Closed GuocunWang closed 1 year ago
@WangGuocun Since your MMDetection is 3.0.0rc5, you will have to use the mmyolo dev branch for now
@WangGuocun Since your MMDetection is 3.0.0rc5, you will have to use the mmyolo dev branch for now
Could you tell me what to do next? I just copy the code in the docs to install mmyolo.
@WangGuocun
git clone https://github.com/open-mmlab/mmyolo.git
cd mmyolo
git checkout dev
I have change it, but still meet the error. But I can run other examples in the docs, such as the cat detection.
(mmyolo) ***:~/mmyolo$ git checkout dev 分支 'dev' 设置为跟踪来自 'origin' 的远程分支 'dev'。 切换到一个新分支 'dev'
..............
01/02 21:18:42 - mmengine - INFO - Saving checkpoint at 10 epochs
01/02 21:18:43 - mmengine - WARNING - save_param_scheduler
is True but self.param_schedulers
is None, so skip saving parameter schedulers
01/02 21:18:45 - mmengine - INFO - Epoch(val) [10][1/1] eta: 0:00:00 time: 1.3469 data_time: 0.8048 memory: 4763
Traceback (most recent call last):
File "tools/train.py", line 116, in
(mmyolo) ***:~/mmyolo$ git branch
@WangGuocun ok let's confirm.
@hhaAndroid Hi, I'm using MMEngine: 0.4.0 MMDetection: 3.0.0rc5 MMYOLO: 0.3.0+b0cf073, Also I've changed the metainfo to lowercase. I got the same error when training yolov7, while I trained yolov5 and yolov6 with my custom dataset successfully in the same environment. Here's the log:
(openmmlab) [ubuntu@VM-0-3-ubuntu ~/mmyolo]$ python tools/train.py configs/custom_dataset/yolov7_l_syncbn_fast_1xb32-100e_ionogram.py
01/09 23:26:36 - mmengine - WARNING - Failed to search registry with scope "mmyolo" in the "log_processor" registry tree. As a workaround, the current "log_processor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmyolo" is a correct scope, or whether the registry is initialized.
01/09 23:26:36 - mmengine - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.8.15 (default, Nov 24 2022, 15:19:38) [GCC 11.2.0]
CUDA available: True
numpy_random_seed: 1381925
GPU 0: Tesla V100-SXM2-32GB
CUDA_HOME: :/usr/local/cuda-11.4:/usr/local/cuda-11.4
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.12.1
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.3.2 (built against CUDA 11.5)
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.13.1
OpenCV: 4.6.0
MMEngine: 0.4.0
Runtime environment:
cudnn_benchmark: True
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: None
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
01/09 23:26:40 - mmengine - INFO - Config:
default_scope = 'mmyolo'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(
type='YOLOv5ParamSchedulerHook',
scheduler_type='cosine',
lr_factor=0.1,
max_epochs=10),
checkpoint=dict(
type='CheckpointHook',
interval=25,
save_param_scheduler=False,
save_best='auto',
max_keep_ckpts=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='mmdet.DetVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=True,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='mmdet.DetLocalVisualizer',
vis_backends=[dict(type='LocalVisBackend'),
dict(type='WandbVisBackend')],
name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = './work_dirs/yolov7_l_syncbn_fast_8x16b-300e_coco_20221123_023601-8113c0eb.pth'
resume = False
file_client_args = dict(backend='disk')
data_root = './Iono4311/'
dataset_type = 'YOLOv5CocoDataset'
class_name = ('E', 'Es-l', 'Es-c', 'F1', 'F2', 'Spread-F')
num_classes = 6
metainfo = dict(
classes=('E', 'Es-l', 'Es-c', 'F1', 'F2', 'Spread-F'),
palette=[(250, 165, 30), (120, 69, 125), (53, 125, 34), (0, 11, 123),
(130, 20, 12), (120, 121, 80)])
img_scale = (640, 640)
work_dir = './work_dirs/yolov7_l_100e'
save_epoch_intervals = 2
train_batch_size_per_gpu = 16
train_num_workers = 4
max_epochs = 10
base_lr = 0.00125
persistent_workers = True
val_batch_size_per_gpu = 1
val_num_workers = 2
batch_shapes_cfg = dict(
type='BatchShapePolicy',
batch_size=1,
img_size=640,
size_divisor=32,
extra_pad_ratio=0.5)
anchors = [[[8, 6], [24, 4], [19, 9]], [[22, 19], [17, 49], [29, 45]],
[[44, 66], [96, 76], [126, 59]]]
strides = [8, 16, 32]
num_det_layers = 3
model = dict(
type='YOLODetector',
data_preprocessor=dict(
type='YOLOv5DetDataPreprocessor',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
bgr_to_rgb=True),
backbone=dict(
type='YOLOv7Backbone',
arch='L',
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='SiLU', inplace=True)),
neck=dict(
type='YOLOv7PAFPN',
block_cfg=dict(
type='ELANBlock',
middle_ratio=0.5,
block_ratio=0.25,
