!323 Remove reduplicated useless proto

Merge pull request !323 from 张晓昆/master

CMakeLists.txt

@@ -195,8 +195,40 @@ target_compile_options(parser_caffe_proto_obj PRIVATE
     $<$<AND:$<STREQUAL:${TARGET_SYSTEM_NAME},Windows>,$<STREQUAL:${CMAKE_CONFIGURATION_TYPES},Release>>:/MT>
 )
 
+############ lib_caffe_parser.so ############
+add_library(_caffe_parser SHARED
+    $<TARGET_OBJECTS:parser_caffe_proto_obj>
+)
+
+target_compile_definitions(_caffe_parser PRIVATE
+    google=ascend_private
+)
+
+target_include_directories(_caffe_parser PRIVATE
+    ${CMAKE_CURRENT_LIST_DIR}
+)
+
+target_link_options(_caffe_parser PRIVATE
+    -Wl,-Bsymbolic
+)
+
+target_link_libraries(_caffe_parser PRIVATE
+    $<BUILD_INTERFACE:intf_pub>
+    -Wl,--no-as-needed
+    ascend_protobuf
+    -Wl,--as-needed
+)
+
+############ install ############
+set(INSTALL_BASE_DIR "")
+set(INSTALL_LIBRARY_DIR lib)
+
+install(TARGETS _caffe_parser OPTIONAL
+    LIBRARY DESTINATION ${INSTALL_LIBRARY_DIR}
+)
+
+
 add_subdirectory(parser)
 add_subdirectory(parser/common)
 add_subdirectory(parser/func_to_graph)
 add_subdirectory(parser/onnx)
-add_subdirectory(parser/proto/caffe)

parser/caffe/proto/ge_ir.proto

@@ -1,193 +0,0 @@
-syntax = "proto3";
-
-package ge.proto;
-
-enum DataType
-{
-    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
-    DT_FLOAT     = 1;  // float type
-    DT_FLOAT16   = 2;  // fp16 type
-    DT_INT8      = 3;  // int8 type
-    DT_UINT8     = 4;  // uint8 type
-    DT_INT16     = 5;  // int16 type
-    DT_UINT16    = 6;  // uint16 type
-    DT_INT32     = 7;  //
-    DT_INT64     = 8;  // int64 type
-    DT_UINT32    = 9;  // unsigned int32
-    DT_UINT64    = 10;  // unsigned int64
-    DT_BOOL      = 11;  // bool type
-    DT_DOUBLE    = 12; // double type
-    DT_STRING = 13;            // string type
-    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
-    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
-    DT_COMPLEX64 = 16;         // complex64 type
-    DT_COMPLEX128 = 17;        // complex128 type
-    DT_QINT8 = 18;             // qint8 type
-    DT_QINT16 = 19;            // qint16 type
-    DT_QINT32 = 20;            // qint32 type
-    DT_QUINT8 = 21;            // quint8 type
-    DT_QUINT16 = 22;           // quint16 type
-    DT_RESOURCE  = 23;         // resource type
-    DT_STRING_REF = 24;        // string_ref type
-    DT_DUAL      = 25;              /**< dual output type */
-    DT_VARIANT = 26;           // variant type
-    DT_BF16 = 27;              // bf16 type
-    DT_INT4 = 28;              // int4 type
-}
-
-message AttrDef
-{
-    message ListValue
-    {
-        enum ListValueType{
-          VT_LIST_NONE = 0;
-          VT_LIST_STRING = 1;
-          VT_LIST_INT = 2;
-          VT_LIST_FLOAT = 3;
-          VT_LIST_BOOL = 4;
-          VT_LIST_BYTES = 5;
-          VT_LIST_TENSOR_DESC = 6;
-          VT_LIST_TENSOR = 7;
-          VT_LIST_GRAPH = 8;
-          VT_LIST_NAMED_ATTRS = 9;
-          VT_LIST_DATA_TYPE = 10;
-        }
-        repeated bytes s             = 2;                    // "list(string)"
-        repeated int64 i             = 3;  // "list(int)"
-        repeated float f             = 4;   // "list(float)"
-        repeated bool  b             = 5;  // "list(bool)"
-        repeated bytes bt            = 7;
-        repeated TensorDescriptor td = 8;
-        repeated TensorDef t         = 9;
-        repeated GraphDef g          = 10;
-	    repeated NamedAttrs na       = 11;
-	    repeated int64 dt            = 12; // list ge::DataType
-
-	    ListValueType val_type       = 20;
-    }
-
-    message ListListInt{
-        message ListInt{
-            repeated int64 list_i             = 1; // list int
-        }
-        repeated ListInt list_list_i             = 1; // list list int
-    }
-
-    oneof value
-    {
-        bytes            s    = 2;  // "string"
-        int64            i    = 3;  // "int"
-        float            f    = 4;  // "float"
-        bool             b    = 5;  // "bool"
-        bytes            bt   = 7;
-        ListValue        list = 1;   // any "list(...)"
-        NamedAttrs       func = 10;  // Used to support attr nesting
-        TensorDescriptor td   = 11;  // GeTensorDesc type
-        TensorDef        t    = 12;  // GeTensor type
-        GraphDef         g    = 13;  // Graph type
-        ListListInt      list_list_int  = 14;  // List List Int type
-        int64            dt   = 15; // ge::DataType
-    }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs
-{
-    string               name = 1;
-    map<string, AttrDef> attr = 2;
-}
-
-// Shape / dimension description, using row-major order
-message ShapeDef
-{
-    repeated int64 dim = 1;  // Size of each dimension
-}
-
-// Multidimensional data description
-message TensorDescriptor
-{
-    string   name   = 1;  // Optional parameter, tensor name
-
-    DataType dtype  = 2;  // tensor datatype
-    ShapeDef shape  = 3;  // Shape / dimension
-    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"
-
-    bool has_out_attr = 9;
-    int64 size = 10;
-    int64 weight_size = 11;
-    bool reuse_input = 12;
-    bool output_tensor = 13;
-    string device_type = 14;
-    bool input_tensor =15;
-    int64 real_dim_cnt = 16;
-    int64 reuse_input_index = 17;
-    int64 data_offset = 18;
-    int64 cmps_size = 19;
-    string cmps_tab = 20;
-    int64 cmps_tab_offset = 21;
-
-	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
-}
-
-// GeTensor definition
-message TensorDef
-{
-    TensorDescriptor desc = 1;  // Tensor description
-    bytes            data = 2;  // Tensor data
-}
-
-
-// Operator description
-message OpDef
-{
-    string name = 1;  // name
-    string type = 2;  // type
-
-    repeated string input = 5;  // input original op name + outgoing index. op_name：index
-
-    map<string, AttrDef> attr = 10;  // Set of operator parameter fields
-
-    bool has_out_attr = 20;
-    int64 id = 21;
-    int64 stream_id =22;
-    repeated string input_name = 23;
-    repeated string src_name = 24;
-    repeated int64 src_index = 25;
-    repeated string dst_name = 26;
-    repeated int64 dst_index = 27;
-    repeated int64 input_i = 28;
-    repeated int64 output_i = 29;
-    repeated int64 workspace = 30;
-    repeated int64 workspace_bytes = 31;
-    repeated bool is_input_const = 32;
-    repeated TensorDescriptor input_desc = 33;
-    repeated TensorDescriptor output_desc = 34;
-    repeated string subgraph_name = 35;
-}
-
-// Graph definition
-message GraphDef
-{
-    string name   = 1;   //  name
-
-    repeated string input  = 4;  // Graph input
-    repeated string output = 5;  // Graph output
-
-    repeated OpDef op      = 6;  // List of operators
-
-	map<string, AttrDef> attr = 11;  // Extended field
-}
-
-// model definition
-message ModelDef
-{
-	string name         = 1;  // name
-	uint32 version      = 2;  // IR Proto verion
-	string custom_version = 3;  // User model version number, passed in by user
-
-    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef
-
-    map<string, AttrDef> attr = 11;  // Extended field
-}
-

parser/caffe/proto/om.proto

@@ -1,396 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-enum TargetType
-{
-    MINI = 0;
-    TINY = 1;
-    LITE = 2;
-}
-
-// offline model
-message ModelDef {
-    string name = 1;
-    uint32 version = 2;
-
-    uint64 memory_size = 10;
-    uint32 stream_num = 11;
-    uint32 event_num = 12;
-    uint64 weight_size = 13;
-    uint32 label_num = 15;
-    repeated OpDef op = 20;
-    TargetType target_type = 23;
-
-    map<string, AttrDef> attr = 30;
-};
-
-// operator define
-message OpDef {
-    string name = 1;
-    string type = 2;
-
-    uint32 id = 3;
-    uint32 stream_id = 4;
-
-    repeated string input_name = 5;
-
-    repeated string src_name = 8;
-    repeated int32 src_index = 9;
-    repeated int64 input = 10;
-    repeated int64 output = 11;
-    repeated TensorDescriptor input_desc = 12;
-    repeated TensorDescriptor output_desc = 13;
-    repeated WeightDef weights = 14;
-    repeated string dst_name = 15;
-    repeated int32 dst_index = 16;
-
-    repeated int64 workspace = 20;
-    repeated uint32 workspace_bytes = 21;
-
-    repeated string weight_name = 22;
-    repeated bool is_input_const = 23;
-
-    map<string, AttrDef> attr = 30;
-
-    QuantizeFactorParams quantize_factor = 31;
-
-    oneof op_params {
-        // start at 100 here
-        SendOpParams sender_param = 100;
-        RecvOpParams receiver_param = 200;
-        ConvolutionOpParams convolution_param = 300;
-        PoolingOpParams pooling_param = 400;
-        EltwiseOpParams eltwise_param = 500;
-        BatchNormOpParams batchnorm_param = 600;
-        ScaleOpParams scale_param = 700;
-        FullConnectionOpParams full_connection_param = 800;
-        SoftmaxOpParams softmax_param = 900;
-        ActivationOpParams activation_param = 1000;
-        ReshapeOpParams reshape_param = 1100;
-    }
-};
-
-message SendOpParams {
-    uint32 event_id = 1;
-};
-
-message RecvOpParams {
-    uint32 event_id = 1;
-};
-
-enum QuantizeScaleType
-{
-    VECTOR_SCALE = 0;
-    SCALAR_SCALE = 1;
-}
-
-enum QuantizeScaleMode
-{
-    NORMAL_MODE = 0;
-    SQRT_MODE = 1;
-}
-
-enum QuantizeAlgorithm
-{
-    NON_OFFSET_ALGO = 0;
-    HALF_OFFSET_ALGO = 1;
-    ALL_OFFSET_ALGO = 2;
-}
-message QuantizeFactor
-{
-    QuantizeScaleMode scale_mode = 1;
-    bytes scale_value = 2;
-    int64 scale_offset = 3;
-    bytes offset_data_value = 4;
-    int64 offset_data_offset = 5;
-    bytes offset_weight_value = 6;
-    int64 offset_weight_offset = 7;
-    bytes offset_pad_value = 8;
-    int64 offset_pad_offset = 9;
-};
-
-message QuantizeCalcFactor
-{
-    bytes offsetw = 1;
-    int64 offsetw_offset = 2;
-    bytes offsetd = 3;
-    int64 offsetd_offset = 4;
-    bytes scalereq = 5;
-    int64 scaledreq_offset = 6;
-    bytes offsetdnext = 7;
-    int64 offsetdnext_offset = 8;
-}
-
-message QuantizeFactorParams
-{
-    QuantizeAlgorithm quantize_algo = 1;
-    QuantizeScaleType scale_type = 2;
-    QuantizeFactor quantize_param = 3;
-    QuantizeFactor dequantize_param = 4;
-    QuantizeFactor requantize_param = 5;
-    QuantizeCalcFactor quantizecalc_param = 6;
-};
-
-message ConvolutionOpParams {
-    int32 mode = 1;
-    int32 algo = 2;
-    int32 pad_mode = 3;
-    uint32 group = 4;
-    uint32 num_output = 5;
-
-    repeated uint32 pad = 10;
-    repeated uint32 stride = 11;
-    repeated uint32 dilation = 12;
-    repeated uint32 kernel = 13;
-
-    float alpha = 20;
-    float beta = 21;
-
-    WeightDef filter = 40;
-    WeightDef bias = 41;
-
-    bool relu_flag = 62;
-    repeated uint32 adj = 70;
-    repeated uint32 target_shape = 71;
-    repeated uint32 before_pad = 72;
-};
-
-message PoolingOpParams {
-    int32 mode = 1;
-    int32 nan_opt = 2;
-    int32 pad_mode = 3;
-    bool global_pooling = 4;
-
-    repeated uint32 window = 10;
-    repeated uint32 pad = 11;
-    repeated uint32 stride = 12;
-    bool ceil_mode = 13;
-    int32 data_mode  = 14;
-
-    float alpha = 20;
-    float beta = 21;
-    repeated uint32 before_pad = 22;
-};
-
-message EltwiseOpParams {
-    int32 mode = 1;
-    repeated float coeff = 2;
-    float alpha = 3;
-    float beta = 4;
-    repeated WeightDef weight = 5;
-    bool relu_flag = 6;
-};
-
-message ActivationOpParams {
-    int32 mode = 1;
-    float coef = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message BatchNormOpParams {
-    int32 mode = 1;
-
-    float alpha = 2;
-    float beta = 3;
-    double epsilon = 4;//optinal,[default = 1e-5]
-    bool use_global_stats = 5; //optinal,by default true,testing mode
-    float  moving_average_fraction = 6; //optinal,[default = .999];
-
-    WeightDef estimated_mean = 7;
-    WeightDef estimated_variance = 8;
-
-    WeightDef scale = 9;
-    WeightDef bias = 10;
-};
-
-message ScaleOpParams {
-    WeightDef scale = 1;
-    WeightDef bias = 2;
-};
-
-message ReshapeOpParams {
-    float alpha = 1;
-    float beta = 2;
-    ShapeDef shape = 3;
-    int32 axis = 4;
-    int32 num_axes = 5;
-    int32 format = 6;
-};
-
-message SoftmaxOpParams {
-    int32 algo = 1;
-    int32 mode = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message FullConnectionOpParams {
-    WeightDef filter = 1;
-    WeightDef bias = 2;
-    uint32 num_output = 3;
-    bool relu_flag = 12;
-};
-
-message FlattenOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 start_axis = 3;
-    int32 end_axis = 4;
-}
-
-message AddLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message AddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message SubOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message BiasAddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    WeightDef bias = 10;
-};
-
-message MatMulOpParams {
-    float alpha = 1;
-    float beta = 2;
-    bool transposeX = 3;
-    bool transposeW = 4;
-
-    WeightDef filter = 10;
-    WeightDef bias = 12;
-};
-
-message RsqrtOpParams {
-    float alpha = 1;
-    float beta = 2;
-};
-
-
-message WeightDef {
-    int32 format = 1;
-    int32 data_type = 2;
-    ShapeDef shape = 3;
-    bytes data = 4;
-    int64 data_offset = 5;
-    uint32 cmps_size = 6;
-    bytes cmps_tab = 7;
-    int64 cmps_tab_offset = 10;
-    CompressInfo cmps_info = 8;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
-}
-
-message ShapeDef {
-    repeated int64 dim = 1;
-}
-
-enum DeviceType {
-  NPU = 0;                   // In default, we will use NPU.
-  CPU = 1;                   // CPU
-}
-
-message AllOffsetQuantizeInfo {
-	float scale  = 1;
-	int32 offset = 2;
-} 
-
-message TensorDescriptor {
-    int32 format = 1;
-    int32 data_type = 2;
-    repeated int64 dim = 3;
-    uint32 size = 4;
-    bool reuse_input = 5;
-    bool output_tensor = 7;
-    DeviceType device_type = 8;
-    bool input_tensor = 9;
-    uint32 real_dim_cnt = 10;
-    uint32 reuse_input_index = 11;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
-}
-
-message CompressInfo {
-    int32 blockRow = 1;                             // block row
-    int32 blockCol = 2;                             // block col
-    int32 fractalK = 3;                             // fractal K
-    int32 fractalN = 4;                             // fractal N
-    int32 lastFractalK = 5;                         // K of last fractal
-    int32 lastFractalN = 6;                         // N of last fractal
-    int32 cubeSize = 7;                             // cube's length
-    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
-}
-
-message AttrDef {
-  message ListValue {
-    repeated string s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated uint32 u = 6 [packed = true];         // "list(uint)"
-    repeated bytes bt = 7;
-  }
-
-  oneof value {
-    string s = 2;                // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    uint32 u = 6;                // "uint32"
-    bytes bt = 7;
-    ListValue list = 1;          // any "list(...)"
-    NamedAttrs func = 10;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs {
-  string name = 1;
-  map<string, AttrDef> attr = 2;
-}
-

parser/caffe/proto/task.proto

@@ -1,179 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-message ModelTaskDef {
-    string version = 1;
-
-    map<string, string> attr = 9; // Extended field
-    repeated TaskDef task = 10;
-
-    uint64 memory_size = 11;
-    uint32 stream_num = 12;
-    uint32 event_num = 13;
-    uint64 weight_size = 14;
-
-    repeated bytes op = 15; // input/output opdef in bytes
-
-    uint64 base_addr = 16;    // base addr
-    uint64 weight_addr = 17;  // weight addr
-    uint32 batch_num = 18;
-}
-
-
-message TaskDef {
-    uint32 id = 1;
-    uint32 type = 2;
-
-    uint32 stream_id = 10;
-    uint32 event_id = 11;
-
-    KernelDef kernel = 20;
-    KernelExDef kernel_ex = 21;
-    KernelHcclDef kernel_hccl = 25;
-    EventExDef event_ex = 26;
-    LogTimeStampDef log_timestamp = 28;
-
-    uint32 label_id = 30;
-
-    MemcpyAsyncDef memcpy_async = 31;
-    StreamSwitchDef stream_switch = 32;
-    StreamActiveDef stream_active = 33;
-    bytes private_def = 34;
-    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
-    StreamSwitchNDef stream_switch_n = 36;
-
-    LabelSetDef label_set = 37;
-    LabelGotoExDef label_goto_ex = 38;
-    LabelSwitchByIndexDef label_switch_by_index = 39;
-    KernelDefWithHandle kernel_with_handle = 40;
-}
-
-message KernelDef {
-    KernelContext context = 1;
-
-    string stub_func = 10;
-    uint32 block_dim = 11;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes sm_desc = 14;
-    bytes flowtable = 15;
-    string so_name = 16;
-    string kernel_name = 17;
-    bytes kernel_ext_info = 18;
-    uint32 kernel_ext_info_size = 19;
-}
-
-message KernelDefWithHandle {
-    KernelContext context = 1;
-
-    uint64 handle = 10;
-    string dev_func = 11;
-    uint32 block_dim = 12;
-    uint32 args_size = 13;
-    bytes args = 14;
-    bytes sm_desc = 15;
-    string original_kernel_key = 16;
-    string node_info = 17;
-}
-
-message KernelContext {
-    uint32 kernel_type = 1;
-    uint32 op_id = 2;                              // OP type in CCE
-    uint32 kernel_func_id = 3;
-    uint32 op_index = 4;                           // TE/Custom operator
-    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
-    bytes args_offset = 6;                         // args offset information
-    uint32 args_count = 7;                         // args count
-    repeated uint32 origin_op_index = 8;
-}
-
-
-message KernelExDef {
-    uint32 flags = 1;
-
-    uint32 op_index = 4;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
-    uint32 task_info_size = 15;
-    bytes kernel_ext_info = 16;
-    uint32 kernel_ext_info_size = 17;
-}
-
-
-message KernelHcclDef {
-    uint32 op_index = 8;
-    string hccl_type = 9;
-}
-
-
-message EventExDef {
-    uint32 op_index = 1;
-    uint32 event_type = 2;
-}
-
-message LogTimeStampDef {
-    uint64 logid = 1;
-    bool notify = 2;
-    uint32 flat = 3;
-}
-
-message MemcpyAsyncDef {
-    uint64 dst = 1;
-    uint64 dst_max = 2;
-    uint64 src = 3;
-    uint64 count = 4;
-    uint32 kind = 5;
-    uint32 op_index = 6;
-}
-
-message StreamSwitchDef {
-    uint32 op_index = 1;
-    uint32 true_stream_id = 2;
-    int64 value = 3;
-    uint64 value_ptr = 4;
-    uint32 data_type = 5;
-}
-
-message StreamActiveDef {
-    uint32 op_index = 1;
-    uint32 active_stream_id = 2;
-}
-
-message StreamSwitchNDef {
-    uint32 op_index = 1;
-    uint32 size = 2;
-    repeated int64 target_value = 3;
-    repeated uint32 true_stream_id = 4;
-    uint32 element_size = 5;
-    uint32 data_type = 6;
-}
-
-message LabelSetDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelGotoExDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelSwitchByIndexDef {
-    uint32 op_index = 1;
-    uint32 label_max = 2;
-}

parser/common/proto/ge_ir.proto

@@ -1,193 +0,0 @@
-syntax = "proto3";
-
-package ge.proto;
-
-enum DataType
-{
-    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
-    DT_FLOAT     = 1;  // float type
-    DT_FLOAT16   = 2;  // fp16 type
-    DT_INT8      = 3;  // int8 type
-    DT_UINT8     = 4;  // uint8 type
-    DT_INT16     = 5;  // int16 type
-    DT_UINT16    = 6;  // uint16 type
-    DT_INT32     = 7;  //
-    DT_INT64     = 8;  // int64 type
-    DT_UINT32    = 9;  // unsigned int32
-    DT_UINT64    = 10;  // unsigned int64
-    DT_BOOL      = 11;  // bool type
-    DT_DOUBLE    = 12; // double type
-    DT_STRING = 13;            // string type
-    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
-    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
-    DT_COMPLEX64 = 16;         // complex64 type
-    DT_COMPLEX128 = 17;        // complex128 type
-    DT_QINT8 = 18;             // qint8 type
-    DT_QINT16 = 19;            // qint16 type
-    DT_QINT32 = 20;            // qint32 type
-    DT_QUINT8 = 21;            // quint8 type
-    DT_QUINT16 = 22;           // quint16 type
-    DT_RESOURCE  = 23;         // resource type
-    DT_STRING_REF = 24;        // string_ref type
-    DT_DUAL      = 25;              /**< dual output type */
-    DT_VARIANT = 26;           // variant type
-    DT_BF16 = 27;              // bf16 type
-    DT_INT4 = 28;              // int4 type
-}
-
-message AttrDef
-{
-    message ListValue
-    {
-        enum ListValueType{
-          VT_LIST_NONE = 0;
-          VT_LIST_STRING = 1;
-          VT_LIST_INT = 2;
-          VT_LIST_FLOAT = 3;
-          VT_LIST_BOOL = 4;
-          VT_LIST_BYTES = 5;
-          VT_LIST_TENSOR_DESC = 6;
-          VT_LIST_TENSOR = 7;
-          VT_LIST_GRAPH = 8;
-          VT_LIST_NAMED_ATTRS = 9;
-          VT_LIST_DATA_TYPE = 10;
-        }
-        repeated bytes s             = 2;                    // "list(string)"
-        repeated int64 i             = 3;  // "list(int)"
-        repeated float f             = 4;   // "list(float)"
-        repeated bool  b             = 5;  // "list(bool)"
-        repeated bytes bt            = 7;
-        repeated TensorDescriptor td = 8;
-        repeated TensorDef t         = 9;
-        repeated GraphDef g          = 10;
-	    repeated NamedAttrs na       = 11;
-	    repeated int64 dt            = 12; // list ge::DataType
-
-	    ListValueType val_type       = 20;
-    }
-
-    message ListListInt{
-        message ListInt{
-            repeated int64 list_i             = 1; // list int
-        }
-        repeated ListInt list_list_i             = 1; // list list int
-    }
-
-    oneof value
-    {
-        bytes            s    = 2;  // "string"
-        int64            i    = 3;  // "int"
-        float            f    = 4;  // "float"
-        bool             b    = 5;  // "bool"
-        bytes            bt   = 7;
-        ListValue        list = 1;   // any "list(...)"
-        NamedAttrs       func = 10;  // Used to support attr nesting
-        TensorDescriptor td   = 11;  // GeTensorDesc type
-        TensorDef        t    = 12;  // GeTensor type
-        GraphDef         g    = 13;  // Graph type
-        ListListInt      list_list_int  = 14;  // List List Int type
-        int64            dt   = 15; // ge::DataType
-    }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs
-{
-    string               name = 1;
-    map<string, AttrDef> attr = 2;
-}
-
-// Shape / dimension description, using row-major order
-message ShapeDef
-{
-    repeated int64 dim = 1;  // Size of each dimension
-}
-
-// Multidimensional data description
-message TensorDescriptor
-{
-    string   name   = 1;  // Optional parameter, tensor name
-
-    DataType dtype  = 2;  // tensor datatype
-    ShapeDef shape  = 3;  // Shape / dimension
-    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"
-
-    bool has_out_attr = 9;
-    int64 size = 10;
-    int64 weight_size = 11;
-    bool reuse_input = 12;
-    bool output_tensor = 13;
-    string device_type = 14;
-    bool input_tensor =15;
-    int64 real_dim_cnt = 16;
-    int64 reuse_input_index = 17;
-    int64 data_offset = 18;
-    int64 cmps_size = 19;
-    string cmps_tab = 20;
-    int64 cmps_tab_offset = 21;
-
-	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
-}
-
-// GeTensor definition
-message TensorDef
-{
-    TensorDescriptor desc = 1;  // Tensor description
-    bytes            data = 2;  // Tensor data
-}
-
-
-// Operator description
-message OpDef
-{
-    string name = 1;  // name
-    string type = 2;  // type
-
-    repeated string input = 5;  // input original op name + outgoing index. op_name：index
-
-    map<string, AttrDef> attr = 10;  // Set of operator parameter fields
-
-    bool has_out_attr = 20;
-    int64 id = 21;
-    int64 stream_id =22;
-    repeated string input_name = 23;
-    repeated string src_name = 24;
-    repeated int64 src_index = 25;
-    repeated string dst_name = 26;
-    repeated int64 dst_index = 27;
-    repeated int64 input_i = 28;
-    repeated int64 output_i = 29;
-    repeated int64 workspace = 30;
-    repeated int64 workspace_bytes = 31;
-    repeated bool is_input_const = 32;
-    repeated TensorDescriptor input_desc = 33;
-    repeated TensorDescriptor output_desc = 34;
-    repeated string subgraph_name = 35;
-}
-
-// Graph definition
-message GraphDef
-{
-    string name   = 1;   //  name
-
-    repeated string input  = 4;  // Graph input
-    repeated string output = 5;  // Graph output
-
-    repeated OpDef op      = 6;  // List of operators
-
-	map<string, AttrDef> attr = 11;  // Extended field
-}
-
-// model definition
-message ModelDef
-{
-	string name         = 1;  // name
-	uint32 version      = 2;  // IR Proto verion
-	string custom_version = 3;  // User model version number, passed in by user
-
-    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef
-
-    map<string, AttrDef> attr = 11;  // Extended field
-}
-

