_C.INPUT.MIN_SIZE_TEST = 800
_C.INPUT.MAX_SIZE_TEST = 1333
_C.INPUT.CROP = CN({"ENABLED": False})
_C.INPUT.CROP.TYPE = "relative_range"
_C.INPUT.CROP.SIZE = [0.9, 0.9]
_C.INPUT.MASK_FORMAT = "polygon"
# -----------------------------------------------------------------------------
# Datasets options
# -----------------------------------------------------------------------------
_C.DATASETS = CN()
_C.DATASETS.TRAIN = ()
_C.DATASETS.PROPOSAL_FILES_TRAIN = ()
_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000
_C.DATASETS.TEST = ()
_C.DATASETS.PROPOSAL_FILES_TEST = ()
_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000
# -----------------------------------------------------------------------------
# DataLoader options
def get_shapenet_cfg():
cfg = CN()
cfg.MODEL = CN()
cfg.MODEL.BACKBONE = "resnet50"
cfg.MODEL.VOXEL_ON = False
cfg.MODEL.MESH_ON = False
# ------------------------------------------------------------------------ #
# Checkpoint
# ------------------------------------------------------------------------ #
cfg.MODEL.CHECKPOINT = "" # path to checkpoint
# ------------------------------------------------------------------------ #
# Voxel Head
# ------------------------------------------------------------------------ #
cfg.MODEL.VOXEL_HEAD = CN()
# The number of convs in the voxel head and the number of channels
cfg.MODEL.VOXEL_HEAD.NUM_CONV = 0
cfg.MODEL.VOXEL_HEAD.CONV_DIM = 256
# Normalization method for the convolution layers. Options: "" (no norm), "GN"
cfg.MODEL.VOXEL_HEAD.NORM = ""
# The number of depth channels for the predicted voxels
cfg.MODEL.VOXEL_HEAD.VOXEL_SIZE = 28
cfg.MODEL.VOXEL_HEAD.LOSS_WEIGHT = 1.0
cfg.MODEL.VOXEL_HEAD.CUBIFY_THRESH = 0.0
# voxel only iterations
cfg.MODEL.VOXEL_HEAD.VOXEL_ONLY_ITERS = 100
##
cfg.MODEL.VOXEL_HEAD.TCONV_USE_BIAS = False
cfg.MODEL.VOXEL_HEAD.LEAKY_VALUE = 0.2
##
# ------------------------------------------------------------------------ #
# Mesh Head
# ------------------------------------------------------------------------ #
cfg.MODEL.MESH_HEAD = CN()
cfg.MODEL.MESH_HEAD.NAME = "VoxMeshHead"
# Numer of stages
cfg.MODEL.MESH_HEAD.NUM_STAGES = 1
cfg.MODEL.MESH_HEAD.NUM_GRAPH_CONVS = 1 # per stage
cfg.MODEL.MESH_HEAD.GRAPH_CONV_DIM = 256
cfg.MODEL.MESH_HEAD.GRAPH_CONV_INIT = "normal"
# Mesh sampling
cfg.MODEL.MESH_HEAD.GT_NUM_SAMPLES = 5000
cfg.MODEL.MESH_HEAD.PRED_NUM_SAMPLES = 5000
# loss weights
cfg.MODEL.MESH_HEAD.CHAMFER_LOSS_WEIGHT = 1.0
cfg.MODEL.MESH_HEAD.NORMALS_LOSS_WEIGHT = 1.0
cfg.MODEL.MESH_HEAD.EDGE_LOSS_WEIGHT = 1.0
# Init ico_sphere level (only for when voxel_on is false)
cfg.MODEL.MESH_HEAD.ICO_SPHERE_LEVEL = -1
# ------------------------------------------------------------------------ #
# Solver
# ------------------------------------------------------------------------ #
cfg.SOLVER = CN()
cfg.SOLVER.LR_SCHEDULER_NAME = "constant" # {'constant', 'cosine'}
cfg.SOLVER.BATCH_SIZE = 32
