How should I make config file to train with my customized dataset?
HitmansGO opened this issue · 5 comments
HitmansGO commented
I had already prepared my customized dataset with S3DIS format. And I also referred to mmdection3d's config file https://mmdetection3d.readthedocs.io/en/latest/user_guides/new_data_model.html
but when I run train.py with my own config file, I got this erro:
Traceback (most recent call last):
File "/home/hfut108/oneformer3d/tools/train.py", line 135, in <module>
main()
File "/home/hfut108/oneformer3d/tools/train.py", line 131, in main
runner.train()
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1777, in train
model = self.train_loop.run() # type: ignore
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/mmengine/runner/loops.py", line 96, in run
self.run_epoch()
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/mmengine/runner/loops.py", line 112, in run_epoch
for idx, data_batch in enumerate(self.dataloader):
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 681, in __next__
data = self._next_data()
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1376, in _next_data
return self._process_data(data)
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1402, in _process_data
data.reraise()
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/torch/_utils.py", line 461, in reraise
raise exception
Exception: Caught Exception in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/mmengine/dataset/dataset_wrapper.py", line 171, in __getitem__
return self.datasets[dataset_idx][sample_idx]
File "/home/hfut108/anaconda3/envs/openmmlab3d/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 418, in __getitem__
raise Exception(f'Cannot find valid image after {self.max_refetch}! '
Exception: Cannot find valid image after 1000! Please check your image path and pipeline
and here is my config file:
import sys
sys.path.append("/home/hfut108")
_base_ = [
'mmdet3d::_base_/default_runtime.py',
]
custom_imports = dict(imports=['oneformer3d.oneformer3d.oneformer3d'])
# model settings
num_channels = 64
num_instance_classes = 2
num_semantic_classes = 2
class_names = ['part','bgpart']
metainfo = dict(classes=class_names)
num_points = 4096
model = dict(
type='S3DISOneFormer3D',
data_preprocessor=dict(type='Det3DDataPreprocessor'),
in_channels=6,
num_channels=num_channels,
voxel_size=0.05,
num_classes=num_instance_classes,
min_spatial_shape=128,
backbone=dict(
type='SpConvUNet',
num_planes=[num_channels * (i + 1) for i in range(5)],
return_blocks=True),
decoder=dict(
type='QueryDecoder',
num_layers=3,
num_classes=num_instance_classes,
num_instance_queries=400,
num_semantic_queries=num_semantic_classes,
num_instance_classes=num_instance_classes,
in_channels=num_channels,
d_model=256,
num_heads=8,
hidden_dim=1024,
dropout=0.0,
activation_fn='gelu',
iter_pred=True,
attn_mask=True,
fix_attention=True,
objectness_flag=True),
criterion=dict(
type='S3DISUnifiedCriterion',
num_semantic_classes=num_semantic_classes,
sem_criterion=dict(
type='S3DISSemanticCriterion',
loss_weight=5.0),
inst_criterion=dict(
type='InstanceCriterion',
matcher=dict(
type='HungarianMatcher',
costs=[
dict(type='QueryClassificationCost', weight=0.5),
dict(type='MaskBCECost', weight=1.0),
dict(type='MaskDiceCost', weight=1.0)]),
loss_weight=[0.5, 1.0, 1.0, 0.5],
num_classes=num_instance_classes,
non_object_weight=0.05,
fix_dice_loss_weight=True,
iter_matcher=True,
fix_mean_loss=True)),
train_cfg=dict(),
test_cfg=dict(
topk_insts=450,
inst_score_thr=0.0,
pan_score_thr=0.4,
npoint_thr=300,
obj_normalization=True,
obj_normalization_thr=0.01,
sp_score_thr=0.15,
nms=True,
matrix_nms_kernel='linear',
num_sem_cls=num_semantic_classes,
stuff_cls=[1],
thing_cls=[0]))
# dataset settings
dataset_type = 'S3DISSegDataset_'
data_root = 'data/s3dis/'
data_prefix = dict(
pts='points',
pts_instance_mask='instance_mask',
pts_semantic_mask='semantic_mask')
train_area = [1, 2, 3, 4, 6]
test_area = 5
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_label_3d=False,
with_bbox_3d=False,
with_mask_3d=True,
with_seg_3d=True),
dict(
type='PointSample_',
num_points=num_points),
dict(type='PointInstClassMapping_',
num_classes=num_instance_classes),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[0.0, 0.0],
scale_ratio_range=[0.9, 1.1],
translation_std=[.1, .1, .1],
shift_height=False),
dict(
type='NormalizePointsColor_',
color_mean=[127.5, 127.5, 127.5]),
dict(
type='Pack3DDetInputs_',
keys=[
'points', 'gt_labels_3d',
'pts_semantic_mask', 'pts_instance_mask'
])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=True,
with_seg_3d=True),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='NormalizePointsColor_',
color_mean=[127.5, 127.5, 127.5])]),
dict(type='Pack3DDetInputs_', keys=['points'])
]
# run settings
train_dataloader = dict(
batch_size=2,
num_workers=3,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='ConcatDataset',
datasets=([
dict(
type=dataset_type,
data_root=data_root,
ann_file=f's3dis_infos_Area_{i}.pkl',
pipeline=train_pipeline,
metainfo=metainfo,
filter_empty_gt=True,
data_prefix=data_prefix,
box_type_3d='Depth',
backend_args=None) for i in train_area])))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
test_mode=True,
data_prefix=data_prefix,
box_type_3d='Depth',
backend_args=None))
test_dataloader = val_dataloader
label2cat = {i: name for i, name in enumerate(class_names)}
metric_meta = dict(
label2cat=label2cat,
ignore_index=[num_semantic_classes],
classes=class_names,
dataset_name='S3DIS')
sem_mapping = [0, 1]
val_evaluator = dict(
type='UnifiedSegMetric',
stuff_class_inds=[1],
thing_class_inds=[0],
min_num_points=1,
id_offset=2**16,
sem_mapping=sem_mapping,
inst_mapping=sem_mapping,
submission_prefix_semantic=None,
submission_prefix_instance=None,
metric_meta=metric_meta)
test_evaluator = val_evaluator
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.05),
clip_grad=dict(max_norm=10, norm_type=2))
param_scheduler = dict(type='PolyLR', begin=0, end=512, power=0.9)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
default_hooks = dict(
checkpoint=dict(
interval=16,
max_keep_ckpts=1,
save_best=['all_ap_50%', 'miou'],
rule='greater'))
load_from = 'work_dirs/tmp/instance-only-oneformer3d_1xb2_scannet-and-structured3d.pth'
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=512, val_interval=16)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
I don't know how to fix this erro, so I really need some help.
Expecting for the solution, thx!
oneformer3d-contributor commented
Please debug smth like for b in YourDataset: print b. Because now you have a problem with dataset. It tries to get i-th element 1000 times, all of the attempts fail (probably bug with path to point clouds or to annotation), so you get this error.
zhaohongxiang-seu commented
@HitmansGO I encountered the same issue, have you resolved it?
ustbwang1jie commented
@HitmansGO have you resolved it?
Lizhinwafu commented
[> @HitmansGO have you resolved it?
](#9 (comment))