mirror of
https://github.com/botastic/SoftGroup.git
synced 2026-07-01 16:00:06 +00:00
add scannet instruction
This commit is contained in:
Thang Vu
4
.gitignore
vendored
4
.gitignore
vendored
@@ -64,6 +64,8 @@ dist/
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*.tsv
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*.npy
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*.zip
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dataset/scannetv2/scans
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dataset/scannetv2/scans_test
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dataset/scannetv2/train
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dataset/scannetv2/val
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dataset/scannetv2/test
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@@ -72,5 +74,3 @@ dataset/scannetv2/scannetv2-labels.combined.tsv
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dataset/s3dis/preprocess
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dataset/s3dis/val_gt
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61
README.md
61
README.md
@@ -54,35 +54,19 @@ conda install -c bioconda google-sparsehash
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4\) Install spconv
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* Verify the version of spconv.
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spconv 1.0, compatible with CUDA < 11 and pytorch < 1.5, is already recursively cloned in `SoftGroup/lib/spconv` in step 2) by default.
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For higher version CUDA and pytorch, spconv 1.2 is suggested. Replace `SoftGroup/lib/spconv` with this fork of spconv.
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* Install the dependencies.
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```
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git clone https://github.com/outsidercsy/spconv.git --recursive
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```
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sudo apt-get install libboost-all-dev
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sudo apt-get install libsparsehash-dev
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Note: In the provided spconv 1.0 and 1.2, spconv\spconv\functional.py is modified to make grad_output contiguous. Make sure you use the modified spconv but not the original one. Or there would be some bugs of optimization.
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* Install the dependent libraries.
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```
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conda install libboost
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conda install -c daleydeng gcc-5 # (optional, install gcc-5.4 in conda env)
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```
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* Compile the spconv library.
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```
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cd SoftGroup/lib/spconv
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python setup.py bdist_wheel
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```
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* Intall the generated .whl file.
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```
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cd SoftGroup/lib/spconv/dist
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pip install {wheel_file_name}.whl
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pip install dist/{WHEEL_FILE_NAME}.whl
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```
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@@ -100,35 +84,36 @@ python setup.py build_ext develop
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1\) Download the [ScanNet](http://www.scan-net.org/) v2 dataset.
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2\) Put the data in the corresponding folders.
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* Copy the files `[scene_id]_vh_clean_2.ply`, `[scene_id]_vh_clean_2.labels.ply`, `[scene_id]_vh_clean_2.0.010000.segs.json` and `[scene_id].aggregation.json` into the `dataset/scannetv2/train` and `dataset/scannetv2/val` folders according to the ScanNet v2 train/val [split](https://github.com/ScanNet/ScanNet/tree/master/Tasks/Benchmark).
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2\) Put the downloaded ``scans`` and ``scans_test`` folder as follows.
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* Copy the files `[scene_id]_vh_clean_2.ply` into the `dataset/scannetv2/test` folder according to the ScanNet v2 test [split](https://github.com/ScanNet/ScanNet/tree/master/Tasks/Benchmark).
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* Put the file `scannetv2-labels.combined.tsv` in the `dataset/scannetv2` folder.
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The dataset files are organized as follows.
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```
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SoftGroup
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├── dataset
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│ ├── scannetv2
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│ │ ├── train
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│ │ │ ├── [scene_id]_vh_clean_2.ply & [scene_id]_vh_clean_2.labels.ply & [scene_id]_vh_clean_2.0.010000.segs.json & [scene_id].aggregation.json
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│ │ ├── val
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│ │ │ ├── [scene_id]_vh_clean_2.ply & [scene_id]_vh_clean_2.labels.ply & [scene_id]_vh_clean_2.0.010000.segs.json & [scene_id].aggregation.json
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│ │ ├── test
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│ │ │ ├── [scene_id]_vh_clean_2.ply
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│ │ ├── scannetv2-labels.combined.tsv
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│ │ ├── scans
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│ │ ├── scans_test
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```
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3\) Generate input files `[scene_id]_inst_nostuff.pth` for instance segmentation.
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3\) Split and preprocess data
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```
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cd SoftGroup/dataset/scannetv2
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python prepare_data_inst.py --data_split train
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python prepare_data_inst.py --data_split val
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python prepare_data_inst.py --data_split test
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bash prepare_data.sh
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```
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The script data into train/val/test folder and preprocess the data. After running the script the scannet dataset structure should look like below.
