# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Class for evaluating object detections with COCO metrics."""
from __future__ import print_function
import json
import multiprocessing
import os
import tempfile
from collections import OrderedDict, defaultdict
import mmcv
import numpy as np
import six
import torch
from xtcocotools.coco import COCO
from xtcocotools.cocoeval import COCOeval
from easycv.core import standard_fields
from easycv.core.evaluation import coco_tools
from easycv.core.post_processing.nms import oks_nms, soft_oks_nms
from easycv.core.standard_fields import DetectionResultFields, InputDataFields
from easycv.framework.errors import KeyError, TypeError, ValueError
from easycv.utils.json_utils import MyEncoder
from .base_evaluator import Evaluator
from .builder import EVALUATORS
from .metric_registry import METRICS
try:
from panopticapi.evaluation import OFFSET, VOID, PQStat
from panopticapi.utils import rgb2id
except ImportError:
PQStat = None
rgb2id = None
VOID = 0
OFFSET = 256 * 256 * 256
[docs]@EVALUATORS.register_module
class CocoDetectionEvaluator(Evaluator):
"""Class to evaluate COCO detection metrics."""
[docs] def __init__(self,
classes,
include_metrics_per_category=False,
all_metrics_per_category=False,
coco_analyze=False,
dataset_name=None,
metric_names=['DetectionBoxes_Precision/mAP']):
"""Constructor.
Args:
classes: a list of class name
include_metrics_per_category: If True, include metrics for each category.
all_metrics_per_category: Whether to include all the summary metrics for
each category in per_category_ap. Be careful with setting it to true if
you have more than handful of ∏, because it will pollute your mldash.
coco_analyze: If True, will analyze the detection result using coco analysis.
dataset_name: If not None, dataset_name will be inserted to each metric name.
"""
super(CocoDetectionEvaluator, self).__init__(dataset_name,
metric_names)
# _image_ids is a dictionary that maps unique image ids to Booleans which
# indicate whether a corresponding detection has been added.
self._image_ids = {}
self._groundtruth_list = []
self._detection_boxes_list = []
self._annotation_id = 1
self._metrics = None
self._analyze_images = None
self._include_metrics_per_category = include_metrics_per_category
self._all_metrics_per_category = all_metrics_per_category
self._analyze = coco_analyze
self._categories = [None] * len(classes)
# categories: A list of dicts, each of which has the following keys -
# 'id': (required) an integer id uniquely identifying this category.
# 'name': (required) string representing category name e.g., 'cat', 'dog'.
for idx, name in enumerate(classes):
self._categories[idx] = {'id': idx, 'name': name}
self._category_id_set = set([cat['id'] for cat in self._categories])
# for json formatted evaluation
self.num_classes = len(classes)
self.class_names = classes
self.dataset_name = dataset_name
self.iou_thrs = np.linspace(
.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
self.proposal_nums = (100, 300, 1000)
self.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
}
self.metric_items = [
'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
]
self.metric = 'bbox'
self.iou_type = 'bbox'
[docs] def clear(self):
"""Clears the state to prepare for a fresh evaluation."""
self._image_ids.clear()
self._groundtruth_list = []
self._detection_boxes_list = []
[docs] def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
If the image has already been added, a warning is logged, and groundtruth is
ignored.
Args:
image_id: A unique string/integer identifier for the image.
groundtruth_dict: A dictionary containing
:InputDataFields.groundtruth_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` groundtruth boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
:InputDataFields.groundtruth_classes: integer numpy array of shape
[num_boxes] containing 1-indexed groundtruth classes for the boxes.
InputDataFields.groundtruth_is_crowd (optional): integer numpy array of
shape [num_boxes] containing iscrowd flag for groundtruth boxes.
"""
if image_id in self._image_ids:
print(
'Ignoring ground truth with image id %s since it was '
' previously added', image_id)
return
groundtruth_is_crowd = groundtruth_dict.get(
standard_fields.InputDataFields.groundtruth_is_crowd)
# Drop groundtruth_is_crowd if empty tensor.
if groundtruth_is_crowd is not None and not groundtruth_is_crowd.shape[
0]:
groundtruth_is_crowd = None
self._groundtruth_list.extend(
coco_tools.ExportSingleImageGroundtruthToCoco(
image_id=image_id,
next_annotation_id=self._annotation_id,
category_id_set=self._category_id_set,
groundtruth_boxes=groundtruth_dict[
standard_fields.InputDataFields.groundtruth_boxes],
groundtruth_classes=groundtruth_dict[
standard_fields.InputDataFields.groundtruth_classes],
groundtruth_is_crowd=groundtruth_is_crowd))
self._annotation_id += groundtruth_dict[
standard_fields.InputDataFields.groundtruth_boxes].shape[0]
# Boolean to indicate whether a detection has been added for this image.
self._image_ids[image_id] = False
[docs] def add_single_detected_image_info(self, image_id, detections_dict):
"""Adds detections for a single image to be used for evaluation.