num_blocks=4,
num_convs_in_block=1),
upsample_feats_cat_first=False,
in_channels=[512, 1024, 1024],
out_channels=[128, 256, 512],
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='SiLU', inplace=True)),
bbox_head=dict(
type='YOLOv7Head',
head_module=dict(
type='YOLOv7HeadModule',
num_classes=6,
in_channels=[256, 512, 1024],
featmap_strides=[8, 16, 32],
num_base_priors=3),
prior_generator=dict(
type='mmdet.YOLOAnchorGenerator',
base_sizes=[[[8, 6], [24, 4], [19, 9]],
[[22, 19], [17, 49], [29, 45]],
[[44, 66], [96, 76], [126, 59]]],
strides=[8, 16, 32]),
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=0.0225),
loss_bbox=dict(
type='IoULoss',
iou_mode='ciou',
bbox_format='xywh',
reduction='mean',
loss_weight=0.05,
return_iou=True),
loss_obj=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=0.7),
obj_level_weights=[4.0, 1.0, 0.4],
prior_match_thr=4.0,
simota_candidate_topk=10,
simota_iou_weight=3.0,
simota_cls_weight=1.0),
test_cfg=dict(
multi_label=True,
nms_pre=30000,
score_thr=0.001,
nms=dict(type='nms', iou_threshold=0.65),
max_per_img=300))
pre_transform = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations', with_bbox=True)
]
mosiac4_pipeline = [
dict(
type='Mosaic',
img_scale=(640, 640),
pad_val=114.0,
pre_transform=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations', with_bbox=True)
]),
dict(
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
max_translate_ratio=0.2,
scaling_ratio_range=(0.1, 2.0),
border=(-320, -320),
border_val=(114, 114, 114))
]
mosiac9_pipeline = [
dict(
type='Mosaic9',
img_scale=(640, 640),
pad_val=114.0,
pre_transform=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations', with_bbox=True)
]),
dict(
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
max_translate_ratio=0.2,
scaling_ratio_range=(0.1, 2.0),
border=(-320, -320),
border_val=(114, 114, 114))
]
randchoice_mosaic_pipeline = dict(
type='RandomChoice',
transforms=[[{
'type':
'Mosaic',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}],
[{
'type':
'Mosaic9',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}]],
prob=[0.8, 0.2])
train_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoice',
transforms=[[{
'type':
'Mosaic',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}],
[{
'type':
'Mosaic9',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}]],
prob=[0.8, 0.2]),
dict(
type='YOLOv5MixUp',
alpha=8.0,
beta=8.0,
prob=0.15,
pre_transform=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoice',
transforms=[[{
'type':
'Mosaic',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}],
[{
'type':
'Mosaic9',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}]],
prob=[0.8, 0.2])
]),
dict(type='YOLOv5HSVRandomAug'),
dict(type='mmdet.RandomFlip', prob=0.5),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
'flip_direction'))
]
train_dataloader = dict(
batch_size=16,
num_workers=4,
persistent_workers=True,
pin_memory=True,
sampler=dict(type='DefaultSampler', shuffle=True),
collate_fn=dict(type='yolov5_collate'),
dataset=dict(
type='YOLOv5CocoDataset',
data_root='./Iono4311/',
ann_file='annotations/train.json',
data_prefix=dict(img='train_images/'),
filter_cfg=dict(filter_empty_gt=False, min_size=32),
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoice',
transforms=[[{
'type':
'Mosaic',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}],
[{
'type':
'Mosaic9',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}]],
prob=[0.8, 0.2]),
dict(
type='YOLOv5MixUp',
alpha=8.0,
beta=8.0,
prob=0.15,
pre_transform=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoice',
transforms=[[{
'type':
'Mosaic',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}],
[{
'type':
'Mosaic9',
'img_scale': (640, 640),
'pad_val':
114.0,
'pre_transform': [{
'type': 'LoadImageFromFile',
'file_client_args': {
'backend': 'disk'
}
}, {
'type': 'LoadAnnotations',
'with_bbox': True
}]
}, {
'type': 'YOLOv5RandomAffine',
'max_rotate_degree': 0.0,
'max_shear_degree': 0.0,
'max_translate_ratio': 0.2,
'scaling_ratio_range': (0.1, 2.0),
'border': (-320, -320),
'border_val': (114, 114, 114)
}]],
prob=[0.8, 0.2])
]),
dict(type='YOLOv5HSVRandomAug'),
dict(type='mmdet.RandomFlip', prob=0.5),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'flip', 'flip_direction'))
]))
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
dict(
type='LetterResize',
scale=(640, 640),
allow_scale_up=False,
pad_val=dict(img=114)),
dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'pad_param'))
