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Repository: microsoft/S2R-DepthNet
Branch: main
Commit: aebc931c7e8c
Files: 22
Total size: 68.9 MB
Directory structure:
gitextract_h1a_g1sl/
├── .github/
│ └── workflows/
│ └── codeql-analysis.yml
├── .gitignore
├── CODE_OF_CONDUCT.md
├── DSAEncoder/
│ └── drn_d_22-4bd2f8ea.pth
├── DSAModules.py
├── LICENSE
├── README.md
├── SECURITY.md
├── SUPPORT.md
├── datasets/
│ ├── kitti/
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── val.txt
│ └── vkitti/
│ ├── test.txt
│ ├── train.txt
│ └── val.txt
├── loaddata.py
├── models/
│ └── modules.py
├── test.py
├── train.py
├── train_loss.py
├── transform.py
└── utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/codeql-analysis.yml
================================================
# For most projects, this workflow file will not need changing; you simply need
# to commit it to your repository.
#
# You may wish to alter this file to override the set of languages analyzed,
# or to provide custom queries or build logic.
#
# ******** NOTE ********
# We have attempted to detect the languages in your repository. Please check
# the `language` matrix defined below to confirm you have the correct set of
# supported CodeQL languages.
#
name: "CodeQL"
on:
push:
branches: [ main ]
pull_request:
# The branches below must be a subset of the branches above
branches: [ main ]
schedule:
- cron: '26 22 * * 4'
jobs:
analyze:
name: Analyze
runs-on: ubuntu-latest
permissions:
actions: read
contents: read
security-events: write
strategy:
fail-fast: false
matrix:
language: [ 'python' ]
# CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
# Learn more:
# https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
steps:
- name: Checkout repository
uses: actions/checkout@v2
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL
uses: github/codeql-action/init@v1
with:
languages: ${{ matrix.language }}
# If you wish to specify custom queries, you can do so here or in a config file.
# By default, queries listed here will override any specified in a config file.
# Prefix the list here with "+" to use these queries and those in the config file.
# queries: ./path/to/local/query, your-org/your-repo/queries@main
# Autobuild attempts to build any compiled languages (C/C++, C#, or Java).
# If this step fails, then you should remove it and run the build manually (see below)
- name: Autobuild
uses: github/codeql-action/autobuild@v1
# ℹ️ Command-line programs to run using the OS shell.
# 📚 https://git.io/JvXDl
# ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
# and modify them (or add more) to build your code if your project
# uses a compiled language
#- run: |
# make bootstrap
# make release
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@v1
================================================
FILE: .gitignore
================================================
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================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Microsoft Open Source Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
Resources:
- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns
================================================
FILE: DSAEncoder/drn_d_22-4bd2f8ea.pth
================================================
[File too large to display: 62.6 MB]
================================================
FILE: DSAModules.py
================================================
# https://docs.opensource.microsoft.com/content/releasing/copyright-headers.html
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import torch
BatchNorm = nn.BatchNorm2d
# __all__ = ['DRN', 'drn26', 'drn42', 'drn58']
webroot = 'https://tigress-web.princeton.edu/~fy/drn/models/'
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'drn-c-26': webroot + 'drn_c_26-ddedf421.pth',
'drn-c-42': webroot + 'drn_c_42-9d336e8c.pth',
'drn-c-58': webroot + 'drn_c_58-0a53a92c.pth',
'drn-d-22': webroot + 'drn_d_22-4bd2f8ea.pth',
'drn-d-38': webroot + 'drn_d_38-eebb45f0.pth',
'drn-d-54': webroot + 'drn_d_54-0e0534ff.pth',
'drn-d-105': webroot + 'drn_d_105-12b40979.pth',
'drn-d-105_ms': webroot + 'drn-d-105_ms_cityscapes.pth'
}
def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=padding, bias=False, dilation=dilation)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
dilation=(1, 1), residual=True):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride,
padding=dilation[0], dilation=dilation[0])
self.bn1 = BatchNorm(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes,
padding=dilation[1], dilation=dilation[1])
self.bn2 = BatchNorm(planes)
self.downsample = downsample
self.stride = stride
self.residual = residual
def forward(self, x):
residual = x
out = self.conv1(x) # 32/64 1/2
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
if self.residual:
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None,
dilation=(1, 1), residual=True):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = BatchNorm(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=dilation[1], bias=False,
dilation=dilation[1])
self.bn2 = BatchNorm(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = BatchNorm(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class DRN(nn.Module):
# BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', **kwargs
def __init__(self, block, layers, num_classes=1000,
channels=(16, 32, 64, 128, 256, 512, 512, 512),
out_map=False, out_middle=False, pool_size=28, arch='D'):
super(DRN, self).__init__()
self.inplanes = channels[0] # 16
self.out_map = out_map
self.out_dim = channels[-1]
self.out_middle = out_middle
self.arch = arch
if arch == 'C':
self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
padding=3, bias=False)
self.bn1 = BatchNorm(channels[0])
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(
BasicBlock, channels[0], layers[0], stride=1)
self.layer2 = self._make_layer(
BasicBlock, channels[1], layers[1], stride=2)
elif arch == 'D':
self.layer0 = nn.Sequential(
nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3, # 3/16 7x7
bias=False),
BatchNorm(channels[0]),
nn.ReLU(inplace=True)
)
self.layer1 = self._make_conv_layers(
channels[0], layers[0], stride=1) # (16, 1, stride=1)
self.layer2 = self._make_conv_layers(
channels[1], layers[1], stride=2) # (32, 1, stride=2)
self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2) # (64, 2)
self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2) # (128, 2)
self.layer5 = self._make_layer(block, channels[4], layers[4],
dilation=2, new_level=False) # (256, 2)
self.layer6 = None if layers[5] == 0 else \
self._make_layer(block, channels[5], layers[5], dilation=4, # (512, 2)
new_level=False)
if arch == 'C':
self.layer7 = None if layers[6] == 0 else \
self._make_layer(BasicBlock, channels[6], layers[6], dilation=2,
new_level=False, residual=False)
self.layer8 = None if layers[7] == 0 else \
self._make_layer(BasicBlock, channels[7], layers[7], dilation=1,
new_level=False, residual=False)
elif arch == 'D':
self.layer7 = None if layers[6] == 0 else \
self._make_conv_layers(channels[6], layers[6], dilation=2) # (512,1)
self.layer8 = None if layers[7] == 0 else \
self._make_conv_layers(channels[7], layers[7], dilation=1) # (512,1)
if num_classes > 0:
self.avgpool = nn.AvgPool2d(pool_size)
self.fc = nn.Conv2d(self.out_dim, num_classes, kernel_size=1,
stride=1, padding=0, bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, BatchNorm):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
new_level=True, residual=True):
assert dilation == 1 or dilation % 2 == 0
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
# 32/64 stride=2
# 128/256
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, #
kernel_size=1, stride=stride, bias=False),
BatchNorm(planes * block.expansion),
)
## 32/64
## 128/256 dilation=(2,2)
layers = list()
layers.append(block(
self.inplanes, planes, stride, downsample,
dilation=(1, 1) if dilation == 1 else (
dilation // 2 if new_level else dilation, dilation),
residual=residual))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, residual=residual,
dilation=(dilation, dilation)))
return nn.Sequential(*layers)
def _make_conv_layers(self, channels, convs, stride=1, dilation=1):
modules = []
# 16
for i in range(convs):
modules.extend([
nn.Conv2d(self.inplanes, channels, kernel_size=3,
stride=stride if i == 0 else 1,
padding=dilation, bias=False, dilation=dilation), # 16/16 3x3 16/32
BatchNorm(channels),
nn.ReLU(inplace=True)])
self.inplanes = channels
return nn.Sequential(*modules)
def forward(self, x):
y = list()
if self.arch == 'C':
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
elif self.arch == 'D':
x = self.layer0(x) # 3/16 7x7
x = self.layer1(x) # 16/16
y.append(x)
x = self.layer2(x) # 16/32 (1/2)
y.append(x)
x = self.layer3(x) # 32/64 (1/4)
y.append(x)
x = self.layer4(x) # 64/128 (1/8)
y.append(x)
x = self.layer5(x) # 128/256 (1/8)
y.append(x)
if self.layer6 is not None:
x = self.layer6(x) # 256/512 (1/8)
y.append(x)
if self.layer7 is not None:
x = self.layer7(x) # 512/512 (1/8)
y.append(x)
if self.layer8 is not None:
x = self.layer8(x) # 512/512 (1/8)
y.append(x)
if self.out_map:
x = self.fc(x)
else:
x = self.avgpool(x) #
x = self.fc(x)
x = x.view(x.size(0), -1)
if self.out_middle:
return x, y
else:
return x
class _UpProjection(nn.Sequential):
def __init__(self, num_input_features, num_output_features):
super(_UpProjection, self).__init__()
#self.reflect_pad_1 = nn.ReflectionPad2d(2)
self.conv1 = nn.Conv2d(num_input_features, num_output_features,
kernel_size=5, stride=1, padding=2, bias=False)
self.bn1 = nn.BatchNorm2d(num_output_features)
self.relu = nn.ReLU(inplace=True)
#self.reflect_pad_2 = nn.ReflectionPad2d(1)
self.conv1_2 = nn.Conv2d(num_output_features, num_output_features,
kernel_size=3, stride=1, padding=1, bias=False)
self.bn1_2 = nn.BatchNorm2d(num_output_features)
#self.reflect_pad_3 = nn.ReflectionPad2d(2)
self.conv2 = nn.Conv2d(num_input_features, num_output_features,
kernel_size=5, stride=1, padding=2, bias=False)
self.bn2 = nn.BatchNorm2d(num_output_features)
def forward(self, x, size):
x = F.interpolate(x, size=size, mode='bilinear',align_corners=True)
#x = F.upsample(x, size=size, mode='bilinear')
x_conv1 = self.relu(self.bn1(self.conv1(x)))
bran1 = self.bn1_2(self.conv1_2(x_conv1))
bran2 = self.bn2(self.conv2(x))
out = self.relu(bran1 + bran2)
return out
class AutoED(nn.Module):
def __init__(self, model):
super(AutoED, self).__init__()
self.E = _E(model)
self.D = _D()
def freeze_bn(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
