def save_ims_from_model(output_dir):
register_kitti_mots_dataset("datasets/KITTI-MOTS/training/image_02",
"datasets/KITTI-MOTS/instances_txt",
("kitti_mots_train", "kitti_mots_test"),
image_extension="png")
cfg = get_cfg()
cfg_file = "Cityscapes/mask_rcnn_R_50_FPN.yaml"
cfg.merge_from_file(model_zoo.get_config_file(cfg_file))
cfg.SEED = 42
cfg.MODEL.WEIGHTS = os.path.join(output_dir, "model_final.pth")
cfg.DATASETS.TRAIN = ("kitti_mots_test", )
cfg.DATASETS.TEST = ("kitti_mots_test",)
predictor = DefaultPredictor(cfg)
mots_test = DatasetCatalog.get("kitti_mots_test")
random.seed(991289902352970059272393463766778531712405567416019953137427298712849420652624107943398234695660286007524334222721892010493181783407)
for data in random.sample(mots_test, 10):
im = cv2.imread(data["file_name"])
basename = os.path.basename(data['file_name'])
outputs = predictor(im)
v = Visualizer(
im[:, :, ::-1], MetadataCatalog.get("coco_2017_val"), scale=1.2)
v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2.imwrite(os.path.join(output_dir, basename), v.get_image()[:, :, ::-1])
def train(output, iou=None, nms=None, rpn=None):
batch_size = 2
DatasetCatalog.clear()
register_kitti_mots_dataset("datasets/KITTI-MOTS/training/image_02",
"datasets/KITTI-MOTS/instances_txt",
("kitti_mots_train", "kitti_mots_test"),
image_extension="png")
cfg_file = "Cityscapes/mask_rcnn_R_50_FPN.yaml"
output_dir = output
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(cfg_file))
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(cfg_file)
cfg.SEED = 42
cfg.DATASETS.TRAIN = ("kitti_mots_train",)
cfg.DATASETS.TEST = ("kitti_mots_test",)
cfg.DATALOADER.NUM_WORKERS = 4
cfg.SOLVER.IMS_PER_BATCH = batch_size
cfg.SOLVER.BASE_LR = 0.0002 * batch_size / 16 # pick a good LR
cfg.SOLVER.MAX_ITER = 7500
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.OUTPUT_DIR = output_dir
if iou is not None:
cfg.MODEL.RPN.IOU_THRESHOLDS = [iou[0], iou[1]]
if nms is not None:
cfg.MODEL.RPN.NMS_THRESH = nms
if rpn is not None:
cfg.MODEL.RPN.PRE_NMS_TOPK_TRAIN = rpn[0]
cfg.MODEL.RPN.PRE_NMS_TOPK_TEST = rpn[1]
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = DefaultTrainer(cfg)
val_loss = ValidationLoss(cfg)
trainer.register_hooks([val_loss])
trainer._hooks = trainer._hooks[:-2] + trainer._hooks[-2:][::-1]
trainer.resume_or_load(resume=True)
trainer.train()
evaluator = COCOEvaluator("kitti_mots_test", cfg, False, output_dir=output_dir)
trainer.test(cfg, trainer.model, evaluators=[evaluator])
plot_losses(cfg)
from detectron2.data import MetadataCatalog
import cv2
from visualizer import plot_losses, show_results
from hooks import ValidationLoss
from kitti_mots_dataset import register_kitti_mots_dataset, get_kiti_mots_dicts
from opt import parse_args
from week6.dataloader import TrainerDA
if __name__ == '__main__':
opts = parse_args()
batch_size = 2
register_kitti_mots_dataset("../datasets/KITTI-MOTS/training/image_02",
"../datasets/KITTI-MOTS/instances_txt",
("kitti_mots_train", "kitti_mots_test"),
image_extension="png")
# register_kitti_mots_dataset("datasets/MOTSChallenge/train/images",
# "datasets/MOTSChallenge/train/instances_txt",
# ("mots_challenge_train", "mots_challenge_test"),
# image_extension="jpg")
cfg_file = opts.config
output_dir = opts.output
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(cfg_file))
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(cfg_file)
if opts.data == "kitti":
import random
from detectron2.data import MetadataCatalog
import cv2
from visualizer import plot_losses, show_results
from hooks import ValidationLoss
from kitti_mots_dataset import register_kitti_mots_dataset, get_kiti_mots_dicts
from opt import parse_args
if __name__ == '__main__':
opts = parse_args()
batch_size = 4
register_kitti_mots_dataset("datasets/KITTI-MOTS/training/image_02",
"datasets/KITTI-MOTS/instances_txt",
("kitti_mots_train", "kitti_mots_test"),
image_extension="png")
register_kitti_mots_dataset(
"datasets/MOTSChallenge/train/images",
"datasets/MOTSChallenge/train/instances_txt",
("mots_challenge_train", "mots_challenge_test"),
image_extension="jpg")
cfg_file = opts.config
output_dir = opts.output
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(cfg_file))
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(cfg_file)