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from yacs.config import CfgNode as CN
_CN = CN()
############## ↓ MODEL Pipeline ↓ ##############
_CN.MODEL = CN()
_CN.MODEL.BACKBONE_TYPE = "FPN"
_CN.MODEL.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)]
_CN.MODEL.FINE_WINDOW_SIZE = 5 # window_size in fine_level, must be odd
_CN.MODEL.FINE_CONCAT_COARSE_FEAT = False
# 1. MODEL-backbone (local feature CNN) config
_CN.MODEL.FPN = CN()
_CN.MODEL.FPN.INITIAL_DIM = 128
_CN.MODEL.FPN.BLOCK_DIMS = [128, 192, 256, 384] # s1, s2, s3
# 2. MODEL-coarse module config
_CN.MODEL.COARSE = CN()
_CN.MODEL.COARSE.D_MODEL = 256
_CN.MODEL.COARSE.D_FFN = 256
_CN.MODEL.COARSE.NHEAD = 8
_CN.MODEL.COARSE.LAYER_NAMES = ["seed", "seed", "seed", "seed", "seed"]
_CN.MODEL.COARSE.ATTENTION = "linear" # options: ['linear', 'full']
_CN.MODEL.COARSE.TEMP_BUG_FIX = True
_CN.MODEL.COARSE.N_TOPICS = 100
_CN.MODEL.COARSE.N_SAMPLES = 6
_CN.MODEL.COARSE.N_TOPIC_TRANSFORMERS = 1
# 3. Coarse-Matching config
_CN.MODEL.MATCH_COARSE = CN()
_CN.MODEL.MATCH_COARSE.THR = 0.2
_CN.MODEL.MATCH_COARSE.BORDER_RM = 2
_CN.MODEL.MATCH_COARSE.MATCH_TYPE = "dual_softmax"
_CN.MODEL.MATCH_COARSE.DSMAX_TEMPERATURE = 0.1
_CN.MODEL.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.2 # training tricks: save GPU memory
_CN.MODEL.MATCH_COARSE.TRAIN_PAD_NUM_GT_MIN = 200 # training tricks: avoid DDP deadlock
_CN.MODEL.MATCH_COARSE.SPARSE_SPVS = True
# 4. MODEL-fine module config
_CN.MODEL.FINE = CN()
_CN.MODEL.FINE.D_MODEL = 128
_CN.MODEL.FINE.D_FFN = 128
_CN.MODEL.FINE.NHEAD = 4
_CN.MODEL.FINE.LAYER_NAMES = ["cross"] * 1
_CN.MODEL.FINE.ATTENTION = "linear"
_CN.MODEL.FINE.N_TOPICS = 1
# 5. MODEL Losses
# -- # coarse-level
_CN.MODEL.LOSS = CN()
_CN.MODEL.LOSS.COARSE_WEIGHT = 1.0
# _CN.MODEL.LOSS.SPARSE_SPVS = False
# -- - -- # focal loss (coarse)
_CN.MODEL.LOSS.FOCAL_ALPHA = 0.25
_CN.MODEL.LOSS.POS_WEIGHT = 1.0
_CN.MODEL.LOSS.NEG_WEIGHT = 1.0
# _CN.MODEL.LOSS.DUAL_SOFTMAX = False # whether coarse-level use dual-softmax or not.
# use `_CN.MODEL.MATCH_COARSE.MATCH_TYPE`
# -- # fine-level
_CN.MODEL.LOSS.FINE_TYPE = "l2_with_std" # ['l2_with_std', 'l2']
_CN.MODEL.LOSS.FINE_WEIGHT = 1.0
_CN.MODEL.LOSS.FINE_CORRECT_THR = 1.0 # for filtering valid fine-level gts (some gt matches might fall out of the fine-level window)
############## Dataset ##############
_CN.DATASET = CN()
# 1. data config
# training and validating
_CN.DATASET.TRAINVAL_DATA_SOURCE = None # options: ['ScanNet', 'MegaDepth']
_CN.DATASET.TRAIN_DATA_ROOT = None
_CN.DATASET.TRAIN_POSE_ROOT = None # (optional directory for poses)
_CN.DATASET.TRAIN_NPZ_ROOT = None
_CN.DATASET.TRAIN_LIST_PATH = None
_CN.DATASET.TRAIN_INTRINSIC_PATH = None
_CN.DATASET.VAL_DATA_ROOT = None
_CN.DATASET.VAL_POSE_ROOT = None # (optional directory for poses)
_CN.DATASET.VAL_NPZ_ROOT = None
_CN.DATASET.VAL_LIST_PATH = (
None # None if val data from all scenes are bundled into a single npz file
)
_CN.DATASET.VAL_INTRINSIC_PATH = None
# testing
_CN.DATASET.TEST_DATA_SOURCE = None
_CN.DATASET.TEST_DATA_ROOT = None
_CN.DATASET.TEST_POSE_ROOT = None # (optional directory for poses)
_CN.DATASET.TEST_NPZ_ROOT = None
_CN.DATASET.TEST_LIST_PATH = (
None # None if test data from all scenes are bundled into a single npz file
)
_CN.DATASET.TEST_INTRINSIC_PATH = None
_CN.DATASET.TEST_IMGSIZE = None
# 2. dataset config
# general options
_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = (
0.4 # discard data with overlap_score < min_overlap_score
)
_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0
_CN.DATASET.AUGMENTATION_TYPE = None # options: [None, 'dark', 'mobile']
# MegaDepth options
_CN.DATASET.MGDPT_IMG_RESIZE = (
640 # resize the longer side, zero-pad bottom-right to square.
