Add nnapi_quickcheck for split operator (#2398)

Add nnapi_quickcheck_split_1 file to verify split operator

Signed-off-by: Jiyoung Yun <jy910.yun@samsung.com>

diff --git a/tools/nnapi_quickcheck/CMakeLists.txt b/tools/nnapi_quickcheck/CMakeLists.txt
index 2795cd2..e6112b8 100644 (file)
--- a/tools/nnapi_quickcheck/CMakeLists.txt
+++ b/tools/nnapi_quickcheck/CMakeLists.txt
@@ -76,3 +76,4 @@ add_nnapi_quickcheck(gather_2)
 add_nnapi_quickcheck(dequantize_1)
 add_nnapi_quickcheck(tanh_1)
 add_nnapi_quickcheck(logistic_quan_1)
+add_nnapi_quickcheck(split_1)

diff --git a/tools/nnapi_quickcheck/tests/split_1.cpp b/tools/nnapi_quickcheck/tests/split_1.cpp
new file mode 100644 (file)
index 0000000..742c5db
--- /dev/null
+++ b/tools/nnapi_quickcheck/tests/split_1.cpp
@@ -0,0 +1,153 @@
+/*
+ * 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 "util/feature/Shape.h"
+
+#include "support/tflite/Diff.h"
+#include "support/tflite/Quantization.h"
+#include "support/tflite/interp/FunctionBuilder.h"
+
+#include <chrono>
+#include <random>
+#include <iostream>
+#include <cassert>
+
+using namespace tflite;
+using namespace tflite::ops::builtin;
+
+TEST(NNAPI_Quickcheck_split_1, simple_test)
+{
+  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 "split_1.lst"
+#undef INT_VALUE
+
+  const int32_t IFM_N = IFM_N_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 NUM_SPLIT = NUM_SPLIT_Value();
+  const int32_t AXIS = AXIS_Value();
+
+  // Set random seed
+  int SEED = std::chrono::system_clock::now().time_since_epoch().count();
+
+  nnfw::util::env::IntAccessor("SEED").access(SEED);
+
+  // 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_N);
+  PRINT_VALUE(IFM_C);
+  PRINT_VALUE(IFM_H);
+  PRINT_VALUE(IFM_W);
+  PRINT_VALUE(NUM_SPLIT);
+  PRINT_VALUE(AXIS);
+#undef PRINT_VALUE
+#undef PRINT_NEWLINE
+
+  const int32_t OFM_N = IFM_N;
+  const int32_t OFM_C = IFM_C;
+  const int32_t OFM_H = IFM_H;
+  const int32_t OFM_W = IFM_W;
+  const int32_t axis[1] = {AXIS};
+
+  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();
+
+    // On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N)
+    interp.AddTensors(NUM_SPLIT + 2);
+
+    // Configure Input Tensor(s)
+    interp.SetTensorParametersReadOnly(0, kTfLiteInt32 /* type */, "axis" /* name */,
+                                       {1} /* dims */, quantization,
+                                       reinterpret_cast<const char *>(axis), 1 * sizeof(int32_t));
+
+    interp.SetTensorParametersReadWrite(1, kTfLiteFloat32 /* type */, "input" /* name */,
+                                        {IFM_N, IFM_H, IFM_W, IFM_C} /* dims */, quantization);
+
+    // Configure Output Tensor
+    std::vector<int> ofm_indexes;
+
+    for (uint32_t n = 0; n < NUM_SPLIT; ++n)
+    {
+      const auto ofm_index = 2 + n;
+
+      interp.SetTensorParametersReadWrite(ofm_index, kTfLiteFloat32 /* type */, "output" /* name */,
+                                          {OFM_N, OFM_H, OFM_W, OFM_C} /* dims */, quantization);
+
+      ofm_indexes.emplace_back(ofm_index);
+    }
+
+    auto *param = reinterpret_cast<TfLiteSplitParams *>(malloc(sizeof(TfLiteSplitParams)));
+
+    param->num_splits = NUM_SPLIT;
+
+    // Add SPLIT Node
+    // Run SPLIT and store its result into Tensor #0
+    //  - Read axis and IFM from Tensor #0 and #1, respectively
+    interp.AddNodeWithParameters({0, 1}, ofm_indexes, nullptr, 0, reinterpret_cast<void *>(param),
+                                 BuiltinOpResolver().FindOp(BuiltinOperator_SPLIT, 1));
+
+    // Set Tensor #1 as Input #0, and Tensor #2 ~ #NUM_SPLIT+1 as Output #0
+    interp.SetInputs({1});
+    interp.SetOutputs(ofm_indexes);
+  };
+
+  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);
+}

diff --git a/tools/nnapi_quickcheck/tests/split_1.lst b/tools/nnapi_quickcheck/tests/split_1.lst
new file mode 100644 (file)
index 0000000..823bf24
--- /dev/null
+++ b/tools/nnapi_quickcheck/tests/split_1.lst
@@ -0,0 +1,10 @@
+#ifndef INT_VALUE
+#error "INT_VALUE should be defined"
+#endif // INT_VALUE
+
+INT_VALUE(IFM_N, 1)
+INT_VALUE(IFM_C, 1)
+INT_VALUE(IFM_H, 5)
+INT_VALUE(IFM_W, 30)
+INT_VALUE(NUM_SPLIT, 5)
+INT_VALUE(AXIS, 1)