training s3dis error: ValueError: Trainer: num_samples should be a positive integer value, but got num_samples=0
Yatoronto opened this issue · comments
detail error imformation:
sh scripts/train.sh -p python -g 1 -d s3dis -c semseg-pt-v3m1-0-base -n semseg-pt-v3m1-0-base
Experiment name: semseg-pt-v3m1-0-base
Python interpreter dir: python
Dataset: s3dis
Config: semseg-pt-v3m1-0-base
GPU Num: 1
=========> CREATE EXP DIR <=========
Experiment dir: /media/wsc/16B68EBDB68E9CBD/PTv3/Pointcept/exp/s3dis/semseg-pt-v3m1-0-base
Loading config in: configs/s3dis/semseg-pt-v3m1-0-base.py
Running code in: exp/s3dis/semseg-pt-v3m1-0-base/code
=========> RUN TASK <=========
[2024-06-13 16:03:02,124 INFO train.py line 128 9903] => Loading config ...
[2024-06-13 16:03:02,124 INFO train.py line 130 9903] Save path: exp/s3dis/semseg-pt-v3m1-0-base
[2024-06-13 16:03:02,670 INFO train.py line 131 9903] Config:
weight = None
resume = False
evaluate = True
test_only = False
seed = 1595974
save_path = 'exp/s3dis/semseg-pt-v3m1-0-base'
num_worker = 24
batch_size = 12
batch_size_val = None
batch_size_test = None
epoch = 3000
eval_epoch = 100
sync_bn = False
enable_amp = True
empty_cache = False
empty_cache_per_epoch = False
find_unused_parameters = False
mix_prob = 0.8
param_dicts = [dict(keyword='block', lr=0.0006)]
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='DefaultSegmentorV2',
num_classes=13,
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)
])
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 = 'S3DISDataset'
data_root = 'data/s3dis'
data = dict(
num_classes=13,
ignore_index=-1,
names=[
'ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter'
],
train=dict(
type='S3DISDataset',
split=('Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_6'),
data_root='data/s3dis',
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='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_grid_coord=True),
dict(type='SphereCrop', sample_rate=0.6, mode='random'),
dict(type='SphereCrop', point_max=204800, mode='random'),
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,
loop=30),
val=dict(
type='S3DISDataset',
split='Area_5',
data_root='data/s3dis',
transform=[
dict(type='CenterShift', apply_z=True),
dict(
type='Copy',
keys_dict=dict(coord='origin_coord',
segment='origin_segment')),
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', 'origin_coord', 'segment',
'origin_segment'),
offset_keys_dict=dict(
offset='coord', origin_offset='origin_coord'),
feat_keys=('color', 'normal'))
],
test_mode=False),
test=dict(
type='S3DISDataset',
split='Area_5',
data_root='data/s3dis',
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=[[{
'type': 'RandomScale',
'scale': [0.9, 0.9]
}], [{
'type': 'RandomScale',
'scale': [0.95, 0.95]
}], [{
'type': 'RandomScale',
'scale': [1, 1]
}], [{
'type': 'RandomScale',
'scale': [1.05, 1.05]
}], [{
'type': 'RandomScale',
'scale': [1.1, 1.1]
}],
[{
'type': 'RandomScale',
'scale': [0.9, 0.9]
}, {
'type': 'RandomFlip',
'p': 1
}],
[{
'type': 'RandomScale',
'scale': [0.95, 0.95]
}, {
'type': 'RandomFlip',
'p': 1
}],
[{
'type': 'RandomScale',
'scale': [1, 1]
}, {
'type': 'RandomFlip',
'p': 1
}],
[{
'type': 'RandomScale',
'scale': [1.05, 1.05]
}, {
'type': 'RandomFlip',
'p': 1
}],
[{
'type': 'RandomScale',
'scale': [1.1, 1.1]
}, {
'type': 'RandomFlip',
'p': 1
}]])))
num_worker_per_gpu = 24
batch_size_per_gpu = 12
batch_size_val_per_gpu = 1
batch_size_test_per_gpu = 1
[2024-06-13 16:03:02,671 INFO train.py line 132 9903] => Building model ...
[2024-06-13 16:03:02,916 INFO train.py line 216 9903] Num params: 46167117
[2024-06-13 16:03:03,640 INFO train.py line 134 9903] => Building writer ...
[2024-06-13 16:03:03,641 INFO train.py line 226 9903] Tensorboard writer logging dir: exp/s3dis/semseg-pt-v3m1-0-base
[2024-06-13 16:03:03,641 INFO train.py line 136 9903] => Building train dataset & dataloader ...
[2024-06-13 16:03:03,642 INFO defaults.py line 68 9903] Totally 0 x 30 samples in ('Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_6') set.
Traceback (most recent call last):
File "/media/wsc/16B68EBDB68E9CBD/PTv3/Pointcept/exp/s3dis/semseg-pt-v3m1-0-base/code/pointcept/utils/registry.py", line 53, in build_from_cfg
return obj_cls(**args)
File "/media/wsc/16B68EBDB68E9CBD/PTv3/Pointcept/exp/s3dis/semseg-pt-v3m1-0-base/code/pointcept/engines/train.py", line 137, in __init__
self.train_loader = self.build_train_loader()
File "/media/wsc/16B68EBDB68E9CBD/PTv3/Pointcept/exp/s3dis/semseg-pt-v3m1-0-base/code/pointcept/engines/train.py", line 248, in build_train_loader
train_loader = torch.utils.data.DataLoader(
File "/home/wsc/anaconda3/envs/ptv3/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 351, in __init__
sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]
File "/home/wsc/anaconda3/envs/ptv3/lib/python3.8/site-packages/torch/utils/data/sampler.py", line 107, in __init__
raise ValueError("num_samples should be a positive integer "
ValueError: num_samples should be a positive integer value, but got num_samples=0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "exp/s3dis/semseg-pt-v3m1-0-base/code/tools/train.py", line 38, in <module>
main()
File "exp/s3dis/semseg-pt-v3m1-0-base/code/tools/train.py", line 27, in main
launch(
File "/media/wsc/16B68EBDB68E9CBD/PTv3/Pointcept/exp/s3dis/semseg-pt-v3m1-0-base/code/pointcept/engines/launch.py", line 89, in launch
main_func(*cfg)
File "exp/s3dis/semseg-pt-v3m1-0-base/code/tools/train.py", line 19, in main_worker
trainer = TRAINERS.build(dict(type=cfg.train.type, cfg=cfg))
File "/media/wsc/16B68EBDB68E9CBD/PTv3/Pointcept/exp/s3dis/semseg-pt-v3m1-0-base/code/pointcept/utils/registry.py", line 214, in build
return self.build_func(*args, **kwargs, registry=self)
File "/media/wsc/16B68EBDB68E9CBD/PTv3/Pointcept/exp/s3dis/semseg-pt-v3m1-0-base/code/pointcept/utils/registry.py", line 56, in build_from_cfg
raise type(e)(f"{obj_cls.__name__}: {e}")
ValueError: Trainer: num_samples should be a positive integer value, but got num_samples=0