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Repository: XuelianCheng/LEAStereo
Branch: master
Commit: b2921eaf2554
Files: 66
Total size: 2.2 MB
Directory structure:
gitextract_u0g_mu_q/
├── .gitignore
├── LICENSE
├── README.md
├── config_utils/
│ ├── decode_args.py
│ ├── predict_args.py
│ ├── search_args.py
│ └── train_args.py
├── create_link.sh
├── dataloaders/
│ ├── __init__.py
│ ├── datasets/
│ │ ├── __init__.py
│ │ └── stereo.py
│ └── lists/
│ ├── kitti2012_test.list
│ ├── kitti2012_train.list
│ ├── kitti2012_train170.list
│ ├── kitti2012_val24.list
│ ├── kitti2015_test.list
│ ├── kitti2015_train.list
│ ├── kitti2015_train180.list
│ ├── kitti2015_val20.list
│ ├── middeval3_test.list
│ ├── middeval3_train.list
│ ├── sceneflow_search_trainA.list
│ ├── sceneflow_search_trainB.list
│ ├── sceneflow_search_val.list
│ ├── sceneflow_test.list
│ └── sceneflow_train.list
├── decode.py
├── decode.sh
├── models/
│ ├── __init__.py
│ ├── build_model.py
│ ├── build_model_2d.py
│ ├── build_model_3d.py
│ ├── cell_level_search_2d.py
│ ├── cell_level_search_3d.py
│ ├── decoding_formulas.py
│ ├── genotypes_2d.py
│ ├── genotypes_3d.py
│ ├── operations_2d.py
│ └── operations_3d.py
├── mypath.py
├── predict.py
├── predict_kitti12.sh
├── predict_kitti15.sh
├── predict_md.sh
├── predict_sf.sh
├── retrain/
│ ├── LEAStereo.py
│ ├── new_model_2d.py
│ ├── new_model_3d.py
│ └── skip_model_3d.py
├── search.py
├── search.sh
├── thop/
│ ├── __init__.py
│ ├── count_hooks.py
│ ├── profile.py
│ └── utils.py
├── train.py
├── train_kitti12.sh
├── train_kitti15.sh
├── train_md.sh
├── train_sf.sh
└── utils/
├── colorize.py
├── copy_state_dict.py
├── lr_scheduler.py
├── multadds_count.py
├── saver.py
└── summaries.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
**/__pycache__/
*.py[cod]
*$py.class
/predict/*
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# Packages #
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# Translations
*.mo
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# Django stuff:
*.log
local_settings.py
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db.sqlite3-journal
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instance/
.webassets-cache
# Scrapy stuff:
.scrapy
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docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
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# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
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env/
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# Spyder project settings
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# Rope project settings
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# mkdocs documentation
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# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2020 Xuelian Cheng
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
================================================
## LEAStereo
This repository contains the code for our NeurIPS 2020 paper `Hierarchical Neural Architecture Searchfor Deep Stereo Matching` [[NeurIPS 20](https://proceedings.neurips.cc/paper/2020/file/fc146be0b230d7e0a92e66a6114b840d-Paper.pdf)]
## Requirements
### Environment
1. Python 3.8.*
2. CUDA 10.0
3. PyTorch
4. TorchVision
### Install
Create a virtual environment and activate it.
```shell
conda create -n leastereo python=3.8
conda activate leastereo
```
The code has been tested with PyTorch 1.6 and Cuda 10.2.
```shell
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.2 -c pytorch
conda install matplotlib path.py tqdm
conda install tensorboard tensorboardX
conda install scipy scikit-image opencv
```
Install Nvidia Apex
Follow the instructions [here](https://github.com/NVIDIA/apex#quick-start). Apex is required for mixed precision training.
Please do not use pip install apex - this will not install the correct package.
### Dataset
To evaluate/train our LEAStereo network, you will need to download the required datasets.
* [SceneFlow](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
* [KITTI2015](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=stereo)
* [KITTI2012](http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stereo)
* [Middlebury 2014](https://vision.middlebury.edu/stereo/submit3/)
Change the first column path in file `create_link.sh` with your actual dataset location. Then run `create_link.sh` that will create symbolic links to wherever the datasets were downloaded in the `datasets` folder. For Middlebury 2014 dataset, we perform our network on half resolution images.
```Shell
├── datasets
├── SceneFlow
├── camera_data
├── disparity
├── frames_finalpass
├── kitti2012
├── testing
├── training
├── kitti2015
├── testing
├── training
├── MiddEval3
├── testH
├── trainingH
```
### Prediction
You can evaluate a trained model using `prediction.sh` for each dataset, that would help you generate *.png or *.pfm images correspoding to different datasets.
```shell
sh predict_sf.sh
sh predict_md.sh
sh predict_kitti12.sh
sh predict_kitti15.sh
```
Results of our model on three benchmark datasets could also be found [here](https://drive.google.com/file/d/1Wcv-WzQToTwAiBfWpONrtyQSgsHrWqWC/view?usp=sharing)
### Architecture Search
Three steps for the architecture search:
#### 1. Search
```shell
sh search.sh
```
#### 2. decode
```shell
sh decode.sh
```
#### 3. retrain
```shell
sh train_sf.sh
sh train_md.sh
sh train_kitti12.sh
sh train_kitti15.sh
```
### Acknowledgements
This repository makes liberal use of code from [[AutoDeeplab](https://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Auto-DeepLab_Hierarchical_Neural_Architecture_Search_for_Semantic_Image_Segmentation_CVPR_2019_paper.html)] [[pytorch code](https://github.com/NoamRosenberg/autodeeplab)(Non-official)].
### Citing
If you find this code useful, please consider to cite our work.
```
@article{cheng2020hierarchical,
title={Hierarchical Neural Architecture Search for Deep Stereo Matching},
author={Cheng, Xuelian and Zhong, Yiran and Harandi, Mehrtash and Dai, Yuchao and Chang, Xiaojun and Li, Hongdong and Drummond, Tom and Ge, Zongyuan},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}
```
================================================
FILE: config_utils/decode_args.py
================================================
import argparse
def obtain_decode_args():
parser = argparse.ArgumentParser(description="LEStereo Decoding..")
parser.add_argument('--dataset', type=str, default='sceneflow',
choices=['sceneflow', 'kitti15', 'kitti12', 'middlebury'],
help='dataset name (default: sceneflow)')
parser.add_argument('--step', type=int, default=3)
parser.add_argument('--resume', type=str, default=None,
help='put the path to resuming file if needed')
return parser.parse_args()
================================================
FILE: config_utils/predict_args.py
================================================
import argparse
def obtain_predict_args():
parser = argparse.ArgumentParser(description='LEStereo Prediction')
parser.add_argument('--crop_height', type=int, required=True, help="crop height")
parser.add_argument('--crop_width', type=int, required=True, help="crop width")
parser.add_argument('--maxdisp', type=int, default=192, help="max disp")
parser.add_argument('--resume', type=str, default='', help="resume from saved model")
parser.add_argument('--cuda', type=bool, default=True, help='use cuda?')
parser.add_argument('--sceneflow', type=int, default=0, help='sceneflow dataset? Default=False')
parser.add_argument('--kitti2012', type=int, default=0, help='kitti 2012? Default=False')
parser.add_argument('--kitti2015', type=int, default=0, help='kitti 2015? Default=False')
parser.add_argument('--middlebury', type=int, default=0, help='Middlebury? Default=False')
parser.add_argument('--data_path', type=str, required=True, help="data root")
parser.add_argument('--test_list', type=str, required=True, help="training list")
parser.add_argument('--save_path', type=str, default='./result/', help="location to save result")
######### LEStereo params####################
parser.add_argument('--fea_num_layers', type=int, default=6)
parser.add_argument('--mat_num_layers', type=int, default=12)
parser.add_argument('--fea_filter_multiplier', type=int, default=8)
parser.add_argument('--mat_filter_multiplier', type=int, default=8)
parser.add_argument('--fea_block_multiplier', type=int, default=4)
parser.add_argument('--mat_block_multiplier', type=int, default=4)
parser.add_argument('--fea_step', type=int, default=3)
parser.add_argument('--mat_step', type=int, default=3)
parser.add_argument('--net_arch_fea', default=None, type=str)
parser.add_argument('--cell_arch_fea', default=None, type=str)
parser.add_argument('--net_arch_mat', default=None, type=str)
parser.add_argument('--cell_arch_mat', default=None, type=str)
args = parser.parse_args()
return args
================================================
FILE: config_utils/search_args.py
================================================
import argparse
def obtain_search_args():
parser = argparse.ArgumentParser(description="LEStereo Searching...")
