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- _base_ = [
- '../_base_/models/retinanet_r50_fpn.py',
- '../common/lsj-200e_coco-detection.py'
- ]
- image_size = (1024, 1024)
- batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
- model = dict(data_preprocessor=dict(batch_augments=batch_augments))
- train_dataloader = dict(batch_size=8, num_workers=4)
- # Enable automatic-mixed-precision training with AmpOptimWrapper.
- optim_wrapper = dict(
- type='AmpOptimWrapper',
- optimizer=dict(
- type='SGD', lr=0.01 * 4, momentum=0.9, weight_decay=0.00004))
- # NOTE: `auto_scale_lr` is for automatically scaling LR,
- # USER SHOULD NOT CHANGE ITS VALUES.
- # base_batch_size = (8 GPUs) x (8 samples per GPU)
- auto_scale_lr = dict(base_batch_size=64)
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