myc634
commented
9 months ago Hi, thanks for your interest, could you please provide the script for install PyTorch?
Closed Zhangjyhhh closed 9 months ago
myc634
commented
9 months ago Hi, thanks for your interest, could you please provide the script for install PyTorch?
Zhangjyhhh
commented
9 months ago @myc634 conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
Zhangjyhhh
commented
9 months ago Hi, thanks for your interest, could you please provide the script for install PyTorch? conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
myc634
commented
9 months ago I don’t know the exact reason causing this issue, but I kindly recommend you following the README, try using pip to install PyTorch.
Zhangjyhhh
commented
9 months ago I don’t know the exact reason causing this issue, but I kindly recommend you following the README, try using pip to install PyTorch.
I solve this issue by running this command "python -m torch.distributed.launch --nproc_per_node=1 --master_port=29505 ./tools/train.py configs/ultralidar_nusc.py --launcher pytorch " And I just have single GPU. Is that right i set "--nproc_per_node=1" ? Do I need to change another file? I run the command above another issue happend,by the way, i use nuscenes datasets v1.0.0 .mini
(ultralidar) jyzhang@Makevoice:~/mmdetection3d/UltraLiDAR_nusc_waymo$ python -m torch.distributed.launch --nproc_per_node=1 --master_port=29505 ./tools/train.py configs/ultralidar_nusc.py --launcher pytorch
/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions
warnings.warn(
plugin
2023-12-24 19:58:31,126 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 2080
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.10.0+cu113
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.3
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
- CuDNN 8.2
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
TorchVision: 0.11.1+cu113
OpenCV: 4.8.1
MMCV: 1.5.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.28.2
MMSegmentation: 0.30.0
MMDetection3D: 1.0.0rc1+97e072b
------------------------------------------------------------
2023-12-24 19:58:31,931 - mmdet - INFO - Distributed training: True
2023-12-24 19:58:32,699 - mmdet - INFO - Config:
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/nusc_stage1'
load_from = None
resume_from = None
workflow = [('train', 1)]
model_type = 'codebook_training'
batch_size = 1
point_cloud_range = [-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]
voxel_size = [0.15625, 0.15625, 0.2]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
plugin = True
plugin_dir = 'plugin/'
num_points = 30
model = dict(
type='UltraLiDAR',
model_type='codebook_training',
pts_bbox_head=dict(
type='CenterHead',
in_channels=256,
tasks=[
dict(num_class=1, class_names=['car']),
dict(num_class=2, class_names=['truck', 'construction_vehicle']),
dict(num_class=2, class_names=['bus', 'trailer']),
dict(num_class=1, class_names=['barrier']),
dict(num_class=2, class_names=['motorcycle', 'bicycle']),
dict(num_class=2, class_names=['pedestrian', 'traffic_cone'])
],
common_heads=dict(
reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
share_conv_channel=64,
bbox_coder=dict(
type='CenterPointBBoxCoder',
pc_range=[-50.0, -50.0],
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_num=500,
score_threshold=0.1,
out_size_factor=8,
voxel_size=[0.15625, 0.15625],
code_size=9),
separate_head=dict(
type='SeparateHead', init_bias=-2.19, final_kernel=3),
loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
norm_bbox=True),
voxelizer=dict(
type='Voxelizer',
x_min=-50.0,
x_max=50.0,
y_min=-50.0,
y_max=50.0,
z_min=-4.0,
z_max=3.0,
step=0.15625,
z_step=0.2),
vector_quantizer=dict(
type='VectorQuantizer',
n_e=1024,
e_dim=1024,
beta=0.25,
cosine_similarity=False),
lidar_encoder=dict(type='VQEncoder', img_size=640, codebook_dim=1024),
lidar_decoder=dict(
type='VQDecoder',
img_size=(640, 640),
num_patches=6400,
codebook_dim=1024),
train_cfg=dict(
pts=dict(
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0],
grid_size=[1024, 1024, 40],
voxel_size=[0.15625, 0.15625, 0.2],
out_size_factor=8,
dense_reg=1,
gaussian_overlap=0.1,
max_objs=500,
min_radius=2,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])),
test_cfg=dict(
pts=dict(
pc_range=[-50.0, -50.0],
post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_per_img=500,
max_pool_nms=False,
min_radius=[4, 12, 10, 1, 0.85, 0.175],
score_threshold=0.1,
out_size_factor=8,
voxel_size=[0.15625, 0.15625],
pre_max_size=1000,
post_max_size=83,
nms_type=[
'rotate', 'rotate', 'rotate', 'circle', 'rotate', 'rotate'
],
nms_thr=[0.2, 0.2, 0.2, 0.2, 0.2, 0.5],
nms_rescale_factor=[
1.0, [0.7, 0.7], [0.4, 0.55], 1.1, [1.0, 1.0], [4.5, 9.0]
])))
dataset_type = 'NuscDataset'
data_root = '/home/jyzhang/datasets/nuScenes/'
file_client_args = dict(backend='disk')
bda_aug_conf = dict(
rot_lim=(-22.5, 22.5),
scale_lim=(0.95, 1.05),
flip_dx_ratio=0.5,
flip_dy_ratio=0.5)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='PointsRangeFilter',
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectRangeFilter',
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectNameFilter',
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]),
dict(
type='DefaultFormatBundle3D',
class_names=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type='NuscDataset',
data_root='/home/jyzhang/datasets/nuScenes/',
ann_file='/home/jyzhang/datasets/nuScenes/nuscenes_infos_train.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True),
dict(
type='PointsRangeFilter',
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectRangeFilter',
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectNameFilter',
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='DefaultFormatBundle3D',
class_names=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='Collect3D',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
],
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
],
modality=dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False),
test_mode=False,
box_type_3d='LiDAR'),
val=dict(
type='NuscDataset',
data_root='/home/jyzhang/datasets/nuScenes/',
ann_file='/home/jyzhang/datasets/nuScenes/nuscenes_infos_val.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True),