If a detection has already been added for this image id, a warning is
logged, and the detection is skipped.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary containing
:DetectionResultFields.detection_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` detection boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
:DetectionResultFields.detection_scores: float32 numpy array of shape
[num_boxes] containing detection scores for the boxes.
:DetectionResultFields.detection_classes: integer numpy array of shape
[num_boxes] containing 1-indexed detection classes for the boxes.
Raises:
ValueError: If groundtruth for the image_id is not available.
"""
if image_id not in self._image_ids:
raise ValueError(
'Missing groundtruth for image id: {}'.format(image_id))
if self._image_ids[image_id]:
print(
'Ignoring detection with image id %s since it was '
'previously added', image_id)
return
self._detection_boxes_list.extend(
coco_tools.ExportSingleImageDetectionBoxesToCoco(
image_id=image_id,
category_id_set=self._category_id_set,
detection_boxes=detections_dict[
standard_fields.DetectionResultFields.detection_boxes],
detection_scores=detections_dict[
standard_fields.DetectionResultFields.detection_scores],
detection_classes=detections_dict[
standard_fields.DetectionResultFields.detection_classes]))
self._image_ids[image_id] = True
def _evaluate(self, analyze=False):
"""Evaluates the detection boxes and returns a dictionary of coco metrics.
Args:
analyze: if set True, will call coco analyze to analyze false positive,
return result images for each class, this process is very slow.
Returns:
A dictionary holding
1. summary_metrics:
'DetectionBoxes_Precision/mAP': mean average precision over classes
averaged over IOU thresholds ranging from .5 to .95 with .05
increments.
'DetectionBoxes_Precision/mAP@.50IOU': mean average precision at 50% IOU
'DetectionBoxes_Precision/mAP@.75IOU': mean average precision at 75% IOU
'DetectionBoxes_Precision/mAP (small)': mean average precision for small
objects (area < 32^2 pixels).
'DetectionBoxes_Precision/mAP (medium)': mean average precision for
medium sized objects (32^2 pixels < area < 96^2 pixels).
'DetectionBoxes_Precision/mAP (large)': mean average precision for large
objects (96^2 pixels < area < 10000^2 pixels).
'DetectionBoxes_Recall/AR@1': average recall with 1 detection.
'DetectionBoxes_Recall/AR@10': average recall with 10 detections.
'DetectionBoxes_Recall/AR@100': average recall with 100 detections.
'DetectionBoxes_Recall/AR@100 (small)': average recall for small objects
with 100.
'DetectionBoxes_Recall/AR@100 (medium)': average recall for medium objects
with 100.
'DetectionBoxes_Recall/AR@100 (large)': average recall for large objects
with 100 detections.
2. per_category_ap: if include_metrics_per_category is True, category
specific results with keys of the form:
'Precision mAP ByCategory/category' (without the supercategory part if
no supercategories exist). For backward compatibility
'PerformanceByCategory' is included in the output regardless of
all_metrics_per_category.
"""
groundtruth_dict = {
'annotations': self._groundtruth_list,
'images': [{
'id': image_id
} for image_id in self._image_ids],
'categories': self._categories
}
coco_wrapped_groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
coco_wrapped_detections = coco_wrapped_groundtruth.LoadAnnotations(
self._detection_boxes_list)
box_evaluator = coco_tools.COCOEvalWrapper(
coco_wrapped_groundtruth,
coco_wrapped_detections,
agnostic_mode=False)
box_metrics, box_per_category_ap = box_evaluator.ComputeMetrics(
include_metrics_per_category=self._include_metrics_per_category,
all_metrics_per_category=self._all_metrics_per_category)
box_metrics.update(box_per_category_ap)
box_metrics = {
'DetectionBoxes_' + key: value
for key, value in iter(box_metrics.items())
}
if self._analyze or analyze:
images = box_evaluator.Analyze()
return box_metrics, images
else:
return box_metrics
def _evaluate_impl(self, prediction_dict, groundtruth_dict):
'''
Args:
prediction_dict: A dict of k-v pair, each v is a list of
tensor or numpy array for detection result. A dictionary containing
:groundtruth_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` groundtruth boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
:groundtruth_classes: integer numpy array of shape
[num_boxes] containing 1-indexed groundtruth classes for the boxes.
InputDataFields.groundtruth_is_crowd (optional): integer numpy array of
shape [num_boxes] containing iscrowd flag for groundtruth boxes.
:img_metas: List of length number of test images,
dict of image meta info, containing filename, ori_img_shape, and so on.
groundtruth_dict: A dict of k-v pair, each v is a list of
tensor or numpy array for groundtruth info. A dictionary containing
:DetectionResultFields.detection_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` detection boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
:DetectionResultFields.detection_scores: float32 numpy array of shape
[num_boxes] containing detection scores for the boxes.
:DetectionResultFields.detection_classes: integer numpy array of shape
[num_boxes] containing 1-indexed detection classes for the boxes.
:groundtruth_is_crowd: integer numpy array of
shape [num_boxes] containing iscrowd flag for groundtruth boxes.
