Simple-does-it-weakly-supervised-instance-and-semantic-segmentation
There are five weakly supervised networks in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017). Respectively, Naive, Box, Box^i, Grabcut+, M∩G+. All of them use cheap-to-generate label, bounding box, during training and don't need other informations except image during testing.
This repo contains a TensorFlow implementation of Grabcut version of semantic segmentation.
My Environment
Environment 1
- Operating System:
- Arch Linux 4.15.15-1
- Memory
- 64GB
- CUDA:
- CUDA V9.0.176
- CUDNN:
- CUDNN 7.0.5-2
- GPU:
- GTX 1070 8G
- Nvidia driver:
- 390.25
- Python:
- python 3.6.4
- Python package:
- tqdm, bs4, opencv-python, pydensecrf, cython...
- Tensorflow:
- tensorflow-gpu 1.5.0
Environment 2
- Operating System:
- Ubuntu 16.04
- Memory
- 64GB
- CUDA:
- CUDA V9.0.176
- CUDNN:
- CUDNN 7
- GPU:
- GTX 1060 6G
- Nvidia driver:
- 390.48
- Python:
- python 3.5.2
- Python package:
- tqdm, bs4, opencv-python, pydensecrf, cython...
- Tensorflow:
- tensorflow-gpu 1.6.0
Downloading the VOC12 dataset
Setup Dataset
My directory structure
./Simple_does_it/
├── Dataset
│ ├── Annotations
│ ├── CRF_masks
│ ├── CRF_pairs
│ ├── Grabcut_inst
│ ├── Grabcut_pairs
│ ├── JPEGImages
│ ├── Pred_masks
│ ├── Pred_pairs
│ ├── SegmentationClass
│ └── Segmentation_label
├── Model
│ ├── Logs
│ └── models
├── Parser_
├── Postprocess
├── Preprocess
└── Util
VOC2012 directory structure
VOCtrainval_11-May-2012
└── VOCdevkit
└── VOC2012
├── Annotations
├── ImageSets
│ ├── Action
│ ├── Layout
│ ├── Main
│ └── Segmentation
├── JPEGImages
├── SegmentationClass
└── SegmentationObject
- Put annotations in 'Annotations'
mv {PATH}/VOCtrainval_11-May-2012/VOCdevkit/VOC2012/Annotations/* {PATH}/Simple_does_it/Dataset/Annotations/
- Put images in 'JPEGImages'
mv {PATH}/VOCtrainval_11-May-2012/VOCdevkit/VOC2012/JPEGImages/* {PATH}/Simple_does_it/Dataset/JPEGImages/
- Put Ground truth in 'SegmentationClass' for computing mIoU and IoU
mv {PATH}/VOCtrainval_11-May-2012/VOCdevkit/VOC2012/SegmentationClass/* {PATH}/Simple_does_it/Dataset/SegmentationClass/
Demo (See Usage for more details)
Download pretrain model training on VOC12 (train set size: 1464)
- Pretrain model
- Move files from VOC12_CKPT to 'models'
- Run test
python ./Model/model.py --restore_target 1020 - Run train (See Training for more details)
python ./Model/model.py --is_train 1 --set_name voc_train.txt --restore_target 1020 - Performance
| set | CRF | mIoU |
|---|---|---|
| train | X | 64.93% |
| train | O | 66.90% |
| val | X | 39.03% |
| val | O | 42.54% |
Download pretrain model training on VOC12 + SBD (train set size: 10582)
- Pretrain model
- Move files from VOC12_SBD_CKPT to 'models'
- Run test
python ./Model/model.py --restore_target 538 - Run train (See Training for more details)
python ./Model/model.py --is_train 1 --set_name train.txt --restore_target 538 - Performance
| set | CRF | mIoU |
|---|---|---|
| train | X | 66.87% |
| train | O | 68.21% |
| val | X | 51.90% |
| val | O | 54.52% |
Training (See Usage for more details)
Download pretrain vgg16
- tensorflow/models
- Put vgg_16.ckpt in 'models'
Extract annotations from 'Annotations' according to 'train.txt' or 'voc_train.txt' for VOC12 + SDB or VOC12
- For VOC12 + SBD (train set size: 10582)
- This will generate a 'train_pairs.txt' for 'grabcut.py'
python ./Dataset/make_train.py - For VOC12 (train set size: 1464)
- This will generate a 'voc_train_pairs.txt' for 'grabcut.py'
python ./Dataset/make_train.py --train_set_name voc_train.txt --train_pair_name voc_train_pairs.txt
Generate label for training by 'grabcut.py'
- Result of grabcut for each bounding box will be stored at 'Grabcut_inst'
- Result of grabcut for each image will be stored at 'Segmentation_label'
- Result of grabcut for each image combing with image and bounding box will be stored at 'Grabcut_pairs'
- Note: If the instance hasn't existed at 'Grabcut_inst', grabcut.py will grabcut that image
- For VOC12 + SBD (train set size: 10582)
python ./Preprocess/grabcut.py - For VOC12 (train set size: 1464)
python ./Preprocess/grabcut.py --train_pair_name voc_train_pairs.txt
Train network
- The event file for tensorboard will be stored at 'Logs'
- Train on VOC12 + SBD (train set size: 10582)
- This will consume lot of memory.