dict(
type='PointsRangeFilter',
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectRangeFilter',
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectNameFilter',
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='DefaultFormatBundle3D',
class_names=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='Collect3D',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
],
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
],
modality=dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False),
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type='NuscDataset',
data_root='/home/jyzhang/datasets/nuScenes/',
ann_file='/home/jyzhang/datasets/nuScenes/nuscenes_infos_val.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True),
dict(
type='PointsRangeFilter',
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectRangeFilter',
point_cloud_range=[-50.0, -50.0, -4.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectNameFilter',
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='DefaultFormatBundle3D',
class_names=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='Collect3D',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
],
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
],
modality=dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False),
test_mode=True,
box_type_3d='LiDAR'))
optimizer = dict(
type='AdamW',
lr=0.0008,
betas=(0.9, 0.95),
paramwise_cfg=dict(
custom_keys=dict(
absolute_pos_embed=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0),
embedding=dict(decay_mult=0.0),
img_backbone=dict(lr_mult=0.1, decay_mult=0.001))),
weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=5, norm_type=2))
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.3333333333333333,
min_lr_ratio=0.001)
runner = dict(type='EpochBasedRunner', max_epochs=80)
checkpoint = None
find_unused_parameters = True
gpu_ids = range(0, 1)
device = 'cuda'
2023-12-24 19:58:32,699 - mmdet - INFO - Set random seed to 0, deterministic: False
/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
2023-12-24 19:58:33,653 - mmdet - INFO - Model:
UltraLiDAR(
(voxelizer): Voxelizer()
(vector_quantizer): VectorQuantizer(
(embedding): Embedding(1024, 1024)
)
(pre_quant): Sequential(
(0): Linear(in_features=1024, out_features=1024, bias=True)
(1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(lidar_encoder): VQEncoder(
(patch_embed): PatchEmbed(
(proj): Conv2d(40, 512, kernel_size=(8, 8), stride=(8, 8))
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
)
(blocks): Sequential(
(0): BasicLayer(
dim=512, input_resolution=(80, 80), depth=12
(blocks): ModuleList(
(0): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(1): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(2): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(3): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(4): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(5): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(6): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
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(drop2): Dropout(p=0.0, inplace=False)
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(drop2): Dropout(p=0.0, inplace=False)
)
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)
)
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(norm): Sequential(
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(1): GELU()
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(pre_quant): Linear(in_features=512, out_features=1024, bias=True)
)
(lidar_decoder): VQDecoder(
(decoder_embed): Linear(in_features=1024, out_features=512, bias=True)
(blocks): BasicLayer(
dim=512, input_resolution=(80, 80), depth=12
(blocks): ModuleList(
(0): SwinTransformerBlock(
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(attn): WindowAttention(
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(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
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(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
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(softmax): Softmax(dim=-1)
)
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(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
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(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
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(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
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(mlp): Mlp(
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(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
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(4): SwinTransformerBlock(
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(attn): WindowAttention(
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(attn_drop): Dropout(p=0.0, inplace=False)
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(proj_drop): Dropout(p=0.0, inplace=False)
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)
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)
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(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
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(mlp): Mlp(
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(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
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(6): SwinTransformerBlock(
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(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(7): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(8): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(9): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(10): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(11): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm): Sequential(
(0): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(1): GELU()
)
(pred): Linear(in_features=512, out_features=2560, bias=True)
)
(aug): Sequential(
(0): RandomVerticalFlip(p=0.5, p_batch=1.0, same_on_batch=False)
(1): RandomHorizontalFlip(p=0.5, p_batch=1.0, same_on_batch=False)
)
)
collecting samples...