Return:
dict, each key is metric_name, value is metric value
'''
num_images_det = len(
prediction_dict[DetectionResultFields.detection_boxes])
num_images_gt = len(
groundtruth_dict[InputDataFields.groundtruth_boxes])
assert num_images_det == num_images_gt, 'detection and groundtruth number is not the same'
groundtruth_is_crowd_list = groundtruth_dict.get(
'groundtruth_is_crowd', None)
for idx, (detection_boxes, detection_classes, detection_scores, img_metas, gt_boxes, gt_classes) in \
enumerate(zip(prediction_dict['detection_boxes'], prediction_dict['detection_classes'],
prediction_dict['detection_scores'], prediction_dict['img_metas'],
groundtruth_dict['groundtruth_boxes'], groundtruth_dict['groundtruth_classes'])):
height, width = img_metas[
'ori_img_shape'][:2] # height, width, channel
image_id = img_metas['filename']
# tensor caculated on other devices is not on device:0, so transfer them
if isinstance(detection_boxes, torch.Tensor):
detection_boxes = detection_boxes.cpu().numpy()
detection_scores = detection_scores.cpu().numpy()
detection_classes = detection_classes.cpu().numpy().astype(
np.int32)
if groundtruth_is_crowd_list is None:
groundtruth_is_crowd = None
else:
groundtruth_is_crowd = groundtruth_is_crowd_list[idx]
groundtruth_dict = {
'groundtruth_boxes': gt_boxes,
'groundtruth_classes': gt_classes,
'groundtruth_is_crowd': groundtruth_is_crowd,
}
self.add_single_ground_truth_image_info(image_id, groundtruth_dict)
if detection_classes is None or detection_scores is None:
detection_classes = np.array([-1])
detection_scores = np.array([0.0])
detection_boxes = np.array([[0.0, 0.0, 0.0, 0.0]])
# add detection info
detection_dict = {
'detection_boxes': detection_boxes,
'detection_scores': detection_scores,
'detection_classes': detection_classes,
}
self.add_single_detected_image_info(image_id, detection_dict)
eval_dict = self._evaluate()
self.clear()
return eval_dict
def _check_mask_type_and_value(array_name, masks):
"""Checks whether mask dtype is uint8 and the values are either 0 or 1."""
if masks.dtype != np.uint8:
raise TypeError('{} must be of type np.uint8. Found {}.'.format(
array_name, masks.dtype))
if np.any(np.logical_and(masks != 0, masks != 1)):
raise ValueError(
'{} elements can only be either 0 or 1.'.format(array_name))
[docs]@EVALUATORS.register_module
class CocoMaskEvaluator(Evaluator):
"""Class to evaluate COCO detection metrics."""
[docs] def __init__(self,
classes,
include_metrics_per_category=False,
dataset_name=None,
metric_names=['DetectionMasks_Precision/mAP']):
"""Constructor.
Args:
categories: A list of dicts, each of which has the following keys
:id: (required) an integer id uniquely identifying this category.
:name: (required) string representing category name e.g., 'cat', 'dog'.
include_metrics_per_category: If True, include metrics for each category.
"""
super(CocoMaskEvaluator, self).__init__(dataset_name, metric_names)
self._image_id_to_mask_shape_map = {}
self._image_ids_with_detections = set([])
self._groundtruth_list = []
self._detection_masks_list = []
self._annotation_id = 1
self._include_metrics_per_category = include_metrics_per_category
self._categories = [None] * len(classes)
# categories: A list of dicts, each of which has the following keys -
# 'id': (required) an integer id uniquely identifying this category.
# 'name': (required) string representing category name e.g., 'cat', 'dog'.
for idx, name in enumerate(classes):
self._categories[idx] = {'id': idx, 'name': name}
self._category_id_set = set([cat['id'] for cat in self._categories])
[docs] def clear(self):
"""Clears the state to prepare for a fresh evaluation."""
self._image_id_to_mask_shape_map.clear()
self._image_ids_with_detections.clear()
self._groundtruth_list = []
self._detection_masks_list = []
[docs] def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
"""Adds groundtruth for a single image to be used for evaluation.
If the image has already been added, a warning is logged, and groundtruth is
ignored.
Args:
image_id: A unique string/integer identifier for the image.
groundtruth_dict: A dictionary containing
:InputDataFields.groundtruth_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` groundtruth boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
:InputDataFields.groundtruth_classes: integer numpy array of shape
[num_boxes] containing 1-indexed groundtruth classes for the boxes.
:InputDataFields.groundtruth_instance_masks: uint8 numpy array of shape
[num_boxes, image_height, image_width] containing groundtruth masks
corresponding to the boxes. The elements of the array must be in
{0, 1}.