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```
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SoftGroup
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├── dataset
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│ ├── scannetv2
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│ │ ├── scans
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│ │ ├── scans_test
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│ │ ├── train
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│ │ ├── val
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│ │ ├── test
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│ │ ├── val_gt
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```
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## Training
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```
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CUDA_VISIBLE_DEVICES=0 python train.py --config config/softgroup_default_scannet.yaml
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8
dataset/scannetv2/prepare_data.sh
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8
dataset/scannetv2/prepare_data.sh
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@@ -0,0 +1,8 @@
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#!/bin/bash
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echo Copy data
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python split_data.py
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echo Preprocess data
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python prepare_data_inst.py --data_split train
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python prepare_data_inst.py --data_split val
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python prepare_data_inst.py --data_split test
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python prepare_data_inst_gttxt.py
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@@ -97,4 +97,4 @@ if opt.data_split == 'test':
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else:
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p.map(f, files)
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p.close()
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p.join()
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p.join()
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100
dataset/scannetv2/scannetv2_test.txt
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dataset/scannetv2/scannetv2_test.txt
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dataset/scannetv2/scannetv2_train.txt
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dataset/scannetv2/scannetv2_train.txt
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Load Diff
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dataset/scannetv2/scannetv2_val.txt
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dataset/scannetv2/scannetv2_val.txt
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scene0355_01
|
||||
scene0146_00
|
||||
scene0146_01
|
||||
scene0146_02
|
||||
scene0196_00
|
||||
scene0702_00
|
||||
scene0702_01
|
||||
scene0702_02
|
||||
scene0314_00
|
||||
scene0277_00
|
||||
scene0277_01
|
||||
scene0277_02
|
||||
scene0095_00
|
||||
scene0095_01
|
||||
scene0015_00
|
||||
scene0100_00
|
||||
scene0100_01
|
||||
scene0100_02
|
||||
scene0558_00
|
||||
scene0558_01
|
||||
scene0558_02
|
||||
scene0685_00
|
||||
scene0685_01
|
||||
scene0685_02
|
||||
36
dataset/scannetv2/split_data.py
Normal file
36
dataset/scannetv2/split_data.py
Normal file
@@ -0,0 +1,36 @@
|
||||
import os
|
||||
import shutil
|
||||
|
||||
# split scans specified in scannetv2_{train/val/test}.txt
|
||||
|
||||
splits = ['train', 'val', 'test']
|
||||
|
||||
for split in splits:
|
||||
print('processing', split)
|
||||
f_name = 'scannetv2_{}.txt'.format(split)
|
||||
f = open(f_name, 'r')
|
||||
scans = f.readlines()
|
||||
os.makedirs(split, exist_ok=True)
|
||||
for scan_name in scans:
|
||||
scan = scan_name.strip() # strip white space
|
||||
if split == 'test':
|
||||
src = 'scans_test/{}/{}_vh_clean_2.ply'.format(scan, scan)
|
||||
dest = '{}/{}_vh_clean_2.ply'.format(split, scan)
|
||||
shutil.copyfile(src, dest)
|
||||
else:
|
||||
src = 'scans/{}/{}_vh_clean_2.ply'.format(scan, scan)
|
||||
dest = '{}/{}_vh_clean_2.ply'.format(split, scan)
|
||||
shutil.copyfile(src, dest)
|
||||
|
||||
src = 'scans/{}/{}_vh_clean_2.labels.ply'.format(scan, scan)
|
||||
dest = '{}/{}_vh_clean_2.labels.ply'.format(split, scan)
|
||||
shutil.copyfile(src, dest)
|
||||
|
||||
src = 'scans/{}/{}_vh_clean_2.0.010000.segs.json'.format(scan, scan)
|
||||
dest = '{}/{}_vh_clean_2.0.010000.segs.json'.format(split, scan)
|
||||
shutil.copyfile(src, dest)
|
||||
|
||||
src = 'scans/{}/{}.aggregation.json'.format(scan, scan)
|
||||
dest = '{}/{}.aggregation.json'.format(split, scan)
|
||||
shutil.copyfile(src, dest)
|
||||
print('done')
|
||||
@@ -2,6 +2,6 @@ torch==1.1
|
||||
cmake>=3.13.2
|
||||
plyfile
|
||||
tensorboardX
|
||||
pyyaml
|
||||
pyyaml==5.4.1
|
||||
scipy
|
||||
six
|
||||
|
||||
Reference in New Issue
Block a user