1. sixx_make_cfg.py

ROOT_DIR = '/home/oschung_skcc/git'

import os
import os.path as osp
WORK_DIR = osp.dirname( osp.dirname(osp.realpath(__file__)) )

from mmcv import Config
cfg = Config.fromfile(osp.join(ROOT_DIR, "mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py"))
# print(cfg.pretty_text)
config_file     = osp.join(WORK_DIR, 'configs/faster_rcnn_r50_fpn_1x_tidy.py')
checkpoint_file = osp.join(WORK_DIR, 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth')

from mmdet.apis import set_random_seed
# dataset에 대한 환경 파라미터 수정. 

cfg.dataset_type = 'KittyTinyDataset'       # 'CocoDataset' 
cfg.data_root = WORK_DIR                    # 'data/coco/'

###---sixx---:: DATA 
# train, val, test dataset에 대한 
# type, WORK_DIR, ann_file, img_prefix 환경 파라미터 수정. 
cfg.data.train.type = 'KittyTinyDataset'             # 'CocoDataset' 
cfg.data.train.data_root = WORK_DIR
cfg.data.train.ann_file = 'data/train.txt'                # 'data/coco/annotations/instances_train2017.json'
cfg.data.train.img_prefix = 'data/image_2'  # 'data/coco/train2017/'

cfg.data.val.type = 'KittyTinyDataset'
cfg.data.val.data_root = WORK_DIR
cfg.data.val.ann_file = 'data/valid.txt'
cfg.data.val.img_prefix = 'data/image_2'

cfg.data.test.type = 'KittyTinyDataset'
cfg.data.test.data_root = WORK_DIR
cfg.data.test.ann_file = 'data/valid.txt'
cfg.data.test.img_prefix = 'data/image_2'

###---sixx--::: train_pipeline

###---sixx--::: test_pipeline

###---sixx---:: MODEL 
cfg.model.roi_head.bbox_head.num_classes = 4         #  class의 80 갯수 수정. 

cfg.load_from = checkpoint_file                      # pretrained 모델 (경로확인)
cfg.work_dir = osp.join(WORK_DIR, 'tutorial_exps')   # 학습 weight 파일로 로그를 저장하기 위한 디렉토리 설정. 

# schedule 이나 default_runtime의 설정값 수정 
# 학습율 변경 환경 파라미터 설정. 
cfg.optimizer.lr = 0.02 / 8          # 0.02
cfg.lr_config.warmup = None          # linear
cfg.log_config.interval = 10
# config 수행 시마다 policy값이 없어지는 bug로 인하여 설정. 
cfg.lr_config.policy = 'step'
# Change the evaluation metric since we use customized dataset.
cfg.evaluation.metric = 'mAP'        # bbox
# We can set the evaluation interval to reduce the evaluation times
cfg.evaluation.interval = 12         # 1
# We can set the checkpoint saving interval to reduce the storage cost
cfg.checkpoint_config.interval = 12  #1

# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)

# print(f'Config:\
{cfg.pretty_text}')

###############################################################################
with open (config_file, 'w') as f:
    print(cfg.pretty_text, file=f)

import datetime
print(f"---created custom config file by sixx on {datetime.datetime.now()}")
print(f"{cfg.dataset_type} :: {osp.relpath(config_file)}")

2. sixx_middle_dataset

RROOT_DIR = '/home/oschung_skcc/git'
import os
import os.path as osp
WORK_DIR = os.path.dirname( os.path.dirname(os.path.realpath(__file__)) )  # 'mymm/kitty_tiny'

DATA_DIR = osp.join(WORK_DIR, 'data')
IMG_PREFIX = 'image_2' 
ANN_PREFIX = 'label_2'

import sys
sys.path.append('/home/oschung_skcc/git/mmdetection') # ( os.path.dirname(os.path.abspath(__file__)) )
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.custom  import CustomDataset

import mmcv
import cv2
import numpy as np
import pandas as pd

# annotation xml 파일 파싱해서 bbox정보 추출
def get_bboxes_from_xml(annPath):
    annLines = mmcv.list_from_file(annPath)
    content  = [line.strip().split(' ') for line in annLines]
    bbox_names = [x[0] for x in content]
    bboxes = [ [float(info) for info in x[4:8]] for x in content]

    return bbox_names, bboxes

# imgFileNm = '000006'
# ann_file  = '/home/oschung_skcc/git/mymm/kitty_tiny/data/label_2/000006.txt'
@DATASETS.register_module(force=True)
class KittyTinyDataset(CustomDataset):
    CLASSES = ('Car', 'Truck', 'Pedestrian', 'Cyclist')
    
    def load_annotations(self, ann_file):
        # mmdetection 프레임웍이 Config에서 ann_file(path)인자를 찾아 파라미터로 사용.
        cat2label = {k:i for i, k in enumerate(self.CLASSES)}    
        annFileNm_list = mmcv.list_from_file(self.ann_file)

        data_info = []
        for imgFileNm in annFileNm_list:
            if imgFileNm is None:
                continue

            ### IMAGE metadata 
            imgBaseNm = str(imgFileNm)+'.jpeg'
            imgPath = osp.join(DATA_DIR, IMG_PREFIX, imgBaseNm)
            image = cv2.imread(imgPath)
            height, width = image.shape[:2]
            img_metaData = {
                'filename': imgBaseNm,  
                'width':  width, 
                'height': height 
            }

            ### Annotation metadata   
            annBaseNm = str(imgFileNm)+'.txt'
            annPath = osp.join(DATA_DIR, ANN_PREFIX, annBaseNm)
            if not osp.exists(annPath):
                continue
            elif os.stat(annPath).st_size==0:
                continue           

            gt_bboxes = []
            gt_labels = []
            gt_bboxes_ignore = []
            gt_labels_ignore = []

            bbox_names, bboxes = get_bboxes_from_xml(annPath)
            for bboxNm, bbox in zip(bbox_names, bboxes):
                if bboxNm in cat2label:
                    gt_bboxes.append(bbox)
                    gt_labels.append(cat2label[bboxNm])
                else: 
                    gt_bboxes_ignore.append(bbox)
                    gt_labels_ignore.append(-1)
            ann_metaData = {
                'bboxes':        np.array(gt_bboxes,        dtype=np.float32).reshape(-1, 4),
                'labels':        np.array(gt_labels,        dtype=np.c