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PaddleDetection
未验证 提交 36b8f4a8 编写于 作者: G George Ni 提交者： GitHub
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[MOT] remove mot metric (#3360) 

* remove mot metric

* lazy import motmetrics
上级 bfc439ce
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Showing with 182 addition and 321 deletion
ppdet/engine/__init__.py
浏览文件 @ 36b8f4a8
......@@ -14,8 +14,6 @@
from . import trainer
from .trainer import *
from . import tracker
from .tracker import *
from . import callbacks
from .callbacks import *
......@@ -24,6 +22,9 @@ from . import env
from .env import *
__all__ = trainer.__all__ \
+ tracker.__all__ \
+ callbacks.__all__ \
+ env.__all__
from . import tracker
from .tracker import *
__all__ = __all__ + tracker.__all__ \
ppdet/engine/trainer.py
浏览文件 @ 36b8f4a8
......@@ -34,7 +34,6 @@ from ppdet.optimizer import ModelEMA
from ppdet.core.workspace import create
from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
from ppdet.utils.visualizer import visualize_results, save_result
from ppdet.metrics import JDEDetMetric, JDEReIDMetric
from ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, get_infer_results, KeyPointTopDownCOCOEval
from ppdet.data.source.category import get_categories
import ppdet.utils.stats as stats
......@@ -204,10 +203,6 @@ class Trainer(object):
len(eval_dataset), self.cfg.num_joints,
self.cfg.save_dir)
]
elif self.cfg.metric == 'MOTDet':
self._metrics = [JDEDetMetric(), ]
elif self.cfg.metric == 'ReID':
self._metrics = [JDEReIDMetric(), ]
else:
logger.warn("Metric not support for metric type {}".format(
self.cfg.metric))
......
ppdet/metrics/__init__.py
浏览文件 @ 36b8f4a8
......@@ -14,10 +14,12 @@
from . import metrics
from . import keypoint_metrics
from . import mot_metrics
from .metrics import *
from .mot_metrics import *
from .keypoint_metrics import *
__all__ = metrics.__all__ + keypoint_metrics.__all__ + mot_metrics.__all__
__all__ = metrics.__all__ + keypoint_metrics.__all__
from . import mot_metrics
from .mot_metrics import *
__all__ = __all__ + mot_metrics.__all__
ppdet/metrics/mot_eval_utils.py 已删除 100644 → 0
浏览文件 @ bfc439ce
# Copyright (c) 2021 PaddlePaddle 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.
import os
import numpy as np
import copy
import motmetrics as mm
mm.lap.default_solver = 'lap'
__all__ = [
'read_mot_results',
'unzip_objs',
'MOTEvaluator',
]
def read_mot_results(filename, is_gt=False, is_ignore=False):
valid_labels = {1}
ignore_labels = {2, 7, 8, 12}
results_dict = dict()
if os.path.isfile(filename):
with open(filename, 'r') as f:
for line in f.readlines():
linelist = line.split(',')
if len(linelist) < 7:
continue
fid = int(linelist[0])
if fid < 1:
continue
results_dict.setdefault(fid, list())
box_size = float(linelist[4]) * float(linelist[5])
if is_gt:
if 'MOT16-' in filename or 'MOT17-' in filename:
label = int(float(linelist[7]))
mark = int(float(linelist[6]))
if mark == 0 or label not in valid_labels:
continue
score = 1
elif is_ignore:
if 'MOT16-' in filename or 'MOT17-' in filename:
label = int(float(linelist[7]))
vis_ratio = float(linelist[8])
if label not in ignore_labels and vis_ratio >= 0:
continue
else:
continue
score = 1
else:
score = float(linelist[6])
tlwh = tuple(map(float, linelist[2:6]))
target_id = int(linelist[1])
results_dict[fid].append((tlwh, target_id, score))
return results_dict
"""
labels={'ped', ... % 1
'person_on_vhcl', ... % 2
'car', ... % 3
'bicycle', ... % 4
'mbike', ... % 5
'non_mot_vhcl', ... % 6
'static_person', ... % 7
'distractor', ... % 8
'occluder', ... % 9
'occluder_on_grnd', ... % 10
'occluder_full', ... % 11
'reflection', ... % 12
'crowd' ... % 13
};
"""
def unzip_objs(objs):