def forward(self, x):
x_, size1, size2, size3 = self.E(x)
out = self.D(x_, size1, size2, size3)
return out
class _E(nn.Module):
def __init__(self, model):
super(_E, self).__init__()
self.base = nn.Sequential(*list(model.children())[:-2])
def forward(self, x):
x1 = self.base[0](x)
x2 = self.base[1](x1)
x3 = self.base[2](x2)
x4 = self.base[3](x3)
x5 = self.base[4](x4)
x6 = self.base[5](x5)
x7 = self.base[6](x6)
x8 = self.base[7](x7)
x9 = self.base[8](x8)
return x9,[x4.size(2),x4.size(3)],[x3.size(2),x3.size(3)], [x.size(2),x.size(3)]
class _D(nn.Module):
def __init__(self):
super(_D, self).__init__()
num_features = 512
self.up1 = _UpProjection(num_input_features=num_features, num_output_features=num_features // 2)
num_features = num_features // 2
self.up2 = _UpProjection(num_input_features=num_features, num_output_features=num_features // 2)
num_features = num_features // 2
self.up3 = _UpProjection(num_input_features=num_features, num_output_features=num_features // 2)
num_features = num_features // 2
self.conv = nn.Conv2d(num_features, 1, kernel_size=3, stride=1, bias=True)
self.reflect_pad = nn.ReflectionPad2d(1)
def forward(self, x, size1, size2, size3):
u1 = self.up1(x, size1)
u2 = self.up2(u1, size2)
u3 = self.up3(u2, size3)
u4 = self.reflect_pad(u3)
out = torch.sigmoid(self.conv(u4))
return out
class DRN_A(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(DRN_A, self).__init__()
self.out_dim = 512 * block.expansion
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
dilation=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
dilation=4)
self.avgpool = nn.AvgPool2d(28, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, BatchNorm):
m.weight.data.fill_(1)
m.bias.data.zero_()
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
# elif isinstance(m, nn.BatchNorm2d):
# nn.init.constant_(m.weight, 1)
# nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,
dilation=(dilation, dilation)))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def drn_a_50(pretrained=False, **kwargs):
model = DRN_A(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def drn_c_26(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-c-26']))
return model
def drn_c_42(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-c-42']))
return model
def drn_c_58(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-c-58']))
return model
def drn_d_22(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-22'], 'DSAEncoder'))
return model
def drn_d_24(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 2, 2], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-24', 'DSAEncoder']))
return model
def drn_d_38(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-38'], 'DSAEncoder'))
return model
def drn_d_40(pretrained=False, **kwargs):
model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-40'], 'DSAEncoder'))
return model
def drn_d_54(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-54'], 'DSAEncoder'))
return model
def drn_d_56(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-56'], 'DSAEncoder'))
return model
def drn_d_105(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 1, 1], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-105'], 'DSAEncoder'))
return model
def drn_d_107(pretrained=False, **kwargs):
model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 2, 2], arch='D', **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['drn-d-107'], 'DSAEncoder'))
return model
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) Microsoft Corporation.
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
================================================
FILE: README.md
================================================
# S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
This is the official PyTorch implementation of the paper [***S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation***](https://arxiv.org/pdf/2104.00877v2.pdf), ***CVPR 2021 (Oral), Xiaotian Chen, Yuwang Wang, Xuejin Chen, and Wenjun Zeng.***
## Citation
```
@inproceedings{Chen2021S2R-DepthNet,
title = {S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation},
author = {Chen, Xiaotian and Wang , Yuwang and Chen, Xuejin and Zeng, Wenjun},
conference={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2021}
}
```
## Introduction
Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the depth of scenes. We are the first to explore the learning of a depth-specific structural representation, which captures the essential feature for depth estimation and ignores irrelevant style information. Our S2R-DepthNet (Synthetic to Real DepthNet) can be well generalized to unseen real-world data directly even though it is only trained on synthetic data. S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domaininvariant structural representation from an image by disentangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation. Without access of any real-world images, our method even outperforms the state-of-the-art unsupervised domain adaptation methods which use real-world images of the target domain for training. In addition, when using a small amount of labeled real-world data, we achieve the state-of-the-art performance under the semi-supervised setting.
