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Diffstat (limited to 'compiler/locomotiv/src/Node/FeatureCodec.test.cpp')
| -rw-r--r-- | compiler/locomotiv/src/Node/FeatureCodec.test.cpp | 223 |
1 files changed, 223 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/FeatureCodec.test.cpp b/compiler/locomotiv/src/Node/FeatureCodec.test.cpp new file mode 100644 index 000000000..c35f0e69a --- /dev/null +++ b/compiler/locomotiv/src/Node/FeatureCodec.test.cpp @@ -0,0 +1,223 @@ +/* + * 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 <loco/IR/PermutingCodec.h> + +#include <nncc/core/ADT/tensor/Shape.h> +#include <nncc/core/ADT/tensor/Buffer.h> +#include <nncc/core/ADT/tensor/LexicalLayout.h> +#include <nncc/core/ADT/tensor/IndexEnumerator.h> + +#include <gtest/gtest.h> + +using nncc::core::ADT::tensor::Index; +using nncc::core::ADT::tensor::Shape; +using nncc::core::ADT::tensor::LexicalLayout; +using nncc::core::ADT::tensor::make_buffer; +using nncc::core::ADT::tensor::IndexEnumerator; +using nncc::core::ADT::tensor::Buffer; + +// This file is intended to test FeatureEncode and FeatureDecode at once +namespace +{ + +class NodeExecution_FeatureCodec : public ::testing::Test +{ +private: + loco::Graph g; + +protected: + /// @brief Make Pull node and set data by given buffer and data type + template <typename DT> loco::Pull *pull_layer(Buffer<DT> &pull_buf, loco::DataType dtype) + { + auto pull = g.nodes()->create<loco::Pull>(); + pull->dtype(dtype); + + auto pull_data = locomotiv::make_data(pull_buf); + locomotiv::annot_data(pull, std::move(pull_data)); + locomotiv::annot_domain(pull, loco::Domain::Tensor); + + return pull; + } + + /// @brief Make FeatureEncode node with given input and encoding permutation + loco::FeatureEncode *feature_encode_layer(loco::Node *input, + const loco::Permutation<loco::Domain::Feature> &perm) + { + auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::Feature>>( + new loco::PermutingEncoder<loco::Domain::Feature>); + + encoder->perm(perm); + + auto enc = g.nodes()->create<loco::FeatureEncode>(); + enc->input(input); + enc->encoder(std::move(encoder)); + + return enc; + } + + /// @brief Make FeatureDecode node with given input and decoding permutation + loco::FeatureDecode *feature_decode_layer(loco::Node *input, + const loco::Permutation<loco::Domain::Feature> &perm) + { + auto decoder = std::unique_ptr<loco::PermutingDecoder<loco::Domain::Feature>>( + new loco::PermutingDecoder<loco::Domain::Feature>); + + decoder->perm(perm); + + auto dec = g.nodes()->create<loco::FeatureDecode>(); + dec->input(input); + dec->decoder(std::move(decoder)); + + return dec; + } +}; + +} // namespace + +TEST_F(NodeExecution_FeatureCodec, s32) +{ + const uint32_t N = 2; + const uint32_t H = 3; + const uint32_t W = 4; + const uint32_t C = 5; + + // Make "NCHW" data for pull node + auto pull_buf = make_buffer<int32_t, LexicalLayout>(Shape{N, C, H, W}); + int32_t i = 0; + for (IndexEnumerator e{pull_buf.shape()}; e.valid(); e.advance()) + { + pull_buf.at(e.current()) = i; + ++i; // Doesn't matter what it is + } + + // Make NCHW permutation for encoder and decoder + loco::Permutation<loco::Domain::Feature> NCHW; + + NCHW.axis(loco::FeatureAxis::Count) = 0; + NCHW.axis(loco::FeatureAxis::Depth) = 1; + NCHW.axis(loco::FeatureAxis::Height) = 2; + NCHW.axis(loco::FeatureAxis::Width) = 3; + + // Pull + auto pull = pull_layer(pull_buf, loco::DataType::S32); + + // FeatureEncode + auto enc = feature_encode_layer(pull, NCHW); + locomotiv::NodeExecution::get().run(enc); + + // Test FeatureEncode + auto enc_data = locomotiv::annot_data(enc); + ASSERT_NE(enc_data, nullptr); + ASSERT_EQ(enc_data->dtype(), loco::DataType::S32); + ASSERT_EQ(*(enc_data->shape()), (Shape{N, H, W, C})); // locomotiv feature is NHWC + auto enc_buf = enc_data->as_s32_bufptr(); + for (uint32_t n = 0; n < N; ++n) + for (uint32_t h = 0; h < H; ++h) + for (uint32_t w = 0; w < W; ++w) + for (uint32_t c = 0; c < C; ++c) + ASSERT_EQ(pull_buf.at(Index{n, c, h, w}), enc_buf->at(Index{n, h, w, c})); + + ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::Feature); + + // FeatureDecode + auto dec = feature_decode_layer(enc, NCHW); + locomotiv::NodeExecution::get().run(dec); + + // Test FeatureDecode: Encode -> Decode == identity + auto dec_data = locomotiv::annot_data(dec); + ASSERT_NE(dec_data, nullptr); + ASSERT_EQ(dec_data->dtype(), loco::DataType::S32); + ASSERT_EQ(*(dec_data->shape()), (Shape{N, C, H, W})); + auto dec_buf = dec_data->as_s32_bufptr(); + for (uint32_t n = 0; n < N; ++n) + for (uint32_t h = 0; h < H; ++h) + for (uint32_t w = 0; w < W; ++w) + for (uint32_t c = 0; c < C; ++c) + ASSERT_EQ(pull_buf.at(Index{n, c, h, w}), dec_buf->at(Index{n, c, h, w})); + + ASSERT_EQ(locomotiv::annot_domain(dec), loco::Domain::Tensor); +} + +TEST_F(NodeExecution_FeatureCodec, f32) +{ + const uint32_t N = 2; + const uint32_t H = 3; + const uint32_t W = 4; + const uint32_t C = 5; + + // Make crazy "CHNW" data for pull node + auto pull_buf = make_buffer<float, LexicalLayout>(Shape{C, H, N, W}); + float f = 0.0f; + for (IndexEnumerator e{pull_buf.shape()}; e.valid(); e.advance()) + { + pull_buf.at(e.current()) = f; + f += 0.1f; // Doesn't matter what it is + } + + // Make CHNW permutation for encoder and decoder + loco::Permutation<loco::Domain::Feature> CHNW; + + CHNW.axis(loco::FeatureAxis::Depth) = 0; + CHNW.axis(loco::FeatureAxis::Height) = 1; + CHNW.axis(loco::FeatureAxis::Count) = 2; + CHNW.axis(loco::FeatureAxis::Width) = 3; + + // Pull + auto pull = pull_layer(pull_buf, loco::DataType::FLOAT32); + + // FeatureEncode + auto enc = feature_encode_layer(pull, CHNW); + locomotiv::NodeExecution::get().run(enc); + + // Test FeatureEncode + auto enc_data = locomotiv::annot_data(enc); + ASSERT_NE(enc_data, nullptr); + ASSERT_EQ(enc_data->dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(*(enc_data->shape()), (Shape{N, H, W, C})); // locomotiv feature is NHWC + auto enc_buf = enc_data->as_f32_bufptr(); + for (uint32_t n = 0; n < N; ++n) + for (uint32_t h = 0; h < H; ++h) + for (uint32_t w = 0; w < W; ++w) + for (uint32_t c = 0; c < C; ++c) + ASSERT_FLOAT_EQ(pull_buf.at(Index{c, h, n, w}), enc_buf->at(Index{n, h, w, c})); + + ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::Feature); + + // FeatureDecode + auto dec = feature_decode_layer(enc, CHNW); + locomotiv::NodeExecution::get().run(dec); + + // Test FeatureDecode: Encode -> Decode == identity + auto dec_data = locomotiv::annot_data(dec); + ASSERT_NE(dec_data, nullptr); + ASSERT_EQ(dec_data->dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(*(dec_data->shape()), (Shape{C, H, N, W})); + auto dec_buf = dec_data->as_f32_bufptr(); + for (uint32_t n = 0; n < N; ++n) + for (uint32_t h = 0; h < H; ++h) + for (uint32_t w = 0; w < W; ++w) + for (uint32_t c = 0; c < C; ++c) + ASSERT_FLOAT_EQ(pull_buf.at(Index{c, h, n, w}), dec_buf->at(Index{c, h, n, w})); + + ASSERT_EQ(locomotiv::annot_domain(dec), loco::Domain::Tensor); +} |