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Diffstat (limited to 'compiler/locomotiv/src/Node/TensorConstantPad.test.cpp')
| -rw-r--r-- | compiler/locomotiv/src/Node/TensorConstantPad.test.cpp | 218 |
1 files changed, 218 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp b/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp new file mode 100644 index 000000000..0f60c5f85 --- /dev/null +++ b/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp @@ -0,0 +1,218 @@ +/* + * Copyright (c) 2019 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 "NodeExecution.h" + +#include "locomotiv/NodeData.h" +#include "NodeDataImpl.h" +#include "NodeDomain.h" + +#include <nncc/core/ADT/tensor/Shape.h> +#include <nncc/core/ADT/tensor/LexicalLayout.h> + +#include <gtest/gtest.h> + +using nncc::core::ADT::tensor::Index; +using nncc::core::ADT::tensor::LexicalLayout; +using nncc::core::ADT::tensor::make_buffer; +using nncc::core::ADT::tensor::Shape; + +TEST(NodeExecution_Pad, tensor_constant_pad_4_dim) +{ + auto g = loco::make_graph(); + + auto inputTensor = g->nodes()->create<loco::Pull>(); + inputTensor->dtype(loco::DataType::FLOAT32); + inputTensor->shape({1, 2, 2, 1}); + auto inputTensor_buf = make_buffer<float, LexicalLayout>(Shape{1, 2, 2, 1}); + inputTensor_buf.at(Index{0, 0, 0, 0}) = 1.0f; + inputTensor_buf.at(Index{0, 0, 1, 0}) = 2.0f; + inputTensor_buf.at(Index{0, 1, 0, 0}) = 3.0f; + inputTensor_buf.at(Index{0, 1, 1, 0}) = 4.0f; + auto inputTensor_data = locomotiv::make_data(inputTensor_buf); + locomotiv::annot_data(inputTensor, std::move(inputTensor_data)); + locomotiv::annot_domain(inputTensor, loco::Domain::Tensor); + + auto constant = g->nodes()->create<loco::ConstGen>(); + constant->dtype(loco::DataType::FLOAT32); + constant->shape({1}); + auto constant_buf = make_buffer<float, LexicalLayout>(Shape{1}); + constant_buf.at(Index{0}) = 0.0f; + auto constant_data = locomotiv::make_data(constant_buf); + locomotiv::annot_data(constant, std::move(constant_data)); + locomotiv::annot_domain(constant, loco::Domain::Tensor); + + auto pad = g->nodes()->create<loco::TensorConstantPad>(); + pad->input(inputTensor); + pad->constant(constant); + + auto padding = pad->padding(); + padding->rank(4); + padding->front(0) = 0; + padding->back(0) = 0; + padding->front(1) = 3; + padding->back(1) = 1; + padding->front(2) = 1; + padding->back(2) = 1; + padding->front(3) = 0; + padding->back(3) = 0; + + locomotiv::NodeExecution::get().run(pad); + + auto pad_data = locomotiv::annot_data(pad); + ASSERT_NE(pad_data, nullptr); + ASSERT_EQ(pad_data->dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(*(pad_data->shape()), Shape({1, 6, 4, 1})); + + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 3, 1, 0}), 1.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 3, 2, 0}), 2.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 4, 1, 0}), 3.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 4, 2, 0}), 4.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 0, 0, 0}), 0.0f); + + ASSERT_EQ(locomotiv::annot_domain(pad), loco::Domain::Tensor); +} + +TEST(NodeExecution_Pad, tensor_constant_pad_1_dim) +{ + auto g = loco::make_graph(); + + auto inputTensor = g->nodes()->create<loco::Pull>(); + inputTensor->dtype(loco::DataType::FLOAT32); + inputTensor->shape({3}); + auto inputTensor_buf = make_buffer<float, LexicalLayout>(Shape{3}); + inputTensor_buf.at(Index{0}) = 1.0f; + inputTensor_buf.at(Index{1}) = 5.0f; + inputTensor_buf.at(Index{2}) = 3.0f; + auto inputTensor_data = locomotiv::make_data(inputTensor_buf); + locomotiv::annot_data(inputTensor, std::move(inputTensor_data)); + locomotiv::annot_domain(inputTensor, loco::Domain::Tensor); + + auto constant = g->nodes()->create<loco::ConstGen>(); + constant->dtype(loco::DataType::FLOAT32); + constant->shape({1}); + auto constant_buf = make_buffer<float, LexicalLayout>(Shape{1}); + constant_buf.at(Index{0}) = 