if len(objs) > 0:
tlwhs, ids, scores = zip(*objs)
else:
tlwhs, ids, scores = [], [], []
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
return tlwhs, ids, scores
class MOTEvaluator(object):
def __init__(self, data_root, seq_name, data_type):
self.data_root = data_root
self.seq_name = seq_name
self.data_type = data_type
self.load_annotations()
self.reset_accumulator()
def load_annotations(self):
assert self.data_type == 'mot'
gt_filename = os.path.join(self.data_root, self.seq_name, 'gt',
'gt.txt')
self.gt_frame_dict = read_mot_results(gt_filename, is_gt=True)
self.gt_ignore_frame_dict = read_mot_results(
gt_filename, is_ignore=True)
def reset_accumulator(self):
self.acc = mm.MOTAccumulator(auto_id=True)
def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
# results
trk_tlwhs = np.copy(trk_tlwhs)
trk_ids = np.copy(trk_ids)
# gts
gt_objs = self.gt_frame_dict.get(frame_id, [])
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
# ignore boxes
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
ignore_tlwhs = unzip_objs(ignore_objs)[0]
# remove ignored results
keep = np.ones(len(trk_tlwhs), dtype=bool)
iou_distance = mm.distances.iou_matrix(
ignore_tlwhs, trk_tlwhs, max_iou=0.5)
if len(iou_distance) > 0:
match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
match_ious = iou_distance[match_is, match_js]
match_js = np.asarray(match_js, dtype=int)
match_js = match_js[np.logical_not(np.isnan(match_ious))]
keep[match_js] = False
trk_tlwhs = trk_tlwhs[keep]
trk_ids = trk_ids[keep]
# get distance matrix
iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
# acc
self.acc.update(gt_ids, trk_ids, iou_distance)
if rtn_events and iou_distance.size > 0 and hasattr(self.acc,
'last_mot_events'):
events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics
else:
events = None
return events
def eval_file(self, filename):
self.reset_accumulator()
result_frame_dict = read_mot_results(filename, is_gt=False)
frames = sorted(list(set(result_frame_dict.keys())))
for frame_id in frames:
trk_objs = result_frame_dict.get(frame_id, [])
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
return self.acc
@staticmethod
def get_summary(accs,
names,
metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1',
'precision', 'recall')):
names = copy.deepcopy(names)
if metrics is None:
metrics = mm.metrics.motchallenge_metrics
metrics = copy.deepcopy(metrics)
mh = mm.metrics.create()
summary = mh.compute_many(
accs, metrics=metrics, names=names, generate_overall=True)
return summary
@staticmethod
def save_summary(summary, filename):
import pandas as pd
writer = pd.ExcelWriter(filename)
summary.to_excel(writer)
writer.save()
ppdet/metrics/mot_metrics.py
浏览文件 @ 36b8f4a8
......@@ -16,134 +16,188 @@ from __future__ import division
from __future__ import print_function
import os
import paddle
import copy
import numpy as np
from scipy import interpolate
import paddle
import paddle.nn.functional as F
from .map_utils import ap_per_class
from ppdet.modeling.bbox_utils import bbox_iou_np_expand
from .mot_eval_utils import MOTEvaluator
from .metrics import Metric
from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
__all__ = ['JDEDetMetric', 'JDEReIDMetric', 'MOTMetric']
class JDEDetMetric(Metric):
def __init__(self, overlap_thresh=0.5):
self.overlap_thresh = overlap_thresh
self.reset()
def reset(self):
self.AP_accum = np.zeros(1)
self.AP_accum_count = np.zeros(1)
def update(self, inputs, outputs):
bboxes = outputs['bbox'][:, 2:].numpy()
scores = outputs['bbox'][:, 1].numpy()
labels = outputs['bbox'][:, 0].numpy()
bbox_lengths = outputs['bbox_num'].numpy()
if bboxes.shape[0] == 1 and bboxes.sum() == 0.0:
return
gt_boxes = inputs['gt_bbox'].numpy()[0]