collected 323 samples in 0.02s
collecting samples...
collected 323 samples in 0.02s
2023-12-24 19:58:37,369 - mmdet - INFO - Start running, host: jyzhang@Makevoice, work_dir: /home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/work_dirs/nusc_stage1
2023-12-24 19:58:37,369 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) CheckpointHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(LOW ) IterTimerHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_val_epoch:
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_epoch:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
after_run:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
2023-12-24 19:58:37,369 - mmdet - INFO - workflow: [('train', 1)], max: 80 epochs
2023-12-24 19:58:37,369 - mmdet - INFO - Checkpoints will be saved to /home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/work_dirs/nusc_stage1 by HardDiskBackend.
Traceback (most recent call last):
File "./tools/train.py", line 277, in <module>
main()
File "./tools/train.py", line 266, in main
train_detector(
File "/home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/tools/mmdet_train.py", line 170, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter
outputs = self.model.train_step(data_batch, self.optimizer,
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/parallel/distributed.py", line 59, in train_step
output = self.module.train_step(*inputs[0], **kwargs[0])
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmdet/models/detectors/base.py", line 248, in train_step
losses = self(**data)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 110, in new_func
return old_func(*args, **kwargs)
File "/home/jyzhang/mmdetection3d/mmdet3d/models/detectors/base.py", line 60, in forward
return self.forward_train(**kwargs)
File "/home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/plugin/models/detectors/ultralidar.py", line 297, in forward_train
losses = self.train_codebook(points)
File "/home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/plugin/models/detectors/ultralidar.py", line 157, in train_codebook
lidar_feats = self.lidar_encoder(voxels)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/plugin/models/necks/vq_layer.py", line 352, in forward
x = self.patch_embed(x)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/timm/models/layers/patch_embed.py", line 35, in forward
x = self.proj(x)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 446, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 442, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [512, 40, 8, 8], expected input[1, 35, 640, 640] to have 40 channels, but got 35 channels instead
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 13261) of binary: /home/jyzhang/anaconda3/envs/ultralidar/bin/python
Traceback (most recent call last):
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in <module>
main()
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main
launch(args)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch
run(args)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/run.py", line 710, in run
elastic_launch(
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 259, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
./tools/train.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-12-24_19:58:47
host : Makevoice
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 13261)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
the above issue is causing by changing parameters"point_cloud_range = [-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]" and i change it back . now i met another issue : by the way, I just have single GPU. Is that right i set "--nproc_per_node=1" ? Do I need to change another file?
