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OSIODeval.py
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OSIODeval.py
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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import numpy as np
import os
import sys
import tempfile
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
from collections import OrderedDict, defaultdict
from functools import lru_cache
import torch
from fvcore.common.file_io import PathManager
from detectron2.data import MetadataCatalog
from detectron2.utils import comm
from detectron2.evaluation.evaluator import DatasetEvaluator
np.set_printoptions(threshold=sys.maxsize)
class OSIODEvaluator(DatasetEvaluator):
"""
Evaluate Pascal VOC style AP for Pascal VOC dataset.
It contains a synchronization, therefore has to be called from all ranks.
Note that the concept of AP can be implemented in different ways and may not
produce identical results. This class mimics the implementation of the official
Pascal VOC Matlab API, and should produce similar but not identical results to the
official API.
"""
def __init__(self, dataset_name, cfg=None):
"""
Args:
dataset_name (str): name of the dataset, e.g., "voc_2007_test"
"""
self._dataset_name = dataset_name
meta = MetadataCatalog.get(dataset_name)
self._anno_file_template = os.path.join(meta.dirname, "Annotations", "{}.xml")
self._image_set_path = os.path.join(meta.dirname, "ImageSets", "Main", meta.split + ".txt")
self._class_names = meta.thing_classes
assert meta.year in [2007, 2012], meta.year
self._is_2007 = False
self._cpu_device = torch.device("cpu")
self._logger = logging.getLogger(__name__)
if cfg is not None:
self.prev_intro_cls = cfg.OWOD.PREV_INTRODUCED_CLS
self.curr_intro_cls = cfg.OWOD.CUR_INTRODUCED_CLS
self.total_num_class = cfg.MODEL.ROI_HEADS.NUM_CLASSES
self.unknown_class_index = self.total_num_class # 80
self.num_seen_classes = self.prev_intro_cls + self.curr_intro_cls
self.known_classes = self._class_names[:self.num_seen_classes]
def reset(self):
self._predictions = defaultdict(list) # class name -> list of prediction strings
def process(self, inputs, outputs):
for input, output in zip(inputs, outputs):
image_id = input["image_id"]
instances = output["instances"].to(self._cpu_device)
boxes = instances.pred_boxes.tensor.numpy()
scores = instances.scores.tolist()
classes = instances.pred_classes.tolist()
for box, score, cls in zip(boxes, scores, classes):
xmin, ymin, xmax, ymax = box
# The inverse of data loading logic in `datasets/pascal_voc.py`
xmin += 1
ymin += 1
self._predictions[cls].append(
f"{image_id} {score:.3f} {xmin:.1f} {ymin:.1f} {xmax:.1f} {ymax:.1f}"
)
def compute_avg_precision_at_many_recall_level_for_unk(self, precisions, recalls):
precs = {}
for r in range(1, 10):
r = r/10
p = self.compute_avg_precision_at_a_recall_level_for_unk(precisions, recalls, recall_level=r)
precs[r] = p
return precs
def compute_avg_precision_at_a_recall_level_for_unk(self, precisions, recalls, recall_level=0.5):
precs = {}
for iou, recall in recalls.items():
prec = []
for cls_id, rec in enumerate(recall):
if cls_id == self.unknown_class_index and len(rec)>0:
p = precisions[iou][cls_id][min(range(len(rec)), key=lambda i: abs(rec[i] - recall_level))]
prec.append(p)
if len(prec) > 0:
precs[iou] = np.mean(prec)
else:
precs[iou] = 0
return precs
def compute_WI_at_many_recall_level(self, recalls, tp_plus_fp_cs, fp_os):
wi_at_recall = {}
for r in range(1, 10):
r = r/10
wi = self.compute_WI_at_a_recall_level(recalls, tp_plus_fp_cs, fp_os, recall_level=r)
wi_at_recall[r] = wi
return wi_at_recall
def compute_WI_at_a_recall_level(self, recalls, tp_plus_fp_cs, fp_os, recall_level=0.5):
wi_at_iou = {}
for iou, recall in recalls.items():
tp_plus_fps = []
fps = []
for cls_id, rec in enumerate(recall):
if cls_id in range(self.num_seen_classes) and len(rec) > 0:
index = min(range(len(rec)), key=lambda i: abs(rec[i] - recall_level))
tp_plus_fp = tp_plus_fp_cs[iou][cls_id][index]
tp_plus_fps.append(tp_plus_fp)
fp = fp_os[iou][cls_id][index]
fps.append(fp)
if len(tp_plus_fps) > 0:
wi_at_iou[iou] = np.mean(fps) / np.mean(tp_plus_fps)
else:
wi_at_iou[iou] = 0
return wi_at_iou
def evaluate(self):
"""
Returns:
dict: has a key "segm", whose value is a dict of "AP", "AP50", and "AP75".