parser.add_argument('--clean-module', type=int, default=0)
parser.add_argument('--dataset', type=str, default='sceneflow',
choices=['sceneflow', 'kitti15', 'kitti12', 'middlebury'],
help='dataset name (default: sceneflow)')
parser.add_argument('--stage', type=str, default='search',
choices=['search', 'train'])
parser.add_argument('--fea_num_layers', type=int, default=6)
parser.add_argument('--mat_num_layers', type=int, default=12)
parser.add_argument('--fea_filter_multiplier', type=int, default=8)
parser.add_argument('--mat_filter_multiplier', type=int, default=8)
parser.add_argument('--fea_block_multiplier', type=int, default=4)
parser.add_argument('--mat_block_multiplier', type=int, default=4)
parser.add_argument('--fea_step', type=int, default=2)
parser.add_argument('--mat_step', type=int, default=2)
parser.add_argument('--workers', type=int, default=0,
metavar='N', help='dataloader threads')
parser.add_argument('--max_disp', type=int, default=192, help="max disp")
parser.add_argument('--crop_height', type=int, default=384,
help="crop height")
parser.add_argument('--crop_width', type=int, default=576,
help="crop width")
parser.add_argument('--freeze-bn', type=bool, default=False,
help='whether to freeze bn parameters (default: False)')
# training hyper params
parser.add_argument('--epochs', type=int, default=None, metavar='N',
help='number of epochs to train (default: auto)')
parser.add_argument('--start_epoch', type=int, default=0,
metavar='N', help='start epochs (default:0)')
parser.add_argument('--alpha_epoch', type=int, default=10,
metavar='N', help='epoch to start training alphas')
parser.add_argument('--batch-size', type=int, default=2,
metavar='N', help='input batch size for \
training (default: auto)')
parser.add_argument('--testBatchSize', type=int, default=1,
metavar='N', help='input batch size for \
testing (default: auto)')
# optimizer params
parser.add_argument('--lr', type=float, default=0.025, metavar='LR',
help='learning rate (default: auto)')
parser.add_argument('--min_lr', type=float, default=0.001)
parser.add_argument('--arch-lr', type=float, default=1e-3, metavar='LR',
help='learning rate for alpha and beta in architect searching process')
parser.add_argument('--lr-scheduler', type=str, default='cos',
choices=['poly', 'step', 'cos'],
help='lr scheduler mode')
parser.add_argument('--momentum', type=float, default=0.9,
metavar='M', help='momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=3e-4,
metavar='M', help='w-decay (default: 5e-4)')
parser.add_argument('--arch-weight-decay', type=float, default=1e-3,
metavar='M', help='w-decay (default: 5e-4)')
parser.add_argument('--nesterov', action='store_true', default=False,
help='whether use nesterov (default: False)')
# cuda, seed and logging
parser.add_argument('--cuda', type=int, default=1,
help='use cuda? Default=True')
parser.add_argument('--gpu-ids', type=str, default='0',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
# finetuning pre-trained models
parser.add_argument('--ft', action='store_true', default=False,
help='finetuning on a different dataset')
# checking point
parser.add_argument('--resume', type=str, default=None,
help='put the path to resuming file if needed')
parser.add_argument('--no-val', action='store_true', default=False,
help='skip validation during training')
args = parser.parse_args()
return args
================================================
FILE: config_utils/train_args.py
================================================
import argparse
def obtain_train_args():
# Training settings
parser = argparse.ArgumentParser(description='LEStereo training...')
parser.add_argument('--maxdisp', type=int, default=192,
help="max disp")
parser.add_argument('--crop_height', type=int, required=True,
help="crop height")
parser.add_argument('--crop_width', type=int, required=True,
help="crop width")
parser.add_argument('--resume', type=str, default='',
help="resume from saved model")
parser.add_argument('--batch_size', type=int, default=1,
help='training batch size')
parser.add_argument('--testBatchSize', type=int, default=8,
help='testing batch size')
parser.add_argument('--nEpochs', type=int, default=2048,
help='number of epochs to train for')
parser.add_argument('--solver', default='adam',choices=['adam','sgd'],
help='solver algorithms')
parser.add_argument('--lr', type=float, default=0.001,
help='Learning Rate. Default=0.001')
parser.add_argument('--cuda', type=int, default=1,
help='use cuda? Default=True')
parser.add_argument('--threads', type=int, default=1,
help='number of threads for data loader to use')
parser.add_argument('--seed', type=int, default=2019,
help='random seed to use. Default=123')
parser.add_argument('--shift', type=int, default=0,
help='random shift of left image. Default=0')
parser.add_argument('--save_path', type=str, default='./checkpoints/',
help="location to save models")
parser.add_argument('--milestones', default=[30,50,300], metavar='N', nargs='*',
help='epochs at which learning rate is divided by 2')
parser.add_argument('--stage', type=str, default='train', choices=['search', 'train'])
parser.add_argument('--dataset', type=str, default='sceneflow',
choices=['sceneflow', 'kitti15', 'kitti12', 'middlebury'], help='dataset name')
######### LEStereo params ##################
parser.add_argument('--fea_num_layers', type=int, default=6)
parser.add_argument('--mat_num_layers', type=int, default=12)
parser.add_argument('--fea_filter_multiplier', type=int, default=8)
parser.add_argument('--mat_filter_multiplier', type=int, default=8)
parser.add_argument('--fea_block_multiplier', type=int, default=4)
parser.add_argument('--mat_block_multiplier', type=int, default=4)
parser.add_argument('--fea_step', type=int, default=2)
parser.add_argument('--mat_step', type=int, default=2)
parser.add_argument('--net_arch_fea', default=None, type=str)
parser.add_argument('--cell_arch_fea', default=None, type=str)
parser.add_argument('--net_arch_mat', default=None, type=str)
parser.add_argument('--cell_arch_mat', default=None, type=str)
args = parser.parse_args()
return args
================================================
FILE: create_link.sh
================================================
ln -s /mnt/data/StereoDataset/dataset/kitti2015 ./dataset
ln -s /mnt/data/StereoDataset/dataset/kitti2012 ./dataset
ln -s /mnt/data/StereoDataset/dataset/SceneFlow ./dataset
ln -s /mnt/data/StereoDataset/dataset/MiddEval3 ./dataset
================================================
FILE: dataloaders/__init__.py
================================================
from torch.utils.data import DataLoader
from dataloaders.datasets import stereo
import pdb
def make_data_loader(args, **kwargs):
############################ sceneflow ###########################
if args.dataset == 'sceneflow':
trainA_list= 'dataloaders/lists/sceneflow_search_trainA.list' #randomly select 10,000 from the original training set
trainB_list= 'dataloaders/lists/sceneflow_search_trainB.list' #randomly select 10,000 from the original training set
val_list = 'dataloaders/lists/sceneflow_search_val.list' #randomly select 1,000 from the original test set
train_list = 'dataloaders/lists/sceneflow_train.list' #original training set: 35,454
test_list = 'dataloaders/lists/sceneflow_test.list' #original test set:4,370
trainA_set = stereo.DatasetFromList(args, trainA_list, [args.crop_height, args.crop_width], True)
trainB_set = stereo.DatasetFromList(args, trainB_list, [args.crop_height, args.crop_width], True)
train_set = stereo.DatasetFromList(args, train_list, [args.crop_height, args.crop_width], True)
val_set = stereo.DatasetFromList(args, val_list, [576,960], False)
test_set = stereo.DatasetFromList(args, test_list, [576,960], False)
if args.stage == 'search':
train_loaderA = DataLoader(trainA_set, batch_size=args.batch_size, shuffle=True, **kwargs)
train_loaderB = DataLoader(trainB_set, batch_size=args.batch_size, shuffle=True, **kwargs)
elif args.stage == 'train':
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs)
else:
raise Exception('parameters not set properly')
val_loader = DataLoader(val_set, batch_size=args.testBatchSize, shuffle=False, **kwargs)
test_loader = DataLoader(test_set, batch_size=args.testBatchSize, shuffle=False, **kwargs)
