实例分割模型库使用教程

实例分割作为计算机视觉的一项基本任务，在方方面面发挥着巨大的作用，本教程使用$Jittor$框架实现了实例分割模型库

实例分割模型库代码：https://github.com/Jittor/InstanceSegmentation-jittor

1 数据集

1.1 COCO数据集下载

sudo wget -c http://images.cocodataset.org/zips/train2017.zip
sudo wget -c http://images.cocodataset.org/zips/val2017.zip
sudo wget -c http://images.cocodataset.org/zips/test2017.zip
sudo wget -c http://images.cocodataset.org/annotations/annotations_trainval2017.zip
sudo wget -c http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip

将压缩包进行解压

unzip train2017.zip
unzip val2017.zip
unzip test2017.zip
unzip annotations_trainval2017.zip
unzip stuff_annotations_trainval2017.zip

数据文件夹如下：

data  
├── coco2017
│   ├── annotations  
│   │   ├── instances_train2017.json 
│   │   ├── instances_val2017.json 
│   ├── images
|   │   ├── train2017  
│   │   │    ├── ####.jpg  
│   │   ├── val2017  
│   │   │    ├── ####.jpg  

1.2 数据集路径配置

Pose2Seg:

下载转换好的pose2seg.json:

下载OCHuman:

images [667MB] & annotations

数据格式：

data  
├── coco2017
│   ├── annotations  
│   │   ├── person_keypoints_train2017_pose2seg.json 
│   │   ├── person_keypoints_val2017_pose2seg.json 
│   ├── train2017  
│   │   ├── ####.jpg  
│   ├── val2017  
│   │   ├── ####.jpg  
├── OCHuman 
│   ├── annotations  
│   │   ├── ochuman_coco_format_test_range_0.00_1.00.json   
│   │   ├── ochuman_coco_format_val_range_0.00_1.00.json   
│   ├── images  
│   │   ├── ####.jpg 

配置train.py和test.py中Dataset的文件路径

class Dataset():
    def __init__(self):
        ImageRoot = './data/coco2017/train2017'
        AnnoFile = './data/coco2017/annotations/person_keypoints_train2017_pose2seg.json'

if dataset == 'OCHumanVal':
     ImageRoot = './data/OCHuman/images'
     AnnoFile = './data/OCHuman/annotations/ochuman_coco_format_val_range_0.00_1.00.json'
elif dataset == 'OCHumanTest':
     ImageRoot = './data/OCHuman/images'
     AnnoFile = './data/OCHuman/annotations/ochuman_coco_format_test_range_0.00_1.00.json'
elif dataset == 'cocoVal':
     ImageRoot = './data/coco2017/val2017'
     AnnoFile = './data/coco2017/annotations/person_keypoints_val2017_pose2seg.json'

Yolact:

修改data/config.py的数据集路径为本地路径

coco2017_dataset = dataset_base.copy({
    'name': 'COCO 2017',
    
    'train_info': './data/coco/annotations/instances_train2017.json',
    'valid_info': './data/coco/annotations/instances_val2017.json',

    'label_map': COCO_LABEL_MAP
})

Detectron:

配置detectron/config/paths_catalog.py

class DatasetCatalog(object):
    DATA_DIR = "datasets"
    DATASETS = {
        "coco_2017_train": {
            "img_dir": "coco/train2017",
            "ann_file": "coco/annotations/instances_train2017.json"
        },
        "coco_2017_val": {
            "img_dir": "coco/val2017",
            "ann_file": "coco/annotations/instances_val2017.json"
        },
        "coco_2014_train": {
            "img_dir": "coco/train2014",
            "ann_file": "coco/annotations/instances_train2014.json"
        },
        "coco_2014_val": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/instances_val2014.json"
        },
        "coco_2014_minival": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/instances_minival2014.json"
        },
        "coco_2014_valminusminival": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/instances_valminusminival2014.json"
        },

2 使用教程

2.1 安装Jittor

sudo apt install python3.7-dev libomp-dev
python3.7 -m pip install jittor
# or install from github(latest version)
# python3.7 -m pip install git+https://github.com/Jittor/jittor.git

2.2 使用Pose2Seg人体分割模型

下载pretrained模型：here

Train:

python train.py

Test:

python test.py --weights last.pkl --coco --OCHuman

2.3 使用Yolact实时分割模型

Pretrained模型 (来源：https://github.com/dbolya/yolact)：

Here are YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on test-dev:

