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https://github.com/open-mmlab/mmdeploy.git
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331292a992
* feat(mmdet3d): test pointpillars and centerpoint on ort, openvino and trt passed * fix(centerpoint): mvx_two_stage input error * fix(review): remove mode decorator * fix(mmdet3d): review advices * fix(regression): update mmdet3d.yml and test ort/openvino passed * unittest(mmdet3d): fix * fix(unittest): fix * fix(mmdet3d): unittest * fix(mmdet3d): unittest * fix(CI): remove mmcv.Config * fix(mmdet3d): unittest * fix(mmdet3d): support torch1.12 * fix(CI): use bigger point cloud file * improvement(mmdet3d): align backend outputs with torch * fix(mmdet3d): remove useless * style(mmdet3d): format code * style(mmdet3d): remove useless * fix(mmdet3d): sync vis_task * unittest(mmdet3d): add test * docs(mmdet3d): add docstring * unittest(ci): add unittest data * fix(mmdet3d): review advices * feat(mmdet3d): convert fail * style(mmdet3d): docstring * style(mmdet3d): docstring
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# For nuScenes dataset, we usually evaluate the model at the end of training.
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# Since the models are trained by 24 epochs by default, we set evaluation
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# interval to be 20. Please change the interval accordingly if you do not
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# use a default schedule.
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# optimizer
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lr = 1e-4
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# This schedule is mainly used by models on nuScenes dataset
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# max_norm=10 is better for SECOND
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(type='AdamW', lr=lr, weight_decay=0.01),
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clip_grad=dict(max_norm=35, norm_type=2))
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# learning rate
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param_scheduler = [
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# learning rate scheduler
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# During the first 8 epochs, learning rate increases from 0 to lr * 10
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# during the next 12 epochs, learning rate decreases from lr * 10 to
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# lr * 1e-4
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dict(
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type='CosineAnnealingLR',
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T_max=8,
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eta_min=lr * 10,
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begin=0,
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end=8,
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by_epoch=True,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=12,
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eta_min=lr * 1e-4,
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begin=8,
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end=20,
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by_epoch=True,
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convert_to_iter_based=True),
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# momentum scheduler
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# During the first 8 epochs, momentum increases from 0 to 0.85 / 0.95
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# during the next 12 epochs, momentum increases from 0.85 / 0.95 to 1
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dict(
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type='CosineAnnealingMomentum',
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T_max=8,
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eta_min=0.85 / 0.95,
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begin=0,
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end=8,
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by_epoch=True,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingMomentum',
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T_max=12,
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eta_min=1,
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begin=8,
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end=20,
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by_epoch=True,
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convert_to_iter_based=True)
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]
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# runtime settings
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train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=20)
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val_cfg = dict()
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test_cfg = dict()
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# Default setting for scaling LR automatically
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# - `enable` means enable scaling LR automatically
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# or not by default.
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# - `base_batch_size` = (8 GPUs) x (4 samples per GPU).
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auto_scale_lr = dict(enable=False, base_batch_size=32)
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