AttributeError: 'PyCenterNetHead' object has no attribute 'init_assigner'

[sw-ot-jaymehta]

Hi

I'm trying to train the PyCenterNet++ with the "[R2-101]-DCN" backbone, below is the error message and environmental details, for reference:

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environmental details
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torch.version :- 1.4.0
torch.cuda.is_available() :- True
mmdet.version :- 2.11.0
get_compiling_cuda_version() :- 10.1
get_compiler_version() :- GCC 7.5

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error message
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[sw-ot-jaymehta]

Hi,

Here is the configuration parameter and code snippet, for reference:

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training reference snippet
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from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.apis import train_detector
from mmcv import Config
import mmcv
import os.path as osp

cfg = Config.fromfile('/PyCenterNet/code/configs/pycenternet/pycenternet_res2_101_fpn_dconv_c3-c5_giou_mstrain_2x_coco.py')

cfg.model.pretrained = '/PyCenterNet/code/mmcv/checkpoints/res2net101_v1d_26w_4s_mmdetv2-f0a600f9.pth'

cfg.dataset_type = 'COCODataset'

#The original learning rate (LR) is set for 8-GPU training.
#We divide it by 8 since we only use one GPU.
cfg.optimizer.lr = 0.02 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 10

#We can set the evaluation interval to reduce the evaluation times
cfg.evaluation.interval = 18
#We can set the checkpoint saving interval to reduce the storage cost
cfg.checkpoint_config.interval = 18

#Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)

#We can also use tensorboard to log the training process
cfg.log_config.hooks = [
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')]

#We can initialize the logger for training and have a look
#at the final config used for training
print(f'Config:\n{cfg.pretty_text}')

cfg.device="cuda"
#Build dataset
print("cfg.data.train", cfg.data.train)
datasets = [build_dataset(cfg.data.train)]
print(datasets)
#Build the detector
model = build_detector(cfg.model)

#Add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
print("osp.abspath(cfg.work_dir)", osp.abspath(cfg.work_dir))
#Create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))

train_detector(model, datasets, cfg, distributed=False, validate=True)