(ultralidar) jyzhang@Makevoice:~/mmdetection3d/UltraLiDAR_nusc_waymo$ python -m torch.distributed.launch --nproc_per_node=1 --master_port=29505 ./tools/train.py configs/ultralidar_nusc.py --launcher pytorch
/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions
warnings.warn(
plugin
2023-12-24 20:41:03,431 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 2080
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.10.0+cu113
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.3
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
- CuDNN 8.2
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
TorchVision: 0.11.1+cu113
OpenCV: 4.8.1
MMCV: 1.5.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.28.2
MMSegmentation: 0.30.0
MMDetection3D: 1.0.0rc1+97e072b
------------------------------------------------------------
2023-12-24 20:41:04,233 - mmdet - INFO - Distributed training: True
2023-12-24 20:41:04,996 - mmdet - INFO - Config:
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/nusc_stage1'
load_from = None
resume_from = None
workflow = [('train', 1)]
model_type = 'codebook_training'
batch_size = 1
point_cloud_range = [-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]
voxel_size = [0.15625, 0.15625, 0.2]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
plugin = True
plugin_dir = 'plugin/'
num_points = 30
model = dict(
type='UltraLiDAR',
model_type='codebook_training',
pts_bbox_head=dict(
type='CenterHead',
in_channels=256,
tasks=[
dict(num_class=1, class_names=['car']),
dict(num_class=2, class_names=['truck', 'construction_vehicle']),
dict(num_class=2, class_names=['bus', 'trailer']),
dict(num_class=1, class_names=['barrier']),
dict(num_class=2, class_names=['motorcycle', 'bicycle']),
dict(num_class=2, class_names=['pedestrian', 'traffic_cone'])
],
common_heads=dict(
reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
share_conv_channel=64,
bbox_coder=dict(
type='CenterPointBBoxCoder',
pc_range=[-50.0, -50.0],
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_num=500,
score_threshold=0.1,
out_size_factor=8,
voxel_size=[0.15625, 0.15625],
code_size=9),
separate_head=dict(
type='SeparateHead', init_bias=-2.19, final_kernel=3),
loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
norm_bbox=True),
voxelizer=dict(
type='Voxelizer',
x_min=-50.0,
x_max=50.0,
y_min=-50.0,
y_max=50.0,
z_min=-5.0,
z_max=3.0,
step=0.15625,
z_step=0.2),
vector_quantizer=dict(
type='VectorQuantizer',
n_e=1024,
e_dim=1024,
beta=0.25,
cosine_similarity=False),
lidar_encoder=dict(type='VQEncoder', img_size=640, codebook_dim=1024),
lidar_decoder=dict(
type='VQDecoder',
img_size=(640, 640),
num_patches=6400,
codebook_dim=1024),
train_cfg=dict(
pts=dict(
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0],
grid_size=[1024, 1024, 40],
voxel_size=[0.15625, 0.15625, 0.2],
out_size_factor=8,
dense_reg=1,
gaussian_overlap=0.1,
max_objs=500,
min_radius=2,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])),
test_cfg=dict(
pts=dict(
pc_range=[-50.0, -50.0],
post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_per_img=500,
max_pool_nms=False,
min_radius=[4, 12, 10, 1, 0.85, 0.175],
score_threshold=0.1,
out_size_factor=8,
voxel_size=[0.15625, 0.15625],
pre_max_size=1000,
post_max_size=83,
nms_type=[
'rotate', 'rotate', 'rotate', 'circle', 'rotate', 'rotate'
],
nms_thr=[0.2, 0.2, 0.2, 0.2, 0.2, 0.5],
nms_rescale_factor=[
1.0, [0.7, 0.7], [0.4, 0.55], 1.1, [1.0, 1.0], [4.5, 9.0]
])))
dataset_type = 'NuscDataset'
data_root = '/home/jyzhang/datasets/nuScenes/'
file_client_args = dict(backend='disk')
bda_aug_conf = dict(
rot_lim=(-22.5, 22.5),
scale_lim=(0.95, 1.05),
flip_dx_ratio=0.5,
flip_dy_ratio=0.5)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='PointsRangeFilter',
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectRangeFilter',
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectNameFilter',
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]),
dict(
type='DefaultFormatBundle3D',
class_names=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
data = dict(
samples_per_gpu=1,
workers_per_gpu=8,
train=dict(
type='NuscDataset',
data_root='/home/jyzhang/datasets/nuScenes/',
ann_file='/home/jyzhang/datasets/nuScenes/nuscenes_infos_train.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True),
dict(
type='PointsRangeFilter',
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectRangeFilter',
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectNameFilter',
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='DefaultFormatBundle3D',
class_names=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='Collect3D',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
],
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
],
modality=dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False),
test_mode=False,
box_type_3d='LiDAR'),
val=dict(
type='NuscDataset',
data_root='/home/jyzhang/datasets/nuScenes/',
ann_file='/home/jyzhang/datasets/nuScenes/nuscenes_infos_val.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True),
dict(
type='PointsRangeFilter',
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectRangeFilter',
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectNameFilter',
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='DefaultFormatBundle3D',
class_names=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='Collect3D',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
],
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
],
modality=dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False),
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type='NuscDataset',
data_root='/home/jyzhang/datasets/nuScenes/',
ann_file='/home/jyzhang/datasets/nuScenes/nuscenes_infos_val.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True),
dict(
type='PointsRangeFilter',
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectRangeFilter',
point_cloud_range=[-50.0, -50.0, -5.0, 50.0, 50.0, 3.0]),
dict(
type='ObjectNameFilter',
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='DefaultFormatBundle3D',
class_names=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian',
'traffic_cone'
]),
dict(
type='Collect3D',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
],
classes=[
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
],
modality=dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False),