0.0f; + auto constant_data = locomotiv::make_data(constant_buf); + locomotiv::annot_data(constant, std::move(constant_data)); + locomotiv::annot_domain(constant, loco::Domain::Tensor); + + auto pad = g->nodes()->create<loco::TensorConstantPad>(); + pad->input(inputTensor); + pad->constant(constant); + auto padding = pad->padding(); + padding->rank(1); + padding->front(0) = 2; + padding->back(0) = 1; + + locomotiv::NodeExecution::get().run(pad); + + auto pad_data = locomotiv::annot_data(pad); + ASSERT_NE(pad_data, nullptr); + ASSERT_EQ(pad_data->dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(*(pad_data->shape()), Shape({6})); + + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0}), 0.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1}), 0.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{2}), 1.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{3}), 5.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{4}), 3.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{5}), 0.0f); + + ASSERT_EQ(locomotiv::annot_domain(pad), loco::Domain::Tensor); +} + +TEST(NodeExecution_Pad, tensor_constant_pad_6_dim) +{ + auto g = loco::make_graph(); + + auto inputTensor = g->nodes()->create<loco::Pull>(); + inputTensor->dtype(loco::DataType::FLOAT32); + inputTensor->shape({2, 1, 3, 2, 1, 2}); + auto inputTensor_buf = make_buffer<float, LexicalLayout>(Shape{2, 1, 3, 2, 1, 2}); + int a, b, c, d, e, f; + float dummy = 1.0f; + for (uint32_t a = 0; a < 2; a++) + { + for (uint32_t b = 0; b < 1; b++) + { + for (uint32_t c = 0; c < 3; c++) + { + for (uint32_t d = 0; d < 2; d++) + { + for (uint32_t e = 0; e < 1; e++) + { + for (uint32_t f = 0; f < 2; f++) + { + inputTensor_buf.at(Index{a, b, c, d, e, f}) = dummy++; + } + } + } + } + } + } + auto inputTensor_data = locomotiv::make_data(inputTensor_buf); + locomotiv::annot_data(inputTensor, std::move(inputTensor_data)); + locomotiv::annot_domain(inputTensor, loco::Domain::Tensor); + + auto constant = g->nodes()->create<loco::ConstGen>(); + constant->dtype(loco::DataType::FLOAT32); + constant->shape({1}); + auto constant_buf = make_buffer<float, LexicalLayout>(Shape{1}); + constant_buf.at(Index{0}) = 0.0f; + auto constant_data = locomotiv::make_data(constant_buf); + locomotiv::annot_data(constant, std::move(constant_data)); + locomotiv::annot_domain(constant, loco::Domain::Tensor); + + auto pad = g->nodes()->create<loco::TensorConstantPad>(); + pad->input(inputTensor); + pad->constant(constant); + auto padding = pad->padding(); + + padding->rank(6); + padding->front(0) = 1; + padding->back(0) = 1; + padding->front(1) = 0; + padding->back(1) = 0; + padding->front(2) = 1; + padding->back(2) = 2; + padding->front(3) = 2; + padding->back(3) = 1; + padding->front(4) = 0; + padding->back(4) = 0; + padding->front(5) = 1; + padding->back(5) = 2; + + locomotiv::NodeExecution::get().run(pad); + + auto pad_data = locomotiv::annot_data(pad); + ASSERT_NE(pad_data, nullptr); + ASSERT_EQ(pad_data->dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(*(pad_data->shape()), Shape({4, 1, 6, 5, 1, 5})); + + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 2, 0, 1}), 1.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 2, 0, 2}), 2.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 3, 0, 1}), 3.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 3, 0, 2}), 4.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 2, 0, 1}), 5.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 2, 0, 2}), 6.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 3, 0, 1}), 7.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 3, 0, 2}), 8.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 3, 2, 0, 1}), 9.0f); + ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 3, 2, 0, 2}), 10.0f); + + ASSERT_EQ(locomotiv::annot_domain(pad), loco::Domain::Tensor); +} |