def inference(config_file, coco_to_kitti_dict):
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(config_file))
cfg.DATALOADER.NUM_WORKERS = 4
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(config_file)
cfg.DATASETS.TRAIN = ("kitti_mots_train", )
cfg.DATASETS.TEST = ("kitti_mots_test", )
cfg.SOLVER.IMS_PER_BATCH = 8
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=False)
evaluator = COCOEvaluator("kitti_mots_test",
cfg,
False,
output_dir="./output/")
val_loader = build_detection_test_loader(cfg, "kitti_mots_test")
inference_on_dataset(trainer.model, val_loader, evaluator)
preds = evaluator._predictions
filtered_preds = filter_preds(preds, coco_to_kitti_dict)
evaluator._predictions = filtered_preds
evaluator.evaluate()
def inference(config_file, correspondences):
# test_set = 'kitti_mots_test'
test_set = 'mots_challenge_train'
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(config_file))
cfg.DATALOADER.NUM_WORKERS = 4
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(config_file)
cfg.DATASETS.TRAIN = (test_set, )
cfg.DATASETS.TEST = (test_set, )
cfg.SOLVER.IMS_PER_BATCH = 16
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
cfg.OUTPUT_DIR = "../week4/output/r50_fpn_cityscapes/"
cfg.MODEL.WEIGHTS = "../week4/output/r50_fpn_cityscapes/model_final.pth"
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=True)
evaluator = COCOEvaluator(test_set, cfg, False, output_dir="./output/")
print(evaluator._metadata.get("thing_classes"))
val_loader = build_detection_test_loader(cfg, test_set)
inference_on_dataset(trainer.model, val_loader, evaluator)
preds = evaluator._predictions
filtered_preds = filter_preds(preds, correspondences)
evaluator._predictions = filtered_preds
evaluator.evaluate()
predictor = DefaultPredictor(cfg)
motschallenge = DatasetCatalog.get(test_set)
show_results(cfg, motschallenge, predictor)