test_mode=True,
box_type_3d='LiDAR'))
optimizer = dict(
type='AdamW',
lr=0.0008,
betas=(0.9, 0.95),
paramwise_cfg=dict(
custom_keys=dict(
absolute_pos_embed=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0),
embedding=dict(decay_mult=0.0),
img_backbone=dict(lr_mult=0.1, decay_mult=0.001))),
weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=5, norm_type=2))
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.3333333333333333,
min_lr_ratio=0.001)
runner = dict(type='EpochBasedRunner', max_epochs=80)
checkpoint = None
find_unused_parameters = True
gpu_ids = range(0, 1)
device = 'cuda'
2023-12-24 20:41:04,996 - mmdet - INFO - Set random seed to 0, deterministic: False
/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
2023-12-24 20:41:05,935 - mmdet - INFO - Model:
UltraLiDAR(
(voxelizer): Voxelizer()
(vector_quantizer): VectorQuantizer(
(embedding): Embedding(1024, 1024)
)
(pre_quant): Sequential(
(0): Linear(in_features=1024, out_features=1024, bias=True)
(1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
(lidar_encoder): VQEncoder(
(patch_embed): PatchEmbed(
(proj): Conv2d(40, 512, kernel_size=(8, 8), stride=(8, 8))
(norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
)
(blocks): Sequential(
(0): BasicLayer(
dim=512, input_resolution=(80, 80), depth=12
(blocks): ModuleList(
(0): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(1): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(2): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(3): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(4): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(5): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(6): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
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(drop2): Dropout(p=0.0, inplace=False)
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(proj_drop): Dropout(p=0.0, inplace=False)
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(mlp): Mlp(
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(drop2): Dropout(p=0.0, inplace=False)
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)
)
)
(norm): Sequential(
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(1): GELU()
)
(pre_quant): Linear(in_features=512, out_features=1024, bias=True)
)
(lidar_decoder): VQDecoder(
(decoder_embed): Linear(in_features=1024, out_features=512, bias=True)
(blocks): BasicLayer(
dim=512, input_resolution=(80, 80), depth=12
(blocks): ModuleList(
(0): SwinTransformerBlock(
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(attn): WindowAttention(
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(softmax): Softmax(dim=-1)
)
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(mlp): Mlp(
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(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
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(mlp): Mlp(
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(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
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(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
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(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
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(mlp): Mlp(
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(act): GELU()
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(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
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(4): SwinTransformerBlock(
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(attn): WindowAttention(
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(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
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(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
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(attn): WindowAttention(
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(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
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(mlp): Mlp(
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(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
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(6): SwinTransformerBlock(
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(attn): WindowAttention(
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(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(7): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(8): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(9): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(10): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(11): SwinTransformerBlock(
(norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=512, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=512, out_features=512, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=512, out_features=2048, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=2048, out_features=512, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
)
)
(norm): Sequential(
(0): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
(1): GELU()
)
(pred): Linear(in_features=512, out_features=2560, bias=True)
)
(aug): Sequential(
(0): RandomVerticalFlip(p=0.5, p_batch=1.0, same_on_batch=False)
(1): RandomHorizontalFlip(p=0.5, p_batch=1.0, same_on_batch=False)
)
)
collecting samples...
collected 323 samples in 0.02s
collecting samples...
collected 323 samples in 0.02s
2023-12-24 20:41:09,639 - mmdet - INFO - Start running, host: jyzhang@Makevoice, work_dir: /home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/work_dirs/nusc_stage1
2023-12-24 20:41:09,640 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) CheckpointHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(LOW ) IterTimerHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_val_epoch:
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_epoch:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
after_run:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
2023-12-24 20:41:09,640 - mmdet - INFO - workflow: [('train', 1)], max: 80 epochs
2023-12-24 20:41:09,640 - mmdet - INFO - Checkpoints will be saved to /home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/work_dirs/nusc_stage1 by HardDiskBackend.
