This repo is an application of PC-DARTS. We search architectures on Webface dataset and evaluate the model on LFW. Official version can be accepted from the link: yuhuixu1993/PC-DARTS
In this repo, my works are as below:
- sample the proxy task to accelerate the search period
- design the stems to fit webface dataset
- use multi-process to accelerate LFW evalution
- visualize the architecture parameters including alpha and beta by using tensorboard
- parameters adjustment
We get LFW_ACC 98.58% after 20 epochs fine-tune with dropout 0.4 based on warmup-epoch-15 PC-DARTS-LFW pretrained model. The result 98.38% of ResNet50 is reached after 30 epochs fine-tune with dropout 0.5 based on ImageNet pretrained model.
| Model | LFW_ACC | Params |
|---|---|---|
| PC-DARTS-LFW | 98.58% | 13.04M |
| ResNet50 | 98.38% | 25.5M |
| warmup epochs | LFW_ACC | Params | geno result |
|---|---|---|---|
| 10 | 97.93% | 13.36M | Genotype(normal=[('sep_conv_5x5', 0), ('sep_conv_5x5', 1), ('sep_conv_5x5', 1), ('sep_conv_5x5', 2), ('sep_conv_5x5', 3), ('sep_conv_5x5', 1), ('sep_conv_5x5', 4), ('max_pool_3x3', 3)], normal_concat=range(2, 6), reduce=[('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('max_pool_3x3', 1), ('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('max_pool_3x3', 2), ('skip_connect', 1)], reduce_concat=range(2, 6)) |
| 15 | 98.2% | 13.04M | Genotype(normal=[('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_3x3', 2), ('sep_conv_5x5', 3), ('avg_pool_3x3', 2), ('sep_conv_3x3', 4), ('sep_conv_3x3', 1)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 2), ('sep_conv_5x5', 1), ('max_pool_3x3', 2), ('max_pool_3x3', 2), ('sep_conv_3x3', 3)], reduce_concat=range(2, 6)) |
| 25 | 97.93% | 12.76M | Genotype(normal=[('sep_conv_5x5', 1), ('sep_conv_5x5', 0), ('sep_conv_5x5', 1), ('dil_conv_3x3', 0), ('sep_conv_5x5', 3), ('sep_conv_5x5', 1), ('sep_conv_5x5', 4), ('max_pool_3x3', 2)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 2), ('max_pool_3x3', 3)], reduce_concat=range(2, 6)) |
we get 10% Webface dataset to search. batch-size 128 case needs 20G memory. 1 search epoch needs ~13 min. One time LFW evaluation needs ~16 min.
total 80 epochs, initial learning rate 0.4, linear decay data augmentation: color-jittor 0.4, label-smooth 0.2, random erase prob-0.9 mode-const count-1
we visualize all architecture parameters including 8 alpha and 1 beta as below.
[00, 01, 0, 1, 2, 3] represents 6 nodes in a cell. [00, 01] is input nodes. ori represents alpha parameters. res represents 8 alphas multiplying betas, which determines which path is selected.
Note: the same parameter line corresponds to different color.
- Enviroment
- torch1.3
- Dataset you can dowload Webface and LFW dataset from Baidu WangPan (password:030r) Then change the code-the augment about data path.
python train_search_face.py
Firstly, change your architechture in genotypes.py and then run train_face.py
python train_face.py
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