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/*
* Copyright (c) 2020 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 "GenModelTest.h"
#include <memory>
TEST_F(GenModelTest, OneOp_Concat_ShareSubTensor)
{
CircleGen cgen;
int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
int rhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
int shared_subtensor = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32});
int concat_out = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32});
std::vector<int32_t> padding_data{0, 0, 1, 1, 1, 1, 0, 0};
uint32_t padding_buf = cgen.addBuffer(padding_data);
int padding = cgen.addTensor({{4, 2}, circle::TensorType::TensorType_INT32, padding_buf});
int pad_out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_FLOAT32});
cgen.addOperatorAdd({{lhs, rhs}, {shared_subtensor}}, circle::ActivationFunctionType_NONE);
cgen.addOperatorConcatenation({{rhs, shared_subtensor}, {concat_out}}, 3,
circle::ActivationFunctionType_NONE);
cgen.addOperatorPad({{shared_subtensor, padding}, {pad_out}});
cgen.setInputsAndOutputs({lhs, rhs}, {pad_out, concat_out});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->addTestCase(uniformTCD<float>(
{{1, 3, 2, 4}, {5, 4, 7, 4}},
{{0, 0, 0, 0, 0, 6, 7, 0, 0, 9, 8, 0, 0, 0, 0, 0}, {5, 6, 4, 7, 7, 9, 4, 8}}));
_context->setBackends({"acl_cl", "acl_neon", "cpu"});
SUCCEED();
}
struct ConcatVariationParam
{
TestCaseData tcd;
circle::TensorType type = circle::TensorType::TensorType_FLOAT32;
float scale = 0.0f;
int64_t zero_point = 0;
};
class ConcatVariation : public GenModelTest,
public ::testing::WithParamInterface<ConcatVariationParam>
{
};
// Input shape: {2, 3} / {2, 3}
// Output shape: {4, 3}
INSTANTIATE_TEST_SUITE_P(
GenModelTest, ConcatVariation,
::testing::Values(
// Float
ConcatVariationParam{uniformTCD<float>({{1, 2, 3, 4, 5, 6}, {7, 8, 9, 10, 11, 12}},
{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}})},
// Uint8
ConcatVariationParam{uniformTCD<uint8_t>({{1, 2, 3, 4, 5, 6}, {7, 8, 9, 10, 11, 12}},
{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}}),
circle::TensorType::TensorType_UINT8, 1.0f, -2},
// Int8
ConcatVariationParam{uniformTCD<int8_t>({{1, 2, 3, 4, 5, 6}, {7, 8, 9, 10, 11, 12}},
{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}}),
circle::TensorType::TensorType_INT8, 1.0f, -2},
// Int16
// TODO Enable when nnfw api support int16 type
// ConcatVariationParam{
// uniformTCD<int16_t>({{1, 2, 3, 4, 5, 6}, {7, 8, 9, 10, 11, 12}},
// {{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}}),
// circle::TensorType::TensorType_INT16, 1.0f, 0},
// Int32
ConcatVariationParam{uniformTCD<int32_t>({{1, 2, 3, 4, 5, 6}, {7, 8, 9, 10, 11, 12}},
{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}}),
circle::TensorType::TensorType_INT32},
// Int64
ConcatVariationParam{uniformTCD<int64_t>({{1, 2, 3, 4, 5, 6}, {7, 8, 9, 10, 11, 12}},
{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}}),
circle::TensorType::TensorType_INT64}));
TEST_P(ConcatVariation, Test)
{
auto ¶m = GetParam();
CircleGen cgen;
int input1 = cgen.addTensor({{2, 3}, param.type}, param.scale, param.zero_point);
int input2 = cgen.addTensor({{2, 3}, param.type}, param.scale, param.zero_point);
int output = cgen.addTensor({{4, 3}, param.type}, param.scale, param.zero_point);
cgen.addOperatorConcatenation({{input1, input2}, {output}}, 0,
circle::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({input1, input2}, {output});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->addTestCase(param.tcd);
_context->setBackends({"acl_cl", "acl_neon", "cpu"});
SUCCEED();
}
TEST_F(GenModelTest, OneOp_Concat_Subtensor_4D)
{
CircleGen cgen;
int in1 = cgen.addTensor({{1, 1, 1, 20}, circle::TensorType::TensorType_FLOAT32});
int in2 = cgen.addTensor({{1, 1, 1, 10}, circle::TensorType::TensorType_FLOAT32});
std::vector<int32_t> axis_data{3};
uint32_t axis_buf = cgen.addBuffer(axis_data);
int axis = cgen.addTensor({{1}, circle::TensorType::TensorType_INT32, axis_buf});
int s_out1 = cgen.addTensor({{1, 1, 1, 5}, circle::TensorType::TensorType_FLOAT32});
int s_out2 = cgen.addTensor({{1, 1, 1, 5}, circle::TensorType::TensorType_FLOAT32});
int s_out3 = cgen.addTensor({{1, 1, 1, 5}, circle::TensorType::TensorType_FLOAT32});
int s_out4 = cgen.addTensor({{1, 1, 1, 5}, circle::TensorType::TensorType_FLOAT32});
int c_out1 = cgen.addTensor({{1, 1, 1, 10}, circle::TensorType::TensorType_FLOAT32});
