text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  from .config import CfgNode as CN
  
  # -----------------------------------------------------------------------------
  # Convention about Training / Test specific parameters
  # -----------------------------------------------------------------------------
  # Whenever an argument can be either used for training or for testing, the
  # corresponding name will be post-fixed by a _TRAIN for a training parameter,
  # or _TEST for a test-specific parameter.
  # For example, the number of images during training will be
  # IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
  # IMAGES_PER_BATCH_TEST
  
  
  # -----------------------------------------------------------------------------
  # Config definition
  # -----------------------------------------------------------------------------
  
  _C = CN()
  
  # The version number, to upgrade from old configs to new ones if any
  # changes happen. It's recommended to keep a VERSION in your config file.
  _C.VERSION = 2
  
  _C.MODEL = CN()
  _C.MODEL.LOAD_PROPOSALS = False
  _C.MODEL.MASK_ON = False
  _C.MODEL.KEYPOINT_ON = False
  _C.MODEL.DEVICE = "cuda"
  _C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
  
  # Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file
  # to be loaded to the model. You can find available models in the model zoo.
  _C.MODEL.WEIGHTS = ""
  
  # Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR).
  # To train on images of different number of channels, just set different mean & std.
  # Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
  _C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
  # When using pre-trained models in Detectron1 or any MSRA models,
  # std has been absorbed into its conv1 weights, so the std needs to be set 1.
  # Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
  _C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
  
  
  # -----------------------------------------------------------------------------
  # INPUT
  # -----------------------------------------------------------------------------
  _C.INPUT = CN()
  # Size of the smallest side of the image during training
  _C.INPUT.MIN_SIZE_TRAIN = (800,)
  # Sample size of smallest side by choice or random selection from range give by
  # INPUT.MIN_SIZE_TRAIN
  _C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice"
  # Maximum size of the side of the image during training
  _C.INPUT.MAX_SIZE_TRAIN = 1333
  # Size of the smallest side of the image during testing. Set to zero to disable resize in testing.
  _C.INPUT.MIN_SIZE_TEST = 800
  # Maximum size of the side of the image during testing
  _C.INPUT.MAX_SIZE_TEST = 1333
  
  # `True` if cropping is used for data augmentation during training
  _C.INPUT.CROP = CN({"ENABLED": False})
  # Cropping type:
  # - "relative" crop (H * CROP.SIZE[0], W * CROP.SIZE[1]) part of an input of size (H, W)
  # - "relative_range" uniformly sample relative crop size from between [CROP.SIZE[0], [CROP.SIZE[1]].
  #   and  [1, 1] and use it as in "relative" scenario.
  # - "absolute" crop part of an input with absolute size: (CROP.SIZE[0], CROP.SIZE[1]).
  # - "absolute_range", for an input of size (H, W), uniformly sample H_crop in
  #   [CROP.SIZE[0], min(H, CROP.SIZE[1])] and W_crop in [CROP.SIZE[0], min(W, CROP.SIZE[1])]
  _C.INPUT.CROP.TYPE = "relative_range"
  # Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of
  # pixels if CROP.TYPE is "absolute"
  _C.INPUT.CROP.SIZE = [0.9, 0.9]
  
  
  # Whether the model needs RGB, YUV, HSV etc.
  # Should be one of the modes defined here, as we use PIL to read the image:
  # https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes
  # with BGR being the one exception. One can set image format to BGR, we will
  # internally use RGB for conversion and flip the channels over
  _C.INPUT.FORMAT = "BGR"
  # The ground truth mask format that the model will use.
  # Mask R-CNN supports either "polygon" or "bitmask" as ground truth.
  _C.INPUT.MASK_FORMAT = "polygon"  # alternative: "bitmask"
  