if args.stage == 'search':
return train_loaderA, train_loaderB, val_loader, test_loader
elif args.stage == 'train':
return train_loader, test_loader
############################ kitti15 ###########################
elif args.dataset == 'kitti15':
train_list= 'dataloaders/lists/kitti2015_train180.list'
test_list = 'dataloaders/lists/kitti2015_val20.list'
train_set = stereo.DatasetFromList(args, train_list, [args.crop_height, args.crop_width], True)
test_set = stereo.DatasetFromList(args, test_list, [384,1248], False)
train_loader= DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = DataLoader(test_set, batch_size=args.testBatchSize, shuffle=False, **kwargs)
return train_loader, test_loader
############################ kitti12 ###########################
elif args.dataset == 'kitti12':
train_list= 'dataloaders/lists/kitti2012_train170.list'
test_list = 'dataloaders/lists/kitti2012_val24.list'
train_set = stereo.DatasetFromList(args, train_list, [args.crop_height, args.crop_width], True)
test_set = stereo.DatasetFromList(args, test_list, [384,1248], False)
train_loader= DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = DataLoader(test_set, batch_size=args.testBatchSize, shuffle=False, **kwargs)
return train_loader, test_loader
############################ middlebury ###########################
elif args.dataset == 'middlebury':
train_list= 'dataloaders/lists/middeval3_train.list'
test_list = 'dataloaders/lists/middeval3_train.list'
train_set = stereo.DatasetFromList(args, train_list, [args.crop_height, args.crop_width], True)
test_set = stereo.DatasetFromList(args, test_list, [1008,1512], False)
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = DataLoader(test_set, batch_size=args.testBatchSize, shuffle=False, **kwargs)
return train_loader, test_loader
else:
raise NotImplementedError
================================================
FILE: dataloaders/datasets/__init__.py
================================================
================================================
FILE: dataloaders/datasets/stereo.py
================================================
import torch.utils.data as data
import skimage
import skimage.io
import skimage.transform
from PIL import Image
import numpy as np
import random
from struct import unpack
import re
import sys
from mypath import Path
import pdb
def readPFM(file):
with open(file, "rb") as f:
# Line 1: PF=>RGB (3 channels), Pf=>Greyscale (1 channel)
type = f.readline().decode('latin-1')
if "PF" in type:
channels = 3
elif "Pf" in type:
channels = 1
else:
sys.exit(1)
# Line 2: width height
line = f.readline().decode('latin-1')
width, height = re.findall('\d+', line)
width = int(width)
height = int(height)
# Line 3: +ve number means big endian, negative means little endian
line = f.readline().decode('latin-1')
BigEndian = True
if "-" in line:
BigEndian = False
# Slurp all binary data
samples = width * height * channels;
buffer = f.read(samples * 4)
# Unpack floats with appropriate endianness
if BigEndian:
fmt = ">"
else:
fmt = "<"
fmt = fmt + str(samples) + "f"
img = unpack(fmt, buffer)
img = np.reshape(img, (height, width))
img = np.flipud(img)
return img, height, width
def train_transform(temp_data, crop_height, crop_width, left_right=False, shift=0):
_, h, w = np.shape(temp_data)
if h > crop_height and w <= crop_width:
temp = temp_data
temp_data = np.zeros([8, h+shift, crop_width + shift], 'float32')
temp_data[6:7,:,:] = 1000
temp_data[:, h + shift - h: h + shift, crop_width + shift - w: crop_width + shift] = temp
_, h, w = np.shape(temp_data)
if h <= crop_height and w <= crop_width:
temp = temp_data
temp_data = np.zeros([8, crop_height + shift, crop_width + shift], 'float32')
temp_data[6: 7, :, :] = 1000
temp_data[:, crop_height + shift - h: crop_height + shift, crop_width + shift - w: crop_width + shift] = temp
_, h, w = np.shape(temp_data)
if shift > 0:
start_x = random.randint(0, w - crop_width)
shift_x = random.randint(-shift, shift)
if shift_x + start_x < 0 or shift_x + start_x + crop_width > w:
shift_x = 0
start_y = random.randint(0, h - crop_height)
left = temp_data[0: 3, start_y: start_y + crop_height, start_x + shift_x: start_x + shift_x + crop_width]
right = temp_data[3: 6, start_y: start_y + crop_height, start_x: start_x + crop_width]
target = temp_data[6: 7, start_y: start_y + crop_height, start_x + shift_x : start_x+shift_x + crop_width]
target = target - shift_x
return left, right, target
if h <= crop_height and w <= crop_width:
temp = temp_data
temp_data = np.zeros([8, crop_height, crop_width], 'float32')
temp_data[:, crop_height - h: crop_height, crop_width - w: crop_width] = temp
else:
start_x = random.randint(0, w - crop_width)
start_y = random.randint(0, h - crop_height)
temp_data = temp_data[:, start_y: start_y + crop_height, start_x: start_x + crop_width]
if random.randint(0, 1) == 0 and left_right:
right = temp_data[0: 3, :, :]
left = temp_data[3: 6, :, :]
target = temp_data[7: 8, :, :]
return left, right, target
else:
left = temp_data[0: 3, :, :]
right = temp_data[3: 6, :, :]
target = temp_data[6: 7, :, :]
return left, right, target
def test_transform(temp_data, crop_height, crop_width, left_right=False):
_, h, w = np.shape(temp_data)
if h <= crop_height and w <= crop_width:
temp = temp_data
temp_data = np.zeros([8,crop_height,crop_width], 'float32')
temp_data[6: 7, :, :] = 1000
temp_data[:, crop_height - h: crop_height, crop_width - w: crop_width] = temp
else:
start_x = (w-crop_width)//2
start_y = (h-crop_height)//2
temp_data = temp_data[:, start_y: start_y + crop_height, start_x: start_x + crop_width]
left = temp_data[0: 3, :, :]
right = temp_data[3: 6, :, :]
target = temp_data[6: 7, :, :]
return left, right, target
def load_data_sceneflow(data_path, current_file):
A = current_file
filename = data_path + 'frames_finalpass/' + A[0: len(A) - 1]
left =Image.open(filename)
filename = data_path + 'frames_finalpass/' + A[0: len(A) - 14] + 'right/' + A[len(A) - 9:len(A) - 1]
right = Image.open(filename)
filename = data_path + 'disparity/' + A[0: len(A) - 4] + 'pfm'
disp_left, height, width = readPFM(filename)
filename = data_path + 'disparity/' + A[0: len(A) - 14] + 'right/' + A[len(A) - 9: len(A) - 4] + 'pfm'
disp_right, height, width = readPFM(filename)
size = np.shape(left)
height = size[0]
width = size[1]
temp_data = np.zeros([8, height, width], 'float32')
left = np.asarray(left)
right = np.asarray(right)
r = left[:, :, 0]
g = left[:, :, 1]
b = left[:,:,2]
temp_data[0, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[1, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[2, :, :] = (b - np.mean(b[:])) / np.std(b[:])
r=right[:, :, 0]
g=right[:, :, 1]
b=right[:, :, 2]
temp_data[3, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[4, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[5, :, :] = (b - np.mean(b[:])) / np.std(b[:])
temp_data[6: 7, :, :] = width * 2
temp_data[6, :, :] = disp_left
temp_data[7, :, :] = disp_right
return temp_data
def load_kitti2015_data(file_path, current_file):
""" load current file from the list"""
filename = file_path + 'image_2/' + current_file[0: len(current_file) - 1]
left = Image.open(filename)
filename = file_path + 'image_3/' + current_file[0: len(current_file) - 1]
right = Image.open(filename)
filename = file_path + 'disp_occ_0/' + current_file[0: len(current_file) - 1]
disp_left = Image.open(filename)
temp = np.asarray(disp_left)
size = np.shape(left)
height = size[0]
width = size[1]
temp_data = np.zeros([8, height, width], 'float32')
left = np.asarray(left)
right = np.asarray(right)
disp_left = np.asarray(disp_left)
r = left[:, :, 0]
g = left[:, :, 1]
b = left[:, :, 2]
temp_data[0, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[1, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[2, :, :] = (b - np.mean(b[:])) / np.std(b[:])
r = right[:, :, 0]
g = right[:, :, 1]
b = right[:, :, 2]
temp_data[3, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[4, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[5, :, :] = (b - np.mean(b[:])) / np.std(b[:])
temp_data[6: 7, :, :] = width * 2
temp_data[6, :, :] = disp_left[:, :]
temp = temp_data[6, :, :]
temp[temp < 0.1] = width * 2 * 256
temp_data[6, :, :] = temp / 256.