int c_out2 = cgen.addTensor({{1, 1, 1, 10}, circle::TensorType::TensorType_FLOAT32});
int c_out3 = cgen.addTensor({{1, 1, 1, 10}, circle::TensorType::TensorType_FLOAT32});
int a_out1 = cgen.addTensor({{1, 1, 1, 10}, circle::TensorType::TensorType_FLOAT32});
int a_out2 = cgen.addTensor({{1, 1, 1, 10}, circle::TensorType::TensorType_FLOAT32});
int a_out3 = cgen.addTensor({{1, 1, 1, 10}, circle::TensorType::TensorType_FLOAT32});
int final_out = cgen.addTensor({{1, 1, 1, 35}, circle::TensorType::TensorType_FLOAT32});
cgen.addOperatorSplit({{axis, in1}, {s_out1, s_out2, s_out3, s_out4}}, 4);
cgen.addOperatorConcatenation({{s_out1, s_out2}, {c_out1}}, 3,
circle::ActivationFunctionType::ActivationFunctionType_NONE);
cgen.addOperatorConcatenation({{s_out1, s_out3}, {c_out2}}, 3,
circle::ActivationFunctionType::ActivationFunctionType_NONE);
cgen.addOperatorConcatenation({{s_out1, s_out4}, {c_out3}}, 3,
circle::ActivationFunctionType::ActivationFunctionType_NONE);
cgen.addOperatorAdd({{c_out1, in2}, {a_out1}},
circle::ActivationFunctionType::ActivationFunctionType_NONE);
cgen.addOperatorAdd({{c_out2, in2}, {a_out2}},
circle::ActivationFunctionType::ActivationFunctionType_NONE);
cgen.addOperatorAdd({{c_out3, in2}, {a_out3}},
circle::ActivationFunctionType::ActivationFunctionType_NONE);
cgen.addOperatorConcatenation({{s_out1, a_out1, a_out2, a_out3}, {final_out}}, 3,
circle::ActivationFunctionType::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({in1, in2}, {s_out1, s_out2, s_out3, s_out4, c_out1, c_out2, c_out3,
a_out1, a_out2, a_out3, final_out});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->addTestCase(uniformTCD<float>(
{
// inputs
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}, // in1
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0} // in2
},
{
// outputs
{1, 2, 3, 4, 5}, // s_out1
{6, 7, 8, 9, 10}, // s_out2
{11, 12, 13, 14, 15}, // s_out3
{16, 17, 18, 19, 20}, // s_out4
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, // c_out1
{1, 2, 3, 4, 5, 11, 12, 13, 14, 15}, // c_out2
{1, 2, 3, 4, 5, 16, 17, 18, 19, 20}, // c_out3
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, // a_out1
{1, 2, 3, 4, 5, 11, 12, 13, 14, 15}, // a_out2
{1, 2, 3, 4, 5, 16, 17, 18, 19, 20}, // a_out3
{1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3,
4, 5, 11, 12, 13, 14, 15, 1, 2, 3, 4, 5, 16, 17, 18, 19, 20} // final_out
}));
_context->setBackends({"acl_cl", "acl_neon", "cpu"});
SUCCEED();
}
TEST_P(ConcatVariation, neg_InvalidAxis)
{
auto ¶m = GetParam();
CircleGen cgen;
int input1 = cgen.addTensor({{2, 3}, param.type}, param.scale, param.zero_point);
int input2 = cgen.addTensor({{2, 3}, param.type}, param.scale, param.zero_point);
int output = cgen.addTensor({{4, 3}, param.type}, param.scale, param.zero_point);
int axis = 2;
cgen.addOperatorConcatenation({{input1, input2}, {output}}, axis,
circle::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({input1, input2}, {output});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->setBackends({"cpu"});
_context->expectFailCompile();
SUCCEED();
}
TEST_P(ConcatVariation, neg_InvalidRank)
{
auto ¶m = GetParam();
CircleGen cgen;
int input1 = cgen.addTensor({{2, 3}, param.type}, param.scale, param.zero_point);
int input2 = cgen.addTensor({{1, 2, 3}, param.type}, param.scale, param.zero_point);
int output = cgen.addTensor({{1, 4, 3}, param.type}, param.scale, param.zero_point);
int axis = 0;
cgen.addOperatorConcatenation({{input1, input2}, {output}}, axis,
circle::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({input1, input2}, {output});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->setBackends({"acl_cl", "acl_neon", "cpu"});
_context->expectFailCompile();
SUCCEED();
}
TEST_P(ConcatVariation, neg_InvalidDimension)
{
auto ¶m = GetParam();
CircleGen cgen;
int input1 = cgen.addTensor({{2, 3}, param.type}, param.scale, param.zero_point);
int input2 = cgen.addTensor({{3, 2}, param.type}, param.scale, param.zero_point);
int output = cgen.addTensor({{4, 3}, param.type}, param.scale, param.zero_point);
int axis = 0;
cgen.addOperatorConcatenation({{input1, input2}, {output}}, axis,
circle::ActivationFunctionType_NONE);
cgen.setInputsAndOutputs({input1, input2}, {output});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->setBackends({"acl_cl", "acl_neon", "cpu"});
_context->expectFailCompile();
SUCCEED();
}
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