- The train set is so large.
- Data dtyp will be casted from np.uint8 to np.float16 for mean substraction.
- Eliminate mean substraction for lower memory usage.
- Change the dtype in ./Dataset/load.py from np.float16 to np.uint8
- Comment mean substraction in ./Model/model.py
python ./Model/model.py --is_train 1 --set_name train.txt - This will consume lot of memory.
- Train on VOC12 (train set size: 1464)
python ./Model/model.py --is_train 1 --set_name voc_train.txt
Testing (See Usage for more details)
Test network
- Result will be stored at 'Pred_masks'
- Result combing with image will be stored at 'Pred_pairs'
- Result after dense CRF will be stored at 'CRF_masks'
- Result after dense CRF combing with image will be stored at 'CRF_pairs'
- Test on VOC12 (val set size: 1449)
python ./Model/model.py --restore_target {num}
Performance (See Usage for more details)
Evaluate mIoU and IoU
- Compute mIoU and IoU
python ./Dataset/mIoU.py
Usage
Parser_/parser.py
- Parse the command line argument
Util/divied.py
- Generating train.txt and test.txt according to 'JPEGImages'
- Not necessary
usage: divied.py [-h] [--dataset DATASET] [--img_dir_name IMG_DIR_NAME]
[--train_set_ratio TRAIN_SET_RATIO]
[--train_set_name TRAIN_SET_NAME]
[--test_set_name TEST_SET_NAME]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET path to dataset (default: Util/../Parser_/../Dataset)
--img_dir_name IMG_DIR_NAME
name for image directory (default: JPEGImages)
--train_set_ratio TRAIN_SET_RATIO
ratio for training set, [0,10] (default: 7)
--train_set_name TRAIN_SET_NAME
name for training set (default: train.txt)
--test_set_name TEST_SET_NAME
name for testing set (default: val.txt)
Dataset/make_train.py
- Extract annotations from 'Annotations' according to 'train.txt'
- Content: {image name}###{image name + num + class + .png}###{bbox ymin}###{bbox xmin}###{bbox ymax}###{bbox xmax}###{class}
- Example: 2011_003038###2011_003038_3_15.png###115###1###233###136###person
usage: make_train.py [-h] [--dataset DATASET]
[--train_set_name TRAIN_SET_NAME]
[--ann_dir_name ANN_DIR_NAME]
[--train_pair_name TRAIN_PAIR_NAME]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET path to dataset (default:
Dataset/../Parser_/../Dataset)
--train_set_name TRAIN_SET_NAME
name for training set (default: train.txt)
--ann_dir_name ANN_DIR_NAME
name for annotation directory (default: Annotations)
--train_pair_name TRAIN_PAIR_NAME
name for training pair (default: train_pairs.txt)
Preprocess/grabcut.py
- Grabcut a traditional computer vision method
- Input bounding box and image then generating label for training
usage: grabcut.py [-h] [--dataset DATASET] [--img_dir_name IMG_DIR_NAME]
[--train_pair_name TRAIN_PAIR_NAME]
[--grabcut_dir_name GRABCUT_DIR_NAME]
[--img_grabcuts_dir IMG_GRABCUTS_DIR]
[--pool_size POOL_SIZE] [--grabcut_iter GRABCUT_ITER]
[--label_dir_name LABEL_DIR_NAME]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET path to dataset (default:
./Preprocess/../Parser_/../Dataset)
--img_dir_name IMG_DIR_NAME
name for image directory (default: JPEGImages)
--train_pair_name TRAIN_PAIR_NAME
name for training pair (default: train_pairs.txt)
--grabcut_dir_name GRABCUT_DIR_NAME
name for grabcut directory (default: Grabcut_inst)
--img_grabcuts_dir IMG_GRABCUTS_DIR
name for image with grabcuts directory (default:
Grabcut_pairs)
--pool_size POOL_SIZE
pool for multiprocess (default: 4)
--grabcut_iter GRABCUT_ITER
grabcut iteration (default: 3)
--label_dir_name LABEL_DIR_NAME
name for label directory (default: Segmentation_label)
Model/model.py
- Deeplab-Largefov
usage: model.py [-h] [--dataset DATASET] [--set_name SET_NAME]
