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V3det update #11615

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0ad8e92
update evaluation code and model performance
yhcao6 Apr 3, 2024
746fff6
format
yhcao6 Apr 4, 2024
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22 changes: 11 additions & 11 deletions configs/v3det/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -61,17 +61,17 @@ data/
## Results and Models

| Backbone | Model | Lr schd | box AP | Config | Download |
| :------: | :-------------: | :-----: | :----: | :----------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
| R-50 | Faster R-CNN | 2x | 25.4 | [config](./faster_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//faster_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| R-50 | Cascade R-CNN | 2x | 31.6 | [config](./cascade_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//cascade_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| R-50 | FCOS | 2x | 9.4 | [config](./fcos_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//fcos_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| R-50 | Deformable-DETR | 50e | 34.4 | [config](./deformable-detr-refine-twostage_r50_8xb4_sample1e-3_v3det_50e.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight/Deformable_DETR_V3Det_R50) |
| R-50 | DINO | 36e | 33.5 | [config](./dino-4scale_r50_8xb2_sample1e-3_v3det_36e.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight/DINO_V3Det_R50) |
| Swin-B | Faster R-CNN | 2x | 37.6 | [config](./faster_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//faster_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| Swin-B | Cascade R-CNN | 2x | 42.5 | [config](./cascade_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//cascade_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| Swin-B | FCOS | 2x | 21.0 | [config](./fcos_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//fcos_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| Swin-B | Deformable-DETR | 50e | 42.5 | [config](./deformable-detr-refine-twostage_swin_16xb2_sample1e-3_v3det_50e.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight/Deformable_DETR_V3Det_SwinB) |
| Swin-B | DINO | 36e | 42.0 | [config](./dino-4scale_swin_16xb1_sample1e-3_v3det_36e.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight/DINO_V3Det_SwinB) |
| :------: | :-------------: | :-----: |:------:| :----------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
| R-50 | Faster R-CNN | 2x | 25.8 | [config](./faster_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//faster_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| R-50 | Cascade R-CNN | 2x | 32.1 | [config](./cascade_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//cascade_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| R-50 | FCOS | 2x | 9.6 | [config](./fcos_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//fcos_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| R-50 | Deformable-DETR | 50e | 35.0 | [config](./deformable-detr-refine-twostage_r50_8xb4_sample1e-3_v3det_50e.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight/Deformable_DETR_V3Det_R50) |
| R-50 | DINO | 36e | 34.0 | [config](./dino-4scale_r50_8xb2_sample1e-3_v3det_36e.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight/DINO_V3Det_R50) |
| Swin-B | Faster R-CNN | 2x | 38.2 | [config](./faster_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//faster_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| Swin-B | Cascade R-CNN | 2x | 43.2 | [config](./cascade_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//cascade_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| Swin-B | FCOS | 2x | 21.5 | [config](./fcos_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight//fcos_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x) |
| Swin-B | Deformable-DETR | 50e | 43.1 | [config](./deformable-detr-refine-twostage_swin_16xb2_sample1e-3_v3det_50e.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight/Deformable_DETR_V3Det_SwinB) |
| Swin-B | DINO | 36e | 42.6 | [config](./dino-4scale_swin_16xb1_sample1e-3_v3det_36e.py) | [model](https://download.openxlab.org.cn/models/V3Det/V3Det/weight/DINO_V3Det_SwinB) |

## Citation

Expand Down
207 changes: 192 additions & 15 deletions mmdet/datasets/api_wrappers/cocoeval_mp.py
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Original file line number Diff line number Diff line change
@@ -1,18 +1,64 @@
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import itertools
import multiprocessing as mp
import time
from collections import defaultdict

import mmengine
import numpy as np
import torch.multiprocessing as mp
from mmengine.logging import MMLogger
from pycocotools.cocoeval import COCOeval
from pycocotools.cocoeval import COCOeval, Params
from tqdm import tqdm


class COCOevalMP(COCOeval):

def __init__(self,
cocoGt=None,
cocoDt=None,
iouType='bbox',
num_proc=8,
tree_ann_path='data/V3Det/annotations/v3det_'
'2023_v1_category_tree.json',
ignore_parent_child_gts=True):
"""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
self.num_proc = num_proc # num of process
self.tree_ann_path = tree_ann_path
self.ignore_parent_child_gts = ignore_parent_child_gts
if not mmengine.exists(tree_ann_path):
print(f'{tree_ann_path} not exist')
raise FileNotFoundError
if cocoGt is not None:
self.params.imgIds = sorted(cocoGt.getImgIds())
self.params.catIds = sorted(cocoGt.getCatIds())
# split base novel cat ids
self.base_inds = []
self.novel_inds = []
for i, c in enumerate(self.cocoGt.dataset['categories']):
if c['novel']:
self.novel_inds.append(i)
else:
self.base_inds.append(i)

def _prepare(self):
'''
Prepare ._gts and ._dts for evaluation based on params
Expand All @@ -39,10 +85,6 @@ def _toMask(anns, coco):
if (dt['category_id'] in cat_ids) and (dt['image_id']
in img_ids):
dts.append(dt)
# gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) # noqa
# dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) # noqa
# gts=self.cocoGt.dataset['annotations']
# dts=self.cocoDt.dataset['annotations']
else:
gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))
Expand All @@ -63,6 +105,44 @@ def _toMask(anns, coco):
self._gts[gt['image_id'], gt['category_id']].append(gt)
for dt in dts:
self._dts[dt['image_id'], dt['category_id']].append(dt)

