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diff --git a/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp b/tools/nnapi_quickcheck/tests/max_pool_quan_1.cpp
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+/*
+ * 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);
+}