<br>
#### The following figure shows the overview of S2RDepthNet.
<br>
#### Examples of Depth-specific Structural Representation.<br>
<div align=center><img width="616" height="351" src="./img/intro.PNG"/></div>
## Usage
### Dependencies
- [Python3.6.9](https://www.python.org/downloads/)
- [PyTorch(1.7.1)](https://pytorch.org/)
- [torchvision(0.8.2+cu101)](https://pypi.org/project/torchvision/)
- [TensorboardX(2.1)](https://pypi.org/project/tensorboardX/)
- [matplotlib(3.3.4)](https://matplotlib.org/)
- [Pillow(8.1.2)](https://pypi.org/project/Pillow/)
## Datasets
The outdoor Synthetic Dataset is [vKITTI](http://www.europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds) and outdoor Real dataset is [KITTI](http://www.cvlibs.net/datasets/kitti/)
## TODO
- [x] Trianing Structure Encoder
## Pretrained Models
We also provide our trained models for inference(outdoor and indoor scenes). [Models Link](https://1drv.ms/u/s!AlwUS_9Kpa8qgkb9ldQbMAMgNd_I?e=aX2PWY)
### Train
As an example, use the following command to train S2RDepthNet on vKITTI.<br>
#### *Train Structure Decoder*
python train.py --syn_dataset VKITTI \
--syn_root "the path of vKITTI dataset" \
--syn_train_datafile datasets/vkitti/train.txt \
--batchSize 32 \
--loadSize 192 640 \
--Shared_Struct_Encoder_path "the path of pretrained Struct encoder(.pth)" \
--train_stage TrainStructDecoder
#### *Train DSA Module and DP module*
python train.py --syn_dataset VKITTI \
--syn_root "the path of vKITTI dataset" \
--syn_train_datafile datasets/vkitti/train.txt \
--batchSize 32 \
--loadSize 192 640 \
--Shared_Struct_Encoder_path "the path of pretrained Struct encoder(.pth)" \
--Struct_Decoder_path "the path of pretrained Structure decoder(.pth)" \
--train_stage TrainDSAandDPModule
### Evaluation
Use the following command to evaluate the trained S2RDepthNet on KITTI test data.<br>
python test.py --dataset KITTI --root "the path of kitti dataset" --test_datafile datasets/kitti/test.txt --loadSize 192 640 --Shared_Struct_Encoder_path "the path of pretrained Struct encoder(.pth)" --Struct_Decoder_path "the path of pretrained Structure decoder(.pth)" --DSAModle_path "the path of pretrained DSAModle(.pth)" --DepthNet_path "the path of pretrained DepthNet(.pth)" --out_dir "Path to save results"
Use the following command to evaluate the trained S2RDepthNet on NYUD-v2 test data.<br>
python test.py --dataset NYUD_V2 --root "the path of NYUD_V2 dataset" --test_datafile datasets/nyudv2/nyu2_test.csv --loadSize 192 256 --Shared_Struct_Encoder_path "the path of pretrained Struct encoder(.pth)" --Struct_Decoder_path "the path of pretrained Structure decoder(.pth)" --DSAModle_path "the path of pretrained DSAModle(.pth)" --DepthNet_path "the path of pretrained DepthNet(.pth)" --out_dir "Path to save results"
## Acknowledgement
We borrowed code from [GASDA](https://github.com/sshan-zhao/GASDA) and [VisualizationOC](https://github.com/JunjH/Visualizing-CNNs-for-monocular-depth-estimation).
================================================
FILE: SECURITY.md
================================================
<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->
## Security
Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).
If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below.
## Reporting Security Issues
**Please do not report security vulnerabilities through public GitHub issues.**
Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report).
If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc).
You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc).
Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
* Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
* Full paths of source file(s) related to the manifestation of the issue
* The location of the affected source code (tag/branch/commit or direct URL)
* Any special configuration required to reproduce the issue
* Step-by-step instructions to reproduce the issue
* Proof-of-concept or exploit code (if possible)
* Impact of the issue, including how an attacker might exploit the issue
This information will help us triage your report more quickly.
If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://microsoft.com/msrc/bounty) page for more details about our active programs.
## Preferred Languages
We prefer all communications to be in English.
## Policy
Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).
<!-- END MICROSOFT SECURITY.MD BLOCK -->
================================================
FILE: SUPPORT.md
================================================
# TODO: The maintainer of this repo has not yet edited this file
**REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project?
- **No CSS support:** Fill out this template with information about how to file issues and get help.
- **Yes CSS support:** Fill out an intake form at [aka.ms/spot](https://aka.ms/spot). CSS will work with/help you to determine next steps. More details also available at [aka.ms/onboardsupport](https://aka.ms/onboardsupport).
- **Not sure?** Fill out a SPOT intake as though the answer were "Yes". CSS will help you decide.
*Then remove this first heading from this SUPPORT.MD file before publishing your repo.*
# Support
## How to file issues and get help
This project uses GitHub Issues to track bugs and feature requests. Please search the existing
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feature request as a new Issue.
For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE
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## Microsoft Support Policy
Support for this **PROJECT or PRODUCT** is limited to the resources listed above.
