mirror of https://github.com/FoundationVision/GLEE
638 lines
28 KiB
Python
638 lines
28 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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from .config import CfgNode as CN
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# NOTE: given the new config system
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# (https://detectron2.readthedocs.io/en/latest/tutorials/lazyconfigs.html),
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# we will stop adding new functionalities to default CfgNode.
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# -----------------------------------------------------------------------------
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# Convention about Training / Test specific parameters
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# -----------------------------------------------------------------------------
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# Whenever an argument can be either used for training or for testing, the
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# corresponding name will be post-fixed by a _TRAIN for a training parameter,
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# or _TEST for a test-specific parameter.
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# For example, the number of images during training will be
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# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
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# IMAGES_PER_BATCH_TEST
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# -----------------------------------------------------------------------------
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# Config definition
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# -----------------------------------------------------------------------------
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_C = CN()
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# The version number, to upgrade from old configs to new ones if any
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# changes happen. It's recommended to keep a VERSION in your config file.
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_C.VERSION = 2
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_C.MODEL = CN()
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_C.MODEL.LOAD_PROPOSALS = False
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_C.MODEL.MASK_ON = False
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_C.MODEL.KEYPOINT_ON = False
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_C.MODEL.DEVICE = "cuda"
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_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
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# Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file
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# to be loaded to the model. You can find available models in the model zoo.
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_C.MODEL.WEIGHTS = ""
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# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR).
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# To train on images of different number of channels, just set different mean & std.
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# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
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_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
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# When using pre-trained models in Detectron1 or any MSRA models,
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# std has been absorbed into its conv1 weights, so the std needs to be set 1.
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# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
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_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
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# -----------------------------------------------------------------------------
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# INPUT
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# -----------------------------------------------------------------------------
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_C.INPUT = CN()
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# By default, {MIN,MAX}_SIZE options are used in transforms.ResizeShortestEdge.
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# Please refer to ResizeShortestEdge for detailed definition.
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# Size of the smallest side of the image during training
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_C.INPUT.MIN_SIZE_TRAIN = (800,)
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# Sample size of smallest side by choice or random selection from range give by
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# INPUT.MIN_SIZE_TRAIN
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_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice"
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# Maximum size of the side of the image during training
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_C.INPUT.MAX_SIZE_TRAIN = 1333
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# Size of the smallest side of the image during testing. Set to zero to disable resize in testing.
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_C.INPUT.MIN_SIZE_TEST = 800
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# Maximum size of the side of the image during testing
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_C.INPUT.MAX_SIZE_TEST = 1333
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# Mode for flipping images used in data augmentation during training
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# choose one of ["horizontal, "vertical", "none"]
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_C.INPUT.RANDOM_FLIP = "horizontal"
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# `True` if cropping is used for data augmentation during training
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_C.INPUT.CROP = CN({"ENABLED": False})
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# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation.
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_C.INPUT.CROP.TYPE = "relative_range"
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# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of
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# pixels if CROP.TYPE is "absolute"
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_C.INPUT.CROP.SIZE = [0.9, 0.9]
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# Whether the model needs RGB, YUV, HSV etc.
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# Should be one of the modes defined here, as we use PIL to read the image:
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# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes
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# with BGR being the one exception. One can set image format to BGR, we will
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# internally use RGB for conversion and flip the channels over
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_C.INPUT.FORMAT = "BGR"
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# The ground truth mask format that the model will use.
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# Mask R-CNN supports either "polygon" or "bitmask" as ground truth.
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_C.INPUT.MASK_FORMAT = "polygon" # alternative: "bitmask"
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# -----------------------------------------------------------------------------
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# Dataset
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# -----------------------------------------------------------------------------
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_C.DATASETS = CN()
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# List of the dataset names for training. Must be registered in DatasetCatalog
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# Samples from these datasets will be merged and used as one dataset.
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_C.DATASETS.TRAIN = ()
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# List of the pre-computed proposal files for training, which must be consistent
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# with datasets listed in DATASETS.TRAIN.
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_C.DATASETS.PROPOSAL_FILES_TRAIN = ()
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# Number of top scoring precomputed proposals to keep for training
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_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000
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# List of the dataset names for testing. Must be registered in DatasetCatalog
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_C.DATASETS.TEST = ()
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# List of the pre-computed proposal files for test, which must be consistent
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# with datasets listed in DATASETS.TEST.
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_C.DATASETS.PROPOSAL_FILES_TEST = ()
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# Number of top scoring precomputed proposals to keep for test
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_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000
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# -----------------------------------------------------------------------------
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# DataLoader
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# -----------------------------------------------------------------------------
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_C.DATALOADER = CN()
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# Number of data loading threads
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_C.DATALOADER.NUM_WORKERS = 4
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# If True, each batch should contain only images for which the aspect ratio
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# is compatible. This groups portrait images together, and landscape images
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# are not batched with portrait images.
