<小米开源框架MACE> 创建模型部署文件

文章内容翻译自 MACE 官方手册，记录本人阅读与开发过程，力求不失原意，但推荐阅读原文。
https://media.readthedocs.org/pdf/mace/latest/mace.pdf
Github地址：https://github.com/xiaomi/mace

声明：如有侵权，请联系作者删除

创建模型部署文件

部署 MACE 模型的第一步是创建一个 YAML 模型部署文件。

文件描述了模型的部署情况，每个文件会生成一个静态库（如果指定了多个ABI类型，则每一个均会生成对应的静态库）。部署文件可以包含一个或多个模型，例如，一个智能相机应用可能包含人脸识别、物体识别、语音识别模型，这些都可以定义在一个部署文件中。

范例

下面是一个 Android 演示程序的部署文件例子。

# The name of library
library_name: mobilenet
target_abis: [arm64-v8a]
embed_model_data: 1
# The build mode for model(s).
# 'code' stand for transfer model(s) into cpp code, 'proto' for model(s) in protobuf file(s).
build_type: code
linkshared: 0
# One yaml config file can contain multi models' config message.
models:
  mobilenet_v1: # model tag, which will be used in model loading and must be specific.
    platform: tensorflow
    # support local path, http:// and https://
    model_file_path: https://cnbj1.fds.api.xiaomi.com/mace/miai-models/mobilenet-v1/mobilenet-v1-1.0.pb
    model_sha256_checksum: 71b10f540ece33c49a7b51f5d4095fc9bd78ce46ebf0300487b2ee23d71294e6
    subgraphs:
      - input_tensors: input
        input_shapes: 1,224,224,3
        output_tensors: MobilenetV1/Predictions/Reshape_1
        output_shapes: 1,1001
    runtime: cpu+gpu
    limit_opencl_kernel_time: 0
    nnlib_graph_mode: 0
    obfuscate: 0
    winograd: 0
  mobilenet_v2:
    platform: tensorflow
    model_file_path: https://cnbj1.fds.api.xiaomi.com/mace/miai-models/mobilenet-v2/mobilenet-v2-1.0.pb
    model_sha256_checksum: 369f9a5f38f3c15b4311c1c84c032ce868da9f371b5f78c13d3ea3c537389bb4
    subgraphs:
      - input_tensors: input
        input_shapes: 1,224,224,3
        output_tensors: MobilenetV2/Predictions/Reshape_1
        output_shapes: 1,1001
    runtime: cpu+gpu
    limit_opencl_kernel_time: 0
    nnlib_graph_mode: 0
    obfuscate: 0
    winograd: 0

配置

library_name	library name.
target_abis	The target ABI to build, can be one or more of 'host', 'armeabi-v7a' or 'arm64-v8a'.
target_socs	[optional] build for specified socs if you just want use the model for that socs.
embed_model_data	Whether embedding model weights as the code, default to 0.
build_type	model build type, can be ['proto', 'code']. 'proto' for converting model to ProtoBuf file and 'code' for converting model to c++ code.
linkshared	[optional] Use dynamic linking for libmace library when setting to 1, or static linking when setting to 0, default to 0.
model_name	model name. should be unique if there are multiple models. LIMIT: if build_type is code, model_name will used in c++ code so that model_name must fulfill c++ name specification.
platform	The source framework, one of [tensorflow, caffe].
model_file_path	The path of the model file, can be local or remote.
model_sha256_checksum	The SHA256 checksum of the model file.
weight_file_path	[optional] The path of the model weights file, used by Caffe model.
weight_sha256_checksum	[optional] The SHA256 checksum of the weight file, used by Caffe model.
subgraphs	subgraphs key. ** DO NOT EDIT **
input_tensors	The input tensor names (tensorflow), top name of inputs' layer (caffe). one or more strings.
output_tensors	The output tensor names (tensorflow), top name of outputs' layer (caffe). one or more strings.
input_shapes	The shapes of the input tensors, in NHWC order.
output_shapes	The shapes of the output tensors, in NHWC order.
input_ranges	The numerical range of the input tensors, default [-1, 1]. It is only for test.
validation_inputs_data	[optional] Specify Numpy validation inputs. When not provided, [-1, 1] random values will be used.
runtime	The running device, one of [cpu, gpu, dsp, cpu_gpu]. cpu_gpu contains cpu and gpu model definition so you can run the model on both cpu and gpu.
data_type	[optional] The data type used for specified runtime. [fp16_fp32, fp32_fp32] for gpu, default is fp16_fp32. [fp32] for cpu. [uint8] for dsp.
limit_opencl_kernel_time	[optional] Whether splitting the OpenCL kernel within 1 ms to keep UI responsiveness, default to 0.
nnlib_graph_mode	[optional] Control the DSP precision and performance, default to 0 usually works for most cases.
obfuscate	[optional] Whether to obfuscate the model operator name, default to 0.
winograd	[optional] Whether to enable Winograd convolution, will increase memory consumption.