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769 lines
40 KiB
769 lines
40 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <math.h>
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#include <stdint.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <iostream>
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#include <vector>
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#include "common.h"
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#include "model.h"
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#define MAX_HEAD_SIZE 50
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using namespace std;
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using namespace ge;
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void update_op_format(Operator ops, Format format) {
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printf("set format begin.........\n");
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ge::TensorDesc tensor_desc_x = ops.GetInputDesc("x");
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ge::TensorDesc tensor_desc_y = ops.GetOutputDesc("y");
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Format f_x0 = tensor_desc_x.GetFormat();
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Format f_y0 = tensor_desc_x.GetFormat();
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printf("before set x format:%d \n", f_x0);
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printf("before set y format:%d \n", f_y0);
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printf("format to be set is :%d \n", format);
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tensor_desc_x.SetFormat(format);
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tensor_desc_y.SetFormat(format);
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ops.UpdateInputDesc("x", tensor_desc_x);
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ops.UpdateOutputDesc("y", tensor_desc_y);
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Format f_x = tensor_desc_x.GetFormat();
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Format f_y = tensor_desc_y.GetFormat();
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printf("after set x format:%d \n", f_x);
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printf("after set y format:%d \n", f_y);
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}
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/// getDimInfo: get dim info from data file
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/// param:
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/// fp: the testing datafile object
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///
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/// return :
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/// dim_info: array to store the info of the dim in datafile, like [4,3,3,6,3,162(3*3*6*3)],4 is dim size,3,3,6,3 is the
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/// dim shape data_size: the size of the testing data including the data file
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void getDimInfo(FILE *fp, std::vector<uint64_t> &dim_info) {
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// get dim info from hisi testing data file
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uint32_t *dim_buffer = (uint32_t *)malloc(MAX_HEAD_SIZE * sizeof(uint32_t));
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fread(dim_buffer, sizeof(uint32_t), MAX_HEAD_SIZE, fp);
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dim_info.push_back(*dim_buffer); // get dim size
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// get data shape to compute the datasize
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uint64_t data_size = 1;
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uint32_t i = 1;
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for (; i <= dim_info[0]; i++) {
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dim_info.push_back(*(dim_buffer + i));
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data_size *= *(dim_buffer + i);
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}
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dim_info.push_back(data_size);
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free(dim_buffer);
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}
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/// readTestDataFile: read test date from hisi .t datafile
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/// param:
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/// infile: the path of hisi .t datafile
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/// return:
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/// dim_info: array to store the info of the dim in datafile, like [4,3,3,6,3],4 is dim size,3,3,6,3 is the dim shape
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void *readTestDataFile(std::string infile, std::vector<uint64_t> &dim_info) {
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FILE *fp;
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fp = fopen(infile.c_str(), "r");
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if (fp == NULL) {
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printf("ERROR: cant't open file %s\n", infile.c_str());
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return NULL;
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} else {
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getDimInfo(fp, dim_info);
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uint64_t data_size = dim_info[dim_info.size() - 1];
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fclose(fp);
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fp = fopen(infile.c_str(), "r");
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if (fp == NULL) {
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printf("ERROR: cant't open file %s\n", infile.c_str());
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return NULL;
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}
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uint32_t *memory = (uint32_t *)malloc((dim_info[0] + 1 + data_size) * sizeof(uint32_t));
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fread(memory, sizeof(uint32_t), (dim_info[0] + 1 + data_size), fp);
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fclose(fp);
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return memory + (dim_info[0] + 1);
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}
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}
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void *readUint8TestDataFile(std::string infile, int size) {
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FILE *fp;
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fp = fopen(infile.c_str(), "r");
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if (fp == NULL) {
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printf("ERROR: cant't open file %s\n", infile.c_str());
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return NULL;
