Swin - error/debug 모음
0
1. cocoeval.py 수정
File "/root/anaconda3/envs/cbnet/lib/python3.7/site-packages/pycocotools/cocoeval.py", line 195, in <listcomp>
iscrowd = [int(o['iscrowd']) for o in gt]
KeyError: 'iscrowd'
커스텀 coco 데이터셋에 iscrowd 필드가 없는 경우 cocoeval.py를 아래 코드로 수정해 준다.
__author__ = 'tsungyi'
import copy
import datetime
import time
from collections import defaultdict
import numpy as np
from . import mask as maskUtils
class COCOeval:
# Interface for evaluating detection on the Microsoft COCO dataset.
#
# The usage for CocoEval is as follows:
# cocoGt=..., cocoDt=... # load dataset and results
# E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object
# E.params.recThrs = ...; # set parameters as desired
# E.evaluate(); # run per image evaluation
# E.accumulate(); # accumulate per image results
# E.summarize(); # display summary metrics of results
# For example usage see evalDemo.m and http://mscoco.org/.
#
# The evaluation parameters are as follows (defaults in brackets):
# imgIds - [all] N img ids to use for evaluation
# catIds - [all] K cat ids to use for evaluation
# iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation
# recThrs - [0:.01:1] R=101 recall thresholds for evaluation
# areaRng - [...] A=4 object area ranges for evaluation
# maxDets - [1 10 100] M=3 thresholds on max detections per image
# iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints'
# iouType replaced the now DEPRECATED useSegm parameter.
# useCats - [1] if true use category labels for evaluation
# Note: if useCats=0 category labels are ignored as in proposal scoring.
# Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.
#
# evaluate(): evaluates detections on every image and every category and
# concats the results into the "evalImgs" with fields:
# dtIds - [1xD] id for each of the D detections (dt)
# gtIds - [1xG] id for each of the G ground truths (gt)
# dtMatches - [TxD] matching gt id at each IoU or 0
# gtMatches - [TxG] matching dt id at each IoU or 0
# dtScores - [1xD] confidence of each dt
# gtIgnore - [1xG] ignore flag for each gt
# dtIgnore - [TxD] ignore flag for each dt at each IoU
#
# accumulate(): accumulates the per-image, per-category evaluation
# results in "evalImgs" into the dictionary "eval" with fields:
# params - parameters used for evaluation
# date - date evaluation was performed
# counts - [T,R,K,A,M] parameter dimensions (see above)
# precision - [TxRxKxAxM] precision for every evaluation setting
# recall - [TxKxAxM] max recall for every evaluation setting
# Note: precision and recall==-1 for settings with no gt objects.
#
# See also coco, mask, pycocoDemo, pycocoEvalDemo
#
# Microsoft COCO Toolbox. version 2.0
# Data, paper, and tutorials available at: http://mscoco.org/
# Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
# Licensed under the Simplified BSD License [see coco/license.txt]
def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'):
'''
Initialize CocoEval using coco APIs for gt and dt
:param cocoGt: coco object with ground truth annotations
:param cocoDt: coco object with detection results
:return: None
'''
if not iouType:
print('iouType not specified. use default iouType segm')
self.cocoGt = cocoGt # ground truth COCO API
self.cocoDt = cocoDt # detections COCO API
self.evalImgs = defaultdict(
list) # per-image per-category evaluation results [KxAxI] elements
self.eval = {} # accumulated evaluation results
self._gts = defaultdict(list) # gt for evaluation
self._dts = defaultdict(list) # dt for evaluation
self.params = Params(iouType=iouType) # parameters
self._paramsEval = {} # parameters for evaluation
self.stats = [] # result summarization
self.ious = {} # ious between all gts and dts
if cocoGt is not None:
self.params.imgIds = sorted(cocoGt.getImgIds())
self.params.catIds = sorted(cocoGt.getCatIds())
def _prepare(self):
'''
Prepare ._gts and ._dts for evaluation based on params
:return: None
'''
def _toMask(anns, coco):
# modify ann['segmentation'] by reference
for ann in anns:
rle = coco.annToRLE(ann)
ann['segmentation'] = rle
p = self.params
if p.useCats:
gts = self.cocoGt.loadAnns(
self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
dts = self.cocoDt.loadAnns(
self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
else:
gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))
# convert ground truth to mask if iouType == 'segm'
if p.iouType == 'segm':
_toMask(gts, self.cocoGt)
_toMask(dts, self.cocoDt)
# set ignore flag
for gt in gts:
gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0
gt['ignore'] = 'iscrowd' in gt and gt['iscrowd']
if p.iouType == 'keypoints':
gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore']
self._gts = defaultdict(list) # gt for evaluation
self._dts = defaultdict(list) # dt for evaluation
for gt in gts:
self._gts[gt['image_id'], gt['category_id']].append(gt)
for dt in dts:
self._dts[dt['image_id'], dt['category_id']].append(dt)
self.evalImgs = defaultdict(
list) # per-image per-category evaluation results
self.eval = {} # accumulated evaluation results
def evaluate(self):
'''
Run per image evaluation on given images and store results
(a list of dict) in self.evalImgs
:return: None
'''
tic = time.time()
print('Running per image evaluation...')
