一、安装
地址：MaskRCNN-Benchmark(Pytorch版本)
首先要阅读官网说明的环境要求，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
我要制作的原始数据格式是训练文件在一个文件（train），标注文件是csv格式，内容如下：
第一步，先把全部有标记的图片且分为训练集，验证集，分别存储在两个文件夹中，代码如下：
代码语言：javascript复制#!/usr/bin/env python
# coding=UTF-8
'''
@Description:
@Author: HuangQinJian
@LastEditors: HuangQinJian
@Date: 2019-05-01 12:56:08
@LastEditTime: 2019-05-01 13:11:38
'''
import pandas as pd
import random
import os
import shutil
if not os.path.exists('trained/'):
os.mkdir('trained/')
if not os.path.exists('val/'):
os.mkdir('val/')
val_rate = 0.15
img_path = 'train/'
img_list = os.listdir(img_path)
train = pd.read_csv('train_label_fix.csv')
# print(img_list)
random.shuffle(img_list)
total_num = len(img_list)
val_num = int(total_num*val_rate)
train_num = total_num-val_num
for i in range(train_num):
img_name = img_list[i]
shutil.copy('train/' img_name, 'trained/' img_name)
for j in range(val_num):
img_name = img_list[j train_num]
shutil.copy('train/' img_name, 'val/' img_name)第二步，把csv格式的标注文件转换成coco的格式，代码如下：
代码语言：javascript复制#!/usr/bin/env python
# coding=UTF-8
'''
@Description:
@Author: HuangQinJian
@LastEditors: HuangQinJian
@Date: 2019-04-23 11:28:23
@LastEditTime: 2019-05-01 13:15:57
'''
import sys
import os
import json
import cv2
import pandas as pd
START_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = {}
def convert(csv_path, img_path, json_file):
"""
csv_path : csv文件的路径
img_path : 存放图片的文件夹
json_file : 保存生成的json文件路径
"""
json_dict = {"images": [], "type": "instances", "annotations": [],
"categories": []}
bnd_id = START_BOUNDING_BOX_ID
categories = PRE_DEFINE_CATEGORIES
csv = pd.read_csv(csv_path)
img_nameList = os.listdir(img_path)
img_num = len(img_nameList)
print("图片总数为{0}".format(img_num))
for i in range(img_num):
# for i in range(30):
image_id = i 1
img_name = img_nameList[i]
if img_name == '60f3ea2534804c9b806e7d5ae1e229cf.jpg' or img_name == '6b292bacb2024d9b9f2d0620f489b1e4.jpg':
continue
# 可能需要根据具体格式修改的地方
lines = csv[csv.filename == img_name]
img = cv2.imread(os.path.join(img_path, img_name))
height, width, _ = img.shape
image = {'file_name': img_name, 'height': height, 'width': width,
'id': image_id}
print(image)
json_dict['images'].append(image)
for j in range(len(lines)):
# 可能需要根据具体格式修改的地方
category = str(lines.iloc[j]['type'])
if category not in categories:
new_id = len(categories)
categories[category] = new_id
category_id = categories[category]
# 可能需要根据具体格式修改的地方
xmin = int(lines.iloc[j]['X1'])
ymin = int(lines.iloc[j]['Y1'])
xmax = int(lines.iloc[j]['X3'])
ymax = int(lines.iloc[j]['Y3'])
# print(xmin, ymin, xmax, ymax)
assert(xmax > xmin)
assert(ymax > ymin)
o_width = abs(xmax - xmin)
o_height = abs(ymax - ymin)
ann = {'area': o_width*o_height, 'iscrowd': 0, 'image_id':
image_id, 'bbox': [xmin, ymin, o_width, o_height],
'category_id': category_id, 'id': bnd_id, 'ignore': 0,
'segmentation': []}
json_dict['annotations'].append(ann)
bnd_id = bnd_id 1
for cate, cid in categories.items():
cat = {'supercategory': 'none', 'id': cid, 'name': cate}
json_dict['categories'].append(cat)
json_fp = open(json_file, 'w')
json_str = json.dumps(json_dict, indent=4)
json_fp.write(json_str)
json_fp.close()
