<小米开源框架MACE> 创建模型部署文件
2018-06-30 本文已影响22人
Kaami
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. |
