# -*- coding = utf-8 -*- # Copyright (c) Facebook, Inc. and its affiliates. # pyre-ignore-all-errors from detectron2.config import CfgNode as CN def add_dataset_category_config(cfg: CN) -> None: """ Add config for additional category-related dataset options - category whitelisting - category mapping """ _C = cfg _C.DATASETS.CATEGORY_MAPS = CN(new_allowed=True) _C.DATASETS.WHITELISTED_CATEGORIES = CN(new_allowed=True) # class to mesh mapping _C.DATASETS.CLASS_TO_MESH_NAME_MAPPING = CN(new_allowed=True) def add_evaluation_config(cfg: CN) -> None: _C = cfg _C.DENSEPOSE_EVALUATION = CN() # evaluator type, possible values: # - "iou": evaluator for models that produce iou data # - "cse": evaluator for models that produce cse data _C.DENSEPOSE_EVALUATION.TYPE = "iou" # storage for DensePose results, possible values: # - "none": no explicit storage, all the results are stored in the # dictionary with predictions, memory intensive; # historically the default storage type # - "ram": RAM storage, uses per-process RAM storage, which is # reduced to a single process storage on later stages, # less memory intensive # - "file": file storage, uses per-process file-based storage, # the least memory intensive, but may create bottlenecks # on file system accesses _C.DENSEPOSE_EVALUATION.STORAGE = "none" # minimum threshold for IOU values: the lower its values is, # the more matches are produced (and the higher the AP score) _C.DENSEPOSE_EVALUATION.MIN_IOU_THRESHOLD = 0.5 # Non-distributed inference is slower (at inference time) but can avoid RAM OOM _C.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE = True # evaluate mesh alignment based on vertex embeddings, only makes sense in CSE context _C.DENSEPOSE_EVALUATION.EVALUATE_MESH_ALIGNMENT = False # meshes to compute mesh alignment for _C.DENSEPOSE_EVALUATION.MESH_ALIGNMENT_MESH_NAMES = [] def add_bootstrap_config(cfg: CN) -> None: """ """ _C = cfg _C.BOOTSTRAP_DATASETS = [] _C.BOOTSTRAP_MODEL = CN() _C.BOOTSTRAP_MODEL.WEIGHTS = "" _C.BOOTSTRAP_MODEL.DEVICE = "cuda" def get_bootstrap_dataset_config() -> CN: _C = CN() _C.DATASET = "" # ratio used to mix data loaders _C.RATIO = 0.1 # image loader _C.IMAGE_LOADER = CN(new_allowed=True) _C.IMAGE_LOADER.TYPE = "" _C.IMAGE_LOADER.BATCH_SIZE = 4 _C.IMAGE_LOADER.NUM_WORKERS = 4 _C.IMAGE_LOADER.CATEGORIES = [] _C.IMAGE_LOADER.MAX_COUNT_PER_CATEGORY = 1_000_000 _C.IMAGE_LOADER.CATEGORY_TO_CLASS_MAPPING = CN(new_allowed=True) # inference _C.INFERENCE = CN() # batch size for model inputs _C.INFERENCE.INPUT_BATCH_SIZE = 4 # batch size to group model outputs _C.INFERENCE.OUTPUT_BATCH_SIZE = 2 # sampled data _C.DATA_SAMPLER = CN(new_allowed=True) _C.DATA_SAMPLER.TYPE = "" _C.DATA_SAMPLER.USE_GROUND_TRUTH_CATEGORIES = False # filter _C.FILTER = CN(new_allowed=True) _C.FILTER.TYPE = "" return _C def load_bootstrap_config(cfg: CN) -> None: """ Bootstrap datasets are given as a list of `dict` that are not automatically converted into CfgNode. This method processes all bootstrap dataset entries and ensures that they are in CfgNode format and comply with the specification """ if not cfg.BOOTSTRAP_DATASETS: return bootstrap_datasets_cfgnodes = [] for dataset_cfg in cfg.BOOTSTRAP_DATASETS: _C = get_bootstrap_dataset_config().clone() _C.merge_from_other_cfg(CN(dataset_cfg)) bootstrap_datasets_cfgnodes.append(_C) cfg.BOOTSTRAP_DATASETS = bootstrap_datasets_cfgnodes def add_densepose_head_cse_config(cfg: CN) -> None: """ Add configuration options for Continuous Surface Embeddings (CSE) """ _C = cfg _C.MODEL.ROI_DENSEPOSE_HEAD.CSE = CN() # Dimensionality D of the embedding space _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE = 16 # Embedder specifications for various mesh IDs _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS = CN(new_allowed=True) # normalization coefficient for embedding distances _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_DIST_GAUSS_SIGMA = 0.01 # normalization coefficient for geodesic distances _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.GEODESIC_DIST_GAUSS_SIGMA = 0.01 # embedding loss weight _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_WEIGHT = 0.6 # embedding loss name, currently the following options are supported: # - EmbeddingLoss: cross-entropy on vertex labels # - SoftEmbeddingLoss: cross-entropy on vertex label combined with # Gaussian penalty on distance between vertices _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_NAME = "EmbeddingLoss" # optimizer hyperparameters _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.FEATURES_LR_FACTOR = 1.0 _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_LR_FACTOR = 1.0 # Shape to shape cycle consistency loss parameters: _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False}) # shape to shape cycle consistency loss weight _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.025 # norm type used for loss computation _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.NORM_P = 2 # normalization term for embedding similarity matrices _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.TEMPERATURE = 0.05 # maximum number of vertices to include into shape to shape cycle loss # if negative or zero, all vertices are considered # if positive, random subset of vertices of given size is considered _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.MAX_NUM_VERTICES = 4936 # Pixel to shape cycle consistency loss parameters: _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False}) # pixel to shape cycle consistency loss weight _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.0001 # norm type used for loss computation _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NORM_P = 2 # map images to all meshes and back (if false, use only gt meshes from the batch) _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.USE_ALL_MESHES_NOT_GT_ONLY = False # Randomly select at most this number of pixels from every instance # if negative or zero, all vertices are considered _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NUM_PIXELS_TO_SAMPLE = 100 # normalization factor for pixel to pixel distances (higher value = smoother distribution) _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.PIXEL_SIGMA = 5.0 _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_PIXEL_TO_VERTEX = 0.05 _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_VERTEX_TO_PIXEL = 0.05 def add_densepose_head_config(cfg: CN) -> None: """ Add config for densepose head. """ _C = cfg _C.MODEL.DENSEPOSE_ON = True _C.MODEL.ROI_DENSEPOSE_HEAD = CN() _C.MODEL.ROI_DENSEPOSE_HEAD.NAME = "" _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS = 8 # Number of parts used for point labels _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES = 24 _C.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL = 4 _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM = 512 _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL = 3 _C.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE = 2 _C.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE = 112 _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE = "ROIAlignV2" _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION = 28 _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO = 2 _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS = 2 # 15 or 2 # Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD) _C.MODEL.ROI_DENSEPOSE_HEAD.FG_IOU_THRESHOLD = 0.7 # Loss weights for annotation masks.(14 Parts) _C.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS = 5.0 # Loss weights for surface parts. (24 Parts) _C.MODEL.ROI_DENSEPOSE_HEAD.PART_WEIGHTS = 1.0 # Loss weights for UV regression. _C.MODEL.ROI_DENSEPOSE_HEAD.POINT_REGRESSION_WEIGHTS = 0.01 # Coarse segmentation is trained using instance segmentation task data _C.