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Diffstat (limited to 'tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp')
| -rw-r--r-- | tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp | 189 |
1 files changed, 189 insertions, 0 deletions
diff --git a/tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp b/tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp new file mode 100644 index 000000000..1cb75fea0 --- /dev/null +++ b/tools/nnapi_quickcheck/tests/fully_connected_quan_1.cpp @@ -0,0 +1,189 @@ +/* + * 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 "util/environment.h" + +#include "support/tflite/Diff.h" +#include "support/tflite/Quantization.h" +#include "support/tflite/interp/FunctionBuilder.h" + +#include <iostream> +#include <cassert> + +#include <chrono> +#include <random> + +using namespace tflite; +using namespace tflite::ops::builtin; + +template <typename T> T *make_malloc(void) { return reinterpret_cast<T *>(malloc(sizeof(T))); } + +TEST(NNAPI_Quickcheck_fully_connected_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 "fully_connected_quan_1.lst" +#undef INT_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 = IFM_C_Value() * IFM_H_Value() * IFM_W_Value(); + + const int32_t OUT_LEN = KER_H; + + // 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(IFM_C); + PRINT_VALUE(IFM_H); + PRINT_VALUE(IFM_W); + PRINT_NEWLINE(); + + PRINT_VALUE(KER_H); + PRINT_VALUE(KER_W); + PRINT_NEWLINE(); + + PRINT_VALUE(OUT_LEN); +#undef PRINT_VALUE +#undef PRINT_NEWLINE + + // Configure Kernel Data + const uint32_t kernel_size = KER_H * KER_W; + float kernel_data[kernel_size] = { + 0.0f, + }; + + // Fill kernel data with random data + { + std::normal_distribution<float> kernel_dist(-1.0f, +1.0f); + + for (uint32_t off = 0; off < kernel_size; ++off) + { + kernel_data[off++] = kernel_dist(random); + } + } + + // Configure Bias Data + const auto bias_size = KER_H; + int32_t bias_data[bias_size] = { + 0, + }; + + // Fill bias data with random data + { + std::normal_distribution<float> bias_dist(-1.0f, +1.0f); + + for (uint32_t off = 0; off < bias_size; ++off) + { + bias_data[off] = static_cast<int32_t>(bias_dist(random)); + } + } + + 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 = make_default_quantization(); + quantization.scale = FLOAT_NEAREST_TO_1; + quantization.zero_point = 0; + + // On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N) + interp.AddTensors(4); + + // Configure OFM + interp.SetTensorParametersReadWrite(0, kTfLiteUInt8 /* type */, "output" /* name */, + {1 /*N*/, KER_H} /* dims */, quantization); + + // Configure IFM + interp.SetTensorParametersReadWrite(1, kTfLiteUInt8 /* type */, "input" /* name */, + {1 /*N*/, IFM_H, IFM_W, IFM_C} /* dims */, quantization); + + // NOTE kernel_data & bias_data should live longer than interpreter! + interp.SetTensorParametersReadOnly( + 2, kTfLiteUInt8 /* type */, "filter" /* name */, {KER_H, KER_W} /* dims */, quantization, + reinterpret_cast<const char *>(kernel_data), kernel_size * sizeof(uint8_t)); + + interp.SetTensorParametersReadOnly( + 3, kTfLiteInt32 /* type */, "bias" /* name */, {bias_size} /* dims */, quantization, + reinterpret_cast<const char *>(bias_data), bias_size * sizeof(int32_t)); + + // Add Fully Connected Node + // + // NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free + // So, param should be allocated with malloc + auto param = make_malloc<TfLiteFullyConnectedParams>(); + + param->activation = kTfLiteActRelu; + + // Run Convolution and store its result into Tensor #0 + // - Read IFM from Tensor #1 + // - Read Filter from Tensor #2, + // - Read Bias from Tensor #3 + interp.AddNodeWithParameters({1, 2, 3}, {0}, nullptr, 0, reinterpret_cast<void *>(param), + BuiltinOpResolver().FindOp(BuiltinOperator_FULLY_CONNECTED, 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); +} |