2 * Copyright (c) 2020 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 "kernels/Pad.h"
18 #include "kernels/TestUtils.h"
19 #include "luci_interpreter/TestMemoryManager.h"
21 namespace luci_interpreter
28 using namespace testing;
30 float GetTolerance(float min, float max) { return (max - min) / 255.0; }
34 std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
35 float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
36 std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-1.0f, 1.0f);
37 std::vector<float> input_data{-0.8, 0.2, 0.9, 0.7, 0.1, -0.3};
38 std::vector<int32_t> paddings_data{0, 0, 0, 2, 1, 3, 0, 0};
39 Tensor input_tensor = makeInputTensor<DataType::U8>(
40 {1, 2, 3, 1}, quant_param.first, quant_param.second, input_data, memory_manager.get());
41 Tensor paddings_tensor =
42 makeInputTensor<DataType::S32>({4, 2}, paddings_data, memory_manager.get());
43 Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second);
45 Pad kernel(&input_tensor, &paddings_tensor, &output_tensor);
47 memory_manager->allocate_memory(output_tensor);
50 std::vector<float> ref_output_data{0, -0.8, 0.2, 0.9, 0, 0, 0, 0, 0.7, 0.1, -0.3, 0, 0, 0,
51 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
52 EXPECT_THAT(dequantizeTensorData(output_tensor),
53 FloatArrayNear(ref_output_data, kQuantizedTolerance));
54 EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 4, 7, 1}));
59 std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
60 std::vector<float> input_data{1, 2, 3, 4, 5, 6};
61 std::vector<int32_t> paddings_data{1, 0, 0, 2, 0, 3, 0, 0};
63 makeInputTensor<DataType::FLOAT32>({1, 2, 3, 1}, input_data, memory_manager.get());
64 Tensor paddings_tensor =
65 makeInputTensor<DataType::S32>({4, 2}, paddings_data, memory_manager.get());
66 Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
68 Pad kernel(&input_tensor, &paddings_tensor, &output_tensor);
70 memory_manager->allocate_memory(output_tensor);
73 std::vector<float> ref_output_data{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
74 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0, 0, 0, 4, 5,
75 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
76 std::initializer_list<int32_t> ref_output_shape{2, 4, 6, 1};
77 EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
78 EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
82 } // namespace kernels
83 } // namespace luci_interpreter