alibaba-miil / hardcorenas Goto Github PK
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License: MIT License
Very impressive improvement in speed over other SOTA. Thanks for sharing the code.
I ran the following command to search 25 msec latency model for P100 GPU.
CUDA_VISIBLE_DEVICES=0 python -u ./search.py path_to_imagenet --train_percent=80 --bcfw_steps=10000 --initial-checkpoint=./pretrained/w_star_076b2ed3.pth --inference_time_limit=25 --lut_filename=./latency/LUT_GPU.pkl --batch-size 6 --amp 0
I am pointing to pre-trained model at, https://miil-public-eu.oss-eu-central-1.aliyuncs.com/public/HardCoReNAS/w_star_076b2ed3.pth
I got the following results,
Test child model: [8333/8333] Time: 0.821 (0.026) Loss: 3.6528 (0.9987) Acc@1: 50.0000 (76.4600) Acc@5: 50.0000 (93.1140)
Latency_predicted=0.07242456756666653, latency_measured=0.26521933794021607, diff=0.19279477037354953
How do I interpret this result? Did I get 72 msec model with 76.46 Top1 accuracy. I was expecting latency close to 25 msec.
Hi, thanks very much for your work.
I use following code to reproduce results on P100, but the result is inconsistent with the paper report.
python -u ./search.py
--train_percent=80
--bcfw_steps=10000
--initial-checkpoint=<A path to the one-shot model's weights>
--inference_time_limit 25 #ms
results:
25 ms constraints->searched model test:actual latency 62.48ms (paper report: 27ms)
acc(76.364/93.018) flops(467M) params(6.849M)
I'm very confused. Could you help me?
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