| |
| // Copyright (c) 2020-2023, ARM Limited. |
| // |
| // Licensed under the Apache License, Version 2.0 (the "License"); |
| // you may not use this file except in compliance with the License. |
| // You may obtain a copy of the License at |
| // |
| // http://www.apache.org/licenses/LICENSE-2.0 |
| // |
| // Unless required by applicable law or agreed to in writing, software |
| // distributed under the License is distributed on an "AS IS" BASIS, |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| // See the License for the specific language governing permissions and |
| // limitations under the License. |
| |
| #include "model_runner.h" |
| #include "version.h" |
| |
| #include "arith_util.h" |
| #include "command_line_utils.h" |
| #include "custom_op_interface.h" |
| #include "custom_registry.h" |
| #include "ops/op_factory.h" |
| #include "subgraph_traverser.h" |
| #include "tosa_serialization_handler.h" |
| |
| #include <Eigen/CXX11/Tensor> |
| #include <fstream> |
| #include <iostream> |
| #include <nlohmann/json.hpp> |
| #include <stdio.h> |
| |
| using namespace TosaReference; |
| using namespace tosa; |
| using json = nlohmann::json; |
| |
| int initTestDesc(json& test_desc); |
| |
| int readInputTensors(SubgraphTraverser& gt, json& test_desc); |
| int writeFinalTensors(SubgraphTraverser& gt, json& test_desc, const std::string& filename_prefix); |
| int readVariableTensors(SubgraphTraverser& gt, json test_desc); |
| int writeVariableTensors(SubgraphTraverser& gt, json test_desc); |
| int loadSharedLibs(std::string& custom_op_lib_path); |
| int loadGraph(TosaSerializationHandler& tsh, json& test_desc); |
| void parse_value(const std::string& text, tosa_level_t& value); |
| const std::string getResultFilenamePrefix(); |
| bool isComplianceAbsModeNeeded(json& test_desc); |
| |
| int main(int argc, char** argv) |
| { |
| TosaVersion model_version(TOSA_REFERENCE_MODEL_VERSION_MAJOR, TOSA_REFERENCE_MODEL_VERSION_MINOR, |
| TOSA_REFERENCE_MODEL_VERSION_PATCH, TOSA_REFERENCE_MODEL_VERSION_DRAFT); |
| |
| // Initialize configuration and debug subsystems |
| g_func_debug.init_debug(0); |
| |
| if (func_model_parse_cmd_line(g_func_config, g_func_debug, argc, argv, model_version.to_string().c_str())) |
| { |
| return 1; |
| } |
| |
| TosaSerializationHandler tsh; |
| TosaVersion::compat_t is_compat = TosaVersion::is_compatible(model_version, tsh.GetVersion()); |
| |
| switch (is_compat) |
| { |
| case TosaVersion::compat_t::COMPLETELY_COMPATIBLE: |
| break; |
| case TosaVersion::compat_t::BACKWARD_COMPATIBLE: |
| printf("WARNING: Reference model version %s is backward compatible with serializer version %s\n", |
| model_version.to_string().c_str(), tsh.GetVersion().to_string().c_str()); |
| break; |
| case TosaVersion::compat_t::NOT_COMPATIBLE: |
| printf("ERROR: Reference model version %s is not compatible with serializer version %s\n", |
| model_version.to_string().c_str(), tsh.GetVersion().to_string().c_str()); |
| return TOSA_VERSION_MISMATCH; |
| } |
| |
| json test_desc; |
| |
| // Initialize test descriptor |
| if (initTestDesc(test_desc)) |
| { |
| FATAL_ERROR("Unable to load test json"); |
| } |
| |
| // load shared libs if specified |
| if (g_func_config.custom_op_lib_path != "") |
| { |
| if (loadSharedLibs(g_func_config.custom_op_lib_path)) |
| { |
| FATAL_ERROR("Shared library specified but not loaded successfully"); |
| } |
| } |
| |
| if (loadGraph(tsh, test_desc)) |
| { |
| FATAL_ERROR("Unable to load graph"); |
| } |
| |
| GraphStatus status = GraphStatus::TOSA_VALID; |
| |
| if (isComplianceAbsModeNeeded(test_desc) && !g_func_config.precise_mode) |