return temp_data
def load_kitti2012_data(file_path, current_file):
""" load current file from the list"""
filename = file_path + 'colored_0/' + current_file[0: len(current_file) - 1]
left = Image.open(filename)
filename = file_path+'colored_1/' + current_file[0: len(current_file) - 1]
right = Image.open(filename)
filename = file_path+'disp_noc/' + current_file[0: len(current_file) - 1] #disp_occ
disp_left = Image.open(filename)
temp = np.asarray(disp_left)
size = np.shape(left)
height = size[0]
width = size[1]
temp_data = np.zeros([8, height, width], 'float32')
left = np.asarray(left)
right = np.asarray(right)
disp_left = np.asarray(disp_left)
r = left[:, :, 0]
g = left[:, :, 1]
b = left[:, :, 2]
temp_data[0, :, :] = (r-np.mean(r[:])) / np.std(r[:])
temp_data[1, :, :] = (g-np.mean(g[:])) / np.std(g[:])
temp_data[2, :, :] = (b-np.mean(b[:])) / np.std(b[:])
r=right[:, :, 0]
g=right[:, :, 1]
b=right[:, :, 2]
temp_data[3, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[4, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[5, :, :] = (b - np.mean(b[:])) / np.std(b[:])
temp_data[6: 7, :, :] = width * 2
temp_data[6, :, :] = disp_left[:, :]
temp = temp_data[6, :, :]
temp[temp < 0.1] = width * 2 * 256
temp_data[6, :, :] = temp / 256.
return temp_data
def load_data_md(file_path, current_file, eth=False):
""" load current file from the list"""
imgl = file_path + current_file[0: len(current_file) - 1]
gt_l = imgl.replace('im0.png','disp0GT.pfm')
imgr = imgl.replace('im0.png','im1.png')
left = Image.open(imgl)
right = Image.open(imgr)
disp_left, height, width = readPFM(gt_l)
pdb.set_trace()
temp_data = np.zeros([8, height, width], 'float32')
left = np.asarray(left)
right = np.asarray(right)
disp_left = np.asarray(disp_left)
r = left[:, :, 0]
g = left[:, :, 1]
b = left[:, :, 2]
temp_data[0, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[1, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[2, :, :] = (b - np.mean(b[:])) / np.std(b[:])
r = right[:, :, 0]
g = right[:, :, 1]
b = right[:, :, 2]
temp_data[3, :, :] = (r - np.mean(r[:])) / np.std(r[:])
temp_data[4, :, :] = (g - np.mean(g[:])) / np.std(g[:])
temp_data[5, :, :] = (b - np.mean(b[:])) / np.std(b[:])
temp_data[6: 7, :, :] = width * 2
temp_data[6, :, :] = disp_left[:, :]
temp = temp_data[6, :, :]
temp[temp < 0.1] = width * 2 * 256
temp_data[6, :, :] = temp #/ 256.
return temp_data
class DatasetFromList(data.Dataset):
def __init__(self, args, file_list, crop_size=[256, 256], training=True, left_right=False, shift=0):
super(DatasetFromList, self).__init__()
f = open(file_list, 'r')
self.args = args
self.file_list = f.readlines()
self.training = training
self.crop_height = crop_size[0]
self.crop_width = crop_size[1]
self.left_right = left_right
self.shift = shift
def __getitem__(self, index):
if self.args.dataset == 'kitti12': #load kitti2012 dataset
temp_data = load_kitti2012_data(Path.db_root_dir('kitti12'), self.file_list[index])
elif self.args.dataset == 'kitti15': #load kitti2015 dataset
temp_data = load_kitti2015_data(Path.db_root_dir('kitti15'), self.file_list[index])
elif self.args.dataset == 'sceneflow': #load sceneflow dataset
temp_data = load_data_sceneflow(Path.db_root_dir('sceneflow'), self.file_list[index])
elif self.args.dataset == 'middlebury': #load middbury dataset
temp_data = load_data_md(Path.db_root_dir('middlebury'), self.file_list[index])
if self.training:
input1, input2, target = train_transform(temp_data, self.crop_height, self.crop_width, self.left_right, self.shift)
return input1, input2, target
else:
input1, input2, target = test_transform(temp_data, self.crop_height, self.crop_width)
return input1, input2, target
def __len__(self):
return len(self.file_list)
================================================
FILE: dataloaders/lists/kitti2012_test.list
================================================
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================================================
FILE: dataloaders/lists/kitti2012_train.list
================================================
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FILE: dataloaders/lists/kitti2012_train170.list
================================================
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================================================
FILE: dataloaders/lists/kitti2012_val24.list
================================================
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FILE: dataloaders/lists/kitti2015_test.list
================================================
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================================================
FILE: dataloaders/lists/kitti2015_train.list
================================================
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================================================
FILE: dataloaders/lists/kitti2015_train180.list
================================================
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================================================
FILE: dataloaders/lists/kitti2015_val20.list
================================================
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FILE: dataloaders/lists/middeval3_test.list
================================================
Australia/im0.png
AustraliaP/im0.png
Bicycle2/im0.png
Classroom2/im0.png
Classroom2E/im0.png
Computer/im0.png
Crusade/im0.png
CrusadeP/im0.png
Djembe/im0.png
DjembeL/im0.png
Hoops/im0.png
Livingroom/im0.png
Newkuba/im0.png
Plants/im0.png
Staircase/im0.png
================================================
FILE: dataloaders/lists/middeval3_train.list
================================================
Playtable/im0.png
ArtL/im0.png
Jadeplant/im0.png
PlaytableP/im0.png
PianoL/im0.png
Piano/im0.png
Adirondack/im0.png
Teddy/im0.png
Recycle/im0.png
Motorcycle/im0.png
MotorcycleE/im0.png
Vintage/im0.png
Playroom/im0.png
Shelves/im0.png
Pipes/im0.png
================================================
FILE: dataloaders/lists/sceneflow_search_trainA.list
================================================
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TRAIN/B/0448/left/0006.png
TRAIN/flower_storm_x2/left/0023.png
TRAIN/B/0131/left/0013.png
TRAIN/top_view_x2/left/0061.png
TRAIN/A/0004/left/0008.png
TRAIN/A/0631/left/0015.png
TRAIN/C/0235/left/0012.png
TRAIN/A/0471/left/0006.png
TRAIN/B/0209/left/0009.png
TRAIN/funnyworld_augmented0_x2/left/0238.png
TRAIN/A/
gitextract_u0g_mu_q/
├── .gitignore
├── LICENSE
├── README.md
├── config_utils/
│ ├── decode_args.py
│ ├── predict_args.py
│ ├── search_args.py
│ └── train_args.py
├── create_link.sh
├── dataloaders/
│ ├── __init__.py
│ ├── datasets/
│ │ ├── __init__.py
│ │ └── stereo.py
│ └── lists/
│ ├── kitti2012_test.list
│ ├── kitti2012_train.list
│ ├── kitti2012_train170.list
│ ├── kitti2012_val24.list
│ ├── kitti2015_test.list
