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Diffstat (limited to 'compiler/locomotiv/src/Node/FilterEncode.test.cpp')
| -rw-r--r-- | compiler/locomotiv/src/Node/FilterEncode.test.cpp | 144 |
1 files changed, 144 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/FilterEncode.test.cpp b/compiler/locomotiv/src/Node/FilterEncode.test.cpp new file mode 100644 index 000000000..79b8308e2 --- /dev/null +++ b/compiler/locomotiv/src/Node/FilterEncode.test.cpp @@ -0,0 +1,144 @@ +/* + * 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; + +TEST(NodeExecution_FilterEncode, s32) +{ + const uint32_t N = 2; + const uint32_t H = 3; + const uint32_t W = 4; + const uint32_t C = 5; + + auto g = loco::make_graph(); + + // Pull + auto pull = g->nodes()->create<loco::Pull>(); + pull->dtype(loco::DataType::S32); + + // Make and assign "NCHW" data to pull node + auto pull_buf = make_buffer<int32_t, LexicalLayout>(Shape{N, C, H, W}); + int32_t i = 1; + for (IndexEnumerator e{pull_buf.shape()}; e.valid(); e.advance()) + { + pull_buf.at(e.current()) = i; + ++i; // Doesn't matter what it is + } + auto pull_data = locomotiv::make_data(pull_buf); + locomotiv::annot_data(pull, std::move(pull_data)); + locomotiv::annot_domain(pull, loco::Domain::Tensor); + + // Encoder to correctly read input tensor as NCHW + auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::Filter>>( + new loco::PermutingEncoder<loco::Domain::Filter>); + encoder->perm()->axis(loco::FilterAxis::Count) = 0; + encoder->perm()->axis(loco::FilterAxis::Depth) = 1; + encoder->perm()->axis(loco::FilterAxis::Height) = 2; + encoder->perm()->axis(loco::FilterAxis::Width) = 3; + + // FilterEncode + auto enc = g->nodes()->create<loco::FilterEncode>(); + enc->input(pull); + enc->encoder(std::move(encoder)); + + locomotiv::NodeExecution::get().run(enc); + + 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 filter 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::Filter); +} + +TEST(NodeExecution_FilterEncode, f32) +{ + const uint32_t N = 2; + const uint32_t H = 3; + const uint32_t W = 4; + const uint32_t C = 5; + + auto g = loco::make_graph(); + + // Pull + auto pull = g->nodes()->create<loco::Pull>(); + pull->dtype(loco::DataType::FLOAT32); + + // Make and assign crazy "CHNW" data to pull node + auto pull_buf = make_buffer<float, LexicalLayout>(Shape{C, H, N, W}); + float f = 1; + 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 + } + auto pull_data = locomotiv::make_data(pull_buf); + locomotiv::annot_data(pull, std::move(pull_data)); + locomotiv::annot_domain(pull, loco::Domain::Tensor); + + // Encoder to correctly read input tensor as CHNW + auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::Filter>>( + new loco::PermutingEncoder<loco::Domain::Filter>); + encoder->perm()->axis(loco::FilterAxis::Depth) = 0; + encoder->perm()->axis(loco::FilterAxis::Height) = 1; + encoder->perm()->axis(loco::FilterAxis::Count) = 2; + encoder->perm()->axis(loco::FilterAxis::Width) = 3; + + // FilterEncode + auto enc = g->nodes()->create<loco::FilterEncode>(); + enc->input(pull); + enc->encoder(std::move(encoder)); + + locomotiv::NodeExecution::get().run(enc); + + 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 filter 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::Filter); +} |