firefire_用ATSS训练自己的目标检测模型!!_

### ATSS训练自己的数据集 [[Repo](https://github.com/sfzhang15/ATSS)] [[paper](https://arxiv.org/abs/1912.02424)] ### 1.Introduction anchor-based和anchor-free最本质的区别是正负样本的定义（**how to define positive and negative training samples**). ATSS（Adaptive Training Sample Selection)可以自动选择正负样本基于一些统计学的指标。ATSS提高了anchor based和anchor free的模型的识别精度，使他们之间的gap变小。 ### 2.Installation 对于环境的要求 ```shell Pytorch>=1.0 torchvision==0.2.1 cocoapi yacs matplotlib GCC>=4.9 <6.0 python-opencv ``` 安装： ```shell # 自行安装pytorch和torchvision pip3 install ninja yacs cython matplotlib tqdm # 安装cocoapi git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI python3 setup.py build_ext install # atss git clone https://github.com/sfzhang15/ATSS.git cd ATSS # cuda 9.0 ,9.2 sudo CUDA_HOST_COMPILER=/usr/bin/gcc-5 python3 setup.py build develop --no-deps ``` ### 3.数据准备 将数据按照MS COCO的数据格式准备,在项目根目录下新建文件夹`datasets/myData`  修改项目根目录下`ATSS\atss_core\config\paths_catalog.py`文件 ```python class DatasetCatalog(object): DATA_DIR = "datasets" DATASETS = { "coco_2017_train": { "img_dir": "myData/train", "ann_file": "myData/annotations/instances_train.json" }, "coco_2017_val": { "img_dir": "myData/val", "ann_file": "myData/annotations/instances_val.json" }, ``` ### 4.修改模型配置文件 修改模型配置文件`ATSS\configs\atss`,在该文件夹下新建文件夹比如`wei_score`,将`atss_dcnv2_X_101_64x4d_FPN_2x.yaml`配置文件拷贝到该文件夹并做如下修改（部分参数可自行修改） ```yaml MODEL: META_ARCHITECTURE: "GeneralizedRCNN" WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-64x4d" #<--------预训练权重加载 RPN_ONLY: True ATSS_ON: True BACKBONE: CONV_BODY: "R-101-FPN-RETINANET" RESNETS: STRIDE_IN_1X1: False BACKBONE_OUT_CHANNELS: 256 NUM_GROUPS: 64 WIDTH_PER_GROUP: 4 STAGE_WITH_DCN: (False, False, True, True) WITH_MODULATED_DCN: True DEFORMABLE_GROUPS: 1 RETINANET: USE_C5: False ATSS: ANCHOR_SIZES: (64, 128, 256, 512, 1024) # 8S ASPECT_RATIOS: (1.0,) SCALES_PER_OCTAVE: 1 USE_DCN_IN_TOWER: True POSITIVE_TYPE: 'ATSS' # how to select positves: ATSS (Ours) , SSC (FCOS), IoU (RetinaNet) TOPK: 9 # topk for selecting candidate positive samples from each level REGRESSION_TYPE: 'BOX' # regressing from a 'BOX' or a 'POINT' DATASETS: TRAIN: ("coco_2017_train",) #<----------与数据集对应 TEST: ("coco_2017_val",) INPUT: MIN_SIZE_RANGE_TRAIN: (640, 800) MAX_SIZE_TRAIN: 1333 MIN_SIZE_TEST: 800 MAX_SIZE_TEST: 1333 DATALOADER: SIZE_DIVISIBILITY: 32 SOLVER: BASE_LR: 0.01 WEIGHT_DECAY: 0.0001 STEPS: (120000, 160000) MAX_ITER: 180000 IMS_PER_BATCH: 16 #<-----------batch size可以修改 WARMUP_METHOD: "constant" TEST: BBOX_AUG: ENABLED: True #<--------------多尺度测试 VOTE: True VOTE_TH: 0.66 MERGE_TYPE: "soft-vote" H_FLIP: True SCALES: (400, 500, 600, 640, 700, 900, 1000, 1100, 1200, 1300, 1400, 1800) SCALE_RANGES: [[96, 10000], [96, 10000], [64, 10000], [64, 10000], [64, 10000], [0, 10000], [0, 10000], [0, 256], [0, 256], [0, 192], [0, 192], [0, 96]] MAX_SIZE: 3000 SCALE_H_FLIP: True ``` ### 5.模型训练 ```shell python3 -m torch.distributed.launch \ --nproc_per_node=8 \ --master_port=$((RANDOM + 10000)) \ tools/train_net.py \ --config-file configs/atss/atss_R_50_FPN_1x.yaml \ DATALOADER.NUM_WORKERS 2 \ OUTPUT_DIR training_dir/atss_R_50_FPN_1x # 1.