│ ├── kitti2015_train.list
│ ├── kitti2015_train180.list
│ ├── kitti2015_val20.list
│ ├── middeval3_test.list
│ ├── middeval3_train.list
│ ├── sceneflow_search_trainA.list
│ ├── sceneflow_search_trainB.list
│ ├── sceneflow_search_val.list
│ ├── sceneflow_test.list
│ └── sceneflow_train.list
├── decode.py
├── decode.sh
├── models/
│ ├── __init__.py
│ ├── build_model.py
│ ├── build_model_2d.py
│ ├── build_model_3d.py
│ ├── cell_level_search_2d.py
│ ├── cell_level_search_3d.py
│ ├── decoding_formulas.py
│ ├── genotypes_2d.py
│ ├── genotypes_3d.py
│ ├── operations_2d.py
│ └── operations_3d.py
├── mypath.py
├── predict.py
├── predict_kitti12.sh
├── predict_kitti15.sh
├── predict_md.sh
├── predict_sf.sh
├── retrain/
│ ├── LEAStereo.py
│ ├── new_model_2d.py
│ ├── new_model_3d.py
│ └── skip_model_3d.py
├── search.py
├── search.sh
├── thop/
│ ├── __init__.py
│ ├── count_hooks.py
│ ├── profile.py
│ └── utils.py
├── train.py
├── train_kitti12.sh
├── train_kitti15.sh
├── train_md.sh
├── train_sf.sh
└── utils/
├── colorize.py
├── copy_state_dict.py
├── lr_scheduler.py
├── multadds_count.py
├── saver.py
└── summaries.py
SYMBOL INDEX (231 symbols across 32 files)
FILE: config_utils/decode_args.py
function obtain_decode_args (line 3) | def obtain_decode_args():
FILE: config_utils/predict_args.py
function obtain_predict_args (line 3) | def obtain_predict_args():
FILE: config_utils/search_args.py
function obtain_search_args (line 3) | def obtain_search_args():
FILE: config_utils/train_args.py
function obtain_train_args (line 3) | def obtain_train_args():
FILE: dataloaders/__init__.py
function make_data_loader (line 5) | def make_data_loader(args, **kwargs):
FILE: dataloaders/datasets/stereo.py
function readPFM (line 14) | def readPFM(file):
function train_transform (line 48) | def train_transform(temp_data, crop_height, crop_width, left_right=False...
function test_transform (line 94) | def test_transform(temp_data, crop_height, crop_width, left_right=False):
function load_data_sceneflow (line 114) | def load_data_sceneflow(data_path, current_file):
function load_kitti2015_data (line 147) | def load_kitti2015_data(file_path, current_file):
function load_kitti2012_data (line 186) | def load_kitti2012_data(file_path, current_file):
function load_data_md (line 226) | def load_data_md(file_path, current_file, eth=False):
class DatasetFromList (line 264) | class DatasetFromList(data.Dataset):
method __init__ (line 265) | def __init__(self, args, file_list, crop_size=[256, 256], training=Tru...
method __getitem__ (line 276) | def __getitem__(self, index):
method __len__ (line 294) | def __len__(self):
FILE: decode.py
class Loader (line 8) | class Loader(object):
method __init__ (line 9) | def __init__(self, args):
method retreive_alphas_betas (line 27) | def retreive_alphas_betas(self):
method decode_architecture (line 30) | def decode_architecture(self):
method decode_cell (line 35) | def decode_cell(self):
function get_new_network_cell (line 40) | def get_new_network_cell():
FILE: models/build_model.py
class AutoStereo (line 9) | class AutoStereo(nn.Module):
method __init__ (line 10) | def __init__(self, maxdisp=192, Fea_Layers=6, Fea_Filter=8, Fea_Block=...
method forward (line 40) | def forward(self, x, y):
FILE: models/build_model_2d.py
class DispEntropy (line 9) | class DispEntropy(nn.Module):
method __init__ (line 10) | def __init__(self, maxdisp):
method forward (line 15) | def forward(self, x):
class DisparityRegression (line 24) | class DisparityRegression(nn.Module):
method __init__ (line 25) | def __init__(self, maxdisp):
method forward (line 29) | def forward(self, x):
class Disp (line 37) | class Disp(nn.Module):
method __init__ (line 38) | def __init__(self, maxdisp=192):
method forward (line 44) | def forward(self, x):
class AutoFeature (line 52) | class AutoFeature(nn.Module):
method __init__ (line 53) | def __init__(self, num_layers, filter_multiplier=8, block_multiplier=4...
method forward (line 172) | def forward(self, x):
method _initialize_alphas_betas (line 414) | def _initialize_alphas_betas(self):
method arch_parameters (line 432) | def arch_parameters(self):
method weight_parameters (line 435) | def weight_parameters(self):
method genotype (line 438) | def genotype(self):
FILE: models/build_model_3d.py
class AutoMatching (line 9) | class AutoMatching(nn.Module):
method __init__ (line 10) | def __init__(self, num_layers, filter_multiplier=8, block_multiplier=2...
method forward (line 128) | def forward(self, x):
method _initialize_alphas_betas (line 367) | def _initialize_alphas_betas(self):
method arch_parameters (line 386) | def arch_parameters(self):
method weight_parameters (line 389) | def weight_parameters(self):
method genotype (line 392) | def genotype(self):
FILE: models/cell_level_search_2d.py
class MixedOp (line 5) | class MixedOp(nn.Module):
method __init__ (line 7) | def __init__(self, C, stride):
method forward (line 16) | def forward(self, x, weights):
class Cell (line 20) | class Cell(nn.Module):
method __init__ (line 22) | def __init__(self, steps, block_multiplier, prev_prev_fmultiplier,
method scale_dimension (line 66) | def scale_dimension(self, dim, scale):
method prev_feature_resize (line 70) | def prev_feature_resize(self, prev_feature, mode):
method forward (line 80) | def forward(self, s0, s1_down, s1_same, s1_up, n_alphas):
method _initialize_weights (line 139) | def _initialize_weights(self):
FILE: models/cell_level_search_3d.py
class MixedOp (line 5) | class MixedOp(nn.Module):
method __init__ (line 7) | def __init__(self, C, stride):
method forward (line 16) | def forward(self, x, weights):
class Cell (line 20) | class Cell(nn.Module):
method __init__ (line 22) | def __init__(self, steps, block_multiplier, prev_prev_fmultiplier,
method scale_dimension (line 66) | def scale_dimension(self, dim, scale):
method prev_feature_resize (line 70) | def prev_feature_resize(self, prev_feature, mode):
method forward (line 81) | def forward(self, s0, s1_down, s1_same, s1_up, n_alphas):
method _initialize_weights (line 140) | def _initialize_weights(self):
FILE: models/decoding_formulas.py
function network_layer_to_space (line 6) | def network_layer_to_space(net_arch):
class Decoder (line 32) | class Decoder(object):
method __init__ (line 33) | def __init__(self, alphas, betas, steps):
method viterbi_decode (line 59) | def viterbi_decode(self):
method genotype_decode (line 93) | def genotype_decode(self):
FILE: models/operations_2d.py
class NaiveBN (line 10) | class NaiveBN(nn.Module):
method __init__ (line 11) | def __init__(self, C_out, momentum=0.1):
method forward (line 19) | def forward(self, x):
method _initialize_weights (line 22) | def _initialize_weights(self):
class ConvBR (line 31) | class ConvBR(nn.Module):
method __init__ (line 32) | def __init__(self, C_in, C_out, kernel_size, stride, padding, bn=True,...