parser/common/proto/insert_op.proto

@@ -1,140 +0,0 @@
-syntax = "proto3";
-
-package domi;
-
-message InsertNewOps {
-	repeated AippOpParams aipp_op = 1;
-	repeated MultiShapeOpParams multi_shape_op = 2;
-}
-
-message AippOpParams {
-	enum InputFormat {
-		UNDEFINED = 0;
-		YUV420SP_U8 = 1;
-		XRGB8888_U8 = 2;
-		RGB888_U8   = 3;
-		YUV400_U8   = 4;
-		NC1HWC0DI_FP16 = 5;
-		NC1HWC0DI_S8 = 6;
-		ARGB8888_U8 = 7;
-		YUYV_U8 = 8;
-		YUV422SP_U8 = 9;
-		AYUV444_U8 = 10;
-		RAW10 = 11;
-		RAW12 = 12;
-		RAW16 = 13;
-		RAW24 = 14;
-		RGB16 = 15;
-		RGB20 = 16;
-		RGB24 = 17;
-		RGB8_IR = 18;
-		RGB16_IR = 19;
-		RGB24_IR = 20;
-	}
-
-	enum AippMode {
-		undefined = 0;
-		static = 1;
-		dynamic = 2;
-	}
-
-	// AIPP模式，区分静态AIPP和动态AIPP
-	AippMode aipp_mode = 1;
-
-	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
-	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
-	uint32 related_input_rank = 2;
-
-        // related_input_name is optional and the top name of data node which inserts aipp
-        string related_input_name = 6;
-
-	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
-	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
-	// 配置值 <= Data算子输出边的个数。
-	repeated uint32 input_edge_idx = 3;
-
-	// [Begin] 动态AIPP参数，配置静态AIPP时无效
-	uint32 max_src_image_size = 4;
-
-	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
-	bool support_rotation = 5;
-
-	// [End] 动态AIPP参数
-
-
-	// [Begin] 静态AIPP参数，配置动态AIPP时无效
-	InputFormat input_format = 51;
-	bool csc_switch = 52;
-	float cpadding_value = 53;
-	bool rbuv_swap_switch = 54;
-	bool ax_swap_switch = 55;
-	bool single_line_mode = 56;
-
-	int32 src_image_size_w = 57;
-	int32 src_image_size_h = 58;
-
-	bool crop = 59;
-	int32 load_start_pos_w = 60;
-	int32 load_start_pos_h = 61;
-	int32 crop_size_w = 62;
-	int32 crop_size_h = 63;
-
-	bool resize = 64;
-	int32 resize_output_w = 65;
-	int32 resize_output_h = 66;
-
-	bool padding = 67;
-	int32 left_padding_size = 68;
-	int32 right_padding_size = 69;
-	int32 top_padding_size = 70;
-	int32 bottom_padding_size = 71;
-	float padding_value = 72;
-
-	int32 mean_chn_0 = 10;
-	int32 mean_chn_1 = 11;
-	int32 mean_chn_2 = 12;
-	int32 mean_chn_3 = 19;
-	float min_chn_0 = 13;
-	float min_chn_1 = 14;
-	float min_chn_2 = 15;
-	float min_chn_3 = 20;
-	repeated float var_reci_chn_0 = 16;
-	repeated float var_reci_chn_1 = 17;
-	repeated float var_reci_chn_2 = 18;
-	repeated float var_reci_chn_3 = 21;
-
-	repeated int32 matrix_r0c0 = 30;
-	repeated int32 matrix_r0c1 = 31;
-	repeated int32 matrix_r0c2 = 32;
-	repeated int32 matrix_r1c0 = 33;
-	repeated int32 matrix_r1c1 = 34;
-	repeated int32 matrix_r1c2 = 35;
-	repeated int32 matrix_r2c0 = 36;
-	repeated int32 matrix_r2c1 = 37;
-	repeated int32 matrix_r2c2 = 38;
-	repeated int32 output_bias_0 = 39;
-	repeated int32 output_bias_1 = 40;
-	repeated int32 output_bias_2 = 41;
-	repeated int32 input_bias_0 = 42;
-	repeated int32 input_bias_1 = 43;
-	repeated int32 input_bias_2 = 44;
-
-	// [End] 静态AIPP参数
-
-       // The n number that is used for raw/rgbir data into f16 transformation.
-       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
-       uint32 raw_rgbir_to_f16_n = 45;
-}
-
-message MultiShapeOpParams {
-	enum MultiShapeMode {
-		batch      = 0;             //动态batch
-		resolution = 1;             //动态分辨率，扩展用
-	}
-
-	MultiShapeMode    mode                      = 1;        //算子模式
-	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入
-
-
-	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
-}

parser/common/proto/om.proto

@@ -1,396 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-enum TargetType
-{
-    MINI = 0;
-    TINY = 1;
-    LITE = 2;
-}
-
-// offline model
-message ModelDef {
-    string name = 1;
-    uint32 version = 2;
-
-    uint64 memory_size = 10;
-    uint32 stream_num = 11;
-    uint32 event_num = 12;
-    uint64 weight_size = 13;
-    uint32 label_num = 15;
-    repeated OpDef op = 20;
-    TargetType target_type = 23;
-
-    map<string, AttrDef> attr = 30;
-};
-
-// operator define
-message OpDef {
-    string name = 1;
-    string type = 2;
-
-    uint32 id = 3;
-    uint32 stream_id = 4;
-
-    repeated string input_name = 5;
-
-    repeated string src_name = 8;
-    repeated int32 src_index = 9;
-    repeated int64 input = 10;
-    repeated int64 output = 11;
-    repeated TensorDescriptor input_desc = 12;
-    repeated TensorDescriptor output_desc = 13;
-    repeated WeightDef weights = 14;
-    repeated string dst_name = 15;
-    repeated int32 dst_index = 16;
-
-    repeated int64 workspace = 20;
-    repeated uint32 workspace_bytes = 21;
-
-    repeated string weight_name = 22;
-    repeated bool is_input_const = 23;
-
-    map<string, AttrDef> attr = 30;
-
-    QuantizeFactorParams quantize_factor = 31;
-
-    oneof op_params {
-        // start at 100 here
-        SendOpParams sender_param = 100;
-        RecvOpParams receiver_param = 200;
-        ConvolutionOpParams convolution_param = 300;
-        PoolingOpParams pooling_param = 400;
-        EltwiseOpParams eltwise_param = 500;
-        BatchNormOpParams batchnorm_param = 600;
-        ScaleOpParams scale_param = 700;
-        FullConnectionOpParams full_connection_param = 800;
-        SoftmaxOpParams softmax_param = 900;
-        ActivationOpParams activation_param = 1000;
-        ReshapeOpParams reshape_param = 1100;
-    }
-};
-
-message SendOpParams {
-    uint32 event_id = 1;
-};
-
-message RecvOpParams {
-    uint32 event_id = 1;
-};
-
-enum QuantizeScaleType
-{
-    VECTOR_SCALE = 0;
-    SCALAR_SCALE = 1;
-}
-
-enum QuantizeScaleMode
-{
-    NORMAL_MODE = 0;
-    SQRT_MODE = 1;
-}
-
-enum QuantizeAlgorithm
-{
-    NON_OFFSET_ALGO = 0;
-    HALF_OFFSET_ALGO = 1;
-    ALL_OFFSET_ALGO = 2;
-}
-message QuantizeFactor
-{
-    QuantizeScaleMode scale_mode = 1;
-    bytes scale_value = 2;
-    int64 scale_offset = 3;
-    bytes offset_data_value = 4;
-    int64 offset_data_offset = 5;
-    bytes offset_weight_value = 6;
-    int64 offset_weight_offset = 7;
-    bytes offset_pad_value = 8;
-    int64 offset_pad_offset = 9;
-};
-
-message QuantizeCalcFactor
-{
-    bytes offsetw = 1;
-    int64 offsetw_offset = 2;
-    bytes offsetd = 3;
-    int64 offsetd_offset = 4;
-    bytes scalereq = 5;
-    int64 scaledreq_offset = 6;
-    bytes offsetdnext = 7;
-    int64 offsetdnext_offset = 8;
-}
-
-message QuantizeFactorParams
-{
-    QuantizeAlgorithm quantize_algo = 1;
-    QuantizeScaleType scale_type = 2;
-    QuantizeFactor quantize_param = 3;
-    QuantizeFactor dequantize_param = 4;
-    QuantizeFactor requantize_param = 5;
-    QuantizeCalcFactor quantizecalc_param = 6;
-};
-
-message ConvolutionOpParams {
-    int32 mode = 1;
-    int32 algo = 2;
-    int32 pad_mode = 3;
-    uint32 group = 4;
-    uint32 num_output = 5;
-
-    repeated uint32 pad = 10;
-    repeated uint32 stride = 11;
-    repeated uint32 dilation = 12;
-    repeated uint32 kernel = 13;
-
-    float alpha = 20;
-    float beta = 21;
-
-    WeightDef filter = 40;
-    WeightDef bias = 41;
-
-    bool relu_flag = 62;
-    repeated uint32 adj = 70;
-    repeated uint32 target_shape = 71;
-    repeated uint32 before_pad = 72;
-};
-
-message PoolingOpParams {
-    int32 mode = 1;
-    int32 nan_opt = 2;
-    int32 pad_mode = 3;
-    bool global_pooling = 4;
-
-    repeated uint32 window = 10;
-    repeated uint32 pad = 11;
-    repeated uint32 stride = 12;
-    bool ceil_mode = 13;
-    int32 data_mode  = 14;
-
-    float alpha = 20;
-    float beta = 21;
-    repeated uint32 before_pad = 22;
-};
-
-message EltwiseOpParams {
-    int32 mode = 1;
-    repeated float coeff = 2;
-    float alpha = 3;
-    float beta = 4;
-    repeated WeightDef weight = 5;
-    bool relu_flag = 6;
-};
-
-message ActivationOpParams {
-    int32 mode = 1;
-    float coef = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message BatchNormOpParams {
-    int32 mode = 1;
-
-    float alpha = 2;
-    float beta = 3;
-    double epsilon = 4;//optinal,[default = 1e-5]
-    bool use_global_stats = 5; //optinal,by default true,testing mode
-    float  moving_average_fraction = 6; //optinal,[default = .999];
-
-    WeightDef estimated_mean = 7;
-    WeightDef estimated_variance = 8;
-
-    WeightDef scale = 9;
-    WeightDef bias = 10;
-};
-
-message ScaleOpParams {
-    WeightDef scale = 1;
-    WeightDef bias = 2;
-};
-
-message ReshapeOpParams {
-    float alpha = 1;
-    float beta = 2;
-    ShapeDef shape = 3;
-    int32 axis = 4;
-    int32 num_axes = 5;
-    int32 format = 6;
-};
-
-message SoftmaxOpParams {
-    int32 algo = 1;
-    int32 mode = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message FullConnectionOpParams {
-    WeightDef filter = 1;
-    WeightDef bias = 2;
-    uint32 num_output = 3;
-    bool relu_flag = 12;
-};
-
-message FlattenOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 start_axis = 3;
-    int32 end_axis = 4;
-}
-
-message AddLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message AddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message SubOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message BiasAddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    WeightDef bias = 10;
-};
-
-message MatMulOpParams {
-    float alpha = 1;
-    float beta = 2;
-    bool transposeX = 3;
-    bool transposeW = 4;
-
-    WeightDef filter = 10;
-    WeightDef bias = 12;
-};
-
-message RsqrtOpParams {
-    float alpha = 1;
-    float beta = 2;
-};
-
-
-message WeightDef {
-    int32 format = 1;
-    int32 data_type = 2;
-    ShapeDef shape = 3;
-    bytes data = 4;
-    int64 data_offset = 5;
-    uint32 cmps_size = 6;
-    bytes cmps_tab = 7;
-    int64 cmps_tab_offset = 10;
-    CompressInfo cmps_info = 8;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
-}
-
-message ShapeDef {
-    repeated int64 dim = 1;
-}
-
-enum DeviceType {
-  NPU = 0;                   // In default, we will use NPU.
-  CPU = 1;                   // CPU
-}
-
-message AllOffsetQuantizeInfo {
-	float scale  = 1;
-	int32 offset = 2;
-} 
-
-message TensorDescriptor {
-    int32 format = 1;
-    int32 data_type = 2;
-    repeated int64 dim = 3;
-    uint32 size = 4;
-    bool reuse_input = 5;
-    bool output_tensor = 7;
-    DeviceType device_type = 8;
-    bool input_tensor = 9;
-    uint32 real_dim_cnt = 10;
-    uint32 reuse_input_index = 11;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
-}
-
-message CompressInfo {
-    int32 blockRow = 1;                             // block row
-    int32 blockCol = 2;                             // block col
-    int32 fractalK = 3;                             // fractal K
-    int32 fractalN = 4;                             // fractal N
-    int32 lastFractalK = 5;                         // K of last fractal
-    int32 lastFractalN = 6;                         // N of last fractal
-    int32 cubeSize = 7;                             // cube's length
-    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
-}
-
-message AttrDef {
-  message ListValue {
-    repeated string s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated uint32 u = 6 [packed = true];         // "list(uint)"
-    repeated bytes bt = 7;
-  }
-
-  oneof value {
-    string s = 2;                // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    uint32 u = 6;                // "uint32"
-    bytes bt = 7;
-    ListValue list = 1;          // any "list(...)"
-    NamedAttrs func = 10;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs {
-  string name = 1;
-  map<string, AttrDef> attr = 2;
-}
-

parser/common/proto/tensorflow/attr_value.proto

@@ -1,62 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "AttrValueProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensor.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing the value for an attr used to configure an Op.
-// Comment indicates the corresponding attr type.  Only the field matching the
-// attr type may be filled.
-message AttrValue {
-  // LINT.IfChange
-  message ListValue {
-    repeated bytes s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated DataType type = 6 [packed = true];  // "list(type)"
-    repeated TensorShapeProto shape = 7;         // "list(shape)"
-    repeated TensorProto tensor = 8;             // "list(tensor)"
-    repeated NameAttrList func = 9;              // "list(attr)"
-  }
-  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
-
-  oneof value {
-    bytes s = 2;                 // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    DataType type = 6;           // "type"
-    TensorShapeProto shape = 7;  // "shape"
-    TensorProto tensor = 8;      // "tensor"
-    ListValue list = 1;          // any "list(...)"
-
-    // "func" represents a function. func.name is a function's name or
-    // a primitive op's name. func.attr.first is the name of an attr
-    // defined for that function. func.attr.second is the value for
-    // that attr in the instantiation.
-    NameAttrList func = 10;
-
-    // This is a placeholder only used in nodes defined inside a
-    // function.  It indicates the attr value will be supplied when
-    // the function is instantiated.  For example, let us suppose a
-    // node "N" in function "FN". "N" has an attr "A" with value
-    // placeholder = "foo". When FN is instantiated with attr "foo"
-    // set to "bar", the instantiated node N's attr A will have been
-    // given the value "bar".
-    string placeholder = 9;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NameAttrList {
-  string name = 1;
-  map<string, AttrValue> attr = 2;
-}

parser/common/proto/tensorflow/function.proto

@@ -1,100 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "FunctionProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "node_def.proto";
-import "op_def.proto";
-
-// A library is a set of named functions.
-message FunctionDefLibrary {
-  repeated FunctionDef function = 1;
-  repeated GradientDef gradient = 2;
-}
-
-// A function can be instantiated when the runtime can bind every attr
-// with a value. When a GraphDef has a call to a function, it must
-// have binding for every attr defined in the signature.
-//   * device spec, etc.
-message FunctionDef {
-  // The definition of the function's name, arguments, return values,
-  // attrs etc.
-  OpDef signature = 1;
-
-  // Attributes specific to this function definition.
-  map<string, AttrValue> attr = 5;
-
-  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
-  reserved 2;
-
-  // In both of the following fields, there is the need to specify an
-  // output that is used as either the input to another node (in
-  // `node_def`) or as a return value of the function (in `ret`).
-  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
-  // list in some cases (instead of just single outputs).  Also, we
-  // need to be able to deal with lists of unknown length (so the
-  // output index may not be known at function definition time).  So
-  // we use the following format instead:
-  // * "fun_in" where "fun_in" is the name of a function input arg in
-  //   the `signature` field above.  This represents that input, whether
-  //   it is a single tensor or a list.
-  // * "fun_in:0" gives the first element of a function input arg (a
-  //   non-list input is considered a list of length 1 for these
-  //   purposes).
-  // * "node:out" where "node" is the name of a node in `node_def` and
-  //   "out" is the name one of its op's output arguments (the name
-  //   comes from the OpDef of the node's op). This represents that
-  //   node's output, whether it is a single tensor or a list.
-  //   Note: We enforce that an op's output arguments are never
-  //   renamed in the backwards-compatibility test.
-  // * "node:out:0" gives the first element of a node output arg (a
-  //   non-list output is considered a list of length 1 for these
-  //   purposes).
-  //
-  // NOT CURRENTLY SUPPORTED (but may be in the future):
-  // * "node:out:-1" gives last element in a node output list
-  // * "node:out:1:" gives a list with all but the first element in a
-  //   node output list
-  // * "node:out::-1" gives a list with all but the last element in a
-  //   node output list
-
-  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
-  // may have values of type `placeholder` and the `input` field uses
-  // the "output" format above.
-
-  // By convention, "op" in node_def is resolved by consulting with a
-  // user-defined library first. If not resolved, "func" is assumed to
-  // be a builtin op.
-  repeated NodeDef node_def = 3;
-
-  // A mapping from the output arg names from `signature` to the
-  // outputs from `node_def` that should be returned by the function.
-  map<string, string> ret = 4;
-}
-
-// GradientDef defines the gradient function of a function defined in
-// a function library.
-//
-// A gradient function g (specified by gradient_func) for a function f
-// (specified by function_name) must follow the following:
-//
-// The function 'f' must be a numerical function which takes N inputs
-// and produces M outputs. Its gradient function 'g', which is a
-// function taking N + M inputs and produces N outputs.
-//
-// I.e. if we have
-//    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
-// then, g is
-//    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
-//                                      dL/dy1, dL/dy2, ..., dL/dy_M),
-// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
-// loss function). dL/dx_i is the partial derivative of L with respect
-// to x_i.
-message GradientDef {
-  string function_name = 1;  // The function name.
-  string gradient_func = 2;  // The gradient function's name.
-}

parser/common/proto/tensorflow/graph.proto

@@ -1,56 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "GraphProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "node_def.proto";
-import "function.proto";
-import "versions.proto";
-
-// Represents the graph of operations
-message GraphDef {
-  repeated NodeDef node = 1;
-
-  // Compatibility versions of the graph.  See core/public/version.h for version
-  // history.  The GraphDef version is distinct from the TensorFlow version, and
-  // each release of TensorFlow will support a range of GraphDef versions.
-  VersionDef versions = 4;
-
-  // Deprecated single version field; use versions above instead.  Since all
-  // GraphDef changes before "versions" was introduced were forward
-  // compatible, this field is entirely ignored.
-  int32 version = 3 [deprecated = true];
-
-  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-  //
-  // "library" provides user-defined functions.
-  //
-  // Naming:
-  //   * library.function.name are in a flat namespace.
-  //     NOTE: We may need to change it to be hierarchical to support
-  //     different orgs. E.g.,
-  //     { "/google/nn", { ... }},
-  //     { "/google/vision", { ... }}
-  //     { "/org_foo/module_bar", { ... }}
-  //     map<string, FunctionDefLib> named_lib;
-  //   * If node[i].op is the name of one function in "library",
-  //     node[i] is deemed as a function call. Otherwise, node[i].op
-  //     must be a primitive operation supported by the runtime.
-  //
-  //
-  // Function call semantics:
-  //
-  //   * The callee may start execution as soon as some of its inputs
-  //     are ready. The caller may want to use Tuple() mechanism to
-  //     ensure all inputs are ready in the same time.
-  //
-  //   * The consumer of return values may start executing as soon as
-  //     the return values the consumer depends on are ready.  The
-  //     consumer may want to use Tuple() mechanism to ensure the
-  //     consumer does not start until all return values of the callee
-  //     function are ready.
-  FunctionDefLibrary library = 2;
-};

parser/common/proto/tensorflow/graph_library.proto

@@ -1,14 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-
-import "graph.proto";
-
-message GeGraphDef {
-  string name = 1;
-  GraphDef graph = 2;
-}
-
-message GraphDefLibrary {
-  repeated GeGraphDef graph_def = 1;
-};

parser/common/proto/tensorflow/node_def.proto

@@ -1,63 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "NodeProto";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-
-message NodeDef {
-  // The name given to this operator. Used for naming inputs,
-  // logging, visualization, etc.  Unique within a single GraphDef.
-  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-  string name = 1;
-
-  // The operation name.  There may be custom parameters in attrs.
-  // Op names starting with an underscore are reserved for internal use.
-  string op = 2;
-
-  // Each input is "node:src_output" with "node" being a string name and
-  // "src_output" indicating which output tensor to use from "node". If
-  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-  // may optionally be followed by control inputs that have the format
-  // "^node".
-  repeated string input = 3;
-
-  // A (possibly partial) specification for the device on which this
-  // node should be placed.
-  // The expected syntax for this string is as follows:
-  //
-  // DEVICE_SPEC ::= PARTIAL_SPEC
-  //
-  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-  // CONSTRAINT ::= ("job:" JOB_NAME)
-  //              | ("replica:" [1-9][0-9]*)
-  //              | ("task:" [1-9][0-9]*)
-  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
-  //
-  // Valid values for this string include:
-  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
-  // * "/job:worker/device:GPU:3"                   (partial specification)
-  // * ""                                    (no specification)
-  //
-  // If the constraints do not resolve to a single device (or if this
-  // field is empty or not present), the runtime will attempt to
-  // choose a device automatically.
-  string device = 4;
-
-  // Operation-specific graph-construction-time configuration.
-  // Note that this should include all attrs defined in the
-  // corresponding OpDef, including those with a value matching
-  // the default -- this allows the default to change and makes
-  // NodeDefs easier to interpret on their own.  However, if
-  // an attr with a default is not specified in this list, the
-  // default will be used.
-  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-  // one of the names from the corresponding OpDef's attr field).
-  // The values must have a type matching the corresponding OpDef
-  // attr's type field.
-  // Add some examples here showing best practices.
-  map<string, AttrValue> attr = 5;
-};

parser/common/proto/tensorflow/op_def.proto

@@ -1,164 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "OpDefProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "types.proto";
-
-// Defines an operation. A NodeDef in a GraphDef specifies an Op by
-// using the "op" field which should match the name of a OpDef.
-// LINT.IfChange
-message OpDef {
-  // Op names starting with an underscore are reserved for internal use.
-  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-  string name = 1;
-
-  // For describing inputs and outputs.
-  message ArgDef {
-    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-    string name = 1;
-
-    // Human readable description.
-    string description = 2;
-
-    // Describes the type of one or more tensors that are accepted/produced
-    // by this input/output arg.  The only legal combinations are:
-    // * For a single tensor: either the "type" field is set or the
-    //   "type_attr" field is set to the name of an attr with type "type".
-    // * For a sequence of tensors with the same type: the "number_attr"
-    //   field will be set to the name of an attr with type "int", and
-    //   either the "type" or "type_attr" field will be set as for
-    //   single tensors.
-    // * For a sequence of tensors, the "type_list_attr" field will be set
-    //   to the name of an attr with type "list(type)".
-    DataType type = 3;
-    string type_attr = 4;    // if specified, attr must have type "type"
-    string number_attr = 5;  // if specified, attr must have type "int"
-    // If specified, attr must have type "list(type)", and none of
-    // type, type_attr, and number_attr may be specified.
-    string type_list_attr = 6;
-
-    // For inputs: if true, the inputs are required to be refs.
-    //   By default, inputs can be either refs or non-refs.
-    // For outputs: if true, outputs are refs, otherwise they are not.
-    bool is_ref = 16;
-  };
-
-  // Description of the input(s).
-  repeated ArgDef input_arg = 2;
-
-  // Description of the output(s).
-  repeated ArgDef output_arg = 3;
-
-  // Description of the graph-construction-time configuration of this
-  // Op.  That is to say, this describes the attr fields that will
-  // be specified in the NodeDef.
-  message AttrDef {
-    // A descriptive name for the argument.  May be used, e.g. by the
-    // Python client, as a keyword argument name, and so should match
-    // the regexp "[a-z][a-z0-9_]+".
-    string name = 1;
-
-    // One of the type names from attr_value.proto ("string", "list(string)",
-    // "int", etc.).
-    string type = 2;
-
-    // A reasonable default for this attribute if the user does not supply
-    // a value.  If not specified, the user must supply a value.
-    AttrValue default_value = 3;
-
-    // Human-readable description.
-    string description = 4;
-
-
-    // --- Constraints ---
-    // These constraints are only in effect if specified.  Default is no
-    // constraints.
-
-    // For type == "int", this is a minimum value.  For "list(___)"
-    // types, this is the minimum length.
-    bool has_minimum = 5;
-    int64 minimum = 6;
-
-    // The set of allowed values.  Has type that is the "list" version
-    // of the "type" field above (uses the "list" field of AttrValue).
-    // If type == "type" or "list(type)" above, then the "type" field
-    // of "allowed_values.list" has the set of allowed DataTypes.
-    // If type == "string" or "list(string)", then the "s" field of
-    // "allowed_values.list" has the set of allowed strings.
-    AttrValue allowed_values = 7;
-  }
-  repeated AttrDef attr = 4;
-
-  // Optional deprecation based on GraphDef versions.
-  OpDeprecation deprecation = 8;
-
-  // One-line human-readable description of what the Op does.
-  string summary = 5;
-
-  // Additional, longer human-readable description of what the Op does.
-  string description = 6;
-
-  // -------------------------------------------------------------------------
-  // Which optimizations this operation can participate in.
-
-  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-  bool is_commutative = 18;
-
-  // If is_aggregate is true, then this operation accepts N >= 2
-  // inputs and produces 1 output all of the same type.  Should be
-  // associative and commutative, and produce output with the same
-  // shape as the input.  The optimizer may replace an aggregate op
-  // taking input from multiple devices with a tree of aggregate ops
-  // that aggregate locally within each device (and possibly within
-  // groups of nearby devices) before communicating.
-  bool is_aggregate = 16;  // for things like add
-
-  // Other optimizations go here, like
-  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.
-
-  // -------------------------------------------------------------------------
-  // Optimization constraints.
-
-  // Ops are marked as stateful if their behavior depends on some state beyond
-  // their input tensors (e.g. variable reading op) or if they have
-  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
-  // must always produce the same output for the same input and have
-  // no side-effects.
-  //
-  // By default Ops may be moved between devices.  Stateful ops should
-  // either not be moved, or should only be moved if that state can also
-  // be moved (e.g. via some sort of save / restore).
-  // Stateful ops are guaranteed to never be optimized away by Common
-  // Subexpression Elimination (CSE).
-  bool is_stateful = 17;  // for things like variables, queue
-
-  // -------------------------------------------------------------------------
-  // Non-standard options.
-
-  // By default, all inputs to an Op must be initialized Tensors.  Ops
-  // that may initialize tensors for the first time should set this
-  // field to true, to allow the Op to take an uninitialized Tensor as
-  // input.
-  bool allows_uninitialized_input = 19;  // for Assign, etc.
-};
-// LINT.ThenChange(
-//     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)
-
-// Information about version-dependent deprecation of an op
-message OpDeprecation {
-  // First GraphDef version at which the op is disallowed.
-  int32 version = 1;
-
-  // Explanation of why it was deprecated and what to use instead.
-  string explanation = 2;
-};
-
-// A collection of OpDefs
-message OpList {
-  repeated OpDef op = 1;
-};

