mmpretrain/configs/hrnet/hrnet-w32_4xb32_in1k.py

_base_ = [
    '../_base_/models/hrnet/hrnet-w32.py',
    '../_base_/datasets/imagenet_bs32_pil_resize.py',
    '../_base_/schedules/imagenet_bs256_coslr.py',
    '../_base_/default_runtime.py'
]

# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
# base_batch_size = (4 GPUs) x (32 samples per GPU)
auto_scale_lr = dict(base_batch_size=128)
[Feature] Support HRNet and add pre-trained models (#660) * Support HRNet * Add HRNet configs * Fix a bug in backward * Add configs and update docs. * Not use bias in conv before batch norm * Defaults to use `norm_eval=False` * Add unit tests and support out_channels in HRFuseScales * Update checkpoint path * Update docstring. * Remove incorrect files * Improve according to comments 2022-01-28 10:54:14 +08:00			`_base_ = [`
			`'../_base_/models/hrnet/hrnet-w32.py',`
			`'../_base_/datasets/imagenet_bs32_pil_resize.py',`
			`'../_base_/schedules/imagenet_bs256_coslr.py',`
			`'../_base_/default_runtime.py'`
			`]`
[Refactor] add auto_scale_lr 2022-07-14 19:15:49 +08:00
Update auto_scale_lr fields 2022-07-15 15:20:17 +08:00			# NOTE: `auto_scale_lr` is for automatically scaling LR
			`# based on the actual training batch size.`
[Refactor] add auto_scale_lr 2022-07-14 19:15:49 +08:00			`# base_batch_size = (4 GPUs) x (32 samples per GPU)`
			`auto_scale_lr = dict(base_batch_size=128)`