def lvis_eval(result_files,
result_types,
lvis,
max_dets=(100, 300, 1000),
existing_json=None):
for res_type in result_types:
assert res_type in [
'proposal', 'proposal_fast', 'proposal_fast_percat', 'bbox',
'segm', 'keypoints'
]
if mmcv.is_str(lvis):
lvis = LVIS(lvis)
assert isinstance(lvis, LVIS)
if result_types == ['proposal_fast']:
ar = lvis_fast_eval_recall(result_files, lvis, np.array(max_dets))
for i, num in enumerate(max_dets):
print('AR@{}\t= {:.4f}'.format(num, ar[i]))
return
elif result_types == ['proposal_fast_percat']:
assert existing_json is not None
per_cat_recall = {}
for cat_id in range(1, 1231):
ar = lvis_fast_eval_recall(result_files,
lvis,
np.array(max_dets),
category_id=cat_id)
for i, num in enumerate(max_dets):
per_cat_recall.update({cat_id: ar})
print('cat{} AR@{}\t= {:.4f}'.format(cat_id, num, ar[i]))
pickle.dump(per_cat_recall,
open('./{}_per_cat_recall.pt'.format(existing_json), 'wb'))
return
for res_type in result_types:
result_file = result_files[res_type]
assert result_file.endswith('.json')
iou_type = 'bbox' if res_type == 'proposal' else res_type
lvisEval = LVISEval(ANNOTATION_PATH, result_file, iou_type,
max_dets) # original - without max_dets
# lvisEval.params.imgIds = img_ids
if res_type == 'proposal':
lvisEval.params.use_cats = 0
lvisEval.params.max_dets = list(max_dets)
lvisEval.run()
lvisEval.print_results()
def _evaluate_predictions_on_lvis(lvis_gt,
lvis_results,
iou_type,
class_names=None):
"""
Args:
iou_type (str):
kpt_oks_sigmas (list[float]):
class_names (None or list[str]): if provided, will use it to predict
per-category AP.
Returns:
a dict of {metric name: score}
"""
metrics = {
"bbox":
["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
"segm":
["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
}[iou_type]
logger = logging.getLogger(__name__)
if len(lvis_results) == 0: # TODO: check if needed
logger.warn("No predictions from the model! Set scores to -1")
return {metric: -1 for metric in metrics}
if iou_type == "segm":
lvis_results = copy.deepcopy(lvis_results)
# When evaluating mask AP, if the results contain bbox, LVIS API will
# use the box area as the area of the instance, instead of the mask area.
# This leads to a different definition of small/medium/large.
# We remove the bbox field to let mask AP use mask area.
for c in lvis_results:
c.pop("bbox", None)
from lvis import LVISEval, LVISResults
lvis_results = LVISResults(lvis_gt, lvis_results)
lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type)
lvis_eval.run()
lvis_eval.print_results()
# Pull the standard metrics from the LVIS results
results = lvis_eval.get_results()
results = {metric: float(results[metric] * 100) for metric in metrics}
logger.info("Evaluation results for {}: \n".format(iou_type) +
create_small_table(results))
return results
def eval_partial_results(epoch, dset_name, validation_path):
results = []
mAP = -1
directory = 'bbox_results/temp_res'
for filename in os.listdir(directory):
if filename.endswith(".json"):
temp_name = os.path.join(directory, filename)
with open(temp_name, 'rb') as f:
results = list(itertools.chain(results, pickle.load(f)))
cwd = os.getenv('owd')
validation_path = os.path.join(cwd, validation_path)
if not os.path.exists(f'bbox_results/{dset_name}/'):
os.makedirs(f'bbox_results/{dset_name}/')
json.dump(results,
open(f'./bbox_results/{dset_name}/results_{epoch}.json', 'w'),
indent=4)
resFile = f'./bbox_results/{dset_name}/results_{epoch}.json'
if (dset_name == 'coco') | (dset_name == 'drones'):
cocoGt = COCO(validation_path)
try:
cocoDt = cocoGt.loadRes(resFile)
except IndexError:
print('empty list return zero map')
return 0
cocoDt.loadAnns()
# running evaluation
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
mAP = cocoEval.stats[0]
elif (dset_name == 'lvis'):
lvis_eval = LVISEval(validation_path, resFile, 'bbox')
