class LVISEval:
def __init__(self, lvis_gt, lvis_dt, iou_type="segm"):
"""Constructor for LVISEval.
Args:
lvis_gt (LVIS class instance, or str containing path of annotation file)
lvis_dt (LVISResult class instance, or str containing path of result file,
or list of dict)
iou_type (str): segm or bbox evaluation
"""
# self.logger = logging.getLogger(__name__)
if iou_type not in ["bbox", "segm"]:
raise ValueError("iou_type: {} is not supported.".format(iou_type))
if isinstance(lvis_gt, LVIS):
self.lvis_gt = lvis_gt
elif isinstance(lvis_gt, str):
self.lvis_gt = LVIS(lvis_gt)
else:
raise TypeError("Unsupported type {} of lvis_gt.".format(lvis_gt))
if isinstance(lvis_dt, LVISResults):
self.lvis_dt = lvis_dt
elif isinstance(lvis_dt, (str, list)):
self.lvis_dt = LVISResults(self.lvis_gt, lvis_dt)
else:
raise TypeError("Unsupported type {} of lvis_dt.".format(lvis_dt))
# per-image per-category evaluation results
self.eval_imgs = defaultdict(list)
self.eval = {} # accumulated evaluation results
self._gts = defaultdict(list) # gt for evaluation
self._dts = defaultdict(list) # dt for evaluation
self.params = Params(iou_type=iou_type) # parameters
self.results = OrderedDict()
self.ious = {} # ious between all gts and dts
self.params.img_ids = sorted(self.lvis_gt.get_img_ids())
self.params.cat_ids = sorted(self.lvis_gt.get_cat_ids())
def _to_mask(self, anns, lvis):
for ann in anns:
rle = lvis.ann_to_rle(ann)
ann["segmentation"] = rle
def _prepare(self):
"""Prepare self._gts and self._dts for evaluation based on params."""
cat_ids = self.params.cat_ids if self.params.cat_ids else None
gts = self.lvis_gt.load_anns(
self.lvis_gt.get_ann_ids(img_ids=self.params.img_ids,
cat_ids=cat_ids))
dts = self.lvis_dt.load_anns(
self.lvis_dt.get_ann_ids(img_ids=self.params.img_ids,
cat_ids=cat_ids))
# convert ground truth to mask if iou_type == 'segm'
if self.params.iou_type == "segm":
self._to_mask(gts, self.lvis_gt)
self._to_mask(dts, self.lvis_dt)
# set ignore flag
for gt in gts:
if "ignore" not in gt:
gt["ignore"] = 0
for gt in gts:
self._gts[gt["image_id"], gt["category_id"]].append(gt)
# For federated dataset evaluation we will filter out all dt for an
# image which belong to categories not present in gt and not present in
# the negative list for an image. In other words detector is not penalized
# for categories about which we don't have gt information about their
# presence or absence in an image.
img_data = self.lvis_gt.load_imgs(ids=self.params.img_ids)
# per image map of categories not present in image
img_nl = {d["id"]: d["neg_category_ids"] for d in img_data}
# per image list of categories present in image
img_pl = defaultdict(set)
for ann in gts:
img_pl[ann["image_id"]].add(ann["category_id"])
# per image map of categoires which have missing gt. For these
# categories we don't penalize the detector for flase positives.
self.img_nel = {
d["id"]: d["not_exhaustive_category_ids"]
for d in img_data
}
for dt in dts:
img_id, cat_id = dt["image_id"], dt["category_id"]
if cat_id not in img_nl[img_id] and cat_id not in img_pl[img_id]:
continue
self._dts[img_id, cat_id].append(dt)
self.freq_groups = self._prepare_freq_group()
def _prepare_freq_group(self):
freq_groups = [[] for _ in self.params.img_count_lbl]
cat_data = self.lvis_gt.load_cats(self.params.cat_ids)
for idx, _cat_data in enumerate(cat_data):
frequency = _cat_data["frequency"]
freq_groups[self.params.img_count_lbl.index(frequency)].append(idx)
return freq_groups
def evaluate(self):
"""
Run per image evaluation on given images and store results
(a list of dict) in self.eval_imgs.
"""
print("Running per image evaluation.")
print("Evaluate annotation type *{}*".format(self.params.iou_type))
self.params.img_ids = list(np.unique(self.params.img_ids))
if self.params.use_cats:
cat_ids = self.params.cat_ids
else:
cat_ids = [-1]
self._prepare()
self.ious = {(img_id, cat_id): self.compute_iou(img_id, cat_id)
for img_id in self.params.img_ids for cat_id in cat_ids}
# loop through images, area range, max detection number
self.eval_imgs = [
self.evaluate_img(img_id, cat_id, area_rng) for cat_id in cat_ids
for area_rng in self.params.area_rng
for img_id in self.params.img_ids
]
def _get_gt_dt(self, img_id, cat_id):
"""Create gt, dt which are list of anns/dets. If use_cats is true
only anns/dets corresponding to tuple (img_id, cat_id) will be
used. Else, all anns/dets in image are used and cat_id is not used.
