mmrotate学习（5）mmrotate测试训练好的模型map

测试map

上一篇训练教程mmrotate框架训练数据集，选用的iou=50，
我想测试iou在75之下的map的值，和iou在0.05-0.95递增的情况的平均map，需要进行以下操作：

测试AP75

1.修改mmrotate/datasets/dota.py

主要有一个参数：
1）iou_thr: iou阈值，修改成75

2.修改 test.py 中的参数

主要有三个参数：
1）config: 使用的模型文件 ；
2）checkpoint：训练得到的模型权重文件;
3）show-dir: 预测结果存放的路径

补充：测试用的数据集就是训练的步骤4中的数据集路径

3.输入指令

python test.py --eval mAP
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测试AP（iou在0.05-0.95递增的情况的平均map）

需要借助其他的代码
1.修改mmrotate/datasets/dota.py
在类class DOTADataset(CustomDataset)下添加以下子函数

def save_dota_txt(self, results, outfolder,conf_thresh=0.01):
            import math
            if not os.path.exists(outfolder):
                os.mkdir(outfolder)
            for i in range(len(self)):
                imgname = self.data_infos[i]['filename']
                bboxresult = results[i]
                txt_name = os.path.join(outfolder,imgname.split('.')[0]+'.txt')
                f = open(txt_name, 'w')
                for cls, bboxes in zip(self.CLASSES, bboxresult):
                    for box in bboxes:
                        if box[-1] < conf_thresh: 
                            continue
                        xc, yc, w, h, ag, score = box.tolist()
                        wx, wy = w / 2 * math.cos(ag), w / 2 * math.sin(ag)
                        hx, hy = -h / 2 * math.sin(ag), h / 2 * math.cos(ag)
                        p1 = (xc - wx - hx, yc - wy - hy)
                        p2 = (xc + wx - hx, yc + wy - hy)
                        p3 = (xc + wx + hx, yc + wy + hy)
                        p4 = (xc - wx + hx, yc - wy + hy)
                        ps = (np.concatenate([p1, p2, p3, p4]))
                        points = ' '.join(ps.astype(float).astype(str))
                        # soft label and eval result
                        f.write(points + ' ' + cls + ' ' + str(box[-1]) + '\n')
                        # hard label
                        # if float(box[-1]) > 0.1:
                            # f.write(points + ' ' + cls  + ' 0\n')
                f.close()
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2.借用evaluation.py，修改map步长

import numpy as np
import os
from shapely.geometry import Polygon
import sys

sys.path.append('/home/disk/bing/mmrotate-main/DOTA_devkit')
import polyiou


def parse_gt(filename):
    """
    :param filename: ground truth file to parse
    :return: all instances in a picture
    """
    objects = []
    with open(filename, 'r') as f:
        while True:
            line = f.readline()
            if line:
                splitlines = line.strip().split(' ')
                object_struct = {}
                if (len(splitlines) < 9):
                    continue
                object_struct['name'] = splitlines[8]

                object_struct['difficult'] = float(splitlines[9])
                object_struct['bbox'] = [
                    float(splitlines[0]),
                    float(splitlines[1]),
                    float(splitlines[2]),
                    float(splitlines[3]),
                    float(splitlines[4]),
                    float(splitlines[5]),
                    float(splitlines[6]),
                    float(splitlines[7])
                ]
                objects.append(object_struct)
            else:
                break
    return objects


def voc_ap(rec, prec, use_07_metric=False):
    """ ap = voc_ap(rec, prec, [use_07_metric])
    Compute VOC AP given precision and recall.
    If use_07_metric is true, uses the
    VOC 07 11 point method (default:False).
    """
    if use_07_metric:
        # 11 point metric
        ap = 0.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec >= t) == 0:
                p = 0
            else:
                p = np.max(prec[rec >= t])
            ap = ap + p / 11.
    else:
        # correct AP calculation
        # first append sentinel values at the end
        mrec = np.concatenate(([0.], rec, [1.]))
        mpre = np.concatenate(([0.], prec, [0.]))

        # compute the precision envelope
        for i in range(mpre.size - 1, 0, -1):
            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])

        # to calculate area under PR curve, look for points
        # where X axis (recall) changes value
        i = np.where(mrec[1:] != mrec[:-1])[0]

