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[Refactor] Refactor detection dataset metainfo to lowercase and updat…
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…e detection metric logics (#98)

* [Refactor] Refactor detection dataset metainfo to lowercase

* minor fix

* update

* support keep coco eval information and support load CLASSES to avoid BC

* update typehint and docstring

* add logger in cocodetection

* add RELATION_MATRIX to avoid bc-breaking

* Update mmeval/metrics/coco_detection.py

Co-authored-by: RangiLyu <[email protected]>

* fix comments

* empty log level info->warning

* default log level change to INFO

* fix comments

* Update mmeval/metrics/coco_detection.py

Co-authored-by: Qian Zhao <[email protected]>

* fix comments

* fix comments

* fix logic

* fix typo

* add comments for get_classes

* suppport logger

* update ut

* add requiorements

* update requirements

* using rich to print table

* Update mmeval/metrics/coco_detection.py

Co-authored-by: Qian Zhao <[email protected]>

* fix comments

* rename test coco detection ut

* add print results flag

* minor fix

* Update mmeval/metrics/coco_detection.py

Co-authored-by: Zaida Zhou <[email protected]>

* Update mmeval/metrics/oid_map.py

Co-authored-by: Zaida Zhou <[email protected]>

* Update mmeval/metrics/voc_map.py

Co-authored-by: Zaida Zhou <[email protected]>

---------

Co-authored-by: RangiLyu <[email protected]>
Co-authored-by: Qian Zhao <[email protected]>
Co-authored-by: Zaida Zhou <[email protected]>
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@BIGWangYuDong @RangiLyu
@C1rN09 @zhouzaida
4 people authored Mar 14, 2023
1 parent a9cf49b commit 976f399
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Showing 7 changed files with 259 additions and 96 deletions.
210 changes: 164 additions & 46 deletions mmeval/metrics/coco_detection.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,16 @@
# Copyright (c) OpenMMLab. All rights reserved.
import contextlib
import datetime
import io
import itertools
import numpy as np
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
from json import dump
from rich.console import Console
from rich.table import Table
from typing import Dict, List, Optional, Sequence, Union

from mmeval.core.base_metric import BaseMetric
Expand Down Expand Up @@ -38,7 +43,7 @@ class COCODetection(BaseMetric):
classwise (bool): Whether to return the computed results of each
class. Defaults to False.
proposal_nums (Sequence[int]): Numbers of proposals to be evaluated.
Defaults to (100, 300, 1000).
Defaults to (1, 10, 100).
metric_items (List[str], optional): Metric result names to be
recorded in the evaluation result. Defaults to None.
format_only (bool): Format the output results without perform
Expand All @@ -54,6 +59,10 @@ class COCODetection(BaseMetric):
ann_file. Defaults to True.
backend_args (dict, optional): Arguments to instantiate the
preifx of uri corresponding backend. Defaults to None.
print_results (bool): Whether to print the results. Defaults to True.
logger (Logger, optional): logger used to record messages. When set to
``None``, the default logger will be used.
Defaults to None.
**kwargs: Keyword parameters passed to :class:`BaseMetric`.
Examples:
Expand All @@ -66,7 +75,7 @@ class COCODetection(BaseMetric):
>>>
>>> num_classes = 4
>>> fake_dataset_metas = {
... 'CLASSES': tuple([str(i) for i in range(num_classes)])
... 'classes': tuple([str(i) for i in range(num_classes)])
... }
>>>
>>> coco_det_metric = COCODetection(
Expand Down Expand Up @@ -146,12 +155,13 @@ def __init__(self,
metric: Union[str, List[str]] = 'bbox',
iou_thrs: Union[float, Sequence[float], None] = None,
classwise: bool = False,
proposal_nums: Sequence[int] = (100, 300, 1000),
proposal_nums: Sequence[int] = (1, 10, 100),
metric_items: Optional[Sequence[str]] = None,
format_only: bool = False,
outfile_prefix: Optional[str] = None,
gt_mask_area: bool = True,
backend_args: Optional[dict] = None,
print_results: bool = True,
**kwargs) -> None:
if not HAS_COCOAPI:
raise RuntimeError('Failed to import `COCO` and `COCOeval` from '
Expand All @@ -165,8 +175,8 @@ def __init__(self,
for metric in self.metrics:
if metric not in allowed_metrics:
raise KeyError(
"metric should be one of 'bbox', 'segm', 'proposal', "
f"'proposal_fast', but got {metric}.")
"metric should be one of 'bbox', 'segm', and 'proposal', "
f'but got {metric}.')