if self.ignore_parent_child_gts:
# for each category, maintain its child categories
cat_tree = mmengine.load(self.tree_ann_path)
catid2treeid = cat_tree['categoryid2treeid']
treeid2catid = {v: k for k, v in catid2treeid.items()}
ori_ancestor2descendant = cat_tree['ancestor2descendant']
ancestor2descendant = dict()
for k, v in ori_ancestor2descendant.items():
if k in treeid2catid:
ancestor2descendant[k] = v
ancestor2descendant_catid = defaultdict(set)
for tree_id in ancestor2descendant:
cat_id = treeid2catid[tree_id]
descendant_ids = ancestor2descendant[tree_id]
for descendant_id in descendant_ids:
if descendant_id not in treeid2catid:
continue
descendant_catid = treeid2catid[descendant_id]
ancestor2descendant_catid[int(cat_id)].add(
int(descendant_catid))
self.ancestor2descendant_catid = ancestor2descendant_catid
# If a gt has child category cat_A, and dts of this image
# has this category, add this gt to gt<img_id, cat_A>
for gt in gts:
ignore_cats = []
for child_cat_id in self.ancestor2descendant_catid[
gt['category_id']]:
if len(self._dts[gt['image_id'], child_cat_id]) > 0:
ignore_cats.append(child_cat_id)
if len(ignore_cats) == 0:
continue
ignore_gt = copy.deepcopy(gt)
ignore_gt['category_id'] = ignore_cats
ignore_gt['ignore'] = 1
for child_cat_id in ignore_cats:
self._gts[gt['image_id'], child_cat_id].append(ignore_gt)

self.evalImgs = defaultdict(
list) # per-image per-category evaluation results
self.eval = {} # accumulated evaluation results
Expand Down Expand Up @@ -91,7 +171,7 @@ def evaluate(self):
# loop through images, area range, max detection number
catIds = p.catIds if p.useCats else [-1]

nproc = 8
nproc = self.num_proc
split_size = len(catIds) // nproc
mp_params = []
for i in range(nproc):
Expand All @@ -102,7 +182,7 @@ def evaluate(self):
mp_params.append((catIds[begin:end], ))

MMLogger.get_current_instance().info(
'start multi processing evaluation ...')
f'start multi processing evaluation with nproc: {nproc}...')
with mp.Pool(nproc) as pool:
self.evalImgs = pool.starmap(self._evaluateImg, mp_params)

Expand All @@ -116,14 +196,12 @@ def _evaluateImg(self, catids_chunk):
self._prepare()
p = self.params
maxDet = max(p.maxDets)
all_params = []
for catId in catids_chunk:
for areaRng in p.areaRng:
for imgId in p.imgIds:
all_params.append((catId, areaRng, imgId))
all_params = itertools.product(catids_chunk, p.areaRng, p.imgIds)
all_params_len = len(catids_chunk) * len(p.areaRng) * len(p.imgIds)
evalImgs = [
self.evaluateImg(imgId, catId, areaRng, maxDet)
for catId, areaRng, imgId in tqdm(all_params)
for catId, areaRng, imgId in tqdm(
all_params, total=all_params_len)
]
return evalImgs

Expand Down Expand Up @@ -209,7 +287,7 @@ def evaluateImg(self, imgId, catId, aRng, maxDet):
'dtIgnore': dtIg,
}

def summarize(self):
def summarize(self, is_ovd=False):
"""Compute and display summary metrics for evaluation results.

Note this function can *only* be applied on the default parameter
Expand Down Expand Up @@ -272,6 +350,102 @@ def _summarizeDets():
stats = np.array(stats)
return stats

def _summarizeOVDs():

def _summarize(ap=1,
iouThr=None,
areaRng='all',
maxDets=100,
cat_kind=None):
assert cat_kind in ('Base', 'Novel')
if cat_kind == 'Novel':
cat_inds = self.novel_inds
else:
cat_inds = self.base_inds
p = self.params
iStr = (' {:<18} {} @[ IoU={:<9} | area={:>6s} | '
'maxDets={:>3d} ] = {:0.3f}') # noqa
titleStr = f'{cat_kind} Average Precision' if ap == 1 \
else f'{cat_kind} 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[:, :, cat_inds, 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[:, cat_inds, 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

stats = []
for cat_kind in ('Base', 'Novel'):
print(f'\nSummarize {cat_kind} Classes:')
stats.append(
_summarize(
1, maxDets=self.params.maxDets[-1], cat_kind=cat_kind))
stats.append(
_summarize(
1,
iouThr=.5,
maxDets=self.params.maxDets[-1],
cat_kind=cat_kind))
stats.append(
_summarize(
1,
iouThr=.75,
maxDets=self.params.maxDets[-1],
cat_kind=cat_kind))
for area_rng in ('small', 'medium', 'large'):
stats.append(
_summarize(
1,
areaRng=area_rng,
maxDets=self.params.maxDets[-1],
cat_kind=cat_kind))
for max_det in self.params.maxDets:
stats.append(
_summarize(0, maxDets=max_det, cat_kind=cat_kind))
for area_rng in ('small', 'medium', 'large'):
stats.append(
_summarize(
0,
areaRng=area_rng,
maxDets=self.params.maxDets[-1],
cat_kind=cat_kind))
stats = np.array(stats)

print()
print('-' * 45)
print(f'Compute OVD AP: (bAP + 3 * nAP) / 4 '
f'= {(stats[0] + 3 * stats[10]) / 4.:.4f}')
print('-' * 45)
return stats

def _summarizeKps():
stats = np.zeros((10, ))
stats[0] = _summarize(1, maxDets=20)
Expand All @@ -293,4 +467,7 @@ def _summarizeKps():
summarize = _summarizeDets
elif iouType == 'keypoints':
summarize = _summarizeKps
if is_ovd:
assert iouType == 'bbox'
summarize = _summarizeOVDs
self.stats = summarize()