================================================
FILE: datasets/kitti/test.txt
================================================
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FILE: datasets/kitti/train.txt
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2011_09_28/2011_09_28_drive_0001_sync/image_02/data/0000000105.png 2011_09_28/2011_09_28_drive_0001_sync/image_03/data/0000000105.png 2011_09_28 2011_09_28/2011_09_28_drive_0001_sync/velodyne_points/data/0000000105.bin
2011_09_26/2011_09_26_drive_0057_sync/image_02/data/0000000116.png 2011_09_26/2011_09_26_drive_0057_sync/image_03/data/0000000116.png 2011_09_26 2011_09_26/2011_09_26_drive_0057_sync/velodyne_points/data/0000000116.bin
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2011_09_26/2011_09_26_drive_0032_sync/image_02/data/0000000280.png 2011_09_26/2011_09_26_drive_0032_sync/image_03/data/0000000280.png 2011_09_26 2011_09_26/2011_09_26_drive_0032_sync/velodyne_points/data/0000000280.bin
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2011_09_30/2011_09_30_drive_0020_sync/image_02/data/0000000934.png 2011_09_30/2011_09_30_drive_0020_sync/image_03/data/0000000934.png 2011_09_30 2011_09_30/2011_09_30_drive_0020_sync/velodyne_points/data/0000000934.bin
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2011_09_26/2011_09_26_drive_0028_sync/image_02/data/0000000309.png 2011_09_26/2011_09_26_drive_0028_sync/image_03/data/0000000309.png 2011_09_26 2011_09_26/2011_09_26_drive_0028_sync/velodyne_points/data/0000000309.bin
2011_09_30/2011_09_30_drive_0034_sync/image_02/data/0000000743.png 2011_09_30/2011_09_30_drive_0034_sync/image_03/data/0000000743.png 2011_09_30 2011_09_30/2011_09_30_drive_0034_sync/velodyne_points/data/0000000743.bin
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000001281.png 2011_10_03/2011_10_03_drive_0034_sync/image_03/data/0000001281.png 2011_10_03 2011_10_03/2011_10_03_drive_0034_sync/velodyne_points/data/0000001281.bin
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000003239.png 2011_09_30/2011_09_30_drive_0028_sync/image_03/data/0000003239.png 2011_09_30 2011_09_30/2011_09_30_drive_0028_sync/velodyne_points/data/0000003239.bin
2011_09_30/2011_09_30_drive_0020_sync/image_02/data/0000000979.png 2011_09_30/2011_09_30_drive_0020_sync/image_03/data/0000000979.png 2011_09_30 2011_09_30/2011_09_30_drive_0020_sync/velodyne_points/data/0000000979.bin
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000004073.png 2011_09_30/2011_09_30_drive_0028_sync/image_03/data/0000004073.png 2011_09_30 2011_09_30/2011_09_30_drive_0028_sync/velodyne_points/data/0000004073.bin
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000004027.png 2011_10_03/2011_10_03_drive_0034_sync/image_03/data/0000004027.png 2011_10_03 2011_10_03/2011_10_03_drive_0034_sync/velodyne_points/data/0000004027.bin
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000003873.png 2011_10_03/2011_10_03_drive_0034_sync/image_03/data/0000003873.png 2011_10_03 2011_10_03/2011_10_03_drive_0034_sync/velodyne_points/data/0000003873.bin
2011_09_26/2011_09_26_drive_0057_sync/image_02/data/0000000125.png 2011_09_26/2011_09_26_drive_0057_sync/image_03/data/0000000125.png 2011_09_26 2011_09_26/2011_09_26_drive_0057_sync/velodyne_points/data/0000000125.bin
2011_09_26/2011_09_26_drive_0022_sync/image_02/data/0000000532.png 2011_09_26/2011_09_26_drive_0022_sync/image_03/data/0000000532.png 2011_09_26 2011_09_26/2011_09_26_drive_0022_sync/velodyne_points/data/0000000532.bin
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000003862.png 2011_09_30/2011_09_30_drive_0028_sync/image_03/data/0000003862.png 2011_09_30 2011_09_30/2011_09_30_drive_0028_sync/velodyne_points/data/0000003862.bin
2011_09_26/2011_09_26_drive_0019_sync/image_02/data/0000000012.png 2011_09_26/2011_09_26_drive_0019_sync/image_03/data/0000000012.png 2011_09_26 2011_09_26/2011_09_26_drive_0019_sync/velodyne_points/data/0000000012.bin
2011_09_26/2011_09_26_drive_0019_sync/image_02/data/0000000031.png 2011_09_26/2011_09_26_drive_0019_sync/image_03/data/0000000031.png 2011_09_26 2011_09_26/2011_09_26_drive_0019_sync/velodyne_points/data/0000000031.bin
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000001405.png 2011_09_30/2011_09_30_drive_0033_sync/image_03/data/0000001405.png 2011_09_30 2011_09_30/2011_09_30_drive_0033_sync/velodyne_points/data/0000001405.bin
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000001459.png 2011_09_30/2011_09_30_drive_0033_sync/image_03/data/0000001459.png 2011_09_30 2011_09_30/2011_09_30_drive_0033_sync/velodyne_points/data/0000001459.bin
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000003282.png 2011_10_03/2011_10_03_drive_0034_sync/image_03/data/0000003282.png 2011_10_03 2011_10_03/2011_10_03_drive_0034_sync/velodyne_points/data/0000003282.bin
2011_09_30/2011_09_30_drive_0020_sync/image_02/data/0000000551.png 2011_09_30/2011_09_30_drive_0020_sync/image_03/data/0000000551.png 2011_09_30 2011_09_30/2011_09_30_drive_0020_sync/velodyne_points/data/0000000551.bin
2011_09_26/2011_09_26_drive_0051_sync/image_02/data/0000000042.png 2011_09_26/2011_09_26_drive_0051_sync/image_03/data/0000000042.png 2011_09_26 2011_09_26/2011_09_26_drive_0051_sync/velodyne_points/data/0000000042.bin
2011_09_26/2011_09_26_drive_0070_sync/image_02/data/0000000340.png 2011_09_26/2011_09_26_drive_0070_sync/image_03/data/0000000340.png 2011_09_26 2011_09_26/2011_09_26_drive_0070_sync/velodyne_points/data/0000000340.bin
2011_09_30/2011_09_30_drive_0034_sync/image_02/data/0000000707.png 2011_09_30/2011_09_30_drive_0034_sync/image_03/data/0000000707.png 2011_09_30 2011_09_30/2011_09_30_drive_0034_sync/velodyne_points/data/0000000707.bin