parser/common/proto/tensorflow/resource_handle.proto

@@ -1,29 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "ResourceHandle";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Protocol buffer representing a handle to a tensorflow resource. Handles are
-// not valid across executions, but can be serialized back and forth from within
-// a single run.
-message ResourceHandleProto {
-  // Unique name for the device containing the resource.
-  string device = 1;
-
-  // Container in which this resource is placed.
-  string container = 2;
-
-  // Unique name of this resource.
-  string name = 3;
-
-  // Hash code for the type of the resource. Is only valid in the same device
-  // and in the same execution.
-  uint64 hash_code = 4;
-
-  // For debug-only, the name of the type pointed to by this handle, if
-  // available.
-  string maybe_type_name = 5;
-};

parser/common/proto/tensorflow/tensor.proto

@@ -1,94 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "resource_handle.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing a tensor.
-message TensorProto {
-  DataType dtype = 1;
-
-  // Shape of the tensor.
-  TensorShapeProto tensor_shape = 2;
-
-  // Only one of the representations below is set, one of "tensor_contents" and
-  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
-  // contain repeated fields it would require another extra set of messages.
-
-  // Version number.
-  //
-  // In version 0, if the "repeated xxx" representations contain only one
-  // element, that element is repeated to fill the shape.  This makes it easy
-  // to represent a constant Tensor with a single value.
-  int32 version_number = 3;
-
-  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
-  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
-  // can be used for all tensor types. The purpose of this representation is to
-  // reduce serialization overhead during RPC call by avoiding serialization of
-  // many repeated small items.
-  bytes tensor_content = 4;
-
-  // Type specific representations that make it easy to create tensor protos in
-  // all languages.  Only the representation corresponding to "dtype" can
-  // be set.  The values hold the flattened representation of the tensor in
-  // row major order.
-
-  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
-  // have some pointless zero padding for each value here.
-  repeated int32 half_val = 13 [packed = true];
-
-  // DT_FLOAT.
-  repeated float float_val = 5 [packed = true];
-
-  // DT_DOUBLE.
-  repeated double double_val = 6 [packed = true];
-
-  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
-  repeated int32 int_val = 7 [packed = true];
-
-  // DT_STRING
-  repeated bytes string_val = 8;
-
-  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
-  // and imaginary parts of i-th single precision complex.
-  repeated float scomplex_val = 9 [packed = true];
-
-  // DT_INT64
-  repeated int64 int64_val = 10 [packed = true];
-
-  // DT_BOOL
-  repeated bool bool_val = 11 [packed = true];
-
-  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
-  // and imaginary parts of i-th double precision complex.
-  repeated double dcomplex_val = 12 [packed = true];
-
-  // DT_RESOURCE
-  repeated ResourceHandleProto resource_handle_val = 14;
-
-  // DT_VARIANT
-  repeated VariantTensorDataProto variant_val = 15;
-
-  // DT_UINT32
-  repeated uint32 uint32_val = 16 [packed = true];
-
-  // DT_UINT64
-  repeated uint64 uint64_val = 17 [packed = true];
-};
-
-// Protocol buffer representing the serialization format of DT_VARIANT tensors.
-message VariantTensorDataProto {
-  // Name of the type of objects being serialized.
-  string type_name = 1;
-  // Portions of the object that are not Tensors.
-  bytes metadata = 2;
-  // Tensors contained within objects being serialized.
-  repeated TensorProto tensors = 3;
-}

parser/common/proto/tensorflow/tensor_shape.proto

@@ -1,45 +0,0 @@
-// Protocol buffer representing the shape of tensors.
-
-syntax = "proto3";
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorShapeProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-package domi.tensorflow;
-
-// Dimensions of a tensor.
-message TensorShapeProto {
-  // One dimension of the tensor.
-  message Dim {
-    // Size of the tensor in that dimension.
-    // This value must be >= -1, but values of -1 are reserved for "unknown"
-    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
-    // that work with TensorShapeProto may fail at runtime when deserializing
-    // a TensorShapeProto containing a dim value of -1.
-    int64 size = 1;
-
-    // Optional name of the tensor dimension.
-    string name = 2;
-  };
-
-  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
-  // for a 30 x 40 2D tensor.  If an entry has size -1, this
-  // corresponds to a dimension of unknown size. The names are
-  // optional.
-  //
-  // The order of entries in "dim" matters: It indicates the layout of the
-  // values in the tensor in-memory representation.
-  //
-  // The first entry in "dim" is the outermost dimension used to layout the
-  // values, the last entry is the innermost dimension.  This matches the
-  // in-memory layout of RowMajor Eigen tensors.
-  //
-  // If "dim.size()" > 0, "unknown_rank" must be false.
-  repeated Dim dim = 2;
-
-  // If true, the number of dimensions in the shape is unknown.
-  //
-  // If true, "dim.size()" must be 0.
-  bool unknown_rank = 3;
-};

parser/common/proto/tensorflow/types.proto

@@ -1,74 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TypesProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// LINT.IfChange
-enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0;
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1;
-  DT_DOUBLE = 2;
-  DT_INT32 = 3;
-  DT_UINT8 = 4;
-  DT_INT16 = 5;
-  DT_INT8 = 6;
-  DT_STRING = 7;
-  DT_COMPLEX64 = 8;  // Single-precision complex
-  DT_INT64 = 9;
-  DT_BOOL = 10;
-  DT_QINT8 = 11;     // Quantized int8
-  DT_QUINT8 = 12;    // Quantized uint8
-  DT_QINT32 = 13;    // Quantized int32
-  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
-  DT_QINT16 = 15;    // Quantized int16
-  DT_QUINT16 = 16;   // Quantized uint16
-  DT_UINT16 = 17;
-  DT_COMPLEX128 = 18;  // Double-precision complex
-  DT_HALF = 19;
-  DT_RESOURCE = 20;
-  DT_VARIANT = 21;  // Arbitrary C++ data types
-  DT_UINT32 = 22;
-  DT_UINT64 = 23;
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101;
-  DT_DOUBLE_REF = 102;
-  DT_INT32_REF = 103;
-  DT_UINT8_REF = 104;
-  DT_INT16_REF = 105;
-  DT_INT8_REF = 106;
-  DT_STRING_REF = 107;
-  DT_COMPLEX64_REF = 108;
-  DT_INT64_REF = 109;
-  DT_BOOL_REF = 110;
-  DT_QINT8_REF = 111;
-  DT_QUINT8_REF = 112;
-  DT_QINT32_REF = 113;
-  DT_BFLOAT16_REF = 114;
-  DT_QINT16_REF = 115;
-  DT_QUINT16_REF = 116;
-  DT_UINT16_REF = 117;
-  DT_COMPLEX128_REF = 118;
-  DT_HALF_REF = 119;
-  DT_RESOURCE_REF = 120;
-  DT_VARIANT_REF = 121;
-  DT_UINT32_REF = 122;
-  DT_UINT64_REF = 123;
-}
-// LINT.ThenChange(
-//    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
-//    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)

parser/common/proto/tensorflow/versions.proto

@@ -1,31 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "VersionsProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Version information for a piece of serialized data
-//
-// There are different types of versions for each type of data
-// (GraphDef, etc.), but they all have the same common shape
-// described here.
-//
-// Each consumer has "consumer" and "min_producer" versions (specified
-// elsewhere).  A consumer is allowed to consume this data if
-//
-//   producer >= min_producer
-//   consumer >= min_consumer
-//   consumer not in bad_consumers
-//
-message VersionDef {
-  // The version of the code that produced this data.
-  int32 producer = 1;
-
-  // Any consumer below this version is not allowed to consume this data.
-  int32 min_consumer = 2;
-
-  // Specific consumer versions which are disallowed (e.g. due to bugs).
-  repeated int32 bad_consumers = 3;
-};

parser/func_to_graph/proto/attr_value.proto

@@ -1,62 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "AttrValueProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensor.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing the value for an attr used to configure an Op.
-// Comment indicates the corresponding attr type.  Only the field matching the
-// attr type may be filled.
-message AttrValue {
-  // LINT.IfChange
-  message ListValue {
-    repeated bytes s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated DataType type = 6 [packed = true];  // "list(type)"
-    repeated TensorShapeProto shape = 7;         // "list(shape)"
-    repeated TensorProto tensor = 8;             // "list(tensor)"
-    repeated NameAttrList func = 9;              // "list(attr)"
-  }
-  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
-
-  oneof value {
-    bytes s = 2;                 // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    DataType type = 6;           // "type"
-    TensorShapeProto shape = 7;  // "shape"
-    TensorProto tensor = 8;      // "tensor"
-    ListValue list = 1;          // any "list(...)"
-
-    // "func" represents a function. func.name is a function's name or
-    // a primitive op's name. func.attr.first is the name of an attr
-    // defined for that function. func.attr.second is the value for
-    // that attr in the instantiation.
-    NameAttrList func = 10;
-
-    // This is a placeholder only used in nodes defined inside a
-    // function.  It indicates the attr value will be supplied when
-    // the function is instantiated.  For example, let us suppose a
-    // node "N" in function "FN". "N" has an attr "A" with value
-    // placeholder = "foo". When FN is instantiated with attr "foo"
-    // set to "bar", the instantiated node N's attr A will have been
-    // given the value "bar".
-    string placeholder = 9;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NameAttrList {
-  string name = 1;
-  map<string, AttrValue> attr = 2;
-}

parser/func_to_graph/proto/function.proto

@@ -1,100 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "FunctionProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "node_def.proto";
-import "op_def.proto";
-
-// A library is a set of named functions.
-message FunctionDefLibrary {
-  repeated FunctionDef function = 1;
-  repeated GradientDef gradient = 2;
-}
-
-// A function can be instantiated when the runtime can bind every attr
-// with a value. When a GraphDef has a call to a function, it must
-// have binding for every attr defined in the signature.
-//   * device spec, etc.
-message FunctionDef {
-  // The definition of the function's name, arguments, return values,
-  // attrs etc.
-  OpDef signature = 1;
-
-  // Attributes specific to this function definition.
-  map<string, AttrValue> attr = 5;
-
-  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
-  reserved 2;
-
-  // In both of the following fields, there is the need to specify an
-  // output that is used as either the input to another node (in
-  // `node_def`) or as a return value of the function (in `ret`).
-  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
-  // list in some cases (instead of just single outputs).  Also, we
-  // need to be able to deal with lists of unknown length (so the
-  // output index may not be known at function definition time).  So
-  // we use the following format instead:
-  // * "fun_in" where "fun_in" is the name of a function input arg in
-  //   the `signature` field above.  This represents that input, whether
-  //   it is a single tensor or a list.
-  // * "fun_in:0" gives the first element of a function input arg (a
-  //   non-list input is considered a list of length 1 for these
-  //   purposes).
-  // * "node:out" where "node" is the name of a node in `node_def` and
-  //   "out" is the name one of its op's output arguments (the name
-  //   comes from the OpDef of the node's op). This represents that
-  //   node's output, whether it is a single tensor or a list.
-  //   Note: We enforce that an op's output arguments are never
-  //   renamed in the backwards-compatibility test.
-  // * "node:out:0" gives the first element of a node output arg (a
-  //   non-list output is considered a list of length 1 for these
-  //   purposes).
-  //
-  // NOT CURRENTLY SUPPORTED (but may be in the future):
-  // * "node:out:-1" gives last element in a node output list
-  // * "node:out:1:" gives a list with all but the first element in a
-  //   node output list
-  // * "node:out::-1" gives a list with all but the last element in a
-  //   node output list
-
-  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
-  // may have values of type `placeholder` and the `input` field uses
-  // the "output" format above.
-
-  // By convention, "op" in node_def is resolved by consulting with a
-  // user-defined library first. If not resolved, "func" is assumed to
-  // be a builtin op.
-  repeated NodeDef node_def = 3;
-
-  // A mapping from the output arg names from `signature` to the
-  // outputs from `node_def` that should be returned by the function.
-  map<string, string> ret = 4;
-}
-
-// GradientDef defines the gradient function of a function defined in
-// a function library.
-//
-// A gradient function g (specified by gradient_func) for a function f
-// (specified by function_name) must follow the following:
-//
-// The function 'f' must be a numerical function which takes N inputs
-// and produces M outputs. Its gradient function 'g', which is a
-// function taking N + M inputs and produces N outputs.
-//
-// I.e. if we have
-//    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
-// then, g is
-//    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
-//                                      dL/dy1, dL/dy2, ..., dL/dy_M),
-// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
-// loss function). dL/dx_i is the partial derivative of L with respect
-// to x_i.
-message GradientDef {
-  string function_name = 1;  // The function name.
-  string gradient_func = 2;  // The gradient function's name.
-}

parser/func_to_graph/proto/graph.proto

@@ -1,56 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "GraphProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "node_def.proto";
-import "function.proto";
-import "versions.proto";
-
-// Represents the graph of operations
-message GraphDef {
-  repeated NodeDef node = 1;
-
-  // Compatibility versions of the graph.  See core/public/version.h for version
-  // history.  The GraphDef version is distinct from the TensorFlow version, and
-  // each release of TensorFlow will support a range of GraphDef versions.
-  VersionDef versions = 4;
-
-  // Deprecated single version field; use versions above instead.  Since all
-  // GraphDef changes before "versions" was introduced were forward
-  // compatible, this field is entirely ignored.
-  int32 version = 3 [deprecated = true];
-
-  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-  //
-  // "library" provides user-defined functions.
-  //
-  // Naming:
-  //   * library.function.name are in a flat namespace.
-  //     NOTE: We may need to change it to be hierarchical to support
-  //     different orgs. E.g.,
-  //     { "/google/nn", { ... }},
-  //     { "/google/vision", { ... }}
-  //     { "/org_foo/module_bar", { ... }}
-  //     map<string, FunctionDefLib> named_lib;
-  //   * If node[i].op is the name of one function in "library",
-  //     node[i] is deemed as a function call. Otherwise, node[i].op
-  //     must be a primitive operation supported by the runtime.
-  //
-  //
-  // Function call semantics:
-  //
-  //   * The callee may start execution as soon as some of its inputs
-  //     are ready. The caller may want to use Tuple() mechanism to
-  //     ensure all inputs are ready in the same time.
-  //
-  //   * The consumer of return values may start executing as soon as
-  //     the return values the consumer depends on are ready.  The
-  //     consumer may want to use Tuple() mechanism to ensure the
-  //     consumer does not start until all return values of the callee
-  //     function are ready.
-  FunctionDefLibrary library = 2;
-};

parser/func_to_graph/proto/graph_library.proto

@@ -1,14 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-
-import "graph.proto";
-
-message GeGraphDef {
-  string name = 1;
-  GraphDef graph = 2;
-}
-
-message GraphDefLibrary {
-  repeated GeGraphDef graph_def = 1;
-};

parser/func_to_graph/proto/node_def.proto

@@ -1,63 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "NodeProto";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-
-message NodeDef {
-  // The name given to this operator. Used for naming inputs,
-  // logging, visualization, etc.  Unique within a single GraphDef.
-  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-  string name = 1;
-
-  // The operation name.  There may be custom parameters in attrs.
-  // Op names starting with an underscore are reserved for internal use.
-  string op = 2;
-
-  // Each input is "node:src_output" with "node" being a string name and
-  // "src_output" indicating which output tensor to use from "node". If
-  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-  // may optionally be followed by control inputs that have the format
-  // "^node".
-  repeated string input = 3;
-
-  // A (possibly partial) specification for the device on which this
-  // node should be placed.
-  // The expected syntax for this string is as follows:
-  //
-  // DEVICE_SPEC ::= PARTIAL_SPEC
-  //
-  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-  // CONSTRAINT ::= ("job:" JOB_NAME)
-  //              | ("replica:" [1-9][0-9]*)
-  //              | ("task:" [1-9][0-9]*)
-  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
-  //
-  // Valid values for this string include:
-  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
-  // * "/job:worker/device:GPU:3"                   (partial specification)
-  // * ""                                    (no specification)
-  //
-  // If the constraints do not resolve to a single device (or if this
-  // field is empty or not present), the runtime will attempt to
-  // choose a device automatically.
-  string device = 4;
-
-  // Operation-specific graph-construction-time configuration.
-  // Note that this should include all attrs defined in the
-  // corresponding OpDef, including those with a value matching
-  // the default -- this allows the default to change and makes
-  // NodeDefs easier to interpret on their own.  However, if
-  // an attr with a default is not specified in this list, the
-  // default will be used.
-  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-  // one of the names from the corresponding OpDef's attr field).
-  // The values must have a type matching the corresponding OpDef
-  // attr's type field.
-  // Add some examples here showing best practices.
-  map<string, AttrValue> attr = 5;
-};

parser/func_to_graph/proto/op_def.proto

@@ -1,164 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "OpDefProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "types.proto";
-
-// Defines an operation. A NodeDef in a GraphDef specifies an Op by
-// using the "op" field which should match the name of a OpDef.
-// LINT.IfChange
-message OpDef {
-  // Op names starting with an underscore are reserved for internal use.
-  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-  string name = 1;
-
-  // For describing inputs and outputs.
-  message ArgDef {
-    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-    string name = 1;
-
-    // Human readable description.
-    string description = 2;
-
-    // Describes the type of one or more tensors that are accepted/produced
-    // by this input/output arg.  The only legal combinations are:
-    // * For a single tensor: either the "type" field is set or the
-    //   "type_attr" field is set to the name of an attr with type "type".
-    // * For a sequence of tensors with the same type: the "number_attr"
-    //   field will be set to the name of an attr with type "int", and
-    //   either the "type" or "type_attr" field will be set as for
-    //   single tensors.
-    // * For a sequence of tensors, the "type_list_attr" field will be set
-    //   to the name of an attr with type "list(type)".
-    DataType type = 3;
-    string type_attr = 4;    // if specified, attr must have type "type"
-    string number_attr = 5;  // if specified, attr must have type "int"
-    // If specified, attr must have type "list(type)", and none of
-    // type, type_attr, and number_attr may be specified.
-    string type_list_attr = 6;
-
-    // For inputs: if true, the inputs are required to be refs.
-    //   By default, inputs can be either refs or non-refs.
-    // For outputs: if true, outputs are refs, otherwise they are not.
-    bool is_ref = 16;
-  };
-
-  // Description of the input(s).
-  repeated ArgDef input_arg = 2;
-
-  // Description of the output(s).
-  repeated ArgDef output_arg = 3;
-
-  // Description of the graph-construction-time configuration of this
-  // Op.  That is to say, this describes the attr fields that will
-  // be specified in the NodeDef.
-  message AttrDef {
-    // A descriptive name for the argument.  May be used, e.g. by the
-    // Python client, as a keyword argument name, and so should match
-    // the regexp "[a-z][a-z0-9_]+".
-    string name = 1;
-
-    // One of the type names from attr_value.proto ("string", "list(string)",
-    // "int", etc.).
-    string type = 2;
-
-    // A reasonable default for this attribute if the user does not supply
-    // a value.  If not specified, the user must supply a value.
-    AttrValue default_value = 3;
-
-    // Human-readable description.
-    string description = 4;
-
-
-    // --- Constraints ---
-    // These constraints are only in effect if specified.  Default is no
-    // constraints.
-
-    // For type == "int", this is a minimum value.  For "list(___)"
-    // types, this is the minimum length.
-    bool has_minimum = 5;
-    int64 minimum = 6;
-
-    // The set of allowed values.  Has type that is the "list" version
-    // of the "type" field above (uses the "list" field of AttrValue).
-    // If type == "type" or "list(type)" above, then the "type" field
-    // of "allowed_values.list" has the set of allowed DataTypes.
-    // If type == "string" or "list(string)", then the "s" field of
-    // "allowed_values.list" has the set of allowed strings.
-    AttrValue allowed_values = 7;
-  }
-  repeated AttrDef attr = 4;
-
-  // Optional deprecation based on GraphDef versions.
-  OpDeprecation deprecation = 8;
-
-  // One-line human-readable description of what the Op does.
-  string summary = 5;
-
-  // Additional, longer human-readable description of what the Op does.
-  string description = 6;
-
-  // -------------------------------------------------------------------------
-  // Which optimizations this operation can participate in.
-
-  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-  bool is_commutative = 18;
-
-  // If is_aggregate is true, then this operation accepts N >= 2
-  // inputs and produces 1 output all of the same type.  Should be
-  // associative and commutative, and produce output with the same
-  // shape as the input.  The optimizer may replace an aggregate op
-  // taking input from multiple devices with a tree of aggregate ops
-  // that aggregate locally within each device (and possibly within
-  // groups of nearby devices) before communicating.
-  bool is_aggregate = 16;  // for things like add
-
-  // Other optimizations go here, like
-  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.
-
-  // -------------------------------------------------------------------------
-  // Optimization constraints.
-
-  // Ops are marked as stateful if their behavior depends on some state beyond
-  // their input tensors (e.g. variable reading op) or if they have
-  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
-  // must always produce the same output for the same input and have
-  // no side-effects.
-  //
-  // By default Ops may be moved between devices.  Stateful ops should
-  // either not be moved, or should only be moved if that state can also
-  // be moved (e.g. via some sort of save / restore).
-  // Stateful ops are guaranteed to never be optimized away by Common
-  // Subexpression Elimination (CSE).
-  bool is_stateful = 17;  // for things like variables, queue
-
-  // -------------------------------------------------------------------------
-  // Non-standard options.
-
-  // By default, all inputs to an Op must be initialized Tensors.  Ops
-  // that may initialize tensors for the first time should set this
-  // field to true, to allow the Op to take an uninitialized Tensor as
-  // input.
-  bool allows_uninitialized_input = 19;  // for Assign, etc.
-};
-// LINT.ThenChange(
-//     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)
-
-// Information about version-dependent deprecation of an op
-message OpDeprecation {
-  // First GraphDef version at which the op is disallowed.
-  int32 version = 1;
-
-  // Explanation of why it was deprecated and what to use instead.
-  string explanation = 2;
-};
-
-// A collection of OpDefs
-message OpList {
-  repeated OpDef op = 1;
-};

parser/func_to_graph/proto/resource_handle.proto

@@ -1,29 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "ResourceHandle";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Protocol buffer representing a handle to a tensorflow resource. Handles are
-// not valid across executions, but can be serialized back and forth from within
-// a single run.
-message ResourceHandleProto {
-  // Unique name for the device containing the resource.
-  string device = 1;
-
-  // Container in which this resource is placed.
-  string container = 2;
-
-  // Unique name of this resource.
-  string name = 3;
-
-  // Hash code for the type of the resource. Is only valid in the same device
-  // and in the same execution.
-  uint64 hash_code = 4;
-
-  // For debug-only, the name of the type pointed to by this handle, if
-  // available.
-  string maybe_type_name = 5;
-};