p = self.params
# add backward compatibility if useSegm is specified in params
if p.useSegm is not None:
p.iouType = 'segm' if p.useSegm == 1 else 'bbox'
print('useSegm (deprecated) is not None. Running {} evaluation'.
format(p.iouType))
print('Evaluate annotation type *{}*'.format(p.iouType))
p.imgIds = list(np.unique(p.imgIds))
if p.useCats:
p.catIds = list(np.unique(p.catIds))
p.maxDets = sorted(p.maxDets)
self.params = p
self._prepare()
# loop through images, area range, max detection number
catIds = p.catIds if p.useCats else [-1]
if p.iouType == 'segm' or p.iouType == 'bbox':
computeIoU = self.computeIoU
elif p.iouType == 'keypoints':
computeIoU = self.computeOks
self.ious = {(imgId, catId): computeIoU(imgId, catId)
for imgId in p.imgIds for catId in catIds}
evaluateImg = self.evaluateImg
maxDet = p.maxDets[-1]
self.evalImgs = [
evaluateImg(imgId, catId, areaRng, maxDet) for catId in catIds
for areaRng in p.areaRng for imgId in p.imgIds
]
self._paramsEval = copy.deepcopy(self.params)
toc = time.time()
print('DONE (t={:0.2f}s).'.format(toc - tic))
def computeIoU(self, imgId, catId):
p = self.params
if p.useCats:
gt = self._gts[imgId, catId]
dt = self._dts[imgId, catId]
else:
gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
if len(gt) == 0 and len(dt) == 0:
return []
inds = np.argsort([-d['score'] for d in dt], kind='mergesort')
dt = [dt[i] for i in inds]
if len(dt) > p.maxDets[-1]:
dt = dt[0:p.maxDets[-1]]
if p.iouType == 'segm':
g = [g['segmentation'] for g in gt]
d = [d['segmentation'] for d in dt]
elif p.iouType == 'bbox':
g = [g['bbox'] for g in gt]
d = [d['bbox'] for d in dt]
else:
raise Exception('unknown iouType for iou computation')
# print(d)
# print(g)
# print(gt)
# compute iou between each dt and gt region
iscrowd = [0 for i in range(len(gt))]
ious = maskUtils.iou(d, g, iscrowd)
return ious
def computeOks(self, imgId, catId):
p = self.params
# dimention here should be Nxm
gts = self._gts[imgId, catId]
dts = self._dts[imgId, catId]
inds = np.argsort([-d['score'] for d in dts], kind='mergesort')
dts = [dts[i] for i in inds]
if len(dts) > p.maxDets[-1]:
dts = dts[0:p.maxDets[-1]]
# if len(gts) == 0 and len(dts) == 0:
if len(gts) == 0 or len(dts) == 0:
return []
ious = np.zeros((len(dts), len(gts)))
sigmas = p.kpt_oks_sigmas
vars = (sigmas * 2)**2
k = len(sigmas)
# compute oks between each detection and ground truth object
for j, gt in enumerate(gts):
# create bounds for ignore regions(double the gt bbox)
g = np.array(gt['keypoints'])
xg = g[0::3]
yg = g[1::3]
vg = g[2::3]
k1 = np.count_nonzero(vg > 0)
bb = gt['bbox']
x0 = bb[0] - bb[2]
x1 = bb[0] + bb[2] * 2
y0 = bb[1] - bb[3]
y1 = bb[1] + bb[3] * 2
for i, dt in enumerate(dts):
d = np.array(dt['keypoints'])
xd = d[0::3]
yd = d[1::3]
if k1 > 0:
# measure the per-keypoint distance if keypoints visible
dx = xd - xg
dy = yd - yg
else:
