[2024-02-25 21:58:30,227 INFO train.py line 128 1064569] => Loading config ...
[2024-02-25 21:58:30,227 INFO train.py line 130 1064569] Save path: exp/scannet/semseg-pt-v2m2-0-base
[2024-02-25 21:58:31,229 INFO train.py line 131 1064569] Config:
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
                           }]])))
num_worker_per_gpu = 8
batch_size_per_gpu = 6
batch_size_val_per_gpu = 1
batch_size_test_per_gpu = 1

[2024-02-25 21:58:31,230 INFO train.py line 132 1064569] => Building model ...
[2024-02-25 21:58:31,329 INFO train.py line 209 1064569] Num params: 11323948
[2024-02-25 21:58:32,250 INFO train.py line 134 1064569] => Building writer ...
[2024-02-25 21:58:32,280 INFO train.py line 219 1064569] Tensorboard writer logging dir: exp/scannet/semseg-pt-v2m2-0-base
[2024-02-25 21:58:32,280 INFO train.py line 136 1064569] => Building train dataset & dataloader ...
[2024-02-25 21:58:32,289 INFO scannet.py line 72 1064569] Totally 1201 x 9 samples in train set.
[2024-02-25 21:58:32,290 INFO train.py line 138 1064569] => Building val dataset & dataloader ...
[2024-02-25 21:58:32,293 INFO scannet.py line 72 1064569] Totally 312 x 1 samples in val set.
[2024-02-25 21:58:32,293 INFO train.py line 140 1064569] => Building optimize, scheduler, scaler(amp) ...
[2024-02-25 21:58:32,300 INFO train.py line 144 1064569] => Building hooks ...
[2024-02-25 21:58:32,300 INFO misc.py line 213 1064569] => Loading checkpoint & weight ...
[2024-02-25 21:58:32,301 INFO misc.py line 250 1064569] No weight found at: None
[2024-02-25 21:58:32,301 INFO train.py line 151 1064569] >>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>>