this
+ # case will be to sum the background channel(mostly the channel 0) to
+ # all the rest channels expect for the background channel itself.
+ # This will allow us to avoid detecting intersections between a class and the
+ # background. This also force us to change the logical AND we were doing to
+ # obtain the border mask when we were working with predicted labels.
+ # With predicted labels won't see this problem because all the pixels belongs
+ # to at least one class
+ if self.background_class is not None:
+ input_tensor[:, self.include_idx] = input_tensor[:, self.include_idx] + \
+ input_tensor[:, self.exclude_idx]
+
+ # C denotes a number of channels, N, H and W are dismissed
+ C = input_tensor.shape[1]
+
+ # Create an empty erode kernel and send it to 'device'
+ erode_kernel = torch.zeros((C, 1, 3, 3), device=self.device)
+ if self.kern_conn == 4:
+ erode_kernel[:] = torch.tensor([[0, 1, 0],
+ [1, 1, 1],
+ [0, 1, 0]], device=self.device)
+ else:
+ erode_kernel[:] = torch.tensor([[1, 1, 1],
+ [1, 1, 1],
+ [1, 1, 1]], device=self.device)
+
+ # to check connectivity
+ kernel_sum = erode_kernel[0][0].sum() # should be kern_conn + 1
+
+ # erode the input image border_size times
+ erode_input = input_tensor
+ for _ in range(self.border_size):
+ eroded_output = F.conv2d(erode_input, erode_kernel, groups=C, padding=1)
+ # Pick the elements that match the kernel_sum to obtain the eroded
+ # output and convert to dtype=float32
+ eroded_output = (eroded_output == kernel_sum).float()
+ erode_input = eroded_output
+
+ # We want to sum up all the channels into 1 unique border mask
+ # Even when we added the background to all the rest of the channels, there
+ # might be "bodies" in the background channel, thus, the erosion process can
+ # output "false positives" were this "bodies" are present in the background.
+ # We need to obtain the background mask and add it to the eroded bodies to
+ # obtain a consisent output once we calculate the border mask
+ if self.background_class is not None:
+ background_mask = (eroded_output[:, self.exclude_idx] == 1)
+
+ # The eroded_bodies mask will consist in all the pixels were the convolution
+ # returned 1 for all the channels, therefore we need to sum up all the
+ # channels into one unique tensor and add the background mask to avoid having
+ # the background in the border mask output
+ eroded_bodies = (eroded_output.sum(1, keepdim=True) == 1)
+ if self.background_class is not None:
+ eroded_bodies = eroded_bodies + background_mask
+
+ # we want the opposite
+ borders = 1 - eroded_bodies
+
+ # unbatch?
+ if must_unbatch:
+ borders = borders[0]
+ # import cv2
+ # import numpy as np
+ # bordersprint = (borders * 255).squeeze().cpu().numpy().astype(np.uint8)
+ # cv2.imshow("border", bordersprint)
+ # cv2.waitKey(0)
+
+ return borders
+
+
+if __name__ == "__main__":
+ import argparse
+ parser = argparse.ArgumentParser("./borderMask.py")
+ parser.add_argument(
+ '--scan', '-s',
+ type=str,
+ required=True,
+ help='Scan to get xyz. No Default',
+ )
+ parser.add_argument(
+ '--label', '-l',
+ type=str,
+ required=True,
+ help='Label to calculate border mask. No Default',
+ )
+ parser.add_argument(
+ '--exclude_class', '-e',
+ type=int,
+ required=False,
+ default=None,
+ help='Label to ignore. No Default',
+ )
+ parser.add_argument(
+ '--border', '-b',
+ type=int,
+ required=False,
+ default=1,
+ help='Border size. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--conn', '-c',
+ type=int,
+ required=False,
+ default=4,
+ help='Kernel connectivity. Defaults to %(default)s',
+ )
+ FLAGS, unparsed = parser.parse_known_args()
+
+ # print summary of what we will do
+ print("----------")
+ print("INTERFACE:")
+ print("Scan", FLAGS.scan)
+ print("Label", FLAGS.label)
+ print("Exclude class", FLAGS.exclude_class)
+ print("Border", FLAGS.border)
+ print("Connectivity", FLAGS.conn)
+ print("----------\n")
+
+ # get device
+ if torch.cuda.is_available():
+ device = torch.device('cuda')
+ else:
+ device = torch.device('cpu')
+
+ # define the border mask
+ bm = borderMask(300, device, FLAGS.border,
+ FLAGS.conn, FLAGS.exclude_class)
+
+ # imports for inference part
+ import cv2
+ import numpy as np
+ from common.laserscan import LaserScan, SemLaserScan
+
+ # open label and project
+ scan = SemLaserScan(project=True, max_classes=300)
+ scan.open_scan(FLAGS.scan)
+ scan.open_label(FLAGS.label)
+
+ # get the things I need
+ proj_range = torch.from_numpy(scan.proj_range).to(device)
+ proj_sem_label = torch.from_numpy(scan.proj_sem_label).long().to(device)
+ proj_sem_color = torch.from_numpy(scan.proj_sem_color).to(device)
+
+ # run the border mask
+ border_mask = bm(proj_sem_label)
+
+ # bring to numpy and normalize for showing
+ proj_range = proj_range.cpu().numpy()
+ proj_sem_label = proj_sem_label.cpu().numpy()
+ proj_sem_color = proj_sem_color.cpu().numpy()
+ border_mask = border_mask.cpu().numpy().squeeze()
+
+ # norm
+ proj_range = (proj_range / proj_range.max() * 255).astype(np.uint8)
+ border_mask = (border_mask * 255).astype(np.uint8)
+
+ # show
+ cv2.imshow("range", proj_range)
+ cv2.imshow("label", proj_sem_color)
+ cv2.imshow("border", border_mask)
+ cv2.waitKey(0)
+ cv2.destroyAllWindows()
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/requirements.txt b/pytorch_code/lidar-bonnetal/train/tasks/semantic/requirements.txt
new file mode 100644
index 0000000..2dd6e5c
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/requirements.txt
@@ -0,0 +1,11 @@
+numpy==1.14.0
+scipy==1.4.1
+torch==1.1.0
+tensorflow==1.13.1
+vispy==0.5.3
+torchvision==0.2.2.post3
+opencv_contrib_python==4.1.0.25
+matplotlib==2.2.3
+Pillow==6.1.0
+PyYAML==5.1.1
+thop
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/train.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/train.py
new file mode 100755
index 0000000..30bc68c
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/train.py
@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+import sys
+import argparse
+import subprocess
+import datetime
+import yaml
+from shutil import copyfile
+import os
+import shutil
+import __init__ as booger
+
+from tasks.semantic.modules.trainer import *
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser("./train.py")
+ parser.add_argument(
+ '--dataset', '-d',
+ type=str,
+ required=True,
+ help='Dataset to train with. No Default',
+ )
+ parser.add_argument(
+ '--arch_cfg', '-ac',
+ type=str,
+ required=True,
+ help='Architecture yaml cfg file. See /config/arch for sample. No default!',
+ )
+ parser.add_argument(
+ '--data_cfg', '-dc',
+ type=str,
+ required=False,
+ default='config/labels/semantic-kitti.yaml',
+ help='Classification yaml cfg file. See /config/labels for sample. No default!',
+ )
+ parser.add_argument(
+ '--log', '-l',
+ type=str,
+ default=os.path.expanduser("~") + '/logs/' +
+ datetime.datetime.now().strftime("%Y-%-m-%d-%H:%M") + '/',
+ help='Directory to put the log data. Default: ~/logs/date+time'
+ )
+ parser.add_argument(
+ '--pretrained', '-p',
+ type=str,
+ required=False,
+ default=None,
+ help='Directory to get the pretrained model. If not passed, do from scratch!'
+ )
+ FLAGS, unparsed = parser.parse_known_args()
+
+ # print summary of what we will do
+ print("----------")
+ print("INTERFACE:")
+ print("dataset", FLAGS.dataset)
+ print("arch_cfg", FLAGS.arch_cfg)
+ print("data_cfg", FLAGS.data_cfg)
+ print("log", FLAGS.log)
+ print("pretrained", FLAGS.pretrained)
+ print("----------\n")
+ print("Commit hash (training version): ", str(
+ subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']).strip()))
+ print("----------\n")
+
+ # open arch config file
+ try:
+ print("Opening arch config file %s" % FLAGS.arch_cfg)
+ ARCH = yaml.safe_load(open(FLAGS.arch_cfg, 'r'))
+ except Exception as e:
+ print(e)
+ print("Error opening arch yaml file.")
+ quit()
+
+ # open data config file
+ try:
+ print("Opening data config file %s" % FLAGS.data_cfg)
+ DATA = yaml.safe_load(open(FLAGS.data_cfg, 'r'))
+ except Exception as e:
+ print(e)
+ print("Error opening data yaml file.")
+ quit()
+
+ # create log folder
+ try:
+ print(FLAGS.log)
+ if os.path.isdir(FLAGS.log):
+ shutil.rmtree(FLAGS.log)
+ os.makedirs(FLAGS.log)
+ except Exception as e:
+ print(e)
+ print("Error creating log directory. Check permissions!")
+ quit()
+
+ # does model folder exist?
+ if FLAGS.pretrained is not None:
+ if os.path.isdir(FLAGS.pretrained):
+ print("model folder exists! Using model from %s" % (FLAGS.pretrained))
+ else:
+ print("model folder doesnt exist! Start with random weights...")
+ else:
+ print("No pretrained directory found.")
+
+ # copy all files to log folder (to remember what we did, and make inference
+ # easier). Also, standardize name to be able to open it later
+ try:
+ print("Copying files to %s for further reference." % FLAGS.log)
+ copyfile(FLAGS.arch_cfg, FLAGS.log + "/arch_cfg.yaml")
+ copyfile(FLAGS.data_cfg, FLAGS.log + "/data_cfg.yaml")
+ except Exception as e:
+ print(e)
+ print("Error copying files, check permissions. Exiting...")
+ quit()
+
+ # create trainer and start the training
+ trainer = Trainer(ARCH, DATA, FLAGS.dataset, FLAGS.log, FLAGS.pretrained)
+ trainer.train()
diff --git a/pytorch_code/lidar-bonnetal/train/tasks/semantic/visualize.py b/pytorch_code/lidar-bonnetal/train/tasks/semantic/visualize.py
new file mode 100755
index 0000000..cd7db3f
--- /dev/null
+++ b/pytorch_code/lidar-bonnetal/train/tasks/semantic/visualize.py
@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+# This file is covered by the LICENSE file in the root of this project.
+
+import argparse
+import os
+import yaml
+import __init__ as booger
+
+from common.laserscan import LaserScan, SemLaserScan
+from common.laserscanvis import LaserScanVis
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser("./visualize.py")
+ parser.add_argument(
+ '--dataset', '-d',
+ type=str,
+ required=True,
+ help='Dataset to visualize. No Default',
+ )
+ parser.add_argument(
+ '--config', '-c',
+ type=str,
+ required=False,
+ default="config/labels/semantic-kitti.yaml",
+ help='Dataset config file. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--sequence', '-s',
+ type=str,
+ default="00",
+ required=False,
+ help='Sequence to visualize. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--predictions', '-p',
+ type=str,
+ default=None,
+ required=False,
+ help='Alternate location for labels, to use predictions folder. '
+ 'Must point to directory containing the predictions in the proper format '
+ ' (see readme)'
+ 'Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--ignore_semantics', '-i',
+ dest='ignore_semantics',
+ default=False,
+ action='store_true',
+ help='Ignore semantics. Visualizes uncolored pointclouds.'
+ 'Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--offset',
+ type=int,
+ default=0,
+ required=False,
+ help='Sequence to start. Defaults to %(default)s',
+ )
+ parser.add_argument(
+ '--ignore_safety',
+ dest='ignore_safety',
+ default=False,
+ action='store_true',
+ help='Normally you want the number of labels and ptcls to be the same,'
+ ', but if you are not done inferring this is not the case, so this disables'
+ ' that safety.'