lvis_eval.run()
metrics = lvis_eval.get_results()
lvis_eval.print_results()
mAP = metrics['AP']
return (mAP)
def eval_results(results, dset_name, validation_path):
cwd = os.getenv('owd')
validation_path = os.path.join(cwd, validation_path)
if not os.path.exists(f'bbox_results/{dset_name}/'):
os.makedirs(f'bbox_results/{dset_name}/')
rid = (random.randint(0, 1000000))
json.dump(results,
open(f'./bbox_results/{dset_name}/results_{rid}.json', 'w'),
indent=4)
resFile = f'./bbox_results/{dset_name}/results_{rid}.json'
if (dset_name == 'coco') | (dset_name == 'drones'):
cocoGt = COCO(validation_path)
try:
cocoDt = cocoGt.loadRes(resFile)
except IndexError:
print('empty list return zero map')
return 0
cocoDt.loadAnns()
# running evaluation
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
mAP = cocoEval.stats[0]
elif (dset_name == 'lvis'):
try:
lvis_eval = LVISEval(validation_path, resFile, 'bbox')
except IndexError:
print('empty list return zero map')
return 0
lvis_eval.run()
metrics = lvis_eval.get_results()
lvis_eval.print_results()
mAP = metrics['AP']
os.remove(resFile)
return (mAP)
def lvis_eval(resfile, res_type):
print('*********evaluating *{}*'.format(res_type))
# tmp_file = osp.join(runner.work_dir, 'temp_0')
# result_files = results2json(self.dataset, results, tmp_file)
ANNOTATION_PATH = 'data/lvis/annotations/lvis_v0.5_val.json'
# cocoGt = self.dataset.coco
# imgIds = cocoGt.getImgIds()
# for res_type in res_types:
# # try:
# # cocoDt = cocoGt.loadRes(result_files[res_type])
# # except IndexError:
# # print('No prediction found.')
# # break
iou_type = res_type
lvis_eval = LVISEval(ANNOTATION_PATH, resfile, iou_type)
lvis_eval.run()
lvis_eval.print_results()
def _evaluate_predictions_on_lvis(lvis_gt, lvis_results, iou_type):
"""
Evaluate the lvis results using LVISEval API.
"""
assert len(lvis_results) > 0
if iou_type == "segm":
lvis_results = copy.deepcopy(lvis_results)
# When evaluating mask AP, if the results contain bbox, LVIS API will
# use the box area as the area of the instance, instead of the mask area.
# This leads to a different definition of small/medium/large.
# We remove the bbox field to let mask AP use mask area.
for c in lvis_results:
c.pop("bbox", None)
lvis_results = LVISResults(lvis_gt, lvis_results)
lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type)
lvis_eval.run()
lvis_eval.print_results()
return lvis_eval
def evaluate(self, runner, results):
tmp_file = osp.join(runner.work_dir, 'temp_0')
result_files = results2json(self.dataset, results, tmp_file)
res_types = ['bbox', 'segm'
] if runner.model.module.with_mask else ['bbox']
ANNOTATION_PATH = 'data/lvis/annotations/lvis_v0.5_val.json'
# cocoGt = self.dataset.coco
# imgIds = cocoGt.getImgIds()
for res_type in res_types:
# try:
# cocoDt = cocoGt.loadRes(result_files[res_type])
# except IndexError:
# print('No prediction found.')
# break
iou_type = res_type
lvis_eval = LVISEval(ANNOTATION_PATH, result_files[res_type],
iou_type)
lvis_eval.run()
lvis_eval.print_results()
def main():
config = fetch_config()
print_args_stdout(config)
ipdb.set_trace()
print("Running eval.")
lvis_eval = LVISEval(config.ann_path, config.results_path, config.ann_type)
lvis_eval.run()
lvis_eval.print_results()
print("Finished eval.")
ipdb.set_trace()
# All precision values: 10 x 101 x 1230 x 4
# precision has dims (iou, recall, cls, area range)
precisions = lvis_eval.eval['precision']
with open(config.ann_path, 'r') as outfile:
gt = json.load(outfile)
cat_metas = gt['categories']
cats = []
for cat_meta in cat_metas:
cats.append((cat_meta['id'], cat_meta['name']))
cats.sort(key=itemgetter(0))
class_names = [cat[1] for cat in cats]
area_type = 0
results_per_category, per_cat_results = fetch_aps(precisions, class_names, area_type)
print("mAP for area type {}: {}".format(area_type, evaluate_map(results_per_category)))