        # and sum (\Delta recall) * prec
        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap


def parse_predict(detpath, classname):
    image_ids = []
    confidence = []
    BB = []
    files = os.listdir(detpath)
    for file in files:
        image_name = file.split('.')[0]
        R = [
            obj for obj in parse_gt(os.path.join(detpath, file))
            if obj['name'] == classname
        ]
        bbox = [x['bbox'] for x in R]
        scores = [x['difficult'] for x in R]
        image_names = [image_name for x in R]
        image_ids += image_names
        confidence += scores
        BB += bbox
    return np.array(image_ids), np.array(confidence), np.array(BB)


def cal_iou(g, p):
    g = np.asarray(g)
    p = np.asarray(p)
    g = Polygon(g[:8].reshape((4, 2)))
    #print('p:', p)
    p = Polygon(p[:8].reshape((4, 2)))
    if not g.is_valid or not p.is_valid:
        return 0
    inter = Polygon(g).intersection(Polygon(p)).area
    union = g.area + p.area - inter
    if union == 0:
        return 0
    else:
        return inter / union


def voc_eval(
        detpath,
        annopath,
        classname,
        # cachedir,
        ovthresh=0.5,
        use_07_metric=False):

    recs = {}
    for txt in os.listdir(annopath):
        recs[txt[:-4]] = parse_gt(os.path.join(annopath, txt))

    class_recs = {}
    npos = 0
    for txt in os.listdir(annopath):
        R = [obj for obj in recs[txt[:-4]] if obj['name'] == classname]
        bbox = np.array([x['bbox'] for x in R])
        difficult = np.array([False for x in R])
        det = [False] * len(R)
        npos = npos + len(R)
        class_recs[txt[:-4]] = {
            'bbox': bbox,
            'difficult': difficult,
            'det': det
        }

    image_ids, confidence, BB = parse_predict(detpath, classname)
    sorted_ind = np.argsort(-confidence)
    if BB.shape[0] == 0:
        return 0, 0, 0
    BB = BB[sorted_ind, :]
    image_ids = [image_ids[x] for x in sorted_ind]

    nd = len(image_ids)
    tp = np.zeros(nd)
    fp = np.zeros(nd)
    for d in range(nd):
        R = class_recs[image_ids[d]]
        bb = BB[d, :].astype(float)
        ovmax = -np.inf
        BBGT = R['bbox'].astype(float)

        ## compute det bb with each BBGT

        if BBGT.size > 0:
            # overlaps = np.array([cal_iou(bb_GT, bb) for bb_GT in BBGT])
            # ovmax = np.max(overlaps)
            # jmax = np.argmax(overlaps)

            BBGT_xmin = np.min(BBGT[:, 0::2], axis=1)
            BBGT_ymin = np.min(BBGT[:, 1::2], axis=1)
            BBGT_xmax = np.max(BBGT[:, 0::2], axis=1)
            BBGT_ymax = np.max(BBGT[:, 1::2], axis=1)
            bb_xmin = np.min(bb[0::2])
            bb_ymin = np.min(bb[1::2])
            bb_xmax = np.max(bb[0::2])
            bb_ymax = np.max(bb[1::2])

            ixmin = np.maximum(BBGT_xmin, bb_xmin)
            iymin = np.maximum(BBGT_ymin, bb_ymin)
            ixmax = np.minimum(BBGT_xmax, bb_xmax)
            iymax = np.minimum(BBGT_ymax, bb_ymax)
            iw = np.maximum(ixmax - ixmin + 1., 0.)
            ih = np.maximum(iymax - iymin + 1., 0.)
            inters = iw * ih

            # union
            uni = ((bb_xmax - bb_xmin + 1.) * (bb_ymax - bb_ymin + 1.) +
                   (BBGT_xmax - BBGT_xmin + 1.) *
                   (BBGT_ymax - BBGT_ymin + 1.) - inters)

            overlaps = inters / uni

            BBGT_keep_mask = overlaps > 0
            BBGT_keep = BBGT[BBGT_keep_mask, :]
            BBGT_keep_index = np.where(overlaps > 0)[0]

            # pdb.set_trace()
            def calcoverlaps(BBGT_keep, bb):
                overlaps = []
                for index, GT in enumerate(BBGT_keep):
                    overlap = polyiou.iou_poly(
                        polyiou.VectorDouble(BBGT_keep[index]),
                        polyiou.VectorDouble(bb))
                    overlaps.append(overlap)
                return overlaps

            if len(BBGT_keep) > 0:
                overlaps = calcoverlaps(BBGT_keep, bb)

                ovmax = np.max(overlaps)
                jmax = np.argmax(overlaps)
                # pdb.set_trace()
                jmax = BBGT_keep_index[jmax]

        if ovmax > ovthresh:
            if not R['difficult'][jmax]:
                if not R['det'][jmax]:
                    tp[d] = 1.
                    R['det'][jmax] = 1
                else:
                    fp[d] = 1.
        else:
            fp[d] = 1.