+ 'Defaults to %(default)s',
+ )
+ FLAGS, unparsed = parser.parse_known_args()
+
+ # print summary of what we will do
+ print("*" * 80)
+ print("INTERFACE:")
+ print("Dataset", FLAGS.dataset)
+ print("Config", FLAGS.config)
+ print("Sequence", FLAGS.sequence)
+ print("Predictions", FLAGS.predictions)
+ print("ignore_semantics", FLAGS.ignore_semantics)
+ print("ignore_safety", FLAGS.ignore_safety)
+ print("offset", FLAGS.offset)
+ print("*" * 80)
+
+ # open config file
+ try:
+ print("Opening config file %s" % FLAGS.config)
+ CFG = yaml.safe_load(open(FLAGS.config, 'r'))
+ except Exception as e:
+ print(e)
+ print("Error opening yaml file.")
+ quit()
+
+ # fix sequence name
+ FLAGS.sequence = '{0:02d}'.format(int(FLAGS.sequence))
+
+ # does sequence folder exist?
+ scan_paths = os.path.join(FLAGS.dataset, "sequences",
+ FLAGS.sequence, "velodyne")
+ if os.path.isdir(scan_paths):
+ print("Sequence folder exists! Using sequence from %s" % scan_paths)
+ else:
+ print("Sequence folder doesn't exist! Exiting...")
+ quit()
+
+ # populate the pointclouds
+ scan_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(scan_paths)) for f in fn]
+ scan_names.sort()
+
+ # does sequence folder exist?
+ if not FLAGS.ignore_semantics:
+ if FLAGS.predictions is not None:
+ label_paths = os.path.join(FLAGS.predictions, "sequences",
+ FLAGS.sequence, "predictions")
+ else:
+ label_paths = os.path.join(FLAGS.dataset, "sequences",
+ FLAGS.sequence, "labels")
+ if os.path.isdir(label_paths):
+ print("Labels folder exists! Using labels from %s" % label_paths)
+ else:
+ print("Labels folder doesn't exist! Exiting...")
+ quit()
+ # populate the pointclouds
+ label_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(
+ os.path.expanduser(label_paths)) for f in fn]
+ label_names.sort()
+
+ # check that there are same amount of labels and scans
+ if not FLAGS.ignore_safety:
+ assert(len(label_names) == len(scan_names))
+
+ # create a scan
+ if FLAGS.ignore_semantics:
+ scan = LaserScan(project=True) # project all opened scans to spheric proj
+ else:
+ color_dict = CFG["color_map"]
+ scan = SemLaserScan(color_dict, project=True)
+
+ # create a visualizer
+ semantics = not FLAGS.ignore_semantics
+ if not semantics:
+ label_names = None
+ vis = LaserScanVis(scan=scan,
+ scan_names=scan_names,
+ label_names=label_names,
+ offset=FLAGS.offset,
+ semantics=semantics,
+ instances=False)
+
+ # print instructions
+ print("To navigate:")
+ print("\tb: back (previous scan)")
+ print("\tn: next (next scan)")
+ print("\tq: quit (exit program)")
+
+ # run the visualizer
+ vis.run()
diff --git a/tensorflow_code/LICENSE b/tensorflow_code/LICENSE
new file mode 100644
index 0000000..0f6fecc
--- /dev/null
+++ b/tensorflow_code/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Pierre Biasutti
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/tensorflow_code/README.md b/tensorflow_code/README.md
new file mode 100644
index 0000000..e3be2b5
--- /dev/null
+++ b/tensorflow_code/README.md
@@ -0,0 +1,43 @@
+# 3D-MiniNet: Tensorflow Implementation
+
+The original code is the one implemented in pytorch. This is just a port to tensorflow.
+## Requirements
+The instructions have been tested on Ubuntu 16.04 with Python 3.6 and Tensorflow 1.13 with GPU support.
+
+Download the SqueezeSeg dataset as explained [here](https://github.com/xuanyuzhou98/SqueezeSegV2) in Section "Dataset". Then, in **make_tfrecord_pn.py** line 29, set the variable "**semantic_base**" to the path of the SqueezeSeg dataset folder.
+
+```python
+semantic_base = "/path/to/squeezeseg/dataset/"
+```
+
+## Train and validate 3D-MiniNet
+You can generate the _training_ and _validation_ **TFRecords** by running the following command:
+```bash
+python make_tfrecord_pn.py --config=config/3dmininet.cfg
+```
+
+Once done, you can start training the model by running:
+```bash
+python train.py --config=config/3dmininet.cfg --gpu=0
+```
+You can set which GPU is being used by tuning the `--gpu` parameter.
+
+While training, you can run the validation as following:
+```bash
+python test.py --config=config/3dmininet.cfg --gpu=1
+```
+This script will run the validation on each new checkpoint as soon as they are created, and will store the scores in a text file.
+
+During training, the code will produce logs for Tensorboard, as specified in the configuration file (see after). You can run Tensorboard while training with the following command:
+```bash
+tensorboard --logdir=training_path/logs
+```
+
+## Data Augmentation
+
+The current data augmentation is disable (commented on the training file).
+The data augmentation explained in the paper is for a 360º view LIDAR and this dataset is not.
+For this dataset other data augmentation was implemented see the data [augmentation file](augmentation.py).
+For enabling the data augmentation just uncomment the line 268 from the training file.
+**The data augmentation is specific for this dataset and dataset format so if you are using other dataset, do not use this one.**
+Besides, the current daat augmentation is done in the 2D space (images), not on the 3D space (point cloud) which is preferable.
\ No newline at end of file
diff --git a/tensorflow_code/augmentation.py b/tensorflow_code/augmentation.py
new file mode 100644
index 0000000..96a8424
--- /dev/null
+++ b/tensorflow_code/augmentation.py
@@ -0,0 +1,139 @@
+
+import random
+from scipy import misc,ndimage
+import cv2
+import scipy.misc
+import numpy as np
+
+def crop_center(img_np, cropx, cropy):
+ y, x = img_np.shape
+ startx = x // 2 - (cropx // 2)
+ starty = y // 2 - (cropy // 2)
+ return img_np[starty:starty + cropy, startx:startx + cropx]
+
+
+
+def augmentation(points_data, label_data):
+ """
+ All of this augmentation is done in the 2D space, not on the 3D space.
+ """
+
+ for b in range(points_data.shape[0]):
+ # Read
+ point_i = points_data[b, ...]
+ label_i = label_data[b, ...]
+ x = point_i[:, :, 0]
+ y = point_i[:, :, 1]
+ z = point_i[:, :, 2]
+ remission = point_i[:, :, 3]
+ depth = point_i[:, :, 4]
+
+ x = np.reshape(x, (64, 512))
+ y = np.reshape(y, (64, 512))
+ z = np.reshape(z, (64, 512))
+ remission = np.reshape(remission, (64, 512))
+ depth = np.reshape(depth, (64, 512))
+
+ label1 = label_i[:, :, 0]
+ label2 = label_i[:, :, 1]
+ label3 = label_i[:, :, 2]
+ label4 = label_i[:, :, 3]
+ label5 = label_i[:, :, 4]
+ label6 = label_i[:, :, 5]
+
+
+ # Augmentation 50% times
+ aug = random.random() > 0.5
+
+ if random.random() > 0.5 and aug: # Random flipping
+ x = np.fliplr(x)
+ y = np.fliplr(y)
+ # when you flip left_to_right, change the sign of the axis
+ y = - y
+ z = np.fliplr(z)
+ depth = np.fliplr(depth)
+ remission = np.fliplr(remission)
+ label1 = np.fliplr(label1)
+ label2= np.fliplr(label2)
+ label3 = np.fliplr(label3)
+ label4= np.fliplr(label4)
+ label5= np.fliplr(label5)
+ label6 = np.fliplr(label6)
+
+
+ if random.random() > 0.5 and aug: # Random shifts
+ x_shift= random.randint(0, 512) - 256
+ y_shift = random.randint(0, 24) - 12
+ x = ndimage.shift(x, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ y = ndimage.shift(y, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ z = ndimage.shift(z, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ depth = ndimage.shift(depth, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ remission = ndimage.shift(remission, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ label1 = ndimage.shift(label1, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ label2 = ndimage.shift(label2, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ label3 = ndimage.shift(label3, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ label5 = ndimage.shift(label5, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ label4 = ndimage.shift(label4, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+ label6 = ndimage.shift(label6, (y_shift, x_shift), None, order=0, mode='constant', cval=0)
+
+ if random.random() > 0.5 and aug: # Random zooms (in/out)
+ zoom = random.random() + 1
+ if random.random() > 0.5:
+ zoom = 1. / zoom
+
+ x = ndimage.zoom(x, zoom, output=None, order=0, mode='constant', cval=0.0)
+ y = ndimage.zoom(y, zoom, output=None, order=0, mode='constant', cval=0.0)
+ z = ndimage.zoom(z, zoom, output=None, order=0, mode='constant', cval=0.0)
+ depth = ndimage.zoom(depth, zoom, output=None, order=0, mode='constant', cval=0.0)
+ remission = ndimage.zoom(remission, zoom, output=None, order=0, mode='constant', cval=0.0)
+ label1 = ndimage.zoom(label1, zoom, output=None, order=0, mode='constant', cval=0.0)
+ label2 = ndimage.zoom(label2, zoom, output=None, order=0, mode='constant', cval=0.0)
+ label3 = ndimage.zoom(label3, zoom, output=None, order=0, mode='constant', cval=0.0)
+ label4 = ndimage.zoom(label4, zoom, output=None, order=0, mode='constant', cval=0.0)
+ label5 = ndimage.zoom(label5, zoom, output=None, order=0, mode='constant', cval=0.0)
+ label6 = ndimage.zoom(label6, zoom, output=None, order=0, mode='constant', cval=0.0)
+
+ if zoom > 1:
+
+ x = crop_center(x, 512, 64)
+ y = crop_center(y, 512, 64)
+ z = crop_center(z, 512, 64)
+ depth = crop_center(depth, 512, 64)
+ remission = crop_center(remission, 512, 64)
+ label1 = crop_center(label1, 512, 64)
+ label2 = crop_center(label2, 512, 64)
+ label3 = crop_center(label3, 512, 64)
+ label4 = crop_center(label4, 512, 64)
+ label5 = crop_center(label5, 512, 64)
+ label6 = crop_center(label6, 512, 64)
+
+ else:
+ x = cv2.resize(x, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ y = cv2.resize(y, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ z = cv2.resize(z, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ depth = cv2.resize(depth, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ remission = cv2.resize(remission, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ label1 = cv2.resize(label1, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ label2 = cv2.resize(label2, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ label3 = cv2.resize(label3, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ label4 = cv2.resize(label4, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ label5 = cv2.resize(label5, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+ label6 = cv2.resize(label6, dsize=(512, 64), interpolation=cv2.INTER_NEAREST)
+
+
+
+ label_data[b, :,:,0]= label1
