# 库的名字
library_name: library_name
# 配置文件名会被用作生成库的名称：libmace-${library_name}.a
target_abis: [armeabi-v7a, arm64-v8a]
# 具体机型的soc编号，可以使用`adb shell getprop | grep ro.board.platform | cut -d [ -f3 | cut -d ] -f1`获取
target_socs: [msm8998]
embed_model_data: 1
build_type: code # 模型build类型。code表示将模型转为代码，proto表示将模型转为protobuf文件
models: # 一个配置文件可以包含多个模型的配置信息，最终生成的库中包含多个模型
  first_net: # 模型的标签，在调度模型的时候，会用这个变量，必须唯一
    platform: tensorflow
    model_file_path: path/to/model64.pb # also support http:// and https://
    model_sha256_checksum: 7f7462333406e7dea87222737590ebb7d94490194d2f21a7d72bafa87e64e9f9
    subgraphs:
      - input_tensors: input_node
        input_shapes: 1,64,64,3
        output_tensors: output_node
        output_shapes: 1,64,64,2
    runtime: gpu
    data_type: fp16_fp32
    limit_opencl_kernel_time: 0
    nnlib_graph_mode: 0
    obfuscate: 1
    winograd: 0
    input_files:
      - path/to/input_files # support http://
  second_net:
    platform: caffe
    model_file_path: path/to/model.prototxt
    weight_file_path: path/to/weight.caffemodel
    model_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
    weight_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
    subgraphs:
      - input_tensors:
          - input_node0
          - input_node1
        input_shapes:
          - 1,256,256,3
          - 1,128,128,3
        output_tensors:
          - output_node0
          - output_node1
        output_shapes:
          - 1,256,256,2
          - 1,1,1,2
    runtime: cpu
    limit_opencl_kernel_time: 1
    nnlib_graph_mode: 0
    obfuscate: 1
    winograd: 0
    input_files:
      - path/to/input_files # support http://