"""
if self.params.use_cats:
gt = self._gts[img_id, cat_id]
dt = self._dts[img_id, cat_id]
else:
gt = [
_ann for _cat_id in self.params.cat_ids
for _ann in self._gts[img_id, cat_id]
]
dt = [
_ann for _cat_id in self.params.cat_ids
for _ann in self._dts[img_id, cat_id]
]
return gt, dt
def compute_iou(self, img_id, cat_id):
gt, dt = self._get_gt_dt(img_id, cat_id)
if len(gt) == 0 and len(dt) == 0:
return []
# Sort detections in decreasing order of score.
idx = np.argsort([-d["score"] for d in dt], kind="mergesort")
dt = [dt[i] for i in idx]
iscrowd = [int(False)] * len(gt)
if self.params.iou_type == "segm":
ann_type = "segmentation"
elif self.params.iou_type == "bbox":
ann_type = "bbox"
else:
raise ValueError("Unknown iou_type for iou computation.")
gt = [g[ann_type] for g in gt]
dt = [d[ann_type] for d in dt]
# compute iou between each dt and gt region
# will return array of shape len(dt), len(gt)
ious = mask_utils.iou(dt, gt, iscrowd)
return ious
def evaluate_img(self, img_id, cat_id, area_rng):
"""Perform evaluation for single category and image."""
gt, dt = self._get_gt_dt(img_id, cat_id)
if len(gt) == 0 and len(dt) == 0:
return None
# Add another filed _ignore to only consider anns based on area range.
for g in gt:
if g["ignore"] or (g["area"] < area_rng[0]
or g["area"] > area_rng[1]):
g["_ignore"] = 1
else:
g["_ignore"] = 0
# Sort gt ignore last
gt_idx = np.argsort([g["_ignore"] for g in gt], kind="mergesort")
gt = [gt[i] for i in gt_idx]
# Sort dt highest score first
dt_idx = np.argsort([-d["score"] for d in dt], kind="mergesort")
dt = [dt[i] for i in dt_idx]
# load computed ious
ious = (self.ious[img_id, cat_id][:, gt_idx]
if len(self.ious[img_id, cat_id]) > 0 else self.ious[img_id,
cat_id])
num_thrs = len(self.params.iou_thrs)
num_gt = len(gt)
num_dt = len(dt)
# Array to store the "id" of the matched dt/gt
gt_m = np.zeros((num_thrs, num_gt))
dt_m = np.zeros((num_thrs, num_dt))
gt_ig = np.array([g["_ignore"] for g in gt])
dt_ig = np.zeros((num_thrs, num_dt))
for iou_thr_idx, iou_thr in enumerate(self.params.iou_thrs):
if len(ious) == 0:
break
for dt_idx, _dt in enumerate(dt):
iou = min([iou_thr, 1 - 1e-10])
# information about best match so far (m=-1 -> unmatched)
# store the gt_idx which matched for _dt
m = -1
for gt_idx, _ in enumerate(gt):
# if this gt already matched continue
if gt_m[iou_thr_idx, gt_idx] > 0:
continue
# if _dt matched to reg gt, and on ignore gt, stop
if m > -1 and gt_ig[m] == 0 and gt_ig[gt_idx] == 1:
break
# continue to next gt unless better match made
if ious[dt_idx, gt_idx] < iou:
continue
# if match successful and best so far, store appropriately
iou = ious[dt_idx, gt_idx]
m = gt_idx
# No match found for _dt, go to next _dt
if m == -1:
continue
# if gt to ignore for some reason update dt_ig.
# Should not be used in evaluation.
dt_ig[iou_thr_idx, dt_idx] = gt_ig[m]
# _dt match found, update gt_m, and dt_m with "id"
dt_m[iou_thr_idx, dt_idx] = gt[m]["id"]
gt_m[iou_thr_idx, m] = _dt["id"]
# For LVIS we will ignore any unmatched detection if that category was
# not exhaustively annotated in gt.
dt_ig_mask = [
d["area"] < area_rng[0] or d["area"] > area_rng[1]
or d["category_id"] in self.img_nel[d["image_id"]] for d in dt
]
dt_ig_mask = np.array(dt_ig_mask).reshape((1, num_dt)) # 1 X num_dt
dt_ig_mask = np.repeat(dt_ig_mask, num_thrs, 0) # num_thrs X num_dt
# Based on dt_ig_mask ignore any unmatched detection by updating dt_ig
dt_ig = np.logical_or(dt_ig, np.logical_and(dt_m == 0, dt_ig_mask))
# store results for given image and category
return {
"image_id": img_id,
"category_id": cat_id,
"area_rng": area_rng,
"dt_ids": [d["id"] for d in dt],
"gt_ids": [g["id"] for g in gt],
"dt_matches": dt_m,
"gt_matches": gt_m,
"dt_scores": [d["score"] for d in dt],
"gt_ignore": gt_ig,
"dt_ignore": dt_ig,
}
def accumulate(self):
"""Accumulate per image evaluation results and store the result in
self.eval.