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000000900.png 2011_09_30/2011_09_30_drive_0033_sync/image_03/data/0000000900.png 2011_09_30 2011_09_30/2011_09_30_drive_0033_sync/velodyne_points/data/0000000900.bin
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000001337.png 2011_10_03/2011_10_03_drive_0034_sync/image_03/data/0000001337.png 2011_10_03 2011_10_03/2011_10_03_drive_0034_sync/velodyne_points/data/0000001337.bin
2011_09_30/2011_09_30_drive_0033_sync/image_02/data/0000000892.png 2011_09_30/2011_09_30_drive_0033_sync/image_03/data/0000000892.png 2011_09_30 2011_09_30/2011_09_30_drive_0033_sync/velodyne_points/data/0000000892.bin
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000000196.png 2011_09_30/2011_09_30_drive_0028_sync/image_03/data/0000000196.png 2011_09_30 2011_09_30/2011_09_30_drive_0028_sync/velodyne_points/data/0000000196.bin
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000000820.png 2011_09_30/2011_09_30_drive_0028_sync/image_03/data/0000000820.png 2011_09_30 2011_09_30/2011_09_30_drive_0028_sync/velodyne_points/data/0000000820.bin
2011_09_30/2011_09_30_drive_0028_sync/image_02/data/0000001385.png 2011_09_30/2011_09_30_drive_0028_sync/image_03/data/0000001385.png 2011_09_30 2011_09_30/2011_09_30_drive_0028_sync/velodyne_points/data/0000001385.bin
2011_09_30/2011_09_30_drive_0034_sync/image_02/data/0000000414.png 2011_09_30/2011_09_30_drive_0034_sync/image_03/data/0000000414.png 2011_09_30 2011_09_30/2011_09_30_drive_0034_sync/velodyne_points/data/0000000414.bin
2011_09_26/2011_09_26_drive_0113_sync/image_02/data/0000000024.png 2011_09_26/2011_09_26_drive_0113_sync/image_03/data/0000000024.png 2011_09_26 2011_09_26/2011_09_26_drive_0113_sync/velodyne_points/data/0000000024.bin
2011_10_03/2011_10_03_drive_0034_sync/image_02/data/0000002698.png 2011_10_03/2011_10_03_drive_0034_sync/image_03/data/0000002698.png 2011_10_03 2011_10_03/2011_10_03_drive_0034_sync/velodyne_
gitextract_h1a_g1sl/ ├── .github/ │ └── workflows/ │ └── codeql-analysis.yml ├── .gitignore ├── CODE_OF_CONDUCT.md ├── DSAEncoder/ │ └── drn_d_22-4bd2f8ea.pth ├── DSAModules.py ├── LICENSE ├── README.md ├── SECURITY.md ├── SUPPORT.md ├── datasets/ │ ├── kitti/ │ │ ├── test.txt │ │ ├── train.txt │ │ └── val.txt │ └── vkitti/ │ ├── test.txt │ ├── train.txt │ └── val.txt ├── loaddata.py ├── models/ │ └── modules.py ├── test.py ├── train.py ├── train_loss.py ├── transform.py └── utils.py
SYMBOL INDEX (147 symbols across 8 files)
FILE: DSAModules.py
function conv3x3 (line 28) | def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):
class BasicBlock (line 33) | class BasicBlock(nn.Module):
method __init__ (line 36) | def __init__(self, inplanes, planes, stride=1, downsample=None,
method forward (line 50) | def forward(self, x):
class Bottleneck (line 69) | class Bottleneck(nn.Module):
method __init__ (line 72) | def __init__(self, inplanes, planes, stride=1, downsample=None,
method forward (line 87) | def forward(self, x):
class DRN (line 110) | class DRN(nn.Module):
method __init__ (line 112) | def __init__(self, block, layers, num_classes=1000,
method _make_layer (line 178) | def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
method _make_conv_layers (line 206) | def _make_conv_layers(self, channels, convs, stride=1, dilation=1):
method forward (line 219) | def forward(self, x):
class _UpProjection (line 267) | class _UpProjection(nn.Sequential):
method __init__ (line 269) | def __init__(self, num_input_features, num_output_features):
method forward (line 287) | def forward(self, x, size):
class AutoED (line 300) | class AutoED(nn.Module):
method __init__ (line 301) | def __init__(self, model):
method freeze_bn (line 306) | def freeze_bn(self):
method forward (line 312) | def forward(self, x):
class _E (line 319) | class _E(nn.Module):
method __init__ (line 320) | def __init__(self, model):
method forward (line 324) | def forward(self, x):
class _D (line 338) | class _D(nn.Module):
method __init__ (line 339) | def __init__(self):
method forward (line 355) | def forward(self, x, size1, size2, size3):
class DRN_A (line 369) | class DRN_A(nn.Module):
method __init__ (line 371) | def __init__(self, block, layers, num_classes=1000):
method _make_layer (line 404) | def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
method forward (line 422) | def forward(self, x):
function drn_a_50 (line 440) | def drn_a_50(pretrained=False, **kwargs):
function drn_c_26 (line 447) | def drn_c_26(pretrained=False, **kwargs):
function drn_c_42 (line 454) | def drn_c_42(pretrained=False, **kwargs):
function drn_c_58 (line 461) | def drn_c_58(pretrained=False, **kwargs):
function drn_d_22 (line 468) | def drn_d_22(pretrained=False, **kwargs):
function drn_d_24 (line 475) | def drn_d_24(pretrained=False, **kwargs):
function drn_d_38 (line 482) | def drn_d_38(pretrained=False, **kwargs):
function drn_d_40 (line 489) | def drn_d_40(pretrained=False, **kwargs):
function drn_d_54 (line 496) | def drn_d_54(pretrained=False, **kwargs):
function drn_d_56 (line 503) | def drn_d_56(pretrained=False, **kwargs):
function drn_d_105 (line 510) | def drn_d_105(pretrained=False, **kwargs):
function drn_d_107 (line 517) | def drn_d_107(pretrained=False, **kwargs):
FILE: loaddata.py
function get_dataset (line 18) | def get_dataset(root, data_file='train.list',
class KittiDataset (line 39) | class KittiDataset(data.Dataset):
method __init__ (line 40) | def __init__(self, root='./datasets', data_file='tgt_train.list', phas...