| { |
| // Warn about precise mode for dot product or abs error compliance |
| DEBUG_INFO(CONFIG, "DOT_PRODUCT/ABS_ERROR compliance: NOTE - enable precise mode for compliance results") |
| } |
| |
| // max of 2 runs, second run only happens when precise_mode is set, to do an abs_mode run |
| for (int run = 0; run < 2; run++) |
| { |
| SubgraphTraverser main_gt(tsh.GetMainRegion()->GetBlockByName("main"), &tsh, nullptr); |
| |
| if (main_gt.initializeGraph()) |
| { |
| WARNING("Unable to initialize main graph traverser."); |
| goto done; |
| } |
| |
| if (main_gt.linkTensorsAndNodes()) |
| { |
| WARNING("Failed to link tensors and nodes"); |
| goto done; |
| } |
| |
| if (main_gt.validateGraph()) |
| { |
| WARNING("Failed to validate graph. Evaluation aborted."); |
| goto done; |
| } |
| |
| if (main_gt.allocateInputTensors()) |
| { |
| WARNING("Failed to allocate input tensors. Evaluation aborted."); |
| goto done; |
| } |
| |
| if (g_func_config.validate_only) |
| { |
| goto done; |
| } |
| |
| if (readInputTensors(main_gt, test_desc)) |
| { |
| FATAL_ERROR("Unable to read input tensors"); |
| } |
| |
| if (!g_func_config.eval) |
| { |
| goto done; |
| } |
| |
| if (g_func_config.initialize_variable_tensor_from_numpy) |
| { |
| if (readVariableTensors(main_gt, test_desc)) |
| { |
| FATAL_ERROR("Unable to read variable tensors"); |
| } |
| } |
| |
| // evaluateAll() returns 1 if graph evaluation is forced to be terminated earlier. |
| if (main_gt.evaluateAll()) |
| { |
| ASSERT_MSG(main_gt.getGraphStatus() != GraphStatus::TOSA_VALID, |
| "Upon evaluateAll() returning 1, graph can not be VALID."); |
| } |
| else |
| { |
| ASSERT_MSG(main_gt.getGraphStatus() == GraphStatus::TOSA_VALID || |
| main_gt.getGraphStatus() == GraphStatus::TOSA_UNPREDICTABLE, |
| "Upon evaluateAll() returning 0, graph can only be VALID/UNPREDICTABLE."); |
| } |
| |
| // Only generate output tensor if graph is valid. |
| if (main_gt.getGraphStatus() == GraphStatus::TOSA_VALID) |
| { |
| // make sure output tensor is evaluated and show its value |
| int num_output_tensors = main_gt.getNumOutputTensors(); |
| bool all_output_valid = true; |
| for (int i = 0; i < num_output_tensors; i++) |
| { |
| const Tensor* ct = main_gt.getOutputTensor(i); |
| ASSERT_MEM(ct); |
| if (!ct->getIsValid()) |
| { |
| ct->dumpTensorParams(g_func_debug.func_debug_file); |
| if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT)) |
| { |
| ct->dumpTensor(g_func_debug.func_debug_file); |
| } |
| all_output_valid = false; |
| } |
| } |
| if (!all_output_valid) |
| { |
| main_gt.dumpGraph(g_func_debug.func_debug_file); |
| FATAL_ERROR( |
| "SubgraphTraverser \"main\" error: Output tensors are not all valid at the end of evaluation."); |
| } |
| |
| if (g_func_config.output_tensors) |
| { |
| if (writeFinalTensors(main_gt, test_desc, getResultFilenamePrefix())) |
| { |
| WARNING("Errors encountered in saving output tensors"); |
| } |
| |
| if (writeVariableTensors(main_gt, test_desc)) |
| { |
| WARNING("Errors encountered in writing variable tensors"); |
| } |
| } |
| } |
| |
| done: |
| status = main_gt.getGraphStatus(); |
| switch (status) |
| { |
| case GraphStatus::TOSA_VALID: |
| // Result is valid. |
| break; |
| case GraphStatus::TOSA_UNPREDICTABLE: |
| fprintf(stderr, "Graph result: UNPREDICTABLE.\n"); |
| break; |
| case GraphStatus::TOSA_ERROR: |
| fprintf(stderr, "Graph result: ERROR.\n"); |
| break; |
| default: |
| fprintf(stderr, "Unknown graph status code=%d.\n", (int)main_gt.getGraphStatus()); |
| } |
| |
| if (run == 0 && status == GraphStatus::TOSA_VALID && g_func_config.precise_mode && g_func_config.eval && |