parser/func_to_graph/proto/tensor.proto

@@ -1,94 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "resource_handle.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing a tensor.
-message TensorProto {
-  DataType dtype = 1;
-
-  // Shape of the tensor.
-  TensorShapeProto tensor_shape = 2;
-
-  // Only one of the representations below is set, one of "tensor_contents" and
-  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
-  // contain repeated fields it would require another extra set of messages.
-
-  // Version number.
-  //
-  // In version 0, if the "repeated xxx" representations contain only one
-  // element, that element is repeated to fill the shape.  This makes it easy
-  // to represent a constant Tensor with a single value.
-  int32 version_number = 3;
-
-  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
-  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
-  // can be used for all tensor types. The purpose of this representation is to
-  // reduce serialization overhead during RPC call by avoiding serialization of
-  // many repeated small items.
-  bytes tensor_content = 4;
-
-  // Type specific representations that make it easy to create tensor protos in
-  // all languages.  Only the representation corresponding to "dtype" can
-  // be set.  The values hold the flattened representation of the tensor in
-  // row major order.
-
-  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
-  // have some pointless zero padding for each value here.
-  repeated int32 half_val = 13 [packed = true];
-
-  // DT_FLOAT.
-  repeated float float_val = 5 [packed = true];
-
-  // DT_DOUBLE.
-  repeated double double_val = 6 [packed = true];
-
-  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
-  repeated int32 int_val = 7 [packed = true];
-
-  // DT_STRING
-  repeated bytes string_val = 8;
-
-  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
-  // and imaginary parts of i-th single precision complex.
-  repeated float scomplex_val = 9 [packed = true];
-
-  // DT_INT64
-  repeated int64 int64_val = 10 [packed = true];
-
-  // DT_BOOL
-  repeated bool bool_val = 11 [packed = true];
-
-  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
-  // and imaginary parts of i-th double precision complex.
-  repeated double dcomplex_val = 12 [packed = true];
-
-  // DT_RESOURCE
-  repeated ResourceHandleProto resource_handle_val = 14;
-
-  // DT_VARIANT
-  repeated VariantTensorDataProto variant_val = 15;
-
-  // DT_UINT32
-  repeated uint32 uint32_val = 16 [packed = true];
-
-  // DT_UINT64
-  repeated uint64 uint64_val = 17 [packed = true];
-};
-
-// Protocol buffer representing the serialization format of DT_VARIANT tensors.
-message VariantTensorDataProto {
-  // Name of the type of objects being serialized.
-  string type_name = 1;
-  // Portions of the object that are not Tensors.
-  bytes metadata = 2;
-  // Tensors contained within objects being serialized.
-  repeated TensorProto tensors = 3;
-}

parser/func_to_graph/proto/tensor_shape.proto

@@ -1,45 +0,0 @@
-// Protocol buffer representing the shape of tensors.
-
-syntax = "proto3";
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorShapeProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-package domi.tensorflow;
-
-// Dimensions of a tensor.
-message TensorShapeProto {
-  // One dimension of the tensor.
-  message Dim {
-    // Size of the tensor in that dimension.
-    // This value must be >= -1, but values of -1 are reserved for "unknown"
-    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
-    // that work with TensorShapeProto may fail at runtime when deserializing
-    // a TensorShapeProto containing a dim value of -1.
-    int64 size = 1;
-
-    // Optional name of the tensor dimension.
-    string name = 2;
-  };
-
-  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
-  // for a 30 x 40 2D tensor.  If an entry has size -1, this
-  // corresponds to a dimension of unknown size. The names are
-  // optional.
-  //
-  // The order of entries in "dim" matters: It indicates the layout of the
-  // values in the tensor in-memory representation.
-  //
-  // The first entry in "dim" is the outermost dimension used to layout the
-  // values, the last entry is the innermost dimension.  This matches the
-  // in-memory layout of RowMajor Eigen tensors.
-  //
-  // If "dim.size()" > 0, "unknown_rank" must be false.
-  repeated Dim dim = 2;
-
-  // If true, the number of dimensions in the shape is unknown.
-  //
-  // If true, "dim.size()" must be 0.
-  bool unknown_rank = 3;
-};

parser/func_to_graph/proto/types.proto

@@ -1,74 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TypesProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// LINT.IfChange
-enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0;
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1;
-  DT_DOUBLE = 2;
-  DT_INT32 = 3;
-  DT_UINT8 = 4;
-  DT_INT16 = 5;
-  DT_INT8 = 6;
-  DT_STRING = 7;
-  DT_COMPLEX64 = 8;  // Single-precision complex
-  DT_INT64 = 9;
-  DT_BOOL = 10;
-  DT_QINT8 = 11;     // Quantized int8
-  DT_QUINT8 = 12;    // Quantized uint8
-  DT_QINT32 = 13;    // Quantized int32
-  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
-  DT_QINT16 = 15;    // Quantized int16
-  DT_QUINT16 = 16;   // Quantized uint16
-  DT_UINT16 = 17;
-  DT_COMPLEX128 = 18;  // Double-precision complex
-  DT_HALF = 19;
-  DT_RESOURCE = 20;
-  DT_VARIANT = 21;  // Arbitrary C++ data types
-  DT_UINT32 = 22;
-  DT_UINT64 = 23;
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101;
-  DT_DOUBLE_REF = 102;
-  DT_INT32_REF = 103;
-  DT_UINT8_REF = 104;
-  DT_INT16_REF = 105;
-  DT_INT8_REF = 106;
-  DT_STRING_REF = 107;
-  DT_COMPLEX64_REF = 108;
-  DT_INT64_REF = 109;
-  DT_BOOL_REF = 110;
-  DT_QINT8_REF = 111;
-  DT_QUINT8_REF = 112;
-  DT_QINT32_REF = 113;
-  DT_BFLOAT16_REF = 114;
-  DT_QINT16_REF = 115;
-  DT_QUINT16_REF = 116;
-  DT_UINT16_REF = 117;
-  DT_COMPLEX128_REF = 118;
-  DT_HALF_REF = 119;
-  DT_RESOURCE_REF = 120;
-  DT_VARIANT_REF = 121;
-  DT_UINT32_REF = 122;
-  DT_UINT64_REF = 123;
-}
-// LINT.ThenChange(
-//    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
-//    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)

parser/func_to_graph/proto/versions.proto

@@ -1,31 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "VersionsProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Version information for a piece of serialized data
-//
-// There are different types of versions for each type of data
-// (GraphDef, etc.), but they all have the same common shape
-// described here.
-//
-// Each consumer has "consumer" and "min_producer" versions (specified
-// elsewhere).  A consumer is allowed to consume this data if
-//
-//   producer >= min_producer
-//   consumer >= min_consumer
-//   consumer not in bad_consumers
-//
-message VersionDef {
-  // The version of the code that produced this data.
-  int32 producer = 1;
-
-  // Any consumer below this version is not allowed to consume this data.
-  int32 min_consumer = 2;
-
-  // Specific consumer versions which are disallowed (e.g. due to bugs).
-  repeated int32 bad_consumers = 3;
-};

parser/onnx/proto/om.proto

@@ -1,396 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-enum TargetType
-{
-    MINI = 0;
-    TINY = 1;
-    LITE = 2;
-}
-
-// offline model
-message ModelDef {
-    string name = 1;
-    uint32 version = 2;
-
-    uint64 memory_size = 10;
-    uint32 stream_num = 11;
-    uint32 event_num = 12;
-    uint64 weight_size = 13;
-    uint32 label_num = 15;
-    repeated OpDef op = 20;
-    TargetType target_type = 23;
-
-    map<string, AttrDef> attr = 30;
-};
-
-// operator define
-message OpDef {
-    string name = 1;
-    string type = 2;
-
-    uint32 id = 3;
-    uint32 stream_id = 4;
-
-    repeated string input_name = 5;
-
-    repeated string src_name = 8;
-    repeated int32 src_index = 9;
-    repeated int64 input = 10;
-    repeated int64 output = 11;
-    repeated TensorDescriptor input_desc = 12;
-    repeated TensorDescriptor output_desc = 13;
-    repeated WeightDef weights = 14;
-    repeated string dst_name = 15;
-    repeated int32 dst_index = 16;
-
-    repeated int64 workspace = 20;
-    repeated uint32 workspace_bytes = 21;
-
-    repeated string weight_name = 22;
-    repeated bool is_input_const = 23;
-
-    map<string, AttrDef> attr = 30;
-
-    QuantizeFactorParams quantize_factor = 31;
-
-    oneof op_params {
-        // start at 100 here
-        SendOpParams sender_param = 100;
-        RecvOpParams receiver_param = 200;
-        ConvolutionOpParams convolution_param = 300;
-        PoolingOpParams pooling_param = 400;
-        EltwiseOpParams eltwise_param = 500;
-        BatchNormOpParams batchnorm_param = 600;
-        ScaleOpParams scale_param = 700;
-        FullConnectionOpParams full_connection_param = 800;
-        SoftmaxOpParams softmax_param = 900;
-        ActivationOpParams activation_param = 1000;
-        ReshapeOpParams reshape_param = 1100;
-    }
-};
-
-message SendOpParams {
-    uint32 event_id = 1;
-};
-
-message RecvOpParams {
-    uint32 event_id = 1;
-};
-
-enum QuantizeScaleType
-{
-    VECTOR_SCALE = 0;
-    SCALAR_SCALE = 1;
-}
-
-enum QuantizeScaleMode
-{
-    NORMAL_MODE = 0;
-    SQRT_MODE = 1;
-}
-
-enum QuantizeAlgorithm
-{
-    NON_OFFSET_ALGO = 0;
-    HALF_OFFSET_ALGO = 1;
-    ALL_OFFSET_ALGO = 2;
-}
-message QuantizeFactor
-{
-    QuantizeScaleMode scale_mode = 1;
-    bytes scale_value = 2;
-    int64 scale_offset = 3;
-    bytes offset_data_value = 4;
-    int64 offset_data_offset = 5;
-    bytes offset_weight_value = 6;
-    int64 offset_weight_offset = 7;
-    bytes offset_pad_value = 8;
-    int64 offset_pad_offset = 9;
-};
-
-message QuantizeCalcFactor
-{
-    bytes offsetw = 1;
-    int64 offsetw_offset = 2;
-    bytes offsetd = 3;
-    int64 offsetd_offset = 4;
-    bytes scalereq = 5;
-    int64 scaledreq_offset = 6;
-    bytes offsetdnext = 7;
-    int64 offsetdnext_offset = 8;
-}
-
-message QuantizeFactorParams
-{
-    QuantizeAlgorithm quantize_algo = 1;
-    QuantizeScaleType scale_type = 2;
-    QuantizeFactor quantize_param = 3;
-    QuantizeFactor dequantize_param = 4;
-    QuantizeFactor requantize_param = 5;
-    QuantizeCalcFactor quantizecalc_param = 6;
-};
-
-message ConvolutionOpParams {
-    int32 mode = 1;
-    int32 algo = 2;
-    int32 pad_mode = 3;
-    uint32 group = 4;
-    uint32 num_output = 5;
-
-    repeated uint32 pad = 10;
-    repeated uint32 stride = 11;
-    repeated uint32 dilation = 12;
-    repeated uint32 kernel = 13;
-
-    float alpha = 20;
-    float beta = 21;
-
-    WeightDef filter = 40;
-    WeightDef bias = 41;
-
-    bool relu_flag = 62;
-    repeated uint32 adj = 70;
-    repeated uint32 target_shape = 71;
-    repeated uint32 before_pad = 72;
-};
-
-message PoolingOpParams {
-    int32 mode = 1;
-    int32 nan_opt = 2;
-    int32 pad_mode = 3;
-    bool global_pooling = 4;
-
-    repeated uint32 window = 10;
-    repeated uint32 pad = 11;
-    repeated uint32 stride = 12;
-    bool ceil_mode = 13;
-    int32 data_mode  = 14;
-
-    float alpha = 20;
-    float beta = 21;
-    repeated uint32 before_pad = 22;
-};
-
-message EltwiseOpParams {
-    int32 mode = 1;
-    repeated float coeff = 2;
-    float alpha = 3;
-    float beta = 4;
-    repeated WeightDef weight = 5;
-    bool relu_flag = 6;
-};
-
-message ActivationOpParams {
-    int32 mode = 1;
-    float coef = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message BatchNormOpParams {
-    int32 mode = 1;
-
-    float alpha = 2;
-    float beta = 3;
-    double epsilon = 4;//optinal,[default = 1e-5]
-    bool use_global_stats = 5; //optinal,by default true,testing mode
-    float  moving_average_fraction = 6; //optinal,[default = .999];
-
-    WeightDef estimated_mean = 7;
-    WeightDef estimated_variance = 8;
-
-    WeightDef scale = 9;
-    WeightDef bias = 10;
-};
-
-message ScaleOpParams {
-    WeightDef scale = 1;
-    WeightDef bias = 2;
-};
-
-message ReshapeOpParams {
-    float alpha = 1;
-    float beta = 2;
-    ShapeDef shape = 3;
-    int32 axis = 4;
-    int32 num_axes = 5;
-    int32 format = 6;
-};
-
-message SoftmaxOpParams {
-    int32 algo = 1;
-    int32 mode = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message FullConnectionOpParams {
-    WeightDef filter = 1;
-    WeightDef bias = 2;
-    uint32 num_output = 3;
-    bool relu_flag = 12;
-};
-
-message FlattenOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 start_axis = 3;
-    int32 end_axis = 4;
-}
-
-message AddLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message AddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message SubOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message BiasAddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    WeightDef bias = 10;
-};
-
-message MatMulOpParams {
-    float alpha = 1;
-    float beta = 2;
-    bool transposeX = 3;
-    bool transposeW = 4;
-
-    WeightDef filter = 10;
-    WeightDef bias = 12;
-};
-
-message RsqrtOpParams {
-    float alpha = 1;
-    float beta = 2;
-};
-
-
-message WeightDef {
-    int32 format = 1;
-    int32 data_type = 2;
-    ShapeDef shape = 3;
-    bytes data = 4;
-    int64 data_offset = 5;
-    uint32 cmps_size = 6;
-    bytes cmps_tab = 7;
-    int64 cmps_tab_offset = 10;
-    CompressInfo cmps_info = 8;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
-}
-
-message ShapeDef {
-    repeated int64 dim = 1;
-}
-
-enum DeviceType {
-  NPU = 0;                   // In default, we will use NPU.
-  CPU = 1;                   // CPU
-}
-
-message AllOffsetQuantizeInfo {
-	float scale  = 1;
-	int32 offset = 2;
-} 
-
-message TensorDescriptor {
-    int32 format = 1;
-    int32 data_type = 2;
-    repeated int64 dim = 3;
-    uint32 size = 4;
-    bool reuse_input = 5;
-    bool output_tensor = 7;
-    DeviceType device_type = 8;
-    bool input_tensor = 9;
-    uint32 real_dim_cnt = 10;
-    uint32 reuse_input_index = 11;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
-}
-
-message CompressInfo {
-    int32 blockRow = 1;                             // block row
-    int32 blockCol = 2;                             // block col
-    int32 fractalK = 3;                             // fractal K
-    int32 fractalN = 4;                             // fractal N
-    int32 lastFractalK = 5;                         // K of last fractal
-    int32 lastFractalN = 6;                         // N of last fractal
-    int32 cubeSize = 7;                             // cube's length
-    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
-}
-
-message AttrDef {
-  message ListValue {
-    repeated string s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated uint32 u = 6 [packed = true];         // "list(uint)"
-    repeated bytes bt = 7;
-  }
-
-  oneof value {
-    string s = 2;                // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    uint32 u = 6;                // "uint32"
-    bytes bt = 7;
-    ListValue list = 1;          // any "list(...)"
-    NamedAttrs func = 10;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs {
-  string name = 1;
-  map<string, AttrDef> attr = 2;
-}
-

parser/onnx/proto/onnx/ge_onnx.proto

@@ -1,563 +0,0 @@
-// Copyright (c) ONNX Project Contributors.
-// Licensed under the MIT license.
-
-syntax = "proto3";
-
-package ge.onnx;
-
-// Overview
-//
-// ONNX is an open specification that is comprised of the following components:
-//
-// 1)  A definition of an extensible computation graph model.
-// 2)  Definitions of standard data types.
-// 3)  Definitions of built-in operators.
-//
-// This document describes the syntax of models and their computation graphs,
-// as well as the standard data types. Together, they are referred to as the ONNX
-// Intermediate Representation, or 'IR' for short.
-//
-// The normative semantic specification of the ONNX IR is found in docs/IR.md.
-// Definitions of the built-in neural network operators may be found in docs/Operators.md.
-
-// Notes
-//
-// Release
-//
-// We are still in the very early stage of defining ONNX. The current
-// version of ONNX is a starting point. While we are actively working
-// towards a complete spec, we would like to get the community involved
-// by sharing our working version of ONNX.
-//
-// Protobuf compatibility
-//
-// To simplify framework compatibility, ONNX is defined using the subset of protobuf
-// that is compatible with both protobuf v2 and v3. This means that we do not use any
-// protobuf features that are only available in one of the two versions.
-//
-// Here are the most notable contortions we have to carry out to work around
-// these limitations:
-//
-//   - No 'map' (added protobuf 3.0). We instead represent mappings as lists
-//     of key-value pairs, where order does not matter and duplicates
-//     are not allowed.
-
-
-// Versioning
-//
-// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
-//
-// To be compatible with both proto2 and proto3, we will use a version number
-// that is not defined by the default value but an explicit enum number.
-enum Version {
-  // proto3 requires the first enum value to be zero.
-  // We add this just to appease the compiler.
-  _START_VERSION = 0;
-  // The version field is always serialized and we will use it to store the
-  // version that the  graph is generated from. This helps us set up version
-  // control.
-  // For the IR, we are using simple numbers starting with with 0x00000001,
-  // which was the version we published on Oct 10, 2017.
-  IR_VERSION_2017_10_10 = 0x0000000000000001;
-
-  // IR_VERSION 2 published on Oct 30, 2017
-  // - Added type discriminator to AttributeProto to support proto3 users
-  IR_VERSION_2017_10_30 = 0x0000000000000002;
-
-  // IR VERSION 3 published on Nov 3, 2017
-  // - For operator versioning:
-  //    - Added new message OperatorSetIdProto
-  //    - Added opset_import in ModelProto
-  // - For vendor extensions, added domain in NodeProto
-  IR_VERSION_2017_11_3 = 0x0000000000000003;
-
-  // IR VERSION 4 published on Jan 22, 2019
-  // - Relax constraint that initializers should be a subset of graph inputs
-  // - Add type BFLOAT16
-  IR_VERSION_2019_1_22 = 0x0000000000000004;
-
-  // IR VERSION 5 published on March 18, 2019
-  // - Add message TensorAnnotation.
-  // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
-  IR_VERSION_2019_3_18 = 0x0000000000000005;
-
-  // IR VERSION 6 published on Sep 19, 2019
-  // - Add support for sparse tensor constants stored in model.
-  //   - Add message SparseTensorProto
-  //   - Add sparse initializers
-  IR_VERSION = 0x0000000000000006;
-}
-
-// Attributes
-//
-// A named attribute containing either singular float, integer, string, graph,
-// and tensor values, or repeated float, integer, string, graph, and tensor values.
-// An AttributeProto MUST contain the name field, and *only one* of the
-// following content fields, effectively enforcing a C/C++ union equivalent.
-message AttributeProto {
-
-  // Note: this enum is structurally identical to the OpSchema::AttrType
-  // enum defined in schema.h.  If you rev one, you likely need to rev the other.
-  enum AttributeType {
-    UNDEFINED = 0;
-    FLOAT = 1;
-    INT = 2;
-    STRING = 3;
-    TENSOR = 4;
-    GRAPH = 5;
-    SPARSE_TENSOR = 11;
-
-    FLOATS = 6;
-    INTS = 7;
-    STRINGS = 8;
-    TENSORS = 9;
-    GRAPHS = 10;
-    SPARSE_TENSORS = 12;
-  }
-
-  // The name field MUST be present for this version of the IR.
-  string name = 1;           // namespace Attribute
-
-  // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
-  // In this case, this AttributeProto does not contain data, and it's a reference of attribute
-  // in parent scope.
-  // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
-  string ref_attr_name = 21;
-
-  // A human-readable documentation for this attribute. Markdown is allowed.
-  string doc_string = 13;
-
-  // The type field MUST be present for this version of the IR.
-  // For 0.0.1 versions of the IR, this field was not defined, and
-  // implementations needed to use has_field hueristics to determine
-  // which value field was in use.  For IR_VERSION 0.0.2 or later, this
-  // field MUST be set and match the f|i|s|t|... field in use.  This
-  // change was made to accomodate proto3 implementations.
-  AttributeType type = 20;   // discriminator that indicates which field below is in use
-
-  // Exactly ONE of the following fields must be present for this version of the IR
-  float f = 2;               // float
-  int64 i = 3;               // int
-  bytes s = 4;               // UTF-8 string
-  TensorProto t = 5;         // tensor value
-  GraphProto g = 6;          // graph
-  SparseTensorProto sparse_tensor = 22;  // sparse tensor value
-  // Do not use field below, it's deprecated.
-  // optional ValueProto v = 12;         // value - subsumes everything but graph
-
-  repeated float floats = 7;          // list of floats
-  repeated int64 ints = 8;            // list of ints
-  repeated bytes strings = 9;         // list of UTF-8 strings
-  repeated TensorProto tensors = 10;  // list of tensors
-  repeated GraphProto graphs = 11;    // list of graph
-  repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
-}
-
-// Defines information on value, including the name, the type, and
-// the shape of the value.
-message ValueInfoProto {
-  // This field MUST be present in this version of the IR.
-  string name = 1;     // namespace Value
-  // This field MUST be present in this version of the IR for
-  // inputs and outputs of the top-level graph.
-  TypeProto type = 2;
-  // A human-readable documentation for this value. Markdown is allowed.
-  string doc_string = 3;
-}
-
-// Nodes
-//
-// Computation graphs are made up of a DAG of nodes, which represent what is
-// commonly called a "layer" or "pipeline stage" in machine learning frameworks.
-//
-// For example, it can be a node of type "Conv" that takes in an image, a filter
-// tensor and a bias tensor, and produces the convolved output.
-message NodeProto {
-  repeated string input = 1;    // namespace Value
-  repeated string output = 2;   // namespace Value
-
-  // An optional identifier for this node in a graph.
-  // This field MAY be absent in ths version of the IR.
-  string name = 3;     // namespace Node
-
-  // The symbolic identifier of the Operator to execute.
-  string op_type = 4;  // namespace Operator
-  // The domain of the OperatorSet that specifies the operator named by op_type.
-  string domain = 7;   // namespace Domain
-
-  // Additional named attributes.
-  repeated AttributeProto attribute = 5;
-
-  // A human-readable documentation for this node. Markdown is allowed.
-  string doc_string = 6;
-}
-
-// Models
-//
-// ModelProto is a top-level file/container format for bundling a ML model and
-// associating its computation graph with metadata.
-//
-// The semantics of the model are described by the associated GraphProto.
-message ModelProto {
-  // The version of the IR this model targets. See Version enum above.
-  // This field MUST be present.
-  int64 ir_version = 1;
-
-  // The OperatorSets this model relies on.
-  // All ModelProtos MUST have at least one entry that
-  // specifies which version of the ONNX OperatorSet is
-  // being imported.
-  //
-  // All nodes in the ModelProto's graph will bind against the operator
-  // with the same-domain/same-op_type operator with the HIGHEST version
-  // in the referenced operator sets.
-  repeated OperatorSetIdProto opset_import = 8;
-
-  // The name of the framework or tool used to generate this model.
-  // This field SHOULD be present to indicate which implementation/tool/framework
-  // emitted the model.
-  string producer_name = 2;
-
-  // The version of the framework or tool used to generate this model.
-  // This field SHOULD be present to indicate which implementation/tool/framework
-  // emitted the model.
-  string producer_version = 3;
-
-  // Domain name of the model.
-  // We use reverse domain names as name space indicators. For example:
-  // `com.facebook.fair` or `com.microsoft.cognitiveservices`
-  //
-  // Together with `model_version` and GraphProto.name, this forms the unique identity of
-  // the graph.
-  string domain = 4;
-
-  // The version of the graph encoded. See Version enum below.
-  int64 model_version = 5;
-
-  // A human-readable documentation for this model. Markdown is allowed.
-  string doc_string = 6;
-
-  // The parameterized graph that is evaluated to execute the model.
-  GraphProto graph = 7;
-
-  // Named metadata values; keys should be distinct.
-  repeated StringStringEntryProto metadata_props = 14;
-};
-
-// StringStringEntryProto follows the pattern for cross-proto-version maps.
-// See https://developers.google.com/protocol-buffers/docs/proto3#maps
-message StringStringEntryProto {
-  string key = 1;
-  string value= 2;
-};
-
-message TensorAnnotation {
-  string tensor_name = 1;
-  // <key, value> pairs to annotate tensor specified by <tensor_name> above.
-  // The keys used in the mapping below must be pre-defined in ONNX spec.
-  // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as
-  // quantization parameter keys.
-  repeated StringStringEntryProto quant_parameter_tensor_names = 2;
-}
-
-
-
-// Graphs
-//
-// A graph defines the computational logic of a model and is comprised of a parameterized
-// list of nodes that form a directed acyclic graph based on their inputs and outputs.
-// This is the equivalent of the "network" or "graph" in many deep learning
-// frameworks.
-message GraphProto {
-  // The nodes in the graph, sorted topologically.
-  repeated NodeProto node = 1;
-
-  // The name of the graph.
-  string name = 2;   // namespace Graph
-
-  // A list of named tensor values, used to specify constant inputs of the graph.
-  // Each TensorProto entry must have a distinct name (within the list) that
-  // MAY also appear in the input list.
-  repeated TensorProto initializer = 5;
-
-  // Initializers (see above) stored in sparse format.
-  repeated SparseTensorProto sparse_initializer = 15;
-
-  // A human-readable documentation for this graph. Markdown is allowed.
-  string doc_string = 10;
-
-  // The inputs and outputs of the graph.
-  repeated ValueInfoProto input = 11;
-  repeated ValueInfoProto output = 12;
-
-  // Information for the values in the graph. The ValueInfoProto.name's
-  // must be distinct. It is optional for a value to appear in value_info list.
-  repeated ValueInfoProto value_info = 13;
-
-  // This field carries information to indicate the mapping among a tensor and its
-  // quantization parameter tensors. For example:
-  // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated,
-  // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.
-  repeated TensorAnnotation quantization_annotation = 14;
-
-  // DO NOT USE the following fields, they were deprecated from earlier versions.
-  // repeated string input = 3;
-  // repeated string output = 4;
-  // optional int64 ir_version = 6;
-  // optional int64 producer_version = 7;
-  // optional string producer_tag = 8;
-  // optional string domain = 9;
-}
-
-// Tensors
-//
-// A serialized tensor value.
-message TensorProto {
-  enum DataType {
-    UNDEFINED = 0;
-    // Basic types.
-    FLOAT = 1;   // float
-    UINT8 = 2;   // uint8_t
-    INT8 = 3;    // int8_t
-    UINT16 = 4;  // uint16_t
-    INT16 = 5;   // int16_t
-    INT32 = 6;   // int32_t
-    INT64 = 7;   // int64_t
-    STRING = 8;  // string
-    BOOL = 9;    // bool
-
-    // IEEE754 half-precision floating-point format (16 bits wide).
-    // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits.
-    FLOAT16 = 10;
-
-    DOUBLE = 11;
-    UINT32 = 12;
-    UINT64 = 13;
-    COMPLEX64 = 14;     // complex with float32 real and imaginary components
-    COMPLEX128 = 15;    // complex with float64 real and imaginary components
-
-    // Non-IEEE floating-point format based on IEEE754 single-precision
-    // floating-point number truncated to 16 bits.
-    // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits.
-    BFLOAT16 = 16;
-
-    // Future extensions go here.
-  }
-
-  // The shape of the tensor.
-  repeated int64 dims = 1;
-
-  // The data type of the tensor.
-  // This field MUST have a valid TensorProto.DataType value
-  int32 data_type = 2;
-
-  // For very large tensors, we may want to store them in chunks, in which
-  // case the following fields will specify the segment that is stored in
-  // the current TensorProto.
-  message Segment {
-    int64 begin = 1;
-    int64 end = 2;
-  }
-  Segment segment = 3;
-
-  // Tensor content must be organized in row-major order.
-  //
-  // Depending on the data_type field, exactly one of the fields below with
-  // name ending in _data is used to store the elements of the tensor.
-
-  // For float and complex64 values
-  // Complex64 tensors are encoded as a single array of floats,
-  // with the real components appearing in odd numbered positions,
-  // and the corresponding imaginary component apparing in the
-  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
-  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
-  // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
-  repeated float float_data = 4 [packed = true];
-
-  // For int32, uint8, int8, uint16, int16, bool, and float16 values
-  // float16 values must be bit-wise converted to an uint16_t prior
-  // to writing to the buffer.
-  // When this field is present, the data_type field MUST be
-  // INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16
-  repeated int32 int32_data = 5 [packed = true];
-
-  // For strings.
-  // Each element of string_data is a UTF-8 encoded Unicode
-  // string. No trailing null, no leading BOM. The protobuf "string"
-  // scalar type is not used to match ML community conventions.
-  // When this field is present, the data_type field MUST be STRING
-  repeated bytes string_data = 6;
-
-  // For int64.
-  // When this field is present, the data_type field MUST be INT64
-  repeated int64 int64_data = 7 [packed = true];
-
-  // Optionally, a name for the tensor.
-  string name = 8; // namespace Value
-
-  // A human-readable documentation for this tensor. Markdown is allowed.
-  string doc_string = 12;
-
-  // Serializations can either use one of the fields above, or use this
-  // raw bytes field. The only exception is the string case, where one is
-  // required to store the content in the repeated bytes string_data field.
-  //
-  // When this raw_data field is used to store tensor value, elements MUST
-  // be stored in as fixed-width, little-endian order.
-  // Floating-point data types MUST be stored in IEEE 754 format.
-  // Complex64 elements must be written as two consecutive FLOAT values, real component first.
-  // Complex128 elements must be written as two consecutive DOUBLE values, real component first.
-  // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
-  //
-  // Note: the advantage of specific field rather than the raw_data field is
-  // that in some cases (e.g. int data), protobuf does a better packing via
-  // variable length storage, and may lead to smaller binary footprint.
-  // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
-  bytes raw_data = 9;
-
-  // Data can be stored inside the protobuf file using type-specific fields or raw_data.
-  // Alternatively, raw bytes data can be stored in an external file, using the external_data field.
-  // external_data stores key-value pairs describing data location. Recognized keys are:
-  // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX
-  //                           protobuf model was stored
-  // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string.
-  //                         Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
-  // - "length" (optional) - number of bytes containing data. Integer stored as string.
-  // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key.
-  repeated StringStringEntryProto external_data = 13;
-
-  // Location of the data for this tensor. MUST be one of:
-  // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field.
-  // - EXTERNAL - data stored in an external location as described by external_data field.
-  enum DataLocation {
-    DEFAULT = 0;
-    EXTERNAL = 1;
-  }
-
-  // If value not set, data is stored in raw_data (if set) otherwise in type-specified field.
-  DataLocation data_location = 14;
-
-  // For double
-  // Complex128 tensors are encoded as a single array of doubles,
-  // with the real components appearing in odd numbered positions,
-  // and the corresponding imaginary component apparing in the
-  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
-  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
-  // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
-  repeated double double_data = 10 [packed = true];
-
-  // For uint64 and uint32 values
-  // When this field is present, the data_type field MUST be
-  // UINT32 or UINT64
-  repeated uint64 uint64_data = 11 [packed = true];
-}
-
-// A serialized sparse-tensor value
-message SparseTensorProto {
-  // The sequence of non-default values are encoded as a tensor of shape [NNZ].
-  // The default-value is zero for numeric tensors, and empty-string for string tensors.
-  TensorProto values = 1;
-
-  // The indices of the non-default values, which may be stored in one of two formats.
-  // (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
-  // corresponding to the j-th index of the i-th value (in the values tensor).
-  // (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
-  // must be the linearized-index of the i-th value (in the values tensor).
-  // The linearized-index can be converted into an index tuple (k_1,...,k_rank)
-  // using the shape provided below.
-  // The indices must appear in ascending order without duplication.
-  // In the first format, the ordering is lexicographic-ordering:
-  // e.g., index-value [1,4] must appear before [2,1]
-  TensorProto indices = 2;
-
-  // The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
-  repeated int64 dims = 3;
-}
-
-// Defines a tensor shape. A dimension can be either an integer value
-// or a symbolic variable. A symbolic variable represents an unknown
-// dimension.
-message TensorShapeProto {
-  message Dimension {
-    oneof value {
-      int64 dim_value = 1;
-      string dim_param = 2;   // namespace Shape
-    };
-    // Standard denotation can optionally be used to denote tensor
-    // dimensions with standard semantic descriptions to ensure
-    // that operations are applied to the correct axis of a tensor.
-    // Refer to https://github.com/onnx/onnx/blob/master/docs/DimensionDenotation.md#denotation-definition
-    // for pre-defined dimension denotations.
-    string denotation = 3;
-  };
-  repeated Dimension dim = 1;
-}
-
-// Types
-//
-// The standard ONNX data types.
-message TypeProto {
-
-  message Tensor {
-    // This field MUST NOT have the value of UNDEFINED
-    // This field MUST have a valid TensorProto.DataType value
-    // This field MUST be present for this version of the IR.
-    int32 elem_type = 1;
-    TensorShapeProto shape = 2;
-  }
-
-  // repeated T
-  message Sequence {
-    // The type and optional shape of each element of the sequence.
-    // This field MUST be present for this version of the IR.
-    TypeProto elem_type = 1;
-  };
-
-  // map<K,V>
-  message Map {
-    // This field MUST have a valid TensorProto.DataType value
-    // This field MUST be present for this version of the IR.
-    // This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
-    int32 key_type = 1;
-    // This field MUST be present for this version of the IR.
-    TypeProto value_type = 2;
-  };
-
-  oneof value {
-    // The type of a tensor.
-    Tensor tensor_type = 1;
-
-    // NOTE:  DNN-only implementations of ONNX MAY elect to not support non-tensor values
-    //        as input and output to graphs and nodes. These types are needed to naturally
-    //        support classical ML operators.  DNN operators SHOULD restrict their input
-    //        and output types to tensors.
-
-    // The type of a sequence.
-    Sequence sequence_type = 4;
-
-    // The type of a map.
-    Map map_type = 5;
-
-  }
-
-  // An optional denotation can be used to denote the whole
-  // type with a standard semantic description as to what is
-  // stored inside. Refer to https://github.com/onnx/onnx/blob/master/docs/TypeDenotation.md#type-denotation-definition
-  // for pre-defined type denotations.
-  string denotation = 6;
-}
-
-// Operator Sets
-//
-// OperatorSets are uniquely identified by a (domain, opset_version) pair.
-message OperatorSetIdProto {
-  // The domain of the operator set being identified.
-  // The empty string ("") or absence of this field implies the operator
-  // set that is defined as part of the ONNX specification.
-  // This field MUST be present in this version of the IR when referring to any other operator set.
-  string domain = 1;
-
-  // The version of the operator set being identified.
-  // This field MUST be present in this version of the IR.
-  int64 version = 2;
-}

