Mmdetection: mabe a bug in version 2.1.0
I upgrade mmdetection from 2.0.0 to 2.1.0, but I can't train with this error:ValueError: need at least one array to concatenate.
So I use version 2.0.0 and all work well, so I think maybe there is a bug in version 2.1.0
bug
heboyong
All 8 comments
Hi @heboyong
What command did you run?
Could you please fill the error-template?
xvjiarui
on 10 Jun 2020
Even i'm facing similar issue.
I cloned and built it.
I'm training cityscapes as coco on ms_rcnn.
What I'm running:
./tools/dist_train.sh configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_cityscapes_new.py 4
Error Traceback:
(open-mmlab) dksingh@gnode22:~/frameworks/mmdetection$ python tools/train.py configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_cityscapes_new.py --gpus=4
2020-06-10 19:35:19,231 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.7 (default, May 7 2020, 21:25:33) [GCC 7.3.0]
CUDA available: True
CUDA_HOME: /usr/local/cuda-10.2
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GPU 0,1,2,3: GeForce GTX 1080 Ti
GCC: gcc (Ubuntu 5.5.0-12ubuntu1~16.04) 5.5.0 20171010
PyTorch: 1.5.0
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.1 Product Build 20200208 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 10.2
- 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
- CuDNN 7.6.5
- Magma 2.5.2
- Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL -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-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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, USE_STATIC_DISPATCH=OFF,
TorchVision: 0.6.0a0+82fd1c8
OpenCV: 4.2.0
MMCV: 0.5.9
MMDetection: 2.1.0+8bc0b9c
MMDetection Compiler: GCC 5.5
MMDetection CUDA Compiler: 10.2
------------------------------------------------------------
2020-06-10 19:35:19,232 - mmdet - INFO - Distributed training: False
2020-06-10 19:35:20,010 - mmdet - INFO - Config:
classes = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
'bicycle')
model = dict(
type='MaskScoringRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
groups=32,
base_width=4),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='MaskScoringRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=8,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=8,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
num_classes=8)))
train_cfg = dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False,
mask_thr_binary=0.5))
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_thr=0.5),
max_per_img=100,
mask_thr_binary=0.5))
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
classes=('person', 'rider', 'car', 'truck', 'bus', 'train',
'motorcycle', 'bicycle'),
type='CityscapesDataset',
ann_file=
'data/cityscapes/annotations/instancesonly_filtered_gtFine_train.json',
img_prefix='data/cityscapes/leftImg8bit/train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]),
val=dict(
classes=('person', 'rider', 'car', 'truck', 'bus', 'train',
'motorcycle', 'bicycle'),
type='CityscapesDataset',
ann_file=
'data/cityscapes/annotations/instancesonly_filtered_gtFine_val.json',
img_prefix='data/cityscapes/leftImg8bit/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
classes=('person', 'rider', 'car', 'truck', 'bus', 'train',
'motorcycle', 'bicycle'),
type='CityscapesDataset',
ann_file=
'data/cityscapes/annotations/instancesonly_filtered_gtFine_val.json',
img_prefix='data/cityscapes/leftImg8bit/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
evaluation = dict(interval=1, metric=['bbox', 'segm'])
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
total_epochs = 12
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
work_dir = '/ssd_scratch/cvit/dksingh/mmdetection_logs/ms_rcnn_x101_32x4d_fpn_1x_cityscapes_new/'
gpu_ids = range(0, 4)
2020-06-10 19:35:20,884 - mmdet - INFO - load model from: open-mmlab://resnext101_32x4d
loading annotations into memory...
Done (t=0.35s)
creating index...
index created!
loading annotations into memory...
Done (t=0.04s)
creating index...
index created!
2020-06-10 19:35:24,085 - mmdet - INFO - Start running, host: dksingh@gnode22, work_dir: /ssd_scratch/cvit/dksingh/mmdetection_logs/ms_rcnn_x101_32x4d_fpn_1x_cityscapes_new
2020-06-10 19:35:24,085 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
Traceback (most recent call last):
File "tools/train.py", line 161, in <module>
main()
File "tools/train.py", line 157, in main
meta=meta)
File "/home/dksingh/frameworks/mmdetection/mmdet/apis/train.py", line 179, in train_detector
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/dksingh/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/runner.py", line 384, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/dksingh/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/runner.py", line 279, in train
for i, data_batch in enumerate(data_loader):
File "/home/dksingh/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 279, in __iter__
return _MultiProcessingDataLoaderIter(self)
File "/home/dksingh/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 746, in __init__
self._try_put_index()
File "/home/dksingh/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 861, in _try_put_index
index = self._next_index()
File "/home/dksingh/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 339, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/home/dksingh/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/sampler.py", line 200, in __iter__
for idx in self.sampler:
File "/home/dksingh/frameworks/mmdetection/mmdet/datasets/samplers/group_sampler.py", line 36, in __iter__
indices = np.concatenate(indices)
File "<__array_function__ internals>", line 6, in concatenate
ValueError: need at least one array to concatenate
deepakksingh
on 10 Jun 2020
same problem...T.T
JacksonLiu-ai
on 11 Jun 2020
same problem
CarryHJR
on 11 Jun 2020
It seems that it is related to #2396.
I will open a PR to fix it.
xvjiarui
on 12 Jun 2020
I tried to build the version v1.2.0 by performing a checkout to that tag and then doing a build using python setup.py develop but in the logs it shows:
MMCV: 0.5.9 │
MMDetection: 2.1.0+bf9638a │
MMDetection Compiler: GCC 5.5 │
MMDetection CUDA Compiler: 10.2
Is there a procedure to build older versions?
deepakksingh
on 17 Jun 2020
mmdetection sucks, detectron2 is the best
zxs123boss
on 3 Jul 2020
👎3
mmdetection sucks, detectron2 is the best
Please mind your words. Do NOT be offensive.
hellock
on 3 Jul 2020
👍4
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Please mind your words. Do NOT be offensive.