method forward (line 41) | def forward(self, x):
method _initialize_weights (line 48) | def _initialize_weights(self):
class SepConv (line 56) | class SepConv(nn.Module):
method __init__ (line 57) | def __init__(self, C_in, C_out, kernel_size, stride, padding):
method forward (line 72) | def forward(self, x):
method _initialize_weights (line 75) | def _initialize_weights(self):
class Identity (line 83) | class Identity(nn.Module):
method __init__ (line 84) | def __init__(self):
method forward (line 88) | def forward(self, x):
method init_weight (line 91) | def init_weight(self):
method _initialize_weights (line 97) | def _initialize_weights(self):
class FactorizedReduce (line 106) | class FactorizedReduce(nn.Module):
method __init__ (line 107) | def __init__(self, C_in, C_out):
method forward (line 116) | def forward(self, x):
method init_weight (line 122) | def init_weight(self):
method _initialize_weights (line 128) | def _initialize_weights(self):
class DoubleFactorizedReduce (line 137) | class DoubleFactorizedReduce(nn.Module):
method __init__ (line 138) | def __init__(self, C_in, C_out):
method forward (line 147) | def forward(self, x):
method _initialize_weights (line 153) | def _initialize_weights(self):
class FactorizedIncrease (line 161) | class FactorizedIncrease(nn.Module):
method __init__ (line 162) | def __init__(self, in_channel, out_channel):
method forward (line 174) | def forward(self, x):
method _initialize_weights (line 177) | def _initialize_weights(self):
class DoubleFactorizedIncrease (line 186) | class DoubleFactorizedIncrease(nn.Module):
method __init__ (line 187) | def __init__(self, in_channel, out_channel):
method forward (line 203) | def forward(self, x):
method _initialize_weights (line 206) | def _initialize_weights(self):
FILE: models/operations_3d.py
class NaiveBN (line 10) | class NaiveBN(nn.Module):
method __init__ (line 11) | def __init__(self, C_out, momentum=0.1):
method forward (line 19) | def forward(self, x):
method _initialize_weights (line 22) | def _initialize_weights(self):
class ConvBR (line 31) | class ConvBR(nn.Module):
method __init__ (line 32) | def __init__(self, C_in, C_out, kernel_size, stride, padding, bn=True,...
method forward (line 41) | def forward(self, x):
method _initialize_weights (line 49) | def _initialize_weights(self):
class SepConv (line 57) | class SepConv(nn.Module):
method __init__ (line 58) | def __init__(self, C_in, C_out, kernel_size, stride, padding):
method forward (line 73) | def forward(self, x):
method _initialize_weights (line 76) | def _initialize_weights(self):
class Identity (line 84) | class Identity(nn.Module):
method __init__ (line 85) | def __init__(self):
method forward (line 89) | def forward(self, x):
method init_weight (line 92) | def init_weight(self):
method _initialize_weights (line 98) | def _initialize_weights(self):
class FactorizedReduce (line 107) | class FactorizedReduce(nn.Module):
method __init__ (line 108) | def __init__(self, C_in, C_out):
method forward (line 117) | def forward(self, x):
method init_weight (line 123) | def init_weight(self):
method _initialize_weights (line 129) | def _initialize_weights(self):
class DoubleFactorizedReduce (line 138) | class DoubleFactorizedReduce(nn.Module):
method __init__ (line 139) | def __init__(self, C_in, C_out):
method forward (line 148) | def forward(self, x):
method _initialize_weights (line 154) | def _initialize_weights(self):
class FactorizedIncrease (line 162) | class FactorizedIncrease(nn.Module):
method __init__ (line 163) | def __init__(self, in_channel, out_channel):
method forward (line 175) | def forward(self, x):
method _initialize_weights (line 178) | def _initialize_weights(self):
class DoubleFactorizedIncrease (line 187) | class DoubleFactorizedIncrease(nn.Module):
method __init__ (line 188) | def __init__(self, in_channel, out_channel):
method forward (line 204) | def forward(self, x):
method _initialize_weights (line 207) | def _initialize_weights(self):
FILE: mypath.py
class Path (line 1) | class Path(object):
method db_root_dir (line 3) | def db_root_dir(dataset):
FILE: predict.py
function RGBToPyCmap (line 64) | def RGBToPyCmap(rgbdata):
function readPFM (line 86) | def readPFM(file):
function save_pfm (line 122) | def save_pfm(filename, image, scale=1):
function test_transform (line 150) | def test_transform(temp_data, crop_height, crop_width):
function load_data (line 168) | def load_data(leftname, rightname):
function test_md (line 192) | def test_md(leftname, rightname, savename, imgname):
function test_kitti (line 230) | def test_kitti(leftname, rightname, savename):
function test (line 252) | def test(leftname, rightname, savename):
function plot_disparity (line 279) | def plot_disparity(savename, data, max_disp):
FILE: retrain/LEAStereo.py
class LEAStereo (line 11) | class LEAStereo(nn.Module):
method __init__ (line 12) | def __init__(self, args):
method forward (line 27) | def forward(self, x, y):
method get_params (line 45) | def get_params(self):
FILE: retrain/new_model_2d.py
class Cell (line 12) | class Cell(nn.Module):
method __init__ (line 13) | def __init__(self, steps, block_multiplier, prev_prev_fmultiplier,
method scale_dimension (line 38) | def scale_dimension(self, dim, scale):
method forward (line 41) | def forward(self, prev_prev_input, prev_input):
class newFeature (line 78) | class newFeature(nn.Module):
method __init__ (line 79) | def __init__(self, network_arch, cell_arch, cell=Cell, args=None):
method forward (line 140) | def forward(self, x):
method get_params (line 167) | def get_params(self):
FILE: retrain/new_model_3d.py
class Cell (line 11) | class Cell(nn.Module):
method __init__ (line 12) | def __init__(self, steps, block_multiplier, prev_prev_fmultiplier,
method scale_dimension (line 37) | def scale_dimension(self, dim, scale):
method forward (line 40) | def forward(self, prev_prev_input, prev_input):
class newMatching (line 76) | class newMatching(nn.Module):
method __init__ (line 77) | def __init__(self, network_arch, cell_arch, cell=Cell, args=None):
method forward (line 136) | def forward(self, x):
FILE: retrain/skip_model_3d.py
class Cell (line 11) | class Cell(nn.Module):
method __init__ (line 12) | def __init__(self, steps, block_multiplier, prev_prev_fmultiplier,
method scale_dimension (line 37) | def scale_dimension(self, dim, scale):
method forward (line 40) | def forward(self, prev_prev_input, prev_input):
class newMatching (line 76) | class newMatching(nn.Module):
method __init__ (line 77) | def __init__(self, network_arch, cell_arch, cell=Cell, args=None):
method forward (line 138) | def forward(self, x):
FILE: search.py
class Trainer (line 50) | class Trainer(object):
method __init__ (line 51) | def __init__(self, args):
method training (line 151) | def training(self, epoch):
method validation (line 231) | def validation(self, epoch):
FILE: thop/count_hooks.py
function count_convNd (line 9) | def count_convNd(m, x, y):
function count_conv2d (line 20) | def count_conv2d(m, x, y):
function count_convtranspose2d (line 46) | def count_convtranspose2d(m, x, y):
function count_bn (line 76) | def count_bn(m, x, y):
function count_relu (line 86) | def count_relu(m, x, y):
function count_softmax (line 95) | def count_softmax(m, x, y):