]
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
pin_memory=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='YOLOv5CocoDataset',
data_root='./Iono4311/',
test_mode=True,
data_prefix=dict(img='val_images/'),
ann_file='annotations/val.json',
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
dict(
type='LetterResize',
scale=(640, 640),
allow_scale_up=False,
pad_val=dict(img=114)),
dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'pad_param'))
],
batch_shapes_cfg=dict(
type='BatchShapePolicy',
batch_size=1,
img_size=640,
size_divisor=32,
extra_pad_ratio=0.5)))
test_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
pin_memory=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='YOLOv5CocoDataset',
data_root='./Iono4311/',
test_mode=True,
data_prefix=dict(img='test_images/'),
ann_file='annotations/test.json',
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='YOLOv5KeepRatioResize', scale=(640, 640)),
dict(
type='LetterResize',
scale=(640, 640),
allow_scale_up=False,
pad_val=dict(img=114)),
dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'pad_param'))
],
batch_shapes_cfg=dict(
type='BatchShapePolicy',
batch_size=1,
img_size=640,
size_divisor=32,
extra_pad_ratio=0.5)))
param_scheduler = None
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(
type='SGD',
lr=0.00125,
momentum=0.937,
weight_decay=0.0005,
nesterov=True,
batch_size_per_gpu=16),
constructor='YOLOv7OptimWrapperConstructor')
val_evaluator = dict(
type='mmdet.CocoMetric',
proposal_nums=(100, 1, 10),
ann_file='./Iono4311/annotations/val.json',
metric='bbox')
test_evaluator = dict(
type='mmdet.CocoMetric',
proposal_nums=(100, 1, 10),
ann_file='./Iono4311/annotations/test.json',
metric='bbox')
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=10, val_interval=2, val_begin=1)
custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0001,
update_buffers=True,
strict_load=False,
priority=49)
]
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
launcher = 'none'
01/09 23:26:40 - mmengine - WARNING - The "visualizer" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:40 - mmengine - WARNING - The "visualizer" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead.
01/09 23:26:40 - mmengine - WARNING - The "vis_backend" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
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01/09 23:26:54 - mmengine - WARNING - The "model" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:54 - mmengine - WARNING - The "model" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead.
01/09 23:26:54 - mmengine - WARNING - The "task util" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:54 - mmengine - WARNING - The "task util" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead.
01/09 23:26:56 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
01/09 23:26:56 - mmengine - WARNING - The "hook" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:56 - mmengine - WARNING - The "hook" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead.
01/09 23:26:56 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(BELOW_NORMAL) LoggerHook
--------------------
after_load_checkpoint:
(49 ) EMAHook
--------------------
before_train:
(9 ) YOLOv5ParamSchedulerHook
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
--------------------
before_train_iter:
(9 ) YOLOv5ParamSchedulerHook
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(9 ) YOLOv5ParamSchedulerHook
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train_epoch:
(9 ) YOLOv5ParamSchedulerHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_val_epoch:
(49 ) EMAHook
(NORMAL ) IterTimerHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(VERY_LOW ) CheckpointHook
--------------------
before_save_checkpoint:
(49 ) EMAHook
--------------------
before_test_epoch:
(49 ) EMAHook
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) DetVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(49 ) EMAHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
01/09 23:26:59 - mmengine - WARNING - The "loop" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:59 - mmengine - WARNING - The "dataset" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:59 - mmengine - WARNING - The "transform" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:59 - mmengine - WARNING - The "transform" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead.
loading annotations into memory...
