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import sys
import os
from natsort import natsorted
import argparse
from collections import OrderedDict
import io
import contextlib
import itertools
import numpy as np
import mmcv
from mmdet.datasets.api_wrappers import COCO, COCOeval
sys.path.append('/home/caduser/KOTORI/vin-ssl/source')
os.chdir('/home/caduser/KOTORI/vin-ssl/source')
from base_config_track import get_config
from mmdet_tools import mmdet_test
def print_log(msg, logger):
pass
#print(msg)
def evaluate(dataset, results, metric='bbox', logger=None, jsonfile_prefix=None, classwise=False, proposal_nums=(100, 300, 1000), iou_thrs=None, metric_items=None):
"""Evaluation in COCO 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): 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], optional): IoU threshold used for
evaluating recalls/mAPs. If set to a list, the average of all
IoUs will also be computed. If not specified, [0.50, 0.55,
0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used.
Default: None.
metric_items (list[str] | str, optional): Metric items that will
be returned. If not specified, ``['AR@100', 'AR@300',
'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be
used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75',
'mAP_s', 'mAP_m', 'mAP_l']`` will be used when
``metric=='bbox' or metric=='segm'``.
Returns:
dict[str, float]: COCO style evaluation metric.
"""
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(f'metric {metric} is not supported')
if iou_thrs is None:
iou_thrs = np.linspace(
.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
if metric_items is not None:
if not isinstance(metric_items, list):
metric_items = [metric_items]
result_files, tmp_dir = dataset.format_results(results, jsonfile_prefix)
eval_results = OrderedDict()
cocoGt = dataset.coco
results_per_category = []
for metric in metrics:
msg = f'Evaluating {metric}...'
if logger is None:
msg = '\n' + msg
print_log(msg, logger=logger)
if metric == 'proposal_fast':
ar = dataset.fast_eval_recall(
results, proposal_nums, iou_thrs, logger='silent')
log_msg = []
for i, num in enumerate(proposal_nums):
eval_results[f'AR@{num}'] = ar[i]
log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}')
log_msg = ''.join(log_msg)
print_log(log_msg, logger=logger)
continue
iou_type = 'bbox' if metric == 'proposal' else metric
if metric not in result_files:
raise KeyError(f'{metric} is not in results')
try:
predictions = mmcv.load(result_files[metric])
if iou_type == 'segm':
# Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331 # noqa
# When evaluating mask AP, if the results contain bbox,
# cocoapi will use the box area instead of the mask area
# for calculating the instance area. Though the overall AP
# is not affected, this leads to different
# small/medium/large mask AP results.
for x in predictions:
x.pop('bbox')
warnings.simplefilter('once')
warnings.warn(
'The key "bbox" is deleted for more accurate mask AP '
'of small/medium/large instances since v2.12.0. This '
'does not change the overall mAP calculation.',
UserWarning)
cocoDt = cocoGt.loadRes(predictions)
except IndexError:
print_log(
'The testing results of the whole dataset is empty.',
logger=logger,
level=logging.ERROR)
break
cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
cocoEval.params.catIds = dataset.cat_ids
cocoEval.params.imgIds = dataset.img_ids
cocoEval.params.maxDets = list(proposal_nums)
cocoEval.params.iouThrs = iou_thrs
# mapping of cocoEval.stats
coco_metric_names = {
'mAP': 0,
'mAP_50': 1,
'mAP_75': 2,
'mAP_s': 3,
'mAP_m': 4,
'mAP_l': 5,
'AR@100': 6,
'AR@300': 7,
'AR@1000': 8,
'AR_s@1000': 9,
'AR_m@1000': 10,
'AR_l@1000': 11
}
if metric_items is not None:
for metric_item in metric_items:
if metric_item not in coco_metric_names:
raise KeyError(
f'metric item {metric_item} is not supported')
if metric == 'proposal':
cocoEval.params.useCats = 0
cocoEval.evaluate()
cocoEval.accumulate()
# Save coco summarize print information to logger
redirect_string = io.StringIO()
with contextlib.redirect_stdout(redirect_string):
cocoEval.summarize()
print_log('\n' + redirect_string.getvalue(), logger=logger)
if metric_items is None:
metric_items = [