# Print for eye-balling.
# print_aps(results_per_category, class_names, n_cols=6)
# Store results_per_category into a JSON.
with open(config.aps_json_path, 'w') as json_file:
json.dump(per_cat_results, json_file, indent=4)
# Store the 4D precisions tensor as a PKL.
with open(config.prec_pkl_path, 'wb') as pkl_file:
pickle.dump(precisions, pkl_file)
def do_lvis_evaluation(
dataset,
gt_path,
predictions,
output_folder,
iou_types,
iteration,
):
logger = logging.getLogger("maskrcnn_benchmark.inference")
logger.info("Preparing results for LVIS format")
lvis_results = prepare_for_lvis_evaluation(predictions, dataset, iou_types)
if len(lvis_results) == 0:
return {}
dt_path = os.path.join(output_folder, "lvis_dt.json")
import json
with open(dt_path, "w") as f:
json.dump(lvis_results, f)
logger.info("Evaluating predictions")
lvis_eval_info = {}
for iou_type in iou_types:
lvis_eval = LVISEval(gt_path, dt_path, iou_type)
lvis_eval.run()
print(iou_type)
lvis_eval.print_results()
keys = lvis_eval.get_results().keys()
for k in keys:
lvis_eval_info[iou_type + k] = lvis_eval.get_results()[k]
save_path = os.path.join(output_folder, str(iteration))
mkdir(save_path)
lvis_eval_percat = LVISEvalPerCat(gt_path, dt_path, iou_type,
save_path)
lvis_eval_percat.run()
lvis_eval_percat.print_results()
return lvis_eval_info
def _evaluate_predictions_on_lvis(lvis_gt,
lvis_results,
iou_type,
class_names=None):
"""
Args:
iou_type (str):
class_names (None or list[str]): if provided, will use it to predict
per-category AP.
Returns:
a dict of {metric name: score}
"""
metrics = ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"]
logger = logging.getLogger(__name__)
if len(lvis_results) == 0: # TODO: check if needed
logger.warn("No predictions from the model! Set scores to -1")
return {metric: -1 for metric in metrics}
from lvis import LVISEval, LVISResults
lvis_results = LVISResults(lvis_gt, lvis_results)
lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type)
lvis_eval.run()
lvis_eval.print_results()
# Pull the standard metrics from the LVIS results
results = lvis_eval.get_results()
results = {metric: float(results[metric] * 100) for metric in metrics}
logger.info(
"Evaluation results for {}: \n".format(iou_type) + \
create_small_table(results)
)
return results
def evaluate(self,
results,
metric='bbox',
logger=None,
jsonfile_prefix=None,
classwise=False,
proposal_nums=(100, 300, 1000),
iou_thrs=np.arange(0.5, 0.96, 0.05)):
"""Evaluation in LVIS protocol.
Args:
results (list[list | tuple]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated. Options are
'bbox', 'segm', 'proposal', 'proposal_fast'.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
jsonfile_prefix (str | None):
classwise (bool): Whether to evaluating the AP for each class.
proposal_nums (Sequence[int]): Proposal number used for evaluating
recalls, such as recall@100, recall@1000.
Default: (100, 300, 1000).
iou_thrs (Sequence[float]): IoU threshold used for evaluating
recalls. If set to a list, the average recall of all IoUs will
also be computed. Default: 0.5.
Returns:
dict[str, float]: LVIS style metrics.
"""
try:
import lvis
assert lvis.__version__ >= '10.5.3'
from lvis import LVISResults, LVISEval
except AssertionError:
raise AssertionError('Incompatible version of lvis is installed. '
'Run pip uninstall lvis first. Then run pip '
'install mmlvis to install open-mmlab forked '
'lvis. ')
except ImportError:
raise ImportError('Package lvis is not installed. Please run pip '
'install mmlvis to install open-mmlab forked '
'lvis.')
assert isinstance(results, list), 'results must be a list'
assert len(results) == len(self), (
'The length of results is not equal to the dataset len: {} != {}'.