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS = False # For Decoder _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_ON = True _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NUM_CLASSES = 256 _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_CONV_DIMS = 256 _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NORM = "" _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_COMMON_STRIDE = 4 # For DeepLab head _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB = CN() _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM = "GN" _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NONLOCAL_ON = 0 # Predictor class name, must be registered in DENSEPOSE_PREDICTOR_REGISTRY # Some registered predictors: # "DensePoseChartPredictor": predicts segmentation and UV coordinates for predefined charts # "DensePoseChartWithConfidencePredictor": predicts segmentation, UV coordinates # and associated confidences for predefined charts (default) # "DensePoseEmbeddingWithConfidencePredictor": predicts segmentation, embeddings # and associated confidences for CSE _C.MODEL.ROI_DENSEPOSE_HEAD.PREDICTOR_NAME = "DensePoseChartWithConfidencePredictor" # Loss class name, must be registered in DENSEPOSE_LOSS_REGISTRY # Some registered losses: # "DensePoseChartLoss": loss for chart-based models that estimate # segmentation and UV coordinates # "DensePoseChartWithConfidenceLoss": loss for chart-based models that estimate # segmentation, UV coordinates and the corresponding confidences (default) _C.MODEL.ROI_DENSEPOSE_HEAD.LOSS_NAME = "DensePoseChartWithConfidenceLoss" # Confidences # Enable learning UV confidences (variances) along with the actual values _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE = CN({"ENABLED": False}) # UV confidence lower bound _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.EPSILON = 0.01 # Enable learning segmentation confidences (variances) along with the actual values _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE = CN({"ENABLED": False}) # Segmentation confidence lower bound _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.EPSILON = 0.01 # Statistical model type for confidence learning, possible values: # - "iid_iso": statistically independent identically distributed residuals # with isotropic covariance # - "indep_aniso": statistically independent residuals with anisotropic # covariances _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.TYPE = "iid_iso" # List of angles for rotation in data augmentation during training _C.INPUT.ROTATION_ANGLES = [0] _C.TEST.AUG.ROTATION_ANGLES = () # Rotation TTA add_densepose_head_cse_config(cfg) def add_hrnet_config(cfg: CN) -> None: """ Add config for HRNet backbone. """ _C = cfg # For HigherHRNet w32 _C.MODEL.HRNET = CN() _C.MODEL.HRNET.STEM_INPLANES = 64 _C.MODEL.HRNET.STAGE2 = CN() _C.MODEL.HRNET.STAGE2.NUM_MODULES = 1 _C.MODEL.HRNET.STAGE2.NUM_BRANCHES = 2 _C.MODEL.HRNET.STAGE2.BLOCK = "BASIC" _C.MODEL.HRNET.STAGE2.NUM_BLOCKS = [4, 4] _C.MODEL.HRNET.STAGE2.NUM_CHANNELS = [32, 64] _C.MODEL.HRNET.STAGE2.FUSE_METHOD = "SUM" _C.MODEL.HRNET.STAGE3 = CN() _C.MODEL.HRNET.STAGE3.NUM_MODULES = 4 _C.MODEL.HRNET.STAGE3.NUM_BRANCHES = 3 _C.MODEL.HRNET.STAGE3.BLOCK = "BASIC" _C.MODEL.HRNET.STAGE3.NUM_BLOCKS = [4, 4, 4] _C.MODEL.HRNET.STAGE3.NUM_CHANNELS = [32, 64, 128] _C.MODEL.HRNET.STAGE3.FUSE_METHOD = "SUM" _C.MODEL.HRNET.STAGE4 = CN() _C.MODEL.HRNET.STAGE4.NUM_MODULES = 3 _C.MODEL.HRNET.STAGE4.NUM_BRANCHES = 4 _C.MODEL.HRNET.STAGE4.BLOCK = "BASIC" _C.MODEL.HRNET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] _C.MODEL.HRNET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256] _C.MODEL.HRNET.STAGE4.FUSE_METHOD = "SUM" _C.MODEL.HRNET.HRFPN = CN() _C.MODEL.HRNET.HRFPN.OUT_CHANNELS = 256 def add_densepose_config(cfg: CN) -> None: add_densepose_head_config(cfg) add_hrnet_config(cfg) add_bootstrap_config(cfg) add_dataset_category_config(cfg) add_evaluation_config(cfg)