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}
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uint8_t *memory = (uint8_t *)malloc((size) * sizeof(uint8_t));
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fread(memory, sizeof(uint8_t), (size), fp);
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fclose(fp);
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return memory;
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}
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/// allclose
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/// param:
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/// a:compared file a
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/// b:compared file b
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/// count: the count size which will compare
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/// rtol:
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/// atol:
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/// return:
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/// true or false
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bool allclose(float *a, float *b, uint64_t count, float rtol = 1e-05, float atol = 1e-08) {
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uint32_t i = 0;
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for (; i < count; ++i) {
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if (fabs(a[i] - b[i]) > (atol + rtol * fabs(b[i]))) {
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printf("compara failed: i= %d, a[i]=%f, b[i]=%f,atol=%f,rtol=%f\n", i, a[i], b[i], atol, rtol);
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return false;
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}
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}
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return true;
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}
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/// compFp32WithTData: compare the data with the data in hisi .t file
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/// param:
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/// actual_output_data: the result of ge
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/// expected_data_file: the path of hisi .t result file
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/// rtol:
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/// atol:
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/// return:
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/// true of false
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bool compFp32WithTData(float *actual_output_data, std::string expected_data_file, float rtol = 1e-05, float atol = 1e-08) {
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std::vector<uint64_t> dim_info;
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float *expected_output_data = (float *)readTestDataFile(expected_data_file, dim_info);
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uint32_t i = 1;
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uint64_t data_size = 1;
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for (; i <= dim_info[0]; i++) {
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data_size *= dim_info[i];
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}
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return allclose(actual_output_data, expected_output_data, data_size, rtol, atol);
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}
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int SwitchDatatype(DataType dt) {
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int size = 1;
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if (dt == ge::DT_FLOAT) size = 4;
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if (dt == ge::DT_INT32) size = 4;
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if (dt == ge::DT_FLOAT16) size = 2;
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if (dt == ge::DT_INT64) size = 8;
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return size;
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}
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ge::Tensor genTensor(std::vector<int64_t> tensor_shape, Format format, DataType dt) {
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int size = 1;
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for (int i = 0; i < tensor_shape.size(); i++) {
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size = size * tensor_shape[i];
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}
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int data_type_size = SwitchDatatype(dt);
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size = abs(size * data_type_size);
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vector<uint8_t> data_value;
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if (size == 0) {
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TensorDesc input_tensor_desc = TensorDesc(ge::Shape(tensor_shape), format, dt);
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input_tensor_desc.SetRealDimCnt(tensor_shape.size());
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Tensor gen_tensor = Tensor(input_tensor_desc, data_value);
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return gen_tensor;
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}
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for (int i = 0; i < size; i++) {
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data_value.push_back(1);
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}
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TensorDesc input_tensor_desc = TensorDesc(ge::Shape(tensor_shape), format, dt);
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input_tensor_desc.SetRealDimCnt(tensor_shape.size());
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Tensor gen_tensor = Tensor(input_tensor_desc, data_value);
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return gen_tensor;
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}
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ge::Tensor genTensor_withVaule(std::vector<int64_t> tensor_shape, float value) {
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int size = 1;
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for (int i = 0; i < tensor_shape.size(); i++) {
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size = size * tensor_shape[i];
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}
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float *data_value = new float[size];
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for (int i = 0; i < size; i++) {
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*(data_value + i) = value;
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}
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Tensor gen_ge_tensor;
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TensorDesc input_tensor_desc = TensorDesc(ge::Shape(tensor_shape), FORMAT_NCHW);