# do class wise evaluation, default False
self.classwise = classwise
Expand All @@ -188,6 +198,7 @@ def __init__(self,

self.iou_thrs = iou_thrs
self.metric_items = metric_items
self.print_results = print_results
self.format_only = format_only
if self.format_only:
assert outfile_prefix is not None, 'outfile_prefix must be not'
Expand Down Expand Up @@ -323,9 +334,9 @@ def gt_to_coco_json(self, gt_dicts: Sequence[dict],
'not affect the overall AP, but leads to different '
'small/medium/large AP results.')

classes = self.classes
categories = [
dict(id=id, name=name) for id, name in enumerate(
self.dataset_meta['CLASSES']) # type:ignore
dict(id=id, name=name) for id, name in enumerate(classes)
]
image_infos: list = []
annotations: list = []
Expand Down Expand Up @@ -470,7 +481,7 @@ def __call__(self, *args, **kwargs) -> Dict:

return metric_result

def compute_metric(self, results: list) -> Dict[str, float]:
def compute_metric(self, results: list) -> dict:
"""Compute the COCO metrics.
Args:
Expand All @@ -479,8 +490,9 @@ def compute_metric(self, results: list) -> Dict[str, float]:
been synced across all ranks.
Returns:
dict: The computed metric. The keys are the names of
the metrics, and the values are corresponding results.
dict: The computed metric.
The keys are the names of the metrics, and the values are
corresponding results.
"""
tmp_dir = None
if self.outfile_prefix is None:
Expand All @@ -489,34 +501,36 @@ def compute_metric(self, results: list) -> Dict[str, float]:
else:
outfile_prefix = self.outfile_prefix

classes = self.classes
# split gt and prediction list
preds, gts = zip(*results)

if self._coco_api is None:
# use converted gt json file to initialize coco api
print('Converting ground truth to coco format...')
self.logger.info('Converting ground truth to coco format...')
coco_json_path = self.gt_to_coco_json(
gt_dicts=gts, outfile_prefix=outfile_prefix)
self._coco_api = COCO(coco_json_path)

# handle lazy init
if len(self.cat_ids) == 0:
self.cat_ids = self._coco_api.get_cat_ids(
cat_names=self.dataset_meta['CLASSES']) # type: ignore
cat_names=classes) # type: ignore
if len(self.img_ids) == 0:
self.img_ids = self._coco_api.get_img_ids()

# convert predictions to coco format and dump to json file
result_files = self.results2json(preds, outfile_prefix)

eval_results: OrderedDict = OrderedDict()
table_results: OrderedDict = OrderedDict()
if self.format_only:
print('results are saved in '
f'{osp.dirname(outfile_prefix)}')
self.logger.info(
f'Results are saved in {osp.dirname(outfile_prefix)}')
return eval_results

for metric in self.metrics:
print(f'Evaluating {metric}...')
self.logger.info(f'Evaluating {metric}...')

# evaluate proposal, bbox and segm
iou_type = 'bbox' if metric == 'proposal' else metric
Expand All @@ -536,7 +550,8 @@ def compute_metric(self, results: list) -> Dict[str, float]:
coco_dt = self._coco_api.loadRes(predictions)

except IndexError:
print('The testing results of the whole dataset is empty.')
self.logger.warning('The testing results of the '
'whole dataset is empty.')
break

coco_eval = COCOeval(self._coco_api, coco_dt, iou_type)
Expand All @@ -554,12 +569,12 @@ def compute_metric(self, results: list) -> Dict[str, float]:
'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
f'AR@{self.proposal_nums[0]}': 6,
f'AR@{self.proposal_nums[1]}': 7,
f'AR@{self.proposal_nums[2]}': 8,
f'AR_s@{self.proposal_nums[2]}': 9,
f'AR_m@{self.proposal_nums[2]}': 10,
f'AR_l@{self.proposal_nums[2]}': 11
}
metric_items = self.metric_items
if metric_items is not None:
Expand All @@ -572,24 +587,47 @@ def compute_metric(self, results: list) -> Dict[str, float]:
coco_eval.params.useCats = 0
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
redirect_string = io.StringIO()
with contextlib.redirect_stdout(redirect_string):
coco_eval.summarize()
self.logger.info('\n' + redirect_string.getvalue())
if metric_items is None:
metric_items = [
'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000',
'AR_m@1000', 'AR_l@1000'
f'AR@{self.proposal_nums[0]}',
f'AR@{self.proposal_nums[1]}',
f'AR@{self.proposal_nums[2]}',
f'AR_s@{self.proposal_nums[2]}',
f'AR_m@{self.proposal_nums[2]}',
f'AR_l@{self.proposal_nums[2]}'
]

results_list = []
for item in metric_items:
val = float(
f'{coco_eval.stats[coco_metric_names[item]]:.3f}')
results_list.append(f'{val * 100:.1f}')
val = float(coco_eval.stats[coco_metric_names[item]])
results_list.append(f'{round(val * 100, 2):0.2f}')
eval_results[item] = val
eval_results[f'{metric}_result'] = results_list
table_results[f'{metric}_result'] = results_list
else:
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
# Save coco summarize print information to logger
redirect_string = io.StringIO()
with contextlib.redirect_stdout(redirect_string):
coco_eval.summarize()
self.logger.info('\n' + redirect_string.getvalue())
if metric_items is None:
metric_items = [
'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
]

