cubercnn/config/config.py

# Copyright (c) Meta Platforms, Inc. and affiliates
from detectron2.config import CfgNode as CN

def get_cfg_defaults(cfg):

    # A list of category names which will be used
    cfg.DATASETS.CATEGORY_NAMES = []

    # The category names which will be treated as ignore
    # e.g., not counting as background during training
    # or as false positives during evaluation.
    cfg.DATASETS.IGNORE_NAMES = []

    # Should the datasets appear with the same probabilty
    # in batches (e.g., the imbalance from small and large
    # datasets will be accounted for during sampling)
    cfg.DATALOADER.BALANCE_DATASETS = False

    # The thresholds for when to treat a known box
    # as ignore based on too heavy of truncation or 
    # too low of visibility in the image. This affects
    # both training and evaluation ignores.
    cfg.DATASETS.TRUNCATION_THRES = 0.99
    cfg.DATASETS.VISIBILITY_THRES = 0.01
    cfg.DATASETS.MIN_HEIGHT_THRES = 0.00
    cfg.DATASETS.MAX_DEPTH = 1e8

    # Whether modal 2D boxes should be loaded, 
    # or if the full 3D projected boxes should be used.
    cfg.DATASETS.MODAL_2D_BOXES = False

    # Whether truncated 2D boxes should be loaded, 
    # or if the 3D full projected boxes should be used.
    cfg.DATASETS.TRUNC_2D_BOXES = True

    # Threshold used for matching and filtering boxes
    # inside of ignore regions, within the RPN and ROIHeads
    cfg.MODEL.RPN.IGNORE_THRESHOLD = 0.5

    # Configuration for cube head
    cfg.MODEL.ROI_CUBE_HEAD = CN()
    cfg.MODEL.ROI_CUBE_HEAD.NAME = "CubeHead"
    cfg.MODEL.ROI_CUBE_HEAD.POOLER_RESOLUTION = 7
    cfg.MODEL.ROI_CUBE_HEAD.POOLER_SAMPLING_RATIO = 0
    cfg.MODEL.ROI_CUBE_HEAD.POOLER_TYPE = "ROIAlignV2"

    # Settings for the cube head features
    cfg.MODEL.ROI_CUBE_HEAD.NUM_CONV = 0
    cfg.MODEL.ROI_CUBE_HEAD.CONV_DIM = 256
    cfg.MODEL.ROI_CUBE_HEAD.NUM_FC = 2
    cfg.MODEL.ROI_CUBE_HEAD.FC_DIM = 1024
    # proposal method 
    cfg.MODEL.ROI_CUBE_HEAD.NUMBER_OF_PROPOSALS = 1000
    
    # the style to predict Z with currently supported
    # options --> ['direct', 'sigmoid', 'log', 'clusters']
    cfg.MODEL.ROI_CUBE_HEAD.Z_TYPE = "direct"

    # the style to predict pose with currently supported
    # options --> ['6d', 'euler', 'quaternion']
    cfg.MODEL.ROI_CUBE_HEAD.POSE_TYPE = "6d"

    # Whether to scale all 3D losses by inverse depth
    cfg.MODEL.ROI_CUBE_HEAD.INVERSE_Z_WEIGHT = False

    # Virtual depth puts all predictions of depth into
    # a shared virtual space with a shared focal length. 
    cfg.MODEL.ROI_CUBE_HEAD.VIRTUAL_DEPTH = True
    cfg.MODEL.ROI_CUBE_HEAD.VIRTUAL_FOCAL = 512.0

    # If true, then all losses are computed using the 8 corners
    # such that they are all in a shared scale space. 
    # E.g., their scale correlates with their impact on 3D IoU.
    # This way no manual weights need to be set.
    cfg.MODEL.ROI_CUBE_HEAD.DISENTANGLED_LOSS = True

    # When > 1, the outputs of the 3D head will be based on
    # a 2D scale clustering, based on 2D proposal height/width.
    # This parameter describes the number of bins to cluster.
    cfg.MODEL.ROI_CUBE_HEAD.CLUSTER_BINS = 1

    # Whether batch norm is enabled during training. 
    # If false, all BN weights will be frozen. 
    cfg.MODEL.USE_BN = True

    # Whether to predict the pose in allocentric space. 
    # The allocentric space may correlate better with 2D 
    # images compared to egocentric poses. 
    cfg.MODEL.ROI_CUBE_HEAD.ALLOCENTRIC_POSE = True