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gen_ge_tensor.SetTensorDesc(input_tensor_desc);
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gen_ge_tensor.SetData((uint8_t *)data_value, size * 4);
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return gen_ge_tensor;
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}
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Tensor genTesnor_Shape_as_data(std::vector<int64_t> tensor_shape) {
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Format format = FORMAT_NCHW;
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DataType dt = DT_INT32;
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int size = tensor_shape.size();
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int32_t *tensor_data = new int32_t[size];
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std::cout << "shape tensor size:" << size << endl;
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for (int i = 0; i < size; i++) {
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*(tensor_data + i) = tensor_shape[i];
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}
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Tensor gen_tensor;
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TensorDesc input_tensor_desc = TensorDesc(ge::Shape({size}), FORMAT_NCHW, DT_INT32);
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gen_tensor.SetData((uint8_t *)tensor_data, size * GetDatTypeSize(dt));
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gen_tensor.SetTensorDesc(input_tensor_desc);
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return gen_tensor;
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}
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/// train_flag is 0 when infer; train_flag is 1 when train; train_flag is 0 default
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/// run_mode_path is not 0,1,2 when TBE; run_mode_path is 1 when FE; run_mode_path is 0 default
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/// run_mode_path is 2 now when AICPU, ge.enabledlocalFmkop is 1
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ge::Status GEInitialize_api(string train_flag, string run_mode_path) {
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ge::Status ret;
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if (run_mode_path == "0") {
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const std::map<string, string> config = {
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{"device_id", "0,2,4,6"},
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{"rank_table_file", "hccl from csa/paas"},
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{"ge.graphRunMode", train_flag},
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{"ge.aicpuFlag", "1"},
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{"ge.feFlag", "1"},
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{DDK_VERSION_FLAG, "1.60.T17.B830"},
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{"ge.soLoadPath",
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"/usr/local/HiAI/runtime/lib64/plugin/opskernel/libfe.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/"
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"libaicpu_plugin.so"}};
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ret = ge::GEInitialize(config);
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} else if (run_mode_path == "1") {
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const std::map<string, string> config = {
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{"device_id", "0,2,4,6"},
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{"rank_table_file", "hccl from csa/paas"},
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{"ge.graphRunMode", train_flag},
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{"ge.feFlag", "1"},
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{DDK_VERSION_FLAG, "1.60.T17.B830"},
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{TBE_PLUGIN_PATH_FLAG, "/usr/local/HiAI/runtime/lib64/tbe_plugin/bert"},
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{"ge.soLoadPath", "/usr/local/HiAI/runtime/lib64/plugin/opskernel/libfe.so"}};
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ret = ge::GEInitialize(config);
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} else if (run_mode_path == "2") {
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const std::map<string, string> config = {{"device_id", "0,2,4,6"},
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{"rank_table_file", "hccl from csa/paas"},
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{"ge.graphRunMode", train_flag},
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{LOCAL_FMKOP_FLAG, "1"}};
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ret = ge::GEInitialize(config);
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} else {
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const std::map<string, string> config = {
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{"device_id", "0,2,4,6"},
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{"rank_table_file", "hccl from csa/paas"},
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{"ge.graphRunMode", train_flag},
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{DDK_VERSION_FLAG, "1.60.T17.B830"},
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{TBE_PLUGIN_PATH_FLAG, "/usr/local/HiAI/runtime/lib64/tbe_plugin/" + run_mode_path}};
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ret = ge::GEInitialize(config);
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}
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std::cout << "GEInitialize_ret is " << ret << std::endl;
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return ret;
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}
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/// train_flag is infer default
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/// run_mode: is multi group of [fe,aicpu,bert,deeplabv3,mobilenetv2,single_path_nas,ssd]
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/// but bert,deeplabv3,mobilenetv2,single_path_nas,ssd can only set one value from array
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/// eg:"fe,aicpu,bert" or "fe", default is “fe”
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/// "fe,aicpu,bert" remain open fe aicpu and bert
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ge::Status GEInitialize_api_new(string train_flag, string run_mode) {
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ge::Status ret;
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vector<string> modes;
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char *strs = new char[run_mode.length() + 1];
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strcpy(strs, run_mode.c_str());
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const char *delim = ",";
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char *p = strtok(strs, delim);
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while (p) {
