_base_ = ["../_base_/default_runtime.py"] # misc custom setting batch_size = 12 # bs: total bs in all gpus num_worker = 24 mix_prob = 0.8 empty_cache = False enable_amp = True # model settings model = dict( type="DefaultSegmentorV2", num_classes=21, backbone_out_channels=64, backbone=dict( type="PT-v3m1", in_channels=6, order=("z", "z-trans", "hilbert", "hilbert-trans"), stride=(2, 2, 2, 2), enc_depths=(2, 2, 2, 6, 2), enc_channels=(32, 64, 128, 256, 512), enc_num_head=(2, 4, 8, 16, 32), enc_patch_size=(1024, 1024, 1024, 1024, 1024), dec_depths=(2, 2, 2, 2), dec_channels=(64, 64, 128, 256), dec_num_head=(4, 4, 8, 16), dec_patch_size=(1024, 1024, 1024, 1024), mlp_ratio=4, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, drop_path=0.3, shuffle_orders=True, pre_norm=True, enable_rpe=False, enable_flash=True, upcast_attention=False, upcast_softmax=False, cls_mode=False, pdnorm_bn=False, pdnorm_ln=False, pdnorm_decouple=True, pdnorm_adaptive=False, pdnorm_affine=True, pdnorm_conditions=("ScanNet", "S3DIS", "Structured3D"), ), criteria=[ dict(type="CrossEntropyLoss", loss_weight=1.0, ignore_index=-1), dict(type="LovaszLoss", mode="multiclass", loss_weight=1.0, ignore_index=-1), ], ) # scheduler settings epoch = 800 optimizer = dict(type="AdamW", lr=0.006, weight_decay=0.05) scheduler = dict( type="OneCycleLR", max_lr=[0.006, 0.0006], pct_start=0.05, anneal_strategy="cos", div_factor=10.0, final_div_factor=1000.0, ) param_dicts = [dict(keyword="block", lr=0.0006)] # dataset settings dataset_type = "DefaultDataset" data_root = "data/matterport3d" data = dict( num_classes=21, ignore_index=-1, names=( "wall", "floor", "cabinet", "bed", "chair", "sofa", "table", "door", "window", "bookshelf", "picture", "counter", "desk", "curtain", "refrigerator", "shower curtain", "toilet", "sink", "bathtub", "other", "ceiling", ), train=dict( type=dataset_type, split="train", data_root=data_root, transform=[ dict(type="CenterShift", apply_z=True), dict( type="RandomDropout", dropout_ratio=0.2, dropout_application_ratio=0.2 ), # dict(type="RandomRotateTargetAngle", angle=(1/2, 1, 3/2), center=[0, 0, 0], axis="z", p=0.75), dict(type="RandomRotate", angle=[-1, 1], axis="z", center=[0, 0, 0], p=0.5), dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="x", p=0.5), dict(type="RandomRotate", angle=[-1 / 64, 1 / 64], axis="y", p=0.5), dict(type="RandomScale", scale=[0.9, 1.1]), # dict(type="RandomShift", shift=[0.2, 0.2, 0.2]), dict(type="RandomFlip", p=0.5), dict(type="RandomJitter", sigma=0.005, clip=0.02), dict(type="ElasticDistortion", distortion_params=[[0.2, 0.4], [0.8, 1.6]]), dict(type="ChromaticAutoContrast", p=0.2, blend_factor=None), dict(type="ChromaticTranslation", p=0.95, ratio=0.05), dict(type="ChromaticJitter", p=0.95, std=0.05), # dict(type="HueSaturationTranslation", hue_max=0.2, saturation_max=0.2), # dict(type="RandomColorDrop", p=0.2, color_augment=0.0), dict( type="GridSample", grid_size=0.02, hash_type="fnv", mode="train", return_grid_coord=True, ), dict(type="SphereCrop", point_max=102400, mode="random"), dict(type="CenterShift", apply_z=False), dict(type="NormalizeColor"), # dict(type="ShufflePoint"), dict(type="ToTensor"), dict( type="Collect", keys=("coord", "grid_coord", "segment"), feat_keys=("color", "normal"), ), ], test_mode=False, ), val=dict( type=dataset_type, split="val", data_root=data_root, transform=[ dict(type="CenterShift", apply_z=True), dict( type="GridSample", grid_size=0.02, hash_type="fnv", mode="train", return_grid_coord=True, ), dict(type="CenterShift", apply_z=False), dict(type="NormalizeColor"), dict(type="ToTensor"), dict( type="Collect", keys=("coord", "grid_coord", "segment"), feat_keys=("color", "normal"), ), ], test_mode=False, ), test=dict( type=dataset_type, split="val", data_root=data_root, transform=[ dict(type="CenterShift", apply_z=True), dict(type="NormalizeColor"), ], test_mode=True, test_cfg=dict( voxelize=dict( type="GridSample", grid_size=0.02, hash_type="fnv", mode="test", keys=("coord", "color", "normal"), return_grid_coord=True, ), crop=None, post_transform=[ dict(type="CenterShift", apply_z=False), dict(type="ToTensor"), dict( type="Collect", keys=("coord", "grid_coord", "index"), feat_keys=("color", "normal"), ), ], aug_transform=[ [ dict( type="RandomRotateTargetAngle", angle=[0], axis="z", center=[0, 0, 0], p=1, ) ], [ dict( type="RandomRotateTargetAngle", angle=[1 / 2], axis="z", center=[0, 0, 0], p=1, ) ], [ dict( type="RandomRotateTargetAngle", angle=[1], axis="z", center=[0, 0, 0], p=1, ) ], [ dict( type="RandomRotateTargetAngle", angle=[3 / 2], axis="z", center=[0, 0, 0], p=1, ) ], [ dict( type="RandomRotateTargetAngle", angle=[0], axis="z", center=[0, 0, 0], p=1, ), dict(type="RandomScale", scale=[0.95, 0.95]), ], [ dict( type="RandomRotateTargetAngle", angle=[1 / 2], axis="z", center=[0, 0, 0], p=1, ), dict(type="RandomScale", scale=[0.95, 0.95]), ], [ dict( type="RandomRotateTargetAngle", angle=[1], axis="z", center=[0, 0, 0], p=1, ), dict(type="RandomScale", scale=[0.95, 0.95]), ], [ dict( type="RandomRotateTargetAngle", angle=[3 / 2], axis="z", center=[0, 0, 0], p=1, ), dict(type="RandomScale", scale=[0.95, 0.95]), ], [ dict( type="RandomRotateTargetAngle", angle=[0], axis="z", center=[0, 0, 0], p=1, ), dict(type="RandomScale", scale=[1.05, 1.05]), ], [ dict( type="RandomRotateTargetAngle", angle=[1 / 2], axis="z", center=[0, 0, 0], p=1, ), dict(type="RandomScale", scale=[1.05, 1.05]), ], [ dict( type="RandomRotateTargetAngle", angle=[1], axis="z", center=[0, 0, 0], p=1, ), dict(type="RandomScale", scale=[1.05, 1.05]), ], [ dict( type="RandomRotateTargetAngle", angle=[3 / 2], axis="z", center=[0, 0, 0], p=1, ), dict(type="RandomScale", scale=[1.05, 1.05]), ], [dict(type="RandomFlip", p=1)], ], ), ), )