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| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=50), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=1), | |
| sampler_seed=dict(type='DistSamplerSeedHook') | |
| ) | |
| env_cfg = dict( | |
| cudnn_benchmark=False, | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
| dist_cfg=dict(backend='nccl'), | |
| ) | |
| log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) | |
| log_level = 'INFO' | |
| load_from = None | |
| resume = False | |
| # training schedule for 1x | |
| # train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| # Default setting for scaling LR automatically | |
| # - `enable` means enable scaling LR automatically | |
| # or not by default. | |
| # - `base_batch_size` = (8 GPUs) x (2 samples per GPU). | |
| auto_scale_lr = dict(enable=False, base_batch_size=16) | |
| visualizer = dict(type='Visualizer', vis_backends=[dict(type='TensorboardVisBackend')]) | |
| randomness=dict(seed=3407) | |