# measure minimum distance to keypoints in (x0,y0) &
# (x1,y1)
z = np.zeros((k))
dx = np.max((z, x0 - xd), axis=0) + np.max(
(z, xd - x1), axis=0)
dy = np.max((z, y0 - yd), axis=0) + np.max(
(z, yd - y1), axis=0)
e = (dx**2 + dy**2) / vars / (gt['area'] + np.spacing(1)) / 2
if k1 > 0:
e = e[vg > 0]
ious[i, j] = np.sum(np.exp(-e)) / e.shape[0]
return ious
def evaluateImg(self, imgId, catId, aRng, maxDet):
'''
perform evaluation for single category and image
:return: dict (single image results)
'''
p = self.params
if p.useCats:
gt = self._gts[imgId, catId]
dt = self._dts[imgId, catId]
else:
gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
if len(gt) == 0 and len(dt) == 0:
return None
for g in gt:
if g['ignore'] or (g['area'] < aRng[0] or g['area'] > aRng[1]):
g['_ignore'] = 1
else:
g['_ignore'] = 0
# sort dt highest score first, sort gt ignore last
gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort')
gt = [gt[i] for i in gtind]
dtind = np.argsort([-d['score'] for d in dt], kind='mergesort')
dt = [dt[i] for i in dtind[0:maxDet]]
# iscrowd = [int(o['iscrowd']) for o in gt]
iscrowd = [0 for i in range(len(gt))]
# load computed ious
ious = self.ious[imgId, catId][:, gtind] if len(
self.ious[imgId, catId]) > 0 else self.ious[imgId, catId]
T = len(p.iouThrs)
G = len(gt)
D = len(dt)
gtm = np.zeros((T, G))
dtm = np.zeros((T, D))
gtIg = np.array([g['_ignore'] for g in gt])
dtIg = np.zeros((T, D))
if not len(ious) == 0:
for tind, t in enumerate(p.iouThrs):
for dind, d in enumerate(dt):
# information about best match so far (m=-1 -> unmatched)
iou = min([t, 1 - 1e-10])
m = -1
for gind, g in enumerate(gt):
# if this gt already matched, and not a crowd, continue
if gtm[tind, gind] > 0 and not iscrowd[gind]:
continue
# if dt matched to reg gt, and on ignore gt, stop
if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1:
break
# continue to next gt unless better match made
if ious[dind, gind] < iou:
continue
# if match successful and best so far, store
# appropriately
iou = ious[dind, gind]
m = gind
# if match made store id of match for both dt and gt
if m == -1:
continue
dtIg[tind, dind] = gtIg[m]
dtm[tind, dind] = gt[m]['id']
gtm[tind, m] = d['id']
# set unmatched detections outside of area range to ignore
a = np.array([d['area'] < aRng[0] or d['area'] > aRng[1]
for d in dt]).reshape((1, len(dt)))
dtIg = np.logical_or(dtIg, np.logical_and(dtm == 0, np.repeat(a, T,
0)))
# store results for given image and category
return {
'image_id': imgId,
'category_id': catId,
'aRng': aRng,
'maxDet': maxDet,
'dtIds': [d['id'] for d in dt],
'gtIds': [g['id'] for g in gt],
'dtMatches': dtm,
'gtMatches': gtm,
'dtScores': [d['score'] for d in dt],
'gtIgnore': gtIg,
'dtIgnore': dtIg,
}
def accumulate(self, p=None):
'''
Accumulate per image evaluation results and store the result in
self.eval
:param p: input params for evaluation
:return: None
'''
print('Accumulating evaluation results...')
