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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. # 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. | |
_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() | |