Image Size	Backbone	FPS	mAP	Weights
550	Resnet50-FPN	42.5	28.2	yolact_resnet50_54_800000.pth	Mirror
550	Darknet53-FPN	40.0	28.7	yolact_darknet53_54_800000.pth	Mirror
550	Resnet101-FPN	33.5	29.8	[yolact_base_54800000.pth](https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf/view?usp=sharing)	Mirror
700	Resnet101-FPN	23.6	31.2	yolact_im700_54_800000.pth	Mirror

YOLACT++ models (released on December 16th, 2019):

Image Size	Backbone	FPS	mAP	Weights
550	Resnet50-FPN	33.5	34.1	yolact_plus_resnet50_54_800000.pth	Mirror
550	Resnet101-FPN	27.3	34.6	yolact_plus_base_54_800000.pth	Mirror

Train:

# Trains using the base config with a batch size of 8 (the default).
python train.py --config=yolact_base_config

# Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.
python train.py --config=yolact_base_config --batch_size=5

# Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file's name.
python train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1

# Use the help option to see a description of all available command line arguments
python train.py --help

Test:

# Display qualitative results on the specified image.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png

# Process an image and save it to another file.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png

# Process a whole folder of images.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder

2.4 使用Detectron库

安装：

cd detectron.jittor
python setup.py install

配置config （参考configs的样例）：

MODEL:
  META_ARCHITECTURE: "GeneralizedRCNN"
  WEIGHT: "https://download.pytorch.org/models/maskrcnn/e2e_mask_rcnn_R_50_C4_1x.pth"
  RPN:
    PRE_NMS_TOP_N_TEST: 6000
    POST_NMS_TOP_N_TEST: 1000
  ROI_MASK_HEAD:
    PREDICTOR: "MaskRCNNC4Predictor"
    SHARE_BOX_FEATURE_EXTRACTOR: True
  MASK_ON: True
DATASETS:
  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
  TEST: ("coco_2014_minival",)
SOLVER:
  BASE_LR: 0.01
  WEIGHT_DECAY: 0.0001
  STEPS: (120000, 160000)
  MAX_ITER: 180000
  IMS_PER_BATCH: 8

使用：

#coding=utf-8
import requests
from io import BytesIO
from PIL import Image
import cv2
import numpy as np
import jittor as jt 
from detectron.config import cfg
from predictor import COCODemo

def load(url):
    """
    Given an url of an image, downloads the image and
    returns a PIL image
    """
    response = requests.get(url)
    pil_image = Image.open(BytesIO(response.content)).convert("RGB")
    # convert to BGR format
    image = np.array(pil_image)[:, :, [2, 1, 0]]
    return image

# turn on cuda
jt.flags.use_cuda = 1

# set config
config_file = '../configs/maskrcnn_benchmark/e2e_mask_rcnn_R_50_FPN_1x.yaml'
# update the config options with the config file
cfg.merge_from_file(config_file)
#cfg.MODEL.WEIGHT = "weight/maskrcnn_r50.pth"

#set predictor
coco_demo = COCODemo(
    cfg,
    min_image_size=800,
    confidence_threshold=0.5,
)

#load image
pil_image = Image.open('test.jpg').convert("RGB")
image = np.array(pil_image)[:, :, [2, 1, 0]]

# compute predictions
predictions = coco_demo.run_on_opencv_image(image)

# save result
cv2.imwrite('predicton.jpg',predictions)

Train:

python tools/train_net.py --config-file /path/your/configfile

Test:

python tools/test_net.py --config-file /path/your/configfile

3 参考

https://github.com/liruilong940607/Pose2Seg
https://arxiv.org/abs/1803.10683
https://github.com/dbolya/yolact
https://arxiv.org/abs/1904.02689
https://arxiv.org/abs/1912.06218
https://github.com/facebookresearch/maskrcnn-benchmark

Jittor实例分割模型库使用教程

实例分割模型库使用教程

1 数据集

1.1 COCO数据集下载

1.2 数据集路径配置

2 使用教程

2.1 安装Jittor

2.2 使用Pose2Seg人体分割模型

2.3 使用Yolact实时分割模型

2.4 使用Detectron库

3 参考

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