# Config for visualization for wrong prediction visualization.
# _C.TENSORBOARD.ENABLE must be True.
_C.TENSORBOARD.WRONG_PRED_VIS = CfgNode()
_C.TENSORBOARD.WRONG_PRED_VIS.ENABLE = False
# Folder tag to origanize model eval videos under.
_C.TENSORBOARD.WRONG_PRED_VIS.TAG = "Incorrectly classified videos."
# Subset of labels to visualize. Only wrong predictions with true labels
# within this subset is visualized.
_C.TENSORBOARD.WRONG_PRED_VIS.SUBSET_PATH = ""
# ---------------------------------------------------------------------------- #
# Demo options
# ---------------------------------------------------------------------------- #
_C.DEMO = CfgNode()
# Run model in DEMO mode.
_C.DEMO.ENABLE = False
# Path to a json file providing class_name - id mapping
# in the format {"class_name1": id1, "class_name2": id2, ...}.
_C.DEMO.LABEL_FILE_PATH = ""
# Specify a camera device as input. This will be prioritized
# over input video if set.
# If -1, use input video instead.
_C.DEMO.WEBCAM = -1
# Path to input video for demo.
_C.DEMO.INPUT_VIDEO = ""
# e.g.: [layer1 1,2;1,2, layer2, layer3 150,151;3,4]; this means for each array `arr` # along the batch dimension in `layer1`, we take arr[[1, 2], [1, 2]] _C.TENSORBOARD.MODEL_VIS.LAYER_LIST = [] # Top-k predictions to plot on videos _C.TENSORBOARD.MODEL_VIS.TOPK_PREDS = 1 # Colormap to for text boxes and bounding boxes colors _C.TENSORBOARD.MODEL_VIS.COLORMAP = "Pastel2" # Add custom config with default values. custom_config.add_custom_config(_C) # ---------------------------------------------------------------------------- # # Demo options # ---------------------------------------------------------------------------- # _C.DEMO = CfgNode(new_allowed=new_allowed) _C.DEMO.ENABLE = False _C.DEMO.LABEL_FILE_PATH = "" _C.DEMO.DATA_SOURCE = 0 _C.DEMO.DISPLAY_WIDTH = 0 _C.DEMO.DISPLAY_HEIGHT = 0 _C.DEMO.DETECTRON2_OBJECT_DETECTION_MODEL_CFG = "" _C.DEMO.DETECTRON2_OBJECT_DETECTION_MODEL_WEIGHTS = ""