format(len(results), len(self)))
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError('metric {} is not supported'.format(metric))
if jsonfile_prefix is None:
tmp_dir = tempfile.TemporaryDirectory()
jsonfile_prefix = osp.join(tmp_dir.name, 'results')
else:
tmp_dir = None
result_files = self.results2json(results, jsonfile_prefix)
eval_results = {}
# get original api
lvis_gt = self.coco
for metric in metrics:
msg = 'Evaluating {}...'.format(metric)
if logger is None:
msg = '\n' + msg
print_log(msg, logger=logger)
if metric == 'proposal_fast':
ar = self.fast_eval_recall(results,
proposal_nums,
iou_thrs,
logger='silent')
log_msg = []
for i, num in enumerate(proposal_nums):
eval_results['AR@{}'.format(num)] = ar[i]
log_msg.append('\nAR@{}\t{:.4f}'.format(num, ar[i]))
log_msg = ''.join(log_msg)
print_log(log_msg, logger=logger)
continue
if metric not in result_files:
raise KeyError('{} is not in results'.format(metric))
try:
lvis_dt = LVISResults(lvis_gt, result_files[metric])
except IndexError:
print_log('The testing results of the whole dataset is empty.',
logger=logger,
level=logging.ERROR)
break
iou_type = 'bbox' if metric == 'proposal' else metric
lvis_eval = LVISEval(lvis_gt, lvis_dt, iou_type)
lvis_eval.params.imgIds = self.img_ids
if metric == 'proposal':
lvis_eval.params.useCats = 0
lvis_eval.params.maxDets = list(proposal_nums)
lvis_eval.evaluate()
lvis_eval.accumulate()
lvis_eval.summarize()
for k, v in lvis_eval.get_results().items():
if k.startswith('AR'):
val = float('{:.3f}'.format(float(v)))
eval_results[k] = val
else:
lvis_eval.evaluate()
lvis_eval.accumulate()
lvis_eval.summarize()
lvis_results = lvis_eval.get_results()
if classwise: # Compute per-category AP
# Compute per-category AP
# from https://github.com/facebookresearch/detectron2/
precisions = lvis_eval.eval['precision']
# precision: (iou, recall, cls, area range, max dets)
assert len(self.cat_ids) == precisions.shape[2]
results_per_category = []
for idx, catId in enumerate(self.cat_ids):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
nm = self.coco.load_cats(catId)[0]
precision = precisions[:, :, idx, 0, -1]
precision = precision[precision > -1]
if precision.size:
ap = np.mean(precision)
else:
ap = float('nan')
results_per_category.append(
(f'{nm["name"]}', f'{float(ap):0.3f}'))
num_columns = min(6, len(results_per_category) * 2)
results_flatten = list(
itertools.chain(*results_per_category))
headers = ['category', 'AP'] * (num_columns // 2)
results_2d = itertools.zip_longest(*[
results_flatten[i::num_columns]
for i in range(num_columns)
])
table_data = [headers]
table_data += [result for result in results_2d]
table = AsciiTable(table_data)
print_log('\n' + table.table, logger=logger)
for k, v in lvis_results.items():
if k.startswith('AP'):
key = '{}_{}'.format(metric, k)
val = float('{:.3f}'.format(float(v)))
eval_results[key] = val
ap_summary = ' '.join([
'{}:{:.3f}'.format(k, float(v))
for k, v in lvis_results.items() if k.startswith('AP')
])
eval_results['{}_mAP_copypaste'.format(metric)] = ap_summary
lvis_eval.print_results()
if tmp_dir is not None:
tmp_dir.cleanup()
return eval_results
def evaluate(self,
results,
metric='bbox',
logger=None,
jsonfile_prefix=None,
classwise=False,
proposal_nums=300,
iou_thrs=np.arange(0.5, 0.96, 0.05)):
"""Evaluation in COCO protocol.
Args:
results (list): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
jsonfile_prefix (str | None): The prefix of json files. It includes
the file path and the prefix of filename, e.g., "a/b/prefix".
If not specified, a temp file will be created. Default: None.
classwise (bool): Whether to evaluating the AP for each class.
proposal_nums (Sequence[int]): Proposal number used for evaluating
recalls, such as recall@100, recall@1000.
Default: (100, 300, 1000).
iou_thrs (Sequence[float]): IoU threshold used for evaluating
recalls. If set to a list, the average recall of all IoUs will
also be computed. Default: 0.5.