function count_maxpool (line 108) | def count_maxpool(m, x, y):
function count_adap_maxpool (line 116) | def count_adap_maxpool(m, x, y):
function count_avgpool (line 125) | def count_avgpool(m, x, y):
function count_adap_avgpool (line 135) | def count_adap_avgpool(m, x, y):
function count_linear (line 146) | def count_linear(m, x, y):
FILE: thop/profile.py
function profile (line 42) | def profile(model, input_size, custom_ops={}, device="cpu"):
FILE: thop/utils.py
function clever_format (line 2) | def clever_format(num, format="%.2f"):
FILE: train.py
function train (line 80) | def train(epoch):
function val (line 117) | def val():
function save_checkpoint (line 156) | def save_checkpoint(save_path, epoch,state, is_best):
FILE: utils/colorize.py
function get_color_map (line 3) | def get_color_map():
FILE: utils/copy_state_dict.py
function copy_state_dict (line 1) | def copy_state_dict(cur_state_dict, pre_state_dict, prefix = ''):
FILE: utils/lr_scheduler.py
class LR_Scheduler (line 14) | class LR_Scheduler(object):
method __init__ (line 32) | def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0,
method __call__ (line 46) | def __call__(self, optimizer, i, epoch, best_pred):
method _adjust_learning_rate (line 69) | def _adjust_learning_rate(self, optimizer, lr):
FILE: utils/multadds_count.py
function count_parameters_in_MB (line 8) | def count_parameters_in_MB(model):
function comp_multadds (line 12) | def comp_multadds(model, input_size=(3,224,224), half=False):
function comp_multadds_fw (line 27) | def comp_multadds_fw(model, input_data):
function add_flops_counting_methods (line 38) | def add_flops_counting_methods(net_main_module):
function compute_average_flops_cost (line 92) | def compute_average_flops_cost(self):
function start_flops_count (line 112) | def start_flops_count(self):
function stop_flops_count (line 125) | def stop_flops_count(self):
function reset_flops_count (line 138) | def reset_flops_count(self):
function add_flops_mask (line 150) | def add_flops_mask(module, mask):
function remove_flops_mask (line 158) | def remove_flops_mask(module):
function conv_flops_counter_hook (line 165) | def conv_flops_counter_hook(conv_module, input, output):
function linear_flops_counter_hook (line 197) | def linear_flops_counter_hook(linear_module, input, output):
function batch_counter_hook (line 213) | def batch_counter_hook(module, input, output):
function add_batch_counter_variables_or_reset (line 222) | def add_batch_counter_variables_or_reset(module):
function add_batch_counter_hook_function (line 226) | def add_batch_counter_hook_function(module):
function remove_batch_counter_hook_function (line 234) | def remove_batch_counter_hook_function(module):
function add_flops_counter_variable_or_reset (line 241) | def add_flops_counter_variable_or_reset(module):
function add_flops_counter_hook_function (line 246) | def add_flops_counter_hook_function(module):
function remove_flops_counter_hook_function (line 262) | def remove_flops_counter_hook_function(module):
function add_flops_mask_variable_or_reset (line 275) | def add_flops_mask_variable_or_reset(module):
FILE: utils/saver.py
class Saver (line 8) | class Saver(object):
method __init__ (line 10) | def __init__(self, args):
method save_checkpoint (line 20) | def save_checkpoint(self, state, is_best, filename='checkpoint.pth.tar'):
method save_experiment_config (line 45) | def save_experiment_config(self):
FILE: utils/summaries.py
class TensorboardSummary (line 9) | class TensorboardSummary(object):
method __init__ (line 10) | def __init__(self, directory):
method create_summary (line 13) | def create_summary(self):
method tensor2array (line 17) | def tensor2array(self, tensor, max_value=255, colormap='rainbow'):
method visualize_image_stereo (line 45) | def visualize_image_stereo(self, writer, image, target, output, global...
Condensed preview — 66 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,354K chars).
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{
"path": ".gitignore",
"chars": 2259,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n**/__pycache__/\n*.py[cod]\n*$py.class\n/predict/*\n/apex/*\n/run/*\n\n# P"
},
{
"path": "LICENSE",
"chars": 1070,
"preview": "MIT License\n\nCopyright (c) 2020 Xuelian Cheng\n\nPermission is hereby granted, free of charge, to any person obtaining a c"
},
{
"path": "README.md",
"chars": 3488,
"preview": "## LEAStereo\n\nThis repository contains the code for our NeurIPS 2020 paper `Hierarchical Neural Architecture Searchfor D"
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"path": "config_utils/decode_args.py",
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"preview": "import argparse\n\ndef obtain_decode_args():\n parser = argparse.ArgumentParser(description=\"LEStereo Decoding..\")\n p"
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"preview": "import argparse\n\ndef obtain_predict_args():\n\n parser = argparse.ArgumentParser(description='LEStereo Prediction')\n "
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"preview": "import argparse\n\ndef obtain_search_args():\n parser = argparse.ArgumentParser(description=\"LEStereo Searching...\")\n "
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"chars": 3133,
"preview": "import argparse\n\ndef obtain_train_args():\n\n # Training settings\n parser = argparse.ArgumentParser(description='LES"
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"preview": "ln -s /mnt/data/StereoDataset/dataset/kitti2015 ./dataset\nln -s /mnt/data/StereoDataset/dataset/kitti2012 ./dataset\nln -"
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"chars": 0,
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{
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"path": "dataloaders/lists/kitti2015_train.list",
"chars": 2800,
"preview": "000179_10.png\n000128_10.png\n000122_10.png\n000178_10.png\n000173_10.png\n000100_10.png\n000114_10.png\n000037_10.png\n000071_1"
},
{
"path": "dataloaders/lists/kitti2015_train180.list",
"chars": 2520,
"preview": "000179_10.png\n000128_10.png\n000122_10.png\n000178_10.png\n000173_10.png\n000100_10.png\n000114_10.png\n000037_10.png\n000071_1"
},
{
"path": "dataloaders/lists/kitti2015_val20.list",
"chars": 280,
"preview": "000198_10.png\n000059_10.png\n000067_10.png\n000123_10.png\n000133_10.png\n000057_10.png\n000073_10.png\n000120_10.png\n000018_1"
},
{
"path": "dataloaders/lists/middeval3_test.list",
"chars": 256,
"preview": "Australia/im0.png\nAustraliaP/im0.png\nBicycle2/im0.png\nClassroom2/im0.png\nClassroom2E/im0.png\nComputer/im0.png\nCrusade/im"
},
{
"path": "dataloaders/lists/middeval3_train.list",
"chars": 248,
"preview": "Playtable/im0.png\nArtL/im0.png\nJadeplant/im0.png\nPlaytableP/im0.png\nPianoL/im0.png\nPiano/im0.png\nAdirondack/im0.png\nTedd"
},
{
"path": "dataloaders/lists/sceneflow_search_trainA.list",
"chars": 344877,
"preview": "TRAIN/B/0111/left/0009.png\nTRAIN/B/0061/left/0007.png\nTRAIN/C/0672/left/0011.png\nTRAIN/15mm_focallength/scene_backwards/"
},
{
"path": "dataloaders/lists/sceneflow_search_trainB.list",
"chars": 341760,
"preview": "TRAIN/A/0345/left/0014.png\nTRAIN/A/0582/left/0013.png\nTRAIN/lonetree_augmented0_x2/left/0107.png\nTRAIN/A/0186/left/0006."