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string s = p; // transform substr to string
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modes.push_back(s); // save to result array
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p = strtok(NULL, delim);
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}
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std::map<string, string> config = {
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{"device_id", "0,2,4,6"},
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{"rank_table_file", "hccl from csa/paas"},
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{DDK_VERSION_FLAG, "1.60.T17.B830"},
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{"ge.opsProtoLibPath", "/usr/local/HiAI/runtime/ops/op_proto/built-in/libopsproto.so"}};
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if (train_flag == "infer")
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config.insert(pair<string, string>("ge.graphRunMode", "0"));
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else if (train_flag == "train")
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config.insert(pair<string, string>("ge.graphRunMode", "1"));
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else
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std::cout << "GeInitialize give the error param" << std::endl;
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for (int i = 0; i < modes.size(); i++) {
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if (modes[i] == "fe") {
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config.insert(pair<string, string>("ge.feFlag", "1"));
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if (config.find("ge.soLoadPath") != config.end()) {
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config["ge.soLoadPath"] =
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"/usr/local/HiAI/runtime/lib64/plugin/opskernel/libfe.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/"
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"libaicpu_plugin.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/libge_local_engine.so:/usr/local/HiAI/"
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"runtime/lib64/plugin/opskernel/librts_engine.so";
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} else {
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config.insert(pair<string, string>(
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"ge.soLoadPath",
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"/usr/local/HiAI/runtime/lib64/plugin/opskernel/libfe.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/"
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"libge_local_engine.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/librts_engine.so"));
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}
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} else if (modes[i] == "aicpu") {
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config.insert(pair<string, string>("ge.aicpuFlag", "1"));
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if (config.find("ge.soLoadPath") != config.end()) {
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config["ge.soLoadPath"] =
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"/usr/local/HiAI/runtime/lib64/plugin/opskernel/libfe.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/"
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"libaicpu_plugin.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/libge_local_engine.so:/usr/local/HiAI/"
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"runtime/lib64/plugin/opskernel/librts_engine.so";
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} else {
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config.insert(pair<string, string>(
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"ge.soLoadPath",
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"/usr/local/HiAI/runtime/lib64/plugin/opskernel/libaicpu_plugin.so:/usr/local/HiAI/runtime/lib64/plugin/"
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"opskernel/libge_local_engine.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/librts_engine.so"));
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}
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} else if (modes[i] == "bert" || modes[i] == "deeplabv3" || modes[i] == "mobilenetv2" ||
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modes[i] == "single_path_nas" || modes[i] == "ssd") {
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config.insert(pair<string, string>(TBE_PLUGIN_PATH_FLAG, "/usr/local/HiAI/runtime/lib64/tbe_plugin/" + modes[i]));
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} else if (modes[i] == "plugin") {
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} else
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std::cout << "GeInitialize give the error param" << std::endl;
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}
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ret = ge::GEInitialize(config);
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std::cout << "GEInitialize_ret is " << ret << std::endl;
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return ret;
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}
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ge::Status GEFinalize_api() {
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ge::Status ret = ge::GEFinalize();
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std::cout << "GEFinalize ret is " << ret << std::endl;
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return ret;
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}
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/// set train_flag
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/// if run_mode_path is "fe" remain FE process; "fe,plugin" is FE and TBE plugin process
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/// "aicpu" is open aicpu plugin
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int RunGraph_initData(Graph &graph, string op_name, map<string, std::vector<int64_t>> attr_test, string train_flag,
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string run_mode_path) {
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std::map<string, string> options = {{RUN_FLAG, "1"}};
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uint32_t graph_id = 0;
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ge::Status ret = GEInitialize_api_new(train_flag, run_mode_path);
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EXPECT_EQ(ret, ge::SUCCESS);
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ge::Session *session = new Session(options);
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ASSERT_TRUE(session != NULL);
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std::vector<Tensor> input;
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if (attr_test.find("input1") != attr_test.end()) {
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Tensor input_tensor = genTensor(attr_test["input1"]);