  
  # -----------------------------------------------------------------------------
  # Dataset
  # -----------------------------------------------------------------------------
  _C.DATASETS = CN()
  # List of the dataset names for training. Must be registered in DatasetCatalog
  _C.DATASETS.TRAIN = ()
  # List of the pre-computed proposal files for training, which must be consistent
  # with datasets listed in DATASETS.TRAIN.
  _C.DATASETS.PROPOSAL_FILES_TRAIN = ()
  # Number of top scoring precomputed proposals to keep for training
  _C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000
  # List of the dataset names for testing. Must be registered in DatasetCatalog
  _C.DATASETS.TEST = ()
  # List of the pre-computed proposal files for test, which must be consistent
  # with datasets listed in DATASETS.TEST.
  _C.DATASETS.PROPOSAL_FILES_TEST = ()
  # Number of top scoring precomputed proposals to keep for test
  _C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000
  
  
  # -----------------------------------------------------------------------------
  # DataLoader
  # -----------------------------------------------------------------------------
  _C.DATALOADER = CN()
  # Number of data loading threads
  _C.DATALOADER.NUM_WORKERS = 4
  # If True, each batch should contain only images for which the aspect ratio
  # is compatible. This groups portrait images together, and landscape images
  # are not batched with portrait images.
  _C.DATALOADER.ASPECT_RATIO_GROUPING = True
  # Options: TrainingSampler, RepeatFactorTrainingSampler
  _C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
  # Repeat threshold for RepeatFactorTrainingSampler
  _C.DATALOADER.REPEAT_THRESHOLD = 0.0
  # Tf True, when working on datasets that have instance annotations, the
  # training dataloader will filter out images without associated annotations
  _C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
  
  # ---------------------------------------------------------------------------- #
  # Backbone options
  # ---------------------------------------------------------------------------- #
  _C.MODEL.BACKBONE = CN()
  
  _C.MODEL.BACKBONE.NAME = "build_resnet_backbone"
  # Freeze the first several stages so they are not trained.
  # There are 5 stages in ResNet. The first is a convolution, and the following
  # stages are each group of residual blocks.
  _C.MODEL.BACKBONE.FREEZE_AT = 2
  
  
  # ---------------------------------------------------------------------------- #
  # FPN options
  # ---------------------------------------------------------------------------- #
  _C.MODEL.FPN = CN()
  # Names of the input feature maps to be used by FPN
  # They must have contiguous power of 2 strides
  # e.g., ["res2", "res3", "res4", "res5"]
  _C.MODEL.FPN.IN_FEATURES = []
  _C.MODEL.FPN.OUT_CHANNELS = 256
  
  # Options: "" (no norm), "GN"
  _C.MODEL.FPN.NORM = ""
  
  # Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg"
  _C.MODEL.FPN.FUSE_TYPE = "sum"
  
  
  # ---------------------------------------------------------------------------- #
  # Proposal generator options
  # ---------------------------------------------------------------------------- #
  _C.MODEL.PROPOSAL_GENERATOR = CN()
  # Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals"
  _C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN"
  # Proposal height and width both need to be greater than MIN_SIZE
  # (a the scale used during training or inference)
  _C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0
  
  
  # ---------------------------------------------------------------------------- #
  # Anchor generator options
  # ---------------------------------------------------------------------------- #
  _C.MODEL.ANCHOR_GENERATOR = CN()
  # The generator can be any name in the ANCHOR_GENERATOR registry
  _C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator"
  # Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
  # Format: list[list[float]]. SIZES[i] specifies the list of sizes
  # to use for IN_FEATURES[i]; len(SIZES) == len(IN_FEATURES) must be true,
  # or len(SIZES) == 1 is true and size list SIZES[0] is used for all
  # IN_FEATURES.
  _C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]]
  # Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect
  # ratios are generated by an anchor generator.
  # Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W)
  # to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true,
  # or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used
  # for all IN_FEATURES.
  _C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
  # Anchor angles.
  # list[list[float]], the angle in degrees, for each input feature map.
  # ANGLES[i] specifies the list of angles for IN_FEATURES[i].
  _C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]]
  # Relative offset between the center of the first anchor and the top-left corner of the image
  # Value has to be in [0, 1). Recommend to use 0.5, which means half stride.
  # The value is not expected to affect model accuracy.
  _C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0
  