method __len__ (line 64) | def __len__(self):
method read_data (line 67) | def read_data(self, datafiles):
method __getitem__ (line 85) | def __getitem__(self, index):
class VKittiDataset (line 123) | class VKittiDataset(data.Dataset):
method __init__ (line 124) | def __init__(self, root='./datasets', data_file='src_train.list',
method __len__ (line 148) | def __len__(self):
method read_data (line 151) | def read_data(self, datafiles):
method __getitem__ (line 159) | def __getitem__(self, index):
class NYUD_V2Dataset (line 192) | class NYUD_V2Dataset(data.Dataset):
method __init__ (line 193) | def __init__(self, root='./datasets', data_file='nyu_data.txt', phase=...
method __len__ (line 204) | def __len__(self):
method read_data (line 207) | def read_data(self, datafiles):
method __getitem__ (line 215) | def __getitem__(self, index):
class SUNCGDataset (line 251) | class SUNCGDataset(data.Dataset):
method __init__ (line 252) | def __init__(self, root='./suncg_datasets', data_file='suncg_train.txt',
method __len__ (line 275) | def __len__(self):
method read_data (line 278) | def read_data(self, datafiles):
method __getitem__ (line 288) | def __getitem__(self, index):
class DepthToTensor (line 322) | class DepthToTensor(object):
method __call__ (line 323) | def __call__(self, input):
function creat_train_dataloader (line 330) | def creat_train_dataloader(dataset, root, data_file, batchsize, nThreads,
function create_test_dataloader (line 349) | def create_test_dataloader(dataset, root, data_file, batchsize, nThreads...
class KITTI (line 376) | class KITTI:
method read_calib_file (line 377) | def read_calib_file(self, path):
method get_fb (line 395) | def get_fb(self, calib_dir, cam=2):
method load_velodyne_points (line 414) | def load_velodyne_points(self, file_name):
method lin_interp (line 420) | def lin_interp(self, shape, xyd):
method sub2ind (line 433) | def sub2ind(self, metrixSize, rowSub, colSub):
method get_depth (line 441) | def get_depth(self, calib_dir, velo_file_name, im_shape, cam=2, interp...
FILE: models/modules.py
class AdaptiveInstanceNorm2d (line 10) | class AdaptiveInstanceNorm2d(nn.Module):
method __init__ (line 11) | def __init__(self, num_features, eps=1e-5, momentum=0.1):
method forward (line 23) | def forward(self, x):
method __repr__ (line 38) | def __repr__(self):
class ResBlocks (line 41) | class ResBlocks(nn.Module):
method __init__ (line 42) | def __init__(self, num_blocks, dim, norm='in', activation='relu', pad_...
method forward (line 51) | def forward(self, x):
class ResBlock (line 55) | class ResBlock(nn.Module):
method __init__ (line 56) | def __init__(self, dim, norm='in', activation='relu', pad_type='zero'):
method forward (line 84) | def forward(self, x):
function get_nonlinearity_layer (line 96) | def get_nonlinearity_layer(activation_type='PReLU'):
class Conv2dBlock (line 109) | class Conv2dBlock(nn.Module):
method __init__ (line 111) | def __init__(self, input_dim ,output_dim, kernel_size, stride,
method forward (line 164) | def forward(self, x):
class LayerNorm (line 173) | class LayerNorm(nn.Module):
method __init__ (line 174) | def __init__(self, num_features, eps=1e-5, affine=True):
method forward (line 184) | def forward(self, x):
class Struct_Encoder (line 204) | class Struct_Encoder(nn.Module):
method __init__ (line 205) | def __init__(self, n_downsample, n_res, input_dim, dim, norm, activ, p...
method forward (line 216) | def forward(self, x):
class _UpProjection (line 224) | class _UpProjection(nn.Sequential):
method __init__ (line 226) | def __init__(self, num_input_features, num_output_features):
method forward (line 241) | def forward(self, x, size):
class _EncoderBlock (line 253) | class _EncoderBlock(nn.Module):
method __init__ (line 254) | def __init__(self, input_nc, middle_nc, output_nc, norm_layer=nn.Batch...
method forward (line 268) | def forward(self, x):
class _InceptionBlock (line 271) | class _InceptionBlock(nn.Module):
method __init__ (line 272) | def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, non...