[--label_dir_name LABEL_DIR_NAME]
[--img_dir_name IMG_DIR_NAME] [--classes CLASSES]
[--batch_size BATCH_SIZE] [--epoch EPOCH]
[--learning_rate LEARNING_RATE] [--momentum MOMENTUM]
[--keep_prob KEEP_PROB] [--is_train IS_TRAIN]
[--save_step SAVE_STEP] [--pred_dir_name PRED_DIR_NAME]
[--pair_dir_name PAIR_DIR_NAME] [--crf_dir_name CRF_DIR_NAME]
[--crf_pair_dir_name CRF_PAIR_DIR_NAME] [--width WIDTH]
[--height HEIGHT] [--restore_target RESTORE_TARGET]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET path to dataset (default:
./Model/../Parser_/../Dataset)
--set_name SET_NAME name for set (default: val.txt)
--label_dir_name LABEL_DIR_NAME
name for label directory (default: Segmentation_label)
--img_dir_name IMG_DIR_NAME
name for image directory (default: JPEGImages)
--classes CLASSES number of classes for segmentation (default: 21)
--batch_size BATCH_SIZE
batch size for training (default: 16)
--epoch EPOCH epoch for training (default: 2000)
--learning_rate LEARNING_RATE
learning rate for training (default: 0.01)
--momentum MOMENTUM momentum for optimizer (default: 0.9)
--keep_prob KEEP_PROB
probability for dropout (default: 0.5)
--is_train IS_TRAIN training or testing [1 = True / 0 = False] (default:
0)
--save_step SAVE_STEP
step for saving weight (default: 2)
--pred_dir_name PRED_DIR_NAME
name for prediction masks directory (default:
Pred_masks)
--pair_dir_name PAIR_DIR_NAME
name for pairs directory (default: Pred_pairs)
--crf_dir_name CRF_DIR_NAME
name for crf prediction masks directory (default:
CRF_masks)
--crf_pair_dir_name CRF_PAIR_DIR_NAME
name for crf pairs directory (default: CRF_pairs)
--width WIDTH width for resize (default: 513)
--height HEIGHT height for resize (default: 513)
--restore_target RESTORE_TARGET
target for restore (default: 0)
Dataset/mIoU.py
- Compute mIoU and IoU
usage: mIoU.py [-h] [--dataset DATASET] [--set_name SET_NAME]
[--GT_dir_name GT_DIR_NAME] [--Pred_dir_name PRED_DIR_NAME]
[--classes CLASSES]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET path to dataset (default:
./Dataset/../Parser_/../Dataset)
--set_name SET_NAME name for set (default: val.txt)
--GT_dir_name GT_DIR_NAME
name for ground truth directory (default:
SegmentationClass)
--Pred_dir_name PRED_DIR_NAME
name for prediction directory (default: CRF_masks)
--classes CLASSES number of classes (default: 21)
Dataset/load.py
- Loading data for training / testing according to train.txt / val.txt
Dataset/save_result.py
- Save result during testing
Dataset/voc12_class.py
- Map the class to grayscale value
Dataset/voc12_color.py
- Map the grayscale value to RGB
Postprocess/dense_CRF.py
- Dense CRF a machine learning method
- Refine the result
Reference
- [1] Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, Bernt Schiele. Simple Does It: Weakly Supervised Instance and Semantic Segmentation. CVPR 2017
- [2] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. ICLR2015
- [3] philferriere. Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).
- [4] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. arXive 2016
- [5] Philipp Krähenbühl, Vladlen Koltun. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. NIPS 2011
1 Oct 23, 2021
337 Dec 15, 2022
12 Dec 12, 2022
113 Dec 27, 2022
![[CVPR'22] Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast](https://user-images.githubusercontent.com/83934424/157233454-9a0fbae6-2e05-4285-9042-70af1449ad96.png)
96 Dec 30, 2022
124 Dec 27, 2022
60 Nov 18, 2022
31 Dec 8, 2022
112 Jan 2, 2023
39 Sep 20, 2022
48 Dec 18, 2022
54 Dec 12, 2022
74 Dec 27, 2022
110 Dec 7, 2022
16 Nov 14, 2022
44 Dec 12, 2022
16 Nov 26, 2022
3 Oct 15, 2021