"""
print("Accumulating evaluation results.")
if not self.eval_imgs:
print("Please run evaluate first.")
if self.params.use_cats:
cat_ids = self.params.cat_ids
else:
cat_ids = [-1]
num_thrs = len(self.params.iou_thrs)
num_recalls = len(self.params.rec_thrs)
num_cats = len(cat_ids)
num_area_rngs = len(self.params.area_rng)
num_imgs = len(self.params.img_ids)
# -1 for absent categories
precision = -np.ones((num_thrs, num_recalls, num_cats, num_area_rngs))
recall = -np.ones((num_thrs, num_cats, num_area_rngs))
# Initialize dt_pointers
dt_pointers = {}
for cat_idx in range(num_cats):
dt_pointers[cat_idx] = {}
for area_idx in range(num_area_rngs):
dt_pointers[cat_idx][area_idx] = {}
# Per category evaluation
for cat_idx in range(num_cats):
Nk = cat_idx * num_area_rngs * num_imgs
for area_idx in range(num_area_rngs):
Na = area_idx * num_imgs
E = [
self.eval_imgs[Nk + Na + img_idx]
for img_idx in range(num_imgs)
]
# Remove elements which are None
E = [e for e in E if not e is None]
if len(E) == 0:
continue
# Append all scores: shape (N,)
dt_scores = np.concatenate([e["dt_scores"] for e in E], axis=0)
dt_ids = np.concatenate([e["dt_ids"] for e in E], axis=0)
dt_idx = np.argsort(-dt_scores, kind="mergesort")
dt_scores = dt_scores[dt_idx]
dt_ids = dt_ids[dt_idx]
dt_m = np.concatenate([e["dt_matches"] for e in E],
axis=1)[:, dt_idx]
dt_ig = np.concatenate([e["dt_ignore"] for e in E],
axis=1)[:, dt_idx]
gt_ig = np.concatenate([e["gt_ignore"] for e in E])
# num gt anns to consider
num_gt = np.count_nonzero(gt_ig == 0)
if num_gt == 0:
continue
tps = np.logical_and(dt_m, np.logical_not(dt_ig))
fps = np.logical_and(np.logical_not(dt_m),
np.logical_not(dt_ig))
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
dt_pointers[cat_idx][area_idx] = {
"dt_ids": dt_ids,
"tps": tps,
"fps": fps,
}
for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
tp = np.array(tp)
fp = np.array(fp)
num_tp = len(tp)
rc = tp / num_gt
if num_tp:
recall[iou_thr_idx, cat_idx, area_idx] = rc[-1]
else:
recall[iou_thr_idx, cat_idx, area_idx] = 0
# np.spacing(1) ~= eps
pr = tp / (fp + tp + np.spacing(1))
pr = pr.tolist()
# Replace each precision value with the maximum precision
# value to the right of that recall level. This ensures
# that the calculated AP value will be less suspectable
# to small variations in the ranking.
for i in range(num_tp - 1, 0, -1):
if pr[i] > pr[i - 1]:
pr[i - 1] = pr[i]
rec_thrs_insert_idx = np.searchsorted(rc,
self.params.rec_thrs,
side="left")
pr_at_recall = [0.0] * num_recalls
try:
for _idx, pr_idx in enumerate(rec_thrs_insert_idx):
pr_at_recall[_idx] = pr[pr_idx]
except:
pass
precision[iou_thr_idx, :, cat_idx,
area_idx] = np.array(pr_at_recall)
self.eval = {
"params": self.params,
"counts": [num_thrs, num_recalls, num_cats, num_area_rngs],
"date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"precision": precision,
"recall": recall,
"dt_pointers": dt_pointers,
}
def _summarize(self,
summary_type,
iou_thr=None,
area_rng="all",
freq_group_idx=None):
aidx = [
idx for idx, _area_rng in enumerate(self.params.area_rng_lbl)
if _area_rng == area_rng
]
if summary_type == 'ap':
s = self.eval["precision"]
if iou_thr is not None:
tidx = np.where(iou_thr == self.params.iou_thrs)[0]
s = s[tidx]
if freq_group_idx is not None:
s = s[:, :, self.freq_groups[freq_group_idx], aidx]
else:
s = s[:, :, :, aidx]
else:
s = self.eval["recall"]
if iou_thr is not None:
tidx = np.where(iou_thr == self.params.iou_thrs)[0]
s = s[tidx]
s = s[:, :, aidx]
if len(s[s > -1]) == 0:
mean_s = -1
else:
mean_s = np.mean(s[s > -1])
return mean_s
def summarize(self):
"""Compute and display summary metrics for evaluation results."""
if not self.eval:
raise RuntimeError("Please run accumulate() first.")
max_dets = self.params.max_dets
self.results["AP"] = self._summarize('ap')
self.results["AP50"] = self._summarize('ap', iou_thr=0.50)
self.results["AP75"] = self._summarize('ap', iou_thr=0.75)
self.results["APs"] = self._summarize('ap', area_rng="small")
self.results["APm"] = self._summarize('ap', area_rng="medium")
self.results["APl"] = self._summarize('ap', area_rng="large")
self.results["APr"] = self._summarize('ap', freq_group_idx=0)
self.results["APc"] = self._summarize('ap', freq_group_idx=1)
self.results["APf"] = self._summarize('ap', freq_group_idx=2)
key = "AR@{}".format(max_dets)
self.results[key] = self._summarize('ar')
for area_rng in ["small", "medium", "large"]:
key = "AR{}@{}".format(area_rng[0], max_dets)
self.results[key] = self._summarize('ar', area_rng=area_rng)
def run(self):
"""Wrapper function which calculates the results."""
