# DF-Traffic-Sign-Identification **Repository Path**: jx2333/DF-Traffic-Sign-Identification ## Basic Information - **Project Name**: DF-Traffic-Sign-Identification - **Description**: DF-Traffic-Sign-Identification - **Primary Language**: Python - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-05-06 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Baseline **一、安装** 地址：[MaskRCNN-Benchmark(Pytorch版本)](https://github.com/facebookresearch/maskrcnn-benchmark) 首先要阅读官网说明的[环境要求](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/INSTALL.md)，**千万不要一股脑直接安装，不然后面程序很有可能会报错！！！** > > - PyTorch 1.0 from a nightly release. It will not work with 1.0 nor 1.0.1. Installation instructions can be found in https://pytorch.org/get-started/locally/ >- torchvision from master >- cocoapi >- yacs >- matplotlib >- GCC >= 4.9 >- OpenCV ``` # first, make sure that your conda is setup properly with the right environment # for that, check that `which conda`, `which pip` and `which python` points to the # right path. From a clean conda env, this is what you need to do conda create --name maskrcnn_benchmark conda activate maskrcnn_benchmark # this installs the right pip and dependencies for the fresh python conda install ipython # maskrcnn_benchmark and coco api dependencies pip install ninja yacs cython matplotlib tqdm opencv-python # follow PyTorch installation in https://pytorch.org/get-started/locally/ # we give the instructions for CUDA 9.0 conda install -c pytorch pytorch-nightly torchvision cudatoolkit=9.0 export INSTALL_DIR=$PWD # install pycocotools cd $INSTALL_DIR git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI python setup.py build_ext install # install apex cd $INSTALL_DIR git clone https://github.com/NVIDIA/apex.git cd apex python setup.py install --cuda_ext --cpp_ext # install PyTorch Detection cd $INSTALL_DIR git clone https://github.com/facebookresearch/maskrcnn-benchmark.git cd maskrcnn-benchmark # the following will install the lib with # symbolic links, so that you can modify # the files if you want and won't need to # re-build it python setup.py build develop unset INSTALL_DIR # or if you are on macOS # MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop ``` 一定要按上面的说明一步一步来，千万别省略，**不然后面程序很有可能会报错！！！** --- **二、数据准备** 转换成COCO格式即可！ --- **三、文件配置** 在训练自己的数据集过程中需要修改的地方可能很多，下面我就列出常用的几个： - 修改`maskrcnn_benchmark/config/paths_catalog.py`中数据集路径： ``` class DatasetCatalog(object): # 看自己的实际情况修改路径！！！ # 看自己的实际情况修改路径！！！ # 看自己的实际情况修改路径！！！ DATA_DIR = "" 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" }, # 改成训练集所在路径！！！ # 改成训练集所在路径！！！ # 改成训练集所在路径！！！ "coco_2014_train": { "img_dir": "/data1/hqj/traffic-sign-identification/trained", "ann_file": "/data1/hqj/traffic-sign-identification/trained.json" }, # 改成验证集所在路径！！！ # 改成验证集所在路径！！！ # 改成验证集所在路径！！！ "coco_2014_val": { "img_dir": "/data1/hqj/traffic-sign-identification/val", "ann_file": "/data1/hqj/traffic-sign-identification/val.json" }, # 改成测试集所在路径！！！ # 改成测试集所在路径！！！ # 改成测试集所在路径！！！ "coco_2014_test": { "img_dir": "/data1/hqj/traffic-sign-identification/test" ... ``` - config下的配置文件： 由于这个文件下的参数很多，往往需要根据自己的具体需求改，我就列出自己的配置（使用的是`e2e_faster_rcnn_X_101_32x8d_FPN_1x.yaml`，**其中我有注释的必须改**，比如 `NUM_CLASSES`）： ``` INPUT: MIN_SIZE_TRAIN: (1000,) MAX_SIZE_TRAIN: 1667 MIN_SIZE_TEST: 1000 MAX_SIZE_TEST: 1667 MODEL: META_ARCHITECTURE: "GeneralizedRCNN" WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d" BACKBONE: CONV_BODY: "R-101-FPN" RPN: USE_FPN: True BATCH_SIZE_PER_IMAGE: 128 ANCHOR_SIZES: (16, 32, 64, 128, 256) ANCHOR_STRIDE: (4, 8, 16, 32, 64) PRE_NMS_TOP_N_TRAIN: 2000 PRE_NMS_TOP_N_TEST: 1000 POST_NMS_TOP_N_TEST: 1000 FPN_POST_NMS_TOP_N_TEST: 1000 FPN_POST_NMS_TOP_N_TRAIN: 1000 ASPECT_RATIOS : (1.0,) FPN: USE_GN: True ROI_HEADS: # 是否使用FPN USE_FPN: True ROI_BOX_HEAD: USE_GN: True POOLER_RESOLUTION: 7 POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125) POOLER_SAMPLING_RATIO: 2 FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor" PREDICTOR: "FPNPredictor" # 修改成自己任务所需要检测的类别数+1 NUM_CLASSES: 22 RESNETS: BACKBONE_OUT_CHANNELS: 256 STRIDE_IN_1X1: False NUM_GROUPS: 32 WIDTH_PER_GROUP: 8 DATASETS: # paths_catalog.py文件中的配置，数据集指定时如果仅有一个数据集不要忘了逗号（如：("coco_2014_val",)） TRAIN: ("coco_2014_train",) TEST: ("coco_2014_val",) DATALOADER: SIZE_DIVISIBILITY: 32 SOLVER: BASE_LR: 0.001 WEIGHT_DECAY: 0.0001 STEPS: (240000, 320000) MAX_ITER: 360000 # 很重要的设置，具体可以参见官网说明：https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/README.md IMS_PER_BATCH: 1 # 保存模型的间隔 CHECKPOINT_PERIOD: 18000 # 输出文件路径 OUTPUT_DIR: "./weight/" ``` - 如果只做检测任务的话，删除 `maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/coco.py` 中 82-84这三行比较保险。 ![在这里插入图片描述](https://img-blog.csdnimg.cn/2019050116471368.jpg) - `maskrcnn_benchmark/engine/trainer.py` 中 第 90 行可设置输出日志的间隔（默认20，我感觉输出太频繁，看你自己） --- **四、模型训练** - 单GPU 官网给出的是： ``` python /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "/path/to/config/file.yaml" ``` 但是这个默认会使用第一个GPU，如果想指定GPU的话，可以使用以下命令： ``` # 3是要使用GPU的ID CUDA_VISIBLE_DEVICES=3 python /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "/path/to/config/file.yaml" ``` 如果出现内存溢出的情况，这时候就需要调整参数，具体可以参见官网：[内存溢出解决](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/README.md) - 多GPU 官网给出的是： ``` export NGPUS=8 python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "path/to/config/file.yaml" MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN images_per_gpu x 1000 ``` 但是这个默认会随机使用GPU，如果想指定GPU的话，可以使用以下命令： ``` # --nproc_per_node=4 是指使用GPU的数目为4 CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "path/to/config/file.yaml" ``` 遗憾的是，多GPU在我的服务器上一直运行不成功，还请大家帮忙解决！！！ 问题地址：[Multi-GPU training error](https://github.com/facebookresearch/maskrcnn-benchmark/issues/735) --- **五、模型验证** - 修改 config 配置文件中 `WEIGHT: "../weight/model_final.pth"`（此处应为训练完保存的权重） - 运行命令： ``` CUDA_VISIBLE_DEVICES=5 python tools/test_net.py --config-file "/path/to/config/file.yaml" TEST.IMS_PER_BATCH 8 ``` 其中`TEST.IMS_PER_BATCH 8`也可以在config文件中直接配置： ``` TEST: IMS_PER_BATCH: 8 ``` --- **六、模型预测** - 修改 config 配置文件中 `WEIGHT: "../weight/model_final.pth"`（此处应为训练完保存的权重） - 修改`demo/predictor.py`中 CATEGORIES ，替换成自己数据的物体类别（如果想可视化结果，没有可以不改，可以参考`demo/`下面的例子）： ``` class COCODemo(object): # COCO categories for pretty print CATEGORIES = [ "__background", ... ] ``` - 新建一个文件 `demo/predict.py`（需要修改的地方已做注释） ``` #!