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Running
on
Zero
| _base_ = [ | |
| '../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py' | |
| ] | |
| # model setting | |
| model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) | |
| # dsdl dataset settings | |
| # please visit our platform [OpenDataLab](https://opendatalab.com/) | |
| # to downloaded dsdl dataset. | |
| dataset_type = 'DSDLDetDataset' | |
| data_root = 'data/VOC07-det' | |
| img_prefix = 'original' | |
| train_ann = 'dsdl/set-train/train.yaml' | |
| val_ann = 'dsdl/set-test/test.yaml' | |
| specific_key_path = dict(ignore_flag='./objects/*/difficult') | |
| backend_args = None | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=backend_args), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(type='Resize', scale=(1000, 600), keep_ratio=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PackDetInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=backend_args), | |
| dict(type='Resize', scale=(1000, 600), keep_ratio=True), | |
| # avoid bboxes being resized | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor', 'instances')) | |
| ] | |
| train_dataloader = dict( | |
| dataset=dict( | |
| type=dataset_type, | |
| specific_key_path=specific_key_path, | |
| data_root=data_root, | |
| ann_file=train_ann, | |
| data_prefix=dict(img_path=img_prefix), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32, bbox_min_size=32), | |
| pipeline=train_pipeline)) | |
| val_dataloader = dict( | |
| dataset=dict( | |
| type=dataset_type, | |
| specific_key_path=specific_key_path, | |
| data_root=data_root, | |
| ann_file=val_ann, | |
| data_prefix=dict(img_path=img_prefix), | |
| test_mode=True, | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| # Pascal VOC2007 uses `11points` as default evaluate mode, while PASCAL | |
| # VOC2012 defaults to use 'area'. | |
| val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='11points') | |
| # val_evaluator = dict(type='CocoMetric', metric='bbox') | |
| test_evaluator = val_evaluator | |
| # training schedule, voc dataset is repeated 3 times, in | |
| # `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12 | |
| max_epochs = 12 | |
| train_cfg = dict( | |
| type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=3) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| # learning rate | |
| param_scheduler = [ | |
| dict( | |
| type='MultiStepLR', | |
| begin=0, | |
| end=max_epochs, | |
| by_epoch=True, | |
| milestones=[9], | |
| gamma=0.1) | |
| ] | |
| # optimizer | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)) | |
| # 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) | |