  # ---------------------------------------------------------------------------- #
  # RPN options
  # ---------------------------------------------------------------------------- #
  _C.MODEL.RPN = CN()
  _C.MODEL.RPN.HEAD_NAME = "StandardRPNHead"  # used by RPN_HEAD_REGISTRY
  
  # Names of the input feature maps to be used by RPN
  # e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
  _C.MODEL.RPN.IN_FEATURES = ["res4"]
  # Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels
  # Set to -1 or a large value, e.g. 100000, to disable pruning anchors
  _C.MODEL.RPN.BOUNDARY_THRESH = -1
  # IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD]
  # Minimum overlap required between an anchor and ground-truth box for the
  # (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
  # ==> positive RPN example: 1)
  # Maximum overlap allowed between an anchor and ground-truth box for the
  # (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
  # ==> negative RPN example: 0)
  # Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD)
  # are ignored (-1)
  _C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7]
  _C.MODEL.RPN.IOU_LABELS = [0, -1, 1]
  # Total number of RPN examples per image
  _C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
  # Target fraction of foreground (positive) examples per RPN minibatch
  _C.MODEL.RPN.POSITIVE_FRACTION = 0.5
  # Options are: "smooth_l1", "giou"
  _C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1"
  _C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0
  # Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets
  _C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
  # The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
  _C.MODEL.RPN.SMOOTH_L1_BETA = 0.0
  _C.MODEL.RPN.LOSS_WEIGHT = 1.0
  # Number of top scoring RPN proposals to keep before applying NMS
  # When FPN is used, this is *per FPN level* (not total)
  _C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000
  _C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000
  # Number of top scoring RPN proposals to keep after applying NMS
  # When FPN is used, this limit is applied per level and then again to the union
  # of proposals from all levels
  # NOTE: When FPN is used, the meaning of this config is different from Detectron1.
  # It means per-batch topk in Detectron1, but per-image topk here.
  # See the "find_top_rpn_proposals" function for details.
  _C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000
  _C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
  # NMS threshold used on RPN proposals
  _C.MODEL.RPN.NMS_THRESH = 0.7
  
  # ---------------------------------------------------------------------------- #
  # ROI HEADS options
  # ---------------------------------------------------------------------------- #
  _C.MODEL.ROI_HEADS = CN()
  _C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads"
  # Number of foreground classes
  _C.MODEL.ROI_HEADS.NUM_CLASSES = 80
  # Names of the input feature maps to be used by ROI heads
  # Currently all heads (box, mask, ...) use the same input feature map list
  # e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
  _C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"]
  # IOU overlap ratios [IOU_THRESHOLD]
  # Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD)
  # Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD)
  _C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5]
  _C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1]
  # RoI minibatch size *per image* (number of regions of interest [ROIs])
  # Total number of RoIs per training minibatch =
  #   ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH
  # E.g., a common configuration is: 512 * 16 = 8192
  _C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
  # Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
  _C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
  
  # Only used on test mode
  
  # Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
  # balance obtaining high recall with not having too many low precision
  # detections that will slow down inference post processing steps (like NMS)
  # A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down
  # inference.
  _C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05
  # Overlap threshold used for non-maximum suppression (suppress boxes with
  # IoU >= this threshold)
  _C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5
  # If True, augment proposals with ground-truth boxes before sampling proposals to
  # train ROI heads.
  _C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True
  
  # ---------------------------------------------------------------------------- #
  # Box Head
  # ---------------------------------------------------------------------------- #
  _C.MODEL.ROI_BOX_HEAD = CN()
  # C4 don't use head name option
  # Options for non-C4 models: FastRCNNConvFCHead,
  _C.MODEL.ROI_BOX_HEAD.NAME = ""
  # Options are: "smooth_l1", "giou"
  _C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1"
  # The final scaling coefficient on the box regression loss, used to balance the magnitude of its
  # gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`.
  _C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0
  # Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
  # These are empirically chosen to approximately lead to unit variance targets
  _C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0)
  # The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
  _C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0
  _C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
  _C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
  # Type of pooling operation applied to the incoming feature map for each RoI
  _C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
  