)
_CN.DATASET.MGDPT_IMG_PAD = True # pad img to square with size = MGDPT_IMG_RESIZE
_CN.DATASET.MGDPT_DEPTH_PAD = True # pad depthmap to square with size = 2000
_CN.DATASET.MGDPT_DF = 8
############## Trainer ##############
_CN.TRAINER = CN()
_CN.TRAINER.WORLD_SIZE = 1
_CN.TRAINER.CANONICAL_BS = 64
_CN.TRAINER.CANONICAL_LR = 6e-3
_CN.TRAINER.SCALING = None # this will be calculated automatically
_CN.TRAINER.FIND_LR = False # use learning rate finder from pytorch-lightning
# optimizer
_CN.TRAINER.OPTIMIZER = "adamw" # [adam, adamw]
_CN.TRAINER.TRUE_LR = None # this will be calculated automatically at runtime
_CN.TRAINER.ADAM_DECAY = 0.0 # ADAM: for adam
_CN.TRAINER.ADAMW_DECAY = 0.01
# step-based warm-up
_CN.TRAINER.WARMUP_TYPE = "linear" # [linear, constant]
_CN.TRAINER.WARMUP_RATIO = 0.0
_CN.TRAINER.WARMUP_STEP = 4800
# learning rate scheduler
_CN.TRAINER.SCHEDULER = "MultiStepLR" # [MultiStepLR, CosineAnnealing, ExponentialLR]
_CN.TRAINER.SCHEDULER_INTERVAL = "epoch" # [epoch, step]
_CN.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12] # MSLR: MultiStepLR
_CN.TRAINER.MSLR_GAMMA = 0.5
_CN.TRAINER.COSA_TMAX = 30 # COSA: CosineAnnealing
_CN.TRAINER.ELR_GAMMA = 0.999992 # ELR: ExponentialLR, this value for 'step' interval
# plotting related
_CN.TRAINER.ENABLE_PLOTTING = True
_CN.TRAINER.N_VAL_PAIRS_TO_PLOT = 32 # number of val/test paris for plotting
_CN.TRAINER.PLOT_MODE = "evaluation" # ['evaluation', 'confidence']
_CN.TRAINER.PLOT_MATCHES_ALPHA = "dynamic"
# geometric metrics and pose solver
_CN.TRAINER.EPI_ERR_THR = (
5e-4 # recommendation: 5e-4 for ScanNet, 1e-4 for MegaDepth (from SuperGlue)
)
_CN.TRAINER.POSE_GEO_MODEL = "E" # ['E', 'F', 'H']
_CN.TRAINER.POSE_ESTIMATION_METHOD = "RANSAC" # [RANSAC, DEGENSAC, MAGSAC]
_CN.TRAINER.RANSAC_PIXEL_THR = 0.5
_CN.TRAINER.RANSAC_CONF = 0.99999
_CN.TRAINER.RANSAC_MAX_ITERS = 10000
_CN.TRAINER.USE_MAGSACPP = False
# data sampler for train_dataloader
_CN.TRAINER.DATA_SAMPLER = (
"scene_balance" # options: ['scene_balance', 'random', 'normal']
)
# 'scene_balance' config
_CN.TRAINER.N_SAMPLES_PER_SUBSET = 200
_CN.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT = (
True # whether sample each scene with replacement or not
)
_CN.TRAINER.SB_SUBSET_SHUFFLE = (
True # after sampling from scenes, whether shuffle within the epoch or not
)
_CN.TRAINER.SB_REPEAT = 1 # repeat N times for training the sampled data
# 'random' config
_CN.TRAINER.RDM_REPLACEMENT = True
_CN.TRAINER.RDM_NUM_SAMPLES = None
# gradient clipping
_CN.TRAINER.GRADIENT_CLIPPING = 0.5
# reproducibility
# This seed affects the data sampling. With the same seed, the data sampling is promised
# to be the same. When resume training from a checkpoint, it's better to use a different
# seed, otherwise the sampled data will be exactly the same as before resuming, which will
# cause less unique data items sampled during the entire training.
# Use of different seed values might affect the final training result, since not all data items
# are used during training on ScanNet. (60M pairs of images sampled during traing from 230M pairs in total.)
_CN.TRAINER.SEED = 66
def get_cfg_defaults():
"""Get a yacs CfgNode object with default values for my_project."""
# Return a clone so that the defaults will not be altered
# This is for the "local variable" use pattern
return _CN.clone()
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