"""
if image_id in self._image_id_to_mask_shape_map:
print(
'Ignoring ground truth with image id %s since it was '
'previously added', image_id)
return
groundtruth_instance_masks = groundtruth_dict[
standard_fields.InputDataFields.groundtruth_instance_masks]
_check_mask_type_and_value(
standard_fields.InputDataFields.groundtruth_instance_masks,
groundtruth_instance_masks)
self._groundtruth_list.extend(
coco_tools.ExportSingleImageGroundtruthToCoco(
image_id=image_id,
next_annotation_id=self._annotation_id,
category_id_set=self._category_id_set,
groundtruth_boxes=groundtruth_dict[
standard_fields.InputDataFields.groundtruth_boxes],
groundtruth_classes=groundtruth_dict[
standard_fields.InputDataFields.groundtruth_classes],
groundtruth_masks=groundtruth_instance_masks,
groundtruth_is_crowd=groundtruth_dict.get(
standard_fields.InputDataFields.groundtruth_is_crowd,
None)))
self._annotation_id += groundtruth_dict[
standard_fields.InputDataFields.groundtruth_boxes].shape[0]
self._image_id_to_mask_shape_map[image_id] = groundtruth_dict[
standard_fields.InputDataFields.groundtruth_instance_masks].shape
[docs] def add_single_detected_image_info(self, image_id, detections_dict):
"""Adds detections for a single image to be used for evaluation.
If a detection has already been added for this image id, a warning is
logged, and the detection is skipped.
Args:
image_id: A unique string/integer identifier for the image.
detections_dict: A dictionary containing -
DetectionResultFields.detection_scores: float32 numpy array of shape
[num_boxes] containing detection scores for the boxes.
DetectionResultFields.detection_classes: integer numpy array of shape
[num_boxes] containing 1-indexed detection classes for the boxes.
DetectionResultFields.detection_masks: optional uint8 numpy array of
shape [num_boxes, image_height, image_width] containing instance
masks corresponding to the boxes. The elements of the array must be
in {0, 1}.
Raises:
ValueError: If groundtruth for the image_id is not available or if
spatial shapes of groundtruth_instance_masks and detection_masks are
incompatible.
"""
if image_id not in self._image_id_to_mask_shape_map:
raise ValueError(
'Missing groundtruth for image id: {}'.format(image_id))
if image_id in self._image_ids_with_detections:
print(
'Ignoring detection with image id %s since it was '
'previously added', image_id)
return
groundtruth_masks_shape = self._image_id_to_mask_shape_map[image_id]
detection_masks = detections_dict[
standard_fields.DetectionResultFields.detection_masks]
if (len(detection_masks) and groundtruth_masks_shape[0] != 0
and groundtruth_masks_shape[1:] != detection_masks.shape[1:]):
raise ValueError(
'Spatial shape of groundtruth masks and detection masks '
'are incompatible: {} vs {}'.format(groundtruth_masks_shape,
detection_masks.shape))
_check_mask_type_and_value(
standard_fields.DetectionResultFields.detection_masks,
detection_masks)
self._detection_masks_list.extend(
coco_tools.ExportSingleImageDetectionMasksToCoco(
image_id=image_id,
category_id_set=self._category_id_set,
detection_masks=detection_masks,
detection_scores=detections_dict[
standard_fields.DetectionResultFields.detection_scores],
detection_classes=detections_dict[
standard_fields.DetectionResultFields.detection_classes]))
self._image_ids_with_detections.update([image_id])
def _evaluate(self):
"""Evaluates the detection masks and returns a dictionary of coco metrics.
Returns:
A dictionary holding -
1. summary_metrics:
'DetectionMasks_Precision/mAP': mean average precision over classes
averaged over IOU thresholds ranging from .5 to .95 with .05 increments.
'DetectionMasks_Precision/mAP@.50IOU': mean average precision at 50% IOU.
'DetectionMasks_Precision/mAP@.75IOU': mean average precision at 75% IOU.
'DetectionMasks_Precision/mAP (small)': mean average precision for small
objects (area < 32^2 pixels).
'DetectionMasks_Precision/mAP (medium)': mean average precision for medium
sized objects (32^2 pixels < area < 96^2 pixels).
'DetectionMasks_Precision/mAP (large)': mean average precision for large
objects (96^2 pixels < area < 10000^2 pixels).
'DetectionMasks_Recall/AR@1': average recall with 1 detection.
'DetectionMasks_Recall/AR@10': average recall with 10 detections.
'DetectionMasks_Recall/AR@100': average recall with 100 detections.
'DetectionMasks_Recall/AR@100 (small)': average recall for small objects
with 100 detections.
'DetectionMasks_Recall/AR@100 (medium)': average recall for medium objects
with 100 detections.
'DetectionMasks_Recall/AR@100 (large)': average recall for large objects
with 100 detections.
2. per_category_ap: if include_metrics_per_category is True, category
specific results with keys of the form:
'Precision mAP ByCategory/category' (without the supercategory part if
no supercategories exist). For backward compatibility
'PerformanceByCategory' is included in the output regardless of
all_metrics_per_category.