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_C.DATALOADER.ASPECT_RATIO_GROUPING = True
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# Options: TrainingSampler, RepeatFactorTrainingSampler
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_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
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# Repeat threshold for RepeatFactorTrainingSampler
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_C.DATALOADER.REPEAT_THRESHOLD = 0.0
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# Tf True, when working on datasets that have instance annotations, the
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# training dataloader will filter out images without associated annotations
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_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
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# ---------------------------------------------------------------------------- #
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# Backbone options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.BACKBONE = CN()
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_C.MODEL.BACKBONE.NAME = "build_resnet_backbone"
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# Freeze the first several stages so they are not trained.
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# There are 5 stages in ResNet. The first is a convolution, and the following
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# stages are each group of residual blocks.
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_C.MODEL.BACKBONE.FREEZE_AT = 2
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# ---------------------------------------------------------------------------- #
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# FPN options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.FPN = CN()
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# Names of the input feature maps to be used by FPN
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# They must have contiguous power of 2 strides
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# e.g., ["res2", "res3", "res4", "res5"]
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_C.MODEL.FPN.IN_FEATURES = []
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_C.MODEL.FPN.OUT_CHANNELS = 256
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# Options: "" (no norm), "GN"
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_C.MODEL.FPN.NORM = ""
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# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg"
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_C.MODEL.FPN.FUSE_TYPE = "sum"
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# ---------------------------------------------------------------------------- #
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# Proposal generator options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.PROPOSAL_GENERATOR = CN()
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# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals"
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_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN"
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# Proposal height and width both need to be greater than MIN_SIZE
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# (a the scale used during training or inference)
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_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0
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# ---------------------------------------------------------------------------- #
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# Anchor generator options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.ANCHOR_GENERATOR = CN()
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# The generator can be any name in the ANCHOR_GENERATOR registry
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_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator"
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# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
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# Format: list[list[float]]. SIZES[i] specifies the list of sizes to use for
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# IN_FEATURES[i]; len(SIZES) must be equal to len(IN_FEATURES) or 1.
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# When len(SIZES) == 1, SIZES[0] is used for all IN_FEATURES.
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_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]]
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# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect
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# ratios are generated by an anchor generator.
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# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W)
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# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true,
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# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used
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# for all IN_FEATURES.
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_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
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# Anchor angles.
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# list[list[float]], the angle in degrees, for each input feature map.
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# ANGLES[i] specifies the list of angles for IN_FEATURES[i].
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_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]]
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# Relative offset between the center of the first anchor and the top-left corner of the image
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# Value has to be in [0, 1). Recommend to use 0.5, which means half stride.
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# The value is not expected to affect model accuracy.
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_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0
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# ---------------------------------------------------------------------------- #
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# RPN options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.RPN = CN()
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_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead" # used by RPN_HEAD_REGISTRY
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# Names of the input feature maps to be used by RPN
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# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
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_C.MODEL.RPN.IN_FEATURES = ["res4"]
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# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels
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# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
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_C.MODEL.RPN.BOUNDARY_THRESH = -1
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# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD]
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# Minimum overlap required between an anchor and ground-truth box for the
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# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
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# ==> positive RPN example: 1)
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# Maximum overlap allowed between an anchor and ground-truth box for the
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# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
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# ==> negative RPN example: 0)
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# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD)
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# are ignored (-1)
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_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7]
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_C.MODEL.RPN.IOU_LABELS = [0, -1, 1]
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# Number of regions per image used to train RPN
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_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
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# Target fraction of foreground (positive) examples per RPN minibatch
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_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
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# Options are: "smooth_l1", "giou", "diou", "ciou"
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_C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1"
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_C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0
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# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets
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_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
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# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
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_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0
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_C.MODEL.RPN.LOSS_WEIGHT = 1.0
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# Number of top scoring RPN proposals to keep before applying NMS
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# When FPN is used, this is *per FPN level* (not total)
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_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000
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_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000
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# Number of top scoring RPN proposals to keep after applying NMS
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# When FPN is used, this limit is applied per level and then again to the union
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# of proposals from all levels
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# NOTE: When FPN is used, the meaning of this config is different from Detectron1.
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# It means per-batch topk in Detectron1, but per-image topk here.
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# See the "find_top_rpn_proposals" function for details.
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_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000
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_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
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# NMS threshold used on RPN proposals
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_C.MODEL.RPN.NMS_THRESH = 0.7
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# Set this to -1 to use the same number of output channels as input channels.
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_C.MODEL.RPN.CONV_DIMS = [-1]
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# ---------------------------------------------------------------------------- #
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# ROI HEADS options
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# ---------------------------------------------------------------------------- #
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_C.MODEL.ROI_HEADS = CN()
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_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads"
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# Number of foreground classes
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_C.MODEL.ROI_HEADS.NUM_CLASSES = 80
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# Names of the input feature maps to be used by ROI heads
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# Currently all heads (box, mask, ...) use the same input feature map list
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# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
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_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"]
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# IOU overlap ratios [IOU_THRESHOLD]
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# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD)
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# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD)
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_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5]
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_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1]
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# RoI minibatch size *per image* (number of regions of interest [ROIs]) during training
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# Total number of RoIs per training minibatch =
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# ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH
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# E.g., a common configuration is: 512 * 16 = 8192
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_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
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# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
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_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
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# Only used on test mode
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# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
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# balance obtaining high recall with not having too many low precision
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# detections that will slow down inference post processing steps (like NMS)
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# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down
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# inference.