+ label_data[b, :,:,1] = label2
+ label_data[b, :,:,2] = label3
+ label_data[b, :,:,3]= label4
+ label_data[b, :,:,4] = label5
+ label_data[b, :,:,5] = label6
+
+
+ points_data[b, :,:, 4] = np.reshape(depth, (32768, 1))
+ points_data[b, :,:, 3] =np.reshape(remission, (32768, 1))
+ points_data[b, :,:, 2] = np.reshape(z, (32768, 1))
+ points_data[b, :,:,1] = np.reshape(y, (32768, 1))
+ points_data[b, :,:, 0] = np.reshape(x, (32768, 1))
+
+ return points_data, label_data
\ No newline at end of file
diff --git a/tensorflow_code/config/3dmininet.cfg b/tensorflow_code/config/3dmininet.cfg
new file mode 100644
index 0000000..5a89ddf
--- /dev/null
+++ b/tensorflow_code/config/3dmininet.cfg
@@ -0,0 +1,34 @@
+[DATA]
+train : ../boundingbox/avod/rangeimage/pnl_train.txt
+validation : ../boundingbox/avod/rangeimage/pnl_val.txt
+tfrecord_train : data/pnl_train.tfrecord
+tfrecord_val : data/pnl_val.tfrecord
+augmentation : ["original"]
+n_size : [3, 3]
+channels : xyzdr
+pointnet : True
+
+[NETWORK]
+n_classes : 4
+img_width : 512
+img_height: 64
+
+[TRAINING]
+unet_depth : 5
+batch_size : 8
+learning_rate : 0.0003
+lr_decay_interval: 1000000
+lr_decay_value : 0.1
+focal_loss : True
+num_iterations : 1000000
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+
+[TRAINING_OUTPUT]
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+path : training_pnl2/
+logs : logs/
+model : model.ckpt
+save_interval: 2000
+
+[TEST]
+output_path : validation/
diff --git a/tensorflow_code/data/pnl_train.tfrecord b/tensorflow_code/data/pnl_train.tfrecord
new file mode 100644
index 0000000..e69de29
diff --git a/tensorflow_code/data/pnl_train.tfrecord.txt b/tensorflow_code/data/pnl_train.tfrecord.txt
new file mode 100644
index 0000000..e69de29
diff --git a/tensorflow_code/data/semantic_all.txt b/tensorflow_code/data/semantic_all.txt
new file mode 100644
index 0000000..288b4a4
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diff --git a/tensorflow_code/data/semantic_train.txt b/tensorflow_code/data/semantic_train.txt
new file mode 100644
index 0000000..e270c01
--- /dev/null
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diff --git a/tensorflow_code/data/semantic_val.txt b/tensorflow_code/data/semantic_val.txt
new file mode 100644
index 0000000..b0cd5a6
--- /dev/null
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diff --git a/tensorflow_code/data_loader.py b/tensorflow_code/data_loader.py
new file mode 100644
index 0000000..9c1f5d8
--- /dev/null
+++ b/tensorflow_code/data_loader.py
@@ -0,0 +1,209 @@
+import sys
+import numpy as np
+import cv2
+import os
+import math
+from scipy import interpolate
+
+def lindepth_to_mask(depth_linear, img_height, img_width):
+ return np.reshape(depth_linear, (img_height, img_width, 1)) > 0
+
+
+def clip_normalize(data, interval, log_transformed=False):
+ data_clip = np.clip(data, interval[0], interval[1])
+ if log_transformed:
+ #return (np.log(data_clip) - np.log(interval[0])) / (np.log(interval[1]) - np.log(interval[0]))
+ return data_clip
+ else:
+ return (data_clip - interval[0]) / (interval[1] - interval[0])
+
+
+def clip_mask_normalize(data, mask, interval, log_transformed=False):
+ outerval = np.logical_and(data < interval[1], data > interval[0])
+ mask = np.logical_and(mask, outerval)
+ data_clip = np.clip(data, interval[0], interval[1])
+
+ if log_transformed:
+ #return (np.log(data_clip) - np.log(interval[0])) / (np.log(interval[1]) - np.log(interval[0])), mask
+ return np.log(data_clip), mask
+ else:
+ return (data_clip - interval[0]) / (interval[1] - interval[0]), mask
+
+
+def fill_sky(data, mask, new_val):
+ ret, labels = cv2.connectedComponents(np.asarray(mask == 0).astype(np.uint8))
+ sky_label = labels[0, math.floor(mask.shape[1] / 2)]
+
+ cv2.imwrite("./validation/test.png", labels)
+ for c in range(data.shape[2]):
+ data[:,:,c] = np.where(labels == sky_label, new_val, data[:,:,c])
+
+ return data
+
+
+
+def apply_mask(data, mask):
+ tmp = np.zeros((data.shape[0], data.shape[1]))
+
+ if len(data.shape) == 2:
+ data[np.squeeze(mask)] == 0
+ else:
+ for c in range(data.shape[2]):
+ data[:,:,c] = np.where(np.squeeze(mask) == 1, data[:,:,c], tmp)
+
+ return data
+
+
+def ri_to_depth_height_mask(ri, depth_clip, height_clip):
+ mask = ri[:,:,0] > 0
+
+ depth, mask = clip_mask_normalize(np.sqrt(ri[:,:,0]**2 + ri[:,:,1]**2), mask, depth_clip, log_transformed = True)
+
+ height, mask = clip_mask_normalize(ri[:,:,2], mask, height_clip)
+
+ img = apply_mask(np.dstack((depth, height)).astype(np.float32), mask)
+
+ mask = mask
+
+ return img, mask
+
+def ri_to_depth_height_intensity_mask(ri, depth_clip, height_clip):
+ mask = ri[:,:,0] > 0
+
+ depth, mask = clip_mask_normalize(np.sqrt(ri[:,:,0]**2 + ri[:,:,1]**2), mask, depth_clip, log_transformed = True)
+
+ height, mask = clip_mask_normalize(ri[:,:,2], mask, height_clip)
+
+ ref = ri[:,:,3]
+
+ img = apply_mask(np.dstack((depth, height, ref)).astype(np.float32), mask)
+
+ mask = mask
+
+ return img, mask
+
+def ri_to_depth_height_intensity_mask_noclip(ri, depth_clip, height_clip):
+ mask = ri[:,:,0] > 0
+
+ depth = np.sqrt(ri[:,:,0]**2 + ri[:,:,1]**2)
+
+ height = ri[:,:,2]
+
+ ref = ri[:,:,3]
+
+ img = apply_mask(np.dstack((depth, height, ref)).astype(np.float32), mask)
+
+ mask = mask
+
+ return img, mask
+
+def ri_to_depth_height_mask_noclip(ri):
+ mask = ri[:,:,0] > 0
+
+ depth = np.sqrt(ri[:,:,0]**2 + ri[:,:,1]**2)
+
+ height = ri[:,:,2]
+
+ img = apply_mask(np.dstack((depth, height)).astype(np.float32), mask)
+
+ mask = mask
+
+ return img, mask
+
+def ri_to_xyz_mask(ri):
+ mask = ri[:,:,0] > 0
+
+ img = ri[:,:,0:3]
+
+ mask = mask
+
+ return img, mask
+
+def ri_to_xyz_intensity_depth_mask(ri):
+ mask = ri[:,:,0] > 0
+
+ img = ri[:,:,0:5]
+
+ mask = mask
+
+ return img, mask
+
+
+def interp_data(d, mask):
+ interp_output = np.zeros(d.shape)
+ x = np.arange(0, d.shape[1])
+ y = np.arange(0, d.shape[0])
+
+ xx, yy = np.meshgrid(x, y)
+
+ x1 = xx[mask]
+ y1 = yy[mask]
+ for c in range(d.shape[2]):
+ newarr = d[:,:,c]
+ newarr = newarr[mask]
+ interp_output[:,:,c] = interpolate.griddata((x1, y1), newarr.ravel(), (xx, yy), method='nearest')
+
+ return interp_output
+
+
+def pointnetize(groundtruth, n_size=[3, 3]):
+ y_offset = int(math.floor(n_size[0] / 2))
+ x_offset = int(math.floor(n_size[1] / 2))
+ n_len = n_size[0] * n_size[1]
+ mid_offset = int(math.floor(n_len / 2))
+ n_indices = np.delete(np.arange(n_len), mid_offset)
+
+ groundtruth_pad = np.pad(groundtruth, ((y_offset, y_offset),(x_offset, x_offset), (0, 0)), "symmetric")
+
+ n_output = np.zeros((groundtruth.shape[0], groundtruth.shape[1], n_len - 1, groundtruth.shape[2]))
+ p_output = np.zeros((groundtruth.shape[0], groundtruth.shape[1], 1, groundtruth.shape[2]))
+
+ for y in range(0, groundtruth.shape[0]):
+ for x in range(0, groundtruth.shape[1]):
+ patch = groundtruth_pad[y:y+n_size[0], x:x+n_size[1],:]
+ lin_patch = np.reshape(patch, (n_len, -1))
+
+ if lin_patch[mid_offset,0] != 0: # If center pixel is not empty
+ p = lin_patch[mid_offset, :]
+ n = lin_patch[n_indices, :]
+
+ mask_filled = n[:,0] != 0
+
+ n[mask_filled, 0:3] = n[mask_filled, 0:3]# - p[0:3] # Defined points in local coordinates
+
+ n_output[y,x,:,:] = n
+ p_output[y,x,:,:] = p
+
+ return p_output, n_output
+
+
+def gt_to_label(groundtruth, mask, n_classes):
+
+ # Compute weigthed mask
+ contours = np.zeros((mask.shape[0], mask.shape[1]), dtype=bool)
+
+ if np.amax(groundtruth) > n_classes-1:
+ print("[WARNING] There are more classes than expected !")
+
+ for c in range(1, int(np.amax(groundtruth))+1):
+ channel = (groundtruth == c).astype(np.float32)
+ gt_dilate = cv2.dilate(channel, cv2.getStructuringElement(cv2.MORPH_RECT,(3,3)))
+ gt_dilate = gt_dilate - channel
+ contours = np.logical_or(contours, gt_dilate == 1.0)
+
+ contours = contours.astype(np.float32) * mask
+
+ dist = cv2.distanceTransform((1 - contours).astype(np.uint8), cv2.DIST_L2, cv2.DIST_MASK_PRECISE)
+
+ weight_map = 0.1 + 1.0 * np.exp(- dist / (2.0 * 3.0**2.0))
+ weight_map = weight_map * mask
+
+ # Create output label for training
+ label = np.zeros((groundtruth.shape[0], groundtruth.shape[1], n_classes + 1))
+ for y in range(groundtruth.shape[0]):
+ for x in range(groundtruth.shape[1]):
+ label[y, x, int(groundtruth[y, x])] = 1.0
+
+ label[:,:,n_classes] = weight_map
+
+ return label
diff --git a/tensorflow_code/make_tfrecord_pn.py b/tensorflow_code/make_tfrecord_pn.py
new file mode 100644
index 0000000..b8dba2b
--- /dev/null
+++ b/tensorflow_code/make_tfrecord_pn.py
@@ -0,0 +1,120 @@
+import sys
+import tensorflow as tf
+import numpy as np
+import cv2
+import csv
+import os
+import glob
+import math
+import matplotlib.pyplot as plt
+
+
+# SQUEEZESEG DATASET
+# Channels description:
+# 0: X
+# 1: Y
+# 2: Z
+# 3: REFLECTANCE
+# 4: DEPTH
+# 5: LABEL
+
+
+# imports Settings manager
+sys.path.append('./')
+import data_loader
+from settings import Settings
+CONFIG = Settings(required_args=["config"])
+
+semantic_base = "lidar_2d/"
+
+
+# Generates tfrecords for training
+def make_tfrecord():
+
+ # Creates each tfrecord (train and val)
+ for dataset in ["train", "val"]:
+
+ # Get path
+ dataset_output = CONFIG.TFRECORD_TRAIN if dataset == "train" else CONFIG.TFRECORD_VAL
+
+ with tf.python_io.TFRecordWriter(dataset_output) as writer:
+
+ file_list_name = open(dataset_output + ".txt", "w")
+
+ if dataset == "val":
+ file_list = open("./data/semantic_val.txt","r")
+ else:
+ file_list = open("./data/semantic_train.txt", "r")
+
+ # Going through each example
+ line_num = 1
+ for file in file_list:
+
+ augmentation_list = ["normal"] if dataset is "val" else CONFIG.AUGMENTATION
+
+ # Augmentation settings
+ for aug_type in augmentation_list:
+
+ print("[{}] >> Processing file \"{}\" ({}), with augmentation : {}".format(dataset, file[:-1], line_num, aug_type))
+
+ # Load labels
+ data = np.load(semantic_base + file[:-1] + ".npy")
+
+ mask = data[:,:,0] != 0
+
+ #data = data_loader.interp_data(data[:,:,0:5], mask)
+
+ p, n = data_loader.pointnetize(data[:,:,0:5], n_size=CONFIG.N_SIZE)
+ groundtruth = data_loader.apply_mask(data[:,:,5], mask)
+
+ # Compute weigthed mask
+ contours = np.zeros((mask.shape[0], mask.shape[1]), dtype=bool)
+
+ if np.amax(groundtruth) > CONFIG.N_CLASSES-1:
+ print("[WARNING] There are more classes than expected !")
