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Running
on
Zero
| _base_ = '../_base_/default_runtime.py' | |
| # dataset settings | |
| dataset_type = 'CocoDataset' | |
| data_root = 'data/coco/' | |
| image_size = (1024, 1024) | |
| # Example to use different file client | |
| # Method 1: simply set the data root and let the file I/O module | |
| # automatically infer from prefix (not support LMDB and Memcache yet) | |
| # data_root = 's3://openmmlab/datasets/detection/coco/' | |
| # Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6 | |
| # backend_args = dict( | |
| # backend='petrel', | |
| # path_mapping=dict({ | |
| # './data/': 's3://openmmlab/datasets/detection/', | |
| # 'data/': 's3://openmmlab/datasets/detection/' | |
| # })) | |
| backend_args = None | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=backend_args), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='RandomResize', | |
| scale=image_size, | |
| ratio_range=(0.1, 2.0), | |
| keep_ratio=True), | |
| dict( | |
| type='RandomCrop', | |
| crop_type='absolute_range', | |
| crop_size=image_size, | |
| recompute_bbox=True, | |
| allow_negative_crop=True), | |
| dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PackDetInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=backend_args), | |
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ] | |
| # Use RepeatDataset to speed up training | |
| train_dataloader = dict( | |
| batch_size=2, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=dict( | |
| type='RepeatDataset', | |
| times=4, # simply change this from 2 to 16 for 50e - 400e training. | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/instances_train2017.json', | |
| data_prefix=dict(img='train2017/'), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=train_pipeline, | |
| backend_args=backend_args))) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/instances_val2017.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=test_pipeline, | |
| backend_args=backend_args)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file=data_root + 'annotations/instances_val2017.json', | |
| metric='bbox', | |
| format_only=False, | |
| backend_args=backend_args) | |
| test_evaluator = val_evaluator | |
| max_epochs = 25 | |
| train_cfg = dict( | |
| type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=5) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| # optimizer assumes bs=64 | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004)) | |
| # learning rate | |
| param_scheduler = [ | |
| dict( | |
| type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, end=500), | |
| dict( | |
| type='MultiStepLR', | |
| begin=0, | |
| end=max_epochs, | |
| by_epoch=True, | |
| milestones=[22, 24], | |
| gamma=0.1) | |
| ] | |
| # only keep latest 2 checkpoints | |
| default_hooks = dict(checkpoint=dict(max_keep_ckpts=2)) | |
| # NOTE: `auto_scale_lr` is for automatically scaling LR, | |
| # USER SHOULD NOT CHANGE ITS VALUES. | |
| # base_batch_size = (32 GPUs) x (2 samples per GPU) | |
| auto_scale_lr = dict(base_batch_size=64) | |