• 项目应用场景

• • 面向 ViT 算法部署场景，项目采用 ggml 推理框架 + Cpp 来实现，支持低比特量化，如 4bit 量化、5bit 量化、8bit 量化。算法部署平台包括通用 CPU、AMD CPU 等。

• 项目效果

• 项目细节 ==> 具体参见项目 README.md

• • (1) 模型转换，将 pytorch 模型转换为 GGUF

# install torch and timm
pip install torch timm

# list available models if needed; note that not all models are supported
python convert-pth-to-ggml.py --list

# convert the weights to gguf : vit tiny with patch size of 16 and an image 
# size of 384 pre-trained on ImageNet21k and fine-tuned on ImageNet1k
python convert-pth-to-ggml.py --model_name vit_tiny_patch16_384.augreg_in21k_ft_in1k --ftype 1

• • (2) 工程编译

# build ggml and vit 
mkdir build && cd build
cmake .. && make -j4

# run inference
./bin/vit -t 4 -m ../ggml-model-f16.gguf -i ../assets/tench.jpg

• • (3) 执行推理

usage: ./bin/vit [options]

options:
  -h, --help              show this help message and exit
  -s SEED, --seed SEED    RNG seed (default: -1)
  -t N, --threads N       number of threads to use during computation (default: 4)
  -m FNAME, --model FNAME model path (default: ../ggml-model-f16.bin)
  -i FNAME, --inp FNAME   input file (default: ../assets/tench.jpg)
  -k N, --topk N          top k classes to print (default: 5)
  -e FLOAT, --epsilon     epsilon (default: 0.000001)

• • (4) 量化

usage: ./bin/quantize /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type                              
  type = 2 - q4_0                                           
  type = 3 - q4_1                            
  type = 6 - q5_0                     
  type = 7 - q5_1                                                             
  type = 8 - q8_0                                                            

• • (5) 执行推理

python vaihingen_test.py -c config/vaihingen/dcswin.py -o fig_results/vaihingen/dcswin --rgb -t 'd4'

• 项目获取

• • https://download.csdn.net/download/weixin_42405819/89100807