method forward (line 292) | def forward(self, x):
class GaussianNoiseLayer (line 306) | class GaussianNoiseLayer(nn.Module):
method __init__ (line 307) | def __init__(self):
method forward (line 310) | def forward(self, x):
class _DecoderUpBlock (line 317) | class _DecoderUpBlock(nn.Module):
method __init__ (line 318) | def __init__(self, input_nc, middle_nc, output_nc, norm_layer=nn.Batch...
method forward (line 333) | def forward(self, x):
class _OutputBlock (line 336) | class _OutputBlock(nn.Module):
method __init__ (line 337) | def __init__(self, input_nc, output_nc, kernel_size=3, use_bias=False):
method forward (line 347) | def forward(self, x):
class Struct_Decoder (line 351) | class Struct_Decoder(nn.Module):
method __init__ (line 352) | def __init__(self):
method forward (line 366) | def forward(self, x):
function get_norm_layer (line 373) | def get_norm_layer(norm_type='instance'):
class Depth_Net (line 385) | class Depth_Net(nn.Module):
method __init__ (line 386) | def __init__(self, input_nc=1, output_nc=1, ngf=64, layers=4, norm='ba...
method forward (line 442) | def forward(self, input):
FILE: test.py
function save_test (line 40) | def save_test(handle, result1_log):
function kitti_metrics_preprocess (line 65) | def kitti_metrics_preprocess(pred, gt):
function kitti_compute_metrics (line 97) | def kitti_compute_metrics(pred, gt):
function nyu_compute_metrics (line 125) | def nyu_compute_metrics(pred, gt):
function nyu_metrics_preprocess (line 160) | def nyu_metrics_preprocess(pred, gt):
function main (line 190) | def main():
FILE: train.py
function main (line 45) | def main():
FILE: train_loss.py
function depth_loss (line 11) | def depth_loss(output, depth_gt):
function gradient_x (line 29) | def gradient_x(img):
function gradient_y (line 35) | def gradient_y(img):
function struct_weighted_loss (line 44) | def struct_weighted_loss(struct_map, depth, train_iteration, hyper_w):
FILE: transform.py
class RandomHorizontalFlip (line 10) | class RandomHorizontalFlip(object):
method __init__ (line 11) | def __init__(self, prob=None):
method __call__ (line 13) | def __call__(self, img):
class RandomVerticalFlip (line 18) | class RandomVerticalFlip(object):
method __init__ (line 19) | def __init__(self, img):
class RandomImgAugment (line 25) | class RandomImgAugment(object):
method __init__ (line 27) | def __init__(self, no_flip, no_rotation, no_augment, size=None, datase...
method __call__ (line 34) | def __call__(self, inputs):
FILE: utils.py
function makedir (line 11) | def makedir(directory):
function colormap (line 16) | def colormap(image, cmap="jet"):
function update_learning_rate (line 36) | def update_learning_rate(optimizers, scheduler):
function get_scheduler (line 43) | def get_scheduler(optimizer, opt):
function adjust_gt (line 57) | def adjust_gt(gt_depth, pred_depth):
function init_weights (line 65) | def init_weights(net, init_type='normal', gain=0.02):
class AverageMeter (line 88) | class AverageMeter(object):
method __init__ (line 89) | def __init__(self):
method reset (line 92) | def reset(self):
method update (line 98) | def update(self, val, n=1):
Condensed preview — 22 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (6,711K chars).
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{
"path": ".github/workflows/codeql-analysis.yml",
"chars": 2433,
"preview": "# For most projects, this workflow file will not need changing; you simply need\n# to commit it to your repository.\n#\n# Y"
},
{
"path": ".gitignore",
"chars": 6002,
"preview": "## Ignore Visual Studio temporary files, build results, and\n## files generated by popular Visual Studio add-ons.\n##\n## G"
},
{
"path": "CODE_OF_CONDUCT.md",
"chars": 444,
"preview": "# Microsoft Open Source Code of Conduct\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https://op"
},
{
"path": "DSAModules.py",
"chars": 18398,
"preview": "# https://docs.opensource.microsoft.com/content/releasing/copyright-headers.html\nimport torch.nn as nn\nimport math\nimpor"
},
{
"path": "LICENSE",
"chars": 1141,
"preview": " MIT License\n\n Copyright (c) Microsoft Corporation.\n\n Permission is hereby granted, free of charge, to any pers"
},
{
"path": "README.md",
"chars": 5363,
"preview": "# S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation\n\nThis is the official PyTorch implemen"
},
{
"path": "SECURITY.md",
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"preview": "<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->\n\n## Security\n\nMicrosoft takes the security of our software products an"
},
{
"path": "SUPPORT.md",
"chars": 1315,
"preview": "# TODO: The maintainer of this repo has not yet edited this file\r\n\r\n**REPO OWNER**: Do you want Customer Service & Suppo"
},
{
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"chars": 152643,
"preview": "2011_09_26/2011_09_26_drive_0002_sync/image_02/data/0000000069.png 2011_09_26/2011_09_26_drive_0002_sync/image_03/data/0"
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{
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"path": "loaddata.py",
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]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the microsoft/S2R-DepthNet GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 22 files (68.9 MB), approximately 1.7M tokens, and a symbol index with 147 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.