alibaba-miil / hardcorenas Goto Github PK

The given pretrained weight can not download.

Hope this problem can be fixed.

Thanks.

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?