blob: b0c663ac8bc1223cdb03cc33cf7775982223edeb [file] [log] [blame]
//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ParserFlatbuffersFixture.hpp"
TEST_SUITE("TensorflowLiteParser_Tile")
{
struct TileFixture : public ParserFlatbuffersFixture
{
explicit TileFixture(const std::string& inputShape,
const std::string& outputShape,
const std::string& multiplesShape,
const std::string& multiplesData,
const std::string& dataType = "FLOAT32",
const std::string& scale = "1.0",
const std::string& offset = "0")
{
m_JsonString = R"(
{
"version": 3,
"operator_codes": [
{
"deprecated_builtin_code": 69,
"version": 1,
"builtin_code": "TILE"
}
],
"subgraphs": [
{
"tensors": [
{
"shape": )" + inputShape + R"(,
"type": )" + dataType + R"(,
"buffer": 1,
"name": "inputTensor",
"quantization": {
"min": [ 0.0 ],
"max": [ 255.0 ],
"scale": [ )" + scale + R"( ],
"zero_point": [ )" + offset + R"( ],
},
"is_variable": false,
"has_rank": true
},
{
"shape": )" + multiplesShape + R"(,
"type": "INT32",
"buffer": 2,
"name": "multiples",
"quantization": {
"details_type": "NONE",
"quantized_dimension": 0
},
"is_variable": false,
"has_rank": true
},
{
"shape": )" + outputShape + R"(,
"type": )" + dataType + R"(,
"buffer": 3,
"name": "outputTensor",
"quantization": {
"min": [ 0.0 ],
"max": [ 255.0 ],
"scale": [ )" + scale + R"( ],
"zero_point": [ )" + offset + R"( ],
},
"is_variable": false,
"has_rank": true
}
],
"inputs": [ 0 ],
"outputs": [ 2 ],
"operators": [
{
"opcode_index": 0,
"inputs": [ 0, 1 ],
"outputs": [ 2 ],
"builtin_options_type": "NONE",
"custom_options_format": "FLEXBUFFERS"
}
],
} ],
"buffers" : [
{ },
{ },
{ "data": )" + multiplesData + R"(, },
]
}
)";
SetupSingleInputSingleOutput("inputTensor", "outputTensor");
}
};
struct SimpleTileFixture : public TileFixture
{
SimpleTileFixture() : TileFixture("[ 2, 2 ]", "[ 4, 6 ]", "[ 2 ]", "[ 2, 0, 0, 0, 3, 0, 0, 0 ]") {}
};
TEST_CASE_FIXTURE(SimpleTileFixture, "ParseTile")
{
RunTest<2, armnn::DataType::Float32, armnn::DataType::Float32>
(0,
{{ "inputTensor", { 1, 2,
3, 4 }}},
{{ "outputTensor", { 1, 2, 1, 2, 1, 2,
3, 4, 3, 4, 3, 4,
1, 2, 1, 2, 1, 2,
3, 4, 3, 4, 3, 4, }}});
}
}