"""
groundtruth_dict = {
'annotations':
self._groundtruth_list,
'images': [{
'id': image_id,
'height': shape[1],
'width': shape[2]
} for image_id, shape in six.iteritems(
self._image_id_to_mask_shape_map)],
'categories':
self._categories
}
coco_wrapped_groundtruth = coco_tools.COCOWrapper(
groundtruth_dict, detection_type='segmentation')
coco_wrapped_detection_masks = coco_wrapped_groundtruth.LoadAnnotations(
self._detection_masks_list)
mask_evaluator = coco_tools.COCOEvalWrapper(
coco_wrapped_groundtruth,
coco_wrapped_detection_masks,
agnostic_mode=False,
iou_type='segm')
mask_metrics, mask_per_category_ap = mask_evaluator.ComputeMetrics(
include_metrics_per_category=self._include_metrics_per_category)
mask_metrics.update(mask_per_category_ap)
mask_metrics = {
'DetectionMasks_' + key: value
for key, value in six.iteritems(mask_metrics)
}
return mask_metrics
def _evaluate_impl(self, prediction_dict, groundtruth_dict):
"""Evaluate with prediction and groundtruth dict
Args:
detections_dict: A dictionary containing -
:DetectionResultFields.detection_scores: float32 numpy array of shape
[num_boxes] containing detection scores for the boxes.
:DetectionResultFields.detection_classes: integer numpy array of shape
[num_boxes] containing 1-indexed detection classes for the boxes.
:DetectionResultFields.detection_masks: mask rle code or optional uint8
numpy array of shape [num_boxes, image_height, image_width] containing
instance masks corresponding to the boxes. The elements of the array
must be in {0, 1}.
:img_metas: List of length number of test images,
dict of image meta info, containing filename, ori_img_shape, and so on.
groundtruth_dict: A dictionary containing
:InputDataFields.groundtruth_boxes: float32 numpy array of shape
[num_boxes, 4] containing `num_boxes` groundtruth boxes of the format
[ymin, xmin, ymax, xmax] in absolute image coordinates.
:InputDataFields.groundtruth_classes: integer numpy array of shape
[num_boxes] containing 1-indexed groundtruth classes for the boxes.
:InputDataFields.groundtruth_instance_masks: mask rle code or uint8
numpy array of shape [num_boxes, image_height, image_width] containing
groundtruth masks corresponding to the boxes. The elements of the array
must be in {0, 1}.
:groundtruth_is_crowd: integer numpy array of
shape [num_boxes] containing iscrowd flag for groundtruth boxes.
"""
num_images_det = len(
prediction_dict[DetectionResultFields.detection_boxes])
num_images_gt = len(
groundtruth_dict[InputDataFields.groundtruth_boxes])
assert num_images_det == num_images_gt, 'detection and groundtruth number is not the same'
groundtruth_is_crowd_list = groundtruth_dict.get(
'groundtruth_is_crowd', None)
for idx, (detection_masks, detection_classes, detection_scores, img_metas, gt_boxes, gt_masks, gt_classes) in \
enumerate(zip(prediction_dict['detection_masks'], prediction_dict['detection_classes'],
prediction_dict['detection_scores'], prediction_dict['img_metas'],
groundtruth_dict['groundtruth_boxes'], groundtruth_dict['groundtruth_instance_masks'],
groundtruth_dict['groundtruth_classes'])):
height, width = img_metas[
'ori_img_shape'][:2] # height, width, channel
image_id = img_metas['filename']
# tensor caculated on other devices is not on device:0, so transfer them
if isinstance(detection_masks, torch.Tensor):
detection_masks = detection_masks.cpu().numpy()
detection_scores = detection_scores.cpu().numpy()
detection_classes = detection_classes.cpu().numpy().astype(
np.int32)
if groundtruth_is_crowd_list is None:
groundtruth_is_crowd = None
else:
groundtruth_is_crowd = groundtruth_is_crowd_list[idx]
if len(gt_masks) == 0:
gt_masks = np.array([], dtype=np.uint8).reshape(
(0, height, width))
else:
gt_masks = np.array([
self._ann_to_mask(mask, height, width) for mask in gt_masks
],
dtype=np.uint8)
groundtruth_dict = {
'groundtruth_boxes': gt_boxes,
'groundtruth_instance_masks': gt_masks,
'groundtruth_classes': gt_classes,
'groundtruth_is_crowd': groundtruth_is_crowd,
}
self.add_single_ground_truth_image_info(image_id, groundtruth_dict)
detection_masks = np.array([
self._ann_to_mask(mask, height, width)
for mask in detection_masks
],
dtype=np.uint8)
# add detection info
detection_dict = {
'detection_masks': detection_masks,
'detection_scores': detection_scores,
'detection_classes': detection_classes,
}
self.add_single_detected_image_info(image_id, detection_dict)
eval_dict = self._evaluate()
self.clear()
return eval_dict
def _ann_to_mask(self, segmentation, height, width):
from xtcocotools import mask as maskUtils
segm = segmentation
h = height
w = width
if type(segm) == list:
# polygon -- a single object might consist of multiple parts
# we merge all parts into one mask rle code
rles = maskUtils.frPyObjects(segm, h, w)
rle = maskUtils.merge(rles)
elif type(segm['counts']) == list:
# uncompressed RLE
rle = maskUtils.frPyObjects(segm, h, w)
else:
# rle
rle = segm
m = maskUtils.decode(rle)
return m
[docs]@EVALUATORS.register_module
class CoCoPoseTopDownEvaluator(Evaluator):
"""Class to evaluate COCO keypoint topdown metrics.