    # print('check fp:', sum(fp))
    # print('check tp', sum(tp))

    # print('npos num:', npos)
    fp = np.cumsum(fp)
    tp = np.cumsum(tp)

    rec = tp / float(npos)
    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
    ap = voc_ap(rec, prec, use_07_metric)

    return rec, prec, ap


def evalution(detpath,annopath):
    # predict folder
    # detpath = '/home/cv123/data/zyj/ReDet_back/eval/predota_kaggle768_on_fair'
    print(detpath)
    print(annopath)
    # # gt folder

    # classes = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field',
    #            'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
    #            'basketball-court', 'storage-tank', 'soccer-ball-field',
    #            'roundabout', 'harbor', 'swimming-pool', 'helicopter']
    classes = ['ship',]

    ap_50 = 0
    ap_75 = 0
    map = 0
    for cls in classes:
        aps = []
        for iou_thr in [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]:
            rec, prec, ap = voc_eval(
                detpath, annopath, cls, ovthresh=iou_thr, use_07_metric=True)
            aps.append(ap * 100)

        ap_50 += aps[0]
        ap_75 += aps[5]
        map += np.array(aps).mean()
        print(cls, ' ', 'AP50: {:.2f}\tAP75: {:.2f}'.format(aps[0], aps[5]))

    print('AP50: {:.2f}\tAP75: {:.2f}\t mAP: {:.2f}'.format(
        ap_50 / len(classes), ap_75 / len(classes), map / len(classes)))
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主要有两个参数：
1）sys.path.append(‘/home/disk/bing/mmrotate-main/DOTA_devkit’)
需要借用DOTA_devkit，一般将evaluation.py放在DOTA_devkit文件夹的同级目录下
2）classes = [‘ship’,]

3.修改DOTA_devkit/ResultMerge_multi_process.py

修改sys.path.append(‘/home/disk/bing/mmrotate-main/DOTA_devkit’)路径

4.借用test.py

import argparse
import os
import os.path as osp
import time
import warnings

import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                         wrap_fp16_model)
from mmdet.apis import multi_gpu_test, single_gpu_test
from mmdet.datasets import build_dataloader, replace_ImageToTensor

from mmrotate.datasets import build_dataset
from mmrotate.models import build_detector
from mmrotate.utils import setup_multi_processes, compat_cfg

from DOTA_devkit.ResultMerge_multi_process import mergebypoly2

from evaluation import evalution

def forward(config_path,checkpointpath,test_img_path,outpath,imgsize,batchsize):


    cfg = Config.fromfile(config_path)

    cfg = compat_cfg(cfg)

    # set multi-process settings
    setup_multi_processes(cfg)

    cfg.model.pretrained = None
    if cfg.model.get('neck'):
        if isinstance(cfg.model.neck, list):
            for neck_cfg in cfg.model.neck:
                if neck_cfg.get('rfp_backbone'):
                    if neck_cfg.rfp_backbone.get('pretrained'):
                        neck_cfg.rfp_backbone.pretrained = None
        elif cfg.model.neck.get('rfp_backbone'):
            if cfg.model.neck.rfp_backbone.get('pretrained'):
                cfg.model.neck.rfp_backbone.pretrained = None

    samples_per_gpu = batchsize

    distributed = False

    test_dict=dict(
        type='DOTADataset',
        test_mode = True,
        img_prefix=test_img_path,
        ann_file=test_img_path,
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(imgsize, imgsize),
                flip=False,
                transforms=[
                    dict(type='RResize'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='DefaultFormatBundle'),
                    dict(type='Collect', keys=['img'])
                ])
        ],
        version='le90')

    
    dataset = build_dataset(test_dict)
    data_loader = build_dataloader(
        dataset,
        samples_per_gpu=samples_per_gpu,
        workers_per_gpu=min(20, samples_per_gpu),
        dist=distributed,
        shuffle=False)

    # build the model and load checkpoint
    cfg.model.train_cfg = None
    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    checkpoint = load_checkpoint(model, checkpointpath, map_location='cpu')

    if 'CLASSES' in checkpoint.get('meta', {}):
        model.CLASSES = checkpoint['meta']['CLASSES']
    else:
        model.CLASSES = dataset.CLASSES

    if not distributed:
        model = MMDataParallel(model, device_ids=range(1))
        outputs = single_gpu_test(model, data_loader)


    dataset.save_dota_txt(outputs,outfolder=outpath)



if __name__ == '__main__':

    #cfg and checkpoint
    config_path = "/home/disk/bing/mmrotate-main/configs/oriented_reppoints/oriented_reppoints_r50_fpn_1x_dota_le135.py"
    checkpointpath = "/home/disk/bing/mmrotate-main/run/oriented_reppoints/epoch_12.pth"

    #test
    annopath = "/home/disk/bing/datasets/dota/val/labels"
    test_img_path = "/home/disk/bing/datasets/dota/val/images"

    #output txt folder
    outpath = "/home/disk/bing/datasets/dota/val/oriented_reppoints_epoch12_test"
   
    img_size = 1024
    batchsize = 2

    #forward
    forward(config_path,checkpointpath,test_img_path,outpath,img_size,batchsize)
    dst_folder = outpath
    evalution(dst_folder, annopath)


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主要有五个参数：
1）config_path
2）checkpointpath
3）annopath
4）test_img_path
5）outpath
一般将test.py放在evaluation.py的同级目录下

5.输入指令

python test.py
1