cfg.SOLVER.BATCH_SIZE_EVAL = 8
cfg.SOLVER.NUM_EPOCHS = 25
cfg.SOLVER.BASE_LR = 0.0001
cfg.SOLVER.OPTIMIZER = "adam" # {'sgd', 'adam'}
cfg.SOLVER.MOMENTUM = 0.9
cfg.SOLVER.WARMUP_ITERS = 500
cfg.SOLVER.WARMUP_FACTOR = 0.1
cfg.SOLVER.CHECKPOINT_PERIOD = 24949 # in iters
cfg.SOLVER.LOGGING_PERIOD = 50 # in iters
# stable training
cfg.SOLVER.SKIP_LOSS_THRESH = 50.0
cfg.SOLVER.LOSS_SKIP_GAMMA = 0.9
# ------------------------------------------------------------------------ #
# Datasets
# ------------------------------------------------------------------------ #
cfg.DATASETS = CN()
cfg.DATASETS.NAME = "shapenet"
# ------------------------------------------------------------------------ #
# Misc options
# ------------------------------------------------------------------------ #
# Directory where output files are written
cfg.OUTPUT_DIR = "./output"
return cfg
def get_shapenet_cfg():
cfg = CN()
cfg.MODEL = CN()
cfg.MODEL.VOXEL_ON = False
cfg.MODEL.MESH_ON = False
# options: predicted_depth_only | rendered_depth_only | input_concat | input_diff | feature_concat | feature_diff
cfg.MODEL.CONTRASTIVE_DEPTH_TYPE = "input_concat"
cfg.MODEL.USE_GT_DEPTH = False
# options: multihead_attention | simple_attention | stats
cfg.MODEL.FEATURE_FUSION_METHOD = "multihead_attention"
cfg.MODEL.MULTIHEAD_ATTENTION = CN()
# -1 maintains same feature dimensions as before attention
cfg.MODEL.MULTIHEAD_ATTENTION.FEATURE_DIMS = 960
cfg.MODEL.MULTIHEAD_ATTENTION.NUM_HEADS = 10
# ------------------------------------------------------------------------ #
# Checkpoint
# ------------------------------------------------------------------------ #
cfg.MODEL.CHECKPOINT = "" # path to checkpoint
# ------------------------------------------------------------------------ #
# Voxel Head
# ------------------------------------------------------------------------ #
cfg.MODEL.VOXEL_HEAD = CN()
# The number of convs in the voxel head and the number of channels
cfg.MODEL.VOXEL_HEAD.NUM_CONV = 0
cfg.MODEL.VOXEL_HEAD.CONV_DIM = 256
# Normalization method for the convolution layers. Options: "" (no norm), "GN"
cfg.MODEL.VOXEL_HEAD.NORM = ""
# The number of depth channels for the predicted voxels
cfg.MODEL.VOXEL_HEAD.VOXEL_SIZE = 28
cfg.MODEL.VOXEL_HEAD.LOSS_WEIGHT = 1.0
cfg.MODEL.VOXEL_HEAD.CUBIFY_THRESH = 0.0
# voxel only iterations
cfg.MODEL.VOXEL_HEAD.VOXEL_ONLY_ITERS = 100
# Whether voxel weights are frozen
cfg.MODEL.VOXEL_HEAD.FREEZE = False
# Whether to use single view voxel prediction
# without probabilistic merging
cfg.MODEL.VOXEL_HEAD.SINGLE_VIEW = False
cfg.MODEL.VOXEL_HEAD.RGB_FEATURES_INPUT = True
cfg.MODEL.VOXEL_HEAD.DEPTH_FEATURES_INPUT = True
cfg.MODEL.VOXEL_HEAD.RGB_BACKBONE = "resnet50"
cfg.MODEL.VOXEL_HEAD.DEPTH_BACKBONE = "vgg"
# ------------------------------------------------------------------------ #
# Mesh Head
# ------------------------------------------------------------------------ #
cfg.MODEL.MESH_HEAD = CN()