gt_labels = inputs['gt_class'].numpy()[0]
if gt_labels.shape[0] == 0:
return
correct = []
detected = []
for i in range(bboxes.shape[0]):
obj_pred = 0
pred_bbox = bboxes[i].reshape(1, 4)
# Compute iou with target boxes
iou = bbox_iou_np_expand(pred_bbox, gt_boxes, x1y1x2y2=True)[0]
# Extract index of largest overlap
best_i = np.argmax(iou)
# If overlap exceeds threshold and classification is correct mark as correct
if iou[best_i] > self.overlap_thresh and obj_pred == gt_labels[
best_i] and best_i not in detected:
correct.append(1)
detected.append(best_i)
__all__ = ['MOTEvaluator', 'MOTMetric']
def read_mot_results(filename, is_gt=False, is_ignore=False):
valid_labels = {1}
ignore_labels = {2, 7, 8, 12}
results_dict = dict()
if os.path.isfile(filename):
with open(filename, 'r') as f:
for line in f.readlines():
linelist = line.split(',')
if len(linelist) < 7:
continue
fid = int(linelist[0])
if fid < 1:
continue
results_dict.setdefault(fid, list())
box_size = float(linelist[4]) * float(linelist[5])
if is_gt:
if 'MOT16-' in filename or 'MOT17-' in filename:
label = int(float(linelist[7]))
mark = int(float(linelist[6]))
if mark == 0 or label not in valid_labels:
continue
score = 1
elif is_ignore:
if 'MOT16-' in filename or 'MOT17-' in filename:
label = int(float(linelist[7]))
vis_ratio = float(linelist[8])
if label not in ignore_labels and vis_ratio >= 0:
continue
else:
correct.append(0)
# Compute Average Precision (AP) per class
target_cls = list(gt_labels.T[0])
AP, AP_class, R, P = ap_per_class(
tp=correct,
conf=scores,
pred_cls=np.zeros_like(scores),
target_cls=target_cls)
self.AP_accum_count += np.bincount(AP_class, minlength=1)
self.AP_accum += np.bincount(AP_class, minlength=1, weights=AP)
def accumulate(self):
logger.info("Accumulating evaluatation results...")
self.map_stat = self.AP_accum[0] / (self.AP_accum_count[0] + 1E-16)
def log(self):
map_stat = 100. * self.map_stat
logger.info("mAP({:.2f}) = {:.2f}%".format(self.overlap_thresh,
map_stat))
def get_results(self):
return self.map_stat
continue
score = 1
else:
score = float(linelist[6])
tlwh = tuple(map(float, linelist[2:6]))
target_id = int(linelist[1])
results_dict[fid].append((tlwh, target_id, score))
return results_dict
"""
labels={'ped', ... % 1
'person_on_vhcl', ... % 2
'car', ... % 3
'bicycle', ... % 4
'mbike', ... % 5
'non_mot_vhcl', ... % 6
'static_person', ... % 7
'distractor', ... % 8
'occluder', ... % 9
'occluder_on_grnd', ... % 10
'occluder_full', ... % 11
'reflection', ... % 12
'crowd' ... % 13
};
"""
def unzip_objs(objs):
if len(objs) > 0:
tlwhs, ids, scores = zip(*objs)
else:
tlwhs, ids, scores = [], [], []
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
return tlwhs, ids, scores
class JDEReIDMetric(Metric):
def __init__(self, far_levels=[1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]):
self.far_levels = far_levels
self.reset()
class MOTEvaluator(object):
def __init__(self, data_root, seq_name, data_type):
self.data_root = data_root
self.seq_name = seq_name
self.data_type = data_type
def reset(self):
self.embedding = []
self.id_labels = []
self.eval_results = {}
self.load_annotations()
self.reset_accumulator()
def update(self, inputs, outputs):
for out in outputs:
feat, label = out[:-1].clone().detach(), int(out[-1])
if label != -1:
self.embedding.append(feat)
self.id_labels.append(label)
def load_annotations(self):
assert self.data_type == 'mot'
gt_filename = os.path.join(self.data_root, self.seq_name, 'gt',
'gt.txt')
self.gt_frame_dict = read_mot_results(gt_filename, is_gt=True)
self.gt_ignore_frame_dict = read_mot_results(
gt_filename, is_ignore=True)
def accumulate(self):
logger.info("Computing pairwise similairity...")