self.evaluate()
self.accumulate()
self.summarize()
def print_results(self):
template = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} catIds={:>3s}] = {:0.3f}"
for key, value in self.results.items():
max_dets = self.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(self.params.iou_thrs[0],
self.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"
print(
template.format(title, _type, iou, area_rng, max_dets,
cat_group_name, value))
def get_results(self):
if not self.results:
self.logger.warn("results is empty. Call run().")
return self.results
class LvisDataset(CustomDataset):
def load_annotations(self, ann_file, LT_ann_file=None):
self.lvis = LVIS(ann_file)
self.cat_ids = self.lvis.get_cat_ids()
if self.CLASSES is None:
self.CLASSES = self.cat_ids
self.cat2label = {
cat_id: i + 1
for i, cat_id in enumerate(self.cat_ids)
}
self.categories = self.cat_ids
if LT_ann_file is not None:
self.img_ids = []
for LT_ann_file in LT_ann_file:
self.img_ids += mmcv.list_from_file(LT_ann_file)
self.img_ids = [ int(x) for x in self.img_ids]
else:
self.img_ids = self.lvis.get_img_ids()
img_infos = []
for i in self.img_ids:
info = self.lvis.load_imgs([i])[0]
info['filename'] = info['file_name'][-16:]
img_infos.append(info)
return img_infos
def get_ann_info(self, idx):
img_id = self.img_infos[idx]['id']
ann_ids = self.lvis.get_ann_ids(img_ids=[img_id])
ann_info = self.lvis.load_anns(ann_ids)
ann = self._parse_ann_info(ann_info)
if self.see_only:
ann['labels'] = ann['labels'][list(self.see_only)]
return ann
def _filter_imgs(self, min_size=32):
"""Filter images too small or without ground truths."""
valid_inds = []
ids_with_ann = set(_['image_id'] for _ in self.lvis.anns.values())
for i, img_info in enumerate(self.img_infos):
if self.img_ids[i] not in ids_with_ann:
continue
if min(img_info['width'], img_info['height']) >= min_size:
valid_inds.append(i)
return valid_inds
def _parse_ann_info(self, ann_info):
"""Parse label annotation. """
gt_labels = np.zeros((len(self.categories),), dtype=np.int64)
for i, ann in enumerate(ann_info):
if ann.get('ignore', False):
continue
x1, y1, w, h = ann['bbox']
if ann['area'] <= 0 or w < 1 or h < 1:
continue
gt_labels[self.cat2label[ann['category_id']]-1] = 1
ann = dict(labels=gt_labels)
return ann
class TaoDataset(CustomDataset):
def load_annotations(self, ann_file):
self.lvis = LVIS(ann_file)
self.full_cat_ids = self.lvis.get_cat_ids()
self.full_cat2label = {
cat_id: i + 1
for i, cat_id in enumerate(self.full_cat_ids)
}
self.CLASSES = tuple(
[item['name'] for item in self.lvis.dataset['categories']])
self.cat_ids = self.lvis.get_cat_ids()
self.cat2label = {
cat_id: i + 1
for i, cat_id in enumerate(self.cat_ids)
}
self.img_ids = self.lvis.get_img_ids()
img_infos = []
for i in self.img_ids:
info = self.lvis.load_imgs([i])[0]
#info['filename'] = info['file_name'].split('_')[-1]
info['filename'] = info['file_name']
img_infos.append(info)
return img_infos
def get_ann_info(self, idx):
img_id = self.img_infos[idx]['id']
ann_ids = self.lvis.get_ann_ids(img_ids=[img_id])
ann_info = self.lvis.load_anns(ann_ids)
return self._parse_ann_info(self.img_infos[idx], ann_info)
def get_ann_info_withoutparse(self, idx):
img_id = self.img_infos[idx]['id']
ann_ids = self.lvis.get_ann_ids(img_ids=[img_id])
ann_info = self.lvis.load_anns(ann_ids)
return ann_info
def _filter_imgs(self, min_size=32):
"""Filter images too small or without ground truths."""
valid_inds = []
ids_with_ann = set(_['image_id'] for _ in self.lvis.anns.values())
for i, img_info in enumerate(self.img_infos):
if self.img_ids[i] not in ids_with_ann:
continue
if min(img_info['width'], img_info['height']) >= min_size:
valid_inds.append(i)
return valid_inds
def _parse_ann_info(self, img_info, ann_info):
"""Parse bbox and mask annotation.
Args:
ann_info (list[dict]): Annotation info of an image.
with_mask (bool): Whether to parse mask annotations.
Returns:
dict: A dict containing the following keys: bboxes, bboxes_ignore,
labels, masks, mask_polys, poly_lens.
"""
gt_bboxes = []
gt_labels = []
gt_bboxes_ignore = []