parser/proto/caffe/CMakeLists.txt

@@ -1,31 +0,0 @@
-############ lib_caffe_parser.so ############
-add_library(_caffe_parser SHARED
-    $<TARGET_OBJECTS:parser_caffe_proto_obj>
-)
-
-target_compile_definitions(_caffe_parser PRIVATE
-    google=ascend_private
-)
-
-target_include_directories(_caffe_parser PRIVATE
-    ${CMAKE_CURRENT_LIST_DIR}
-)
-
-target_link_options(_caffe_parser PRIVATE
-    -Wl,-Bsymbolic
-)
-
-target_link_libraries(_caffe_parser PRIVATE
-    $<BUILD_INTERFACE:intf_pub>
-    -Wl,--no-as-needed
-    ascend_protobuf
-    -Wl,--as-needed
-)
-
-############ install ############
-set(INSTALL_BASE_DIR "")
-set(INSTALL_LIBRARY_DIR lib)
-
-install(TARGETS _caffe_parser OPTIONAL
-    LIBRARY DESTINATION ${INSTALL_LIBRARY_DIR}
-)

parser/proto/caffe/module.mk

@@ -1,21 +0,0 @@
-LOCAL_PATH := $(call my-dir)
-
-include $(CLEAR_VARS)
-
-LOCAL_MODULE := lib_caffe_parser
-
-LOCAL_CFLAGS += -Dgoogle=ascend_private
-ifeq ($(DEBUG), 1)
-LOCAL_CFLAGS += -g -O0
-endif
-
-LOCAL_SRC_FILES := \
-    caffe.proto \
-
-LOCAL_C_INCLUDES := \
-    third_party/protobuf/include \
-
-LOCAL_SHARED_LIBRARIES := \
-    libascend_protobuf \
-
-include $(BUILD_HOST_SHARED_LIBRARY)

parser/proto/ge_ir.proto

@@ -1,193 +0,0 @@
-syntax = "proto3";
-
-package ge.proto;
-
-enum DataType
-{
-    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
-    DT_FLOAT     = 1;  // float type
-    DT_FLOAT16   = 2;  // fp16 type
-    DT_INT8      = 3;  // int8 type
-    DT_UINT8     = 4;  // uint8 type
-    DT_INT16     = 5;  // int16 type
-    DT_UINT16    = 6;  // uint16 type
-    DT_INT32     = 7;  //
-    DT_INT64     = 8;  // int64 type
-    DT_UINT32    = 9;  // unsigned int32
-    DT_UINT64    = 10;  // unsigned int64
-    DT_BOOL      = 11;  // bool type
-    DT_DOUBLE    = 12; // double type
-    DT_STRING = 13;            // string type
-    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
-    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
-    DT_COMPLEX64 = 16;         // complex64 type
-    DT_COMPLEX128 = 17;        // complex128 type
-    DT_QINT8 = 18;             // qint8 type
-    DT_QINT16 = 19;            // qint16 type
-    DT_QINT32 = 20;            // qint32 type
-    DT_QUINT8 = 21;            // quint8 type
-    DT_QUINT16 = 22;           // quint16 type
-    DT_RESOURCE  = 23;         // resource type
-    DT_STRING_REF = 24;        // string_ref type
-    DT_DUAL      = 25;              /**< dual output type */
-    DT_VARIANT = 26;           // variant type
-    DT_BF16 = 27;              // bf16 type
-    DT_INT4 = 28;              // int4 type
-}
-
-message AttrDef
-{
-    message ListValue
-    {
-        enum ListValueType{
-          VT_LIST_NONE = 0;
-          VT_LIST_STRING = 1;
-          VT_LIST_INT = 2;
-          VT_LIST_FLOAT = 3;
-          VT_LIST_BOOL = 4;
-          VT_LIST_BYTES = 5;
-          VT_LIST_TENSOR_DESC = 6;
-          VT_LIST_TENSOR = 7;
-          VT_LIST_GRAPH = 8;
-          VT_LIST_NAMED_ATTRS = 9;
-          VT_LIST_DATA_TYPE = 10;
-        }
-        repeated bytes s             = 2;                    // "list(string)"
-        repeated int64 i             = 3;  // "list(int)"
-        repeated float f             = 4;   // "list(float)"
-        repeated bool  b             = 5;  // "list(bool)"
-        repeated bytes bt            = 7;
-        repeated TensorDescriptor td = 8;
-        repeated TensorDef t         = 9;
-        repeated GraphDef g          = 10;
-	    repeated NamedAttrs na       = 11;
-	    repeated int64 dt            = 12; // list ge::DataType
-
-	    ListValueType val_type       = 20;
-    }
-
-    message ListListInt{
-        message ListInt{
-            repeated int64 list_i             = 1; // list int
-        }
-        repeated ListInt list_list_i             = 1; // list list int
-    }
-
-    oneof value
-    {
-        bytes            s    = 2;  // "string"
-        int64            i    = 3;  // "int"
-        float            f    = 4;  // "float"
-        bool             b    = 5;  // "bool"
-        bytes            bt   = 7;
-        ListValue        list = 1;   // any "list(...)"
-        NamedAttrs       func = 10;  // Used to support attr nesting
-        TensorDescriptor td   = 11;  // GeTensorDesc type
-        TensorDef        t    = 12;  // GeTensor type
-        GraphDef         g    = 13;  // Graph type
-        ListListInt      list_list_int  = 14;  // List List Int type
-        int64            dt   = 15; // ge::DataType
-    }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs
-{
-    string               name = 1;
-    map<string, AttrDef> attr = 2;
-}
-
-// Shape / dimension description, using row-major order
-message ShapeDef
-{
-    repeated int64 dim = 1;  // Size of each dimension
-}
-
-// Multidimensional data description
-message TensorDescriptor
-{
-    string   name   = 1;  // Optional parameter, tensor name
-
-    DataType dtype  = 2;  // tensor datatype
-    ShapeDef shape  = 3;  // Shape / dimension
-    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"
-
-    bool has_out_attr = 9;
-    int64 size = 10;
-    int64 weight_size = 11;
-    bool reuse_input = 12;
-    bool output_tensor = 13;
-    string device_type = 14;
-    bool input_tensor =15;
-    int64 real_dim_cnt = 16;
-    int64 reuse_input_index = 17;
-    int64 data_offset = 18;
-    int64 cmps_size = 19;
-    string cmps_tab = 20;
-    int64 cmps_tab_offset = 21;
-
-	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
-}
-
-// GeTensor definition
-message TensorDef
-{
-    TensorDescriptor desc = 1;  // Tensor description
-    bytes            data = 2;  // Tensor data
-}
-
-
-// Operator description
-message OpDef
-{
-    string name = 1;  // name
-    string type = 2;  // type
-
-    repeated string input = 5;  // input original op name + outgoing index. op_name：index
-
-    map<string, AttrDef> attr = 10;  // Set of operator parameter fields
-
-    bool has_out_attr = 20;
-    int64 id = 21;
-    int64 stream_id =22;
-    repeated string input_name = 23;
-    repeated string src_name = 24;
-    repeated int64 src_index = 25;
-    repeated string dst_name = 26;
-    repeated int64 dst_index = 27;
-    repeated int64 input_i = 28;
-    repeated int64 output_i = 29;
-    repeated int64 workspace = 30;
-    repeated int64 workspace_bytes = 31;
-    repeated bool is_input_const = 32;
-    repeated TensorDescriptor input_desc = 33;
-    repeated TensorDescriptor output_desc = 34;
-    repeated string subgraph_name = 35;
-}
-
-// Graph definition
-message GraphDef
-{
-    string name   = 1;   //  name
-
-    repeated string input  = 4;  // Graph input
-    repeated string output = 5;  // Graph output
-
-    repeated OpDef op      = 6;  // List of operators
-
-	map<string, AttrDef> attr = 11;  // Extended field
-}
-
-// model definition
-message ModelDef
-{
-	string name         = 1;  // name
-	uint32 version      = 2;  // IR Proto verion
-	string custom_version = 3;  // User model version number, passed in by user
-
-    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef
-
-    map<string, AttrDef> attr = 11;  // Extended field
-}
-

parser/proto/insert_op.proto

@@ -1,140 +0,0 @@
-syntax = "proto3";
-
-package domi;
-
-message InsertNewOps {
-	repeated AippOpParams aipp_op = 1;
-	repeated MultiShapeOpParams multi_shape_op = 2;
-}
-
-message AippOpParams {
-	enum InputFormat {
-		UNDEFINED = 0;
-		YUV420SP_U8 = 1;
-		XRGB8888_U8 = 2;
-		RGB888_U8   = 3;
-		YUV400_U8   = 4;
-		NC1HWC0DI_FP16 = 5;
-		NC1HWC0DI_S8 = 6;
-		ARGB8888_U8 = 7;
-		YUYV_U8 = 8;
-		YUV422SP_U8 = 9;
-		AYUV444_U8 = 10;
-		RAW10 = 11;
-		RAW12 = 12;
-		RAW16 = 13;
-		RAW24 = 14;
-		RGB16 = 15;
-		RGB20 = 16;
-		RGB24 = 17;
-		RGB8_IR = 18;
-		RGB16_IR = 19;
-		RGB24_IR = 20;
-	}
-
-	enum AippMode {
-		undefined = 0;
-		static = 1;
-		dynamic = 2;
-	}
-
-	// AIPP模式，区分静态AIPP和动态AIPP
-	AippMode aipp_mode = 1;
-
-	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
-	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
-	uint32 related_input_rank = 2;
-
-        // related_input_name is optional and the top name of data node which inserts aipp
-        string related_input_name = 6;
-
-	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
-	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
-	// 配置值 <= Data算子输出边的个数。
-	repeated uint32 input_edge_idx = 3;
-
-	// [Begin] 动态AIPP参数，配置静态AIPP时无效
-	uint32 max_src_image_size = 4;
-
-	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
-	bool support_rotation = 5;
-
-	// [End] 动态AIPP参数
-
-
-	// [Begin] 静态AIPP参数，配置动态AIPP时无效
-	InputFormat input_format = 51;
-	bool csc_switch = 52;
-	float cpadding_value = 53;
-	bool rbuv_swap_switch = 54;
-	bool ax_swap_switch = 55;
-	bool single_line_mode = 56;
-
-	int32 src_image_size_w = 57;
-	int32 src_image_size_h = 58;
-
-	bool crop = 59;
-	int32 load_start_pos_w = 60;
-	int32 load_start_pos_h = 61;
-	int32 crop_size_w = 62;
-	int32 crop_size_h = 63;
-
-	bool resize = 64;
-	int32 resize_output_w = 65;
-	int32 resize_output_h = 66;
-
-	bool padding = 67;
-	int32 left_padding_size = 68;
-	int32 right_padding_size = 69;
-	int32 top_padding_size = 70;
-	int32 bottom_padding_size = 71;
-	float padding_value = 72;
-
-	int32 mean_chn_0 = 10;
-	int32 mean_chn_1 = 11;
-	int32 mean_chn_2 = 12;
-	int32 mean_chn_3 = 19;
-	float min_chn_0 = 13;
-	float min_chn_1 = 14;
-	float min_chn_2 = 15;
-	float min_chn_3 = 20;
-	repeated float var_reci_chn_0 = 16;
-	repeated float var_reci_chn_1 = 17;
-	repeated float var_reci_chn_2 = 18;
-	repeated float var_reci_chn_3 = 21;
-
-	repeated int32 matrix_r0c0 = 30;
-	repeated int32 matrix_r0c1 = 31;
-	repeated int32 matrix_r0c2 = 32;
-	repeated int32 matrix_r1c0 = 33;
-	repeated int32 matrix_r1c1 = 34;
-	repeated int32 matrix_r1c2 = 35;
-	repeated int32 matrix_r2c0 = 36;
-	repeated int32 matrix_r2c1 = 37;
-	repeated int32 matrix_r2c2 = 38;
-	repeated int32 output_bias_0 = 39;
-	repeated int32 output_bias_1 = 40;
-	repeated int32 output_bias_2 = 41;
-	repeated int32 input_bias_0 = 42;
-	repeated int32 input_bias_1 = 43;
-	repeated int32 input_bias_2 = 44;
-
-	// [End] 静态AIPP参数
-
-       // The n number that is used for raw/rgbir data into f16 transformation.
-       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
-       uint32 raw_rgbir_to_f16_n = 45;
-}
-
-message MultiShapeOpParams {
-	enum MultiShapeMode {
-		batch      = 0;             //动态batch
-		resolution = 1;             //动态分辨率，扩展用
-	}
-
-	MultiShapeMode    mode                      = 1;        //算子模式
-	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入
-
-
-	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
-}

parser/proto/om.proto

@@ -1,396 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-enum TargetType
-{
-    MINI = 0;
-    TINY = 1;
-    LITE = 2;
-}
-
-// offline model
-message ModelDef {
-    string name = 1;
-    uint32 version = 2;
-
-    uint64 memory_size = 10;
-    uint32 stream_num = 11;
-    uint32 event_num = 12;
-    uint64 weight_size = 13;
-    uint32 label_num = 15;
-    repeated OpDef op = 20;
-    TargetType target_type = 23;
-
-    map<string, AttrDef> attr = 30;
-};
-
-// operator define
-message OpDef {
-    string name = 1;
-    string type = 2;
-
-    uint32 id = 3;
-    uint32 stream_id = 4;
-
-    repeated string input_name = 5;
-
-    repeated string src_name = 8;
-    repeated int32 src_index = 9;
-    repeated int64 input = 10;
-    repeated int64 output = 11;
-    repeated TensorDescriptor input_desc = 12;
-    repeated TensorDescriptor output_desc = 13;
-    repeated WeightDef weights = 14;
-    repeated string dst_name = 15;
-    repeated int32 dst_index = 16;
-
-    repeated int64 workspace = 20;
-    repeated uint32 workspace_bytes = 21;
-
-    repeated string weight_name = 22;
-    repeated bool is_input_const = 23;
-
-    map<string, AttrDef> attr = 30;
-
-    QuantizeFactorParams quantize_factor = 31;
-
-    oneof op_params {
-        // start at 100 here
-        SendOpParams sender_param = 100;
-        RecvOpParams receiver_param = 200;
-        ConvolutionOpParams convolution_param = 300;
-        PoolingOpParams pooling_param = 400;
-        EltwiseOpParams eltwise_param = 500;
-        BatchNormOpParams batchnorm_param = 600;
-        ScaleOpParams scale_param = 700;
-        FullConnectionOpParams full_connection_param = 800;
-        SoftmaxOpParams softmax_param = 900;
-        ActivationOpParams activation_param = 1000;
-        ReshapeOpParams reshape_param = 1100;
-    }
-};
-
-message SendOpParams {
-    uint32 event_id = 1;
-};
-
-message RecvOpParams {
-    uint32 event_id = 1;
-};
-
-enum QuantizeScaleType
-{
-    VECTOR_SCALE = 0;
-    SCALAR_SCALE = 1;
-}
-
-enum QuantizeScaleMode
-{
-    NORMAL_MODE = 0;
-    SQRT_MODE = 1;
-}
-
-enum QuantizeAlgorithm
-{
-    NON_OFFSET_ALGO = 0;
-    HALF_OFFSET_ALGO = 1;
-    ALL_OFFSET_ALGO = 2;
-}
-message QuantizeFactor
-{
-    QuantizeScaleMode scale_mode = 1;
-    bytes scale_value = 2;
-    int64 scale_offset = 3;
-    bytes offset_data_value = 4;
-    int64 offset_data_offset = 5;
-    bytes offset_weight_value = 6;
-    int64 offset_weight_offset = 7;
-    bytes offset_pad_value = 8;
-    int64 offset_pad_offset = 9;
-};
-
-message QuantizeCalcFactor
-{
-    bytes offsetw = 1;
-    int64 offsetw_offset = 2;
-    bytes offsetd = 3;
-    int64 offsetd_offset = 4;
-    bytes scalereq = 5;
-    int64 scaledreq_offset = 6;
-    bytes offsetdnext = 7;
-    int64 offsetdnext_offset = 8;
-}
-
-message QuantizeFactorParams
-{
-    QuantizeAlgorithm quantize_algo = 1;
-    QuantizeScaleType scale_type = 2;
-    QuantizeFactor quantize_param = 3;
-    QuantizeFactor dequantize_param = 4;
-    QuantizeFactor requantize_param = 5;
-    QuantizeCalcFactor quantizecalc_param = 6;
-};
-
-message ConvolutionOpParams {
-    int32 mode = 1;
-    int32 algo = 2;
-    int32 pad_mode = 3;
-    uint32 group = 4;
-    uint32 num_output = 5;
-
-    repeated uint32 pad = 10;
-    repeated uint32 stride = 11;
-    repeated uint32 dilation = 12;
-    repeated uint32 kernel = 13;
-
-    float alpha = 20;
-    float beta = 21;
-
-    WeightDef filter = 40;
-    WeightDef bias = 41;
-
-    bool relu_flag = 62;
-    repeated uint32 adj = 70;
-    repeated uint32 target_shape = 71;
-    repeated uint32 before_pad = 72;
-};
-
-message PoolingOpParams {
-    int32 mode = 1;
-    int32 nan_opt = 2;
-    int32 pad_mode = 3;
-    bool global_pooling = 4;
-
-    repeated uint32 window = 10;
-    repeated uint32 pad = 11;
-    repeated uint32 stride = 12;
-    bool ceil_mode = 13;
-    int32 data_mode  = 14;
-
-    float alpha = 20;
-    float beta = 21;
-    repeated uint32 before_pad = 22;
-};
-
-message EltwiseOpParams {
-    int32 mode = 1;
-    repeated float coeff = 2;
-    float alpha = 3;
-    float beta = 4;
-    repeated WeightDef weight = 5;
-    bool relu_flag = 6;
-};
-
-message ActivationOpParams {
-    int32 mode = 1;
-    float coef = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message BatchNormOpParams {
-    int32 mode = 1;
-
-    float alpha = 2;
-    float beta = 3;
-    double epsilon = 4;//optinal,[default = 1e-5]
-    bool use_global_stats = 5; //optinal,by default true,testing mode
-    float  moving_average_fraction = 6; //optinal,[default = .999];
-
-    WeightDef estimated_mean = 7;
-    WeightDef estimated_variance = 8;
-
-    WeightDef scale = 9;
-    WeightDef bias = 10;
-};
-
-message ScaleOpParams {
-    WeightDef scale = 1;
-    WeightDef bias = 2;
-};
-
-message ReshapeOpParams {
-    float alpha = 1;
-    float beta = 2;
-    ShapeDef shape = 3;
-    int32 axis = 4;
-    int32 num_axes = 5;
-    int32 format = 6;
-};
-
-message SoftmaxOpParams {
-    int32 algo = 1;
-    int32 mode = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message FullConnectionOpParams {
-    WeightDef filter = 1;
-    WeightDef bias = 2;
-    uint32 num_output = 3;
-    bool relu_flag = 12;
-};
-
-message FlattenOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 start_axis = 3;
-    int32 end_axis = 4;
-}
-
-message AddLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message AddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message SubOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message BiasAddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    WeightDef bias = 10;
-};
-
-message MatMulOpParams {
-    float alpha = 1;
-    float beta = 2;
-    bool transposeX = 3;
-    bool transposeW = 4;
-
-    WeightDef filter = 10;
-    WeightDef bias = 12;
-};
-
-message RsqrtOpParams {
-    float alpha = 1;
-    float beta = 2;
-};
-
-
-message WeightDef {
-    int32 format = 1;
-    int32 data_type = 2;
-    ShapeDef shape = 3;
-    bytes data = 4;
-    int64 data_offset = 5;
-    uint32 cmps_size = 6;
-    bytes cmps_tab = 7;
-    int64 cmps_tab_offset = 10;
-    CompressInfo cmps_info = 8;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
-}
-
-message ShapeDef {
-    repeated int64 dim = 1;
-}
-
-enum DeviceType {
-  NPU = 0;                   // In default, we will use NPU.
-  CPU = 1;                   // CPU
-}
-
-message AllOffsetQuantizeInfo {
-	float scale  = 1;
-	int32 offset = 2;
-} 
-
-message TensorDescriptor {
-    int32 format = 1;
-    int32 data_type = 2;
-    repeated int64 dim = 3;
-    uint32 size = 4;
-    bool reuse_input = 5;
-    bool output_tensor = 7;
-    DeviceType device_type = 8;
-    bool input_tensor = 9;
-    uint32 real_dim_cnt = 10;
-    uint32 reuse_input_index = 11;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
-}
-
-message CompressInfo {
-    int32 blockRow = 1;                             // block row
-    int32 blockCol = 2;                             // block col
-    int32 fractalK = 3;                             // fractal K
-    int32 fractalN = 4;                             // fractal N
-    int32 lastFractalK = 5;                         // K of last fractal
-    int32 lastFractalN = 6;                         // N of last fractal
-    int32 cubeSize = 7;                             // cube's length
-    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
-}
-
-message AttrDef {
-  message ListValue {
-    repeated string s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated uint32 u = 6 [packed = true];         // "list(uint)"
-    repeated bytes bt = 7;
-  }
-
-  oneof value {
-    string s = 2;                // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    uint32 u = 6;                // "uint32"
-    bytes bt = 7;
-    ListValue list = 1;          // any "list(...)"
-    NamedAttrs func = 10;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs {
-  string name = 1;
-  map<string, AttrDef> attr = 2;
-}
-

