From: 박천교/On-Device Lab(SR)/Engineer/삼성전자 Date: Wed, 29 May 2019 04:44:27 +0000 (+0900) Subject: [locomotiv] Support FeatureEncode execution (#3602) X-Git-Tag: nncc_backup~506 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=9fda5c7ca3cbce62637dda4fee858950004cd5cd;p=platform%2Fcore%2Fml%2Fnnfw.git [locomotiv] Support FeatureEncode execution (#3602) * [locomotiv] Support FeatureEncode execution This commit supports FeatureEncode execution for loco interpreter. Note that locomotiv saves all feature data as NHWC. Signed-off-by: Cheongyo Bahk * Fix typo --- diff --git a/contrib/locomotiv/src/Node.lst b/contrib/locomotiv/src/Node.lst index 3dc5fdd..dced28d 100644 --- a/contrib/locomotiv/src/Node.lst +++ b/contrib/locomotiv/src/Node.lst @@ -5,6 +5,7 @@ // NODE(Name) : alphabetic order please NODE(ConstGen) +NODE(FeatureEncode) NODE(Forward) NODE(MaxPool2D) NODE(Pull) diff --git a/contrib/locomotiv/src/Node/FeatureEncode.cpp b/contrib/locomotiv/src/Node/FeatureEncode.cpp new file mode 100644 index 0000000..c05a0bb --- /dev/null +++ b/contrib/locomotiv/src/Node/FeatureEncode.cpp @@ -0,0 +1,117 @@ +/* + * 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 "NodeDataImpl.h" + +#include +#include + +#include +#include + +namespace +{ + +using nncc::core::ADT::tensor::Buffer; +using nncc::core::ADT::tensor::make_buffer; +using nncc::core::ADT::tensor::LexicalLayout; +using nncc::core::ADT::tensor::Shape; +using nncc::core::ADT::tensor::IndexEnumerator; + +template +std::unique_ptr feature_encode(const loco::FeatureEncode *node, + const Buffer *input_buf) +{ + auto encoder = node->encoder(); + + // Make TensorShape from input + loco::TensorShape input_shape; + input_shape.rank(input_buf->shape().rank()); + assert(input_shape.rank() == 4); + for (uint32_t i = 0; i < input_shape.rank(); ++i) + { + input_shape.dim(i) = loco::make_dimension(input_buf->shape().dim(i)); + } + + loco::FeatureShape node_shape = encoder->shape(input_shape); + + // Make NHWC buffer from FeatureShape + Buffer node_buf = + make_buffer(Shape{node_shape.count().value(), node_shape.height().value(), + node_shape.width().value(), node_shape.depth().value()}); + + // Copy buffer in an order arranged by encoder + for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance()) + { + loco::FeatureIndex index; + index.batch() = e.current().at(0); + index.row() = e.current().at(1); + index.column() = e.current().at(2); + index.channel() = e.current().at(3); + + node_buf.at(e.current()) = input_buf->at(encoder->value(index)); + } + + return locomotiv::make_data(node_buf); +} + +} // namespace + +namespace locomotiv +{ + +void NodeExecution::execute(loco::FeatureEncode *enc) +{ + auto input_data = annot_data(enc->input()); + + if (!input_data) + { + throw std::runtime_error("Input of FeatureEncode not ready"); + } + + if (input_data->shape()->rank() != 4) + { + throw std::runtime_error("Input shape mismatch"); + } + + std::unique_ptr enc_data = nullptr; + + switch (input_data->dtype()) + { + case loco::DataType::S32: + { + auto input_buf = input_data->as_s32_bufptr(); + enc_data = feature_encode(enc, input_buf); + break; + } + case loco::DataType::FLOAT32: + { + auto input_buf = input_data->as_f32_bufptr(); + enc_data = feature_encode(enc, input_buf); + break; + } + default: + throw std::runtime_error("NYI for this DataType"); + } + + assert(enc_data != nullptr); + erase_annot_data(enc); + annot_data(enc, std::move(enc_data)); +} + +} // namespace locomotiv diff --git a/contrib/locomotiv/src/Node/FeatureEncode.test.cpp b/contrib/locomotiv/src/Node/FeatureEncode.test.cpp new file mode 100644 index 0000000..a4bfd9b --- /dev/null +++ b/contrib/locomotiv/src/Node/FeatureEncode.test.cpp @@ -0,0 +1,137 @@ +/* + * 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 + +#include +#include +#include +#include + +#include + +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_FeatureEncode, 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(); + pull->dtype(loco::DataType::S32); + + // Make and assign "NCHW" data to pull node + auto pull_buf = make_buffer(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 *= -3; // Doesn't matter what it is + } + auto pull_data = locomotiv::make_data(pull_buf); + locomotiv::annot_data(pull, std::move(pull_data)); + + // Encoder to correctly read input tensor as NCHW + auto encoder = std::unique_ptr>( + new loco::PermutingEncoder); + encoder->perm()->axis(loco::FeatureAxis::Count) = 0; + encoder->perm()->axis(loco::FeatureAxis::Height) = 2; + encoder->perm()->axis(loco::FeatureAxis::Width) = 3; + encoder->perm()->axis(loco::FeatureAxis::Depth) = 1; + + // FeatureEncode + auto enc = g->nodes()->create(); + 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 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})); +} + +TEST(NodeExecution_FeatureEncode, 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(); + pull->dtype(loco::DataType::FLOAT32); + + // Make and assign crazy "CHNW" data to pull node + auto pull_buf = make_buffer(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 = f + 1 / f; // Doesn't matter what it is + } + auto pull_data = locomotiv::make_data(pull_buf); + locomotiv::annot_data(pull, std::move(pull_data)); + + // Encoder to correctly read input tensor as CHNW + auto encoder = std::unique_ptr>( + new loco::PermutingEncoder); + encoder->perm()->axis(loco::FeatureAxis::Count) = 2; + encoder->perm()->axis(loco::FeatureAxis::Height) = 1; + encoder->perm()->axis(loco::FeatureAxis::Width) = 3; + encoder->perm()->axis(loco::FeatureAxis::Depth) = 0; + + // FeatureEncode + auto enc = g->nodes()->create(); + 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 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})); +}