# Two formats are provided.
# 1. mask: a binary map of the same size of the image.
# 2. polys: each mask consists of one or several polys, each poly is a
# list of float.
gt_masks = []
for i, ann in enumerate(ann_info):
if ann.get('ignore', False):
continue
x1, y1, w, h = ann['bbox']
if ann['area'] <= 0 or w < 1 or h < 1:
continue
bbox = [x1, y1, x1 + w - 1, y1 + h - 1]
if 'iscrowd' in ann.keys():
if ann['iscrowd']:
gt_bboxes_ignore.append(bbox)
else:
gt_bboxes.append(bbox)
gt_labels.append(self.cat2label[ann['category_id']])
gt_masks.append(self.lvis.ann_to_mask(ann))
if gt_bboxes:
gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
gt_labels = np.array(gt_labels, dtype=np.int64)
else:
gt_bboxes = np.zeros((0, 4), dtype=np.float32)
gt_labels = np.array([], dtype=np.int64)
if gt_bboxes_ignore:
gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
else:
gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
seg_map = img_info['filename'].replace('jpg', 'png')
ann = dict(bboxes=gt_bboxes,
labels=gt_labels,
bboxes_ignore=gt_bboxes_ignore,
masks=gt_masks,
seg_map=seg_map)
return ann
class LvisGtAnnVis():
def __init__(self, ann_file):
self.lvis = LVIS(ann_file)
CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic_light', 'fire_hydrant',
'stop_sign', 'parking_meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports_ball', 'kite', 'baseball_bat',
'baseball_glove', 'skateboard', 'surfboard', 'tennis_racket',
'bottle', 'wine_glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot_dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted_plant', 'bed', 'dining_table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
'vase', 'scissors', 'teddy_bear', 'hair_drier', 'toothbrush')##use CLASSES[self.cat2label[35]] to find the class name
self.cat_ids = self.lvis.get_cat_ids()
# self.cat2label = {
# cat_id: i + 1
# for i, cat_id in enumerate(self.cat_ids)
# }
self.img_ids = self.lvis.get_img_ids()
img_infos = []
for i in self.img_ids:
info = self.lvis.load_imgs([i])[0]
info['filename'] = info['file_name']
img_infos.append(info)
self.img_infos = img_infos
# self.img_prefix = './data/lvis/val2017'
self.img_prefix = './data/lvis/train2017'
self.filter_to_keep_finetune_classes()
###filter to keep only the finetune classes with zero_ap but not with fewshot training instances
def filter_to_keep_finetune_classes(self):
self.lvis._filter_anns_finetune_classes()## first filter anns
keep_img_info_ids = []## then filter imgs
for idx in range(len(self.img_infos)):
img_id = self.img_infos[idx]['id']
ann_ids = self.lvis.get_ann_ids(img_ids=[img_id])
for ann_id in ann_ids:
if ann_id in self.lvis.anns.keys():
if idx not in keep_img_info_ids:
keep_img_info_ids.append(idx)
self.img_infos = [self.img_infos[i] for i in keep_img_info_ids]
def show(self, idx):
img_id = self.img_infos[idx]['id']
ann_ids = self.lvis.get_ann_ids(img_ids=[img_id])
ann_info = self.lvis.load_anns(ann_ids)
# for ann in ann_info:
# if ann['iscrowd'] == 1:
# is_crowd_id_val.append(idx)
if self.img_infos[idx]['filename'].startswith('COCO'):##for val set
imdata = cv2.imread(osp.join(self.img_prefix, self.img_infos[idx]['filename'][13:]))
else:
imdata = cv2.imread(osp.join(self.img_prefix, self.img_infos[idx]['filename']))
imdata = cv2.cvtColor(imdata, cv2.COLOR_BGR2RGB)
plt.imshow(imdata)
plt.axis('off')
self.lvis.showanns(ann_info)
plt.show()
class LvisGtAnnVis():
def __init__(self, ann_file):
self.lvis = LVIS(ann_file)
CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic_light', 'fire_hydrant',
'stop_sign', 'parking_meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports_ball',
'kite', 'baseball_bat', 'baseball_glove', 'skateboard',
'surfboard', 'tennis_racket', 'bottle', 'wine_glass', 'cup',
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot_dog',
'pizza', 'donut', 'cake', 'chair', 'couch', 'potted_plant',
'bed', 'dining_table', 'toilet', 'tv', 'laptop', 'mouse',
'remote', 'keyboard', 'cell_phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy_bear', 'hair_drier', 'toothbrush'
) ##use CLASSES[self.cat2label[35]] to find the class name
self.cat_ids = self.lvis.get_cat_ids()
# self.cat2label = {
# cat_id: i + 1
# for i, cat_id in enumerate(self.cat_ids)
# }
self.img_ids = self.lvis.get_img_ids()
img_infos = []
for i in self.img_ids:
info = self.lvis.load_imgs([i])[0]
info['filename'] = info['file_name']
img_infos.append(info)
self.img_infos = img_infos
# self.img_prefix = './data/lvis/val2017'
self.img_prefix = './data/lvis/train2017'
valid_inds = self._filter_imgs()
self.img_infos = [self.img_infos[i] for i in valid_inds]
def _filter_imgs(self, min_size=32):
"""Filter images too small or without ground truths."""
valid_inds = []
ids_with_ann = set(_['image_id'] for _ in self.lvis.anns.values())
for i, img_info in enumerate(self.img_infos):
if self.img_ids[i] not in ids_with_ann:
continue
if min(img_info['width'], img_info['height']) >= min_size:
valid_inds.append(i)
return valid_inds
def show(self, idx):
img_id = self.img_infos[idx]['id']
ann_ids = self.lvis.get_ann_ids(img_ids=[img_id])
ann_info = self.lvis.load_anns(ann_ids)
# for ann in ann_info:
# if ann['iscrowd'] == 1:
# is_crowd_id_val.append(idx)
if self.img_infos[idx]['filename'].startswith('COCO'): ##for val set
imdata = cv2.imread(
osp.join(self.img_prefix,
self.img_infos[idx]['filename'][13:]))
else:
imdata = cv2.imread(
osp.join(self.img_prefix, self.img_infos[idx]['filename']))
imdata = cv2.cvtColor(imdata, cv2.COLOR_BGR2RGB)
plt.imshow(imdata)
plt.axis('off')
self.lvis.showanns(ann_info)
plt.show()
class OIDEval:
def __init__(
self,
lvis_gt,
lvis_dt,
iou_type="bbox",
expand_pred_label=False,
oid_hierarchy_path='./datasets/oid/annotations/challenge-2019-label500-hierarchy.json'
):
"""Constructor for OIDEval.
