I have a bug when I launch train.py

[SylvainArd]

Thanks for your error report and we appreciate it a lot.

Checklist

1. I have searched related issues but cannot get the expected help.
2. The bug has not been fixed in the latest version.

Describe the bug
A clear and concise description of what the bug is.
An error when I launch train.py

Reproduction

1. What command or script did you run?
   a batch file containing :
   call conda activate solov2
   cd C:\Users\MASTER\Desktop\SOLO-master\SOLO-master
   set PYTHONPATH=C:\Users\MASTER\Desktop\SOLO-master;%PYTHONPATH%
   python tools/train.py configs/solov2/solov2_light_448_r18_fpn_8gpu_3x.py
   pause

A placeholder for the command.

3. Did you make any modifications on the code or config? Did you understand what you have modified?
   yes I added a my_dataset.py in mmdet/datasets with the content :
   from .coco import CocoDataset
   from .registry import DATASETS
   @DATASETS.register_module
   class MyDataset(CocoDataset):
   CLASSES = ['null', 'points']

and I have change the configs/solov2/solov2_light_448_r18_fpn_8gpu_3x.py file to :

model settings

model = dict(
type='SOLOv2',
pretrained='torchvision://resnet18',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3), # C2, C3, C4, C5
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[64, 128, 256, 512],
out_channels=256,
start_level=0,
num_outs=5),
bbox_head=dict(
type='SOLOv2Head',
num_classes=1,
in_channels=256,
stacked_convs=2,
seg_feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 56), (28, 112), (56, 224), (112, 448), (224, 896)),
sigma=0.2,
num_grids=[40, 36, 24, 16, 12],
ins_out_channels=128,
loss_ins=dict(
type='DiceLoss',
use_sigmoid=True,
loss_weight=3.0),
loss_cate=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0)),
mask_feat_head=dict(
type='MaskFeatHead',
in_channels=256,
out_channels=128,
start_level=0,
end_level=3,
num_classes=128,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),
)

training and testing settings

train_cfg = dict()
test_cfg = dict(
nms_pre=500,
score_thr=0.1,
mask_thr=0.5,
update_thr=0.05,
kernel='gaussian', # gaussian/linear
sigma=2.0,
max_per_img=100)

dataset settings

dataset_type = 'MyDataset'
data_root = 'datasets/'
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=[(768, 512), (768, 480), (768, 448),
(768, 416), (768, 384), (768, 352)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
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=(768, 448),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'trainval.json',
img_prefix=data_root + 'trainval/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'trainval.json',
img_prefix=data_root + 'trainval/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'trainval.json',
img_prefix=data_root + 'trainval/',
pipeline=test_pipeline))

optimizer

optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))

learning policy

lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.01,
step=[27, 33])
checkpoint_config = dict(interval=1)

yapf:disable

log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])

yapf:enable

runtime settings

total_epochs = 36
device_ids = range(8)
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/solov2_light_release_r18_fpn_8gpu_3x'
load_from = None
resume_from = None
workflow = [('train', 1)]

I have added at root directory a folder "datasets" with a folder of images in it and a trainval.json file containing the coco annotations
my polygons instances are of category "points"
5. What dataset did you use?

Environment

1. Please run python tools/collect_env.py to collect necessary environment infomation and paste it here.
   I have the error :
   Traceback (most recent call last):
   File "tools/collect_env.py", line 12, in
   from mmdet.ops import get_compiler_version, get_compiling_cuda_version
   ModuleNotFoundError: No module named 'mmdet.ops'
   when I run python tools/collect_env.py
2. You may add addition that may be helpful for locating the problem, such as
   • How you installed PyTorch [e.g., pip, conda, source]
     conda create --name solov2 python=3.7 -y
     conda activate solov2
     conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forgey
     pip install mmdet mmcv==2.0.0
   • Other environment variables that may be related (such as $PATH, $LD_LIBRARY_PATH, $PYTHONPATH, etc.)

Error traceback
If applicable, paste the error trackback here.

(solov2) C:\Users\MASTER\Desktop\SOLO-master\SOLO-master>python tools/train.py configs/solov2/solov2_light_448_r18_fpn_8gpu_3x.py
Traceback (most recent call last):
  File "tools/train.py", line 9, in <module>
    from mmcv import Config
ImportError: cannot import name 'Config' from 'mmcv' (C:\ProgramData\Anaconda3\envs\solov2\lib\site-packages\mmcv\__init__.py)

Bug fix
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!