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Diffstat (limited to 'tests/nnfw_api/src/one_op_tests/Concat.cc')
-rw-r--r-- | tests/nnfw_api/src/one_op_tests/Concat.cc | 244 |
1 files changed, 0 insertions, 244 deletions
diff --git a/tests/nnfw_api/src/one_op_tests/Concat.cc b/tests/nnfw_api/src/one_op_tests/Concat.cc deleted file mode 100644 index f4397ba66..000000000 --- a/tests/nnfw_api/src/one_op_tests/Concat.cc +++ /dev/null @@ -1,244 +0,0 @@ -/* - * 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_CASE_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(); -} |