Args:
lvis_gt (LVIS class instance, or str containing path of annotation file)
lvis_dt (LVISResult class instance, or str containing path of result file,
or list of dict)
iou_type (str): segm or bbox evaluation
"""
self.logger = logging.getLogger(__name__)
if iou_type not in ["bbox", "segm"]:
raise ValueError("iou_type: {} is not supported.".format(iou_type))
if isinstance(lvis_gt, LVIS):
self.lvis_gt = lvis_gt
elif isinstance(lvis_gt, str):
self.lvis_gt = LVIS(lvis_gt)
else:
raise TypeError("Unsupported type {} of lvis_gt.".format(lvis_gt))
if isinstance(lvis_dt, LVISResults):
self.lvis_dt = lvis_dt
elif isinstance(lvis_dt, (str, list)):
self.lvis_dt = LVISResults(self.lvis_gt, lvis_dt, max_dets=-1)
else:
raise TypeError("Unsupported type {} of lvis_dt.".format(lvis_dt))
if expand_pred_label:
oid_hierarchy = json.load(open(oid_hierarchy_path, 'r'))
cat_info = self.lvis_gt.dataset['categories']
freebase2id = {x['freebase_id']: x['id'] for x in cat_info}
id2freebase = {x['id']: x['freebase_id'] for x in cat_info}
id2name = {x['id']: x['name'] for x in cat_info}
fas = defaultdict(set)
def dfs(hierarchy, cur_id):
all_childs = set()
all_keyed_child = {}
if 'Subcategory' in hierarchy:
for x in hierarchy['Subcategory']:
childs = dfs(x, freebase2id[x['LabelName']])
all_childs.update(childs)
if cur_id != -1:
for c in all_childs:
fas[c].add(cur_id)
all_childs.add(cur_id)
return all_childs
dfs(oid_hierarchy, -1)
expanded_pred = []
id_count = 0
for d in self.lvis_dt.dataset['annotations']:
cur_id = d['category_id']
ids = [cur_id] + [x for x in fas[cur_id]]
for cat_id in ids:
new_box = copy.deepcopy(d)
id_count = id_count + 1
new_box['id'] = id_count
new_box['category_id'] = cat_id
expanded_pred.append(new_box)
print('Expanding original {} preds to {} preds'.format(
len(self.lvis_dt.dataset['annotations']), len(expanded_pred)))
self.lvis_dt.dataset['annotations'] = expanded_pred
self.lvis_dt._create_index()
# per-image per-category evaluation results
self.eval_imgs = defaultdict(list)
self.eval = {} # accumulated evaluation results
self._gts = defaultdict(list) # gt for evaluation
self._dts = defaultdict(list) # dt for evaluation
self.params = Params(iou_type=iou_type) # parameters
self.results = OrderedDict()
self.ious = {} # ious between all gts and dts
self.params.img_ids = sorted(self.lvis_gt.get_img_ids())
self.params.cat_ids = sorted(self.lvis_gt.get_cat_ids())
def _to_mask(self, anns, lvis):
for ann in anns:
rle = lvis.ann_to_rle(ann)
ann["segmentation"] = rle
def _prepare(self):
"""Prepare self._gts and self._dts for evaluation based on params."""
cat_ids = self.params.cat_ids if self.params.cat_ids else None
gts = self.lvis_gt.load_anns(
self.lvis_gt.get_ann_ids(img_ids=self.params.img_ids,
cat_ids=cat_ids))
dts = self.lvis_dt.load_anns(
self.lvis_dt.get_ann_ids(img_ids=self.params.img_ids,
cat_ids=cat_ids))
# convert ground truth to mask if iou_type == 'segm'
if self.params.iou_type == "segm":
self._to_mask(gts, self.lvis_gt)
self._to_mask(dts, self.lvis_dt)
for gt in gts:
self._gts[gt["image_id"], gt["category_id"]].append(gt)
# For federated dataset evaluation we will filter out all dt for an
# image which belong to categories not present in gt and not present in
# the negative list for an image. In other words detector is not penalized
# for categories about which we don't have gt information about their
# presence or absence in an image.
img_data = self.lvis_gt.load_imgs(ids=self.params.img_ids)
# per image map of categories not present in image
img_nl = {d["id"]: d["neg_category_ids"] for d in img_data}
# per image list of categories present in image
img_pl = {d["id"]: d["pos_category_ids"] for d in img_data}
# img_pl = defaultdict(set)
for ann in gts:
# img_pl[ann["image_id"]].add(ann["category_id"])
assert ann["category_id"] in img_pl[ann["image_id"]]
# print('check pos ids OK.')
for dt in dts:
img_id, cat_id = dt["image_id"], dt["category_id"]
if cat_id not in img_nl[img_id] and cat_id not in img_pl[img_id]:
continue
self._dts[img_id, cat_id].append(dt)
def evaluate(self):
"""
Run per image evaluation on given images and store results
(a list of dict) in self.eval_imgs.