Returns:
dict[str: float]
"""
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError('metric {} is not supported'.format(metric))
result_files = self.format_results(results, jsonfile_prefix)
eval_results = {}
cocoGt = self.coco
for metric in metrics:
msg = 'Evaluating {}...'.format(metric)
if logger is None:
msg = '\n' + msg
print_log(msg, logger=logger)
if metric == 'proposal_fast':
ar = self.fast_eval_recall(results,
proposal_nums,
iou_thrs,
logger='silent')
log_msg = []
for i, num in enumerate(proposal_nums):
eval_results['AR@{}'.format(num)] = ar[i]
log_msg.append('\nAR@{}\t{:.4f}'.format(num, ar[i]))
log_msg = ''.join(log_msg)
print_log(log_msg, logger=logger)
continue
if metric not in result_files:
raise KeyError('{} is not in results'.format(metric))
try:
cocoDt = cocoGt.loadRes(result_files[metric])
except IndexError:
print_log('The testing results of the whole dataset is empty.',
logger=logger,
level=logging.ERROR)
break
iou_type = 'bbox' if metric == 'proposal' else metric
# run lvis evaluation
eval_results['lvis'] = {}
lvis_eval = LVISEval(self.ann_file_path, result_files[metric],
iou_type)
lvis_eval.params.max_dets = proposal_nums
lvis_eval.run()
lvis_eval.print_results()
print('=====> The above metric is {}.'.format(iou_type))
keys = lvis_eval.get_results().keys()
for k in keys:
eval_results['lvis'][iou_type + k] = lvis_eval.get_results()[k]
return eval_results
import logging from lvis import LVIS, LVISResults, LVISEval # result and val files for 100 randomly sampled images. # ANNOTATION_PATH = "./data/lvis_val_100.json" # RESULT_PATH = "./data/lvis_results_100.json" ANNOTATION_PATH = "./data/lvis/lvis_v0.5_val.json" RESULT_PATH = './debug_file.pkl.segm.json' # RESULT_PATH = './mask_rcnn_r101_fpn_1x_lvis.pkl.segm.json' ANN_TYPE = 'segm' lvis_eval = LVISEval(ANNOTATION_PATH, RESULT_PATH, ANN_TYPE) lvis_eval.run() lvis_eval.print_results(True)
def main():
args = parse_args()
assert args.out or args.show or args.json_out, \
('Please specify at least one operation (save or show the results) '
'with the argument "--out" or "--show" or "--json_out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
if args.json_out is not None and args.json_out.endswith('.json'):
args.json_out = args.json_out[:-5]
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
while not osp.isfile(args.checkpoint):
print('Waiting for {} to exist...'.format(args.checkpoint))
time.sleep(60)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
# assert not distributed
if not distributed:
model = MMDataParallel(model, device_ids=[0])
# data_loader.dataset.img_infos = data_loader.dataset.img_infos[:10]
outputs = single_gpu_test(model, data_loader)
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir)
rank, _ = get_dist_info()
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
eval_types = args.eval
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = args.out
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
if dataset.ann_file == 'data/coco/annotations/image_info_test-dev2017.json':
result_files = results2json_segm(dataset,
outputs,
args.out,
dump=True)
else:
result_files = results2json_segm(dataset,
outputs,
args.out,
dump=False)
if 'lvis' in dataset.ann_file: ## an ugly fix to make it compatible with coco eval
from lvis import LVISEval
lvisEval = LVISEval(cfg.data.test.ann_file,
result_files, 'segm')
lvisEval.run()
lvisEval.print_results()
#fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(
iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = args.out + '.{}'.format(name)
result_files = results2json(dataset,
outputs_,
result_file,
dump=False)
coco_eval(result_files, eval_types, dataset.coco)
##eval on lvis-77######
cfg.data.test.ann_file = 'data/lvis/lvis_v0.5_val_cocofied.json'
cfg.data.test.img_prefix = 'data/lvis/val2017/'
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# model_orig=model.module
# model = MMDataParallel(model, device_ids=[0]).cuda()
# data_loader.dataset.img_infos = data_loader.dataset.img_infos[:10]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset,
outputs,
'xxx',
dump=False)
from lvis import LVISEval
lvisEval = LVISEval(
'data/lvis/lvis_v0.5_val_cocofied.json', result_files,
'segm')
lvisEval.run()
lvisEval.print_results()
# fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(
iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset,
outputs_,
result_file,
dump=False)
coco_eval(result_files, eval_types, dataset.coco)
# Save predictions in the COCO json format
if args.json_out and rank == 0:
if not isinstance(outputs[0], dict):
results2json(dataset, outputs, args.json_out)
else:
for name in outputs[0]:
outputs_ = [out[name] for out in outputs]
result_file = args.json_out + '.{}'.format(name)
results2json(dataset, outputs_, result_file)
def _evaluate_predictions_on_lvis_per_class(lvis_gt,
lvis_results,
iou_type,
class_names=None):
"""
Args:
iou_type (str):
kpt_oks_sigmas (list[float]):
class_names (None or list[str]): if provided, will use it to predict
per-category AP.