/usr/bin/env python # coding=UTF-8 ''' @Description: @Author: HuangQinJian @LastEditors: HuangQinJian @Date: 2019-05-01 12:36:04 @LastEditTime: 2019-05-03 17:29:23 ''' import os import matplotlib.pylab as pylab import matplotlib.pyplot as plt import numpy as np import pandas as pd from PIL import Image from maskrcnn_benchmark.config import cfg from predictor import COCODemo from tqdm import tqdm # this makes our figures bigger pylab.rcParams['figure.figsize'] = 20, 12 # 替换成自己的配置文件 # 替换成自己的配置文件 # 替换成自己的配置文件 config_file = "../configs/e2e_faster_rcnn_R_50_FPN_1x.yaml" # update the config options with the config file cfg.merge_from_file(config_file) # manual override some options cfg.merge_from_list(["MODEL.DEVICE", "cuda"]) def load(img_path): pil_image = Image.open(img_path).convert("RGB") # convert to BGR format image = np.array(pil_image)[:, :, [2, 1, 0]] return image # 根据自己的需求改 # 根据自己的需求改 # 根据自己的需求改 coco_demo = COCODemo( cfg, min_image_size=1600, confidence_threshold=0.7, ) # 测试图片的路径 # 测试图片的路径 # 测试图片的路径 imgs_dir = '/data1/hqj/traffic-sign-identification/test' img_names = os.listdir(imgs_dir) submit_v4 = pd.DataFrame() empty_v4 = pd.DataFrame() filenameList = [] X1List = [] X2List = [] X3List = [] X4List = [] Y1List = [] Y2List = [] Y3List = [] Y4List = [] TypeList = [] empty_img_name = [] # for img_name in img_names: for i, img_name in enumerate(tqdm(img_names)): path = os.path.join(imgs_dir, img_name) image = load(path) # compute predictions predictions = coco_demo.compute_prediction(image) try: scores = predictions.get_field("scores").numpy() bbox = predictions.bbox[np.argmax(scores)].numpy() labelList = predictions.get_field("labels").numpy() label = labelList[np.argmax(scores)] filenameList.append(img_name) X1List.append(round(bbox[0])) Y1List.append(round(bbox[1])) X2List.append(round(bbox[2])) Y2List.append(round(bbox[1])) X3List.append(round(bbox[2])) Y3List.append(round(bbox[3])) X4List.append(round(bbox[0])) Y4List.append(round(bbox[3])) TypeList.append(label) # print(filenameList, X1List, X2List, X3List, X4List, Y1List, # Y2List, Y3List, Y4List, TypeList) print(label) except: empty_img_name.append(img_name) print(empty_img_name) submit_v4['filename'] = filenameList submit_v4['X1'] = X1List submit_v4['Y1'] = Y1List submit_v4['X2'] = X2List submit_v4['Y2'] = Y2List submit_v4['X3'] = X3List submit_v4['Y3'] = Y3List submit_v4['X4'] = X4List submit_v4['Y4'] = Y4List submit_v4['type'] = TypeList empty_v4['filename'] = empty_img_name submit_v4.to_csv('submit_v4.csv', index=None) empty_v4.to_csv('empty_v4.csv', index=None) ``` - 运行命令： ``` CUDA_VISIBLE_DEVICES=5 python demo/predict.py ``` --- **七、结束语** 1. 若有修改`maskrcnn-benchmark`文件夹下的代码，一定要重新编译！一定要重新编译！一定要重新编译！ 2. 欢迎大家关注我的 [CSDN](https://huangqinjian.blog.csdn.net/)，干货满满！