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calibrate.py
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calibrate.py
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
Usage - config:
$ python calibrate.py --config 'config/opt/opt_Visdrone_detect_S_Hyp0.yaml'
"""
import sys
from pathlib import Path
import os
from torch.distributed import pickle
import yaml
import pickle
import datetime
from argparse import Namespace, ArgumentParser
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from utils.general import LOGGER, Profile, split_dict, increment_path, print_args
from utils.calibration_utils import (
setup_data_model, get_yolo_predictions, get_calibrator, calibration, calc_mAP, NMS
)
from utils.torch_utils import smart_inference_mode
@smart_inference_mode()
def run(
config=ROOT / 'config/opt/opt_Visdrone_detect_S_Hyp0.yaml',
conf_thres=0.02,
iou_thres_obj=0.60,
iou_thres_class=0.30,
where_apply_calib_obj='before_nms',
where_apply_calib_class='after_nms',
calibrator='isotonic',
size_test=0.6,
speed_info=False,
save_dict=True,
plots=True,
):
with open(config) as f:
config = Namespace(**yaml.safe_load(f))
dataloader, model, device, dt = setup_data_model(config, ROOT)
num_classes = model.model.nc
now = datetime.datetime.now()
save_dir = increment_path(Path(config.project) / config.name / f'calibrate_{str(now.year).zfill(4)}_{str(now.month).zfill(2)}_{str(now.day).zfill(2)}__{str(now.hour).zfill(2)}_{str(now.minute).zfill(2)}', exist_ok=config.exist_ok, mkdir=True) # increment run
d_ = {
"FILE": str(FILE),
"save_dir": str(save_dir),
"nc": model.model.nc,
"names": model.names,
"conf_thres": conf_thres,
"iou_thres_obj": iou_thres_obj,
"iou_thres_class": iou_thres_class,
"where_apply_calib_obj": where_apply_calib_obj,
"where_apply_calib_class": where_apply_calib_class,
"calibrator": calibrator,
"size_test": size_test,
}
with open(os.path.join(save_dir, 'config.yaml'), 'w') as file:
yaml.dump(vars(config), file)
dt = Profile(), Profile(), Profile()
with dt[0]:
data_dict = get_yolo_predictions(dataloader, model, config, device, dt)
calib_dict, test_dict = split_dict(data_dict, size_test)
print("Length calib dict: ", len(calib_dict), " and length test dict: ", len(test_dict))
calibrator = get_calibrator(calibrator)
calib_location = "before_nms"
LOGGER.info(f'\nStarting the calibrations: {calib_location}...')
calib_obj = (where_apply_calib_obj == calib_location)
calib_class = (where_apply_calib_class == calib_location)
if plots:
os.mkdir(os.path.join(save_dir, "images"))
plots = os.path.join(save_dir, "images")
else:
plots = None
with dt[1]:
names_after = calibration(
["bbox_xywh", "obj", "class", "bbox_xyxy_scaled"],
calib_location,
calib_obj,
calib_class,
calib_dict,
test_dict,
calibrator,
conf_thres,
iou_thres_obj,
iou_thres_class,
config,
num_classes,
device,
plots
)
name_preds = names_after.copy()
names_after = [names_after[0]+"_nms", names_after[1]+"_nms", names_after[2]+"_nms", names_after[3]+"_nms"]
NMS(names_after, dataloader, calib_dict, name_preds=name_preds, num_classes=num_classes, opt=config, device=device)
NMS(names_after, dataloader, test_dict, name_preds=name_preds, num_classes=num_classes, opt=config, device=device)
names_after[0] = names_after[3]+"_scaled"
calib_location = "after_nms"
LOGGER.info(f'\nStarting the calibrations: {calib_location}...')
calib_obj = (where_apply_calib_obj == calib_location)
calib_class = (where_apply_calib_class == calib_location)
with dt[2]:
names_after = calibration(
names_after,
calib_location,
calib_obj,
calib_class,
calib_dict,
test_dict,
calibrator,
conf_thres,
iou_thres_obj,
iou_thres_class,
config,
num_classes,
device,
plots,
)
# Print speeds
if speed_info:
t = tuple(x.t / len(data_dict) * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms inference, %.1fms before NMS, %.1fms after NMS' % t)
if save_dict:
with open(f'{save_dir}/calib_dict.pickle', 'wb') as f:
pickle.dump(calib_dict, f, protocol=pickle.HIGHEST_PROTOCOL)
with open(f'{save_dir}/test_dict.pickle', 'wb') as f:
pickle.dump(test_dict, f, protocol=pickle.HIGHEST_PROTOCOL)
d_["names_after"] = names_after
with open(os.path.join(save_dir, 'var.yaml'), 'w') as file:
yaml.dump(d_, file)
print("Original Results")
calc_mAP(save_dir, ["bbox_xyxy_scaled_nms", "obj_nms", "class_nms"], device, title="nms", plots=False)
print("Calibrated results --> objectness calibration: ", where_apply_calib_obj, " & class calibration: ", where_apply_calib_class)
calc_mAP(save_dir, names_after, device, title="calib_nms", plots=False)
def parse_opt():
parser = ArgumentParser()
parser.add_argument('--config', type=str, default=ROOT / 'config/opt/opt_Visdrone_detect_S_Hyp0.yaml', help='config_file.yaml path')
parser.add_argument('--conf_thres', type=float, default=0.02, help='confidence threshold')
parser.add_argument('--iou_thres_obj', type=float, default=0.6, help='Objectness IoU threshold')
parser.add_argument('--iou_thres_class', type=float, default=0.3, help='Class IoU threshold')
parser.add_argument('--where_apply_calib_obj', type=str, default='before_nms', help='where to apply objectness calibration')
parser.add_argument('--where_apply_calib_class', type=str, default='after_nms', help='where to apply class calibration')
parser.add_argument('--calibrator', type=str, default='isotonic', help='which calibrator to use')
parser.add_argument('--size_test', type=float, default=0.6, help='the split for the dataset')
parser.add_argument('--speed_info', type=bool, default=False, help='details of the speed')
parser.add_argument('--save_dict', type=bool, default=True, help='should you save the results')
parser.add_argument('--plots', type=bool, default=True, help='save the ECE plots')
opt = parser.parse_args()
print_args(vars(opt))
return opt
def main(opt):
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)