"""
self.logger.info("Running per image evaluation.")
self.logger.info("Evaluate annotation type *{}*".format(
self.params.iou_type))
self.params.img_ids = list(np.unique(self.params.img_ids))
if self.params.use_cats:
cat_ids = self.params.cat_ids
else:
cat_ids = [-1]
self._prepare()
self.ious = {(img_id, cat_id): self.compute_iou(img_id, cat_id)
for img_id in self.params.img_ids for cat_id in cat_ids}
# loop through images, area range, max detection number
print('Evaluating ...')
self.eval_imgs = [
self.evaluate_img_google(img_id, cat_id, area_rng)
for cat_id in cat_ids for area_rng in self.params.area_rng
for img_id in self.params.img_ids
]
def _get_gt_dt(self, img_id, cat_id):
"""Create gt, dt which are list of anns/dets. If use_cats is true
only anns/dets corresponding to tuple (img_id, cat_id) will be
used. Else, all anns/dets in image are used and cat_id is not used.
"""
if self.params.use_cats:
gt = self._gts[img_id, cat_id]
dt = self._dts[img_id, cat_id]
else:
gt = [
_ann for _cat_id in self.params.cat_ids
for _ann in self._gts[img_id, cat_id]
]
dt = [
_ann for _cat_id in self.params.cat_ids
for _ann in self._dts[img_id, cat_id]
]
return gt, dt
def compute_iou(self, img_id, cat_id):
gt, dt = self._get_gt_dt(img_id, cat_id)
if len(gt) == 0 and len(dt) == 0:
return []
# Sort detections in decreasing order of score.
idx = np.argsort([-d["score"] for d in dt], kind="mergesort")
dt = [dt[i] for i in idx]
# iscrowd = [int(False)] * len(gt)
iscrowd = [int('iscrowd' in g and g['iscrowd'] > 0) for g in gt]
if self.params.iou_type == "segm":
ann_type = "segmentation"
elif self.params.iou_type == "bbox":
ann_type = "bbox"
else:
raise ValueError("Unknown iou_type for iou computation.")
gt = [g[ann_type] for g in gt]
dt = [d[ann_type] for d in dt]
# compute iou between each dt and gt region
# will return array of shape len(dt), len(gt)
ious = mask_utils.iou(dt, gt, iscrowd)
return ious
def evaluate_img_google(self, img_id, cat_id, area_rng):
gt, dt = self._get_gt_dt(img_id, cat_id)
if len(gt) == 0 and len(dt) == 0:
return None
if len(dt) == 0:
return {
"image_id": img_id,
"category_id": cat_id,
"area_rng": area_rng,
"dt_ids": [],
"dt_matches": np.array([], dtype=np.int32).reshape(1, -1),
"dt_scores": [],
"dt_ignore": np.array([], dtype=np.int32).reshape(1, -1),
'num_gt': len(gt)
}
no_crowd_inds = [i for i, g in enumerate(gt) \
if ('iscrowd' not in g) or g['iscrowd'] == 0]
crowd_inds = [i for i, g in enumerate(gt) \
if 'iscrowd' in g and g['iscrowd'] == 1]
dt_idx = np.argsort([-d["score"] for d in dt], kind="mergesort")
if len(self.ious[img_id, cat_id]) > 0:
ious = self.ious[img_id, cat_id]
iou = ious[:, no_crowd_inds]
iou = iou[dt_idx]
ioa = ious[:, crowd_inds]
ioa = ioa[dt_idx]
else:
iou = np.zeros((len(dt_idx), 0))
ioa = np.zeros((len(dt_idx), 0))
scores = np.array([dt[i]['score'] for i in dt_idx])
num_detected_boxes = len(dt)
tp_fp_labels = np.zeros(num_detected_boxes, dtype=bool)
is_matched_to_group_of = np.zeros(num_detected_boxes, dtype=bool)
def compute_match_iou(iou):
max_overlap_gt_ids = np.argmax(iou, axis=1)
is_gt_detected = np.zeros(iou.shape[1], dtype=bool)
for i in range(num_detected_boxes):
gt_id = max_overlap_gt_ids[i]
is_evaluatable = (not tp_fp_labels[i] and iou[i, gt_id] >= 0.5
and not is_matched_to_group_of[i])
if is_evaluatable:
if not is_gt_detected[gt_id]:
tp_fp_labels[i] = True
is_gt_detected[gt_id] = True
def compute_match_ioa(ioa):
scores_group_of = np.zeros(ioa.shape[1], dtype=float)
tp_fp_labels_group_of = np.ones(ioa.shape[1], dtype=float)
max_overlap_group_of_gt_ids = np.argmax(ioa, axis=1)
for i in range(num_detected_boxes):
gt_id = max_overlap_group_of_gt_ids[i]
is_evaluatable = (not tp_fp_labels[i] and ioa[i, gt_id] >= 0.5
and not is_matched_to_group_of[i])
if is_evaluatable:
is_matched_to_group_of[i] = True
scores_group_of[gt_id] = max(scores_group_of[gt_id],
scores[i])
selector = np.where((scores_group_of > 0)
& (tp_fp_labels_group_of > 0))
scores_group_of = scores_group_of[selector]
tp_fp_labels_group_of = tp_fp_labels_group_of[selector]
return scores_group_of, tp_fp_labels_group_of
if iou.shape[1] > 0:
compute_match_iou(iou)
scores_box_group_of = np.ndarray([0], dtype=float)
tp_fp_labels_box_group_of = np.ndarray([0], dtype=float)
if ioa.shape[1] > 0:
scores_box_group_of, tp_fp_labels_box_group_of = compute_match_ioa(
ioa)
valid_entries = (~is_matched_to_group_of)
scores = np.concatenate((scores[valid_entries], scores_box_group_of))
tp_fps = np.concatenate((tp_fp_labels[valid_entries].astype(float),
tp_fp_labels_box_group_of))
return {
"image_id":
img_id,
"category_id":
cat_id,
"area_rng":
area_rng,
"dt_matches":
np.array([1 if x > 0 else 0 for x in tp_fps],
dtype=np.int32).reshape(1, -1),
"dt_scores": [x for x in scores],
"dt_ignore":
np.array([0 for x in scores], dtype=np.int32).reshape(1, -1),
'num_gt':
len(gt)
}
def accumulate(self):
"""Accumulate per image evaluation results and store the result in
self.eval.