Returns:
a dict of {metric name: score}
"""
metrics = {
"bbox":
["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
"segm":
["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
}[iou_type]
logger = logging.getLogger(__name__)
if len(lvis_results) == 0: # TODO: check if needed
logger.warn("No predictions from the model! Set scores to -1")
return {metric: -1 for metric in metrics}
if iou_type == "segm":
lvis_results = copy.deepcopy(lvis_results)
# When evaluating mask AP, if the results contain bbox, LVIS API will
# use the box area as the area of the instance, instead of the mask area.
# This leads to a different definition of small/medium/large.
# We remove the bbox field to let mask AP use mask area.
for c in lvis_results:
c.pop("bbox", None)
from lvis import LVISEval, LVISResults
lvis_results = LVISResults(lvis_gt, lvis_results)
lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type)
lvis_eval.evaluate()
lvis_eval.accumulate()
precisions = lvis_eval.eval["precision"]
results_per_category = []
for idx, name in enumerate(class_names):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
precision = precisions[:, :, idx, 0]
precision = precision[precision > -1]
ap = np.mean(precision) if precision.size else float("nan")
results_per_category.append(("{}".format(name), float(ap * 100)))
lvis_eval.summarize()
lvis_eval.print_results()
# Pull the standard metrics from the LVIS results
results = lvis_eval.get_results()
results = {metric: float(results[metric] * 100) for metric in metrics}
logger.info("Evaluation results for {}: \n".format(iou_type) +
create_small_table(results))
results.update({"AP-" + name: ap for name, ap in results_per_category})
return results
def main():
# Use first line of file docstring as description if it exists.
parser = argparse.ArgumentParser(
description=__doc__.split("\n")[0] if __doc__ else "",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("annotations_json", type=Path)
parser.add_argument("results_json", type=Path)
parser.add_argument("output_dir", type=Path)
parser.add_argument("--type", default="segm", choices=["segm", "bbox"])
parser.add_argument("--dets-per-cat", default=10000, type=int)
parser.add_argument("--ious", nargs="*", type=float)
args = parser.parse_args()
args.output_dir.mkdir(exist_ok=True, parents=True)
logger = setup_logger(output=str(args.output_dir.resolve()), name=__file__)
log_path = args.output_dir / "log.txt"
assert args.dets_per_cat > 0
with open(args.results_json, "r") as f:
results = json.load(f)
by_cat = defaultdict(list)
for ann in results:
by_cat[ann["category_id"]].append(ann)
results = []
topk = args.dets_per_cat
missing_dets_cats = set()
for cat, cat_anns in by_cat.items():
if len(cat_anns) < topk:
missing_dets_cats.add(cat)
results.extend(
sorted(cat_anns, key=lambda x: x["score"], reverse=True)[:topk])
if missing_dets_cats:
logger.warning(
f"\n===\n"
f"{len(missing_dets_cats)} classes had less than {topk} detections!\n"
f"Outputting {topk} detections for each class will improve AP further.\n"
f"If using detectron2, please use the lvdevil/infer_topk.py script to "
f"output a results file with {topk} detections for each class.\n"
f"===")
gt = LVIS(args.annotations_json)
results = LVISResults(gt, results, max_dets=-1)
lvis_eval = LVISEval(gt, results, iou_type=args.type)
params = lvis_eval.params
params.max_dets = -1 # No limit on detections per image.
if args.ious:
params.iou_thrs = args.ious
lvis_eval.run()
lvis_eval.print_results()
metrics = {
k: v
for k, v in lvis_eval.results.items() if k.startswith("AP")
}
logger.info("copypaste: %s,%s", ",".join(map(str, metrics.keys())), "path")
logger.info(
"copypaste: %s,%s",
",".join(f"{v*100:.2f}" for v in metrics.values()),
log_path,
)
get_count = num_get[v].sum().astype(np.float64)
acc = get_count / ins_count
print(template.format('(ACC)', '0.50:0.95', 'all', 300, k, acc * 100))
# with open('tempcls.pkl', 'rb') as fin:
# savelist = pickle.load(fin)
# num_get = savelist[0]
# num_ins = savelist[1]
# splitbin = get_split_bin()
# accumulate_acc(num_ins, num_get, splitbin)
# result and val files for 100 randomly sampled images.
ANNOTATION_PATH = "data/lvis/lvis_v0.5_val.json"
RESULT_PATH_BBOX = args.boxjson
print('Eval Bbox:')
ANN_TYPE = 'bbox'
lvis_eval = LVISEval(ANNOTATION_PATH, RESULT_PATH_BBOX, ANN_TYPE)
lvis_eval.run()
lvis_eval.print_results()
if not args.segjson == 'None':
RESULT_PATH_SEGM = args.segjson
print('Eval Segm:')
ANN_TYPE = 'segm'
lvis_eval = LVISEval(ANNOTATION_PATH, RESULT_PATH_SEGM, ANN_TYPE)
lvis_eval.run()
lvis_eval.print_results()
def _non_dist_train(model,
dataset,
cfg,
validate=False,
logger=None,
timestamp=None):
if validate:
raise NotImplementedError('Built-in validation is not implemented '
'yet in not-distributed training. Use '
'distributed training or test.py and '
'*eval.py scripts instead.')