"""
all_predictions = comm.gather(self._predictions, dst=0)
if not comm.is_main_process():
return
predictions = defaultdict(list)
for predictions_per_rank in all_predictions:
for clsid, lines in predictions_per_rank.items():
predictions[clsid].extend(lines)
self._logger.info(
"Evaluating {} using {} metric. "
"Note that results do not use the official Matlab API.".format(
self._dataset_name, 2007 if self._is_2007 else 2012
)
)
with tempfile.TemporaryDirectory(prefix="pascal_voc_eval_") as dirname:
res_file_template = os.path.join(dirname, "{}.txt")
aps = defaultdict(list) # iou -> ap per class
recs = defaultdict(list)
precs = defaultdict(list)
all_recs = defaultdict(list)
all_precs = defaultdict(list)
unk_det_as_knowns = defaultdict(list)
num_unks = defaultdict(list)
tp_plus_fp_cs = defaultdict(list)
fp_os = defaultdict(list)
for cls_id, cls_name in enumerate(self._class_names):
lines = predictions.get(cls_id, [""])
self._logger.info(cls_name + " has " + str(len(lines)) + " predictions.")
with open(res_file_template.format(cls_name), "w") as f:
f.write("\n".join(lines))
# for thresh in range(50, 100, 5):
thresh = 50
rec, prec, ap, unk_det_as_known, num_unk, tp_plus_fp_closed_set, fp_open_set = voc_eval(
res_file_template,
self._anno_file_template,
self._image_set_path,
cls_name,
ovthresh=thresh / 100.0,
use_07_metric=self._is_2007,
known_classes=self.known_classes
)
aps[thresh].append(ap * 100)
unk_det_as_knowns[thresh].append(unk_det_as_known)
num_unks[thresh].append(num_unk)
all_precs[thresh].append(prec)
all_recs[thresh].append(rec)
tp_plus_fp_cs[thresh].append(tp_plus_fp_closed_set)
fp_os[thresh].append(fp_open_set)
try:
recs[thresh].append(rec[-1] * 100)
precs[thresh].append(prec[-1] * 100)
except:
recs[thresh].append(0)
precs[thresh].append(0)
wi = self.compute_WI_at_many_recall_level(all_recs, tp_plus_fp_cs, fp_os)
self._logger.info('Wilderness Impact: ' + str(wi))
avg_precision_unk = self.compute_avg_precision_at_many_recall_level_for_unk(all_precs, all_recs)
self._logger.info('avg_precision: ' + str(avg_precision_unk))
ret = OrderedDict()
mAP = {iou: np.mean(x) for iou, x in aps.items()}
ret["bbox"] = {"AP": np.mean(list(mAP.values())), "AP50": mAP[50]}
total_num_unk_det_as_known = {iou: np.sum(x) for iou, x in unk_det_as_knowns.items()}
total_num_unk = num_unks[50][0]
self._logger.info('Absolute OSE (total_num_unk_det_as_known): ' + str(total_num_unk_det_as_known))
self._logger.info('total_num_unk ' + str(total_num_unk))
# Extra logging of class-wise APs
avg_precs = list(np.mean([x for _, x in aps.items()], axis=0))
self._logger.info(self._class_names)
# self._logger.info("AP__: " + str(['%.1f' % x for x in avg_precs]))
self._logger.info("AP50: " + str(['%.1f' % x for x in aps[50]]))
self._logger.info("Precisions50: " + str(['%.1f' % x for x in precs[50]]))
self._logger.info("Recall50: " + str(['%.1f' % x for x in recs[50]]))
# self._logger.info("AP75: " + str(['%.1f' % x for x in aps[75]]))
if self.prev_intro_cls > 0:
# self._logger.info("\nPrev class AP__: " + str(np.mean(avg_precs[:self.prev_intro_cls])))
self._logger.info("Prev class AP50: " + str(np.mean(aps[50][:self.prev_intro_cls])))
