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Diffstat (limited to 'tools/nnapi_quickcheck/tests/mul_1.cpp')
| -rw-r--r-- | tools/nnapi_quickcheck/tests/mul_1.cpp | 152 |
1 files changed, 152 insertions, 0 deletions
diff --git a/tools/nnapi_quickcheck/tests/mul_1.cpp b/tools/nnapi_quickcheck/tests/mul_1.cpp new file mode 100644 index 000000000..3a4ae5c8e --- /dev/null +++ b/tools/nnapi_quickcheck/tests/mul_1.cpp @@ -0,0 +1,152 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * 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 "gtest/gtest.h" + +#include "support/tflite/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" +#include "tensorflow/contrib/lite/builtin_op_data.h" + +#include "env.h" +#include "memory.h" +#include "util/environment.h" + +#include "support/tflite/Diff.h" +#include "support/tflite/interp/FunctionBuilder.h" + +#include <iostream> +#include <cassert> + +#include <chrono> +#include <random> + +using namespace tflite; +using namespace tflite::ops::builtin; + +TEST(NNAPI_Quickcheck_mul_1, simple_test) +{ + int verbose = 0; + int tolerance = 1; + + nnfw::util::env::IntAccessor("VERBOSE").access(verbose); + nnfw::util::env::IntAccessor("TOLERANCE").access(tolerance); + + // Set random seed + int SEED = std::chrono::system_clock::now().time_since_epoch().count(); + + nnfw::util::env::IntAccessor("SEED").access(SEED); + +#define INT_VALUE(NAME, VALUE) IntVar NAME##_Value(#NAME, VALUE); +#include "mul_1.lst" +#undef INT_VALUE + + const int32_t LEFT_1D = LEFT_1D_Value(); + const int32_t LEFT_2D = LEFT_2D_Value(); + const int32_t LEFT_3D = LEFT_3D_Value(); + + const int32_t RIGHT_W = RIGHT_W_Value(); + + const int32_t OFM_1D = LEFT_1D_Value(); + const int32_t OFM_2D = LEFT_2D_Value(); + const int32_t OFM_3D = LEFT_3D_Value(); + + // Initialize random number generator + std::minstd_rand random(SEED); + + std::cout << "Configurations:" << std::endl; +#define PRINT_NEWLINE() \ + { \ + std::cout << std::endl; \ + } +#define PRINT_VALUE(value) \ + { \ + std::cout << " " << #value << ": " << (value) << std::endl; \ + } + PRINT_VALUE(SEED); + PRINT_NEWLINE(); + + PRINT_VALUE(LEFT_1D); + PRINT_VALUE(LEFT_2D); + PRINT_VALUE(LEFT_3D); + PRINT_NEWLINE(); + + PRINT_VALUE(RIGHT_W); + PRINT_NEWLINE(); + + PRINT_VALUE(OFM_1D); + PRINT_VALUE(OFM_2D); + PRINT_VALUE(OFM_3D); +#undef PRINT_VALUE +#undef PRINT_NEWLINE + + auto setup = [&](Interpreter &interp) { + // Comment from 'context.h' + // + // Parameters for asymmetric quantization. Quantized values can be converted + // back to float using: + // real_value = scale * (quantized_value - zero_point); + // + // Q: Is this necessary? + TfLiteQuantizationParams quantization; + + quantization.scale = 1; + quantization.zero_point = 0; + + // On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N) + interp.AddTensors(3); + + // Configure output + interp.SetTensorParametersReadWrite(0, kTfLiteFloat32 /* type */, "output" /* name */, + {OFM_1D, OFM_2D, OFM_3D} /* dims */, quantization); + + // Configure input(s) + interp.SetTensorParametersReadWrite(1, kTfLiteFloat32 /* type */, "left" /* name */, + {LEFT_1D, LEFT_2D, LEFT_3D} /* dims */, quantization); + + interp.SetTensorParametersReadWrite(2, kTfLiteFloat32 /* type */, "right" /* name */, + {RIGHT_W} /* dims */, quantization); + + // Add MUL Node + // + // NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free + // So, param should be allocated with malloc + auto param = make_alloc<TfLiteAddParams>(); + + param->activation = kTfLiteActNone; + + // Run MUL and store the result into Tensor #0 + // - Read Left from Tensor #1 + // - Read Right from Tensor #2, + interp.AddNodeWithParameters({1, 2}, {0}, nullptr, 0, reinterpret_cast<void *>(param), + BuiltinOpResolver().FindOp(BuiltinOperator_MUL, 1)); + + interp.SetInputs({1, 2}); + interp.SetOutputs({0}); + }; + + const nnfw::support::tflite::interp::FunctionBuilder builder(setup); + + RandomTestParam param; + + param.verbose = verbose; + param.tolerance = tolerance; + param.tensor_logging = 1; + param.log_path = "report/tensor_mul_1.log"; + + int res = RandomTestRunner{SEED, param}.run(builder); + + EXPECT_EQ(res, 0); +} |