"""
[docs] def __init__(self, dataset_name=None, metric_names=['AP'], **kwargs):
super().__init__(dataset_name, metric_names)
self.vis_thr = kwargs.get('vis_thr', 0.2)
self.oks_thr = kwargs.get('oks_thr', 0.9)
self.use_nms = kwargs.get('use_nms', True)
self.soft_nms = kwargs.get('soft_nms', False)
metric = self.metric_names
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['AP']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
def _evaluate_impl(self, prediction_dict, groundtruth_dict, **kwargs):
"""
Args:
prediction_dict
:preds (np.ndarray[N,K,3]): The first two dimensions are
coordinates, score is the third dimension of the array.
:boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
, scale[1],area, score]
:image_ids (list[int]): For example, [1, 2, ...]
:heatmap (np.ndarray[N, K, H, W]): model output heatmap
:bbox_id (list(int)): bbox_id
groundtruth_dict (only support coco style)
:images (list[dict]): dict keywords: 'file_name', 'height', 'width', 'id' and others
:annotaions (list[dict]): dict keywords: 'num_keypoints', 'keypoints', 'image_id' and others
:categories (list[dict]): list of category info
"""
num_joints = kwargs['num_joints']
class2id = kwargs['class2id']
sigmas = kwargs.get('sigmas', None)
image_ids = prediction_dict['image_ids']
preds = prediction_dict['preds']
boxes = prediction_dict['boxes']
bbox_ids = prediction_dict['bbox_ids']
kpts = defaultdict(list)
for i, image_id in enumerate(image_ids):
kpts[image_id].append({
'keypoints': preds[i],
'center': boxes[i][0:2],
'scale': boxes[i][2:4],
'area': boxes[i][4],
'score': boxes[i][5],
'image_id': image_id,
'bbox_id': bbox_ids[i]
})
kpts = self._sort_and_unique_bboxes(kpts)
# rescoring and oks nms
valid_kpts = []
for image_id in kpts.keys():
img_kpts = kpts[image_id]
for n_p in img_kpts:
box_score = n_p['score']
kpt_score = 0
valid_num = 0
for n_jt in range(0, num_joints):
t_s = n_p['keypoints'][n_jt][2]
if t_s > self.vis_thr:
kpt_score = kpt_score + t_s
valid_num = valid_num + 1
if valid_num != 0:
kpt_score = kpt_score / valid_num
# rescoring
n_p['score'] = kpt_score * box_score
if self.use_nms:
nms = soft_oks_nms if self.soft_nms else oks_nms
keep = nms(img_kpts, self.oks_thr, sigmas=sigmas)
valid_kpts.append([img_kpts[_keep] for _keep in keep])
else:
valid_kpts.append(img_kpts)
data_pack = [{
'cat_id': cat_id,
'ann_type': 'keypoints',
'keypoints': valid_kpts
} for cls, cat_id in class2id.items() if not cls == '__background__']
results = self._coco_keypoint_results_one_category_kernel(
data_pack[0], num_joints)
info_str = self._do_python_keypoint_eval(results, groundtruth_dict,
sigmas)
name_value = OrderedDict(info_str)
return name_value
def _sort_and_unique_bboxes(self, kpts, key='bbox_id'):
"""sort kpts and remove the repeated ones."""
for img_id, persons in kpts.items():
num = len(persons)
kpts[img_id] = sorted(kpts[img_id], key=lambda x: x[key])
for i in range(num - 1, 0, -1):
if kpts[img_id][i][key] == kpts[img_id][i - 1][key]:
del kpts[img_id][i]
return kpts
def _coco_keypoint_results_one_category_kernel(self, data_pack,
num_joints):
"""Get coco keypoint results."""
cat_id = data_pack['cat_id']
keypoints = data_pack['keypoints']
cat_results = []
for img_kpts in keypoints:
if len(img_kpts) == 0:
continue
_key_points = np.array(
[img_kpt['keypoints'] for img_kpt in img_kpts])
key_points = _key_points.reshape(-1, num_joints * 3)
result = [{
'image_id': img_kpt['image_id'],
'category_id': cat_id,
'keypoints': key_point.tolist(),
'score': float(img_kpt['score']),
'center': img_kpt['center'].tolist(),
'scale': img_kpt['scale'].tolist()
} for img_kpt, key_point in zip(img_kpts, key_points)]
cat_results.extend(result)
return cat_results
def _do_python_keypoint_eval(self, results, groundtruth, sigmas=None):
"""Keypoint evaluation using COCOAPI."""
with tempfile.TemporaryDirectory() as tmp_dir:
groundtruth_file = os.path.join(tmp_dir,
'groundtruth_keypoints.json')
with open(groundtruth_file, 'w') as f:
json.dump(groundtruth, f, sort_keys=True, indent=4)
coco = COCO(groundtruth_file)
res_file = os.path.join(tmp_dir, 'result_keypoints.json')
with open(res_file, 'w') as f:
json.dump(results, f, sort_keys=True, indent=4, cls=MyEncoder)
coco_det = coco.loadRes(res_file)
coco_eval = COCOeval(coco, coco_det, 'keypoints', sigmas=sigmas)
coco_eval.params.useSegm = None
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
stats_names = [
'AP', 'AP .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5',
'AR .75', 'AR (M)', 'AR (L)'
]
info_str = list(zip(stats_names, coco_eval.stats))
return info_str
[docs]@EVALUATORS.register_module
class CocoPanopticEvaluator(Evaluator):
"""Class to evaluate COCO panoptic metrics.