parser/proto/task.proto

@@ -1,179 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-message ModelTaskDef {
-    string version = 1;
-
-    map<string, string> attr = 9; // Extended field
-    repeated TaskDef task = 10;
-
-    uint64 memory_size = 11;
-    uint32 stream_num = 12;
-    uint32 event_num = 13;
-    uint64 weight_size = 14;
-
-    repeated bytes op = 15; // input/output opdef in bytes
-
-    uint64 base_addr = 16;    // base addr
-    uint64 weight_addr = 17;  // weight addr
-    uint32 batch_num = 18;
-}
-
-
-message TaskDef {
-    uint32 id = 1;
-    uint32 type = 2;
-
-    uint32 stream_id = 10;
-    uint32 event_id = 11;
-
-    KernelDef kernel = 20;
-    KernelExDef kernel_ex = 21;
-    KernelHcclDef kernel_hccl = 25;
-    EventExDef event_ex = 26;
-    LogTimeStampDef log_timestamp = 28;
-
-    uint32 label_id = 30;
-
-    MemcpyAsyncDef memcpy_async = 31;
-    StreamSwitchDef stream_switch = 32;
-    StreamActiveDef stream_active = 33;
-    bytes private_def = 34;
-    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
-    StreamSwitchNDef stream_switch_n = 36;
-
-    LabelSetDef label_set = 37;
-    LabelGotoExDef label_goto_ex = 38;
-    LabelSwitchByIndexDef label_switch_by_index = 39;
-    KernelDefWithHandle kernel_with_handle = 40;
-}
-
-message KernelDef {
-    KernelContext context = 1;
-
-    string stub_func = 10;
-    uint32 block_dim = 11;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes sm_desc = 14;
-    bytes flowtable = 15;
-    string so_name = 16;
-    string kernel_name = 17;
-    bytes kernel_ext_info = 18;
-    uint32 kernel_ext_info_size = 19;
-}
-
-message KernelDefWithHandle {
-    KernelContext context = 1;
-
-    uint64 handle = 10;
-    string dev_func = 11;
-    uint32 block_dim = 12;
-    uint32 args_size = 13;
-    bytes args = 14;
-    bytes sm_desc = 15;
-    string original_kernel_key = 16;
-    string node_info = 17;
-}
-
-message KernelContext {
-    uint32 kernel_type = 1;
-    uint32 op_id = 2;                              // OP type in CCE
-    uint32 kernel_func_id = 3;
-    uint32 op_index = 4;                           // TE/Custom operator
-    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
-    bytes args_offset = 6;                         // args offset information
-    uint32 args_count = 7;                         // args count
-    repeated uint32 origin_op_index = 8;
-}
-
-
-message KernelExDef {
-    uint32 flags = 1;
-
-    uint32 op_index = 4;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
-    uint32 task_info_size = 15;
-    bytes kernel_ext_info = 16;
-    uint32 kernel_ext_info_size = 17;
-}
-
-
-message KernelHcclDef {
-    uint32 op_index = 8;
-    string hccl_type = 9;
-}
-
-
-message EventExDef {
-    uint32 op_index = 1;
-    uint32 event_type = 2;
-}
-
-message LogTimeStampDef {
-    uint64 logid = 1;
-    bool notify = 2;
-    uint32 flat = 3;
-}
-
-message MemcpyAsyncDef {
-    uint64 dst = 1;
-    uint64 dst_max = 2;
-    uint64 src = 3;
-    uint64 count = 4;
-    uint32 kind = 5;
-    uint32 op_index = 6;
-}
-
-message StreamSwitchDef {
-    uint32 op_index = 1;
-    uint32 true_stream_id = 2;
-    int64 value = 3;
-    uint64 value_ptr = 4;
-    uint32 data_type = 5;
-}
-
-message StreamActiveDef {
-    uint32 op_index = 1;
-    uint32 active_stream_id = 2;
-}
-
-message StreamSwitchNDef {
-    uint32 op_index = 1;
-    uint32 size = 2;
-    repeated int64 target_value = 3;
-    repeated uint32 true_stream_id = 4;
-    uint32 element_size = 5;
-    uint32 data_type = 6;
-}
-
-message LabelSetDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelGotoExDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelSwitchByIndexDef {
-    uint32 op_index = 1;
-    uint32 label_max = 2;
-}

parser/proto/tensorflow/attr_value.proto

@@ -1,62 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "AttrValueProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensor.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing the value for an attr used to configure an Op.
-// Comment indicates the corresponding attr type.  Only the field matching the
-// attr type may be filled.
-message AttrValue {
-  // LINT.IfChange
-  message ListValue {
-    repeated bytes s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated DataType type = 6 [packed = true];  // "list(type)"
-    repeated TensorShapeProto shape = 7;         // "list(shape)"
-    repeated TensorProto tensor = 8;             // "list(tensor)"
-    repeated NameAttrList func = 9;              // "list(attr)"
-  }
-  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
-
-  oneof value {
-    bytes s = 2;                 // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    DataType type = 6;           // "type"
-    TensorShapeProto shape = 7;  // "shape"
-    TensorProto tensor = 8;      // "tensor"
-    ListValue list = 1;          // any "list(...)"
-
-    // "func" represents a function. func.name is a function's name or
-    // a primitive op's name. func.attr.first is the name of an attr
-    // defined for that function. func.attr.second is the value for
-    // that attr in the instantiation.
-    NameAttrList func = 10;
-
-    // This is a placeholder only used in nodes defined inside a
-    // function.  It indicates the attr value will be supplied when
-    // the function is instantiated.  For example, let us suppose a
-    // node "N" in function "FN". "N" has an attr "A" with value
-    // placeholder = "foo". When FN is instantiated with attr "foo"
-    // set to "bar", the instantiated node N's attr A will have been
-    // given the value "bar".
-    string placeholder = 9;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NameAttrList {
-  string name = 1;
-  map<string, AttrValue> attr = 2;
-}

parser/proto/tensorflow/function.proto

@@ -1,100 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "FunctionProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "node_def.proto";
-import "op_def.proto";
-
-// A library is a set of named functions.
-message FunctionDefLibrary {
-  repeated FunctionDef function = 1;
-  repeated GradientDef gradient = 2;
-}
-
-// A function can be instantiated when the runtime can bind every attr
-// with a value. When a GraphDef has a call to a function, it must
-// have binding for every attr defined in the signature.
-//   * device spec, etc.
-message FunctionDef {
-  // The definition of the function's name, arguments, return values,
-  // attrs etc.
-  OpDef signature = 1;
-
-  // Attributes specific to this function definition.
-  map<string, AttrValue> attr = 5;
-
-  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
-  reserved 2;
-
-  // In both of the following fields, there is the need to specify an
-  // output that is used as either the input to another node (in
-  // `node_def`) or as a return value of the function (in `ret`).
-  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
-  // list in some cases (instead of just single outputs).  Also, we
-  // need to be able to deal with lists of unknown length (so the
-  // output index may not be known at function definition time).  So
-  // we use the following format instead:
-  // * "fun_in" where "fun_in" is the name of a function input arg in
-  //   the `signature` field above.  This represents that input, whether
-  //   it is a single tensor or a list.
-  // * "fun_in:0" gives the first element of a function input arg (a
-  //   non-list input is considered a list of length 1 for these
-  //   purposes).
-  // * "node:out" where "node" is the name of a node in `node_def` and
-  //   "out" is the name one of its op's output arguments (the name
-  //   comes from the OpDef of the node's op). This represents that
-  //   node's output, whether it is a single tensor or a list.
-  //   Note: We enforce that an op's output arguments are never
-  //   renamed in the backwards-compatibility test.
-  // * "node:out:0" gives the first element of a node output arg (a
-  //   non-list output is considered a list of length 1 for these
-  //   purposes).
-  //
-  // NOT CURRENTLY SUPPORTED (but may be in the future):
-  // * "node:out:-1" gives last element in a node output list
-  // * "node:out:1:" gives a list with all but the first element in a
-  //   node output list
-  // * "node:out::-1" gives a list with all but the last element in a
-  //   node output list
-
-  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
-  // may have values of type `placeholder` and the `input` field uses
-  // the "output" format above.
-
-  // By convention, "op" in node_def is resolved by consulting with a
-  // user-defined library first. If not resolved, "func" is assumed to
-  // be a builtin op.
-  repeated NodeDef node_def = 3;
-
-  // A mapping from the output arg names from `signature` to the
-  // outputs from `node_def` that should be returned by the function.
-  map<string, string> ret = 4;
-}
-
-// GradientDef defines the gradient function of a function defined in
-// a function library.
-//
-// A gradient function g (specified by gradient_func) for a function f
-// (specified by function_name) must follow the following:
-//
-// The function 'f' must be a numerical function which takes N inputs
-// and produces M outputs. Its gradient function 'g', which is a
-// function taking N + M inputs and produces N outputs.
-//
-// I.e. if we have
-//    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
-// then, g is
-//    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
-//                                      dL/dy1, dL/dy2, ..., dL/dy_M),
-// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
-// loss function). dL/dx_i is the partial derivative of L with respect
-// to x_i.
-message GradientDef {
-  string function_name = 1;  // The function name.
-  string gradient_func = 2;  // The gradient function's name.
-}

parser/proto/tensorflow/graph.proto

@@ -1,56 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "GraphProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "node_def.proto";
-import "function.proto";
-import "versions.proto";
-
-// Represents the graph of operations
-message GraphDef {
-  repeated NodeDef node = 1;
-
-  // Compatibility versions of the graph.  See core/public/version.h for version
-  // history.  The GraphDef version is distinct from the TensorFlow version, and
-  // each release of TensorFlow will support a range of GraphDef versions.
-  VersionDef versions = 4;
-
-  // Deprecated single version field; use versions above instead.  Since all
-  // GraphDef changes before "versions" was introduced were forward
-  // compatible, this field is entirely ignored.
-  int32 version = 3 [deprecated = true];
-
-  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-  //
-  // "library" provides user-defined functions.
-  //
-  // Naming:
-  //   * library.function.name are in a flat namespace.
-  //     NOTE: We may need to change it to be hierarchical to support
-  //     different orgs. E.g.,
-  //     { "/google/nn", { ... }},
-  //     { "/google/vision", { ... }}
-  //     { "/org_foo/module_bar", { ... }}
-  //     map<string, FunctionDefLib> named_lib;
-  //   * If node[i].op is the name of one function in "library",
-  //     node[i] is deemed as a function call. Otherwise, node[i].op
-  //     must be a primitive operation supported by the runtime.
-  //
-  //
-  // Function call semantics:
-  //
-  //   * The callee may start execution as soon as some of its inputs
-  //     are ready. The caller may want to use Tuple() mechanism to
-  //     ensure all inputs are ready in the same time.
-  //
-  //   * The consumer of return values may start executing as soon as
-  //     the return values the consumer depends on are ready.  The
-  //     consumer may want to use Tuple() mechanism to ensure the
-  //     consumer does not start until all return values of the callee
-  //     function are ready.
-  FunctionDefLibrary library = 2;
-};

parser/proto/tensorflow/graph_library.proto

@@ -1,14 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-
-import "graph.proto";
-
-message GeGraphDef {
-  string name = 1;
-  GraphDef graph = 2;
-}
-
-message GraphDefLibrary {
-  repeated GeGraphDef graph_def = 1;
-};

parser/proto/tensorflow/node_def.proto

@@ -1,63 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "NodeProto";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-
-message NodeDef {
-  // The name given to this operator. Used for naming inputs,
-  // logging, visualization, etc.  Unique within a single GraphDef.
-  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-  string name = 1;
-
-  // The operation name.  There may be custom parameters in attrs.
-  // Op names starting with an underscore are reserved for internal use.
-  string op = 2;
-
-  // Each input is "node:src_output" with "node" being a string name and
-  // "src_output" indicating which output tensor to use from "node". If
-  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-  // may optionally be followed by control inputs that have the format
-  // "^node".
-  repeated string input = 3;
-
-  // A (possibly partial) specification for the device on which this
-  // node should be placed.
-  // The expected syntax for this string is as follows:
-  //
-  // DEVICE_SPEC ::= PARTIAL_SPEC
-  //
-  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-  // CONSTRAINT ::= ("job:" JOB_NAME)
-  //              | ("replica:" [1-9][0-9]*)
-  //              | ("task:" [1-9][0-9]*)
-  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
-  //
-  // Valid values for this string include:
-  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
-  // * "/job:worker/device:GPU:3"                   (partial specification)
-  // * ""                                    (no specification)
-  //
-  // If the constraints do not resolve to a single device (or if this
-  // field is empty or not present), the runtime will attempt to
-  // choose a device automatically.
-  string device = 4;
-
-  // Operation-specific graph-construction-time configuration.
-  // Note that this should include all attrs defined in the
-  // corresponding OpDef, including those with a value matching
-  // the default -- this allows the default to change and makes
-  // NodeDefs easier to interpret on their own.  However, if
-  // an attr with a default is not specified in this list, the
-  // default will be used.
-  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-  // one of the names from the corresponding OpDef's attr field).
-  // The values must have a type matching the corresponding OpDef
-  // attr's type field.
-  // Add some examples here showing best practices.
-  map<string, AttrValue> attr = 5;
-};

parser/proto/tensorflow/op_def.proto

@@ -1,164 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "OpDefProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "types.proto";
-
-// Defines an operation. A NodeDef in a GraphDef specifies an Op by
-// using the "op" field which should match the name of a OpDef.
-// LINT.IfChange
-message OpDef {
-  // Op names starting with an underscore are reserved for internal use.
-  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-  string name = 1;
-
-  // For describing inputs and outputs.
-  message ArgDef {
-    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-    string name = 1;
-
-    // Human readable description.
-    string description = 2;
-
-    // Describes the type of one or more tensors that are accepted/produced
-    // by this input/output arg.  The only legal combinations are:
-    // * For a single tensor: either the "type" field is set or the
-    //   "type_attr" field is set to the name of an attr with type "type".
-    // * For a sequence of tensors with the same type: the "number_attr"
-    //   field will be set to the name of an attr with type "int", and
-    //   either the "type" or "type_attr" field will be set as for
-    //   single tensors.
-    // * For a sequence of tensors, the "type_list_attr" field will be set
-    //   to the name of an attr with type "list(type)".
-    DataType type = 3;
-    string type_attr = 4;    // if specified, attr must have type "type"
-    string number_attr = 5;  // if specified, attr must have type "int"
-    // If specified, attr must have type "list(type)", and none of
-    // type, type_attr, and number_attr may be specified.
-    string type_list_attr = 6;
-
-    // For inputs: if true, the inputs are required to be refs.
-    //   By default, inputs can be either refs or non-refs.
-    // For outputs: if true, outputs are refs, otherwise they are not.
-    bool is_ref = 16;
-  };
-
-  // Description of the input(s).
-  repeated ArgDef input_arg = 2;
-
-  // Description of the output(s).
-  repeated ArgDef output_arg = 3;
-
-  // Description of the graph-construction-time configuration of this
-  // Op.  That is to say, this describes the attr fields that will
-  // be specified in the NodeDef.
-  message AttrDef {
-    // A descriptive name for the argument.  May be used, e.g. by the
-    // Python client, as a keyword argument name, and so should match
-    // the regexp "[a-z][a-z0-9_]+".
-    string name = 1;
-
-    // One of the type names from attr_value.proto ("string", "list(string)",
-    // "int", etc.).
-    string type = 2;
-
-    // A reasonable default for this attribute if the user does not supply
-    // a value.  If not specified, the user must supply a value.
-    AttrValue default_value = 3;
-
-    // Human-readable description.
-    string description = 4;
-
-
-    // --- Constraints ---
-    // These constraints are only in effect if specified.  Default is no
-    // constraints.
-
-    // For type == "int", this is a minimum value.  For "list(___)"
-    // types, this is the minimum length.
-    bool has_minimum = 5;
-    int64 minimum = 6;
-
-    // The set of allowed values.  Has type that is the "list" version
-    // of the "type" field above (uses the "list" field of AttrValue).
-    // If type == "type" or "list(type)" above, then the "type" field
-    // of "allowed_values.list" has the set of allowed DataTypes.
-    // If type == "string" or "list(string)", then the "s" field of
-    // "allowed_values.list" has the set of allowed strings.
-    AttrValue allowed_values = 7;
-  }
-  repeated AttrDef attr = 4;
-
-  // Optional deprecation based on GraphDef versions.
-  OpDeprecation deprecation = 8;
-
-  // One-line human-readable description of what the Op does.
-  string summary = 5;
-
-  // Additional, longer human-readable description of what the Op does.
-  string description = 6;
-
-  // -------------------------------------------------------------------------
-  // Which optimizations this operation can participate in.
-
-  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-  bool is_commutative = 18;
-
-  // If is_aggregate is true, then this operation accepts N >= 2
-  // inputs and produces 1 output all of the same type.  Should be
-  // associative and commutative, and produce output with the same
-  // shape as the input.  The optimizer may replace an aggregate op
-  // taking input from multiple devices with a tree of aggregate ops
-  // that aggregate locally within each device (and possibly within
-  // groups of nearby devices) before communicating.
-  bool is_aggregate = 16;  // for things like add
-
-  // Other optimizations go here, like
-  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.
-
-  // -------------------------------------------------------------------------
-  // Optimization constraints.
-
-  // Ops are marked as stateful if their behavior depends on some state beyond
-  // their input tensors (e.g. variable reading op) or if they have
-  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
-  // must always produce the same output for the same input and have
-  // no side-effects.
-  //
-  // By default Ops may be moved between devices.  Stateful ops should
-  // either not be moved, or should only be moved if that state can also
-  // be moved (e.g. via some sort of save / restore).
-  // Stateful ops are guaranteed to never be optimized away by Common
-  // Subexpression Elimination (CSE).
-  bool is_stateful = 17;  // for things like variables, queue
-
-  // -------------------------------------------------------------------------
-  // Non-standard options.
-
-  // By default, all inputs to an Op must be initialized Tensors.  Ops
-  // that may initialize tensors for the first time should set this
-  // field to true, to allow the Op to take an uninitialized Tensor as
-  // input.
-  bool allows_uninitialized_input = 19;  // for Assign, etc.
-};
-// LINT.ThenChange(
-//     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)
-
-// Information about version-dependent deprecation of an op
-message OpDeprecation {
-  // First GraphDef version at which the op is disallowed.
-  int32 version = 1;
-
-  // Explanation of why it was deprecated and what to use instead.
-  string explanation = 2;
-};
-
-// A collection of OpDefs
-message OpList {
-  repeated OpDef op = 1;
-};

parser/proto/tensorflow/resource_handle.proto

@@ -1,29 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "ResourceHandle";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Protocol buffer representing a handle to a tensorflow resource. Handles are
-// not valid across executions, but can be serialized back and forth from within
-// a single run.
-message ResourceHandleProto {
-  // Unique name for the device containing the resource.
-  string device = 1;
-
-  // Container in which this resource is placed.
-  string container = 2;
-
-  // Unique name of this resource.
-  string name = 3;
-
-  // Hash code for the type of the resource. Is only valid in the same device
-  // and in the same execution.
-  uint64 hash_code = 4;
-
-  // For debug-only, the name of the type pointed to by this handle, if
-  // available.
-  string maybe_type_name = 5;
-};

parser/proto/tensorflow/tensor.proto

@@ -1,94 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "resource_handle.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing a tensor.
-message TensorProto {
-  DataType dtype = 1;
-
-  // Shape of the tensor.
-  TensorShapeProto tensor_shape = 2;
-
-  // Only one of the representations below is set, one of "tensor_contents" and
-  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
-  // contain repeated fields it would require another extra set of messages.
-
-  // Version number.
-  //
-  // In version 0, if the "repeated xxx" representations contain only one
-  // element, that element is repeated to fill the shape.  This makes it easy
-  // to represent a constant Tensor with a single value.
-  int32 version_number = 3;
-
-  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
-  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
-  // can be used for all tensor types. The purpose of this representation is to
-  // reduce serialization overhead during RPC call by avoiding serialization of
-  // many repeated small items.
-  bytes tensor_content = 4;
-
-  // Type specific representations that make it easy to create tensor protos in
-  // all languages.  Only the representation corresponding to "dtype" can
-  // be set.  The values hold the flattened representation of the tensor in
-  // row major order.
-
-  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
-  // have some pointless zero padding for each value here.
-  repeated int32 half_val = 13 [packed = true];
-
-  // DT_FLOAT.
-  repeated float float_val = 5 [packed = true];
-
-  // DT_DOUBLE.
-  repeated double double_val = 6 [packed = true];
-
-  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
-  repeated int32 int_val = 7 [packed = true];
-
-  // DT_STRING
-  repeated bytes string_val = 8;
-
-  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
-  // and imaginary parts of i-th single precision complex.
-  repeated float scomplex_val = 9 [packed = true];
-
-  // DT_INT64
-  repeated int64 int64_val = 10 [packed = true];
-
-  // DT_BOOL
-  repeated bool bool_val = 11 [packed = true];
-
-  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
-  // and imaginary parts of i-th double precision complex.
-  repeated double dcomplex_val = 12 [packed = true];
-
-  // DT_RESOURCE
-  repeated ResourceHandleProto resource_handle_val = 14;
-
-  // DT_VARIANT
-  repeated VariantTensorDataProto variant_val = 15;
-
-  // DT_UINT32
-  repeated uint32 uint32_val = 16 [packed = true];
-
-  // DT_UINT64
-  repeated uint64 uint64_val = 17 [packed = true];
-};
-
-// Protocol buffer representing the serialization format of DT_VARIANT tensors.
-message VariantTensorDataProto {
-  // Name of the type of objects being serialized.
-  string type_name = 1;
-  // Portions of the object that are not Tensors.
-  bytes metadata = 2;
-  // Tensors contained within objects being serialized.
-  repeated TensorProto tensors = 3;
-}