| isComplianceAbsModeNeeded(test_desc)) |
| { |
| // when first run result is valid and precise mode and eval is true: turn on abs_mode for second run |
| DEBUG_INFO(CONFIG, "DOT_PRODUCT/ABS_ERROR compliance: Evaluating the graph again to produce bounds results") |
| g_func_config.abs_mode = true; |
| continue; |
| } |
| |
| // otherwise, do only one run |
| break; |
| } |
| |
| g_func_debug.fini_debug(); |
| return (int)status; |
| } |
| |
| int loadSharedLibs(std::string& custom_op_lib_path) |
| { |
| // Load the shared_lib |
| void* lib_handle = dlopen(custom_op_lib_path.c_str(), RTLD_LAZY); |
| if (lib_handle == nullptr) |
| { |
| FATAL_ERROR("Library %s does not exist\n", custom_op_lib_path.c_str()); |
| } |
| |
| typedef int (*get_customOp_function_t)(registration_callback_t registration_func); |
| auto get_customOp_creation_funcs = (get_customOp_function_t)dlsym(lib_handle, "getCustomOpCreationFuncs"); |
| if (get_customOp_creation_funcs == nullptr) |
| { |
| FATAL_ERROR("Can't find the getCustomOpCreationFuncs \n"); |
| } |
| |
| return get_customOp_creation_funcs(&MasterRegistry::register_function); |
| } |
| |
| int loadGraph(TosaSerializationHandler& tsh, json& test_desc) |
| { |
| char graph_fullname[1024]; |
| const std::string error_msg1 = "Check \"tosa_file\" in .json specified by --tosa_desc"; |
| const std::string error_msg2 = " or via arguments --tosa_file & --flatbuffer_dir"; |
| |
| if (strlen(test_desc["tosa_file"].get<std::string>().c_str()) <= 0) |
| { |
| FATAL_ERROR("Missing tosa_file.\n%s", error_msg1.c_str()); |
| } |
| |
| snprintf(graph_fullname, sizeof(graph_fullname), "%s/%s", g_func_config.flatbuffer_dir.c_str(), |
| test_desc["tosa_file"].get<std::string>().c_str()); |
| |
| const char JSON_EXT[] = ".json"; |
| int is_json = 0; |
| { |
| // look for JSON file extension |
| size_t suffix_len = strlen(JSON_EXT); |
| size_t str_len = strlen(graph_fullname); |
| |
| if (str_len > suffix_len && strncasecmp(graph_fullname + (str_len - suffix_len), JSON_EXT, suffix_len) == 0) |
| { |
| is_json = 1; |
| } |
| } |
| |
| if (is_json) |
| { |
| if (tsh.LoadFileSchema(g_func_config.operator_fbs.c_str())) |
| { |
| FATAL_ERROR("\nJSON file detected. Unable to load TOSA flatbuffer schema from: %s\nCheck --operator_fbs " |
| "is set correctly", |
| g_func_config.operator_fbs.c_str()); |
| } |
| |
| if (tsh.LoadFileJson(graph_fullname)) |
| { |
| FATAL_ERROR("\nError loading JSON graph file: %s\n%s%s\nCheck --operator_fbs is using correct version", |
| graph_fullname, error_msg1.c_str(), error_msg2.c_str()); |
| } |
| } |
| else |
| { |
| if (tsh.LoadFileTosaFlatbuffer(graph_fullname)) |
| { |
| FATAL_ERROR("\nError loading TOSA flatbuffer file: %s\n%s%s", graph_fullname, error_msg1.c_str(), |
| error_msg2.c_str()); |
| } |
| } |
| |
| return 0; |
| } |
| |
| int readInputTensors(SubgraphTraverser& gt, json& test_desc) |
| { |
| int tensorCount = gt.getNumInputTensors(); |
| Tensor* tensor; |
| char filename[1024]; |
| |
| try |
| { |
| if ((tensorCount != (int)test_desc["ifm_name"].size()) || (tensorCount != (int)test_desc["ifm_file"].size())) |
| { |
| WARNING("Number of input tensors(%d) doesn't match name(%ld)/file(%ld) in test descriptor.", tensorCount, |
| test_desc["ifm_name"].size(), test_desc["ifm_file"].size()); |
| return 1; |
| } |
| |
| for (int i = 0; i < tensorCount; i++) |
| { |
| tensor = gt.getInputTensorByName(test_desc["ifm_name"][i].get<std::string>()); |
| if (!tensor) |
| { |
| WARNING("Unable to find input tensor %s", test_desc["ifm_name"][i].get<std::string>().c_str()); |
| return 1; |
| } |
| |
