bytetrack / yolox /evaluators /mot_evaluator.py
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from collections import defaultdict
from loguru import logger
from tqdm import tqdm
import torch
from yolox.utils import (
gather,
is_main_process,
postprocess,
synchronize,
time_synchronized,
xyxy2xywh
)
from yolox.tracker.byte_tracker import BYTETracker
from yolox.sort_tracker.sort import Sort
from yolox.deepsort_tracker.deepsort import DeepSort
from yolox.motdt_tracker.motdt_tracker import OnlineTracker
import contextlib
import io
import os
import itertools
import json
import tempfile
import time
def write_results(filename, results):
save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\n'
with open(filename, 'w') as f:
for frame_id, tlwhs, track_ids, scores in results:
for tlwh, track_id, score in zip(tlwhs, track_ids, scores):
if track_id < 0:
continue
x1, y1, w, h = tlwh
line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1), h=round(h, 1), s=round(score, 2))
f.write(line)
logger.info('save results to {}'.format(filename))
def write_results_no_score(filename, results):
save_format = '{frame},{id},{x1},{y1},{w},{h},-1,-1,-1,-1\n'
with open(filename, 'w') as f:
for frame_id, tlwhs, track_ids in results:
for tlwh, track_id in zip(tlwhs, track_ids):
if track_id < 0:
continue
x1, y1, w, h = tlwh
line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1), h=round(h, 1))
f.write(line)
logger.info('save results to {}'.format(filename))
class MOTEvaluator:
"""
COCO AP Evaluation class. All the data in the val2017 dataset are processed
and evaluated by COCO API.
"""
def __init__(
self, args, dataloader, img_size, confthre, nmsthre, num_classes):
"""
Args:
dataloader (Dataloader): evaluate dataloader.
img_size (int): image size after preprocess. images are resized
to squares whose shape is (img_size, img_size).
confthre (float): confidence threshold ranging from 0 to 1, which
is defined in the config file.
nmsthre (float): IoU threshold of non-max supression ranging from 0 to 1.
"""
self.dataloader = dataloader
self.img_size = img_size
self.confthre = confthre
self.nmsthre = nmsthre
self.num_classes = num_classes
self.args = args
def evaluate(
self,
model,
distributed=False,
half=False,
trt_file=None,
decoder=None,
test_size=None,
result_folder=None
):
"""
COCO average precision (AP) Evaluation. Iterate inference on the test dataset
and the results are evaluated by COCO API.
NOTE: This function will change training mode to False, please save states if needed.
Args:
model : model to evaluate.
Returns:
ap50_95 (float) : COCO AP of IoU=50:95
ap50 (float) : COCO AP of IoU=50
summary (sr): summary info of evaluation.
"""
# TODO half to amp_test
tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor
model = model.eval()
if half:
model = model.half()
ids = []
data_list = []
results = []
video_names = defaultdict()
progress_bar = tqdm if is_main_process() else iter
inference_time = 0
track_time = 0
n_samples = len(self.dataloader) - 1
if trt_file is not None:
from torch2trt import TRTModule
model_trt = TRTModule()
model_trt.load_state_dict(torch.load(trt_file))
x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()
model(x)
model = model_trt
tracker = BYTETracker(self.args)
ori_thresh = self.args.track_thresh
for cur_iter, (imgs, _, info_imgs, ids) in enumerate(
progress_bar(self.dataloader)
):
with torch.no_grad():
# init tracker
frame_id = info_imgs[2].item()
video_id = info_imgs[3].item()
img_file_name = info_imgs[4]
video_name = img_file_name[0].split('/')[0]
if video_name == 'MOT17-05-FRCNN' or video_name == 'MOT17-06-FRCNN':
self.args.track_buffer = 14
elif video_name == 'MOT17-13-FRCNN' or video_name == 'MOT17-14-FRCNN':
self.args.track_buffer = 25
else:
self.args.track_buffer = 30
if video_name == 'MOT17-01-FRCNN':
self.args.track_thresh = 0.65
elif video_name == 'MOT17-06-FRCNN':
self.args.track_thresh = 0.65
elif video_name == 'MOT17-12-FRCNN':
self.args.track_thresh = 0.7
elif video_name == 'MOT17-14-FRCNN':
self.args.track_thresh = 0.67
else:
self.args.track_thresh = ori_thresh
if video_name == 'MOT20-06' or video_name == 'MOT20-08':
self.args.track_thresh = 0.3
else:
self.args.track_thresh = ori_thresh
if video_name not in video_names:
video_names[video_id] = video_name
if frame_id == 1:
tracker = BYTETracker(self.args)
if len(results) != 0:
result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))
write_results(result_filename, results)
results = []
imgs = imgs.type(tensor_type)