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input.push_back(input_tensor);
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}
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if (attr_test.find("input2") != attr_test.end()) {
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Tensor input_tensor = genTensor(attr_test["input2"]);
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input.push_back(input_tensor);
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}
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if (attr_test.find("input3") != attr_test.end()) {
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Tensor input_tensor = genTensor(attr_test["input3"]);
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input.push_back(input_tensor);
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}
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std::vector<Tensor> output;
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ret = session->AddGraph(graph_id, graph);
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EXPECT_EQ(ret, ge::SUCCESS);
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if (train_flag == "1") {
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setenv("GE_TRAIN", "1", true);
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ret = session->RunGraph(graph_id, input, output);
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setenv("GE_TRAIN", "0", true);
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} else {
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ret = session->RunGraph(graph_id, input, output);
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}
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delete session;
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GEFinalize_api();
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if (ret != ge::SUCCESS) {
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std::cout << " run graph failed" << std::endl;
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return -1;
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} else {
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return 0;
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}
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}
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ge::Status session_add_and_run_graph(ge::Session *session, uint32_t graph_id, Graph &graph, std::vector<Tensor> inputs,
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std::vector<Tensor> &outputs) {
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ge::Status ret = session->AddGraph(graph_id, graph);
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EXPECT_EQ(ret, ge::SUCCESS);
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ret = session->RunGraph(graph_id, inputs, outputs);
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return ret;
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}
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ge::Session *create_session() {
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// Init session
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std::map<string, string> options = {{"a", "b"}, {TRAIN_FLAG, "1"}};
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ge::Session *session = new Session(options);
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ASSERT_TRUE(session != NULL);
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return session;
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}
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ge::Session *create_aipp_session() {
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// Init session
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std::map<string, string> options = {{"a", "b"}, {TRAIN_FLAG, "1"}, {"ge.insertOpFile", "/root/host/ge/aipp.cfg"}};
|
|
ge::Session *session = new Session(options);
|
|
ASSERT_TRUE(session != NULL);
|
|
|
|
return session;
|
|
}
|
|
|
|
int buildCheckPointGraph(Graph &graph, map<string, TensorDesc> variables) {
|
|
std::vector<Operator> inputs{};
|
|
std::vector<Operator> outputs{};
|
|
|
|
for (map<string, TensorDesc>::iterator it = variables.begin(); it != variables.end(); ++it) {
|
|
auto var = op::Variable(string(it->first));
|
|
var.update_output_desc_y(it->second);
|
|
inputs.push_back(var);
|
|
graph.AddOp(var);
|
|
}
|
|
|
|
auto save = op::Save().create_dynamic_input_tensors(inputs.size());
|
|
for (int i = 0; i < inputs.size(); i++) {
|
|
save.set_dynamic_input_tensors(i, inputs[i]);
|
|
}
|
|
|
|
graph.SetInputs(inputs).SetOutputs(outputs);
|
|
return 0;
|
|
}
|
|
|
|
int buildInitGraph(Graph &graph, std::vector<TensorDesc> desc_var, std::vector<std::string> name_var,
|
|
std::vector<float> values_var) {
|
|
std::vector<Operator> inputs{};
|
|
std::vector<Operator> outputs{};
|
|
|
|
for (int i = 0; i < desc_var.size(); i++) {
|
|
desc_var[i].SetRealDimCnt(desc_var[i].GetShape().GetDimNum());
|
|
auto tensor_data = genTensor_withVaule(desc_var[i].GetShape().GetDims(), values_var[i]);
|
|
auto var_constant = op::Constant().set_attr_value(tensor_data);
|
|
var_constant.update_output_desc_y(desc_var[i]);
|
|
|
|
auto var_init = op::Variable(string(name_var[i]));
|
|
var_init.update_output_desc_y(desc_var[i]);
|
|
auto var_assign = op::Assign().set_input_ref(var_init).set_input_value(var_constant);
|
|
inputs.push_back(var_init);
|
|
}
|
|
graph.SetInputs(inputs).SetOutputs(outputs);
|
|
return 0;
|
|
}
|
|
|
|
int buildInitGraph_other_dataType(Graph &graph, std::vector<TensorDesc> desc_var, std::vector<std::string> name_var) {
|
|
std::vector<Operator> inputs{};
|
|
std::vector<Operator> outputs{};
|
|
|
|
for (int i = 0; i < desc_var.size(); i++) {
|
|
desc_var[i].SetRealDimCnt(desc_var[i].GetShape().GetDimNum());
|
|
auto tensor_data = genTensor(desc_var[i].GetShape().GetDims(), desc_var[i].GetFormat(), desc_var[i].GetDataType());
|
|
auto var_constant = op::Constant().set_attr_value(tensor_data);
|
|
var_constant.update_output_desc_y(desc_var[i]);
|
|
|
|
auto var_init = op::Variable(string(name_var[i]));
|
|
var_init.update_output_desc_y(desc_var[i]);
|
|
auto var_assign = op::Assign().set_input_ref(var_init).set_input_value(var_constant);
|
|
inputs.push_back(var_init);
|
|
|
|
graph.AddOp(var_constant);
|
|
graph.AddOp(var_init);
|
|
graph.AddOp(var_assign);
|
|
}
|
|
graph.SetInputs(inputs).SetOutputs(outputs);
|
|
return 0;
|
|
}
|
|
|
|
bool build_multi_input_multi_output_graph(Graph &graph) {
|
|
auto data1 = op::Data("Data1").set_attr_index(0);
|
|
auto data2 = op::Data("Data2").set_attr_index(1);
|
|
|
|
vector<uint64_t> dim_info;
|
|
|
|
auto relu1 = op::Relu("Relu1").set_input_x(data1);
|
|
auto relu2 = op::Relu("Relu2").set_input_x(data2);
|
|
|
|
auto eltwise = op::Eltwise("Eltwise")
|
|
.create_dynamic_input_x(2)
|
|
.set_dynamic_input_x(0, relu1)
|
|
.set_dynamic_input_x(1, relu2)
|
|
.set_attr_N(2)
|
|
.set_attr_mode(1)
|
|
.set_attr_coeff({1, 1});
|
|
|
|
auto eltwise1 = op::Eltwise("Eltwise1")
|
|
.create_dynamic_input_x(2)
|
|
.set_dynamic_input_x(0, eltwise)
|
|
.set_dynamic_input_x(1, eltwise)
|
|
.set_attr_N(2)