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False) for ds in dataset
]
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# # if model.module.bbox_head.freeze_solov2_and_train_combonly:
# if model.module.bbox_head.optimize_list is not None:
# for (key, param) in model.named_parameters():
# # if 'kernel_convs_convcomb' not in key and 'context_fusion_convs' not in key and 'learned_weight' not in key:
# if not any(s in key for s in model.module.bbox_head.optimize_list):
# param.requires_grad=False
# else:
# # print('optimize {}'.format(key))
# logger.info('optimize {}'.format(key))
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(
model, batch_processor, optimizer, cfg.work_dir, logger=logger)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(
**cfg.optimizer_config, **fp16_cfg, distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
## add test after training
if cfg.data.test.ann_file != 'data/lvis/lvis_v0.5_val_lvis_freqset.json': # if val set is lvis freq, only eval on lvis-freq val set
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
model_orig=model.module
model = MMDataParallel(model, device_ids=[0]).cuda()
data_loader.dataset.img_infos = data_loader.dataset.img_infos[:100]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset, outputs, 'xxx', dump=False)
coco_eval(result_files, eval_types, dataset.coco)
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file, dump=False)
coco_eval(result_files, eval_types, dataset.coco)
##eval on lvis-77######
cfg.data.test.ann_file = 'data/lvis/lvis_v0.5_val_cocofied.json'
cfg.data.test.img_prefix = 'data/lvis/val2017/'
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# model_orig=model.module
# model = MMDataParallel(model, device_ids=[0]).cuda()
data_loader.dataset.img_infos = data_loader.dataset.img_infos[:100]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset, outputs, 'xxx', dump=False)
from lvis import LVISEval
lvisEval = LVISEval('data/lvis/lvis_v0.5_val_cocofied.json', result_files, 'segm')
lvisEval.run()
lvisEval.print_results()
# fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file, dump=False)
coco_eval(result_files, eval_types, dataset.coco)
else:
##eval on lvis-freq######
cfg.data.test.test_mode = True
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# model_orig=model.module
# model = MMDataParallel(model, device_ids=[0]).cuda()
data_loader.dataset.img_infos = data_loader.dataset.img_infos[:100]
outputs = single_gpu_test(model, data_loader)
print('\nwriting results to {}'.format('xxx'))
# mmcv.dump(outputs, 'xxx')
eval_types = ['segm']
if eval_types:
print('Starting evaluate {}'.format(' and '.join(eval_types)))
if eval_types == ['proposal_fast']:
result_file = 'xxx'
coco_eval(result_file, eval_types, dataset.coco)
else:
if not isinstance(outputs[0], dict):
result_files = results2json_segm(dataset, outputs, 'xxx', dump=False)
from lvis import LVISEval
lvisEval = LVISEval(cfg.data.test.ann_file, result_files, 'segm')
lvisEval.run()
lvisEval.print_results()
# fix lvis api eval iou_thr error, should be 0.9 but was 0.8999
lvisEval.params.iou_thrs[8] = 0.9
for iou in [0.5, 0.6, 0.7, 0.8, 0.9]:
print('AP at iou {}: {}'.format(iou, lvisEval._summarize('ap', iou_thr=iou)))
else:
for name in outputs[0]:
print('\nEvaluating {}'.format(name))
outputs_ = [out[name] for out in outputs]
result_file = 'xxx' + '.{}'.format(name)
result_files = results2json(dataset, outputs_,
result_file, dump=False)
coco_eval(result_files, eval_types, dataset.coco)
def evaluate(self,
results,
metric=['track'],
logger=None,
resfile_path=None):
if isinstance(metric, list):
metrics = metric
elif isinstance(metric, str):
metrics = [metric]
else:
raise TypeError('metric must be a list or a str.')
allowed_metrics = ['bbox', 'track']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported.')
