项目介绍
本项目主要面向第 $14$ 届全国服务外包创新创业比赛 $A16$ 赛道虚拟试衣赛题，采用 $2D$ 虚拟试衣技术依托于 $VITON$ 开源数据集训练 $DNN$ 网络并着重进行工程化落地应用；项目选用了前沿顶刊论文的 $PFAFN$ 模型，在此基础上对模型进行优化改进，实现了模型压缩和推理加速并使用 $OpenVINO$ 框架进行部署应用，出色地完成了赛题的要求。
项目开发环境
| 开发平台 |
版本 |
开发工具 |
版本 |
| Pycharm |
2022.3.2 |
Visual Studio Code |
1.80.1 |
| Visual Studio |
17.5.5 |
|
|
| 开发环境 |
版本 |
开发环境 |
版本 |
| neural-compressor |
2.2.1 |
nncf |
2.5.0 |
| numpy |
1.23.4 |
onnx |
1.14.0 |
| opencv-python |
4.7.0.72 |
onnxruntime |
1.15.1 |
| openvino |
2022.3.0 |
pandas |
1.3.5 |
| pytorch-fid |
0.3.0 |
rembg |
2.0.50 |
| pytorch |
2.0.0 |
torch-pruning |
1.1.9 |
| intel-openmp |
2021.4.0 |
|
|
模型结构介绍
本项目基于 $PFAFN$ 模型重新设计各个网络模块，具体结构如下图所示：
项目工程化落地
为了满足赛题方的要求，本项目开展了工程化落地部分，主要分为两个部分，模型训练和模型剪枝量化。项目工程化部署总图如下所示：
项目详细技术文档
实验结果：通道剪枝
| Metrics |
GFLOPs |
Para(M) |
SIZE(MB) |
Total SIZE(MB) |
Compresion Ratio |
FID |
FID Loss |
| Original Module |
6.63 |
9.37 |
35.8 |
112.0 |
100.00% |
8.906 |
0.00% |
| Ratio=0.2 with FineTuning |
5.23 |
7.28 |
27.6 |
88.69 |
79.19% |
9.013 |
1.20% |
| Ratio=0.3 with FineTuning |
4.40 |
6.48 |
24.8 |
65.73 |
58.69% |
9.113 |
2.32% |
| Ratio=0.4 with FineTuning |
3.79 |
5.61 |
20.4 |
40.97 |
36.58% |
9.304 |
4.47% |
| Ratio=0.5 with FineTuning |
3.42 |
4.55 |
16.8 |
35.47 |
31.67% |
9.977 |
12.03% |
| Metrics |
GFLOPs |
Para(M) |
SIZE(MB) |
Total SIZE(MB) |
Compresion Ratio |
FID |
FID Loss |
| Original Module |
21.93 |
43.90 |
167 |
167 |
100.00% |
8.906 |
0.00% |
| Ratio=0.2 with FineTuning |
16.54 |
35.02 |
112.3 |
112.3 |
67.25% |
9.212 |
3.44% |
| Ratio=0.25 with FineTuning |
15.45 |
31.93 |
94.39 |
94.39 |
56.52% |
9.405 |
5.60% |
| Ratio=0.3 with FineTuning |
13.90 |
29.89 |
80.25 |
80.25 |
48.05% |
9.679 |
8.68% |
| Ratio=0.35 with FineTuning |
12.78 |
27.31 |
73.49 |
73.49 |
44.01% |
9.835 |
10.43% |
| Ratio=0.4 with FineTuning |
11.20 |
26.12 |
68.52 |
68.52 |
41.03% |
10.527 |
18.20% |
| Model |
Original Model |
Sparsity |
Pruned Model |
FID |
FPS |
| CWM |
112MB |
40% |
40.97MB |
9.504 |
2.92 |
| IGM |
167MB |
25% |
94.39MB |
9.504 |
2.92 |
实验结果：量化感知训练
| Optimization |
CPU-FID |
GPU-FID |
Original Model |
Quantized Model |
| Unquantized |
9.504 |
9.483 |
135.36MB |
135.36MB |
| Quantize CWM |
9.783 |
9.701 |
40.97MB |
10.85MB |
| Quantize IGM |
10.382 |
10.249 |
94.39MB |
24.10MB |
| Quantize CWM & IGM |
11.503 |
11.379 |
135.36MB |
34.95MB |
实验结果：img2col 优化加速
| Runtimes |
CorrTorch(s) |
Img2Col(s) |
FPS |
Acceleration Rate |
| n=1000 |
147.8491 |
94.7902 |
10.81 |
1.5598 |
| n=10000 |
1489.1325 |
927.4293 |
10.77 |
1.6057 |
| Average Time |
0.1488 |
0.029 |
10.79 |
1.6017 |
参考文献
- Y. Ge, Y. Song, R. Zhang, C. Ge, W. Liu, and P. Luo, “Parser-Free Virtual Try-on via Distilling Appearance Flows,” arXiv preprint arXiv:2103.04559, 2021.
- Y. Cheng, D. Wang, P. Zhou and T. Zhang, “Model Compression and Acceleration for Deep
Neural Networks: The Principles, Progress, and Challenges,” in IEEE Signal Processing Magazine,
vol. 35, no. 1, pp. 126-136, Jan. 2018, doi: 10.1109/MSP.2017.2765695.
- PyTorch Quantization Aware Training