# skip the the last iters since batchsize might be not enough for batch inference
is_time_record = cur_iter < len(self.dataloader) - 1
if is_time_record:
start = time.time()
outputs = model(imgs)
if decoder is not None:
outputs = decoder(outputs, dtype=outputs.type())
outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)
if is_time_record:
infer_end = time_synchronized()
inference_time += infer_end - start
output_results = self.convert_to_coco_format(outputs, info_imgs, ids)
data_list.extend(output_results)
# run tracking
if outputs[0] is not None:
online_targets = tracker.update(outputs[0], info_imgs, self.img_size)
online_tlwhs = []
online_ids = []
online_scores = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
online_scores.append(t.score)
# save results
results.append((frame_id, online_tlwhs, online_ids, online_scores))
if is_time_record:
track_end = time_synchronized()
track_time += track_end - infer_end
if cur_iter == len(self.dataloader) - 1:
result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))
write_results(result_filename, results)
statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])
if distributed:
data_list = gather(data_list, dst=0)
data_list = list(itertools.chain(*data_list))
torch.distributed.reduce(statistics, dst=0)
eval_results = self.evaluate_prediction(data_list, statistics)
synchronize()
return eval_results
def evaluate_sort(
self,
model,
distributed=False,
half=False,
trt_file=None,
decoder=None,
test_size=None,
result_folder=None
):
"""
COCO average precision (AP) Evaluation. Iterate inference on the test dataset
and the results are evaluated by COCO API.
NOTE: This function will change training mode to False, please save states if needed.
Args:
model : model to evaluate.
Returns:
ap50_95 (float) : COCO AP of IoU=50:95
ap50 (float) : COCO AP of IoU=50
summary (sr): summary info of evaluation.
"""
# TODO half to amp_test
tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor
model = model.eval()
if half:
model = model.half()
ids = []
data_list = []
results = []
video_names = defaultdict()
progress_bar = tqdm if is_main_process() else iter
inference_time = 0
track_time = 0
n_samples = len(self.dataloader) - 1
if trt_file is not None:
from torch2trt import TRTModule
model_trt = TRTModule()
model_trt.load_state_dict(torch.load(trt_file))
x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()
model(x)
model = model_trt
tracker = Sort(self.args.track_thresh)
for cur_iter, (imgs, _, info_imgs, ids) in enumerate(
progress_bar(self.dataloader)
):
with torch.no_grad():
# init tracker
frame_id = info_imgs[2].item()
video_id = info_imgs[3].item()
img_file_name = info_imgs[4]
video_name = img_file_name[0].split('/')[0]
if video_name not in video_names:
video_names[video_id] = video_name
if frame_id == 1:
tracker = Sort(self.args.track_thresh)
if len(results) != 0:
result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))
write_results_no_score(result_filename, results)
results = []
imgs = imgs.type(tensor_type)
# skip the the last iters since batchsize might be not enough for batch inference
is_time_record = cur_iter < len(self.dataloader) - 1
if is_time_record:
start = time.time()
outputs = model(imgs)
if decoder is not None:
outputs = decoder(outputs, dtype=outputs.type())
outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)
if is_time_record:
infer_end = time_synchronized()
inference_time += infer_end - start
output_results = self.convert_to_coco_format(outputs, info_imgs, ids)
data_list.extend(output_results)
# run tracking
online_targets = tracker.update(outputs[0], info_imgs, self.img_size)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]
tid = t[4]
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
# save results
results.append((frame_id, online_tlwhs, online_ids))
if is_time_record:
track_end = time_synchronized()
track_time += track_end - infer_end
if cur_iter == len(self.dataloader) - 1:
result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))
write_results_no_score(result_filename, results)
statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])
if distributed:
data_list = gather(data_list, dst=0)
data_list = list(itertools.chain(*data_list))
torch.distributed.reduce(statistics, dst=0)
eval_results = self.evaluate_prediction(data_list, statistics)
synchronize()
return eval_results
def evaluate_deepsort(
self,
model,
distributed=False,
half=False,
trt_file=None,
decoder=None,
test_size=None,
result_folder=None,
model_folder=None
):
"""
COCO average precision (AP) Evaluation. Iterate inference on the test dataset
and the results are evaluated by COCO API.
