Update models/detectron2/platform_detector_setup.py

models/detectron2/platform_detector_setup.py

@@ -26,10 +26,10 @@ def get_platform_detector():
     cfg = get_cfg()
     cfg.OUTPUT_DIR = "./output/platform/"
     # model = build_model(cfg)  # returns a torch.nn.Module
-    cfg.merge_from_file(model_zoo.get_config_file("configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
-    cfg.DATASETS.TEST = ()
+    # cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+    # cfg.DATASETS.TEST = ()
     cfg.DATALOADER.NUM_WORKERS = 2
-    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")  # Let training initialize from model zoo
+    # cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")  # Let training initialize from model zoo
     cfg.SOLVER.IMS_PER_BATCH = 2  # This is the real "batch size" commonly known to deep learning people
     cfg.SOLVER.BASE_LR = 0.00025  # pick a good LR
     cfg.SOLVER.MAX_ITER = 300    # 300 iterations seems good enough for this toy dataset; you will need to train longer for a practical dataset