parser/proto/tensorflow/tensor_shape.proto

@@ -1,45 +0,0 @@
-// Protocol buffer representing the shape of tensors.
-
-syntax = "proto3";
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorShapeProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-package domi.tensorflow;
-
-// Dimensions of a tensor.
-message TensorShapeProto {
-  // One dimension of the tensor.
-  message Dim {
-    // Size of the tensor in that dimension.
-    // This value must be >= -1, but values of -1 are reserved for "unknown"
-    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
-    // that work with TensorShapeProto may fail at runtime when deserializing
-    // a TensorShapeProto containing a dim value of -1.
-    int64 size = 1;
-
-    // Optional name of the tensor dimension.
-    string name = 2;
-  };
-
-  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
-  // for a 30 x 40 2D tensor.  If an entry has size -1, this
-  // corresponds to a dimension of unknown size. The names are
-  // optional.
-  //
-  // The order of entries in "dim" matters: It indicates the layout of the
-  // values in the tensor in-memory representation.
-  //
-  // The first entry in "dim" is the outermost dimension used to layout the
-  // values, the last entry is the innermost dimension.  This matches the
-  // in-memory layout of RowMajor Eigen tensors.
-  //
-  // If "dim.size()" > 0, "unknown_rank" must be false.
-  repeated Dim dim = 2;
-
-  // If true, the number of dimensions in the shape is unknown.
-  //
-  // If true, "dim.size()" must be 0.
-  bool unknown_rank = 3;
-};

parser/proto/tensorflow/types.proto

@@ -1,74 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TypesProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// LINT.IfChange
-enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0;
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1;
-  DT_DOUBLE = 2;
-  DT_INT32 = 3;
-  DT_UINT8 = 4;
-  DT_INT16 = 5;
-  DT_INT8 = 6;
-  DT_STRING = 7;
-  DT_COMPLEX64 = 8;  // Single-precision complex
-  DT_INT64 = 9;
-  DT_BOOL = 10;
-  DT_QINT8 = 11;     // Quantized int8
-  DT_QUINT8 = 12;    // Quantized uint8
-  DT_QINT32 = 13;    // Quantized int32
-  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
-  DT_QINT16 = 15;    // Quantized int16
-  DT_QUINT16 = 16;   // Quantized uint16
-  DT_UINT16 = 17;
-  DT_COMPLEX128 = 18;  // Double-precision complex
-  DT_HALF = 19;
-  DT_RESOURCE = 20;
-  DT_VARIANT = 21;  // Arbitrary C++ data types
-  DT_UINT32 = 22;
-  DT_UINT64 = 23;
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101;
-  DT_DOUBLE_REF = 102;
-  DT_INT32_REF = 103;
-  DT_UINT8_REF = 104;
-  DT_INT16_REF = 105;
-  DT_INT8_REF = 106;
-  DT_STRING_REF = 107;
-  DT_COMPLEX64_REF = 108;
-  DT_INT64_REF = 109;
-  DT_BOOL_REF = 110;
-  DT_QINT8_REF = 111;
-  DT_QUINT8_REF = 112;
-  DT_QINT32_REF = 113;
-  DT_BFLOAT16_REF = 114;
-  DT_QINT16_REF = 115;
-  DT_QUINT16_REF = 116;
-  DT_UINT16_REF = 117;
-  DT_COMPLEX128_REF = 118;
-  DT_HALF_REF = 119;
-  DT_RESOURCE_REF = 120;
-  DT_VARIANT_REF = 121;
-  DT_UINT32_REF = 122;
-  DT_UINT64_REF = 123;
-}
-// LINT.ThenChange(
-//    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
-//    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)

parser/proto/tensorflow/versions.proto

@@ -1,31 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "VersionsProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Version information for a piece of serialized data
-//
-// There are different types of versions for each type of data
-// (GraphDef, etc.), but they all have the same common shape
-// described here.
-//
-// Each consumer has "consumer" and "min_producer" versions (specified
-// elsewhere).  A consumer is allowed to consume this data if
-//
-//   producer >= min_producer
-//   consumer >= min_consumer
-//   consumer not in bad_consumers
-//
-message VersionDef {
-  // The version of the code that produced this data.
-  int32 producer = 1;
-
-  // Any consumer below this version is not allowed to consume this data.
-  int32 min_consumer = 2;
-
-  // Specific consumer versions which are disallowed (e.g. due to bugs).
-  repeated int32 bad_consumers = 3;
-};

parser/tensorflow/proto/ge_ir.proto

@@ -1,193 +0,0 @@
-syntax = "proto3";
-
-package ge.proto;
-
-enum DataType
-{
-    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
-    DT_FLOAT     = 1;  // float type
-    DT_FLOAT16   = 2;  // fp16 type
-    DT_INT8      = 3;  // int8 type
-    DT_UINT8     = 4;  // uint8 type
-    DT_INT16     = 5;  // int16 type
-    DT_UINT16    = 6;  // uint16 type
-    DT_INT32     = 7;  //
-    DT_INT64     = 8;  // int64 type
-    DT_UINT32    = 9;  // unsigned int32
-    DT_UINT64    = 10;  // unsigned int64
-    DT_BOOL      = 11;  // bool type
-    DT_DOUBLE    = 12; // double type
-    DT_STRING = 13;            // string type
-    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
-    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
-    DT_COMPLEX64 = 16;         // complex64 type
-    DT_COMPLEX128 = 17;        // complex128 type
-    DT_QINT8 = 18;             // qint8 type
-    DT_QINT16 = 19;            // qint16 type
-    DT_QINT32 = 20;            // qint32 type
-    DT_QUINT8 = 21;            // quint8 type
-    DT_QUINT16 = 22;           // quint16 type
-    DT_RESOURCE  = 23;         // resource type
-    DT_STRING_REF = 24;        // string_ref type
-    DT_DUAL      = 25;              /**< dual output type */
-    DT_VARIANT = 26;           // variant type
-    DT_BF16 = 27;              // bf16 type
-    DT_INT4 = 28;              // int4 type
-}
-
-message AttrDef
-{
-    message ListValue
-    {
-        enum ListValueType{
-          VT_LIST_NONE = 0;
-          VT_LIST_STRING = 1;
-          VT_LIST_INT = 2;
-          VT_LIST_FLOAT = 3;
-          VT_LIST_BOOL = 4;
-          VT_LIST_BYTES = 5;
-          VT_LIST_TENSOR_DESC = 6;
-          VT_LIST_TENSOR = 7;
-          VT_LIST_GRAPH = 8;
-          VT_LIST_NAMED_ATTRS = 9;
-          VT_LIST_DATA_TYPE = 10;
-        }
-        repeated bytes s             = 2;                    // "list(string)"
-        repeated int64 i             = 3;  // "list(int)"
-        repeated float f             = 4;   // "list(float)"
-        repeated bool  b             = 5;  // "list(bool)"
-        repeated bytes bt            = 7;
-        repeated TensorDescriptor td = 8;
-        repeated TensorDef t         = 9;
-        repeated GraphDef g          = 10;
-	    repeated NamedAttrs na       = 11;
-	    repeated int64 dt            = 12; // list ge::DataType
-
-	    ListValueType val_type       = 20;
-    }
-
-    message ListListInt{
-        message ListInt{
-            repeated int64 list_i             = 1; // list int
-        }
-        repeated ListInt list_list_i             = 1; // list list int
-    }
-
-    oneof value
-    {
-        bytes            s    = 2;  // "string"
-        int64            i    = 3;  // "int"
-        float            f    = 4;  // "float"
-        bool             b    = 5;  // "bool"
-        bytes            bt   = 7;
-        ListValue        list = 1;   // any "list(...)"
-        NamedAttrs       func = 10;  // Used to support attr nesting
-        TensorDescriptor td   = 11;  // GeTensorDesc type
-        TensorDef        t    = 12;  // GeTensor type
-        GraphDef         g    = 13;  // Graph type
-        ListListInt      list_list_int  = 14;  // List List Int type
-        int64            dt   = 15; // ge::DataType
-    }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs
-{
-    string               name = 1;
-    map<string, AttrDef> attr = 2;
-}
-
-// Shape / dimension description, using row-major order
-message ShapeDef
-{
-    repeated int64 dim = 1;  // Size of each dimension
-}
-
-// Multidimensional data description
-message TensorDescriptor
-{
-    string   name   = 1;  // Optional parameter, tensor name
-
-    DataType dtype  = 2;  // tensor datatype
-    ShapeDef shape  = 3;  // Shape / dimension
-    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"
-
-    bool has_out_attr = 9;
-    int64 size = 10;
-    int64 weight_size = 11;
-    bool reuse_input = 12;
-    bool output_tensor = 13;
-    string device_type = 14;
-    bool input_tensor =15;
-    int64 real_dim_cnt = 16;
-    int64 reuse_input_index = 17;
-    int64 data_offset = 18;
-    int64 cmps_size = 19;
-    string cmps_tab = 20;
-    int64 cmps_tab_offset = 21;
-
-	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
-}
-
-// GeTensor definition
-message TensorDef
-{
-    TensorDescriptor desc = 1;  // Tensor description
-    bytes            data = 2;  // Tensor data
-}
-
-
-// Operator description
-message OpDef
-{
-    string name = 1;  // name
-    string type = 2;  // type
-
-    repeated string input = 5;  // input original op name + outgoing index. op_name：index
-
-    map<string, AttrDef> attr = 10;  // Set of operator parameter fields
-
-    bool has_out_attr = 20;
-    int64 id = 21;
-    int64 stream_id =22;
-    repeated string input_name = 23;
-    repeated string src_name = 24;
-    repeated int64 src_index = 25;
-    repeated string dst_name = 26;
-    repeated int64 dst_index = 27;
-    repeated int64 input_i = 28;
-    repeated int64 output_i = 29;
-    repeated int64 workspace = 30;
-    repeated int64 workspace_bytes = 31;
-    repeated bool is_input_const = 32;
-    repeated TensorDescriptor input_desc = 33;
-    repeated TensorDescriptor output_desc = 34;
-    repeated string subgraph_name = 35;
-}
-
-// Graph definition
-message GraphDef
-{
-    string name   = 1;   //  name
-
-    repeated string input  = 4;  // Graph input
-    repeated string output = 5;  // Graph output
-
-    repeated OpDef op      = 6;  // List of operators
-
-	map<string, AttrDef> attr = 11;  // Extended field
-}
-
-// model definition
-message ModelDef
-{
-	string name         = 1;  // name
-	uint32 version      = 2;  // IR Proto verion
-	string custom_version = 3;  // User model version number, passed in by user
-
-    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef
-
-    map<string, AttrDef> attr = 11;  // Extended field
-}
-

parser/tensorflow/proto/insert_op.proto

@@ -1,140 +0,0 @@
-syntax = "proto3";
-
-package domi;
-
-message InsertNewOps {
-	repeated AippOpParams aipp_op = 1;
-	repeated MultiShapeOpParams multi_shape_op = 2;
-}
-
-message AippOpParams {
-	enum InputFormat {
-		UNDEFINED = 0;
-		YUV420SP_U8 = 1;
-		XRGB8888_U8 = 2;
-		RGB888_U8   = 3;
-		YUV400_U8   = 4;
-		NC1HWC0DI_FP16 = 5;
-		NC1HWC0DI_S8 = 6;
-		ARGB8888_U8 = 7;
-		YUYV_U8 = 8;
-		YUV422SP_U8 = 9;
-		AYUV444_U8 = 10;
-		RAW10 = 11;
-		RAW12 = 12;
-		RAW16 = 13;
-		RAW24 = 14;
-		RGB16 = 15;
-		RGB20 = 16;
-		RGB24 = 17;
-		RGB8_IR = 18;
-		RGB16_IR = 19;
-		RGB24_IR = 20;
-	}
-
-	enum AippMode {
-		undefined = 0;
-		static = 1;
-		dynamic = 2;
-	}
-
-	// AIPP模式，区分静态AIPP和动态AIPP
-	AippMode aipp_mode = 1;
-
-	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
-	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
-	uint32 related_input_rank = 2;
-
-        // related_input_name is optional and the top name of data node which inserts aipp
-        string related_input_name = 6;
-
-	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
-	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
-	// 配置值 <= Data算子输出边的个数。
-	repeated uint32 input_edge_idx = 3;
-
-	// [Begin] 动态AIPP参数，配置静态AIPP时无效
-	uint32 max_src_image_size = 4;
-
-	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
-	bool support_rotation = 5;
-
-	// [End] 动态AIPP参数
-
-
-	// [Begin] 静态AIPP参数，配置动态AIPP时无效
-	InputFormat input_format = 51;
-	bool csc_switch = 52;
-	float cpadding_value = 53;
-	bool rbuv_swap_switch = 54;
-	bool ax_swap_switch = 55;
-	bool single_line_mode = 56;
-
-	int32 src_image_size_w = 57;
-	int32 src_image_size_h = 58;
-
-	bool crop = 59;
-	int32 load_start_pos_w = 60;
-	int32 load_start_pos_h = 61;
-	int32 crop_size_w = 62;
-	int32 crop_size_h = 63;
-
-	bool resize = 64;
-	int32 resize_output_w = 65;
-	int32 resize_output_h = 66;
-
-	bool padding = 67;
-	int32 left_padding_size = 68;
-	int32 right_padding_size = 69;
-	int32 top_padding_size = 70;
-	int32 bottom_padding_size = 71;
-	float padding_value = 72;
-
-	int32 mean_chn_0 = 10;
-	int32 mean_chn_1 = 11;
-	int32 mean_chn_2 = 12;
-	int32 mean_chn_3 = 19;
-	float min_chn_0 = 13;
-	float min_chn_1 = 14;
-	float min_chn_2 = 15;
-	float min_chn_3 = 20;
-	repeated float var_reci_chn_0 = 16;
-	repeated float var_reci_chn_1 = 17;
-	repeated float var_reci_chn_2 = 18;
-	repeated float var_reci_chn_3 = 21;
-
-	repeated int32 matrix_r0c0 = 30;
-	repeated int32 matrix_r0c1 = 31;
-	repeated int32 matrix_r0c2 = 32;
-	repeated int32 matrix_r1c0 = 33;
-	repeated int32 matrix_r1c1 = 34;
-	repeated int32 matrix_r1c2 = 35;
-	repeated int32 matrix_r2c0 = 36;
-	repeated int32 matrix_r2c1 = 37;
-	repeated int32 matrix_r2c2 = 38;
-	repeated int32 output_bias_0 = 39;
-	repeated int32 output_bias_1 = 40;
-	repeated int32 output_bias_2 = 41;
-	repeated int32 input_bias_0 = 42;
-	repeated int32 input_bias_1 = 43;
-	repeated int32 input_bias_2 = 44;
-
-	// [End] 静态AIPP参数
-
-       // The n number that is used for raw/rgbir data into f16 transformation.
-       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
-       uint32 raw_rgbir_to_f16_n = 45;
-}
-
-message MultiShapeOpParams {
-	enum MultiShapeMode {
-		batch      = 0;             //动态batch
-		resolution = 1;             //动态分辨率，扩展用
-	}
-
-	MultiShapeMode    mode                      = 1;        //算子模式
-	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入
-
-
-	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
-}

parser/tensorflow/proto/om.proto

@@ -1,396 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-enum TargetType
-{
-    MINI = 0;
-    TINY = 1;
-    LITE = 2;
-}
-
-// offline model
-message ModelDef {
-    string name = 1;
-    uint32 version = 2;
-
-    uint64 memory_size = 10;
-    uint32 stream_num = 11;
-    uint32 event_num = 12;
-    uint64 weight_size = 13;
-    uint32 label_num = 15;
-    repeated OpDef op = 20;
-    TargetType target_type = 23;
-
-    map<string, AttrDef> attr = 30;
-};
-
-// operator define
-message OpDef {
-    string name = 1;
-    string type = 2;
-
-    uint32 id = 3;
-    uint32 stream_id = 4;
-
-    repeated string input_name = 5;
-
-    repeated string src_name = 8;
-    repeated int32 src_index = 9;
-    repeated int64 input = 10;
-    repeated int64 output = 11;
-    repeated TensorDescriptor input_desc = 12;
-    repeated TensorDescriptor output_desc = 13;
-    repeated WeightDef weights = 14;
-    repeated string dst_name = 15;
-    repeated int32 dst_index = 16;
-
-    repeated int64 workspace = 20;
-    repeated uint32 workspace_bytes = 21;
-
-    repeated string weight_name = 22;
-    repeated bool is_input_const = 23;
-
-    map<string, AttrDef> attr = 30;
-
-    QuantizeFactorParams quantize_factor = 31;
-
-    oneof op_params {
-        // start at 100 here
-        SendOpParams sender_param = 100;
-        RecvOpParams receiver_param = 200;
-        ConvolutionOpParams convolution_param = 300;
-        PoolingOpParams pooling_param = 400;
-        EltwiseOpParams eltwise_param = 500;
-        BatchNormOpParams batchnorm_param = 600;
-        ScaleOpParams scale_param = 700;
-        FullConnectionOpParams full_connection_param = 800;
-        SoftmaxOpParams softmax_param = 900;
-        ActivationOpParams activation_param = 1000;
-        ReshapeOpParams reshape_param = 1100;
-    }
-};
-
-message SendOpParams {
-    uint32 event_id = 1;
-};
-
-message RecvOpParams {
-    uint32 event_id = 1;
-};
-
-enum QuantizeScaleType
-{
-    VECTOR_SCALE = 0;
-    SCALAR_SCALE = 1;
-}
-
-enum QuantizeScaleMode
-{
-    NORMAL_MODE = 0;
-    SQRT_MODE = 1;
-}
-
-enum QuantizeAlgorithm
-{
-    NON_OFFSET_ALGO = 0;
-    HALF_OFFSET_ALGO = 1;
-    ALL_OFFSET_ALGO = 2;
-}
-message QuantizeFactor
-{
-    QuantizeScaleMode scale_mode = 1;
-    bytes scale_value = 2;
-    int64 scale_offset = 3;
-    bytes offset_data_value = 4;
-    int64 offset_data_offset = 5;
-    bytes offset_weight_value = 6;
-    int64 offset_weight_offset = 7;
-    bytes offset_pad_value = 8;
-    int64 offset_pad_offset = 9;
-};
-
-message QuantizeCalcFactor
-{
-    bytes offsetw = 1;
-    int64 offsetw_offset = 2;
-    bytes offsetd = 3;
-    int64 offsetd_offset = 4;
-    bytes scalereq = 5;
-    int64 scaledreq_offset = 6;
-    bytes offsetdnext = 7;
-    int64 offsetdnext_offset = 8;
-}
-
-message QuantizeFactorParams
-{
-    QuantizeAlgorithm quantize_algo = 1;
-    QuantizeScaleType scale_type = 2;
-    QuantizeFactor quantize_param = 3;
-    QuantizeFactor dequantize_param = 4;
-    QuantizeFactor requantize_param = 5;
-    QuantizeCalcFactor quantizecalc_param = 6;
-};
-
-message ConvolutionOpParams {
-    int32 mode = 1;
-    int32 algo = 2;
-    int32 pad_mode = 3;
-    uint32 group = 4;
-    uint32 num_output = 5;
-
-    repeated uint32 pad = 10;
-    repeated uint32 stride = 11;
-    repeated uint32 dilation = 12;
-    repeated uint32 kernel = 13;
-
-    float alpha = 20;
-    float beta = 21;
-
-    WeightDef filter = 40;
-    WeightDef bias = 41;
-
-    bool relu_flag = 62;
-    repeated uint32 adj = 70;
-    repeated uint32 target_shape = 71;
-    repeated uint32 before_pad = 72;
-};
-
-message PoolingOpParams {
-    int32 mode = 1;
-    int32 nan_opt = 2;
-    int32 pad_mode = 3;
-    bool global_pooling = 4;
-
-    repeated uint32 window = 10;
-    repeated uint32 pad = 11;
-    repeated uint32 stride = 12;
-    bool ceil_mode = 13;
-    int32 data_mode  = 14;
-
-    float alpha = 20;
-    float beta = 21;
-    repeated uint32 before_pad = 22;
-};
-
-message EltwiseOpParams {
-    int32 mode = 1;
-    repeated float coeff = 2;
-    float alpha = 3;
-    float beta = 4;
-    repeated WeightDef weight = 5;
-    bool relu_flag = 6;
-};
-
-message ActivationOpParams {
-    int32 mode = 1;
-    float coef = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message BatchNormOpParams {
-    int32 mode = 1;
-
-    float alpha = 2;
-    float beta = 3;
-    double epsilon = 4;//optinal,[default = 1e-5]
-    bool use_global_stats = 5; //optinal,by default true,testing mode
-    float  moving_average_fraction = 6; //optinal,[default = .999];
-
-    WeightDef estimated_mean = 7;
-    WeightDef estimated_variance = 8;
-
-    WeightDef scale = 9;
-    WeightDef bias = 10;
-};
-
-message ScaleOpParams {
-    WeightDef scale = 1;
-    WeightDef bias = 2;
-};
-
-message ReshapeOpParams {
-    float alpha = 1;
-    float beta = 2;
-    ShapeDef shape = 3;
-    int32 axis = 4;
-    int32 num_axes = 5;
-    int32 format = 6;
-};
-
-message SoftmaxOpParams {
-    int32 algo = 1;
-    int32 mode = 2;
-    float alpha = 3;
-    float beta = 4;
-};
-
-message FullConnectionOpParams {
-    WeightDef filter = 1;
-    WeightDef bias = 2;
-    uint32 num_output = 3;
-    bool relu_flag = 12;
-};
-
-message FlattenOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 start_axis = 3;
-    int32 end_axis = 4;
-}
-
-message AddLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulLimitedOpParams {
-    float alpha = 1;
-    float beta = 2;
-    int32 axis = 3;
-    bool broadcast = 4;
-
-    repeated WeightDef weight = 10;
-};
-
-message AddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message MulOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message SubOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    repeated WeightDef weight = 10;
-};
-
-message BiasAddOpParams {
-    float alpha = 1;
-    float beta = 2;
-
-    WeightDef bias = 10;
-};
-
-message MatMulOpParams {
-    float alpha = 1;
-    float beta = 2;
-    bool transposeX = 3;
-    bool transposeW = 4;
-
-    WeightDef filter = 10;
-    WeightDef bias = 12;
-};
-
-message RsqrtOpParams {
-    float alpha = 1;
-    float beta = 2;
-};
-
-
-message WeightDef {
-    int32 format = 1;
-    int32 data_type = 2;
-    ShapeDef shape = 3;
-    bytes data = 4;
-    int64 data_offset = 5;
-    uint32 cmps_size = 6;
-    bytes cmps_tab = 7;
-    int64 cmps_tab_offset = 10;
-    CompressInfo cmps_info = 8;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
-}
-
-message ShapeDef {
-    repeated int64 dim = 1;
-}
-
-enum DeviceType {
-  NPU = 0;                   // In default, we will use NPU.
-  CPU = 1;                   // CPU
-}
-
-message AllOffsetQuantizeInfo {
-	float scale  = 1;
-	int32 offset = 2;
-} 
-
-message TensorDescriptor {
-    int32 format = 1;
-    int32 data_type = 2;
-    repeated int64 dim = 3;
-    uint32 size = 4;
-    bool reuse_input = 5;
-    bool output_tensor = 7;
-    DeviceType device_type = 8;
-    bool input_tensor = 9;
-    uint32 real_dim_cnt = 10;
-    uint32 reuse_input_index = 11;
-    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
-}
-
-message CompressInfo {
-    int32 blockRow = 1;                             // block row
-    int32 blockCol = 2;                             // block col
-    int32 fractalK = 3;                             // fractal K
-    int32 fractalN = 4;                             // fractal N
-    int32 lastFractalK = 5;                         // K of last fractal
-    int32 lastFractalN = 6;                         // N of last fractal
-    int32 cubeSize = 7;                             // cube's length
-    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
-}
-
-message AttrDef {
-  message ListValue {
-    repeated string s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated uint32 u = 6 [packed = true];         // "list(uint)"
-    repeated bytes bt = 7;
-  }
-
-  oneof value {
-    string s = 2;                // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    uint32 u = 6;                // "uint32"
-    bytes bt = 7;
-    ListValue list = 1;          // any "list(...)"
-    NamedAttrs func = 10;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NamedAttrs {
-  string name = 1;
-  map<string, AttrDef> attr = 2;
-}
-