  _C.MODEL.ROI_BOX_HEAD.NUM_FC = 0
  # Hidden layer dimension for FC layers in the RoI box head
  _C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024
  _C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0
  # Channel dimension for Conv layers in the RoI box head
  _C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256
  # Normalization method for the convolution layers.
  # Options: "" (no norm), "GN", "SyncBN".
  _C.MODEL.ROI_BOX_HEAD.NORM = ""
  # Whether to use class agnostic for bbox regression
  _C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False
  # If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes.
  _C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False
  
  # ---------------------------------------------------------------------------- #
  # Cascaded Box Head
  # ---------------------------------------------------------------------------- #
  _C.MODEL.ROI_BOX_CASCADE_HEAD = CN()
  # The number of cascade stages is implicitly defined by the length of the following two configs.
  _C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = (
      (10.0, 10.0, 5.0, 5.0),
      (20.0, 20.0, 10.0, 10.0),
      (30.0, 30.0, 15.0, 15.0),
  )
  _C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7)
  
  
  # ---------------------------------------------------------------------------- #
  # Mask Head
  # ---------------------------------------------------------------------------- #
  _C.MODEL.ROI_MASK_HEAD = CN()
  _C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
  _C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
  _C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
  _C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0  # The number of convs in the mask head
  _C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256
  # Normalization method for the convolution layers.
  # Options: "" (no norm), "GN", "SyncBN".
  _C.MODEL.ROI_MASK_HEAD.NORM = ""
  # Whether to use class agnostic for mask prediction
  _C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False
  # Type of pooling operation applied to the incoming feature map for each RoI
  _C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2"
  
  
  # ---------------------------------------------------------------------------- #
  # Keypoint Head
  # ---------------------------------------------------------------------------- #
  _C.MODEL.ROI_KEYPOINT_HEAD = CN()
  _C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead"
  _C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14
  _C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0
  _C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8))
  _C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17  # 17 is the number of keypoints in COCO.
  
  # Images with too few (or no) keypoints are excluded from training.
  _C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1
  # Normalize by the total number of visible keypoints in the minibatch if True.
  # Otherwise, normalize by the total number of keypoints that could ever exist
  # in the minibatch.
  # The keypoint softmax loss is only calculated on visible keypoints.
  # Since the number of visible keypoints can vary significantly between
  # minibatches, this has the effect of up-weighting the importance of
  # minibatches with few visible keypoints. (Imagine the extreme case of
  # only one visible keypoint versus N: in the case of N, each one
  # contributes 1/N to the gradient compared to the single keypoint
  # determining the gradient direction). Instead, we can normalize the
  # loss by the total number of keypoints, if it were the case that all
  # keypoints were visible in a full minibatch. (Returning to the example,
  # this means that the one visible keypoint contributes as much as each
  # of the N keypoints.)
  _C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True
  # Multi-task loss weight to use for keypoints
  # Recommended values:
  #   - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True
  #   - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False
  _C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0
  # Type of pooling operation applied to the incoming feature map for each RoI
  _C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2"
  
  # ---------------------------------------------------------------------------- #
  # Semantic Segmentation Head
  # ---------------------------------------------------------------------------- #
  _C.MODEL.SEM_SEG_HEAD = CN()
  _C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead"
  _C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"]
  # Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for
  # the correposnding pixel.
  _C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255
  # Number of classes in the semantic segmentation head
  _C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54
  # Number of channels in the 3x3 convs inside semantic-FPN heads.
  _C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128
  # Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride.
  _C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
  # Normalization method for the convolution layers. Options: "" (no norm), "GN".
  _C.MODEL.SEM_SEG_HEAD.NORM = "GN"
  _C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0
  
  _C.MODEL.PANOPTIC_FPN = CN()
  # Scaling of all losses from instance detection / segmentation head.
  _C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0
  
  # options when combining instance & semantic segmentation outputs
  _C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True})
  _C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5
  _C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096
  _C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5
  
  
  # ---------------------------------------------------------------------------- #
  # RetinaNet Head
  # ---------------------------------------------------------------------------- #
  _C.MODEL.RETINANET = CN()
  
