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Diffstat (limited to 'tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp')
-rw-r--r-- | tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp | 158 |
1 files changed, 158 insertions, 0 deletions
diff --git a/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp b/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp new file mode 100644 index 000000000..5768ddde8 --- /dev/null +++ b/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp @@ -0,0 +1,158 @@ +/* + * 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/Quantization.h" +#include "support/tflite/interp/FunctionBuilder.h" + +#include <chrono> +#include <iostream> + +using namespace tflite; +using namespace tflite::ops::builtin; + +TEST(NNAPI_Quickcheck_max_pool_1, simple_test) +{ + // Set random seed + int SEED = std::chrono::system_clock::now().time_since_epoch().count(); + + nnfw::util::env::IntAccessor("SEED").access(SEED); + + // Set random test parameters + int verbose = 0; + int tolerance = 1; + + nnfw::util::env::IntAccessor("VERBOSE").access(verbose); + nnfw::util::env::IntAccessor("TOLERANCE").access(tolerance); + +#define INT_VALUE(NAME, VALUE) IntVar NAME##_Value(#NAME, VALUE); +#include "max_pool_quan_1.lst" +#undef INT_VALUE + + const TfLitePadding PADDING_TYPE = static_cast<TfLitePadding>(PADDING_TYPE_Value()); + + const int32_t IFM_C = IFM_C_Value(); + const int32_t IFM_H = IFM_H_Value(); + const int32_t IFM_W = IFM_W_Value(); + + const int32_t KER_H = KER_H_Value(); + const int32_t KER_W = KER_W_Value(); + + const int32_t OFM_C = IFM_C; + const int32_t OFM_H = OFM_H_Value(); + const int32_t OFM_W = OFM_W_Value(); + + assert((OFM_H >= (IFM_H - KER_H))); + assert((OFM_W >= (IFM_W - KER_W))); + assert((kTfLitePaddingSame == PADDING_TYPE) || (kTfLitePaddingValid == PADDING_TYPE)); + + 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(PADDING_TYPE); + PRINT_NEWLINE(); + + PRINT_VALUE(IFM_C); + PRINT_VALUE(IFM_H); + PRINT_VALUE(IFM_W); + PRINT_NEWLINE(); + + PRINT_VALUE(KER_H); + PRINT_VALUE(KER_W); + PRINT_NEWLINE(); + + PRINT_VALUE(OFM_C); + PRINT_VALUE(OFM_H); + PRINT_VALUE(OFM_W); +#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 = 1.0f; + quantization.zero_point = 0; + + // On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N) + interp.AddTensors(2); + + // Configure OFM + interp.SetTensorParametersReadWrite(0, kTfLiteFloat32 /* type */, "output" /* name */, + {1 /*N*/, OFM_H, OFM_W, OFM_C} /* dims */, quantization); + + // Configure IFM + interp.SetTensorParametersReadWrite(1, kTfLiteFloat32 /* type */, "input" /* name */, + {1 /*N*/, IFM_H, IFM_W, IFM_C} /* dims */, quantization); + + // Add Max Pooling Node + // + // NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free + // So, param should be allocated with malloc + auto param = make_alloc<TfLitePoolParams>(); + + param->padding = PADDING_TYPE; + param->stride_width = 1; + param->stride_height = 1; + param->filter_width = KER_W; + param->filter_height = KER_H; + param->activation = kTfLiteActNone; + + // Run Convolution and store its result into Tensor #0 + // - Read IFM from Tensor #1 + interp.AddNodeWithParameters({1}, {0}, nullptr, 0, reinterpret_cast<void *>(param), + BuiltinOpResolver().FindOp(BuiltinOperator_MAX_POOL_2D, 1)); + + // Set Tensor #1 as Input #0, and Tensor #0 as Output #0 + interp.SetInputs({1}); + interp.SetOutputs({0}); + }; + + const nnfw::support::tflite::interp::FunctionBuilder builder(setup); + + RandomTestParam param; + + param.verbose = verbose; + param.tolerance = tolerance; + + int res = RandomTestRunner{SEED, param}.run(builder); + + EXPECT_EQ(res, 0); +} |