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_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05
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# Overlap threshold used for non-maximum suppression (suppress boxes with
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# IoU >= this threshold)
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_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5
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# If True, augment proposals with ground-truth boxes before sampling proposals to
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# train ROI heads.
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_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True
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# ---------------------------------------------------------------------------- #
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# Box Head
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# ---------------------------------------------------------------------------- #
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_C.MODEL.ROI_BOX_HEAD = CN()
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# C4 don't use head name option
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# Options for non-C4 models: FastRCNNConvFCHead,
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_C.MODEL.ROI_BOX_HEAD.NAME = ""
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# Options are: "smooth_l1", "giou", "diou", "ciou"
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_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1"
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# The final scaling coefficient on the box regression loss, used to balance the magnitude of its
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# gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`.
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_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0
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# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
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# These are empirically chosen to approximately lead to unit variance targets
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_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0)
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# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
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_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0
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_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
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_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
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# Type of pooling operation applied to the incoming feature map for each RoI
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_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
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_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0
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# Hidden layer dimension for FC layers in the RoI box head
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_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024
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_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0
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# Channel dimension for Conv layers in the RoI box head
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_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256
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# Normalization method for the convolution layers.
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# Options: "" (no norm), "GN", "SyncBN".
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_C.MODEL.ROI_BOX_HEAD.NORM = ""
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# Whether to use class agnostic for bbox regression
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_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False
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# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes.
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_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False
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# ---------------------------------------------------------------------------- #
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# Cascaded Box Head
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# ---------------------------------------------------------------------------- #
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_C.MODEL.ROI_BOX_CASCADE_HEAD = CN()
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# The number of cascade stages is implicitly defined by the length of the following two configs.
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_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = (
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(10.0, 10.0, 5.0, 5.0),
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(20.0, 20.0, 10.0, 10.0),
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(30.0, 30.0, 15.0, 15.0),
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)
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_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7)
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# ---------------------------------------------------------------------------- #
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# Mask Head
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# ---------------------------------------------------------------------------- #
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_C.MODEL.ROI_MASK_HEAD = CN()
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_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
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_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
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_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
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_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0 # The number of convs in the mask head
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_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256
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# Normalization method for the convolution layers.
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# Options: "" (no norm), "GN", "SyncBN".
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_C.MODEL.ROI_MASK_HEAD.NORM = ""
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# Whether to use class agnostic for mask prediction
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_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False
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# Type of pooling operation applied to the incoming feature map for each RoI
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_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2"
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# ---------------------------------------------------------------------------- #
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# Keypoint Head
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# ---------------------------------------------------------------------------- #
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_C.MODEL.ROI_KEYPOINT_HEAD = CN()
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_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead"
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_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14
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_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0
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_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8))
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_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17 # 17 is the number of keypoints in COCO.
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# Images with too few (or no) keypoints are excluded from training.
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_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1
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# Normalize by the total number of visible keypoints in the minibatch if True.
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# Otherwise, normalize by the total number of keypoints that could ever exist
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# in the minibatch.
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# The keypoint softmax loss is only calculated on visible keypoints.
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# Since the number of visible keypoints can vary significantly between
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# 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}) # "COMBINE.ENABLED" is deprecated & not used
|
|
_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
|
|
# Select topk candidates before NMS
|
|
_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
|
|
# Options are: "smooth_l1", "giou", "diou", "ciou"
|
|
_C.MODEL.RETINANET.BBOX_REG_LOSS_TYPE = "smooth_l1"
|
|
|
|
# One of BN, SyncBN, FrozenBN, GN
|
|
# Only supports GN until unshared norm is implemented
|
|
_C.MODEL.RETINANET.NORM = ""
|
|
|
|
|
|
# ---------------------------------------------------------------------------- #
|
|
# 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()
|
|
|
|
# Options: WarmupMultiStepLR, WarmupCosineLR.
|
|
# See detectron2/solver/build.py for definition.
|
|
_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR"
|
|
|
|
_C.SOLVER.MAX_ITER = 40000
|
|
|
|
_C.SOLVER.BASE_LR = 0.001
|
|
# The end lr, only used by WarmupCosineLR
|
|
_C.SOLVER.BASE_LR_END = 0.0
|
|
|
|
_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. This is also the number
|
|
# of training images per step (i.e. per iteration). If we use 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.
|
|
# It takes no effect when set to 0.
|
|
# 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.
|
|
# See documentation of `DefaultTrainer.auto_scale_workers` for details:
|
|
_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 = None # None means following 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
|
|
|
|
# Enable automatic mixed precision for training
|
|
# Note that this does not change model's inference behavior.
|
|
# To use AMP in inference, run inference under autocast()
|
|
_C.SOLVER.AMP = CN({"ENABLED": False})
|
|
|
|
# ---------------------------------------------------------------------------- #
|
|
# 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
|