},
{
"path": "dataloaders/lists/sceneflow_search_val.list",
"chars": 26000,
"preview": "TEST/B/0060/left/0009.png\nTEST/C/0048/left/0013.png\nTEST/C/0015/left/0007.png\nTEST/C/0128/left/0006.png\nTEST/B/0034/left"
},
{
"path": "dataloaders/lists/sceneflow_test.list",
"chars": 113620,
"preview": "TEST/C/0041/left/0015.png\nTEST/C/0041/left/0008.png\nTEST/C/0041/left/0006.png\nTEST/C/0041/left/0010.png\nTEST/C/0041/left"
},
{
"path": "dataloaders/lists/sceneflow_train.list",
"chars": 1219438,
"preview": "TRAIN/B/0111/left/0009.png\nTRAIN/B/0061/left/0007.png\nTRAIN/C/0672/left/0011.png\nTRAIN/15mm_focallength/scene_backwards/"
},
{
"path": "decode.py",
"chars": 2880,
"preview": "import os\nimport sys\nimport numpy as np\nimport torch\nfrom models.decoding_formulas import Decoder\nfrom config_utils.deco"
},
{
"path": "decode.sh",
"chars": 127,
"preview": "CUDA_VISIBLE_DEVICES=1 python decode.py --dataset sceneflow --step 3 \\\n--resume ./run/sceneflow/experiment/checkpoint.pt"
},
{
"path": "models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "models/build_model.py",
"chars": 2286,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom models.build_model_2d import AutoFeature, Disp\nf"
},
{
"path": "models/build_model_2d.py",
"chars": 23445,
"preview": "import torch.nn as nn\nimport torch.nn.functional as F\nimport models.cell_level_search_2d as cell_level_search\nfrom model"
},
{
"path": "models/build_model_3d.py",
"chars": 21526,
"preview": "import torch.nn as nn\nimport torch.nn.functional as F\nimport models.cell_level_search_3d as cell_level_search\nfrom mode"
},
{
"path": "models/cell_level_search_2d.py",
"chars": 5742,
"preview": "import torch.nn.functional as F\nfrom models.operations_2d import *\nfrom models.genotypes_2d import PRIMITIVES\n\nclass Mix"
},
{
"path": "models/cell_level_search_3d.py",
"chars": 6032,
"preview": "import torch.nn.functional as F\nfrom models.operations_3d import *\nfrom models.genotypes_3d import PRIMITIVES\n\nclass Mix"
},
{
"path": "models/decoding_formulas.py",
"chars": 4777,
"preview": "import numpy as np\nimport pdb\nimport torch\nimport torch.nn.functional as F\n\ndef network_layer_to_space(net_arch):\n fo"
},
{
"path": "models/genotypes_2d.py",
"chars": 147,
"preview": "from collections import namedtuple\n\nGenotype = namedtuple('Genotype_2D', 'cell cell_concat')\n\nPRIMITIVES = [\n 'skip_c"
},
{
"path": "models/genotypes_3d.py",
"chars": 147,
"preview": "from collections import namedtuple\n\nGenotype = namedtuple('Genotype', 'cell cell_concat')\n\nPRIMITIVES = [\n 'skip_conn"
},
{
"path": "models/operations_2d.py",
"chars": 7765,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nOPS = {\n 'skip_connect': lambda C, stride: Identi"
},
{
"path": "models/operations_3d.py",
"chars": 7772,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nOPS = {\n 'skip_connect': lambda C, stride: Identi"
},
{
"path": "mypath.py",
"chars": 522,
"preview": "class Path(object):\n @staticmethod\n def db_root_dir(dataset):\n if dataset == 'sceneflow':\n retur"
},
{
"path": "predict.py",
"chars": 11322,
"preview": "from __future__ import print_function\nimport argparse\nimport skimage\nimport skimage.io\nimport skimage.transform\nfrom PIL"
},
{
"path": "predict_kitti12.sh",
"chars": 988,
"preview": "CUDA_VISIBLE_DEVICES=1 python predict.py \\\n --kitti2012=1 --maxdisp=192 \\\n --crop_heigh"
},
{
"path": "predict_kitti15.sh",
"chars": 983,
"preview": "CUDA_VISIBLE_DEVICES=0 python predict.py \\\n --kitti2015=1 --maxdisp=192 \\\n --crop_heigh"
},
{
"path": "predict_md.sh",
"chars": 986,
"preview": "CUDA_VISIBLE_DEVICES=0 python predict.py \\\n --middlebury=1 --maxdisp=408 \\\n --crop_heig"
},
{
"path": "predict_sf.sh",
"chars": 987,
"preview": "CUDA_VISIBLE_DEVICES=1 python predict.py \\\n --sceneflow=1 --maxdisp=192 \\\n --crop_heigh"
},
{
"path": "retrain/LEAStereo.py",
"chars": 2069,
"preview": "import torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom models.build_model_2d import"
},
{
"path": "retrain/new_model_2d.py",
"chars": 7792,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom models.genotypes_2d import PR"
},
{
"path": "retrain/new_model_3d.py",
"chars": 7528,
"preview": "import torch\nimport torch.nn as nn\nimport numpy as np\nfrom models.genotypes_3d import PRIMITIVES\nfrom models.genotypes_3"
},
{
"path": "retrain/skip_model_3d.py",
"chars": 8243,
"preview": "import torch\nimport torch.nn as nn\nimport numpy as np\nfrom models.genotypes_3d import PRIMITIVES\nfrom models.genotypes_3"
},
{
"path": "search.py",
"chars": 13467,
"preview": "import os\nimport torch\nimport numpy as np\nimport torch.nn as nn\nfrom tqdm import tqdm\nfrom collections import OrderedDic"
},
{
"path": "search.sh",
"chars": 684,
"preview": "CUDA_VISIBLE_DEVICES=1 python search.py \\\n --batch-size 1 \\\n --dataset sceneflow "
},
{
"path": "thop/__init__.py",
"chars": 28,
"preview": "from .profile import profile"
},
{
"path": "thop/count_hooks.py",
"chars": 3769,
"preview": "import argparse\n\nimport torch\nimport torch.nn as nn\n\nmultiply_adds = 1\n\n\ndef count_convNd(m, x, y):\n x = x[0]\n\n ke"
},
{
"path": "thop/profile.py",
"chars": 2776,
"preview": "import logging\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.modules.conv import _ConvNd\n\nfrom .count_hooks import *"
},
{
"path": "thop/utils.py",
"chars": 281,
"preview": "\ndef clever_format(num, format=\"%.2f\"):\n if num > 1e12:\n return format % (num / 1e12) + \"T\"\n if num > 1e9:\n"
},
{
"path": "train.py",
"chars": 7670,
"preview": "from __future__ import print_function\nimport argparse\nfrom math import log10\n\nimport sys\nimport shutil\nimport os\nimport "
},
{
"path": "train_kitti12.sh",
"chars": 1107,
"preview": "CUDA_VISIBLE_DEVICES=0 python train.py \\\n --batch_size=4 \\\n --testBatchSize=1 \\\n "
},
{
"path": "train_kitti15.sh",
"chars": 1148,
"preview": "CUDA_VISIBLE_DEVICES=0 python train.py \\\n --batch_size=4 \\\n --testBatchSize=1 \\\n "
},
{
"path": "train_md.sh",
"chars": 1082,
"preview": "CUDA_VISIBLE_DEVICES=0 python train.py \\\n --batch_size=2 \\\n --testBatchSize=1 \\\n "
},
{
"path": "train_sf.sh",
"chars": 905,
"preview": "CUDA_VISIBLE_DEVICES=0 python3 train.py --batch_size=4 \\\n --crop_height=384 \\\n --crop_widt"
},
{
"path": "utils/colorize.py",
"chars": 12843,
"preview": "import numpy as np\n\ndef get_color_map():\n\n return np.array([[0.18995,0.07176,0.23217],\n [0.19"
},
{
"path": "utils/copy_state_dict.py",
"chars": 529,
"preview": "def copy_state_dict(cur_state_dict, pre_state_dict, prefix = ''):\n def _get_params(key):\n key = prefix + key\n "
},
{
"path": "utils/lr_scheduler.py",
"chars": 2789,
"preview": "##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n## Created by: Hang Zhang\n## ECE Department,"
},
{
"path": "utils/multadds_count.py",
"chars": 8943,
"preview": "import torch\nimport numpy as np\n# Original implementation:\n# \thttps://github.com/warmspringwinds/pytorch-segmentation-de"
},
{
"path": "utils/saver.py",
"chars": 2487,
"preview": "import os\nimport shutil\nimport torch\nfrom collections import OrderedDict\nimport glob\n\n\nclass Saver(object):\n\n def __i"
},
{
"path": "utils/summaries.py",
"chars": 2031,
"preview": "import os\nimport torch\nfrom torchvision.utils import make_grid\nfrom tensorboardX import SummaryWriter\nimport numpy as np"
}
]
About this extraction
This page contains the full source code of the XuelianCheng/LEAStereo GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 66 files (2.2 MB), approximately 573.5k tokens, and a symbol index with 231 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.