"""
[docs] def __init__(self,
dataset_name=None,
metric_names=['PQ'],
classes=None,
file_client_args=dict(backend='disk'),
**kwargs):
super().__init__(dataset_name, metric_names)
self.CLASSES = classes
self.file_client = mmcv.FileClient(**file_client_args)
[docs] def evaluate(self,
gt_json,
gt_folder,
pred_json,
pred_folder,
categories,
nproc=32,
classwise=False,
**kwargs):
# match the gt_anns and pred_anns in the same image
matched_annotations_list = []
for gt_ann in gt_json:
img_id = gt_ann['image_id']
if img_id not in pred_json.keys():
continue
# raise Exception('no prediction for the image'
# ' with id: {}'.format(img_id))
matched_annotations_list.append((gt_ann, pred_json[img_id]))
pq_stat = pq_compute_multi_core(
matched_annotations_list,
gt_folder,
pred_folder,
categories,
self.file_client,
nproc=nproc)
metrics = [('All', None), ('Things', True), ('Stuff', False)]
pq_results = {}
for name, isthing in metrics:
pq_results[name], classwise_results = pq_stat.pq_average(
categories, isthing=isthing)
if name == 'All':
pq_results['classwise'] = classwise_results
classwise_results = None
if classwise:
classwise_results = {
k: v
for k, v in zip(self.CLASSES, pq_results['classwise'].values())
}
results = self.parse_pq_results(pq_results)
return results
[docs] def parse_pq_results(self, pq_results):
"""Parse the Panoptic Quality results."""
result = dict()
result['PQ'] = 100 * pq_results['All']['pq']
result['SQ'] = 100 * pq_results['All']['sq']
result['RQ'] = 100 * pq_results['All']['rq']
result['PQ_th'] = 100 * pq_results['Things']['pq']
result['SQ_th'] = 100 * pq_results['Things']['sq']
result['RQ_th'] = 100 * pq_results['Things']['rq']
result['PQ_st'] = 100 * pq_results['Stuff']['pq']
result['SQ_st'] = 100 * pq_results['Stuff']['sq']
result['RQ_st'] = 100 * pq_results['Stuff']['rq']
return result
def _evaluate_impl(self, predictions, labels, **kwargs):
pass
[docs]def pq_compute_single_core(proc_id,
annotation_set,
gt_folder,
pred_folder,
categories,
file_client=None,
print_log=False):
"""The single core function to evaluate the metric of Panoptic
Segmentation.
Same as the function with the same name in `panopticapi`. Only the function
to load the images is changed to use the file client.
Args:
proc_id (int): The id of the mini process.
gt_folder (str): The path of the ground truth images.
pred_folder (str): The path of the prediction images.
categories (str): The categories of the dataset.
file_client (object): The file client of the dataset. If None,
the backend will be set to `disk`.
print_log (bool): Whether to print the log. Defaults to False.
"""
if PQStat is None:
raise RuntimeError(
'panopticapi is not installed, please install it by: '
'pip install git+https://github.com/cocodataset/'
'panopticapi.git.')
if file_client is None:
file_client_args = dict(backend='disk')
file_client = mmcv.FileClient(**file_client_args)
pq_stat = PQStat()
idx = 0
for gt_ann, pred_ann in annotation_set:
if print_log and idx % 100 == 0:
print('Core: {}, {} from {} images processed'.format(
proc_id, idx, len(annotation_set)))
idx += 1
# The gt images can be on the local disk or `ceph`, so we use
# file_client here.
img_bytes = file_client.get(
os.path.join(gt_folder, gt_ann['file_name']))
pan_gt = mmcv.imfrombytes(img_bytes, flag='color', channel_order='rgb')
pan_gt = rgb2id(pan_gt)
