weight = None resume = False evaluate = True test_only = False seed = 13856060 save_path = 'exp/scannet/semseg-pt-v2m2-0-base' num_worker = 16 batch_size = 12 batch_size_val = None batch_size_test = None epoch = 900 eval_epoch = 100 sync_bn = False enable_amp = True empty_cache = False find_unused_parameters = False mix_prob = 0.8 param_dicts = None hooks = [ dict(type='CheckpointLoader'), dict(type='IterationTimer', warmup_iter=2), dict(type='InformationWriter'), dict(type='SemSegEvaluator'), dict(type='CheckpointSaver', save_freq=None), dict(type='PreciseEvaluator', test_last=False) ] train = dict(type='DefaultTrainer') test = dict(type='SemSegTester', verbose=True) model = dict( type='DefaultSegmentor', backbone=dict( type='PT-v2m2', in_channels=9, num_classes=20, patch_embed_depth=1, patch_embed_channels=48, patch_embed_groups=6, patch_embed_neighbours=8, enc_depths=(2, 2, 6, 2), enc_channels=(96, 192, 384, 512), enc_groups=(12, 24, 48, 64), enc_neighbours=(16, 16, 16, 16), dec_depths=(1, 1, 1, 1), dec_channels=(48, 96, 192, 384), dec_groups=(6, 12, 24, 48), dec_neighbours=(16, 16, 16, 16), grid_sizes=(0.06, 0.15, 0.375, 0.9375), attn_qkv_bias=True, pe_multiplier=False, pe_bias=True, attn_drop_rate=0.0, drop_path_rate=0.3, enable_checkpoint=False, unpool_backend='map'), criteria=[dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1)]) optimizer = dict(type='AdamW', lr=0.005, weight_decay=0.02) scheduler = dict( type='OneCycleLR', max_lr=0.005, pct_start=0.05, anneal_strategy='cos', div_factor=10.0, final_div_factor=1000.0) dataset_type = 'ScanNetDataset' data_root = 'data/scannet' data = dict( num_classes=20, ignore_index=-1, names=[ 'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refridgerator', 'shower curtain', 'toilet', 'sink', 'bathtub', 'otherfurniture' ], train=dict( type='ScanNetDataset', split='train', data_root='data/scannet', transform=[ dict(type='CenterShift', apply_z=True), dict( type='RandomDropout', dropout_ratio=0.2, dropout_application_ratio=0.2), dict( type='RandomRotate', angle=[-1, 1], axis='z', center=[0, 0, 0], p=0.5), dict( type='RandomRotate', angle=[-0.015625, 0.015625], axis='x', p=0.5), dict( type='RandomRotate', angle=[-0.015625, 0.015625], axis='y', p=0.5), dict(type='RandomScale', scale=[0.9, 1.1]), 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='GridSample', grid_size=0.02, hash_type='fnv', mode='train', return_min_coord=True), dict(type='SphereCrop', point_max=100000, mode='random'), dict(type='CenterShift', apply_z=False), dict(type='NormalizeColor'), dict(type='ShufflePoint'), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'segment'), feat_keys=('coord', 'color', 'normal')) ], test_mode=False, loop=9), val=dict( type='ScanNetDataset', split='val', data_root='data/scannet', transform=[ dict(type='CenterShift', apply_z=True), dict( type='GridSample', grid_size=0.02, hash_type='fnv', mode='train', return_min_coord=True), dict(type='CenterShift', apply_z=False), dict(type='NormalizeColor'), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'segment'), feat_keys=('coord', 'color', 'normal')) ], test_mode=False), test=dict( type='ScanNetDataset', split='val', data_root='data/scannet', 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')), crop=None, post_transform=[ dict(type='CenterShift', apply_z=False), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'index'), feat_keys=('coord', 'color', 'normal')) ], aug_transform=[[{ 'type': 'RandomRotateTargetAngle', 'angle': [0], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomFlip', 'p': 1 }]])))