tic = time.time()
if not self.evalImgs:
print('Please run evaluate() first')
# allows input customized parameters
if p is None:
p = self.params
p.catIds = p.catIds if p.useCats == 1 else [-1]
T = len(p.iouThrs)
R = len(p.recThrs)
K = len(p.catIds) if p.useCats else 1
A = len(p.areaRng)
M = len(p.maxDets)
precision = -np.ones(
(T, R, K, A, M)) # -1 for the precision of absent categories
recall = -np.ones((T, K, A, M))
scores = -np.ones((T, R, K, A, M))
# create dictionary for future indexing
_pe = self._paramsEval
catIds = _pe.catIds if _pe.useCats else [-1]
setK = set(catIds)
setA = set(map(tuple, _pe.areaRng))
setM = set(_pe.maxDets)
setI = set(_pe.imgIds)
# get inds to evaluate
k_list = [n for n, k in enumerate(p.catIds) if k in setK]
m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
a_list = [
n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng))
if a in setA
]
i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
I0 = len(_pe.imgIds)
A0 = len(_pe.areaRng)
# retrieve E at each category, area range, and max number of detections
for k, k0 in enumerate(k_list):
Nk = k0 * A0 * I0
for a, a0 in enumerate(a_list):
Na = a0 * I0
for m, maxDet in enumerate(m_list):
E = [self.evalImgs[Nk + Na + i] for i in i_list]
E = [e for e in E if e is not None]
if len(E) == 0:
continue
dtScores = np.concatenate(
[e['dtScores'][0:maxDet] for e in E])
# different sorting method generates slightly different
# results. mergesort is used to be consistent as Matlab
# implementation.
inds = np.argsort(-dtScores, kind='mergesort')
dtScoresSorted = dtScores[inds]
dtm = np.concatenate(
[e['dtMatches'][:, 0:maxDet] for e in E], axis=1)[:,
inds]
dtIg = np.concatenate(
[e['dtIgnore'][:, 0:maxDet] for e in E], axis=1)[:,
inds]
gtIg = np.concatenate([e['gtIgnore'] for e in E])
npig = np.count_nonzero(gtIg == 0)
if npig == 0:
continue
tps = np.logical_and(dtm, np.logical_not(dtIg))
fps = np.logical_and(np.logical_not(dtm),
np.logical_not(dtIg))
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
tp = np.array(tp)
fp = np.array(fp)
nd = len(tp)
rc = tp / npig
pr = tp / (fp + tp + np.spacing(1))
q = np.zeros((R, ))
ss = np.zeros((R, ))
if nd:
recall[t, k, a, m] = rc[-1]
else:
recall[t, k, a, m] = 0
# numpy is slow without cython optimization for
# accessing elements use python array gets significant
# speed improvement
pr = pr.tolist()
q = q.tolist()
for i in range(nd - 1, 0, -1):
if pr[i] > pr[i - 1]:
pr[i - 1] = pr[i]
inds = np.searchsorted(rc, p.recThrs, side='left')
try:
for ri, pi in enumerate(inds):
q[ri] = pr[pi]
ss[ri] = dtScoresSorted[pi]
except: # noqa: E722
pass
precision[t, :, k, a, m] = np.array(q)
scores[t, :, k, a, m] = np.array(ss)
self.eval = {
'params': p,
'counts': [T, R, K, A, M],
'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'precision': precision,
'recall': recall,
'scores': scores,
}
toc = time.time()
print('DONE (t={:0.2f}s).'.format(toc - tic))
def summarize(self):
'''
Compute and display summary metrics for evaluation results.