Done (t=0.04s)
creating index...
index created!
01/09 23:26:59 - mmengine - WARNING - The "data sampler" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:59 - mmengine - WARNING - The "optimizer constructor" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:59 - mmengine - INFO - Optimizer groups: 95 .bias, 95 conv.weight, 98 other
01/09 23:26:59 - mmengine - WARNING - The "optimizer" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:59 - mmengine - WARNING - The "optim_wrapper" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
01/09 23:26:59 - mmengine - WARNING - The "metric" registry in mmyolo did not set import location. Fallback to call `mmyolo.utils.register_all_modules` instead.
01/09 23:26:59 - mmengine - WARNING - The "metric" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead.
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
Loads checkpoint by local backend from path: ./work_dirs/yolov7_l_syncbn_fast_8x16b-300e_coco_20221123_023601-8113c0eb.pth
The model and loaded state dict do not match exactly
size mismatch for bbox_head.head_module.convs_pred.0.1.weight: copying a param with shape torch.Size([255, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([33, 256, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.0.1.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([33]).
size mismatch for bbox_head.head_module.convs_pred.0.2.implicit: copying a param with shape torch.Size([1, 255, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 33, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.1.1.weight: copying a param with shape torch.Size([255, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([33, 512, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.1.1.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([33]).
size mismatch for bbox_head.head_module.convs_pred.1.2.implicit: copying a param with shape torch.Size([1, 255, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 33, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.2.1.weight: copying a param with shape torch.Size([255, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([33, 1024, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.2.1.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([33]).
size mismatch for bbox_head.head_module.convs_pred.2.2.implicit: copying a param with shape torch.Size([1, 255, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 33, 1, 1]).
The model and loaded state dict do not match exactly
size mismatch for bbox_head.head_module.convs_pred.0.1.weight: copying a param with shape torch.Size([255, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([33, 256, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.0.1.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([33]).
size mismatch for bbox_head.head_module.convs_pred.0.2.implicit: copying a param with shape torch.Size([1, 255, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 33, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.1.1.weight: copying a param with shape torch.Size([255, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([33, 512, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.1.1.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([33]).
size mismatch for bbox_head.head_module.convs_pred.1.2.implicit: copying a param with shape torch.Size([1, 255, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 33, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.2.1.weight: copying a param with shape torch.Size([255, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([33, 1024, 1, 1]).
size mismatch for bbox_head.head_module.convs_pred.2.1.bias: copying a param with shape torch.Size([255]) from checkpoint, the shape in current model is torch.Size([33]).
size mismatch for bbox_head.head_module.convs_pred.2.2.implicit: copying a param with shape torch.Size([1, 255, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 33, 1, 1]).
01/09 23:27:03 - mmengine - INFO - Load checkpoint from ./work_dirs/yolov7_l_syncbn_fast_8x16b-300e_coco_20221123_023601-8113c0eb.pth
01/09 23:27:03 - mmengine - INFO - Checkpoints will be saved to /home/ubuntu/mmyolo/work_dirs/yolov7_l_100e.