NOTE: This function will change training mode to False, please save states if needed.
Args:
model : model to evaluate.
Returns:
ap50_95 (float) : COCO AP of IoU=50:95
ap50 (float) : COCO AP of IoU=50
summary (sr): summary info of evaluation.
"""
# TODO half to amp_test
tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor
model = model.eval()
if half:
model = model.half()
ids = []
data_list = []
results = []
video_names = defaultdict()
progress_bar = tqdm if is_main_process() else iter
inference_time = 0
track_time = 0
n_samples = len(self.dataloader) - 1
if trt_file is not None:
from torch2trt import TRTModule
model_trt = TRTModule()
model_trt.load_state_dict(torch.load(trt_file))
x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()
model(x)
model = model_trt
tracker = DeepSort(model_folder, min_confidence=self.args.track_thresh)
for cur_iter, (imgs, _, info_imgs, ids) in enumerate(
progress_bar(self.dataloader)
):
with torch.no_grad():
# init tracker
frame_id = info_imgs[2].item()
video_id = info_imgs[3].item()
img_file_name = info_imgs[4]
video_name = img_file_name[0].split('/')[0]
if video_name not in video_names:
video_names[video_id] = video_name
if frame_id == 1:
tracker = DeepSort(model_folder, min_confidence=self.args.track_thresh)
if len(results) != 0:
result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))
write_results_no_score(result_filename, results)
results = []
imgs = imgs.type(tensor_type)
# skip the the last iters since batchsize might be not enough for batch inference
is_time_record = cur_iter < len(self.dataloader) - 1
if is_time_record:
start = time.time()
outputs = model(imgs)
if decoder is not None:
outputs = decoder(outputs, dtype=outputs.type())
outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)
if is_time_record:
infer_end = time_synchronized()
inference_time += infer_end - start
output_results = self.convert_to_coco_format(outputs, info_imgs, ids)
data_list.extend(output_results)
# run tracking
online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]
tid = t[4]
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
# save results
results.append((frame_id, online_tlwhs, online_ids))
if is_time_record:
track_end = time_synchronized()
track_time += track_end - infer_end
if cur_iter == len(self.dataloader) - 1:
result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))
write_results_no_score(result_filename, results)
statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])
if distributed:
data_list = gather(data_list, dst=0)
data_list = list(itertools.chain(*data_list))
torch.distributed.reduce(statistics, dst=0)
eval_results = self.evaluate_prediction(data_list, statistics)
synchronize()
return eval_results
def evaluate_motdt(
self,
model,
distributed=False,
half=False,
trt_file=None,
decoder=None,
test_size=None,
result_folder=None,
model_folder=None
):
"""
COCO average precision (AP) Evaluation. Iterate inference on the test dataset
and the results are evaluated by COCO API.
NOTE: This function will change training mode to False, please save states if needed.
Args:
model : model to evaluate.
Returns:
ap50_95 (float) : COCO AP of IoU=50:95
ap50 (float) : COCO AP of IoU=50
summary (sr): summary info of evaluation.