self._logger.info("Prev class Precisions50: " + str(np.mean(precs[50][:self.prev_intro_cls])))
self._logger.info("Prev class Recall50: " + str(np.mean(recs[50][:self.prev_intro_cls])))
# self._logger.info("Prev class AP75: " + str(np.mean(aps[75][:self.prev_intro_cls])))
# self._logger.info("\nCurrent class AP__: " + str(np.mean(avg_precs[self.prev_intro_cls:self.curr_intro_cls])))
self._logger.info("Current class AP50: " + str(np.mean(aps[50][self.prev_intro_cls:self.prev_intro_cls + self.curr_intro_cls])))
self._logger.info("Current class Precisions50: " + str(np.mean(precs[50][self.prev_intro_cls:self.prev_intro_cls + self.curr_intro_cls])))
self._logger.info("Current class Recall50: " + str(np.mean(recs[50][self.prev_intro_cls:self.prev_intro_cls + self.curr_intro_cls])))
# self._logger.info("Current class AP75: " + str(np.mean(aps[75][self.prev_intro_cls:self.curr_intro_cls])))
# self._logger.info("\nKnown AP__: " + str(np.mean(avg_precs[:self.prev_intro_cls + self.curr_intro_cls])))
self._logger.info("Known AP50: " + str(np.mean(aps[50][:self.prev_intro_cls + self.curr_intro_cls])))
self._logger.info("Known Precisions50: " + str(np.mean(precs[50][:self.prev_intro_cls + self.curr_intro_cls])))
self._logger.info("Known Recall50: " + str(np.mean(recs[50][:self.prev_intro_cls + self.curr_intro_cls])))
# self._logger.info("Known AP75: " + str(np.mean(aps[75][:self.prev_intro_cls + self.curr_intro_cls])))
# self._logger.info("\nUnknown AP__: " + str(avg_precs[-1]))
self._logger.info("Unknown AP50: " + str(aps[50][-1]))
self._logger.info("Unknown Precisions50: " + str(precs[50][-1]))
self._logger.info("Unknown Recall50: " + str(recs[50][-1]))
# self._logger.info("Unknown AP75: " + str(aps[75][-1]))
# self._logger.info("R__: " + str(['%.1f' % x for x in list(np.mean([x for _, x in recs.items()], axis=0))]))
# self._logger.info("R50: " + str(['%.1f' % x for x in recs[50]]))
# self._logger.info("R75: " + str(['%.1f' % x for x in recs[75]]))
#
# self._logger.info("P__: " + str(['%.1f' % x for x in list(np.mean([x for _, x in precs.items()], axis=0))]))
# self._logger.info("P50: " + str(['%.1f' % x for x in precs[50]]))
# self._logger.info("P75: " + str(['%.1f' % x for x in precs[75]]))
return ret
##############################################################################
#
# Below code is modified from
# https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py
# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
"""Python implementation of the PASCAL VOC devkit's AP evaluation code."""
@lru_cache(maxsize=None)
def parse_rec(filename, known_classes):
"""Parse a PASCAL VOC xml file."""
VOC_CLASS_NAMES_COCOFIED = [
"airplane", "dining table", "motorcycle",
"potted plant", "couch", "tv"
]
BASE_VOC_CLASS_NAMES = [
"aeroplane", "diningtable", "motorbike",
"pottedplant", "sofa", "tvmonitor"
]
try:
with PathManager.open(filename) as f:
tree = ET.parse(f)
except:
import traceback
traceback.print_exc()
print()
logger = logging.getLogger(__name__)
logger.info('Not able to load: ' + filename + '. Continuing without aboarting...')