+
+ for c in range(1, int(np.amax(groundtruth))+1):
+ channel = (groundtruth == c).astype(np.float32)
+ gt_dilate = cv2.dilate(channel, cv2.getStructuringElement(cv2.MORPH_RECT,(3,3)))
+ gt_dilate = gt_dilate - channel
+ contours = np.logical_or(contours, gt_dilate == 1.0)
+
+ contours = contours.astype(np.float32) * mask
+
+ dist = cv2.distanceTransform((1 - contours).astype(np.uint8), cv2.DIST_L2, cv2.DIST_MASK_PRECISE)
+
+ # Create output label for training
+ label = np.zeros((groundtruth.shape[0], groundtruth.shape[1], CONFIG.N_CLASSES + 2))
+ for y in range(groundtruth.shape[0]):
+ for x in range(groundtruth.shape[1]):
+ label[y, x, int(groundtruth[y, x])] = 1.0
+
+ label[:,:,CONFIG.N_CLASSES] = dist
+ label[:,:,CONFIG.N_CLASSES+1] = mask
+
+ # Serialize example
+ n_raw = n.astype(np.float32).tostring()
+ p_raw = p.astype(np.float32).tostring()
+ label_raw = label.astype(np.float32).tostring()
+
+ # Create tf.Example
+ example = tf.train.Example(features=tf.train.Features(feature={
+ 'label': tf.train.Feature(bytes_list=tf.train.BytesList(value=[label_raw])),
+ 'neighbors': tf.train.Feature(bytes_list=tf.train.BytesList(value=[n_raw])),
+ 'points': tf.train.Feature(bytes_list=tf.train.BytesList(value=[p_raw]))}))
+
+ # Adding Example to tfrecord
+ writer.write(example.SerializeToString())
+
+ file_list_name.write(semantic_base + file[:-1] + ".npy\n")
+
+ line_num += 1
+
+ print("Process finished, stored {} entries in \"{}\"".format(line_num-1, dataset_output))
+
+ file_list_name.close()
+
+ print("All files created.")
+
+if __name__ == "__main__":
+ make_tfrecord()
diff --git a/tensorflow_code/mininet3d.py b/tensorflow_code/mininet3d.py
new file mode 100644
index 0000000..27a1956
--- /dev/null
+++ b/tensorflow_code/mininet3d.py
@@ -0,0 +1,568 @@
+import tensorflow as tf
+import sys
+sys.path.append('./')
+from settings import Settings
+CONFIG = Settings()
+
+############################ NETWORK ARCHITECTURE ############################
+
+# USEFUL LAYERS
+fc = tf.layers.dense
+conv = tf.layers.conv2d
+deconv = tf.layers.conv2d_transpose
+relu = tf.nn.relu
+maxpool = tf.layers.max_pooling2d
+dropout_layer = tf.layers.dropout
+batchnorm = tf.layers.batch_normalization
+winit = tf.contrib.layers.xavier_initializer()
+repeat = tf.contrib.layers.repeat
+arg_scope = tf.contrib.framework.arg_scope
+l2_regularizer = tf.contrib.layers.l2_regularizer
+
+
+def downsample(input, n_filters_out, is_training, bn=False, use_relu=False, l2=None, name="down"):
+ x = tf.layers.separable_conv2d(input, n_filters_out, (3, 3), strides=2, padding='same', activation=None,
+ dilation_rate=1, use_bias=False, depthwise_initializer=winit,
+ pointwise_initializer=winit,
+ pointwise_regularizer=l2_regularizer(0.00004))
+ x = tf.layers.batch_normalization(x, training=is_training)
+ x = tf.nn.relu(x)
+ return x
+
+
+def upsampling(inputs, scale):
+ return tf.image.resize_bilinear(inputs, size=[tf.shape(inputs)[1] * scale, tf.shape(inputs)[2] * scale],
+ align_corners=True)
+
+
+def residual_separable(input, n_filters, is_training, dropout=0.3, dilation=1, l2=None, name="down"):
+ x= tf.layers.separable_conv2d(input, n_filters, (3, 3), strides=1, padding='same', activation=None,
+ dilation_rate=dilation, use_bias=False, depthwise_initializer=winit, pointwise_initializer=winit,
+ pointwise_regularizer=l2_regularizer(0.00004))
+ x = tf.layers.batch_normalization(x, training=is_training)
+ x = dropout_layer(x, rate=dropout)
+ if input.shape[3] == x.shape[3]:
+ x = tf.add(x, input)
+ x = tf.nn.relu(x)
+ return x
+
+def residual_separable_multi(input, n_filters, is_training, dropout=0.3, dilation=1, l2=None, name="down"):
+ input_b = tf.identity(input)
+ d = tf.keras.layers.DepthwiseConv2D(3, strides=(1, 1), depth_multiplier=1, padding='same', use_bias=False)
+ x = d(input)
+ x = tf.layers.batch_normalization(x, training=is_training)
+ x = tf.nn.relu(x)
+
+ d2= tf.keras.layers.DepthwiseConv2D(3, strides=(1, 1), depth_multiplier=1, padding='same', use_bias=False)
+ d2.dilation_rate = (dilation, dilation)
+ x2 = d2(input)
+ x2 = tf.layers.batch_normalization(x2, training=is_training)
+ x2 = tf.nn.relu(x2)
+
+ x +=x2
+
+ x = tf.layers.conv2d(x, n_filters, 1, strides=1, padding='same', activation=None, kernel_initializer=winit,
+ dilation_rate=1, use_bias=False, kernel_regularizer=l2_regularizer(0.00004))
+ x = tf.layers.batch_normalization(x, training=is_training)
+
+ x = dropout_layer(x, rate=dropout)
+
+ if input.shape[3] == x.shape[3]:
+ x = tf.add(x, input_b)
+
+ x = tf.nn.relu(x)
+ return x
+
+
+def encoder_module(input, n_filters, is_training, dropout=0.3, dilation=[1,1], l2=None, name="down"):
+ x = residual_separable(input, n_filters, is_training, dropout=dropout, dilation=dilation[0], l2=l2, name=name)
+ x = residual_separable(x, n_filters, is_training, dropout=dropout, dilation=dilation[1], l2=l2, name=name)
+ return x
+
+def encoder_module_multi(input, n_filters, is_training, dropout=0.3, dilation=[1,1], l2=None, name="down"):
+ x = residual_separable_multi(input, n_filters, is_training, dropout=dropout, dilation=dilation[0], l2=l2, name=name)
+ x = residual_separable_multi(x, n_filters, is_training, dropout=dropout, dilation=dilation[1], l2=l2, name=name)
+ return x
+
+# Components
+def maxpool_layer(x,size,stride,name):
+ with tf.name_scope(name):
+ x = tf.layers.max_pooling2d(x, size, stride, padding='VALID')
+
+ return x
+
+def conv_layer(x, kernel, depth, train_logical, name):
+ with tf.variable_scope(name):
+ x = tf.layers.conv2d(x, depth, kernel, padding='SAME', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), bias_initializer=tf.zeros_initializer())
+ x = tf.layers.batch_normalization(x, training=train_logical, momentum=0.99, epsilon=0.001, center=True, scale=True)
+ x = tf.nn.relu(x)
+
+ return x
+
+def conv_transpose_layer(x, kernel, depth, stride, name):
+ with tf.variable_scope(name):
+ x = tf.contrib.layers.conv2d_transpose(x, depth, kernel, stride=stride, scope=name)
+
+ return x
+
+
+
+def mininet3d(points, neighbors, train_logical, mask):
+
+
+ with tf.variable_scope('net'):
+
+ '''
+ Calculate Relative features
+ '''
+
+ # neighbours: (?, 32768, 8, 5): (b, points, neigbours, features)
+ # points: (?, 32768, 1, 5): (b, points, neigbours, features)
+ # mask: (?, 64, 512, 1): (b, h, w, 1)
+ input_image = tf.reshape(points, (-1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, 5))
+ # input_image: (?, 64, 512, 5): (b, h, w, 5)
+
+
+ point_groups = tf.image.extract_image_patches(input_image, ksizes=[1, 4, 4, 1], strides=[1, 4, 4, 1], rates=[1, 1, 1, 1], padding='SAME')
+ point_groups = tf.reshape(point_groups, (-1, 16, 128, 16, 5)) # (b , h, w, , neighb, feat)
+ mask_groups = tf.image.extract_image_patches(mask, ksizes=[1, 4, 4, 1], strides=[1, 4, 4, 1], rates=[1, 1, 1, 1], padding='SAME')
+ mask_groups = tf.reshape(mask_groups, (-1, 16, 128, 16, 1)) # (b , h, w, neighb, feat)
+ # point_groups: (?, 16, 128, 16, 5): (b , h, w, neigh, feat)
+
+
+
+ # Get the mean point (taking apart non-valid points
+ point_groups_sumF =tf.reduce_sum(point_groups, axis=3) #(b , h, w, feat)
+ mask_groups_sumF = tf.reduce_sum(mask_groups, axis=3)
+ point_groups_mean = point_groups_sumF / mask_groups_sumF
+ # point_groups_mean: (?, 16, 128, 5): (b , h, w, feat)
+
+ point_groups_mean = tf.expand_dims(point_groups_mean, 3)
+ point_groups_mean = tf.tile(point_groups_mean, [1, 1, 1, 16, 1])
+ is_nan = tf.math.is_nan(point_groups_mean)
+ point_groups_mean = tf.where(is_nan, x=tf.zeros_like(point_groups_mean), y=point_groups_mean)
+
+
+ # substract mean point to points
+ relative_points = point_groups - point_groups_mean
+
+ mask_groups_tile = tf.tile(mask_groups, [1, 1, 1, 1, 5])
+ relative_points = tf.where(tf.cast(mask_groups_tile, dtype=tf.bool), x=relative_points, y=tf.zeros_like(relative_points))
+
+
+
+ # relative_points: (?, 16, 128, 16, 5): (b , h, w, neighb, feat)
+ xyz_rel = relative_points[:, :, :, :, 0:3]
+ relative_distance = tf.expand_dims(tf.norm(xyz_rel, ord='euclidean', axis=-1), axis=-1)
+ input_final = tf.concat([point_groups, relative_points, relative_distance], axis=-1)
+ # input_final: (?, 16, 128, 16, 11): (b , h, w, neighb, feat)
+ # neighbours: (?, 32768, 8, 5): (b, points, neigbours, features)
+
+ input_final = tf.reshape(input_final, (-1, 16*128, 16, 11)) # (?, 16*128, 16, 11)
+
+
+ x_conv = tf.layers.conv2d(input_final, 192, (1, 16), padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), bias_initializer=tf.zeros_initializer())
+ x_conv = tf.layers.batch_normalization(x_conv, training=train_logical, momentum=0.99, epsilon=0.001, center=True, scale=True)
+ x_conv = tf.nn.relu(x_conv)
+
+
+ x = conv_layer(input_final, (1, 1), 24, train_logical, name="point-based_local_1")
+ x = conv_layer(x, (1, 1), 48, train_logical, name="point-based_local_2")
+ x_context = maxpool_layer(x, (1, 16), (1, 1), name="point-maxpool1")
+
+ x = conv_layer(x, (1, 1), 96, train_logical, name="point-based_local_3")
+ x = conv_layer(x, (1, 1), 192, train_logical, name="point-based_local_4")
+
+ x_local = maxpool_layer(x, (1, 16), (1, 1), name="point-maxpool1")
+
+
+
+
+ x_context_image = tf.reshape(x_context, (-1, 16, 128, 48))