result_files, tmp_dir = self.format_results(results, resfile_path)
eval_results = dict()
if 'track' in metrics:
from tao.toolkit.tao import TaoEval
print_log('Evaluating TAO results...', logger)
tao_eval = TaoEval(self.ann_file, result_files['track'])
tao_eval.params.img_ids = self.img_ids
tao_eval.params.cat_ids = self.cat_ids
tao_eval.params.iou_thrs = np.array([0.5, 0.75])
tao_eval.run()
tao_eval.print_results()
tao_results = tao_eval.get_results()
for k, v in tao_results.items():
if isinstance(k, str) and k.startswith('AP'):
key = 'track_{}'.format(k)
val = float('{:.3f}'.format(float(v)))
eval_results[key] = val
if 'bbox' in metrics:
print_log('Evaluating detection results...', logger)
lvis_gt = LVIS(self.ann_file)
lvis_dt = LVISResults(lvis_gt, result_files['bbox'])
lvis_eval = LVISEval(lvis_gt, lvis_dt, 'bbox')
lvis_eval.params.imgIds = self.img_ids
lvis_eval.params.catIds = self.cat_ids
lvis_eval.evaluate()
lvis_eval.accumulate()
lvis_eval.summarize()
lvis_eval.print_results()
lvis_results = lvis_eval.get_results()
for k, v in lvis_results.items():
if k.startswith('AP'):
key = '{}_{}'.format('bbox', k)
val = float('{:.3f}'.format(float(v)))
eval_results[key] = val
ap_summary = ' '.join([
'{}:{:.3f}'.format(k, float(v))
for k, v in lvis_results.items() if k.startswith('AP')
])
eval_results['bbox_mAP_copypaste'] = ap_summary
if tmp_dir is not None:
tmp_dir.cleanup()
return eval_results
def eval_cocofied_lvis_result(self, gt_file, result_file, metric='segm'):
def get_lvis_format_result(lvis_params, lvis_results):
template = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} catIds={:>3s}] = {:0.3f}"
result_list = []
for key, value in lvis_results.items():
max_dets = lvis_params.max_dets
if "AP" in key:
title = "Average Precision"
_type = "(AP)"
else:
title = "Average Recall"
_type = "(AR)"
if len(key) > 2 and key[2].isdigit():
iou_thr = (float(key[2:]) / 100)
iou = "{:0.2f}".format(iou_thr)
else:
iou = "{:0.2f}:{:0.2f}".format(
lvis_params.iou_thrs[0], lvis_params.iou_thrs[-1]
)
if len(key) > 2 and key[2] in ["r", "c", "f"]:
cat_group_name = key[2]
else:
cat_group_name = "all"
if len(key) > 2 and key[2] in ["s", "m", "l"]:
area_rng = key[2]
else:
area_rng = "all"
result_list.append(template.format(title, _type, iou, area_rng, max_dets, cat_group_name, value))
return result_list
print('load gt json')
lvis_gt = LVIS(gt_file)
cat_ids = lvis_gt.get_cat_ids()
print('load pred json')
lvis_dt = LVISResults(lvis_gt, result_file)
print('evaluating')
lvis_eval = LVISEval(lvis_gt, lvis_dt, metric)
lvis_eval.params.imgIds = lvis_gt.get_img_ids()
lvis_eval.evaluate()
lvis_eval.accumulate()
lvis_eval.summarize()
# Compute per-category AP
precisions = lvis_eval.eval['precision']
assert len(cat_ids) == precisions.shape[2]
results_per_category = []
for idx, catId in enumerate(cat_ids):
nm = lvis_gt.load_cats([catId])[0]
precision = precisions[:, :, idx, 0]
precision = precision[precision > -1]
if precision.size:
ap = np.mean(precision)
else:
ap = float('nan')
results_per_category.append(
(f'{nm["name"]}', f'{float(ap):0.3f}'))
num_columns = min(6, len(results_per_category) * 2)
results_flatten = list(
itertools.chain(*results_per_category))
headers = ['category', 'AP'] * (num_columns // 2)
results_2d = itertools.zip_longest(*[
results_flatten[i::num_columns]
for i in range(num_columns)
])
table_data = [headers]
table_data += [result for result in results_2d]
table = AsciiTable(table_data)
print_log('\n' + table.table)
format_summary_result_list = get_lvis_format_result(lvis_eval.params, lvis_eval.results)
format_summary_result = "\n".join(format_summary_result_list)
with open(f"cocofied_per-category-ap-{metric}.txt", 'w') as f:
f.write(table.table + "\n" + format_summary_result)
lvis_eval.print_results()