Traceback (most recent call last):
File "./tools/train.py", line 277, in <module>
main()
File "./tools/train.py", line 266, in main
train_detector(
File "/home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/tools/mmdet_train.py", line 170, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter
outputs = self.model.train_step(data_batch, self.optimizer,
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/parallel/distributed.py", line 59, in train_step
output = self.module.train_step(*inputs[0], **kwargs[0])
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmdet/models/detectors/base.py", line 248, in train_step
losses = self(**data)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 110, in new_func
return old_func(*args, **kwargs)
File "/home/jyzhang/mmdetection3d/mmdet3d/models/detectors/base.py", line 60, in forward
return self.forward_train(**kwargs)
File "/home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/plugin/models/detectors/ultralidar.py", line 297, in forward_train
losses = self.train_codebook(points)
File "/home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/plugin/models/detectors/ultralidar.py", line 161, in train_codebook
lidar_rec = self.lidar_decoder(lidar_quant)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/mmdetection3d/UltraLiDAR_nusc_waymo/plugin/models/necks/vq_layer.py", line 448, in forward
x = self.blocks(x)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/timm/models/swin_transformer.py", line 413, in forward
x = blk(x)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/timm/models/swin_transformer.py", line 310, in forward
x = x + self.drop_path(self.mlp(self.norm2(x)))
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/timm/models/layers/mlp.py", line 26, in forward
x = self.fc1(x)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 103, in forward
return F.linear(input, self.weight, self.bias)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/nn/functional.py", line 1848, in linear
return torch._C._nn.linear(input, weight, bias)
RuntimeError: CUDA out of memory. Tried to allocate 50.00 MiB (GPU 0; 7.79 GiB total capacity; 5.61 GiB already allocated; 56.81 MiB free; 5.79 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 17327) of binary: /home/jyzhang/anaconda3/envs/ultralidar/bin/python
Traceback (most recent call last):
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in <module>
main()
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main
launch(args)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch
run(args)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/run.py", line 710, in run
elastic_launch(
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 259, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
./tools/train.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-12-24_20:41:44
host : Makevoice
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 17327)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
Does that mean I should use more memory GPU? Are there any way to solve it without using larger memory GPU?
myc634
commented
9 months ago Yes, the --nproc_per_node=1 is right. For RTX 2080 GPU, maybe grad checkpointing works for you
Zhangjyhhh
commented
9 months ago Yes, the --nproc_per_node=1 is right. For RTX 2080 GPU, maybe grad checkpointing works for you
thanks for your reply
Zhangjyhhh
commented
9 months ago @myc634 In eval step 0,I met another issue:
(ultralidar) jyzhang@sumig-System-Product-Name:~/mmdetection3d/UltraLiDAR_nusc_waymo$ python -m torch.distributed.launch --nproc_per_node=1 --master_port=29501 ./tools/test.py ./configs/ultralidar_nusc_static_blank_code.py --eval "mIoU"
/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions
warnings.warn(
Traceback (most recent call last):
File "./tools/test.py", line 15, in <module>
from plugin.datasets.builder import build_dataloader
ModuleNotFoundError: No module named 'plugin'
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 3771) of binary: /home/jyzhang/anaconda3/envs/ultralidar/bin/python
Traceback (most recent call last):
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in <module>
main()
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main
launch(args)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch
run(args)
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/run.py", line 710, in run
elastic_launch(
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/jyzhang/anaconda3/envs/ultralidar/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 259, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
./tools/test.py FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-12-25_16:10:43
host : sumig-System-Product-Name
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 3771)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
how to solve it ?
HI! Thanks for your excellent work ! when i run "./tools/dist_train.sh configs/ultralidar_kitti360.py 8" this command , it show:
but actually, in this env, it has torch:
and I check torch.distrubuted:
So ,i don't know how to solve this issue, could you tell me ?