小米開源框架MACE 建立模型部署檔案
阿新 • • 發佈：2019-02-14
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建立模型部署檔案
部署 MACE 模型的第一步是建立一個 YAML 模型部署檔案。
檔案描述了模型的部署情況，每個檔案會生成一個靜態庫（如果指定了多個ABI型別，則每一個均會生成對應的靜態庫）。部署檔案可以包含一個或多個模型，例如，一個智慧相機應用可能包含人臉識別、物體識別、語音識別模型，這些都可以定義在一個部署檔案中。
範例
下面是一個 Android 演示程式的部署檔案例子。
# The name of library
library_name: mobilenet
target_abis: [arm64-v8a]
embed_model_data: 1
# The build mode for model(s). 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. |