Note this functin can *only* be applied on the default parameter
setting
'''
def _summarize(ap=1, iouThr=None, areaRng='all', maxDets=100):
p = self.params
iStr = '{:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' # noqa: E501
titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
typeStr = '(AP)' if ap == 1 else '(AR)'
iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \
if iouThr is None else '{:0.2f}'.format(iouThr)
aind = [
i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng
]
mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
if ap == 1:
# dimension of precision: [TxRxKxAxM]
s = self.eval['precision']
# IoU
if iouThr is not None:
t = np.where(iouThr == p.iouThrs)[0]
s = s[t]
s = s[:, :, :, aind, mind]
else:
# dimension of recall: [TxKxAxM]
s = self.eval['recall']
if iouThr is not None:
t = np.where(iouThr == p.iouThrs)[0]
s = s[t]
s = s[:, :, aind, mind]
if len(s[s > -1]) == 0:
mean_s = -1
else:
mean_s = np.mean(s[s > -1])
print(
iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets,
mean_s))
return mean_s
def _summarizeDets():
stats = np.zeros((12, ))
stats[0] = _summarize(1)
stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2])
stats[2] = _summarize(1,
iouThr=.75,
maxDets=self.params.maxDets[2])
stats[3] = _summarize(1,
areaRng='small',
maxDets=self.params.maxDets[2])
stats[4] = _summarize(1,
areaRng='medium',
maxDets=self.params.maxDets[2])
stats[5] = _summarize(1,
areaRng='large',
maxDets=self.params.maxDets[2])
stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
stats[9] = _summarize(0,
areaRng='small',
maxDets=self.params.maxDets[2])
stats[10] = _summarize(0,
areaRng='medium',
maxDets=self.params.maxDets[2])
stats[11] = _summarize(0,
areaRng='large',
maxDets=self.params.maxDets[2])
return stats
def _summarizeKps():
stats = np.zeros((10, ))
stats[0] = _summarize(1, maxDets=20)
stats[1] = _summarize(1, maxDets=20, iouThr=.5)
stats[2] = _summarize(1, maxDets=20, iouThr=.75)
stats[3] = _summarize(1, maxDets=20, areaRng='medium')
stats[4] = _summarize(1, maxDets=20, areaRng='large')
stats[5] = _summarize(0, maxDets=20)
stats[6] = _summarize(0, maxDets=20, iouThr=.5)
stats[7] = _summarize(0, maxDets=20, iouThr=.75)
stats[8] = _summarize(0, maxDets=20, areaRng='medium')
stats[9] = _summarize(0, maxDets=20, areaRng='large')
return stats
if not self.eval:
raise Exception('Please run accumulate() first')
iouType = self.params.iouType
if iouType == 'segm' or iouType == 'bbox':
summarize = _summarizeDets
elif iouType == 'keypoints':
summarize = _summarizeKps
self.stats = summarize()
def __str__(self):
self.summarize()
class Params:
'''
Params for coco evaluation api
'''
def setDetParams(self):
self.imgIds = []
self.catIds = []
# np.arange causes trouble. the data point on arange is slightly
# larger than the true value
self.iouThrs = np.linspace(.5,
0.95,
int(np.round((0.95 - .5) / .05)) + 1,
endpoint=True)
self.recThrs = np.linspace(.0,
1.00,
int(np.round((1.00 - .0) / .01)) + 1,
endpoint=True)
self.maxDets = [1, 10, 100]
self.areaRng = [[0**2, 1e5**2], [0**2, 32**2], [32**2, 96**2],
[96**2, 1e5**2]]
self.areaRngLbl = ['all', 'small', 'medium', 'large']
self.useCats = 1
def setKpParams(self):
self.imgIds = []
self.catIds = []
# np.arange causes trouble. the data point on arange is slightly
# larger than the true value
self.iouThrs = np.linspace(.5,
0.95,
int(np.round((0.95 - .5) / .05)) + 1,
endpoint=True)
self.recThrs = np.linspace(.0,
1.00,
int(np.round((1.00 - .0) / .01)) + 1,
endpoint=True)
self.maxDets = [20]
self.areaRng = [[0**2, 1e5**2], [32**2, 96**2], [96**2, 1e5**2]]
self.areaRngLbl = ['all', 'medium', 'large']
self.useCats = 1
self.kpt_oks_sigmas = np.array([
.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07,
.87, .87, .89, .89
]) / 10.0
def __init__(self, iouType='segm'):
if iouType == 'segm' or iouType == 'bbox':
self.setDetParams()
elif iouType == 'keypoints':
self.setKpParams()
else:
raise Exception('iouType not supported')
self.iouType = iouType
# useSegm is deprecated
self.useSegm = None
Computer Vision / ADAS / DMS / Face Recognition