cfg.MODEL.MESH_HEAD.NAME = "VoxMeshHead"
# Numer of stages
cfg.MODEL.MESH_HEAD.NUM_STAGES = 1
cfg.MODEL.MESH_HEAD.NUM_GRAPH_CONVS = 1 # per stage
cfg.MODEL.MESH_HEAD.GRAPH_CONV_DIM = 256
cfg.MODEL.MESH_HEAD.GRAPH_CONV_INIT = "normal"
# Mesh sampling
cfg.MODEL.MESH_HEAD.GT_NUM_SAMPLES = 5000
cfg.MODEL.MESH_HEAD.PRED_NUM_SAMPLES = 5000
# whether to upsample mesh for training
cfg.MODEL.MESH_HEAD.UPSAMPLE_PRED_MESH = True
# loss weights
cfg.MODEL.MESH_HEAD.CHAMFER_LOSS_WEIGHT = 1.0
cfg.MODEL.MESH_HEAD.NORMALS_LOSS_WEIGHT = 1.0
cfg.MODEL.MESH_HEAD.EDGE_LOSS_WEIGHT = 1.0
# Init ico_sphere level (only for when voxel_on is false)
cfg.MODEL.MESH_HEAD.ICO_SPHERE_LEVEL = -1
cfg.MODEL.MESH_HEAD.RGB_FEATURES_INPUT = True
cfg.MODEL.MESH_HEAD.DEPTH_FEATURES_INPUT = True
cfg.MODEL.MESH_HEAD.RGB_BACKBONE = "resnet50"
cfg.MODEL.MESH_HEAD.DEPTH_BACKBONE = "vgg"
cfg.MODEL.MVSNET = CN()
cfg.MODEL.MVSNET.FEATURES_LIST = [32, 64, 128, 256]
cfg.MODEL.MVSNET.CHECKPOINT = ""
cfg.MODEL.MVSNET.FREEZE = False
# the depth values are different than Pixel2Mesh and 3D-R2N2
# the depths here are not scaled by the factor 0.57 here
cfg.MODEL.MVSNET.MIN_DEPTH = 0.175
cfg.MODEL.MVSNET.DEPTH_INTERVAL = 0.044
cfg.MODEL.MVSNET.NUM_DEPTHS = 48
cfg.MODEL.MVSNET.INPUT_IMAGE_SIZE = (224, 224)
cfg.MODEL.MVSNET.FOCAL_LENGTH = (248, 248)
cfg.MODEL.MVSNET.PRINCIPAL_POINT = (111.5, 111.5)
# loss weights
cfg.MODEL.MVSNET.PRED_DEPTH_WEIGHT = 0.1
cfg.MODEL.MVSNET.RENDERED_DEPTH_WEIGHT = 0.00
cfg.MODEL.MVSNET.RENDERED_VS_GT_DEPTH_WEIGHT = 0.00
# ------------------------------------------------------------------------ #
# Solver
# ------------------------------------------------------------------------ #
cfg.SOLVER = CN()
cfg.SOLVER.LR_SCHEDULER_NAME = "constant" # {'constant', 'cosine'}
cfg.SOLVER.BATCH_SIZE = 32
cfg.SOLVER.BATCH_SIZE_EVAL = 8
cfg.SOLVER.NUM_EPOCHS = 25
cfg.SOLVER.BASE_LR = 0.0001
cfg.SOLVER.OPTIMIZER = "adam" # {'sgd', 'adam'}
cfg.SOLVER.MOMENTUM = 0.9
cfg.SOLVER.WARMUP_ITERS = 500
cfg.SOLVER.WARMUP_FACTOR = 0.1
cfg.SOLVER.CHECKPOINT_PERIOD = 24949 # in iters
cfg.SOLVER.LOGGING_PERIOD = 50 # in iters
# stable training
cfg.SOLVER.SKIP_LOSS_THRESH = 50.0
cfg.SOLVER.LOSS_SKIP_GAMMA = 0.9
# for saving checkpoint
cfg.SOLVER.EARLY_STOP_METRIC = "[email protected]"
# ------------------------------------------------------------------------ #
# Datasets
# ------------------------------------------------------------------------ #
cfg.DATASETS = CN()
cfg.DATASETS.NAME = "shapenet"
# ['depth', 'multi_view', 'single_view']
cfg.DATASETS.TYPE = "single_view"
cfg.DATASETS.SPLITS_FILE = "./datasets/shapenet/pix2mesh_splits_val05.json"
cfg.DATASETS.INPUT_VIEWS = [0, 6, 7]
# ------------------------------------------------------------------------ #
# Misc options
# ------------------------------------------------------------------------ #
# Directory where output files are written
cfg.OUTPUT_DIR = "./output"
return cfg