  # This is the number of foreground classes.
  _C.MODEL.RETINANET.NUM_CLASSES = 80
  
  _C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
  
  # Convolutions to use in the cls and bbox tower
  # NOTE: this doesn't include the last conv for logits
  _C.MODEL.RETINANET.NUM_CONVS = 4
  
  # IoU overlap ratio [bg, fg] for labeling anchors.
  # Anchors with < bg are labeled negative (0)
  # Anchors  with >= bg and < fg are ignored (-1)
  # Anchors with >= fg are labeled positive (1)
  _C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5]
  _C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1]
  
  # Prior prob for rare case (i.e. foreground) at the beginning of training.
  # This is used to set the bias for the logits layer of the classifier subnet.
  # This improves training stability in the case of heavy class imbalance.
  _C.MODEL.RETINANET.PRIOR_PROB = 0.01
  
  # Inference cls score threshold, only anchors with score > INFERENCE_TH are
  # considered for inference (to improve speed)
  _C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05
  _C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000
  _C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5
  
  # Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets
  _C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
  
  # Loss parameters
  _C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0
  _C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25
  _C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1
  
  
  # ---------------------------------------------------------------------------- #
  # ResNe[X]t options (ResNets = {ResNet, ResNeXt}
  # Note that parts of a resnet may be used for both the backbone and the head
  # These options apply to both
  # ---------------------------------------------------------------------------- #
  _C.MODEL.RESNETS = CN()
  
  _C.MODEL.RESNETS.DEPTH = 50
  _C.MODEL.RESNETS.OUT_FEATURES = ["res4"]  # res4 for C4 backbone, res2..5 for FPN backbone
  
  # Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
  _C.MODEL.RESNETS.NUM_GROUPS = 1
  
  # Options: FrozenBN, GN, "SyncBN", "BN"
  _C.MODEL.RESNETS.NORM = "FrozenBN"
  
  # Baseline width of each group.
  # Scaling this parameters will scale the width of all bottleneck layers.
  _C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
  
  # Place the stride 2 conv on the 1x1 filter
  # Use True only for the original MSRA ResNet; use False for C2 and Torch models
  _C.MODEL.RESNETS.STRIDE_IN_1X1 = True
  
  # Apply dilation in stage "res5"
  _C.MODEL.RESNETS.RES5_DILATION = 1
  
  # Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet
  # For R18 and R34, this needs to be set to 64
  _C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
  _C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
  
  # Apply Deformable Convolution in stages
  # Specify if apply deform_conv on Res2, Res3, Res4, Res5
  _C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False]
  # Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168);
  # Use False for DeformableV1.
  _C.MODEL.RESNETS.DEFORM_MODULATED = False
  # Number of groups in deformable conv.
  _C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1
  
  
  # ---------------------------------------------------------------------------- #
  # Solver
  # ---------------------------------------------------------------------------- #
  _C.SOLVER = CN()
  
  # See detectron2/solver/build.py for LR scheduler options
  _C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR"
  
  _C.SOLVER.MAX_ITER = 40000
  
  _C.SOLVER.BASE_LR = 0.001
  
  _C.SOLVER.MOMENTUM = 0.9
  
  _C.SOLVER.NESTEROV = False
  
  _C.SOLVER.WEIGHT_DECAY = 0.0001
  # The weight decay that's applied to parameters of normalization layers
  # (typically the affine transformation)
  _C.SOLVER.WEIGHT_DECAY_NORM = 0.0
  
  _C.SOLVER.GAMMA = 0.1
  # The iteration number to decrease learning rate by GAMMA.
  _C.SOLVER.STEPS = (30000,)
  
  _C.SOLVER.WARMUP_FACTOR = 1.0 / 1000
  _C.SOLVER.WARMUP_ITERS = 1000
  _C.SOLVER.WARMUP_METHOD = "linear"
  
  # Save a checkpoint after every this number of iterations
  _C.SOLVER.CHECKPOINT_PERIOD = 5000
  
  # Number of images per batch across all machines.
  # If we have 16 GPUs and IMS_PER_BATCH = 32,
  # each GPU will see 2 images per batch.
  # May be adjusted automatically if REFERENCE_WORLD_SIZE is set.
  _C.SOLVER.IMS_PER_BATCH = 16
  