'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000',
'AR_m@1000', 'AR_l@1000'
]
for item in metric_items:
val = float(
f'{cocoEval.stats[coco_metric_names[item]]:.3f}')
eval_results[item] = val
else:
cocoEval.evaluate()
cocoEval.accumulate()
# Save coco summarize print information to logger
redirect_string = io.StringIO()
with contextlib.redirect_stdout(redirect_string):
cocoEval.summarize()
print_log('\n' + redirect_string.getvalue(), logger=logger)
if classwise: # Compute per-category AP
# Compute per-category AP
# from https://github.com/facebookresearch/detectron2/
precisions = cocoEval.eval['precision']
# precision: (iou, recall, cls, area range, max dets)
assert len(dataset.cat_ids) == precisions.shape[2]
for idx, catId in enumerate(dataset.cat_ids):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
nm = dataset.coco.loadCats(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"]}', float(ap)))
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)
])
if metric_items is None:
metric_items = [
'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
]
for metric_item in metric_items:
key = f'{metric}_{metric_item}'
val = float(
f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}'
)
eval_results[key] = val
ap = cocoEval.stats[:6]
eval_results[f'{metric}_mAP_copypaste'] = (
f'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
f'{ap[4]:.3f} {ap[5]:.3f}')
if tmp_dir is not None:
tmp_dir.cleanup()
return eval_results, results_per_category
# define parse
def get_args():
parser = argparse.ArgumentParser(description='Test trained object detection model')
parser.add_argument(
'--experiment_name', '-exp-name', type=str, default='no-exp',help='providing folder store checkpoint models')
return parser.parse_args()
if __name__ == "__main__":
args = get_args()
experiment_name = args.experiment_name
print ("**********" * 3)
print ('Staring evaluation process')
checkpoints = os.listdir(os.path.join('../trained_weights', experiment_name))
checkpoints = natsorted(checkpoints)
checkpoints = [p for p in checkpoints if 'epoch_' in p]
# checkpoint = os.path.join('../trained_weights', experiment_name, checkpoints[-1])
selected_checkpoints = checkpoints[-1:] # change the number of models want to infer here.
dict_results = {}
valid_dict_results = {}
eval_on_valid = False
for checkpoint_name in selected_checkpoints:
print ('-----'*5)
print ('Processing for checkpoint', checkpoint_name)
checkpoint = os.path.join('../trained_weights', experiment_name, checkpoint_name)
results = {}
results_dir = 'results'
os.makedirs(results_dir, exist_ok=True)
results_avg = []
results_avg_ar = []
results_classwise = []
cfg = get_config()
if eval_on_valid:
cfg.data.test['img_prefix'] = './data/' # uncomment lines 267-268 for inference on validation set
cfg.data.test['ann_file'] = './data/valid_annotations.json'
args_result = argparse.Namespace(eval='bbox', out='results/' + experiment_name + '.pkl', checkpoint=None, work_dir=results_dir, fuse_conv_bn=None,
gpu_ids=None, format_only=None, show=None, show_dir=None, show_score_thr=0.3, gpu_collect=None,
tmpdir=None, cfg_options=None, options=None, launcher='none', eval_options=None, local_rank=0)
dataset, outputs = mmdet_test.get_outputs(cfg, checkpoint, args_result)
metrics, results_per_category = evaluate(dataset, outputs, metric='bbox', classwise=True) #, iou_thrs=[0.5])
metrics_ar, _ = evaluate(dataset, outputs, metric='proposal')
results_avg.append([experiment_name, metrics])
results_avg_ar.append([experiment_name, metrics_ar])
results_classwise.append([experiment_name, OrderedDict(results_per_category)])
print('--------------------------------')
valid_dict_results[checkpoint_name] = []
print('Average Precision')
print(list(results_avg[0][1].keys())[:-1])
valid_dict_results[checkpoint_name].append(list(results_avg[0][1].keys())[:-1]) # append output to valid_dict_results
for res in results_avg:
print([res[0], list(res[1].values())[:-1]])
valid_dict_results[checkpoint_name].append([res[0], list(res[1].values())[:-1]])
dict_results[checkpoint_name] = list(results_avg[0][1].values())[1]
print ("Results on testing set")
print (valid_dict_results)
print("**********" * 3)
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