"""
# TODO half to amp_test
tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor
model = model.eval()
if half:
model = model.half()
ids = []
data_list = []
results = []
video_names = defaultdict()
progress_bar = tqdm if is_main_process() else iter
inference_time = 0
track_time = 0
n_samples = len(self.dataloader) - 1
if trt_file is not None:
from torch2trt import TRTModule
model_trt = TRTModule()
model_trt.load_state_dict(torch.load(trt_file))
x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()
model(x)
model = model_trt
tracker = OnlineTracker(model_folder, min_cls_score=self.args.track_thresh)
for cur_iter, (imgs, _, info_imgs, ids) in enumerate(
progress_bar(self.dataloader)
):
with torch.no_grad():
# init tracker
frame_id = info_imgs[2].item()
video_id = info_imgs[3].item()
img_file_name = info_imgs[4]
video_name = img_file_name[0].split('/')[0]
if video_name not in video_names:
video_names[video_id] = video_name
if frame_id == 1:
tracker = OnlineTracker(model_folder, min_cls_score=self.args.track_thresh)
if len(results) != 0:
result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))
write_results(result_filename, results)
results = []
imgs = imgs.type(tensor_type)
# skip the the last iters since batchsize might be not enough for batch inference
is_time_record = cur_iter < len(self.dataloader) - 1
if is_time_record:
start = time.time()
outputs = model(imgs)
if decoder is not None:
outputs = decoder(outputs, dtype=outputs.type())
outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)
if is_time_record:
infer_end = time_synchronized()
inference_time += infer_end - start
output_results = self.convert_to_coco_format(outputs, info_imgs, ids)
data_list.extend(output_results)
# run tracking
online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])
online_tlwhs = []
online_ids = []
online_scores = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
online_scores.append(t.score)
# save results
results.append((frame_id, online_tlwhs, online_ids, online_scores))
if is_time_record:
track_end = time_synchronized()
track_time += track_end - infer_end
if cur_iter == len(self.dataloader) - 1:
result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))
write_results(result_filename, results)
statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])
if distributed:
data_list = gather(data_list, dst=0)
data_list = list(itertools.chain(*data_list))
torch.distributed.reduce(statistics, dst=0)
eval_results = self.evaluate_prediction(data_list, statistics)
synchronize()
return eval_results
def convert_to_coco_format(self, outputs, info_imgs, ids):
data_list = []
for (output, img_h, img_w, img_id) in zip(
outputs, info_imgs[0], info_imgs[1], ids
):
if output is None:
continue
output = output.cpu()
bboxes = output[:, 0:4]
# preprocessing: resize
scale = min(
self.img_size[0] / float(img_h), self.img_size[1] / float(img_w)
)
bboxes /= scale
bboxes = xyxy2xywh(bboxes)
cls = output[:, 6]
scores = output[:, 4] * output[:, 5]
for ind in range(bboxes.shape[0]):
label = self.dataloader.dataset.class_ids[int(cls[ind])]
pred_data = {
"image_id": int(img_id),
"category_id": label,
"bbox": bboxes[ind].numpy().tolist(),
"score": scores[ind].numpy().item(),
"segmentation": [],
} # COCO json format
data_list.append(pred_data)
return data_list
def evaluate_prediction(self, data_dict, statistics):
if not is_main_process():
return 0, 0, None
logger.info("Evaluate in main process...")
annType = ["segm", "bbox", "keypoints"]
inference_time = statistics[0].item()
track_time = statistics[1].item()
n_samples = statistics[2].item()
a_infer_time = 1000 * inference_time / (n_samples * self.dataloader.batch_size)
a_track_time = 1000 * track_time / (n_samples * self.dataloader.batch_size)
time_info = ", ".join(
[
"Average {} time: {:.2f} ms".format(k, v)
for k, v in zip(
["forward", "track", "inference"],
[a_infer_time, a_track_time, (a_infer_time + a_track_time)],
)
]
)
info = time_info + "\n"
# Evaluate the Dt (detection) json comparing with the ground truth
if len(data_dict) > 0:
cocoGt = self.dataloader.dataset.coco
# TODO: since pycocotools can't process dict in py36, write data to json file.
_, tmp = tempfile.mkstemp()
json.dump(data_dict, open(tmp, "w"))
cocoDt = cocoGt.loadRes(tmp)
'''
try:
from yolox.layers import COCOeval_opt as COCOeval
except ImportError:
from pycocotools import cocoeval as COCOeval
logger.warning("Use standard COCOeval.")
'''
#from pycocotools.cocoeval import COCOeval
from yolox.layers import COCOeval_opt as COCOeval
cocoEval = COCOeval(cocoGt, cocoDt, annType[1])
cocoEval.evaluate()
cocoEval.accumulate()
redirect_string = io.StringIO()
with contextlib.redirect_stdout(redirect_string):
cocoEval.summarize()
info += redirect_string.getvalue()
return cocoEval.stats[0], cocoEval.stats[1], info
else:
return 0, 0, info