|
|
.set_attr_mode(1)
|
|
.set_attr_coeff({1, 1});
|
|
|
|
auto eltwise2 = op::Eltwise("Eltwise2")
|
|
.create_dynamic_input_x(2)
|
|
.set_dynamic_input_x(0, eltwise)
|
|
.set_dynamic_input_x(1, eltwise)
|
|
.set_attr_N(2)
|
|
.set_attr_mode(1)
|
|
.set_attr_coeff({1, 1});
|
|
|
|
std::vector<Operator> inputs{data1, data2};
|
|
std::vector<Operator> outputs{eltwise1, eltwise2};
|
|
graph.SetInputs(inputs).SetOutputs(outputs);
|
|
return true;
|
|
}
|
|
|
|
void build_big_graph(Graph &graph, map<string, std::vector<int64_t>> attr) {
|
|
auto data = op::Data("Data").set_attr_index(0);
|
|
auto weight = op::Const("weight1").set_attr_value(genTensor(attr["weight"]));
|
|
vector<int64_t> weight_shape(attr["weight"].begin(), attr["weight"].end());
|
|
TensorDesc weight_desc(ge::Shape(weight_shape), FORMAT_NCHW, DT_FLOAT);
|
|
weight.update_output_desc_y(weight_desc);
|
|
auto conv_1 = op::Conv2D("conv1").set_input_x(data).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
|
|
auto conv_2 = op::Conv2D("conv2").set_input_x(conv_1).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_3 = op::Conv2D("conv3").set_input_x(conv_2).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_4 = op::Conv2D("conv4").set_input_x(conv_3).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_5 = op::Conv2D("conv5").set_input_x(conv_4).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_6 = op::Conv2D("conv6").set_input_x(conv_5).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_7 = op::Conv2D("conv7").set_input_x(conv_6).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_8 = op::Conv2D("conv8").set_input_x(conv_7).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_9 = op::Conv2D("conv9").set_input_x(conv_8).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_10 = op::Conv2D("conv10").set_input_x(conv_9).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_11 = op::Conv2D("conv11").set_input_x(conv_10).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_12 = op::Conv2D("conv12").set_input_x(conv_11).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_13 = op::Conv2D("conv13").set_input_x(conv_12).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_14 = op::Conv2D("conv14").set_input_x(conv_13).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_15 = op::Conv2D("conv15").set_input_x(conv_14).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_16 = op::Conv2D("conv16").set_input_x(conv_15).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_17 = op::Conv2D("conv17").set_input_x(conv_16).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_18 = op::Conv2D("conv18").set_input_x(conv_17).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_19 = op::Conv2D("conv19").set_input_x(conv_18).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_20 = op::Conv2D("conv20").set_input_x(conv_19).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_21 = op::Conv2D("conv21").set_input_x(conv_20).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_22 = op::Conv2D("conv22").set_input_x(conv_21).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_23 = op::Conv2D("conv23").set_input_x(conv_22).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_24 = op::Conv2D("conv24").set_input_x(conv_23).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_25 = op::Conv2D("conv25").set_input_x(conv_24).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_26 = op::Conv2D("conv26").set_input_x(conv_25).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_27 = op::Conv2D("conv27").set_input_x(conv_26).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_28 = op::Conv2D("conv28").set_input_x(conv_27).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_29 = op::Conv2D("conv29").set_input_x(conv_28).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_30 = op::Conv2D("conv30").set_input_x(conv_29).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_31 = op::Conv2D("conv31").set_input_x(conv_30).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_32 = op::Conv2D("conv32").set_input_x(conv_31).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_33 = op::Conv2D("conv33").set_input_x(conv_32).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_34 = op::Conv2D("conv34").set_input_x(conv_33).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_35 = op::Conv2D("conv35").set_input_x(conv_34).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_36 = op::Conv2D("conv36").set_input_x(conv_35).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_37 = op::Conv2D("conv37").set_input_x(conv_36).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_38 = op::Conv2D("conv38").set_input_x(conv_37).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_39 = op::Conv2D("conv39").set_input_x(conv_38).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_40 = op::Conv2D("conv40").set_input_x(conv_39).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_41 = op::Conv2D("conv41").set_input_x(conv_40).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_42 = op::Conv2D("conv42").set_input_x(conv_41).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_43 = op::Conv2D("conv43").set_input_x(conv_42).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_44 = op::Conv2D("conv44").set_input_x(conv_43).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_45 = op::Conv2D("conv45").set_input_x(conv_44).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_46 = op::Conv2D("conv46").set_input_x(conv_45).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_47 = op::Conv2D("conv47").set_input_x(conv_46).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_48 = op::Conv2D("conv48").set_input_x(conv_47).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_49 = op::Conv2D("conv49").set_input_x(conv_48).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_50 = op::Conv2D("conv50").set_input_x(conv_49).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_51 = op::Conv2D("conv51").set_input_x(conv_50).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_52 = op::Conv2D("conv52").set_input_x(conv_51).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_53 = op::Conv2D("conv53").set_input_x(conv_52).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_54 = op::Conv2D("conv54").set_input_x(conv_53).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_55 = op::Conv2D("conv55").set_input_x(conv_54).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_56 = op::Conv2D("conv56").set_input_x(conv_55).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_57 = op::Conv2D("conv57").set_input_x(conv_56).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_58 = op::Conv2D("conv58").set_input_x(conv_57).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_59 = op::Conv2D("conv59").set_input_x(conv_58).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_60 = op::Conv2D("conv60").set_input_x(conv_59).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_61 = op::Conv2D("conv61").set_input_x(conv_60).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_62 = op::Conv2D("conv62").set_input_x(conv_61).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_63 = op::Conv2D("conv63").set_input_x(conv_62).