# The predictions can only be on the local dist now.
pan_pred = mmcv.imread(
os.path.join(pred_folder, pred_ann['file_name']),
flag='color',
channel_order='rgb')
pan_pred = rgb2id(pan_pred)
gt_segms = {el['id']: el for el in gt_ann['segments_info']}
pred_segms = {el['id']: el for el in pred_ann['segments_info']}
# predicted segments area calculation + prediction sanity checks
pred_labels_set = set(el['id'] for el in pred_ann['segments_info'])
labels, labels_cnt = np.unique(pan_pred, return_counts=True)
for label, label_cnt in zip(labels, labels_cnt):
if label not in pred_segms:
if label == VOID:
continue
raise KeyError(
'In the image with ID {} segment with ID {} is '
'presented in PNG and not presented in JSON.'.format(
gt_ann['image_id'], label))
pred_segms[label]['area'] = label_cnt
pred_labels_set.remove(label)
if pred_segms[label]['category_id'] not in categories:
raise KeyError(
'In the image with ID {} segment with ID {} has '
'unknown category_id {}.'.format(
gt_ann['image_id'], label,
pred_segms[label]['category_id']))
if len(pred_labels_set) != 0:
raise KeyError(
'In the image with ID {} the following segment IDs {} '
'are presented in JSON and not presented in PNG.'.format(
gt_ann['image_id'], list(pred_labels_set)))
# confusion matrix calculation
pan_gt_pred = pan_gt.astype(np.uint64) * OFFSET + pan_pred.astype(
np.uint64)
gt_pred_map = {}
labels, labels_cnt = np.unique(pan_gt_pred, return_counts=True)
for label, intersection in zip(labels, labels_cnt):
gt_id = label // OFFSET
pred_id = label % OFFSET
gt_pred_map[(gt_id, pred_id)] = intersection
# count all matched pairs
gt_matched = set()
pred_matched = set()
for label_tuple, intersection in gt_pred_map.items():
gt_label, pred_label = label_tuple
if gt_label not in gt_segms:
continue
if pred_label not in pred_segms:
continue
if gt_segms[gt_label]['iscrowd'] == 1:
continue
if gt_segms[gt_label]['category_id'] != pred_segms[pred_label][
'category_id']:
continue
union = pred_segms[pred_label]['area'] + gt_segms[gt_label][
'area'] - intersection - gt_pred_map.get((VOID, pred_label), 0)
iou = intersection / union
if iou > 0.5:
pq_stat[gt_segms[gt_label]['category_id']].tp += 1
pq_stat[gt_segms[gt_label]['category_id']].iou += iou
gt_matched.add(gt_label)
pred_matched.add(pred_label)
# count false positives
crowd_labels_dict = {}
for gt_label, gt_info in gt_segms.items():
if gt_label in gt_matched:
continue
# crowd segments are ignored
if gt_info['iscrowd'] == 1:
crowd_labels_dict[gt_info['category_id']] = gt_label
continue
pq_stat[gt_info['category_id']].fn += 1
# count false positives
for pred_label, pred_info in pred_segms.items():
if pred_label in pred_matched:
continue
# intersection of the segment with VOID
intersection = gt_pred_map.get((VOID, pred_label), 0)
# plus intersection with corresponding CROWD region if it exists
if pred_info['category_id'] in crowd_labels_dict:
intersection += gt_pred_map.get(
(crowd_labels_dict[pred_info['category_id']], pred_label),
0)
# predicted segment is ignored if more than half of
# the segment correspond to VOID and CROWD regions
if intersection / pred_info['area'] > 0.5:
continue
pq_stat[pred_info['category_id']].fp += 1
if print_log:
print('Core: {}, all {} images processed'.format(
proc_id, len(annotation_set)))
return pq_stat
[docs]def pq_compute_multi_core(matched_annotations_list,
gt_folder,
pred_folder,
categories,
file_client=None,
nproc=32):
"""Evaluate the metrics of Panoptic Segmentation with multithreading.
Same as the function with the same name in `panopticapi`.
Args:
matched_annotations_list (list): The matched annotation list. Each
element is a tuple of annotations of the same image with the
format (gt_anns, pred_anns).
gt_folder (str): The path of the ground truth images.
pred_folder (str): The path of the prediction images.
categories (str): The categories of the dataset.
file_client (object): The file client of the dataset. If None,
the backend will be set to `disk`.
nproc (int): Number of processes for panoptic quality computing.
Defaults to 32. When `nproc` exceeds the number of cpu cores,
the number of cpu cores is used.
"""
if PQStat is None:
raise RuntimeError(
'panopticapi is not installed, please install it by: '
'pip install git+https://github.com/cocodataset/'
'panopticapi.git.')
if file_client is None:
file_client_args = dict(backend='disk')
file_client = mmcv.FileClient(**file_client_args)
cpu_num = min(nproc, multiprocessing.cpu_count())
annotations_split = np.array_split(matched_annotations_list, cpu_num)
print('Number of cores: {}, images per core: {}'.format(
cpu_num, len(annotations_split[0])))
workers = multiprocessing.Pool(processes=cpu_num)
processes = []
for proc_id, annotation_set in enumerate(annotations_split):
p = workers.apply_async(pq_compute_single_core,
(proc_id, annotation_set, gt_folder,
pred_folder, categories, file_client))
processes.append(p)
# Close the process pool, otherwise it will lead to memory
# leaking problems.
workers.close()
workers.join()
pq_stat = PQStat()
for p in processes:
pq_stat += p.get()
return pq_stat
METRICS.register_default_best_metric(CocoDetectionEvaluator,
'DetectionBoxes_Precision/mAP', 'max')
METRICS.register_default_best_metric(CocoMaskEvaluator,
'DetectionMasks_Precision/mAP', 'max')
METRICS.register_default_best_metric(CoCoPoseTopDownEvaluator, 'AP', 'max')
METRICS.register_default_best_metric(CocoPanopticEvaluator, 'PQ', 'max')