| snprintf(filename, sizeof(filename), "%s/%s", g_func_config.flatbuffer_dir.c_str(), |
| test_desc["ifm_file"][i].get<std::string>().c_str()); |
| |
| DEBUG_MED(GT, "Loading input tensor %s from filename: %s", tensor->getName().c_str(), filename); |
| |
| if (!tensor->is_allocated()) |
| { |
| WARNING("Tensor %s is not allocated before being initialized", tensor->getName().c_str()); |
| return 1; |
| } |
| |
| if (tensor->readFromNpyFile(filename)) |
| { |
| WARNING("Unable to read input tensor %s from filename: %s", tensor->getName().c_str(), filename); |
| tensor->dumpTensorParams(g_func_debug.func_debug_file); |
| return 1; |
| } |
| |
| // Push ready consumers to the next node list |
| for (auto gn : tensor->getConsumers()) |
| { |
| if (gn->hasAllInputsReady() && !gn->getOnNextNodeList() && !gn->getEvaluated()) |
| { |
| gt.addToNextNodeList(gn); |
| } |
| } |
| } |
| } |
| catch (nlohmann::json::type_error& e) |
| { |
| WARNING("Fail accessing test descriptor: %s", e.what()); |
| return 1; |
| } |
| |
| if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT)) |
| { |
| gt.dumpNextNodeList(g_func_debug.func_debug_file); |
| } |
| |
| return 0; |
| } |
| |
| const std::string getResultFilenamePrefix() |
| { |
| return g_func_config.abs_mode ? "bounds_" : ""; |
| } |
| |
| // returns true if test_desc contains a "meta" object containing a "compliance" |
| // object which contains "tensors" and one of those has a "mode" whose value is |
| // "DOT_PRODUCT" or "ABS_ERROR" |
| bool isComplianceAbsModeNeeded(json& test_desc) |
| { |
| if (test_desc.contains("meta") && test_desc["meta"].contains("compliance") && |
| test_desc["meta"]["compliance"].contains("tensors")) |
| { |
| for (auto t : test_desc["meta"]["compliance"]["tensors"]) |
| { |
| if (t.contains("mode") && (t["mode"] == "DOT_PRODUCT" || t["mode"] == "ABS_ERROR")) |
| { |
| return true; |
| } |
| } |
| } |
| return false; |
| } |
| |
| int writeFinalTensors(SubgraphTraverser& gt, json& test_desc, const std::string& filename_prefix) |
| { |
| int tensorCount = gt.getNumOutputTensors(); |
| const Tensor* tensor; |
| char filename[1024]; |
| |
| try |
| { |
| if ((tensorCount != (int)test_desc["ofm_name"].size()) || (tensorCount != (int)test_desc["ofm_file"].size())) |
| { |
| WARNING("Number of output tensors(%d) doesn't match name(%ld)/file(%ld) in test descriptor.", tensorCount, |
| test_desc["ofm_name"].size(), test_desc["ofm_file"].size()); |
| return 1; |
| } |
| |
| for (int i = 0; i < tensorCount; i++) |
| { |
| tensor = gt.getOutputTensorByName(test_desc["ofm_name"][i].get<std::string>()); |
| if (!tensor) |
| { |
| WARNING("Unable to find output tensor %s", test_desc["ofm_name"][i].get<std::string>().c_str()); |
| return 1; |
| } |
| |
| snprintf(filename, sizeof(filename), "%s/%s%s", g_func_config.output_dir.c_str(), filename_prefix.c_str(), |
| test_desc["ofm_file"][i].get<std::string>().c_str()); |
| |
| DEBUG_MED(GT, "Writing output tensor[%d] %s to filename: %s", i, tensor->getName().c_str(), filename); |
| |
| if (tensor->writeToNpyFile(filename)) |
| { |
| WARNING("Unable to write output tensor[%d] %s to filename: %s", i, tensor->getName().c_str(), filename); |
| return 1; |
| } |
| } |
| } |
| catch (nlohmann::json::type_error& e) |
| { |
| WARNING("Fail accessing test descriptor: %s", e.what()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| int readVariableTensors(SubgraphTraverser& gt, json test_desc) |
| { |
| int tensorCount = gt.getNumVariableTensors(); |
| Tensor* tensor; |
| char filename[1024]; |
| |
| try |
| { |
| if ((tensorCount != (int)test_desc["variable_name"].size()) || |