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_64 = op::Conv2D("conv64").set_input_x(conv_63).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_65 = op::Conv2D("conv65").set_input_x(conv_64).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_66 = op::Conv2D("conv66").set_input_x(conv_65).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_67 = op::Conv2D("conv67").set_input_x(conv_66).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_68 = op::Conv2D("conv68").set_input_x(conv_67).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_69 = op::Conv2D("conv69").set_input_x(conv_68).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_70 = op::Conv2D("conv70").set_input_x(conv_69).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_71 = op::Conv2D("conv71").set_input_x(conv_70).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_72 = op::Conv2D("conv72").set_input_x(conv_71).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_73 = op::Conv2D("conv73").set_input_x(conv_72).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_74 = op::Conv2D("conv74").set_input_x(conv_73).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_75 = op::Conv2D("conv75").set_input_x(conv_74).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_76 = op::Conv2D("conv76").set_input_x(conv_75).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_77 = op::Conv2D("conv77").set_input_x(conv_76).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_78 = op::Conv2D("conv78").set_input_x(conv_77).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_79 = op::Conv2D("conv79").set_input_x(conv_78).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_80 = op::Conv2D("conv80").set_input_x(conv_79).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_81 = op::Conv2D("conv81").set_input_x(conv_80).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_82 = op::Conv2D("conv82").set_input_x(conv_81).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_83 = op::Conv2D("conv83").set_input_x(conv_82).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
|
|
auto conv_84 = op::Conv2D("conv84").set_input_x(conv_83).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_85 = op::Conv2D("conv85").set_input_x(conv_84).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_86 = op::Conv2D("conv86").set_input_x(conv_85).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_87 = op::Conv2D("conv87").set_input_x(conv_86).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_88 = op::Conv2D("conv88").set_input_x(conv_87).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_89 = op::Conv2D("conv89").set_input_x(conv_88).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_90 = op::Conv2D("conv90").set_input_x(conv_89).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_91 = op::Conv2D("conv91").set_input_x(conv_80).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_92 = op::Conv2D("conv92").set_input_x(conv_91).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_93 = op::Conv2D("conv93").set_input_x(conv_92).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_94 = op::Conv2D("conv94").set_input_x(conv_93).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_95 = op::Conv2D("conv95").set_input_x(conv_94).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_96 = op::Conv2D("conv96").set_input_x(conv_95).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_97 = op::Conv2D("conv97").set_input_x(conv_96).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_98 = op::Conv2D("conv98").set_input_x(conv_97).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_99 = op::Conv2D("conv99").set_input_x(conv_98).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_100 = op::Conv2D("conv100").set_input_x(conv_99).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_101 = op::Conv2D("conv101").set_input_x(conv_100).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_102 = op::Conv2D("conv102").set_input_x(conv_101).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_103 = op::Conv2D("conv103").set_input_x(conv_102).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_104 = op::Conv2D("conv104").set_input_x(conv_103).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_105 = op::Conv2D("conv105").set_input_x(conv_104).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_106 = op::Conv2D("conv106").set_input_x(conv_105).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_107 = op::Conv2D("conv107").set_input_x(conv_106).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_108 = op::Conv2D("conv108").set_input_x(conv_107).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_109 = op::Conv2D("conv109").set_input_x(conv_108).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_110 = op::Conv2D("conv110").set_input_x(conv_109).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_111 = op::Conv2D("conv111").set_input_x(conv_110).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_112 = op::Conv2D("conv112").set_input_x(conv_111).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_113 = op::Conv2D("conv113").set_input_x(conv_112).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_114 = op::Conv2D("conv114").set_input_x(conv_113).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_115 = op::Conv2D("conv115").set_input_x(conv_114).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_116 = op::Conv2D("conv116").set_input_x(conv_115).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_117 = op::Conv2D("conv117").set_input_x(conv_116).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_118 = op::Conv2D("conv118").set_input_x(conv_117).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_119 = op::Conv2D("conv119").set_input_x(conv_118).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_120 = op::Conv2D("conv120").set_input_x(conv_119).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_121 = op::Conv2D("conv121").set_input_x(conv_120).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_122 = op::Conv2D("conv122").set_input_x(conv_121).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_123 = op::Conv2D("conv123").set_input_x(conv_122).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_124 = op::Conv2D("conv124").set_input_x(conv_123).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_125 = op::Conv2D("conv125").set_input_x(conv_124).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_126 = op::Conv2D("conv126").set_input_x(conv_125).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_127 = op::Conv2D("conv127").set_input_x(conv_126).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_128 = op::Conv2D("conv128").set_input_x(conv_127).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_129 = op::Conv2D("conv129").set_input_x(conv_128).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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auto conv_130 = op::Conv2D("conv130").set_input_x(conv_129).set_input_filter(weight).set_attr_pads({0,0,0,0}).set_attr_strides({1,1,1,1});
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std::vector<Operator> inputs{data};