  # The reference number of workers (GPUs) this config is meant to train with.
  # With a non-zero value, it will be used by DefaultTrainer to compute a desired
  # per-worker batch size, and then scale the other related configs (total batch size,
  # learning rate, etc) to match the per-worker batch size if the actual number
  # of workers during training is different from this reference.
  _C.SOLVER.REFERENCE_WORLD_SIZE = 0
  
  # Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for
  # biases. This is not useful (at least for recent models). You should avoid
  # changing these and they exist only to reproduce Detectron v1 training if
  # desired.
  _C.SOLVER.BIAS_LR_FACTOR = 1.0
  _C.SOLVER.WEIGHT_DECAY_BIAS = _C.SOLVER.WEIGHT_DECAY
  
  # Gradient clipping
  _C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False})
  # Type of gradient clipping, currently 2 values are supported:
  # - "value": the absolute values of elements of each gradients are clipped
  # - "norm": the norm of the gradient for each parameter is clipped thus
  #   affecting all elements in the parameter
  _C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value"
  # Maximum absolute value used for clipping gradients
  _C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0
  # Floating point number p for L-p norm to be used with the "norm"
  # gradient clipping type; for L-inf, please specify .inf
  _C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0
  
  # ---------------------------------------------------------------------------- #
  # Specific test options
  # ---------------------------------------------------------------------------- #
  _C.TEST = CN()
  # For end-to-end tests to verify the expected accuracy.
  # Each item is [task, metric, value, tolerance]
  # e.g.: [['bbox', 'AP', 38.5, 0.2]]
  _C.TEST.EXPECTED_RESULTS = []
  # The period (in terms of steps) to evaluate the model during training.
  # Set to 0 to disable.
  _C.TEST.EVAL_PERIOD = 0
  # The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval
  # When empty, it will use the defaults in COCO.
  # Otherwise it should be a list[float] with the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
  _C.TEST.KEYPOINT_OKS_SIGMAS = []
  # Maximum number of detections to return per image during inference (100 is
  # based on the limit established for the COCO dataset).
  _C.TEST.DETECTIONS_PER_IMAGE = 100
  
  _C.TEST.AUG = CN({"ENABLED": False})
  _C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
  _C.TEST.AUG.MAX_SIZE = 4000
  _C.TEST.AUG.FLIP = True
  
  _C.TEST.PRECISE_BN = CN({"ENABLED": False})
  _C.TEST.PRECISE_BN.NUM_ITER = 200
  
  # ---------------------------------------------------------------------------- #
  # Misc options
  # ---------------------------------------------------------------------------- #
  # Directory where output files are written
  _C.OUTPUT_DIR = "./output"
  # Set seed to negative to fully randomize everything.
  # Set seed to positive to use a fixed seed. Note that a fixed seed increases
  # reproducibility but does not guarantee fully deterministic behavior.
  # Disabling all parallelism further increases reproducibility.
  _C.SEED = -1
  # Benchmark different cudnn algorithms.
  # If input images have very different sizes, this option will have large overhead
  # for about 10k iterations. It usually hurts total time, but can benefit for certain models.
  # If input images have the same or similar sizes, benchmark is often helpful.
  _C.CUDNN_BENCHMARK = False
  # The period (in terms of steps) for minibatch visualization at train time.
  # Set to 0 to disable.
  _C.VIS_PERIOD = 0
  
  # global config is for quick hack purposes.
  # You can set them in command line or config files,
  # and access it with:
  #
  # from detectron2.config import global_cfg
  # print(global_cfg.HACK)
  #
  # Do not commit any configs into it.
  _C.GLOBAL = CN()
  _C.GLOBAL.HACK = 1.0
  
  # ---------------------------------------------------------------------------- #
  # TODO:ILOD configs
  # ---------------------------------------------------------------------------- #
  
  _C.SOLVER.OPTIM = 'SGD'
  _C.SOLVER.MASK_LR = 0.00001
  
  _C.DATASETS.MEMORY = ()