如果你使用少的GPU,可以更改--nproc_per_node，指定GPU数量，无需修改其他，batch size不依赖于该参数 # 2.如果你想改变batch size 则修改SOLVER.IMGS_PER_BATCH这个配置项中的参数 # 3.训练的模型江北保存在 OURPUT_DIR # 4.如果想修改训练的backbone,可以修改--config-file ``` ```shell python3 -m torch.distributed.launch \ --nproc_per_node=1 \ tools/train_net.py \ --config-file configs/atss/wei_score/atss_dcnv2_X_101_64x4d_FPN_2x.yaml \ DATALOADER.NUM_WORKERS 2 \ OUTPUT_DIR checkpoint/atss_dcnv2_X_101_64x4d_FPN_2x ``` ```shell python3 tools/train_net.py \ --config-file configs/atss/wei_score/atss_dcnv2_X_101_64x4d_FPN_2x.yaml \ DATALOADER.NUM_WORKERS 2 \ OUTPUT_DIR checkpoint/atss_dcnv2_X_101_64x4d_FPN_2x\ SOLVER.IMS_PER_BATCH 8 # 单卡 V100 32G能开到batch size 8 ``` 出现如下界面，则正常开始训练  训练过程中GPU的占用情况 ```shell nvidia-smi -lms 200 ```  ### 6.模型推断 ```shell python3 tools/test_net.py \ --config-file configs/atss/wei_score/atss_dcnv2_X_101_64x4d_FPN_2x.yaml \ MODEL.WEIGHT ./checkpoint/atss_dcnv2_X_101_64x4d_FPN_2x/model_0010000.pth \ TEST.IMS_PER_BATCH 1\ OUTPUT_DIR result # 1.MODEL.WEIGHT 是训练模型的存放地址 # 2.TEST.IMS_PER_BATCH是测试的batch size 可以设为1 # 3.--config-file可以修改模型的配置文件和训练后的模型保持一致 ``` 关于视频的测试可以参考`demo`文件夹下的测试脚本，内有详细的说明！ 测试结果： ```shell loading annotations into memory... Done (t=0.00s) creating index... index created! 2020-07-28 13:00:05,839 atss_core.inference INFO: Start evaluation on coco_2017_val dataset(285 images). 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 285/285 [37:48<00:00, 7.96s/it] 2020-07-28 13:37:54,177 atss_core.inference INFO: Total run time: 0:37:48.337468 (7.9590788347679275 s / img per device, on 1 devices) 2020-07-28 13:37:54,177 atss_core.inference INFO: Model inference time: 0:37:45.241220 (7.948214806171886 s / img per device, on 1 devices) 2020-07-28 13:37:54,201 atss_core.inference INFO: Preparing results for COCO format 2020-07-28 13:37:54,202 atss_core.inference INFO: Preparing bbox results 2020-07-28 13:37:54,338 atss_core.inference INFO: Evaluating predictions Loading and preparing results... DONE (t=0.36s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=2.24s). Accumulating evaluation results... DONE (t=0.37s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.575 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.762 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.703 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.593 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.449 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.808 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.865 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.843 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.866 Maximum f-measures for classes: [0.7241379310344828, 0.7339771729587358, 0.6219058553386911] Score thresholds for classes (used in demos for visualization purposes): [0.6439446806907654, 0.5525452494621277, 0.6701372265815735] 2020-07-28 13:37:58,352 atss_core.inference INFO: OrderedDi