+ x_context_1 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 1, 1, 1], padding='SAME')
+ x_context_2 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 2, 2, 1], padding='SAME')
+ x_context_3 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 3, 3, 1], padding='SAME')
+ x_context_1 = tf.reshape(x_context_1, (-1, 16, 128, 9, 48))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16, 128, 9, 48))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16, 128, 9, 48))
+ x_context_1 = tf.reshape(x_context_1, (-1, 16*128, 9, 48))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16*128, 9, 48))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16*128, 9, 48))
+ x_context_1 = conv_layer(x_context_1, (1, 1), 96, train_logical, name="point-based_context_1")
+ x_context_2 = conv_layer(x_context_2, (1, 1), 48, train_logical, name="point-based_context_2")
+ x_context_3 = conv_layer(x_context_3, (1, 1), 48, train_logical, name="point-based_context_3")
+ x_context_1 = maxpool_layer(x_context_1, (1, 9), (1, 1), name="point-maxpool1_cont")
+ x_context_2 = maxpool_layer(x_context_2, (1, 9), (1, 1), name="point-maxpool2_cont")
+ x_context_3 = maxpool_layer(x_context_3, (1, 9), (1, 1), name="point-maxpool3_cont")
+ x_context_1 = tf.reshape(x_context_1, (-1, 16, 128, 96))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16, 128, 48))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16, 128, 48))
+
+ x_context = tf.concat([x_context_3, x_context_2, x_context_1], -1)
+
+ x_local = tf.reshape(x_local, (-1, 16, 128, 192))
+ x_conv = tf.reshape(x_conv, (-1, 16, 128, 192))
+
+ x = tf.concat([x_conv, x_local, x_context], -1)
+
+ atten = tf.reduce_mean(x, [1, 2], keep_dims=True)
+ atten = tf.layers.conv2d(atten, 576, (1, 1), padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), bias_initializer=tf.zeros_initializer())
+ atten = tf.sigmoid(atten)
+ x = tf.multiply(x, atten)
+ x = conv_layer(x, (1, 1), 192, train_logical, name="last")
+
+
+ x_branch = downsample(input_image, n_filters_out=192, is_training=train_logical)
+
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x2 = encoder_module(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = downsample(x2, n_filters_out=192, is_training=train_logical)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[2, 2], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[1, 4], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[2, 8], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[2, 2], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[1, 4], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[2, 8], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[2, 2], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[1, 4], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[2, 8], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[2, 2], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=192, is_training=train_logical, dilation=[1, 4], dropout=0.25)
+
+ x = upsampling(x, 2)
+ x = x + x2
+ x = residual_separable(x, 192, train_logical, dropout=0, dilation=1)
+ x = residual_separable(x, 192, train_logical, dropout=0, dilation=1)
+ x = residual_separable(x, 192, train_logical, dropout=0, dilation=1)
+ x = residual_separable(x, 192, train_logical, dropout=0, dilation=1)
+ x = upsampling(x, 2)
+ x = x + x_branch
+
+ x = residual_separable(x, 96, train_logical, dropout=0, dilation=1)
+ x = residual_separable(x, 96, train_logical, dropout=0, dilation=1)
+ x = upsampling(x, 2)
+
+ x = conv_layer(x, (3, 3), CONFIG.N_CLASSES, train_logical, 'fully_connected1')
+
+ y = tf.identity(x, name='y')
+
+ return y
+
+
+
+
+
+
+def mininet3dsmall(points, neighbors, train_logical, mask):
+
+ with tf.variable_scope('net'):
+ '''
+ Calculate Relative features
+ n'''
+
+ # neighbours: (?, 32768, 8, 5): (b, points, neigbours, features)
+ # points: (?, 32768, 1, 5): (b, points, neigbours, features)
+ # mask: (?, 64, 512, 1): (b, h, w, 1)
+ input_image = tf.reshape(points, (-1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, 5))
+ # input_image: (?, 64, 512, 5): (b, h, w, 5)
+
+
+ point_groups = tf.image.extract_image_patches(input_image, ksizes=[1, 4, 4, 1], strides=[1, 4, 4, 1], rates=[1, 1, 1, 1], padding='SAME')
+ point_groups = tf.reshape(point_groups, (-1, 16, 128, 16, 5)) # (b , h, w, , neighb, feat)
+ mask_groups = tf.image.extract_image_patches(mask, ksizes=[1, 4, 4, 1], strides=[1, 4, 4, 1], rates=[1, 1, 1, 1], padding='SAME')
+ mask_groups = tf.reshape(mask_groups, (-1, 16, 128, 16, 1)) # (b , h, w, neighb, feat)
+ # point_groups: (?, 16, 128, 16, 5): (b , h, w, neigh, feat)
+
+
+
+ # Get the mean point (taking apart non-valid points
+ point_groups_sumF =tf.reduce_sum(point_groups, axis=3) #(b , h, w, feat)
+ mask_groups_sumF = tf.reduce_sum(mask_groups, axis=3)
+ point_groups_mean = point_groups_sumF / mask_groups_sumF
+ # point_groups_mean: (?, 16, 128, 5): (b , h, w, feat)
+
+ point_groups_mean = tf.expand_dims(point_groups_mean, 3)
+ point_groups_mean = tf.tile(point_groups_mean, [1, 1, 1, 16, 1])
+ is_nan = tf.math.is_nan(point_groups_mean)
+ point_groups_mean = tf.where(is_nan, x=tf.zeros_like(point_groups_mean), y=point_groups_mean)
+
+
+ # substract mean point to points
+ relative_points = point_groups - point_groups_mean
+
+ mask_groups_tile = tf.tile(mask_groups, [1, 1, 1, 1, 5])
+ relative_points = tf.where(tf.cast(mask_groups_tile, dtype=tf.bool), x=relative_points, y=tf.zeros_like(relative_points))
+
+
+
+ # relative_points: (?, 16, 128, 16, 5): (b , h, w, neighb, feat)
+ xyz_rel = relative_points[:, :, :, :, 0:3]
+ relative_distance = tf.expand_dims(tf.norm(xyz_rel, ord='euclidean', axis=-1), axis=-1)
+ input_final = tf.concat([point_groups, relative_points, relative_distance], axis=-1)
+ # input_final: (?, 16, 128, 16, 11): (b , h, w, neighb, feat)
+ # neighbours: (?, 32768, 8, 5): (b, points, neigbours, features)
+
+ input_final = tf.reshape(input_final, (-1, 16*128, 16, 11)) # (?, 16*128, 16, 11)
+
+
+ x_conv = tf.layers.conv2d(input_final, 128, (1, 16), padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), bias_initializer=tf.zeros_initializer())
+ x_conv = tf.layers.batch_normalization(x_conv, training=train_logical, momentum=0.99, epsilon=0.001, center=True, scale=True)
+ x_conv = tf.nn.relu(x_conv)
+
+
+ x = conv_layer(input_final, (1, 1), 16, train_logical, name="point-based_local_1")
+ x = conv_layer(x, (1, 1), 32, train_logical, name="point-based_local_2")
+ x_context = maxpool_layer(x, (1, 16), (1, 1), name="point-maxpool1")
+
+ x = conv_layer(x, (1, 1), 64, train_logical, name="point-based_local_3")
+ x = conv_layer(x, (1, 1), 128, train_logical, name="point-based_local_4")
+
+ x_local = maxpool_layer(x, (1, 16), (1, 1), name="point-maxpool1")
+
+
+
+
+ x_context_image = tf.reshape(x_context, (-1, 16, 128, 32))
+ x_context_1 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 1, 1, 1], padding='SAME')
+ x_context_2 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 2, 2, 1], padding='SAME')
+ x_context_3 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 3, 3, 1], padding='SAME')
+ x_context_1 = tf.reshape(x_context_1, (-1, 16, 128, 9, 32))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16, 128, 9, 32))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16, 128, 9, 32))
+ x_context_1 = tf.reshape(x_context_1, (-1, 16*128, 9, 32))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16*128, 9, 32))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16*128, 9, 32))
+ x_context_1 = conv_layer(x_context_1, (1, 1), 64, train_logical, name="point-based_context_1")
+ x_context_2 = conv_layer(x_context_2, (1, 1), 32, train_logical, name="point-based_context_2")
+ x_context_3 = conv_layer(x_context_3, (1, 1), 32, train_logical, name="point-based_context_3")
+ x_context_1 = maxpool_layer(x_context_1, (1, 9), (1, 1), name="point-maxpool1_cont")
+ x_context_2 = maxpool_layer(x_context_2, (1, 9), (1, 1), name="point-maxpool2_cont")
+ x_context_3 = maxpool_layer(x_context_3, (1, 9), (1, 1), name="point-maxpool3_cont")
+ x_context_1 = tf.reshape(x_context_1, (-1, 16, 128, 64))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16, 128, 32))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16, 128, 32))
+
+ x_context = tf.concat([x_context_3, x_context_2, x_context_1], -1)
+
+ x_local = tf.reshape(x_local, (-1, 16, 128, 128))
+ x_conv = tf.reshape(x_conv, (-1, 16, 128, 128))
+
+ x = tf.concat([x_conv, x_local, x_context], -1)
+
+ atten = tf.reduce_mean(x, [1, 2], keep_dims=True)
+ atten = tf.layers.conv2d(atten, 128*3, (1, 1), padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), bias_initializer=tf.zeros_initializer())
+ atten = tf.sigmoid(atten)
+ x = tf.multiply(x, atten)
+ x = conv_layer(x, (1, 1), 128, train_logical, name="last")
+
+
+ x_branch = downsample(input_image, n_filters_out=128, is_training=train_logical)
+
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x2 = encoder_module(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = downsample(x2, n_filters_out=128, is_training=train_logical)
+
