| weight = 'exp/scannet/semseg-pt-v3m1-1-ppt-extreme-alc-20240823-massive-no-val/model/model_mod_insseg.pth' |
| resume = False |
| evaluate = True |
| test_only = False |
| seed = 32743774 |
| save_path = 'exp/scannet/instance_segmentation_ppt_pretrain_ft_full' |
| num_worker = 24 |
| batch_size = 12 |
| batch_size_val = None |
| batch_size_test = None |
| epoch = 800 |
| eval_epoch = 100 |
| sync_bn = False |
| enable_amp = True |
| empty_cache = False |
| empty_cache_per_epoch = False |
| find_unused_parameters = True |
| mix_prob = 0 |
| param_dicts = [dict(keyword='block', lr=0.0006)] |
| hooks = [ |
| dict(type='CheckpointLoader', keywords='module.', replacement='module.'), |
| dict(type='IterationTimer', warmup_iter=2), |
| dict(type='InformationWriter'), |
| dict( |
| type='InsSegEvaluator', |
| segment_ignore_index=(-1, 0, 1), |
| instance_ignore_index=-1), |
| dict(type='CheckpointSaver', save_freq=None) |
| ] |
| train = dict(type='DefaultTrainer') |
| test = dict(type='SemSegTester', verbose=True) |
| class_names = [ |
| 'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', |
| 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', |
| 'refridgerator', 'shower curtain', 'toilet', 'sink', 'bathtub', |
| 'otherfurniture' |
| ] |
| num_classes = 20 |
| segment_ignore_index = (-1, 0, 1) |
| model = dict( |
| type='PG-v1m1', |
| backbone=dict( |
| type='PPT-v1m2', |
| backbone=dict( |
| type='PT-v3m1', |
| in_channels=6, |
| order=('z', 'z-trans', 'hilbert', 'hilbert-trans'), |
| stride=(2, 2, 2, 2), |
| enc_depths=(3, 3, 3, 6, 3), |
| enc_channels=(48, 96, 192, 384, 512), |
| enc_num_head=(3, 6, 12, 24, 32), |
| enc_patch_size=(1024, 1024, 1024, 1024, 1024), |
| dec_depths=(3, 3, 3, 3), |
| dec_channels=(64, 96, 192, 384), |
| dec_num_head=(4, 6, 12, 24), |
| 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=True, |
| pdnorm_ln=True, |
| pdnorm_decouple=True, |
| pdnorm_adaptive=False, |
| pdnorm_affine=True, |
| pdnorm_conditions=('ScanNet', 'ScanNet200', 'ScanNet++', |
| 'Structured3D', 'ALC')), |
| criteria=[ |
| dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1), |
| dict( |
| type='LovaszLoss', |
| mode='multiclass', |
| loss_weight=1.0, |
| ignore_index=-1) |
| ], |
| backbone_out_channels=64, |
| backbone_mode=True, |
| context_channels=256, |
| conditions=('ScanNet', 'ScanNet200', 'ScanNet++', 'Structured3D', |
| 'ALC'), |
| num_classes=(20, 200, 100, 25, 185)), |
| backbone_out_channels=64, |
| semantic_num_classes=20, |
| semantic_ignore_index=-1, |
| segment_ignore_index=(-1, 0, 1), |
| instance_ignore_index=-1, |
| cluster_thresh=1.5, |
| cluster_closed_points=300, |
| cluster_propose_points=100, |
| cluster_min_points=50, |
| freeze_backbone=False) |
| 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) |
| 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=0.5), |
| 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=0.1), |
| dict(type='ChromaticJitter', p=0.95, std=0.05), |
| dict( |
| type='GridSample', |
| grid_size=0.02, |
| hash_type='fnv', |
| mode='train', |
| return_grid_coord=True, |
| keys=('coord', 'color', 'normal', 'segment', 'instance')), |
| dict(type='SphereCrop', sample_rate=0.8, mode='random'), |
| dict(type='NormalizeColor'), |
| dict( |
| type='InstanceParser', |
| segment_ignore_index=(-1, 0, 1), |
| instance_ignore_index=-1), |
| dict(type='Add', keys_dict=dict(condition='ScanNet')), |
| dict(type='ToTensor'), |
| dict( |
| type='Collect', |
| keys=('coord', 'grid_coord', 'segment', 'instance', |
| 'instance_centroid', 'bbox', 'condition'), |
| feat_keys=('color', 'normal')) |
| ], |
| test_mode=False, |
| loop=8), |
| val=dict( |
| type='ScanNetDataset', |
| split='val', |
| data_root='data/scannet', |
| transform=[ |
| dict(type='CenterShift', apply_z=True), |
| dict( |
| type='Copy', |
| keys_dict=dict( |
| coord='origin_coord', |
| segment='origin_segment', |
| instance='origin_instance')), |
| dict( |
| type='GridSample', |
| grid_size=0.02, |
| hash_type='fnv', |
| mode='train', |
| return_grid_coord=True, |
| keys=('coord', 'color', 'normal', 'segment', 'instance')), |
| dict(type='CenterShift', apply_z=False), |
| dict(type='NormalizeColor'), |
| dict( |
| type='InstanceParser', |
| segment_ignore_index=(-1, 0, 1), |
| instance_ignore_index=-1), |
| dict(type='Add', keys_dict=dict(condition='ScanNet')), |
| dict(type='ToTensor'), |
| dict( |
| type='Collect', |
| keys=('coord', 'grid_coord', 'segment', 'instance', |
| 'origin_coord', 'origin_segment', 'origin_instance', |
| 'instance_centroid', 'bbox', 'condition'), |
| feat_keys=('color', 'normal'), |
| offset_keys_dict=dict( |
| offset='coord', origin_offset='origin_coord')) |
| ], |
| test_mode=False), |
| test=dict()) |
|
|
