diff options
Diffstat (limited to 'tests/nnfw_api/src/one_op_tests/PadV2.test.cc')
| -rw-r--r-- | tests/nnfw_api/src/one_op_tests/PadV2.test.cc | 160 |
1 files changed, 160 insertions, 0 deletions
diff --git a/tests/nnfw_api/src/one_op_tests/PadV2.test.cc b/tests/nnfw_api/src/one_op_tests/PadV2.test.cc new file mode 100644 index 000000000..3db2187b2 --- /dev/null +++ b/tests/nnfw_api/src/one_op_tests/PadV2.test.cc @@ -0,0 +1,160 @@ +/* + * 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" + +TEST_F(GenModelTest, OneOp_PadV2) +{ + CircleGen cgen; + int in = cgen.addTensor({{1, 2, 2, 1}, 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}); + std::vector<float> padding_value_data{3.0}; + uint32_t padding_value_buf = cgen.addBuffer(padding_value_data); + int padding_value = + cgen.addTensor({{1}, circle::TensorType::TensorType_FLOAT32, padding_value_buf}); + + int out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_FLOAT32}); + + cgen.addOperatorPadV2({{in, padding, padding_value}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase( + uniformTCD<float>({{1, 2, 3, 4}}, {{3, 3, 3, 3, 3, 1, 2, 3, 3, 3, 4, 3, 3, 3, 3, 3}})); + _context->setBackends({"cpu"}); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_PadV2_InvalidPadRank) +{ + CircleGen cgen; + int in = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + std::vector<int32_t> padding_data{1, 1, 1, 1}; + uint32_t padding_buf = cgen.addBuffer(padding_data); + int padding = cgen.addTensor({{4}, circle::TensorType::TensorType_INT32, padding_buf}); + std::vector<float> padding_value_data{3.0}; + uint32_t padding_value_buf = cgen.addBuffer(padding_value_data); + int padding_value = + cgen.addTensor({{1}, circle::TensorType::TensorType_FLOAT32, padding_value_buf}); + + int out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_FLOAT32}); + + cgen.addOperatorPad({{in, padding, padding_value}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"acl_cl", "acl_neon", "cpu"}); + _context->expectFailCompile(); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_PadV2_InvalidPadDim0) +{ + CircleGen cgen; + int in = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + std::vector<int32_t> padding_data{1, 1, 1, 1}; + uint32_t padding_buf = cgen.addBuffer(padding_data); + int padding = cgen.addTensor({{2, 2}, circle::TensorType::TensorType_INT32, padding_buf}); + std::vector<float> padding_value_data{3.0}; + uint32_t padding_value_buf = cgen.addBuffer(padding_value_data); + int padding_value = + cgen.addTensor({{1}, circle::TensorType::TensorType_FLOAT32, padding_value_buf}); + + int out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_FLOAT32}); + + cgen.addOperatorPad({{in, padding, padding_value}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"acl_cl", "acl_neon", "cpu"}); + _context->expectFailCompile(); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_PadV2_InvalidPadDim1) +{ + CircleGen cgen; + int in = cgen.addTensor({{1, 1, 1, 1}, circle::TensorType::TensorType_FLOAT32}); + std::vector<int32_t> padding_data{1, 1, 1, 1}; + uint32_t padding_buf = cgen.addBuffer(padding_data); + int padding = cgen.addTensor({{4, 1}, circle::TensorType::TensorType_INT32, padding_buf}); + std::vector<float> padding_value_data{3.0}; + uint32_t padding_value_buf = cgen.addBuffer(padding_value_data); + int padding_value = + cgen.addTensor({{1}, circle::TensorType::TensorType_FLOAT32, padding_value_buf}); + + int out = cgen.addTensor({{2, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32}); + + cgen.addOperatorPad({{in, padding, padding_value}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"acl_cl", "acl_neon", "cpu"}); + _context->expectFailCompile(); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_PadV2_Type) +{ + CircleGen cgen; + int in = cgen.addTensor({{1, 1, 1, 1}, circle::TensorType::TensorType_FLOAT32}); + std::vector<int32_t> padding_data{1, 1, 1, 1}; + uint32_t padding_buf = cgen.addBuffer(padding_data); + int padding = cgen.addTensor({{4, 2}, circle::TensorType::TensorType_INT32, padding_buf}); + std::vector<uint8_t> padding_value_data{3}; + uint32_t padding_value_buf = cgen.addBuffer(padding_value_data); + int padding_value = + cgen.addTensor({{1}, circle::TensorType::TensorType_UINT8, padding_value_buf}, 1.0, 1); + + int out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_FLOAT32}); + + cgen.addOperatorPadV2({{in, padding, padding_value}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->expectFailModelLoad(); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_PadV2_QuantParam) +{ + CircleGen cgen; + int in = cgen.addTensor({{1, 1, 1, 1}, circle::TensorType::TensorType_UINT8}, 1.0, 2); + std::vector<int32_t> padding_data{1, 1, 1, 1}; + uint32_t padding_buf = cgen.addBuffer(padding_data); + int padding = cgen.addTensor({{4, 2}, circle::TensorType::TensorType_INT32, padding_buf}); + std::vector<uint8_t> padding_value_data{3}; + uint32_t padding_value_buf = cgen.addBuffer(padding_value_data); + int padding_value = + cgen.addTensor({{1}, circle::TensorType::TensorType_UINT8, padding_value_buf}, 1.0, 1); + + int out = cgen.addTensor({{1, 4, 4, 1}, circle::TensorType::TensorType_UINT8}, 1.0, 1); + + cgen.addOperatorPadV2({{in, padding, padding_value}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->expectFailModelLoad(); + + SUCCEED(); +} |