    # Whether to use chamfer distance for disentangled losses
    # of pose. This avoids periodic issues of rotation but 
    # may prevent the pose "direction" from being interpretable.
    cfg.MODEL.ROI_CUBE_HEAD.CHAMFER_POSE = True

    # Should the prediction heads share FC features or not. 
    # These include groups of uv, z, whl, pose.
    cfg.MODEL.ROI_CUBE_HEAD.SHARED_FC = True

    # Check for stable gradients. When inf is detected, skip the update. 
    # This prevents an occasional bad sample from exploding the model. 
    # The threshold below is the allows percent of bad samples. 
    # 0.0 is off, and 0.01 is recommended for minor robustness to exploding.
    cfg.MODEL.STABILIZE = 0.01
    
    # Whether or not to use the dimension priors
    cfg.MODEL.ROI_CUBE_HEAD.DIMS_PRIORS_ENABLED = True

    # How prior dimensions should be computed? 
    # The supported modes are ["exp", "sigmoid"]
    # where exp is unbounded and sigmoid is bounded
    # between +- 3 standard deviations from the mean.
    cfg.MODEL.ROI_CUBE_HEAD.DIMS_PRIORS_FUNC = 'exp'

    # weight for confidence loss. 0 is off.
    cfg.MODEL.ROI_CUBE_HEAD.USE_CONFIDENCE = 1.0

    # Loss weights for XY, Z, Dims, Pose
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_3D = 1.0
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_XY = 1.0
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_POSE = 7.0
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_NORMAL_VEC = 20.0
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_IOU = 1.0
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_SEG = 2.5
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_Z = 1.0
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_DIMS = 20.0
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_DEPTH = 1.0

    cfg.MODEL.DLA = CN()

    # Supported types for DLA backbones are...
    # dla34, dla46_c, dla46x_c, dla60x_c, dla60, dla60x, dla102x, dla102x2, dla169
    cfg.MODEL.DLA.TYPE = 'dla34'

    # Only available for dla34, dla60, dla102
    cfg.MODEL.DLA.TRICKS = False

    # A joint loss for the disentangled loss.
    # All predictions are computed using a corner
    # or chamfers loss depending on chamfer_pose!
    # Recommened to keep this weight small: [0.05, 0.5]
    cfg.MODEL.ROI_CUBE_HEAD.LOSS_W_JOINT = 1.0

    # sgd, adam, adam+amsgrad, adamw, adamw+amsgrad
    cfg.SOLVER.TYPE = 'sgd'

    cfg.MODEL.RESNETS.TORCHVISION = True
    cfg.TEST.DETECTIONS_PER_IMAGE = 100

    cfg.TEST.VISIBILITY_THRES = 1/2.0
    cfg.TEST.TRUNCATION_THRES = 1/2.0

    cfg.INPUT.RANDOM_FLIP = "horizontal"

    # When True, we will use localization uncertainty
    # as the new IoUness score in the RPN.
    cfg.MODEL.RPN.OBJECTNESS_UNCERTAINTY = 'IoUness'

    # If > 0.0 this is the scaling factor that will be applied to
    # an RoI 2D box before doing any pooling to give more context. 
    # Ex. 1.5 makes width and height 50% larger. 
    cfg.MODEL.ROI_CUBE_HEAD.SCALE_ROI_BOXES = 0.0

    # weight path specifically for pretraining (no checkpointables will be loaded)
    cfg.MODEL.WEIGHTS_PRETRAIN = ''

    # ## start of our things
    cfg.MODEL.ROI_CUBE_HEAD.TEST = 'bas'
    cfg.MODEL.ROI_CUBE_HEAD.DIMS_PRIORS_PRECOMPUTED = False

    cfg.PLOT = CN(new_allowed=True)
    cfg.PLOT.OUTPUT_DIR = ''
    cfg.PLOT.EVAL = ''
    cfg.PLOT.MODE2D = '' #either GT or PRED

    cfg.PLOT.SCORING_FUNC = None
    cfg.PLOT.PROPOSAL_FUNC = None
    cfg.PLOT.number_of_proposals = 1000

    cfg.TRAIN = CN(new_allowed=True)
    cfg.TRAIN.pseudo_gt = 'learn'

    # these are meant to be overwritten as an argument
    cfg.log = True
    # (these 2 are mutually exclusive) z_pseudo_gt_patch or z_pseudo_gt_center 
    cfg.loss_functions = ['dims', 'pose_alignment', 'pose_ground', 'iou', 'z', 'z_pseudo_gt_patch', 'depth']
    cfg.MODEL.DEPTH_ON = False #whether to use the depth anything concated features