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[Fix] Align GreedyNAS retraining details with huangtao's code #1
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2. update lr config to align lr curve with huangtao 3. IterBasedRunner -> EpochBaseRunner
2. fix the rand-m9-mstd-0.5 config in mmcls style 3. training with full imagenet
2. align init_cfg 3. add default mutable_cfg 4. eval and save ckpt by epoch
humu789
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Apr 26, 2022
optimizer = dict( | ||
type='RMSpropTF', | ||
lr=0.048, | ||
eps=0.001, | ||
weight_decay=1e-5, | ||
momentum=0.9, | ||
filter_bias_and_bn=True) |
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params = _add_weight_decay(model, lr, weight_decay)
weight_decay = 0
humu789
reviewed
Apr 26, 2022
@@ -5,28 +5,27 @@ | |||
] | |||
|
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init_cfg = [ | |||
dict(type='FanOutNormal', layer=['Conv2d']), |
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nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='linear')
humu789
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Feb 13, 2023
* add function marker and model extractor * add fsaf split & partial mask rcnn split, import extract.py * 1. add value renaming 2. add apply_marks in config to turn on/off marks * rewind changes on pytorch2onnx.py Co-authored-by: q.yao <[email protected]>
humu789
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Feb 13, 2023
Co-authored-by: Yifan Zhou <[email protected]> Co-authored-by: lvhan028 <[email protected]> Co-authored-by: Yifan Zhou <[email protected]> Co-authored-by: lvhan028 <[email protected]>
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Motivation
There are some inconsistencies with Huang Tao's code, and I fixed them in this PR.
Modification
RMSPropTF
toOPTIMIZER
and remove the hard code in the train api.lr_config
.GlobalAveragePooling
)init_cfg
with huangtaorand-m9-mstd-0.5
config in mmcls style