2022-06-01 14:11:53 +08:00
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# Refers to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification
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2021-11-19 14:20:35 +08:00
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_base_ = [
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2022-11-14 17:06:39 +08:00
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'../_base_/models/mobilenet_v3/mobilenet_v3_small_imagenet.py',
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2022-06-01 14:11:53 +08:00
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'../_base_/datasets/imagenet_bs128_mbv3.py',
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'../_base_/default_runtime.py',
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2021-11-19 14:20:35 +08:00
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]
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2022-06-01 14:11:53 +08:00
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# schedule settings
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2022-06-02 17:11:09 +08:00
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optim_wrapper = dict(
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optimizer=dict(
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type='RMSprop',
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lr=0.064,
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alpha=0.9,
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momentum=0.9,
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eps=0.0316,
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weight_decay=1e-5))
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2022-06-01 14:11:53 +08:00
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2022-05-23 17:31:57 +08:00
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param_scheduler = dict(type='StepLR', by_epoch=True, step_size=2, gamma=0.973)
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2022-06-01 14:11:53 +08:00
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2022-07-12 16:10:59 +08:00
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train_cfg = dict(by_epoch=True, max_epochs=600, val_interval=1)
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val_cfg = dict()
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2022-05-23 17:31:57 +08:00
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test_cfg = dict()
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2022-07-14 19:15:49 +08:00
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2022-07-15 15:20:17 +08:00
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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# base_batch_size = (8 GPUs) x (128 samples per GPU)
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auto_scale_lr = dict(base_batch_size=1024)
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