assert len(self.embedding) == len(self.id_labels)
if len(self.embedding) < 1:
return None
embedding = paddle.stack(self.embedding, axis=0)
emb = F.normalize(embedding, axis=1).numpy()
pdist = np.matmul(emb, emb.T)
id_labels = np.array(self.id_labels, dtype='int32').reshape(-1, 1)
n = len(id_labels)
id_lbl = np.tile(id_labels, n).T
gt = id_lbl == id_lbl.T
up_triangle = np.where(np.triu(pdist) - np.eye(n) * pdist != 0)
pdist = pdist[up_triangle]
gt = gt[up_triangle]
# lazy import metrics here
from sklearn import metrics
far, tar, threshold = metrics.roc_curve(gt, pdist)
interp = interpolate.interp1d(far, tar)
tar_at_far = [interp(x) for x in self.far_levels]
for f, fa in enumerate(self.far_levels):
self.eval_results['TPR@FAR={:.7f}'.format(fa)] = ' {:.4f}'.format(
tar_at_far[f])
def reset_accumulator(self):
import motmetrics as mm
mm.lap.default_solver = 'lap'
self.acc = mm.MOTAccumulator(auto_id=True)
def log(self):
for k, v in self.eval_results.items():
logger.info('{}: {}'.format(k, v))
def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
import motmetrics as mm
mm.lap.default_solver = 'lap'
# results
trk_tlwhs = np.copy(trk_tlwhs)
trk_ids = np.copy(trk_ids)
# gts
gt_objs = self.gt_frame_dict.get(frame_id, [])
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
# ignore boxes
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
ignore_tlwhs = unzip_objs(ignore_objs)[0]
# remove ignored results
keep = np.ones(len(trk_tlwhs), dtype=bool)
iou_distance = mm.distances.iou_matrix(
ignore_tlwhs, trk_tlwhs, max_iou=0.5)
if len(iou_distance) > 0:
match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
match_ious = iou_distance[match_is, match_js]
match_js = np.asarray(match_js, dtype=int)
match_js = match_js[np.logical_not(np.isnan(match_ious))]
keep[match_js] = False
trk_tlwhs = trk_tlwhs[keep]
trk_ids = trk_ids[keep]
# get distance matrix
iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
# acc
self.acc.update(gt_ids, trk_ids, iou_distance)
if rtn_events and iou_distance.size > 0 and hasattr(self.acc,
'last_mot_events'):
events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics
else:
events = None
return events
def eval_file(self, filename):
self.reset_accumulator()
result_frame_dict = read_mot_results(filename, is_gt=False)
frames = sorted(list(set(result_frame_dict.keys())))
for frame_id in frames:
trk_objs = result_frame_dict.get(frame_id, [])
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
return self.acc
@staticmethod
def get_summary(accs,
names,
metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1',
'precision', 'recall')):
import motmetrics as mm
mm.lap.default_solver = 'lap'
names = copy.deepcopy(names)
if metrics is None:
metrics = mm.metrics.motchallenge_metrics
metrics = copy.deepcopy(metrics)
def get_results(self):
return self.eval_results
mh = mm.metrics.create()
summary = mh.compute_many(
accs, metrics=metrics, names=names, generate_overall=True)
return summary
@staticmethod
def save_summary(summary, filename):
import pandas as pd
writer = pd.ExcelWriter(filename)
summary.to_excel(writer)
writer.save()
class MOTMetric(Metric):
......
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