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std::vector<Operator> outputs{conv_130};
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graph.SetInputs(inputs).SetOutputs(outputs);
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}
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int GetDatTypeSize(DataType dt) {
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int dailation = 1;
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if (dt == ge::DT_FLOAT)
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dailation = 4;
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else if (dt == ge::DT_FLOAT16)
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dailation = 2;
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else if (dt == ge::DT_INT16)
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dailation = 2;
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else if (dt == ge::DT_UINT16)
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dailation = 2;
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else if (dt == ge::DT_INT32)
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dailation = 4;
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else if (dt == ge::DT_UINT32)
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dailation = 4;
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else if (dt == ge::DT_INT64)
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dailation = 8;
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else if (dt == ge::DT_UINT64)
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dailation = 8;
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else if (dt == ge::DT_INT8)
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dailation = 1;
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return dailation;
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}
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int buildConvGraph_new(Graph &graph, std::vector<TensorDesc> desc_var, std::vector<std::string> name_var, int flag,
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Format format) {
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auto data_x_shape = op::Data("xShape").set_attr_index(0);
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auto var = op::Variable(name_var[0]);
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auto var1 = op::Variable(name_var[1]); //add one seat of ApplyMomentum()
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auto label1 = op::Variable(name_var[2]); //add one seat of ApplyMomentum()
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auto conv2dgrad = op::Conv2DBackpropFilterD("output_1");
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auto test2 = op::ApplyMomentum();
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var.update_output_desc_y(desc_var[0]);
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var1.update_output_desc_y(desc_var[1]);
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label1.update_output_desc_y(desc_var[2]);
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graph.AddOp(var);
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graph.AddOp(var1);
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graph.AddOp(label1);
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auto conv2d = op::Conv2D().set_input_x(data_x_shape).set_input_filter(var).set_attr_strides({1, 1, 1, 1}).set_attr_pads({0,0,0,0});
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update_op_format(conv2d, format);
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ge::TensorDesc tensor_desc_w = conv2d.GetInputDesc("filter");
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tensor_desc_w.SetFormat(format);
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conv2d.UpdateInputDesc("filter", tensor_desc_w);
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if (flag >= 1) {
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conv2dgrad.set_input_x(data_x_shape)
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.set_attr_filter_size(desc_var[0].GetShape().GetDims())
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.set_input_out_backprop(conv2d)
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.set_attr_strides({1, 1, 1, 1})
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.set_attr_pads({0, 0, 0, 0});
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update_op_format(conv2dgrad, format);
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graph.AddOp(conv2dgrad);
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}
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if (flag >= 2) {
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// set conv2dgrad var
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test2.set_input_accum(var1)
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.set_input_grad(conv2dgrad)
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.set_input_lr(label1)
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.set_input_momentum(label1)
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.set_input_var(var);
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graph.AddOp(test2);
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}
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std::vector<Operator> inputs{data_x_shape}; // set all val
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std::vector<Operator> outputs{conv2d};
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graph.SetInputs(inputs).SetOutputs(outputs);
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graph.AddOp(conv2d);
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return 0;
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}
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/// load bin data_fail
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/// input_path: path of bin data_file
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/// shapes: the shape of Tensor
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/// ft: the format of Tensor
|
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/// dt: the dataType of Tensor
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Tensor load_variable_input_data(string input_path, std::vector<int64_t> shapes, Format ft, DataType dt) {
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vector<uint64_t> dim_info1;
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uint8_t *input_data = (uint8_t *)readTestDataFile(input_path, dim_info1); // common.h
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TensorDesc input_tensor_desc = TensorDesc(ge::Shape(shapes), ft, dt);
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input_tensor_desc.SetRealDimCnt(shapes.size());
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Tensor input_tensor = Tensor(input_tensor_desc, input_data, GetDatTypeSize(dt) * dim_info1[dim_info1[0] + 1]);
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return input_tensor;
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}
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