2 * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
8 * http://www.apache.org/licenses/LICENSE-2.0
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
17 #include "gtest/gtest.h"
19 #include "tflite/ext/kernels/register.h"
20 #include "tensorflow/lite/model.h"
21 #include "tensorflow/lite/builtin_op_data.h"
25 #include "misc/environment.h"
27 #include "tflite/Diff.h"
28 #include "tflite/Quantization.h"
29 #include "tflite/interp/FunctionBuilder.h"
34 using namespace tflite;
35 using namespace nnfw::tflite;
37 TEST(NNAPI_Quickcheck_reshape_1, simple_test)
40 int SEED = std::chrono::system_clock::now().time_since_epoch().count();
42 nnfw::misc::env::IntAccessor("SEED").access(SEED);
44 // Set random test parameters
48 nnfw::misc::env::IntAccessor("VERBOSE").access(verbose);
49 nnfw::misc::env::IntAccessor("TOLERANCE").access(tolerance);
51 #define INT_VALUE(NAME, VALUE) IntVar NAME##_Value(#NAME, VALUE);
52 #include "reshape_quan_1.lst"
55 const int32_t IFM_C = IFM_C_Value();
56 const int32_t IFM_H = IFM_H_Value();
57 const int32_t IFM_W = IFM_W_Value();
59 const int32_t OUT_L = IFM_C * IFM_H * IFM_W;
61 std::cout << "Configurations:" << std::endl;
62 #define PRINT_NEWLINE() \
64 std::cout << std::endl; \
66 #define PRINT_VALUE(value) \
68 std::cout << " " << #value << ": " << (value) << std::endl; \
82 const int32_t dims[2] = {1, OUT_L};
84 auto setup = [&](Interpreter &interp) {
85 // Comment from 'context.h'
87 // Parameters for asymmetric quantization. Quantized values can be converted
88 // back to float using:
89 // real_value = scale * (quantized_value - zero_point);
91 // Q: Is this necessary?
92 // A: This may be necessary, because quantization values(scale, zero_point) of TENSOR_INT32 and
93 // TENSOR_QUANT8_ASYMM are passed on to the runtime.
94 TfLiteQuantizationParams quantization;
95 quantization.scale = 1.0f;
96 quantization.zero_point = 0;
98 // On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N)
102 interp.SetTensorParametersReadWrite(0, kTfLiteUInt8 /* type */, "output" /* name */,
103 {1 /*N*/, OUT_L} /* dims */, quantization);
106 interp.SetTensorParametersReadWrite(1, kTfLiteUInt8 /* type */, "input" /* name */,
107 {1 /*N*/, IFM_H, IFM_W, IFM_C} /* dims */, quantization);
110 interp.SetTensorParametersReadOnly(2, kTfLiteInt32 /* type */, "shape" /* name */,
111 {2} /* dims */, quantization,
112 reinterpret_cast<const char *>(dims), 2 * sizeof(int32_t));
116 // NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free
117 // So, param should be allocated with malloc
118 auto param = make_alloc<TfLiteReshapeParams>();
120 param->num_dimensions = 2;
122 param->shape[1] = OUT_L;
124 // Run Reshapeand store its result into Tensor #0
125 interp.AddNodeWithParameters({1, 2}, {0}, nullptr, 0, reinterpret_cast<void *>(param),
126 BuiltinOpResolver().FindOp(BuiltinOperator_RESHAPE, 1));
128 // Set Tensor #1 as Input #0, and Tensor #0 as Output #0
129 interp.SetInputs({1});
130 interp.SetOutputs({0});
133 const nnfw::tflite::FunctionBuilder builder(setup);
135 RandomTestParam param;
137 param.verbose = verbose;
138 param.tolerance = tolerance;
140 int res = RandomTestRunner{SEED, param}.run(builder);