"""
self.logger.info("Accumulating evaluation results.")
if not self.eval_imgs:
self.logger.warn("Please run evaluate first.")
if self.params.use_cats:
cat_ids = self.params.cat_ids
else:
cat_ids = [-1]
num_thrs = 1
num_recalls = 1
num_cats = len(cat_ids)
num_area_rngs = 1
num_imgs = len(self.params.img_ids)
# -1 for absent categories
precision = -np.ones((num_thrs, num_recalls, num_cats, num_area_rngs))
recall = -np.ones((num_thrs, num_cats, num_area_rngs))
# Initialize dt_pointers
dt_pointers = {}
for cat_idx in range(num_cats):
dt_pointers[cat_idx] = {}
for area_idx in range(num_area_rngs):
dt_pointers[cat_idx][area_idx] = {}
# Per category evaluation
for cat_idx in range(num_cats):
Nk = cat_idx * num_area_rngs * num_imgs
for area_idx in range(num_area_rngs):
Na = area_idx * num_imgs
E = [
self.eval_imgs[Nk + Na + img_idx]
for img_idx in range(num_imgs)
]
# Remove elements which are None
E = [e for e in E if not e is None]
if len(E) == 0:
continue
dt_scores = np.concatenate([e["dt_scores"] for e in E], axis=0)
dt_idx = np.argsort(-dt_scores, kind="mergesort")
dt_scores = dt_scores[dt_idx]
dt_m = np.concatenate([e["dt_matches"] for e in E],
axis=1)[:, dt_idx]
dt_ig = np.concatenate([e["dt_ignore"] for e in E],
axis=1)[:, dt_idx]
num_gt = sum([e['num_gt'] for e in E])
if num_gt == 0:
continue
tps = np.logical_and(dt_m, np.logical_not(dt_ig))
fps = np.logical_and(np.logical_not(dt_m),
np.logical_not(dt_ig))
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
dt_pointers[cat_idx][area_idx] = {
"tps": tps,
"fps": fps,
}
for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
tp = np.array(tp)
fp = np.array(fp)
num_tp = len(tp)
rc = tp / num_gt
if num_tp:
recall[iou_thr_idx, cat_idx, area_idx] = rc[-1]
else:
recall[iou_thr_idx, cat_idx, area_idx] = 0
# np.spacing(1) ~= eps
pr = tp / (fp + tp + np.spacing(1))
pr = pr.tolist()
for i in range(num_tp - 1, 0, -1):
if pr[i] > pr[i - 1]:
pr[i - 1] = pr[i]
mAP = compute_average_precision(
np.array(pr, np.float).reshape(-1),
np.array(rc, np.float).reshape(-1))
precision[iou_thr_idx, :, cat_idx, area_idx] = mAP
self.eval = {
"params": self.params,
"counts": [num_thrs, num_recalls, num_cats, num_area_rngs],
"date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"precision": precision,
"recall": recall,
"dt_pointers": dt_pointers,
}
def _summarize(self, summary_type):
s = self.eval["precision"]
if len(s[s > -1]) == 0:
mean_s = -1
else:
mean_s = np.mean(s[s > -1])
# print(s.reshape(1, 1, -1, 1))
return mean_s
def summarize(self):
"""Compute and display summary metrics for evaluation results."""
if not self.eval:
raise RuntimeError("Please run accumulate() first.")
max_dets = self.params.max_dets
self.results["AP50"] = self._summarize('ap')
def run(self):
"""Wrapper function which calculates the results."""
self.evaluate()
self.accumulate()
self.summarize()
def print_results(self):
template = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} catIds={:>3s}] = {:0.3f}"
for key, value in self.results.items():
max_dets = self.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(self.params.iou_thrs[0],
self.params.iou_thrs[-1])
cat_group_name = "all"
area_rng = "all"
print(
template.format(title, _type, iou, area_rng, max_dets,
cat_group_name, value))
def get_results(self):
if not self.results:
self.logger.warn("results is empty. Call run().")
return self.results