| (tensorCount != (int)test_desc["variable_file"].size())) |
| { |
| WARNING("Number of variable tensors(%d) doesn't match name(%ld)/file(%ld)in test descriptor.", tensorCount, |
| test_desc["variable_name"].size(), test_desc["variable_file"].size()); |
| return 1; |
| } |
| |
| for (int i = 0; i < tensorCount; i++) |
| { |
| tensor = gt.getVariableTensorByName(test_desc["variable_name"][i].get<std::string>()); |
| if (!tensor) |
| { |
| WARNING("Unable to find variable tensor %s", test_desc["variable_name"][i].get<std::string>().c_str()); |
| return 1; |
| } |
| |
| snprintf(filename, sizeof(filename), "%s/%s", g_func_config.flatbuffer_dir.c_str(), |
| test_desc["variable_file"][i].get<std::string>().c_str()); |
| |
| DEBUG_MED(GT, "Loading variable tensor %s from filename: %s", tensor->getName().c_str(), filename); |
| |
| if (!tensor->is_allocated()) |
| { |
| WARNING("Tensor %s is not allocated before being initialized", tensor->getName().c_str()); |
| return 1; |
| } |
| |
| if (tensor->readFromNpyFile(filename)) |
| { |
| WARNING("Unable to read variable tensor %s from filename: %s", tensor->getName().c_str(), filename); |
| tensor->dumpTensorParams(g_func_debug.func_debug_file); |
| return 1; |
| } |
| |
| // Push ready consumers to the next node list |
| for (auto gn : tensor->getConsumers()) |
| { |
| if (gn->hasAllInputsReady() && !gn->getOnNextNodeList() && !gn->getEvaluated()) |
| { |
| gt.addToNextNodeList(gn); |
| } |
| } |
| } |
| } |
| catch (nlohmann::json::type_error& e) |
| { |
| WARNING("Fail accessing test descriptor: %s", e.what()); |
| return 1; |
| } |
| |
| if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT)) |
| { |
| gt.dumpNextNodeList(g_func_debug.func_debug_file); |
| } |
| |
| return 0; |
| } |
| |
| int writeVariableTensors(SubgraphTraverser& gt, json test_desc) |
| { |
| int tensorCount = gt.getNumVariableTensors(); |
| const Tensor* tensor; |
| char filename[1024]; |
| |
| try |
| { |
| if ((tensorCount != (int)test_desc["variable_name"].size()) || |
| (tensorCount != (int)test_desc["variable_file"].size())) |
| { |
| WARNING("Number of variable tensors(%d) doesn't match name(%ld)/file(%ld) in test descriptor.", tensorCount, |
| test_desc["variable_name"].size(), test_desc["variable_file"].size()); |
| return 1; |
| } |
| |
| for (int i = 0; i < tensorCount; i++) |
| { |
| tensor = gt.getVariableTensorByName(test_desc["variable_name"][i].get<std::string>()); |
| if (!tensor) |
| { |
| WARNING("Unable to find variable tensor %s", test_desc["variable_name"][i].get<std::string>().c_str()); |
| return 1; |
| } |
| |
| snprintf(filename, sizeof(filename), "%s/%s", g_func_config.output_dir.c_str(), |
| test_desc["variable_file"][i].get<std::string>().c_str()); |
| |
| DEBUG_MED(GT, "Writing variable tensor[%d] %s to filename: %s", i, tensor->getName().c_str(), filename); |
| if (!tensor->is_allocated()) |
| { |
| WARNING("Tensor %s is no longer allocated", tensor->getName().c_str()); |
| return 1; |
| } |
| if (tensor->writeToNpyFile(filename)) |
| { |
| WARNING("Unable to write variable tensor[%d] %s to filename: %s", i, tensor->getName().c_str(), |
| filename); |
| return 1; |
| } |
| } |
| } |
| catch (nlohmann::json::type_error& e) |
| { |
| WARNING("Fail accessing test descriptor: %s", e.what()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| // Read "foo,bar,..." and return std::vector({foo, bar, ...}) |
| std::vector<std::string> parseFromString(std::string raw_str) |
| { |
| bool last_pair = false; |
| std::string::size_type start = 0, end; |
| std::string name; |
| |
| std::vector<std::string> result; |
| do |
| { |