01/09 23:27:34 - mmengine - INFO - Epoch(train) [1][ 50/189] lr: 6.1250e-05 eta: 0:18:42 time: 0.6099 data_time: 0.0162 memory: 22819 loss: 0.1689 loss_cls: 0.0000 loss_obj: 0.1689 loss_bbox: 0.0000
01/09 23:27:58 - mmengine - INFO - Epoch(train) [1][100/189] lr: 1.2375e-04 eta: 0:16:25 time: 0.4917 data_time: 0.0008 memory: 18070 loss: 0.0961 loss_cls: 0.0000 loss_obj: 0.0961 loss_bbox: 0.0000
01/09 23:28:23 - mmengine - INFO - Epoch(train) [1][150/189] lr: 1.8625e-04 eta: 0:15:24 time: 0.4922 data_time: 0.0008 memory: 18070 loss: 0.0661 loss_cls: 0.0000 loss_obj: 0.0661 loss_bbox: 0.0000
01/09 23:28:47 - mmengine - INFO - Exp name: yolov7_l_syncbn_fast_1xb32-100e_ionogram_20230109_232636
01/09 23:29:12 - mmengine - INFO - Epoch(train) [2][ 50/189] lr: 2.9095e-04 eta: 0:14:50 time: 0.5028 data_time: 0.0106 memory: 20765 loss: 0.0405 loss_cls: 0.0000 loss_obj: 0.0405 loss_bbox: 0.0000
01/09 23:29:37 - mmengine - INFO - Epoch(train) [2][100/189] lr: 3.5207e-04 eta: 0:14:10 time: 0.4932 data_time: 0.0009 memory: 18069 loss: 0.0324 loss_cls: 0.0000 loss_obj: 0.0324 loss_bbox: 0.0000
01/09 23:30:01 - mmengine - INFO - Epoch(train) [2][150/189] lr: 4.1319e-04 eta: 0:13:35 time: 0.4935 data_time: 0.0009 memory: 18069 loss: 0.0265 loss_cls: 0.0000 loss_obj: 0.0265 loss_bbox: 0.0000
01/09 23:30:20 - mmengine - INFO - Exp name: yolov7_l_syncbn_fast_1xb32-100e_ionogram_20230109_232636
01/09 23:30:23 - mmengine - INFO - Epoch(val) [2][ 50/646] eta: 0:00:29 time: 0.0491 data_time: 0.0018 memory: 18069
01/09 23:30:24 - mmengine - INFO - Epoch(val) [2][100/646] eta: 0:00:21 time: 0.0302 data_time: 0.0003 memory: 721
01/09 23:30:26 - mmengine - INFO - Epoch(val) [2][150/646] eta: 0:00:17 time: 0.0294 data_time: 0.0003 memory: 721
01/09 23:30:27 - mmengine - INFO - Epoch(val) [2][200/646] eta: 0:00:15 time: 0.0295 data_time: 0.0021 memory: 721
01/09 23:30:29 - mmengine - INFO - Epoch(val) [2][250/646] eta: 0:00:13 time: 0.0300 data_time: 0.0032 memory: 721
01/09 23:30:30 - mmengine - INFO - Epoch(val) [2][300/646] eta: 0:00:11 time: 0.0295 data_time: 0.0030 memory: 721
01/09 23:30:32 - mmengine - INFO - Epoch(val) [2][350/646] eta: 0:00:09 time: 0.0298 data_time: 0.0032 memory: 721
01/09 23:30:33 - mmengine - INFO - Epoch(val) [2][400/646] eta: 0:00:07 time: 0.0301 data_time: 0.0038 memory: 721
01/09 23:30:35 - mmengine - INFO - Epoch(val) [2][450/646] eta: 0:00:06 time: 0.0308 data_time: 0.0046 memory: 721
01/09 23:30:36 - mmengine - INFO - Epoch(val) [2][500/646] eta: 0:00:04 time: 0.0308 data_time: 0.0046 memory: 721
01/09 23:30:38 - mmengine - INFO - Epoch(val) [2][550/646] eta: 0:00:03 time: 0.0301 data_time: 0.0038 memory: 721
01/09 23:30:39 - mmengine - INFO - Epoch(val) [2][600/646] eta: 0:00:01 time: 0.0305 data_time: 0.0037 memory: 721
Traceback (most recent call last):
File "tools/train.py", line 116, in <module>
main()
File "tools/train.py", line 112, in main
runner.train()
File "/home/ubuntu/.conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1678, in train
model = self.train_loop.run() # type: ignore
File "/home/ubuntu/.conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 96, in run
self.runner.val_loop.run()
File "/home/ubuntu/.conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 350, in run
metrics = self.evaluator.evaluate(len(self.dataloader.dataset))
File "/home/ubuntu/.conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/evaluator/evaluator.py", line 79, in evaluate
_results = metric.evaluate(size)
File "/home/ubuntu/.conda/envs/openmmlab/lib/python3.8/site-packages/mmengine/evaluator/metric.py", line 110, in evaluate
_metrics = self.compute_metrics(results) # type: ignore
File "/home/ubuntu/.conda/envs/openmmlab/lib/python3.8/site-packages/mmdet/evaluation/metrics/coco_metric.py", line 395, in compute_metrics
result_files = self.results2json(preds, outfile_prefix)
File "/home/ubuntu/.conda/envs/openmmlab/lib/python3.8/site-packages/mmdet/evaluation/metrics/coco_metric.py", line 216, in results2json
data['category_id'] = self.cat_ids[label]
IndexError: list index out of range
Would you mind giving me a hand with this?