results_list = []
for metric_item in metric_items:
key = f'{metric}_{metric_item}'
val = coco_eval.stats[coco_metric_names[metric_item]]
results_list.append(f'{round(val * 100, 2):0.2f}')
eval_results[key] = float(val)
table_results[f'{metric}_result'] = results_list

if self.classwise: # Compute per-category AP
# Compute per-category AP
# from https://github.com/facebookresearch/detectron2/
Expand All @@ -609,28 +647,108 @@ def compute_metric(self, results: list) -> Dict[str, float]:
else:
ap = float('nan')
results_per_category.append(
(f'{nm["name"]}', f'{round(ap, 3)}'))
eval_results[f'{metric}_{nm["name"]}_precision'] = \
round(ap, 3)
(f'{nm["name"]}', f'{round(ap * 100, 2):0.2f}'))
eval_results[f'{metric}_{nm["name"]}_precision'] = ap

eval_results[f'{metric}_classwise_result'] = \
table_results[f'{metric}_classwise_result'] = \
results_per_category
if metric_items is None:
metric_items = [
'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
]

results_list = []
for metric_item in metric_items:
key = f'{metric}_{metric_item}'
val = coco_eval.stats[coco_metric_names[metric_item]]
results_list.append(f'{round(val, 3) * 100:.1f}')
eval_results[key] = float(f'{round(val, 3)}')
eval_results[f'{metric}_result'] = results_list
if tmp_dir is not None:
tmp_dir.cleanup()
# if the testing results of the whole dataset is empty,
# does not print tables.
if self.print_results and len(table_results) > 0:
self._print_results(table_results)
return eval_results

def _print_results(self, table_results: dict) -> None:
"""Print the evaluation results table.
Args:
table_results (dict): The computed metric.
"""
for metric in self.metrics:
result = table_results[f'{metric}_result']

if metric == 'proposal':
table_title = ' Recall Results (%)'
if self.metric_items is None:
assert len(result) == 6
headers = [
f'AR@{self.proposal_nums[0]}',
f'AR@{self.proposal_nums[1]}',
f'AR@{self.proposal_nums[2]}',
f'AR_s@{self.proposal_nums[2]}',
f'AR_m@{self.proposal_nums[2]}',
f'AR_l@{self.proposal_nums[2]}'
]
else:
assert len(result) == len(self.metric_items) # type: ignore # yapf: disable # noqa: E501
headers = self.metric_items # type: ignore
else:
table_title = f' {metric} Results (%)'
if self.metric_items is None:
assert len(result) == 6
headers = [
f'{metric}_mAP', f'{metric}_mAP_50',
f'{metric}_mAP_75', f'{metric}_mAP_s',
f'{metric}_mAP_m', f'{metric}_mAP_l'
]
else:
assert len(result) == len(self.metric_items)
headers = [
f'{metric}_{item}' for item in self.metric_items
]
table = Table(title=table_title)
console = Console()
for name in headers:
table.add_column(name, justify='left')
table.add_row(*result)
with console.capture() as capture:
console.print(table, end='')
self.logger.info('\n' + capture.get())

if self.classwise and metric != 'proposal':
self.logger.info(
f'Evaluating {metric} metric of each category...')
classwise_table_title = f' {metric} Classwise Results (%)'
classwise_result = table_results[f'{metric}_classwise_result']

num_columns = min(6, len(classwise_result) * 2)
results_flatten = list(itertools.chain(*classwise_result))
headers = ['category', f'{metric}_AP'] * (num_columns // 2)
results_2d = itertools.zip_longest(*[
results_flatten[i::num_columns] for i in range(num_columns)
])

table = Table(title=classwise_table_title)
console = Console()
for name in headers:
table.add_column(name, justify='left')
for _result in results_2d:
table.add_row(*_result)
with console.capture() as capture:
console.print(table, end='')
self.logger.info('\n' + capture.get())

@property
def classes(self) -> list:
"""Get classes from self.dataset_meta."""
if hasattr(self, '_classes'):
return self._classes # type: ignore

if self.dataset_meta and 'classes' in self.dataset_meta:
classes = self.dataset_meta['classes']
elif self.dataset_meta and 'CLASSES' in self.dataset_meta:
classes = self.dataset_meta['CLASSES']
warnings.warn(
'The `CLASSES` in `dataset_meta` is deprecated, '
'use `classes` instead!', DeprecationWarning)
else:
raise RuntimeError('Could not find `classes` in dataset_meta: '
f'{self.dataset_meta}')
self._classes = classes # type: ignore
return classes


# Keep the deprecated metric name as an alias.
# The deprecated Metric names will be removed in 1.0.0!
Expand Down
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