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/*
* Copyright (c) 2022 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"
struct LessEqualVariationParam
{
TestCaseData tcd;
circle::TensorType input_type = circle::TensorType::TensorType_FLOAT32;
const std::vector<std::string> backends = {"acl_cl", "acl_neon", "cpu"};
};
class LessEqualVariation : public GenModelTest,
public ::testing::WithParamInterface<LessEqualVariationParam>
{
};
// Input shape:
// Base: {1, 2, 2, 1}
// Brodcast: {1} on of two input
// Output shape: {1, 2, 2, 1}
// Input type: Non-quantization type
// Output type: BOOL
// Test with different input type and value
INSTANTIATE_TEST_SUITE_P(GenModelTest, LessEqualVariation,
::testing::Values(
// Float type
LessEqualVariationParam{TestCaseData{}
.addInput<float>({0.1, 0.3, 0.2, 0.7})
.addInput<float>({0.1, 0.2, 0.3, 0.4})
.addOutput<bool>({true, false, true, false})},
// Float type - broadcast
LessEqualVariationParam{TestCaseData{}
.addInput<float>({0.1, 0.3, 0.2, 0.7})
.addInput<float>({0.3})
.addOutput<bool>({true, true, true, false})},
// Int32 type
LessEqualVariationParam{TestCaseData{}
.addInput<int32_t>({1, 3, 2, 7})
.addInput<int32_t>({1, 2, 3, 4})
.addOutput<bool>({true, false, true, false}),
circle::TensorType::TensorType_INT32},
// Int32 type - broadcast
LessEqualVariationParam{TestCaseData{}
.addInput<int32_t>({1, 3, 2, 7})
.addInput<int32_t>({5})
.addOutput<bool>({true, true, true, false}),
circle::TensorType::TensorType_INT32},
// Int64 type
// NYI: acl backend
LessEqualVariationParam{TestCaseData{}
.addInput<int64_t>({1, 3, -2, 7})
.addInput<int64_t>({1, 2, 3, 4})
.addOutput<bool>({true, false, true, false}),
circle::TensorType::TensorType_INT64,
{"cpu"}},
// Int64 type - broadcast
// NYI: acl backend
LessEqualVariationParam{TestCaseData{}
.addInput<int64_t>({1, 3, -2, 7})
.addInput<int64_t>({1})
.addOutput<bool>({true, false, true, false}),
circle::TensorType::TensorType_INT64,
{"cpu"}}));
TEST_P(LessEqualVariation, Test)
{
auto ¶m = GetParam();
auto lhs_data = param.tcd.inputs.at(0);
auto rhs_data = param.tcd.inputs.at(1);
bool broadcast_lhs = false;
bool broadcast_rhs = false;
if (lhs_data.size() != rhs_data.size())
{
if (lhs_data.size() < rhs_data.size())
broadcast_lhs = true;
else
broadcast_rhs = true;
}
CircleGen cgen;
const auto output_type = circle::TensorType::TensorType_BOOL;
int lhs = broadcast_lhs ? cgen.addTensor({{1}, param.input_type})
: cgen.addTensor({{1, 2, 2, 1}, param.input_type});
int rhs = broadcast_rhs ? cgen.addTensor({{1}, param.input_type})
: cgen.addTensor({{1, 2, 2, 1}, param.input_type});
int out = cgen.addTensor({{1, 2, 2, 1}, output_type});
cgen.addOperatorLessEqual({{lhs, rhs}, {out}});
cgen.setInputsAndOutputs({lhs, rhs}, {out});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->addTestCase(param.tcd);
_context->setBackends(param.backends);
SUCCEED();
}
TEST_F(GenModelTest, neg_OneOp_LessEqual_DifferentType)
{
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_INT32});
int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_BOOL});
cgen.addOperatorLessEqual({{lhs, rhs}, {out}});
cgen.setInputsAndOutputs({lhs, rhs}, {out});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->setBackends({"acl_cl", "acl_neon", "cpu"});
_context->expectFailModelLoad();
SUCCEED();
}
TEST_F(GenModelTest, neg_OneOp_LessEqual_InvalidType)
{
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 out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32});
cgen.addOperatorLessEqual({{lhs, rhs}, {out}});
cgen.setInputsAndOutputs({lhs, rhs}, {out});
_context = std::make_unique<GenModelTestContext>(cgen.finish());
_context->setBackends({"acl_cl", "acl_neon", "cpu"});
_context->expectFailModelLoad();
SUCCEED();
}
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