The file is fixed as follow. You can try to change it.
metainfo = { 'CLASSES': ('balloon', ), 'PALETTE': [
# 'palette': [
(220, 20, 60),
]
}
Thanks for your reply, I'm sorry that I forgot to add the metainfo
to val_dataloader['dataset']
in the config.
Besides, the latest version MMYOLO 0.3.0 uses lowercase [#428]. So please use
metainfo = {
'classes': ('balloon', ),
'palette': [(220, 20, 60),]
}
instead.
@hhaAndroid hi, My config file has the metainfo. When I changed the backbone following the official docs, I met the same question. How to fix it?
@WangGuocun ok let's confirm. @hhaAndroid @WangGuocun hi, have you confirmed that?
你好,我遇到了同样的问题,只不过我用的是在YOLOV7,找了很多种方法都没有解决,我不知道该怎么办了,恳请得到你们的帮助,万分感谢!
Hello, I'm experiencing the same problem, except that I'm using in YOLOV7, and I've searched for many ways to solve it, I don't know what to do, I'm begging to get your help, thank you very much!
刚说完我就解决了,大家一定要注意先生成针对自己数据集的anchors!!!我的问题就在这里!
Thanks for your reply, I'm sorry that I forgot to add the
metainfo
toval_dataloader['dataset']
in the config. Besides, the latest version MMYOLO 0.3.0 uses lowercase [#428]. So please usemetainfo = { 'classes': ('balloon', ), 'palette': [(220, 20, 60),] }
instead.
Sorry to necro an older post, but this helped me fix a problem I was encountering too. Thank you so much for documenting it here! 😄 ❤️
Prerequisite
🐞 Describe the bug
I train the balloon detection following the quick start but meet the error as follows.
12/31 22:26:13 - mmengine - INFO - Epoch(val) [10][1/1] eta: 0:00:00 time: 1.2651 data_time: 0.7038 memory: 4764
main()
File "tools/train.py", line 102, in main
runner.train()
File "/home//anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1678, in train
model = self.train_loop.run() # type: ignore
File "/home//anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/runner/loops.py", line 96, in run
self.runner.val_loop.run()
File "/home//anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/runner/loops.py", line 350, in run
metrics = self.evaluator.evaluate(len(self.dataloader.dataset))
File "/home//anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/evaluator/evaluator.py", line 79, in evaluate
_results = metric.evaluate(size)
File "/home//anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/evaluator/metric.py", line 110, in evaluate
_metrics = self.compute_metrics(results) # type: ignore
File "/home//anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmdet/evaluation/metrics/coco_metric.py", line 400, in compute_metrics
result_files = self.results2json(preds, outfile_prefix)
File "/home/***/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmdet/evaluation/metrics/coco_metric.py", line 218, in results2json
data['category_id'] = self.cat_ids[label]
IndexError: list index out of range
Traceback (most recent call last): File "tools/train.py", line 106, in
I print the content and find that the label is 0 which is true, but the cat_ids is [ ]. I follow the docs and don't remake anything.
Environment
python mmyolo/utils/collect_env.py sys.platform: linux Python: 3.8.15 | packaged by conda-forge | (default, Nov 22 2022, 08:49:35) [GCC 10.4.0] CUDA available: True numpy_random_seed: 2147483648 GPU 0: NVIDIA GeForce RTX 3090 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.1, V11.1.105 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 PyTorch: 1.10.1 PyTorch compiling details: PyTorch built with:
TorchVision: 0.11.2 OpenCV: 4.7.0 MMEngine: 0.4.0 MMCV: 2.0.0rc3 MMDetection: 3.0.0rc5 MMYOLO: 0.2.0+27487fd
Additional information
No response