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[2, 2], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[1, 4], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[2, 8], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[2, 2], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[1, 4], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[2, 8], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=128, is_training=train_logical, dilation=[2, 2], dropout=0.25)
+
+ x = upsampling(x, 2)
+ x = x + x2
+ x = residual_separable(x, 128, train_logical, dropout=0, dilation=1)
+ x = residual_separable(x, 128, train_logical, dropout=0, dilation=1)
+ x = upsampling(x, 2)
+ x = x + x_branch
+
+ x = residual_separable(x, 64, train_logical, dropout=0, dilation=1)
+ x = upsampling(x, 2)
+ x = conv_layer(x, (3, 3), CONFIG.N_CLASSES, train_logical, 'fully_connected1')
+
+ y = tf.identity(x, name='y')
+
+ return y
+
+
+
+def mininet3dtiny(points, neighbors, train_logical, mask):
+
+ with tf.variable_scope('net'):
+ '''
+ Calculate Relative features
+ '''
+
+
+ # neighbours: (?, 32768, 8, 5): (b, points, neigbours, features)
+ # points: (?, 32768, 1, 5): (b, points, neigbours, features)
+ # mask: (?, 64, 512, 1): (b, h, w, 1)
+ input_image = tf.reshape(points, (-1, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, 5))
+ # input_image: (?, 64, 512, 5): (b, h, w, 5)
+
+
+ point_groups = tf.image.extract_image_patches(input_image, ksizes=[1, 4, 4, 1], strides=[1, 4, 4, 1], rates=[1, 1, 1, 1], padding='SAME')
+ point_groups = tf.reshape(point_groups, (-1, 16, 128, 16, 5)) # (b , h, w, , neighb, feat)
+ mask_groups = tf.image.extract_image_patches(mask, ksizes=[1, 4, 4, 1], strides=[1, 4, 4, 1], rates=[1, 1, 1, 1], padding='SAME')
+ mask_groups = tf.reshape(mask_groups, (-1, 16, 128, 16, 1)) # (b , h, w, neighb, feat)
+ # point_groups: (?, 16, 128, 16, 5): (b , h, w, neigh, feat)
+
+
+
+ # Get the mean point (taking apart non-valid points
+ point_groups_sumF =tf.reduce_sum(point_groups, axis=3) #(b , h, w, feat)
+ mask_groups_sumF = tf.reduce_sum(mask_groups, axis=3)
+ point_groups_mean = point_groups_sumF / mask_groups_sumF
+ # point_groups_mean: (?, 16, 128, 5): (b , h, w, feat)
+
+ point_groups_mean = tf.expand_dims(point_groups_mean, 3)
+ point_groups_mean = tf.tile(point_groups_mean, [1, 1, 1, 16, 1])
+ is_nan = tf.math.is_nan(point_groups_mean)
+ point_groups_mean = tf.where(is_nan, x=tf.zeros_like(point_groups_mean), y=point_groups_mean)
+
+
+ # substract mean point to points
+ relative_points = point_groups - point_groups_mean
+
+ mask_groups_tile = tf.tile(mask_groups, [1, 1, 1, 1, 5])
+ relative_points = tf.where(tf.cast(mask_groups_tile, dtype=tf.bool), x=relative_points, y=tf.zeros_like(relative_points))
+
+
+
+ # relative_points: (?, 16, 128, 16, 5): (b , h, w, neighb, feat)
+ xyz_rel = relative_points[:, :, :, :, 0:3]
+ relative_distance = tf.expand_dims(tf.norm(xyz_rel, ord='euclidean', axis=-1), axis=-1)
+ input_final = tf.concat([point_groups, relative_points, relative_distance], axis=-1)
+ # input_final: (?, 16, 128, 16, 11): (b , h, w, neighb, feat)
+ # neighbours: (?, 32768, 8, 5): (b, points, neigbours, features)
+
+ input_final = tf.reshape(input_final, (-1, 16*128, 16, 11)) # (?, 16*128, 16, 11)
+
+
+ x_conv = tf.layers.conv2d(input_final, 96, (1, 16), padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), bias_initializer=tf.zeros_initializer())
+ x_conv = tf.layers.batch_normalization(x_conv, training=train_logical, momentum=0.99, epsilon=0.001, center=True, scale=True)
+ x_conv = tf.nn.relu(x_conv)
+
+
+ x = conv_layer(input_final, (1, 1), 12, train_logical, name="point-based_local_1")
+ x = conv_layer(x, (1, 1), 24, train_logical, name="point-based_local_2")
+ x_context = maxpool_layer(x, (1, 16), (1, 1), name="point-maxpool1")
+
+ x = conv_layer(x, (1, 1), 48, train_logical, name="point-based_local_3")
+ x = conv_layer(x, (1, 1), 96, train_logical, name="point-based_local_4")
+
+ x_local = maxpool_layer(x, (1, 16), (1, 1), name="point-maxpool1")
+
+
+
+
+ x_context_image = tf.reshape(x_context, (-1, 16, 128, 24))
+ x_context_1 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 1, 1, 1], padding='SAME')
+ x_context_2 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 2, 2, 1], padding='SAME')
+ x_context_3 = tf.image.extract_image_patches(x_context_image, ksizes=[1, 3, 3, 1], strides=[1, 1, 1, 1], rates=[1, 3, 3, 1], padding='SAME')
+ x_context_1 = tf.reshape(x_context_1, (-1, 16, 128, 9, 24))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16, 128, 9, 24))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16, 128, 9, 24))
+ x_context_1 = tf.reshape(x_context_1, (-1, 16*128, 9, 24))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16*128, 9, 24))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16*128, 9, 24))
+ x_context_1 = conv_layer(x_context_1, (1, 1), 48, train_logical, name="point-based_context_1")
+ x_context_2 = conv_layer(x_context_2, (1, 1), 24, train_logical, name="point-based_context_2")
+ x_context_3 = conv_layer(x_context_3, (1, 1), 24, train_logical, name="point-based_context_3")
+ x_context_1 = maxpool_layer(x_context_1, (1, 9), (1, 1), name="point-maxpool1_cont")
+ x_context_2 = maxpool_layer(x_context_2, (1, 9), (1, 1), name="point-maxpool2_cont")
+ x_context_3 = maxpool_layer(x_context_3, (1, 9), (1, 1), name="point-maxpool3_cont")
+ x_context_1 = tf.reshape(x_context_1, (-1, 16, 128, 48))
+ x_context_2 = tf.reshape(x_context_2, (-1, 16, 128, 24))
+ x_context_3 = tf.reshape(x_context_3, (-1, 16, 128, 24))
+
+ x_context = tf.concat([x_context_3, x_context_2, x_context_1], -1)
+
+ x_local = tf.reshape(x_local, (-1, 16, 128, 96))
+ x_conv = tf.reshape(x_conv, (-1, 16, 128, 96))
+
+ x = tf.concat([x_conv, x_local, x_context], -1)
+
+ atten = tf.reduce_mean(x, [1, 2], keep_dims=True)
+ atten = tf.layers.conv2d(atten, 96*3, (1, 1), padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), bias_initializer=tf.zeros_initializer())
+ atten = tf.sigmoid(atten)
+ x = tf.multiply(x, atten)
+ x = conv_layer(x, (1, 1), 96, train_logical, name="last")
+
+
+ x_branch = downsample(input_image, n_filters_out=96, is_training=train_logical)
+
+ x = encoder_module(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x2 = encoder_module(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+
+ x = downsample(x2, n_filters_out=96, is_training=train_logical)
+ x = encoder_module_multi(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=96, is_training=train_logical, dilation=[2, 2], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=96, is_training=train_logical, dilation=[1, 4], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=96, is_training=train_logical, dilation=[2, 8], dropout=0.25)
+ x = encoder_module_multi(x, n_filters=96, is_training=train_logical, dilation=[1, 1], dropout=0.25)
+
+ x = upsampling(x, 2)
+ x = x + x2
+ x = residual_separable(x, 96, train_logical, dropout=0, dilation=1)
+ x = residual_separable(x, 96, train_logical, dropout=0, dilation=1)
+ x = upsampling(x, 2)
+ x = x + x_branch
+
+ x = residual_separable(x, 48, train_logical, dropout=0, dilation=1)
+ x = upsampling(x, 2)
+
+ x = conv_layer(x, (3, 3), CONFIG.N_CLASSES, train_logical, 'fully_connected1')
+
+ y = tf.identity(x, name='y')
+
+ return y
+
+
+
diff --git a/tensorflow_code/settings.py b/tensorflow_code/settings.py
new file mode 100644
index 0000000..ebfd724
--- /dev/null
+++ b/tensorflow_code/settings.py
@@ -0,0 +1,111 @@
+import os
+import glob
+import numpy as np
+from optparse import OptionParser
+import configparser
+
+class Settings(object):
+
+ CONFIG_NAME = None
+
+ N_CLASSES = None
+ IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_DEPTH = None, None, None
+
+ UNET_DEPTH = None
+ BATCH_SIZE = None
+ LEARNING_RATE = None
+ LR_DECAY_INTERVAL = None
+ LR_DECAY_VALUE = None
+ NUM_ITERS = None
+ FOCAL_LOSS = None
+ SAVE_INTERVAL = None
+ VAL_INTERVAL = None
+
+ OUTPUT_PATH = None
+ OUTPUT_MODEL = None
+ OUTPUT_LOGS = None
+
+ TFRECORD_TRAIN = None
+ TFRECORD_VAL = None
+ AUGMENTATION = None
+ N_SIZE = None
+ N_LEN = None
+ CHANNELS = ""
+ POINTNET = False
+
+ TEST_CHECKPOINT = None
+ TEST_OUTPUT_PATH = None
+
+ # Constructor
+ def __init__(self, required_args = ["config", "dataset", "gpu"]):
+ parser = OptionParser()
+
+ if "config" in required_args:
+ parser.add_option("-c", "--config", dest="config", help="Configure configuration")
+ if "gpu" in required_args:
+ parser.add_option("-g", "--gpu", dest="gpu", help="Configure visible GPU")
+ if "checkpoint" in required_args:
+ parser.add_option("-m", "--checkpoint", dest="checkpoint", help="Configure checkpoint to load")
+
+
+ (options, args) = parser.parse_args()
+
+
+ if "gpu" in required_args and options.gpu:
+ os.environ["CUDA_VISIBLE_DEVICES"] = options.gpu
+ elif "gpu" in required_args:
+ print('Please specify --gpu to select a GPU. You can run "gpustat" in order to see which GPU is available on the machine.')
+ exit(0)
+
+ config = None
+ if "config" in required_args and options.config:
+ config = configparser.ConfigParser()
+ config.read(options.config)
+ elif "config" in required_args:
+ print('Please specify --config to select a configuration file.')