| end = raw_str.find(',', start); |
| if (end == std::string::npos) |
| last_pair = true; |
| |
| // The second parameter holds for number of characters to include in the substring, |
| // not for the index of the end of the capture. |
| name = raw_str.substr(start, end - start); |
| |
| result.push_back(name); |
| |
| start = end + 1; // skip comma |
| } while (!last_pair); |
| |
| return result; |
| } |
| |
| int initTestDesc(json& test_desc) |
| { |
| std::ifstream ifs(g_func_config.test_desc); |
| |
| if (ifs.good()) |
| { |
| try |
| { |
| test_desc = nlohmann::json::parse(ifs); |
| } |
| catch (nlohmann::json::parse_error& e) |
| { |
| WARNING("Error parsing test descriptor json: %s", e.what()); |
| return 1; |
| } |
| } |
| else |
| { |
| WARNING("Cannot open input file: %s", g_func_config.test_desc.c_str()); |
| return 1; |
| } |
| |
| // Overwrite flatbuffer_dir/output_dir with dirname(g_func_config.test_desc) if it's not specified. |
| if (g_func_config.flatbuffer_dir.empty() || g_func_config.output_dir.empty()) |
| { |
| auto slash_pos = g_func_config.test_desc.find_last_of("/\\"); |
| std::string test_dir; |
| if (slash_pos != std::string::npos) |
| { |
| test_dir = g_func_config.test_desc.substr(0, slash_pos); |
| } |
| else |
| { |
| test_dir = std::string("."); |
| } |
| if (g_func_config.flatbuffer_dir.empty()) |
| { |
| g_func_config.flatbuffer_dir = test_dir; |
| } |
| if (g_func_config.output_dir.empty()) |
| { |
| g_func_config.output_dir = test_dir; |
| } |
| } |
| |
| // Overwrite test_desc["tosa_file"] if --tosa_file specified. |
| if (!g_func_config.tosa_file.empty()) |
| { |
| test_desc["tosa_file"] = g_func_config.tosa_file; |
| } |
| |
| // Overwrite test_desc["ifm_name"] if --ifm_name specified. |
| if (!g_func_config.ifm_name.empty()) |
| { |
| std::vector<std::string> ifm_name_vec = parseFromString(g_func_config.ifm_name); |
| test_desc["ifm_name"] = ifm_name_vec; |
| } |
| |
| // Overwrite test_desc["ifm_file"] if --ifm_file specified. |
| if (!g_func_config.ifm_file.empty()) |
| { |
| std::vector<std::string> ifm_file_vec = parseFromString(g_func_config.ifm_file); |
| test_desc["ifm_file"] = ifm_file_vec; |
| } |
| |
| // Overwrite test_desc["ofm_name"] if --ofm_name specified. |
| if (!g_func_config.ofm_name.empty()) |
| { |
| std::vector<std::string> ofm_name_vec = parseFromString(g_func_config.ofm_name); |
| test_desc["ofm_name"] = ofm_name_vec; |
| } |
| |
| // Overwrite test_desc["ofm_file"] if --ofm_file specified. |
| if (!g_func_config.ofm_file.empty()) |
| { |
| std::vector<std::string> ofm_file_vec = parseFromString(g_func_config.ofm_file); |
| test_desc["ofm_file"] = ofm_file_vec; |
| } |
| |
| // Overwrite test_desc["variable_name"] if --variable_name= specified. |
| std::string variable_name_str(g_func_config.variable_name); |
| if (!variable_name_str.empty()) |
| { |
| std::vector<std::string> variable_name_vec = parseFromString(variable_name_str); |
| test_desc["variable_name"] = variable_name_vec; |
| } |
| |
| // Overwrite test_desc["variable_file"] if --variable_file= specified. |
| std::string variable_file_str(g_func_config.variable_file); |
| if (!variable_file_str.empty()) |
| { |
| std::vector<std::string> variable_file_vec = parseFromString(variable_file_str); |
| test_desc["variable_file"] = variable_file_vec; |
| } |
| |
| return 0; |
| } |
| |
| void parse_value(const std::string& text, tosa_level_t& value) |
| { |
| |
| if (text == "NONE") |
| value = func_config_t::NONE; |
| else if (text == "EIGHTK") |
| value = func_config_t::EIGHTK; |
| else |
| throw cxxopts::argument_incorrect_type("TOSA_LEVEL"); |
| return; |
| } |