return None
objects = []
for obj in tree.findall("object"):
obj_struct = {}
cls_name = obj.find("name").text
if cls_name in VOC_CLASS_NAMES_COCOFIED:
cls_name = BASE_VOC_CLASS_NAMES[VOC_CLASS_NAMES_COCOFIED.index(cls_name)]
if cls_name not in known_classes:
cls_name = 'unknown'
obj_struct["name"] = cls_name
# obj_struct["pose"] = obj.find("pose").text
# obj_struct["truncated"] = int(obj.find("truncated").text)
obj_struct["difficult"] = int(obj.find("difficult").text)
bbox = obj.find("bndbox")
obj_struct["bbox"] = [
int(bbox.find("xmin").text),
int(bbox.find("ymin").text),
int(bbox.find("xmax").text),
int(bbox.find("ymax").text),
]
objects.append(obj_struct)
return objects
def voc_ap(rec, prec, use_07_metric=False):
"""Compute VOC AP given precision and recall. If use_07_metric is true, uses
the VOC 07 11-point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.0
for t in np.arange(0.0, 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.0
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.0], rec, [1.0]))
mpre = np.concatenate(([0.0], prec, [0.0]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, use_07_metric=False, known_classes=None):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# assumes detections are in detpath.format(classname)
# assumes annotations are in annopath.format(imagename)
# assumes imagesetfile is a text file with each line an image name
# first load gt
# read list of images
with PathManager.open(imagesetfile, "r") as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
imagenames_filtered = []
# load annots
recs = {}
for imagename in imagenames:
rec = parse_rec(annopath.format(imagename), tuple(known_classes))
if rec is not None:
recs[imagename] = rec
imagenames_filtered.append(imagename)
imagenames = imagenames_filtered
# extract gt objects for this class
class_recs = {}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj["name"] == classname]
bbox = np.array([x["bbox"] for x in R])
difficult = np.array([x["difficult"] for x in R]).astype(np.bool)
# difficult = np.array([False for x in R]).astype(np.bool) # treat all "difficult" as GT
det = [False] * len(R)
npos = npos + sum(~difficult)
class_recs[imagename] = {"bbox": bbox, "difficult": difficult, "det": det}
# read dets
detfile = detpath.format(classname)
with open(detfile, "r") as f:
lines = f.readlines()
splitlines = [x.strip().split(" ") for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines]).reshape(-1, 4)
# sort by confidence
sorted_ind = np.argsort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
# if 'unknown' not in classname:
# return tp, fp, 0
# print("class_recs has keys:", len(class_recs))
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R["bbox"].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1.0, 0.0)
ih = np.maximum(iymax - iymin + 1.0, 0.0)
inters = iw * ih
# union
uni = (
(bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0)
+ (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0)
- inters
)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R["difficult"][jmax]:
if not R["det"][jmax]:
tp[d] = 1.0
R["det"][jmax] = 1
else:
fp[d] = 1.0
else:
fp[d] = 1.0
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
# print('tp: ' + str(tp[-1]))
# print('fp: ' + str(fp[-1]))
# print('tp: ')
# print(tp)
# print('fp: ')
# print(fp)
'''
Computing Absolute Open-Set Error (A-OSE) and Wilderness Impact (WI)
===========
Absolute OSE = # of unknown objects classified as known objects of class 'classname'
WI = FP_openset / (TP_closed_set + FP_closed_set)
'''
logger = logging.getLogger(__name__)
# Finding GT of unknown objects
unknown_class_recs = {}
n_unk = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj["name"] == 'unknown']
bbox = np.array([x["bbox"] for x in R])
difficult = np.array([x["difficult"] for x in R]).astype(np.bool)
det = [False] * len(R)
n_unk = n_unk + sum(~difficult)
unknown_class_recs[imagename] = {"bbox": bbox, "difficult": difficult, "det": det}
if classname == 'unknown':
return rec, prec, ap, 0, n_unk, None, None
# Go down each detection and see if it has an overlap with an unknown object.
# If so, it is an unknown object that was classified as known.
is_unk = np.zeros(nd)
for d in range(nd):
R = unknown_class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R["bbox"].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1.0, 0.0)
ih = np.maximum(iymax - iymin + 1.0, 0.0)
inters = iw * ih
# union
uni = (
(bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0)
+ (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0)
- inters
)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
is_unk[d] = 1.0
is_unk_sum = np.sum(is_unk)
# OSE = is_unk / n_unk
# logger.info('Number of unknowns detected knowns (for class '+ classname + ') is ' + str(is_unk))
# logger.info("Num of unknown instances: " + str(n_unk))
# logger.info('OSE: ' + str(OSE))
tp_plus_fp_closed_set = tp+fp
fp_open_set = np.cumsum(is_unk)
return rec, prec, ap, is_unk_sum, n_unk, tp_plus_fp_closed_set, fp_open_set