parser/tensorflow/proto/task.proto

@@ -1,179 +0,0 @@
-/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
- *
- * This program is free software; you can redistribute it and/or modify
- * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
- *
- * This program is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- * Apache License for more details at
- * http://www.apache.org/licenses/LICENSE-2.0
- */
-syntax = "proto3";
-
-package domi;
-
-message ModelTaskDef {
-    string version = 1;
-
-    map<string, string> attr = 9; // Extended field
-    repeated TaskDef task = 10;
-
-    uint64 memory_size = 11;
-    uint32 stream_num = 12;
-    uint32 event_num = 13;
-    uint64 weight_size = 14;
-
-    repeated bytes op = 15; // input/output opdef in bytes
-
-    uint64 base_addr = 16;    // base addr
-    uint64 weight_addr = 17;  // weight addr
-    uint32 batch_num = 18;
-}
-
-
-message TaskDef {
-    uint32 id = 1;
-    uint32 type = 2;
-
-    uint32 stream_id = 10;
-    uint32 event_id = 11;
-
-    KernelDef kernel = 20;
-    KernelExDef kernel_ex = 21;
-    KernelHcclDef kernel_hccl = 25;
-    EventExDef event_ex = 26;
-    LogTimeStampDef log_timestamp = 28;
-
-    uint32 label_id = 30;
-
-    MemcpyAsyncDef memcpy_async = 31;
-    StreamSwitchDef stream_switch = 32;
-    StreamActiveDef stream_active = 33;
-    bytes private_def = 34;
-    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
-    StreamSwitchNDef stream_switch_n = 36;
-
-    LabelSetDef label_set = 37;
-    LabelGotoExDef label_goto_ex = 38;
-    LabelSwitchByIndexDef label_switch_by_index = 39;
-    KernelDefWithHandle kernel_with_handle = 40;
-}
-
-message KernelDef {
-    KernelContext context = 1;
-
-    string stub_func = 10;
-    uint32 block_dim = 11;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes sm_desc = 14;
-    bytes flowtable = 15;
-    string so_name = 16;
-    string kernel_name = 17;
-    bytes kernel_ext_info = 18;
-    uint32 kernel_ext_info_size = 19;
-}
-
-message KernelDefWithHandle {
-    KernelContext context = 1;
-
-    uint64 handle = 10;
-    string dev_func = 11;
-    uint32 block_dim = 12;
-    uint32 args_size = 13;
-    bytes args = 14;
-    bytes sm_desc = 15;
-    string original_kernel_key = 16;
-    string node_info = 17;
-}
-
-message KernelContext {
-    uint32 kernel_type = 1;
-    uint32 op_id = 2;                              // OP type in CCE
-    uint32 kernel_func_id = 3;
-    uint32 op_index = 4;                           // TE/Custom operator
-    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
-    bytes args_offset = 6;                         // args offset information
-    uint32 args_count = 7;                         // args count
-    repeated uint32 origin_op_index = 8;
-}
-
-
-message KernelExDef {
-    uint32 flags = 1;
-
-    uint32 op_index = 4;
-    uint32 args_size = 12;
-    bytes args = 13;
-    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
-    uint32 task_info_size = 15;
-    bytes kernel_ext_info = 16;
-    uint32 kernel_ext_info_size = 17;
-}
-
-
-message KernelHcclDef {
-    uint32 op_index = 8;
-    string hccl_type = 9;
-}
-
-
-message EventExDef {
-    uint32 op_index = 1;
-    uint32 event_type = 2;
-}
-
-message LogTimeStampDef {
-    uint64 logid = 1;
-    bool notify = 2;
-    uint32 flat = 3;
-}
-
-message MemcpyAsyncDef {
-    uint64 dst = 1;
-    uint64 dst_max = 2;
-    uint64 src = 3;
-    uint64 count = 4;
-    uint32 kind = 5;
-    uint32 op_index = 6;
-}
-
-message StreamSwitchDef {
-    uint32 op_index = 1;
-    uint32 true_stream_id = 2;
-    int64 value = 3;
-    uint64 value_ptr = 4;
-    uint32 data_type = 5;
-}
-
-message StreamActiveDef {
-    uint32 op_index = 1;
-    uint32 active_stream_id = 2;
-}
-
-message StreamSwitchNDef {
-    uint32 op_index = 1;
-    uint32 size = 2;
-    repeated int64 target_value = 3;
-    repeated uint32 true_stream_id = 4;
-    uint32 element_size = 5;
-    uint32 data_type = 6;
-}
-
-message LabelSetDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelGotoExDef {
-    uint32 op_index = 1;
-    uint32 label_id = 2;
-    uint32 model_id = 3;
-}
-
-message LabelSwitchByIndexDef {
-    uint32 op_index = 1;
-    uint32 label_max = 2;
-}

parser/tensorflow/proto/tensorflow/attr_value.proto

@@ -1,62 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "AttrValueProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "tensor.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing the value for an attr used to configure an Op.
-// Comment indicates the corresponding attr type.  Only the field matching the
-// attr type may be filled.
-message AttrValue {
-  // LINT.IfChange
-  message ListValue {
-    repeated bytes s = 2;                        // "list(string)"
-    repeated int64 i = 3 [packed = true];        // "list(int)"
-    repeated float f = 4 [packed = true];        // "list(float)"
-    repeated bool b = 5 [packed = true];         // "list(bool)"
-    repeated DataType type = 6 [packed = true];  // "list(type)"
-    repeated TensorShapeProto shape = 7;         // "list(shape)"
-    repeated TensorProto tensor = 8;             // "list(tensor)"
-    repeated NameAttrList func = 9;              // "list(attr)"
-  }
-  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
-
-  oneof value {
-    bytes s = 2;                 // "string"
-    int64 i = 3;                 // "int"
-    float f = 4;                 // "float"
-    bool b = 5;                  // "bool"
-    DataType type = 6;           // "type"
-    TensorShapeProto shape = 7;  // "shape"
-    TensorProto tensor = 8;      // "tensor"
-    ListValue list = 1;          // any "list(...)"
-
-    // "func" represents a function. func.name is a function's name or
-    // a primitive op's name. func.attr.first is the name of an attr
-    // defined for that function. func.attr.second is the value for
-    // that attr in the instantiation.
-    NameAttrList func = 10;
-
-    // This is a placeholder only used in nodes defined inside a
-    // function.  It indicates the attr value will be supplied when
-    // the function is instantiated.  For example, let us suppose a
-    // node "N" in function "FN". "N" has an attr "A" with value
-    // placeholder = "foo". When FN is instantiated with attr "foo"
-    // set to "bar", the instantiated node N's attr A will have been
-    // given the value "bar".
-    string placeholder = 9;
-  }
-}
-
-// A list of attr names and their values. The whole list is attached
-// with a string name.  E.g., MatMul[T=float].
-message NameAttrList {
-  string name = 1;
-  map<string, AttrValue> attr = 2;
-}

parser/tensorflow/proto/tensorflow/function.proto

@@ -1,100 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "FunctionProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "node_def.proto";
-import "op_def.proto";
-
-// A library is a set of named functions.
-message FunctionDefLibrary {
-  repeated FunctionDef function = 1;
-  repeated GradientDef gradient = 2;
-}
-
-// A function can be instantiated when the runtime can bind every attr
-// with a value. When a GraphDef has a call to a function, it must
-// have binding for every attr defined in the signature.
-//   * device spec, etc.
-message FunctionDef {
-  // The definition of the function's name, arguments, return values,
-  // attrs etc.
-  OpDef signature = 1;
-
-  // Attributes specific to this function definition.
-  map<string, AttrValue> attr = 5;
-
-  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
-  reserved 2;
-
-  // In both of the following fields, there is the need to specify an
-  // output that is used as either the input to another node (in
-  // `node_def`) or as a return value of the function (in `ret`).
-  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
-  // list in some cases (instead of just single outputs).  Also, we
-  // need to be able to deal with lists of unknown length (so the
-  // output index may not be known at function definition time).  So
-  // we use the following format instead:
-  // * "fun_in" where "fun_in" is the name of a function input arg in
-  //   the `signature` field above.  This represents that input, whether
-  //   it is a single tensor or a list.
-  // * "fun_in:0" gives the first element of a function input arg (a
-  //   non-list input is considered a list of length 1 for these
-  //   purposes).
-  // * "node:out" where "node" is the name of a node in `node_def` and
-  //   "out" is the name one of its op's output arguments (the name
-  //   comes from the OpDef of the node's op). This represents that
-  //   node's output, whether it is a single tensor or a list.
-  //   Note: We enforce that an op's output arguments are never
-  //   renamed in the backwards-compatibility test.
-  // * "node:out:0" gives the first element of a node output arg (a
-  //   non-list output is considered a list of length 1 for these
-  //   purposes).
-  //
-  // NOT CURRENTLY SUPPORTED (but may be in the future):
-  // * "node:out:-1" gives last element in a node output list
-  // * "node:out:1:" gives a list with all but the first element in a
-  //   node output list
-  // * "node:out::-1" gives a list with all but the last element in a
-  //   node output list
-
-  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
-  // may have values of type `placeholder` and the `input` field uses
-  // the "output" format above.
-
-  // By convention, "op" in node_def is resolved by consulting with a
-  // user-defined library first. If not resolved, "func" is assumed to
-  // be a builtin op.
-  repeated NodeDef node_def = 3;
-
-  // A mapping from the output arg names from `signature` to the
-  // outputs from `node_def` that should be returned by the function.
-  map<string, string> ret = 4;
-}
-
-// GradientDef defines the gradient function of a function defined in
-// a function library.
-//
-// A gradient function g (specified by gradient_func) for a function f
-// (specified by function_name) must follow the following:
-//
-// The function 'f' must be a numerical function which takes N inputs
-// and produces M outputs. Its gradient function 'g', which is a
-// function taking N + M inputs and produces N outputs.
-//
-// I.e. if we have
-//    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
-// then, g is
-//    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
-//                                      dL/dy1, dL/dy2, ..., dL/dy_M),
-// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
-// loss function). dL/dx_i is the partial derivative of L with respect
-// to x_i.
-message GradientDef {
-  string function_name = 1;  // The function name.
-  string gradient_func = 2;  // The gradient function's name.
-}

parser/tensorflow/proto/tensorflow/graph.proto

@@ -1,56 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "GraphProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "node_def.proto";
-import "function.proto";
-import "versions.proto";
-
-// Represents the graph of operations
-message GraphDef {
-  repeated NodeDef node = 1;
-
-  // Compatibility versions of the graph.  See core/public/version.h for version
-  // history.  The GraphDef version is distinct from the TensorFlow version, and
-  // each release of TensorFlow will support a range of GraphDef versions.
-  VersionDef versions = 4;
-
-  // Deprecated single version field; use versions above instead.  Since all
-  // GraphDef changes before "versions" was introduced were forward
-  // compatible, this field is entirely ignored.
-  int32 version = 3 [deprecated = true];
-
-  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
-  //
-  // "library" provides user-defined functions.
-  //
-  // Naming:
-  //   * library.function.name are in a flat namespace.
-  //     NOTE: We may need to change it to be hierarchical to support
-  //     different orgs. E.g.,
-  //     { "/google/nn", { ... }},
-  //     { "/google/vision", { ... }}
-  //     { "/org_foo/module_bar", { ... }}
-  //     map<string, FunctionDefLib> named_lib;
-  //   * If node[i].op is the name of one function in "library",
-  //     node[i] is deemed as a function call. Otherwise, node[i].op
-  //     must be a primitive operation supported by the runtime.
-  //
-  //
-  // Function call semantics:
-  //
-  //   * The callee may start execution as soon as some of its inputs
-  //     are ready. The caller may want to use Tuple() mechanism to
-  //     ensure all inputs are ready in the same time.
-  //
-  //   * The consumer of return values may start executing as soon as
-  //     the return values the consumer depends on are ready.  The
-  //     consumer may want to use Tuple() mechanism to ensure the
-  //     consumer does not start until all return values of the callee
-  //     function are ready.
-  FunctionDefLibrary library = 2;
-};

parser/tensorflow/proto/tensorflow/graph_library.proto

@@ -1,14 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-
-import "graph.proto";
-
-message GeGraphDef {
-  string name = 1;
-  GraphDef graph = 2;
-}
-
-message GraphDefLibrary {
-  repeated GeGraphDef graph_def = 1;
-};

parser/tensorflow/proto/tensorflow/node_def.proto

@@ -1,63 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "NodeProto";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-
-message NodeDef {
-  // The name given to this operator. Used for naming inputs,
-  // logging, visualization, etc.  Unique within a single GraphDef.
-  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-  string name = 1;
-
-  // The operation name.  There may be custom parameters in attrs.
-  // Op names starting with an underscore are reserved for internal use.
-  string op = 2;
-
-  // Each input is "node:src_output" with "node" being a string name and
-  // "src_output" indicating which output tensor to use from "node". If
-  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-  // may optionally be followed by control inputs that have the format
-  // "^node".
-  repeated string input = 3;
-
-  // A (possibly partial) specification for the device on which this
-  // node should be placed.
-  // The expected syntax for this string is as follows:
-  //
-  // DEVICE_SPEC ::= PARTIAL_SPEC
-  //
-  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-  // CONSTRAINT ::= ("job:" JOB_NAME)
-  //              | ("replica:" [1-9][0-9]*)
-  //              | ("task:" [1-9][0-9]*)
-  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
-  //
-  // Valid values for this string include:
-  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
-  // * "/job:worker/device:GPU:3"                   (partial specification)
-  // * ""                                    (no specification)
-  //
-  // If the constraints do not resolve to a single device (or if this
-  // field is empty or not present), the runtime will attempt to
-  // choose a device automatically.
-  string device = 4;
-
-  // Operation-specific graph-construction-time configuration.
-  // Note that this should include all attrs defined in the
-  // corresponding OpDef, including those with a value matching
-  // the default -- this allows the default to change and makes
-  // NodeDefs easier to interpret on their own.  However, if
-  // an attr with a default is not specified in this list, the
-  // default will be used.
-  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-  // one of the names from the corresponding OpDef's attr field).
-  // The values must have a type matching the corresponding OpDef
-  // attr's type field.
-  // Add some examples here showing best practices.
-  map<string, AttrValue> attr = 5;
-};

parser/tensorflow/proto/tensorflow/op_def.proto

@@ -1,164 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "OpDefProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "attr_value.proto";
-import "types.proto";
-
-// Defines an operation. A NodeDef in a GraphDef specifies an Op by
-// using the "op" field which should match the name of a OpDef.
-// LINT.IfChange
-message OpDef {
-  // Op names starting with an underscore are reserved for internal use.
-  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
-  string name = 1;
-
-  // For describing inputs and outputs.
-  message ArgDef {
-    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
-    string name = 1;
-
-    // Human readable description.
-    string description = 2;
-
-    // Describes the type of one or more tensors that are accepted/produced
-    // by this input/output arg.  The only legal combinations are:
-    // * For a single tensor: either the "type" field is set or the
-    //   "type_attr" field is set to the name of an attr with type "type".
-    // * For a sequence of tensors with the same type: the "number_attr"
-    //   field will be set to the name of an attr with type "int", and
-    //   either the "type" or "type_attr" field will be set as for
-    //   single tensors.
-    // * For a sequence of tensors, the "type_list_attr" field will be set
-    //   to the name of an attr with type "list(type)".
-    DataType type = 3;
-    string type_attr = 4;    // if specified, attr must have type "type"
-    string number_attr = 5;  // if specified, attr must have type "int"
-    // If specified, attr must have type "list(type)", and none of
-    // type, type_attr, and number_attr may be specified.
-    string type_list_attr = 6;
-
-    // For inputs: if true, the inputs are required to be refs.
-    //   By default, inputs can be either refs or non-refs.
-    // For outputs: if true, outputs are refs, otherwise they are not.
-    bool is_ref = 16;
-  };
-
-  // Description of the input(s).
-  repeated ArgDef input_arg = 2;
-
-  // Description of the output(s).
-  repeated ArgDef output_arg = 3;
-
-  // Description of the graph-construction-time configuration of this
-  // Op.  That is to say, this describes the attr fields that will
-  // be specified in the NodeDef.
-  message AttrDef {
-    // A descriptive name for the argument.  May be used, e.g. by the
-    // Python client, as a keyword argument name, and so should match
-    // the regexp "[a-z][a-z0-9_]+".
-    string name = 1;
-
-    // One of the type names from attr_value.proto ("string", "list(string)",
-    // "int", etc.).
-    string type = 2;
-
-    // A reasonable default for this attribute if the user does not supply
-    // a value.  If not specified, the user must supply a value.
-    AttrValue default_value = 3;
-
-    // Human-readable description.
-    string description = 4;
-
-
-    // --- Constraints ---
-    // These constraints are only in effect if specified.  Default is no
-    // constraints.
-
-    // For type == "int", this is a minimum value.  For "list(___)"
-    // types, this is the minimum length.
-    bool has_minimum = 5;
-    int64 minimum = 6;
-
-    // The set of allowed values.  Has type that is the "list" version
-    // of the "type" field above (uses the "list" field of AttrValue).
-    // If type == "type" or "list(type)" above, then the "type" field
-    // of "allowed_values.list" has the set of allowed DataTypes.
-    // If type == "string" or "list(string)", then the "s" field of
-    // "allowed_values.list" has the set of allowed strings.
-    AttrValue allowed_values = 7;
-  }
-  repeated AttrDef attr = 4;
-
-  // Optional deprecation based on GraphDef versions.
-  OpDeprecation deprecation = 8;
-
-  // One-line human-readable description of what the Op does.
-  string summary = 5;
-
-  // Additional, longer human-readable description of what the Op does.
-  string description = 6;
-
-  // -------------------------------------------------------------------------
-  // Which optimizations this operation can participate in.
-
-  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
-  bool is_commutative = 18;
-
-  // If is_aggregate is true, then this operation accepts N >= 2
-  // inputs and produces 1 output all of the same type.  Should be
-  // associative and commutative, and produce output with the same
-  // shape as the input.  The optimizer may replace an aggregate op
-  // taking input from multiple devices with a tree of aggregate ops
-  // that aggregate locally within each device (and possibly within
-  // groups of nearby devices) before communicating.
-  bool is_aggregate = 16;  // for things like add
-
-  // Other optimizations go here, like
-  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.
-
-  // -------------------------------------------------------------------------
-  // Optimization constraints.
-
-  // Ops are marked as stateful if their behavior depends on some state beyond
-  // their input tensors (e.g. variable reading op) or if they have
-  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
-  // must always produce the same output for the same input and have
-  // no side-effects.
-  //
-  // By default Ops may be moved between devices.  Stateful ops should
-  // either not be moved, or should only be moved if that state can also
-  // be moved (e.g. via some sort of save / restore).
-  // Stateful ops are guaranteed to never be optimized away by Common
-  // Subexpression Elimination (CSE).
-  bool is_stateful = 17;  // for things like variables, queue
-
-  // -------------------------------------------------------------------------
-  // Non-standard options.
-
-  // By default, all inputs to an Op must be initialized Tensors.  Ops
-  // that may initialize tensors for the first time should set this
-  // field to true, to allow the Op to take an uninitialized Tensor as
-  // input.
-  bool allows_uninitialized_input = 19;  // for Assign, etc.
-};
-// LINT.ThenChange(
-//     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)
-
-// Information about version-dependent deprecation of an op
-message OpDeprecation {
-  // First GraphDef version at which the op is disallowed.
-  int32 version = 1;
-
-  // Explanation of why it was deprecated and what to use instead.
-  string explanation = 2;
-};
-
-// A collection of OpDefs
-message OpList {
-  repeated OpDef op = 1;
-};

parser/tensorflow/proto/tensorflow/resource_handle.proto

@@ -1,29 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "ResourceHandle";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Protocol buffer representing a handle to a tensorflow resource. Handles are
-// not valid across executions, but can be serialized back and forth from within
-// a single run.
-message ResourceHandleProto {
-  // Unique name for the device containing the resource.
-  string device = 1;
-
-  // Container in which this resource is placed.
-  string container = 2;
-
-  // Unique name of this resource.
-  string name = 3;
-
-  // Hash code for the type of the resource. Is only valid in the same device
-  // and in the same execution.
-  uint64 hash_code = 4;
-
-  // For debug-only, the name of the type pointed to by this handle, if
-  // available.
-  string maybe_type_name = 5;
-};

parser/tensorflow/proto/tensorflow/tensor.proto

@@ -1,94 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-import "resource_handle.proto";
-import "tensor_shape.proto";
-import "types.proto";
-
-// Protocol buffer representing a tensor.
-message TensorProto {
-  DataType dtype = 1;
-
-  // Shape of the tensor.
-  TensorShapeProto tensor_shape = 2;
-
-  // Only one of the representations below is set, one of "tensor_contents" and
-  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
-  // contain repeated fields it would require another extra set of messages.
-
-  // Version number.
-  //
-  // In version 0, if the "repeated xxx" representations contain only one
-  // element, that element is repeated to fill the shape.  This makes it easy
-  // to represent a constant Tensor with a single value.
-  int32 version_number = 3;
-
-  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
-  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
-  // can be used for all tensor types. The purpose of this representation is to
-  // reduce serialization overhead during RPC call by avoiding serialization of
-  // many repeated small items.
-  bytes tensor_content = 4;
-
-  // Type specific representations that make it easy to create tensor protos in
-  // all languages.  Only the representation corresponding to "dtype" can
-  // be set.  The values hold the flattened representation of the tensor in
-  // row major order.
-
-  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
-  // have some pointless zero padding for each value here.
-  repeated int32 half_val = 13 [packed = true];
-
-  // DT_FLOAT.
-  repeated float float_val = 5 [packed = true];
-
-  // DT_DOUBLE.
-  repeated double double_val = 6 [packed = true];
-
-  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
-  repeated int32 int_val = 7 [packed = true];
-
-  // DT_STRING
-  repeated bytes string_val = 8;
-
-  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
-  // and imaginary parts of i-th single precision complex.
-  repeated float scomplex_val = 9 [packed = true];
-
-  // DT_INT64
-  repeated int64 int64_val = 10 [packed = true];
-
-  // DT_BOOL
-  repeated bool bool_val = 11 [packed = true];
-
-  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
-  // and imaginary parts of i-th double precision complex.
-  repeated double dcomplex_val = 12 [packed = true];
-
-  // DT_RESOURCE
-  repeated ResourceHandleProto resource_handle_val = 14;
-
-  // DT_VARIANT
-  repeated VariantTensorDataProto variant_val = 15;
-
-  // DT_UINT32
-  repeated uint32 uint32_val = 16 [packed = true];
-
-  // DT_UINT64
-  repeated uint64 uint64_val = 17 [packed = true];
-};
-
-// Protocol buffer representing the serialization format of DT_VARIANT tensors.
-message VariantTensorDataProto {
-  // Name of the type of objects being serialized.
-  string type_name = 1;
-  // Portions of the object that are not Tensors.
-  bytes metadata = 2;
-  // Tensors contained within objects being serialized.
-  repeated TensorProto tensors = 3;
-}

parser/tensorflow/proto/tensorflow/tensor_shape.proto

@@ -1,45 +0,0 @@
-// Protocol buffer representing the shape of tensors.
-
-syntax = "proto3";
-option cc_enable_arenas = true;
-option java_outer_classname = "TensorShapeProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-package domi.tensorflow;
-
-// Dimensions of a tensor.
-message TensorShapeProto {
-  // One dimension of the tensor.
-  message Dim {
-    // Size of the tensor in that dimension.
-    // This value must be >= -1, but values of -1 are reserved for "unknown"
-    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
-    // that work with TensorShapeProto may fail at runtime when deserializing
-    // a TensorShapeProto containing a dim value of -1.
-    int64 size = 1;
-
-    // Optional name of the tensor dimension.
-    string name = 2;
-  };
-
-  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
-  // for a 30 x 40 2D tensor.  If an entry has size -1, this
-  // corresponds to a dimension of unknown size. The names are
-  // optional.
-  //
-  // The order of entries in "dim" matters: It indicates the layout of the
-  // values in the tensor in-memory representation.
-  //
-  // The first entry in "dim" is the outermost dimension used to layout the
-  // values, the last entry is the innermost dimension.  This matches the
-  // in-memory layout of RowMajor Eigen tensors.
-  //
-  // If "dim.size()" > 0, "unknown_rank" must be false.
-  repeated Dim dim = 2;
-
-  // If true, the number of dimensions in the shape is unknown.
-  //
-  // If true, "dim.size()" must be 0.
-  bool unknown_rank = 3;
-};

parser/tensorflow/proto/tensorflow/types.proto

@@ -1,74 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "TypesProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// LINT.IfChange
-enum DataType {
-  // Not a legal value for DataType.  Used to indicate a DataType field
-  // has not been set.
-  DT_INVALID = 0;
-
-  // Data types that all computation devices are expected to be
-  // capable to support.
-  DT_FLOAT = 1;
-  DT_DOUBLE = 2;
-  DT_INT32 = 3;
-  DT_UINT8 = 4;
-  DT_INT16 = 5;
-  DT_INT8 = 6;
-  DT_STRING = 7;
-  DT_COMPLEX64 = 8;  // Single-precision complex
-  DT_INT64 = 9;
-  DT_BOOL = 10;
-  DT_QINT8 = 11;     // Quantized int8
-  DT_QUINT8 = 12;    // Quantized uint8
-  DT_QINT32 = 13;    // Quantized int32
-  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
-  DT_QINT16 = 15;    // Quantized int16
-  DT_QUINT16 = 16;   // Quantized uint16
-  DT_UINT16 = 17;
-  DT_COMPLEX128 = 18;  // Double-precision complex
-  DT_HALF = 19;
-  DT_RESOURCE = 20;
-  DT_VARIANT = 21;  // Arbitrary C++ data types
-  DT_UINT32 = 22;
-  DT_UINT64 = 23;
-
-  // Do not use!  These are only for parameters.  Every enum above
-  // should have a corresponding value below (verified by types_test).
-  DT_FLOAT_REF = 101;
-  DT_DOUBLE_REF = 102;
-  DT_INT32_REF = 103;
-  DT_UINT8_REF = 104;
-  DT_INT16_REF = 105;
-  DT_INT8_REF = 106;
-  DT_STRING_REF = 107;
-  DT_COMPLEX64_REF = 108;
-  DT_INT64_REF = 109;
-  DT_BOOL_REF = 110;
-  DT_QINT8_REF = 111;
-  DT_QUINT8_REF = 112;
-  DT_QINT32_REF = 113;
-  DT_BFLOAT16_REF = 114;
-  DT_QINT16_REF = 115;
-  DT_QUINT16_REF = 116;
-  DT_UINT16_REF = 117;
-  DT_COMPLEX128_REF = 118;
-  DT_HALF_REF = 119;
-  DT_RESOURCE_REF = 120;
-  DT_VARIANT_REF = 121;
-  DT_UINT32_REF = 122;
-  DT_UINT64_REF = 123;
-}
-// LINT.ThenChange(
-//    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
-//    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
-//    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
-//    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)

parser/tensorflow/proto/tensorflow/versions.proto

@@ -1,31 +0,0 @@
-syntax = "proto3";
-
-package domi.tensorflow;
-option cc_enable_arenas = true;
-option java_outer_classname = "VersionsProtos";
-option java_multiple_files = true;
-option java_package = "org.tensorflow.framework";
-
-// Version information for a piece of serialized data
-//
-// There are different types of versions for each type of data
-// (GraphDef, etc.), but they all have the same common shape
-// described here.
-//
-// Each consumer has "consumer" and "min_producer" versions (specified
-// elsewhere).  A consumer is allowed to consume this data if
-//
-//   producer >= min_producer
-//   consumer >= min_consumer
-//   consumer not in bad_consumers
-//
-message VersionDef {
-  // The version of the code that produced this data.
-  int32 producer = 1;
-
-  // Any consumer below this version is not allowed to consume this data.
-  int32 min_consumer = 2;
-
-  // Specific consumer versions which are disallowed (e.g. due to bugs).
-  repeated int32 bad_consumers = 3;
-};