if __name__ == '__main__':
# csv_path = 'data/train_label_fix.csv'
# img_path = 'data/train/'
# json_file = 'train.json'
csv_path = 'train_label_fix.csv'
img_path = 'trained/'
json_file = 'trained.json'
convert(csv_path, img_path, json_file)
csv_path = 'train_label_fix.csv'
img_path = 'val/'
json_file = 'val.json'
convert(csv_path, img_path, json_file)第三步，可视化转换后的coco的格式，以确保转换正确，代码如下：
（注意：在这一步中，需要先下载 cocoapi , 可能出现的 问题）
代码语言：javascript复制#!/usr/bin/env python
# coding=UTF-8
'''
@Description:
@Author: HuangQinJian
@LastEditors: HuangQinJian
@Date: 2019-04-23 13:43:24
@LastEditTime: 2019-04-30 21:29:26
'''
from pycocotools.coco import COCO
import skimage.io as io
import matplotlib.pyplot as plt
import pylab
import cv2
import os
from skimage.io import imsave
import numpy as np
pylab.rcParams['figure.figsize'] = (8.0, 10.0)
img_path = 'data/train/'
annFile = 'train.json'
if not os.path.exists('anno_image_coco/'):
os.makedirs('anno_image_coco/')
def draw_rectangle(coordinates, image, image_name):
for coordinate in coordinates:
left = np.rint(coordinate[0])
right = np.rint(coordinate[1])
top = np.rint(coordinate[2])
bottom = np.rint(coordinate[3])
# 左上角坐标, 右下角坐标
cv2.rectangle(image,
(int(left), int(right)),
(int(top), int(bottom)),
(0, 255, 0),
2)
imsave('anno_image_coco/' image_name, image)
# 初始化标注数据的 COCO api
coco = COCO(annFile)
# display COCO categories and supercategories
cats = coco.loadCats(coco.getCatIds())
nms = [cat['name'] for cat in cats]
# print('COCO categories: n{}n'.format(' '.join(nms)))
nms = set([cat['supercategory'] for cat in cats])
# print('COCO supercategories: n{}'.format(' '.join(nms)))
img_path = 'data/train/'
img_list = os.listdir(img_path)
# for i in range(len(img_list)):
for i in range(7):
imgIds = i 1
img = coco.loadImgs(imgIds)[0]
image_name = img['file_name']
# print(img)
# 加载并显示图片
# I = io.imread('%s/%s' % (img_path, img['file_name']))
# plt.axis('off')
# plt.imshow(I)
# plt.show()
# catIds=[] 说明展示所有类别的box，也可以指定类别
annIds = coco.getAnnIds(imgIds=img['id'], catIds=[], iscrowd=None)
anns = coco.loadAnns(annIds)
# print(anns)
coordinates = []
img_raw = cv2.imread(os.path.join(img_path, image_name))
for j in range(len(anns)):
coordinate = []
coordinate.append(anns[j]['bbox'][0])
coordinate.append(anns[j]['bbox'][1] anns[j]['bbox'][3])
coordinate.append(anns[j]['bbox'][0] anns[j]['bbox'][2])
coordinate.append(anns[j]['bbox'][1])
# print(coordinate)
coordinates.append(coordinate)
# print(coordinates)
draw_rectangle(coordinates, img_raw, image_name)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):
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"
},
# 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_test",)）
TRAIN: ("coco_2014_train","coco_2014_val")
TEST: ("coco_2014_test",)
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。
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、运行代码
- 单GPU
官网给出的是：
代码语言：javascript复制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：
代码语言：javascript复制# 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
- 多GPU
官网给出的是：
代码语言：javascript复制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：
代码语言：javascript复制# --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