+ exit(0)
+
+ self.CONFIG_NAME = options.config
+
+ # SETTING VALUES
+ if "config" in required_args:
+ self.TFRECORD_TRAIN = config["DATA"]["tfrecord_train"]
+ self.TFRECORD_VAL = config["DATA"]["tfrecord_val"]
+ self.AUGMENTATION = eval(config["DATA"]["augmentation"])
+ self.N_SIZE = eval(config["DATA"]["n_size"])
+ self.N_LEN = self.N_SIZE[0] * self.N_SIZE[1] - 1
+ self.CHANNELS = config["DATA"]["channels"]
+ self.POINTNET = eval(config["DATA"]["pointnet"])
+
+ self.N_CLASSES = int(config["NETWORK"]["n_classes"])
+ self.IMAGE_HEIGHT = int(config["NETWORK"]["img_height"])
+ self.IMAGE_WIDTH = int(config["NETWORK"]["img_width"])
+ self.IMAGE_DEPTH = len(self.CHANNELS)
+
+ self.UNET_DEPTH = int(config["TRAINING"]["unet_depth"])
+ self.BATCH_SIZE = int(config["TRAINING"]["batch_size"])
+ self.LEARNING_RATE = float(config["TRAINING"]["learning_rate"])
+ self.LR_DECAY_INTERVAL = float(config["TRAINING"]["lr_decay_interval"])
+ self.LR_DECAY_VALUE = float(config["TRAINING"]["lr_decay_value"])
+ self.NUM_ITERS = int(config["TRAINING"]["num_iterations"])
+ self.FOCAL_LOSS = eval(config["TRAINING"]["focal_loss"])
+ self.SAVE_INTERVAL = int(config["TRAINING_OUTPUT"]["save_interval"])
+ self.VAL_INTERVAL = int(config["TRAINING"]["val_interval"])
+
+ self.OUTPUT_PATH = config["TRAINING_OUTPUT"]["path"]
+ self.OUTPUT_MODEL = self.OUTPUT_PATH + config["TRAINING_OUTPUT"]["model"]
+ self.OUTPUT_LOGS = self.OUTPUT_PATH + config["TRAINING_OUTPUT"]["logs"]
+
+ self.TEST_OUTPUT_PATH = config["TEST"]["output_path"]
+
+ if "checkpoint" in required_args and options.checkpoint:
+ self.TEST_CHECKPOINT = int(options.checkpoint)
+ elif "checkpoint" in required_args:
+ files = glob.glob(self.OUTPUT_MODEL + "-*.index")
+ checkpoints = []
+ for checkpoint in files:
+ checkpoint = checkpoint.replace(self.OUTPUT_MODEL + "-", "")
+ checkpoint = checkpoint.replace(".index", "")
+ checkpoints.append(int(checkpoint))
+
+ self.TEST_CHECKPOINT = max(checkpoints)
diff --git a/tensorflow_code/test.py b/tensorflow_code/test.py
new file mode 100644
index 0000000..5a43628
--- /dev/null
+++ b/tensorflow_code/test.py
@@ -0,0 +1,247 @@
+import tensorflow as tf
+import numpy as np
+import cv2
+
+import os
+import sys
+import time
+
+import matplotlib.pyplot as plt
+
+sys.path.append('./')
+import data_loader
+from settings import Settings
+CONFIG = Settings(required_args=["gpu","config","checkpoint"])
+
+
+# Computes softmax
+def softmax(x):
+ e_x = np.exp(x)
+ return e_x / np.expand_dims(np.sum(e_x, axis=2), axis=2)
+
+# Erase line in stdout
+def erase_line():
+ sys.stdout.write("\033[F")
+
+# Compute scores for a single image
+def compute_iou_per_class(pred, label, mask, n_class):
+
+ pred = np.argmax(pred[...,0:n_class], axis=2) * mask
+ label = label * mask
+
+ ious = np.zeros(n_class)
+ tps = np.zeros(n_class)
+ fns = np.zeros(n_class)
+ fps = np.zeros(n_class)
+
+ for cls_id in range(n_class):
+ tp = np.sum(pred[label == cls_id] == cls_id)
+ fp = np.sum(label[pred == cls_id] != cls_id)
+ fn = np.sum(pred[label == cls_id] != cls_id)
+
+ ious[cls_id] = tp/(tp+fn+fp+0.00000001)
+ tps[cls_id] = tp
+ fps[cls_id] = fp
+ fns[cls_id] = fn
+
+ return ious, tps, fps, fns
+
+# Create a colored image with depth or label colors
+def label_to_img(label_sm, depth, mask):
+ img = np.zeros((label_sm.shape[0], label_sm.shape[1], 3))
+
+ colors = np.array([[0,0,0],[78,205,196],[199,244,100],[255,107,107]])
+
+ label = np.argmax(label_sm, axis=2)
+ label = np.where(mask == 1, label, 0)
+
+ for y in range(0,label.shape[0]):
+ for x in range(0,label.shape[1]):
+ if label[y,x] == 0:
+ img[y,x,:] = [depth[y,x] * 255.0, depth[y,x] * 255.0, depth[y,x] * 255.0]
+ else:
+ img[y,x,:] = colors[label[y,x],:]
+
+ return img / 255.0
+
+# Export pointcloud with colored labels
+def label_to_xyz(label_sm, data, mask, file):
+ colors = np.array([[100,100,100],[78,205,196],[199,244,100],[255,107,107]])
+
+ ys, xs = np.where(mask == 1)
+ label = np.argmax(label_sm, axis=2)
+
+ file = open(file, "w")
+ for p in range(0, ys.shape[0]):
+ x = xs[p]
+ y = ys[p]
+ l = label[y, x]
+ file.write("{} {} {} {} {} {}\n".format(data[y, x, 0], data[y, x, 1], data[y, x, 2], colors[l, 0], colors[l, 1], colors[l, 2]))
+
+ file.close()
+
+
+# Takes a sequence of channels and returns the corresponding indices in the rangeimage
+def seq_to_idx(seq):
+ idx = []
+ if "x" in seq:
+ idx.append(0)
+ if "y" in seq:
+ idx.append(1)
+ if "z" in seq:
+ idx.append(2)
+ if "r" in seq:
+ idx.append(3)
+ if "d" in seq:
+ idx.append(4)
+
+ return np.array(idx, dtype=np.intp)
+
+# Read a single file
+def read_example(string_record):
+
+ # Create example
+ example = tf.train.Example()
+ example.ParseFromString(string_record)
+
+ features = example.features.feature
+
+ points_lin = np.fromstring(features["points"].bytes_list.value[0], dtype=np.float32)
+ neighbors_lin = np.fromstring(features["neighbors"].bytes_list.value[0], dtype=np.float32)
+ label_lin = np.fromstring(features["label"].bytes_list.value[0], dtype=np.float32)
+
+ points = np.reshape(points_lin, (CONFIG.IMAGE_HEIGHT * CONFIG.IMAGE_WIDTH, 1, 5))
+ neighbors = np.reshape(neighbors_lin, (CONFIG.IMAGE_HEIGHT * CONFIG.IMAGE_WIDTH, CONFIG.N_LEN, 5))
+
+ points = np.take(points, seq_to_idx(CONFIG.CHANNELS), axis=2)
+ neighbors = np.take(neighbors, seq_to_idx(CONFIG.CHANNELS), axis=2)
+
+ label = np.reshape(label_lin, (CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.N_CLASSES + 2))
+ groundtruth = np.argmax(label[:,:,0:CONFIG.N_CLASSES], axis=2)
+ mask = label[:,:,CONFIG.N_CLASSES+1] == 1
+
+ return points, neighbors, groundtruth, label[:,:,0:CONFIG.N_CLASSES], mask, label
+
+
+
+# Run test routine
+def test(checkpoint = None, display=False):
+
+ # Which checkpoint should be tested
+ if checkpoint is not None:
+ CONFIG.TEST_CHECKPOINT = checkpoint
+
+ # Create output dir if needed
+ if not os.path.exists(CONFIG.TEST_OUTPUT_PATH):
+ os.makedirs(CONFIG.TEST_OUTPUT_PATH)
+
+ print("Processing dataset file \"{}\" for checkpoint {}:".format(CONFIG.TFRECORD_VAL, str(CONFIG.TEST_CHECKPOINT)))
+
+ graph = tf.Graph()
+ with tf.Session(graph=graph) as sess:
+ print(CONFIG.OUTPUT_MODEL + "-" + str(CONFIG.TEST_CHECKPOINT) + ".meta")
+ loader = tf.train.import_meta_graph(CONFIG.OUTPUT_MODEL + "-" + str(CONFIG.TEST_CHECKPOINT) + ".meta")
+ loader.restore(sess, CONFIG.OUTPUT_MODEL + "-" + str(CONFIG.TEST_CHECKPOINT))
+
+ points = graph.get_tensor_by_name("points_placeholder:0")
+ neighbors = graph.get_tensor_by_name("neighbors_placeholder:0")
+ train_flag = graph.get_tensor_by_name("flag_placeholder:0")
+ labels = graph.get_tensor_by_name("label_placeholder:0")
+ y = graph.get_tensor_by_name("net/y:0")
+
+
+ # Dataset iterator
+ record_iterator = tf.python_io.tf_record_iterator(path=CONFIG.TFRECORD_VAL)
+
+ # Running network on each example
+ line_num = 1
+
+ tps_sum = 0
+ fns_sum = 0
+ fps_sum = 0
+
+ for string_record in record_iterator:
+
+ CONFIG.BATCH_SIZE = 1
+ points_data, neighbors_data, groundtruth, label, mask, labels_data = read_example(string_record)
+
+ ref = np.reshape(points_data, (CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.IMAGE_DEPTH))
+ img = ref
+ groundtruth = data_loader.apply_mask(groundtruth, mask)
+ # Inference
+ data = sess.run(y, feed_dict = {points: [points_data], neighbors: [neighbors_data], labels: [labels_data], train_flag: False})
+ pred = softmax(data[0,:,:,:])
+
+ if display:
+ plt.subplot(4,1,1)
+ plt.imshow(ref[:,:,3] * mask)
+ plt.title("Reflectance (for visualization)")
+ plt.subplot(4,1,2)
+ plt.imshow(pred[:,:,1] * mask)
+ plt.title("Car prob")
+ plt.subplot(4,1,3)
+ plt.imshow(np.argmax(pred, axis=2) * mask)
+ plt.title("Prediction")
+ plt.subplot(4,1,4)
+ plt.imshow(groundtruth)
+ plt.title("Label")
+ plt.show()
+
+
+ iou, tps, fps, fns = compute_iou_per_class(pred, groundtruth, mask, CONFIG.N_CLASSES)
+
+ tps_sum += tps
+ fns_sum += fns
+ fps_sum += fps
+
+ # print(" >> Processed file {}: IoUs {}".format(line_num, iou))
+
+ line_num += 1
+
+
+ ious = tps_sum.astype(np.float)/(tps_sum + fns_sum + fps_sum + 0.000000001)
+ pr = tps_sum.astype(np.float)/(tps_sum + fps_sum + 0.000000001)
+ re = tps_sum.astype(np.float)/(tps_sum + fns_sum + 0.000000001)
+
+ output = "[{}] Accuracy:\n".format(checkpoint)
+ for i in range(1, CONFIG.N_CLASSES):
+ output += "\tPixel-seg: P: {:.3f}, R: {:.3f}, IoU: {:.3f}\n".format(pr[i], re[i], ious[i])
+ output += "\n"
+
+ return output, ious[1:]
+
+
+def ckpt_exists(ckpt):
+ return os.path.isfile(CONFIG.OUTPUT_MODEL + "-" + str(ckpt) + ".meta")
+
+if __name__ == "__main__":
+ file = open("results_" + os.path.basename(CONFIG.CONFIG_NAME)[:-4] + ".txt", "w")
+
+ ckpt = CONFIG.SAVE_INTERVAL
+ best_cpkt = ''
+ best_miou = 0
+ iou_list = []
+ while ckpt <= CONFIG.NUM_ITERS:
+ output, ious = test(checkpoint = ckpt)
+ if np.mean(ious) > best_miou:
+ best_cpkt = ckpt
+ iou_list = ious
+ best_miou = np.mean(ious)
+ print('BEST IOU')
+
+ print(output)
+ file.write(output)
+ file.flush()
+ print('best ious:')
+ print(best_cpkt)
+ print(iou_list)
+ print(best_miou)
+ print('')
+
+ while not ckpt_exists(ckpt + CONFIG.SAVE_INTERVAL) and ckpt < CONFIG.NUM_ITERS:
+ print("Waiting for the next checkpoint ...")
+ time.sleep(60)
+
+ ckpt += CONFIG.SAVE_INTERVAL
+
+ file.close()
diff --git a/tensorflow_code/train.py b/tensorflow_code/train.py
new file mode 100644
index 0000000..391581f
--- /dev/null
+++ b/tensorflow_code/train.py
@@ -0,0 +1,301 @@
+import os
+import tensorflow as tf
+import numpy as np
+import cv2
+import time
+import glob
+import math
+from augmentation import augmentation
+from mininet3d import mininet3d
+import sys
+sys.path.append('./')
+from settings import Settings
+CONFIG = Settings()
+
+
+############################## TFRECORD READER ##############################
+
+# Takes a sequence of channels and returns the corresponding indices in the rangeimage
+def seq_to_idx(seq):
+ idx = []
+ if "x" in seq:
+ idx.append(0)
+ if "y" in seq:
+ idx.append(1)
+ if "z" in seq:
+ idx.append(2)
+ if "r" in seq:
+ idx.append(3)
+ if "d" in seq:
+ idx.append(4)
+
+ return np.array(idx, dtype=np.intp)
+
+
+# Loads a queue of random examples, and returns a batch iterator for each input
+# and output
+def read_example(filename, batch_size):
+
+ # Open tfrecord
+ reader = tf.TFRecordReader()
+ filename_queue = tf.train.string_input_producer([filename], num_epochs=None)
+ _, serialized_example = reader.read(filename_queue)
+
+ # Create random queue
+ min_queue_examples = 500
+ batch = tf.train.shuffle_batch([serialized_example], batch_size=batch_size, capacity=min_queue_examples+100*batch_size, min_after_dequeue=min_queue_examples, num_threads=2)
+
+ # Read a batch
+ parsed_example = tf.parse_example(batch,features={'neighbors': tf.FixedLenFeature([], tf.string),'points': tf.FixedLenFeature([], tf.string),'label': tf.FixedLenFeature([], tf.string)})
+
+ # Decode point cloud
+ idx = seq_to_idx(CONFIG.CHANNELS)
+
+ points_raw = tf.decode_raw(parsed_example['points'], tf.float32)
+ points = tf.reshape(points_raw, [batch_size, CONFIG.IMAGE_HEIGHT * CONFIG.IMAGE_WIDTH, 1 , 5])
+ points = tf.gather(points, seq_to_idx(CONFIG.CHANNELS), axis=3)
+
+ neighbors_raw = tf.decode_raw(parsed_example['neighbors'], tf.float32)
+ neighbors = tf.reshape(neighbors_raw, [batch_size, CONFIG.IMAGE_HEIGHT * CONFIG.IMAGE_WIDTH, CONFIG.N_LEN, 5])
+ neighbors = tf.gather(neighbors, seq_to_idx(CONFIG.CHANNELS), axis=3)
+
+ # Decode label
+ label_raw = tf.decode_raw(parsed_example['label'], tf.float32)
+ label = tf.reshape(label_raw, [batch_size, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.N_CLASSES + 2])
+
+ return points, neighbors, label
+
+
+
+
+
+############################ NETWORK LOSS ############################
+
+# Returns slices of a tensor
+def slice_tensor(x, start, end=None):
+ if end < 0:
+ y = x[...,start:]
+ else:
+ if end is None:
+ end = start
+ y = x[...,start:end + 1]
+
+ return y
+
+
+def u_net_loss(pred, label):
+
+ with tf.variable_scope('loss'):
+ # Retrieve mask on last channel of the label
+ mask = slice_tensor(label, CONFIG.N_CLASSES + 1, -1)
+ dist = slice_tensor(label, CONFIG.N_CLASSES, CONFIG.N_CLASSES)
+ label = slice_tensor(label, 0, CONFIG.N_CLASSES - 1)
+
+ weight_norm = 2.0 * 3.0 ** 2.0
+
+ weights_ce = 0.1 + 1.0 * tf.exp(- dist / weight_norm)
+ weights_ce = weights_ce * mask
+
+ # Compute the cross entropy
+ if CONFIG.FOCAL_LOSS:
+ with tf.name_scope('focal_loss'):
+ epsilon = 1.e-9
+ gamma = 2.
+ pred_softmax = tf.nn.softmax(pred)
+ cross_entropy = tf.multiply(label, -tf.log(pred_softmax))
+ weights_fl = tf.multiply(label, tf.pow(tf.subtract(1., pred_softmax), gamma))
+ weigths_total = weights_fl * weights_ce
+ loss = tf.reduce_sum(weights_ce * weights_fl * cross_entropy) / tf.reduce_sum(weights_ce)
+
+ tf.summary.scalar("global_loss", loss)
+ else:
+ with tf.name_scope('loss'):
+
+ cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=label, logits=pred, name="cross_entropy")
+ loss = tf.reduce_sum(tf.reshape(cross_entropy, [CONFIG.BATCH_SIZE, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, 1]) * weights_ce) / tf.reduce_sum(weights_ce)
+
+ tf.summary.scalar("global_loss", loss)
+
+ # Compute average precision
+ with tf.name_scope('average_precision'):
+ softmax_pred = tf.nn.softmax(pred)
+
+ argmax_pred = tf.math.argmax(softmax_pred, axis=3)
+ mask_bin = tf.squeeze(tf.math.greater(mask, 0))
+ for c in range(1, CONFIG.N_CLASSES):
+ p = tf.math.equal(argmax_pred, c)
+ l = tf.squeeze(tf.math.equal(slice_tensor(label, c, c), 1.0))
+
+ intersection = tf.logical_and(p, l)
+ union = tf.logical_or(p, l)
+
+ iou = tf.reduce_sum(tf.cast(tf.logical_and(intersection, mask_bin), tf.float32)) / (tf.reduce_sum(tf.cast(tf.logical_and(union, mask_bin), tf.float32)) + 0.00000001)
+ tf.summary.scalar("iou_class_0" + str(c), iou)
+
+
+ # Display prediction and groundtruth for object of class 1
+ with tf.variable_scope('predictions'):
+ for i in range(0, CONFIG.N_CLASSES):
+ tf.summary.image('pred_class_0' + str(i),
+ tf.reshape(tf.transpose(slice_tensor(pred, i, i) * mask, perm=[0,3,1,2]), (CONFIG.BATCH_SIZE, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, 1)),
+ max_outputs=CONFIG.BATCH_SIZE)
+
+
+ with tf.variable_scope('labels'):
+ for i in range(0, CONFIG.N_CLASSES):
+ tf.summary.image('label_class_0' + str(i),
+ tf.reshape(tf.transpose(slice_tensor(label, i, i) * mask, perm=[0,3,1,2]), (CONFIG.BATCH_SIZE, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, 1)),
+ max_outputs=CONFIG.BATCH_SIZE)
+
+
+ return loss
+
+
+
+
+############################ TRAINING MANAGER ############################
+
+# Displays configuration
+def print_config():
+ print("\n----------- RIU-NET CONFIGURATION -----------")
+ print("input channels : {}".format(CONFIG.CHANNELS.upper()))
+ print("input dims : {}x{}x{}".format(CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.IMAGE_DEPTH))
+ print("pointnet embeddings: {}".format("yes" if CONFIG.POINTNET == True else "no"))
+ print("focal loss : {}".format("yes" if CONFIG.FOCAL_LOSS == True else "no"))
+ print("# of parameters : {}".format(np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])))
+ print("---------------------------------------------\n")
+# Compute the average example processing time
+def time_to_speed(batch_time, batch_size):
+ return round(float(batch_size) / batch_time, 2)
+
+# Pretty obvious
+def make_dir(directory):
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+
+# Returns last saved checkpoint index
+def get_last_checkpoint(output_model):
+ files = glob.glob(output_model + "-*.index")
+ checkpoints = [-1]
+ for checkpoint in files:
+ checkpoint = checkpoint.replace(output_model + "-", "")
+ checkpoint = checkpoint.replace(".index", "")
+ checkpoints.append(int(checkpoint))
+
+ return max(checkpoints)
+
+# Computes current learning rate given the decay settings
+def get_learning_rate(iteration, start_rate, decay_interval, decay_value):
+ rate = start_rate * (decay_value ** math.floor(iteration / decay_interval))
+ return rate
+
+
+# Training routine
+def train():
+ # Remove deprecated messages
+ tf.logging.set_verbosity(tf.logging.ERROR)
+
+ # Reading TFRecords
+ with tf.name_scope('train_batch'):
+ batch_points, batch_neighbors, batch_label = read_example(CONFIG.TFRECORD_TRAIN, CONFIG.BATCH_SIZE)
+ with tf.name_scope('val_batch'):
+ val_points, val_neighbors, val_label = read_example(CONFIG.TFRECORD_VAL, CONFIG.BATCH_SIZE)
+ with tf.name_scope('learning_rate'):
+ learning_rate = tf.placeholder(tf.float32, shape=[], name="learning_rate_placeholder")
+
+ # Save learning rate
+ tf.summary.scalar("learning_rate", learning_rate, family="learning_rate")
+
+
+ # Creating input placeholder
+ points = tf.placeholder(shape = [None, CONFIG.IMAGE_HEIGHT * CONFIG.IMAGE_WIDTH, 1, CONFIG.IMAGE_DEPTH], dtype=tf.float32, name='points_placeholder')
+ neighbors = tf.placeholder(shape = [None, CONFIG.IMAGE_HEIGHT * CONFIG.IMAGE_WIDTH, CONFIG.N_LEN, CONFIG.IMAGE_DEPTH], dtype=tf.float32, name='neighbors_placeholder')
+ label = tf.placeholder(shape = [None, CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH, CONFIG.N_CLASSES+2], dtype=tf.float32, name='label_placeholder')
+
+ train_flag = tf.placeholder(dtype=tf.bool, name='flag_placeholder')
+
+
+ # Create Network and Loss operator
+ mask = slice_tensor(label, CONFIG.N_CLASSES + 1, -1)
+
+ y = mininet3d(points, neighbors, train_flag, mask)
+ loss = u_net_loss(y, label)
+
+
+ # Creating optimizer
+ opt = tf.train.AdamOptimizer(learning_rate=CONFIG.LEARNING_RATE)
+
+ update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+ with tf.control_dependencies(update_ops):
+ train_step = opt.minimize(loss)
+
+ # Merging summaries
+ summary_op = tf.summary.merge_all()
+
+ # Saver for checkpoints
+ saver = tf.train.Saver(max_to_keep=1000)
+
+ # Starting session
+ sess = tf.Session()
+ init = tf.global_variables_initializer()
+ sess.run(init)
+
+ # Check if a checkpoint already exists and load it
+ start_step = 0
+ if get_last_checkpoint(CONFIG.OUTPUT_MODEL) >= 0:
+ start_step = get_last_checkpoint(CONFIG.OUTPUT_MODEL)
+ print("[From checkpoint] Restoring checkpoint {}".format(start_step))
+ saver.restore(sess, CONFIG.OUTPUT_MODEL + "-" + str(start_step))
+
+
+ # Creating threads for batch sampling
+ coord = tf.train.Coordinator()
+ threads = tf.train.start_queue_runners(sess=sess, coord=coord)
+
+ # Creating output logs
+ train_summary_writer = tf.summary.FileWriter(CONFIG.OUTPUT_LOGS + "train/", tf.get_default_graph())
+ val_summary_writer = tf.summary.FileWriter(CONFIG.OUTPUT_LOGS + "val/", tf.get_default_graph())
+
+ # Display config
+ print_config()
+
+ # Starting iterations
+ for i in range(start_step, CONFIG.NUM_ITERS):
+
+ # Retrieving current batch
+ points_data, neighbors_data, label_data = sess.run([batch_points, batch_neighbors, batch_label])
+
+ # points_data, label_data = augmentation(points_data, label_data)
+
+ # Computing learning rate
+ rate = get_learning_rate(i, CONFIG.LEARNING_RATE, CONFIG.LR_DECAY_INTERVAL, CONFIG.LR_DECAY_VALUE)
+
+ # Training Network on the current batch
+ t = time.time()
+ _, loss_data, data = sess.run([train_step, loss, y], feed_dict={train_flag: True, points: points_data, neighbors: neighbors_data, label: label_data, learning_rate: rate})
+
+ print('[Training] Iteration: {}, loss: {}, {} e/s, {}lr '.format(int(i), loss_data, time_to_speed(time.time() - t, CONFIG.BATCH_SIZE), rate))
+
+ # If summary has to be saved
+ if i % 100 == 0:
+ summary_str = sess.run(summary_op, feed_dict={train_flag: True, points: points_data, neighbors: neighbors_data, label: label_data, learning_rate: rate})
+ train_summary_writer.add_summary(summary_str, i)
+ # If checkpoint has to be saved
+ if (i + 1) % CONFIG.SAVE_INTERVAL == 0:
+ saver.save(sess, CONFIG.OUTPUT_MODEL, global_step=i+1)
+ # If validation should be done
+ if i % CONFIG.VAL_INTERVAL == 0:
+ # Retrieving validation batch
+ points_data, neighbors_data, label_data = sess.run([val_points, val_neighbors, val_label])
+
+ # Running summaries and saving
+ summary_str = sess.run(summary_op, feed_dict={train_flag: False, points: points_data, neighbors: neighbors_data, label: label_data, learning_rate: rate})
+ val_summary_writer.add_summary(summary_str, i)
+
+ print("[Validation] Done on one batch")
+
+ # Saving final network configuration
+ saver.save(sess, CONFIG.OUTPUT_MODEL, global_step=i+1)
+
+if __name__ == "__main__":
+ train()
diff --git a/tensorflow_code/weights/3D-MiniNet-small/checkpoint b/tensorflow_code/weights/3D-MiniNet-small/checkpoint
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+model_checkpoint_path: "model.ckpt-108000"
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diff --git a/tensorflow_code/weights/3D-MiniNet-tiny/checkpoint b/tensorflow_code/weights/3D-MiniNet-tiny/checkpoint
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+model_checkpoint_path: "model.ckpt-119000"
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+model_checkpoint_path: "model.ckpt-114000"
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