From: 박세희/On-Device Lab(SR)/Principal Engineer/삼성전자 Date: Wed, 26 Jun 2019 09:30:56 +0000 (+0900) Subject: [moco_tf] Revise Frontend to use Import (#3986) X-Git-Tag: nncc_backup~260 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=fcbbf512f1af3b30b722316c1dd000e7793ccbd1;p=platform%2Fcore%2Fml%2Fnnfw.git [moco_tf] Revise Frontend to use Import (#3986) This will change Frontend to use Import. Existing test is coverted by Import so it is removed. Signed-off-by: SaeHie Park --- diff --git a/contrib/moco-tf/src/Frontend.cpp b/contrib/moco-tf/src/Frontend.cpp index 70b6b36..c669314 100644 --- a/contrib/moco-tf/src/Frontend.cpp +++ b/contrib/moco-tf/src/Frontend.cpp @@ -15,29 +15,18 @@ */ #include -#include -#include "GraphBuilder.h" -#include "GraphBuilderContext.h" -#include "GraphBuilderRegistry.h" -#include "Transforms.h" - -#include "Annotations/ShapeInferenceData.h" +#include "Import.h" -#include +#include "Transforms.h" -#include -#include #include #include -#include - #include #include #include -#include #include #include #include @@ -107,227 +96,6 @@ void load_tf(std::istream *stream, moco::tf::Frontend::FileType type, } } -void convert_graph(const moco::tf::ModelSignature &signature, tensorflow::GraphDef &tf_graph_def, - loco::Graph *graph) -{ - auto nodedef = stdex::make_unique(); - auto tensor_names = stdex::make_unique(); - auto updates = stdex::make_unique(); - - moco::tf::GraphBuilderContext gb_context(graph, nodedef.get(), tensor_names.get(), updates.get()); - - // Building a loco graph - // 1. Convert all the nodes to loco::Node - // 2. Connect inputs: set all node input(from a string) to actual node object - // 3. Set graph input - // 4. Create loco::Push node and set input and set graph output - - /** - * @brief Prepare tensorflow::NodeDef search table from name - */ - for (const auto &n : tf_graph_def.node()) - { - nodedef->enroll(n.name(), &n); - } - - /** - * @brief 1. Convert all the nodes to loco::Node - * - * @note In each build for a TF node, four things happen - * 1) create corresponding loco::Node(s) - * 2) read and set the attributes to created loco::Node(s) - * 3) register name-loco::Node(last one of Nodes) that will be used as the output - * 4) queue a task to set the input of the loco::Node(first one of the Nodes) - * this is done only for required nodes depending on the operator - * - * @example Placeholder("in") - Identity("out") - * %1 = Pull --> 0x1001 (loco::Node* object address) - * (symboltable: register %1, after the registeration table will contain as below; - * "in" : 0x1001 - * ) - * (queue: this will be empty as Pull does not queue a task to set input; - * ) - * - * %2 = Forward --> 0x1002 - * (symboltable: register %2 and table will look like below; - * "in" : 0x1001 - * "out" : 0x1002 - * ) - * (queue: Forward will queue a task with input "in"; - * 0x1002: {"in"} - * ) - */ - for (const auto &n : tf_graph_def.node()) - { - if (const auto *graph_builder = moco::tf::GraphBuilderRegistry::get().lookup(n.op())) - { - if (!graph_builder->validate(n)) - { - throw std::runtime_error{"Invalid operator: " + n.op()}; - } - - graph_builder->build(n, &gb_context); - } - else - { - throw std::runtime_error{"Not supported: " + n.op()}; - } - } - - /** - * @brief 2. Connect inputs: Iterate updates and call each update input method - * - * @note Continue from above example graph, connecting inputs is done in following steps - * a) iterate queue - * b) call the input method for each update - * c) each update has the loco::Node *node and names of the input to connect - * node = 0x1002 and names = {"in"} - * d) from symbol table, "in" will return 0x1001 - * e) set input of 0x1002 with 0x1001 - */ - for (auto &update : updates->queue()) - { - update->input(tensor_names.get()); - } - - /** - * @brief 3. Set graph input - */ - for (auto input : signature.inputs()) - { - auto node = tensor_names->node(input); - assert(node != nullptr); - - auto graph_input = graph->inputs()->create(); - - loco::Pull *pull_node = dynamic_cast(node); - assert(pull_node != nullptr); - - graph_input->name(input.nodeName()); - graph_input->node(pull_node); - } - - /** - * @brief 4. Create loco::Push node and set graph input and output - */ - for (auto output : signature.outputs()) - { - auto output_node = tensor_names->node(output); - assert(output_node); - - // create loco::Push for output of graph - auto push_node = graph->nodes()->create(); - push_node->from(output_node); // set input of Push to output node - - // set the graph output name and node object - auto graph_output = graph->outputs()->create(); - graph_output->name(output.nodeName()); - graph_output->node(push_node); - } - - // validate graph - assert(loco::valid(graph)); -} - -void dump_shapeinferencedata(loco::Node *node, const std::string &name) -{ - LOGGER(node_shapeinferencedata); - - const moco::tf::ShapeInferenceData *shapedata = node->annot(); - if (shapedata == nullptr) - { - INFO(node_shapeinferencedata) << "ShapeInferenceData is null for " << name << ":" << node - << std::endl; - } - else - { - std::stringstream ss; - - ss << "ShapeInferenceData for " << name << ":" << node; - // clang-format off - switch (shapedata->domain()) - { - case loco::Domain::Tensor: ss << " (Tensor)"; break; - case loco::Domain::Feature: ss << " (Feature)"; break; - case loco::Domain::Filter: ss << " (Filter)"; break; - case loco::Domain::Bias: ss << " (Bias)"; break; - default: assert(false && "Unknown Domain"); break; - } - // clang-format on - ss << " rank(" << shapedata->rank() << ") ["; - for (uint32_t index = 0; index < shapedata->rank(); ++index) - { - if (index) - ss << ","; - if (shapedata->dim(index).known()) - ss << shapedata->dim(index).value(); - else - ss << "?"; - } - ss << "]"; - - INFO(node_shapeinferencedata) << ss.str() << std::endl; - } -} - -void transform_graph(loco::Graph *graph) -{ - LOGGER(transform_graph); - - std::vector> prepare; - std::vector> transforms; - - // Transforms that run only once for preparation and finalization - { - // TODO add one time preparation when needed - } - - // Transforms that run multiple times until there is no transform occured - { - transforms.emplace_back(stdex::make_unique()); - transforms.emplace_back(stdex::make_unique()); - // TODO add more TensorFlow related transformations - } - - // Run preparation - for (auto &tr : prepare) - { - tr->run(graph); - } - - bool changed; - do - { - changed = false; - - for (auto &tr : transforms) - { - INFO(transform_graph) << "Before transform"; - INFO(transform_graph) << locop::fmt(graph); - - if (tr->run(graph)) - changed = true; - - INFO(transform_graph) << "After transform (changed: " << (changed ? 'Y' : 'N') << ")"; - INFO(transform_graph) << locop::fmt(graph); - } - - } while (changed); - - // TODO would be better to run this code only when log is enabled - { - for (uint32_t i = 0; i < graph->outputs()->size(); ++i) - { - loco::Node *node = graph->outputs()->at(i)->node(); - std::string name = "Output(" + std::to_string(i) + ")"; - dump_shapeinferencedata(node, name); - } - } - - // validate graph - assert(loco::valid(graph)); -} - } // namespace namespace moco @@ -343,15 +111,13 @@ Frontend::Frontend() std::unique_ptr Frontend::load(const ModelSignature &signature, const char *modelfile, FileType type) const { + Import import; + tensorflow::GraphDef tf_graph_def; load_tf(modelfile, type, tf_graph_def); - auto graph = loco::make_graph(); - - convert_graph(signature, tf_graph_def, graph.get()); - - transform_graph(graph.get()); + auto graph = import.load(signature, tf_graph_def); return std::move(graph); } @@ -363,11 +129,9 @@ std::unique_ptr Frontend::load(const ModelSignature &signature, std load_tf(stream, type, tf_graph_def); - auto graph = loco::make_graph(); - - convert_graph(signature, tf_graph_def, graph.get()); + Import import; - transform_graph(graph.get()); + auto graph = import.load(signature, tf_graph_def); return std::move(graph); } diff --git a/contrib/moco-tf/src/Frontend.test.cpp b/contrib/moco-tf/src/Frontend.test.cpp deleted file mode 100644 index 81691e9..0000000 --- a/contrib/moco-tf/src/Frontend.test.cpp +++ /dev/null @@ -1,141 +0,0 @@ -/* - * 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 - -#include - -#include - -#include - -#define STRING(content) #content - -TEST(MocoTensotFlowFrontendTest, Dummy) { moco::tf::Frontend frontend; } - -namespace -{ - -struct membuf : std::streambuf -{ - membuf(char const *base, size_t size) - { - char *p(const_cast(base)); - this->setg(p, p, p + size); - } -}; - -struct imemstream : virtual membuf, std::istream -{ - imemstream(char const *base, size_t size) - : membuf(base, size), std::istream(static_cast(this)) - { - } -}; - -// clang-format off -const char *basic_pbtxtdata = STRING( -node { - name: "Placeholder" - op: "Placeholder" - attr { - key: "dtype" - value { - type: DT_FLOAT - } - } - attr { - key: "shape" - value { - shape { - dim { - size: 1 - } - dim { - size: 2 - } - dim { - size: 1 - } - dim { - size: 2 - } - } - } - } -} -node { - name: "output/identity" - op: "Identity" - input: "Placeholder" - attr { - key: "T" - value { - type: DT_FLOAT - } - } -} -); -// clang-format on - -} // namespace - -TEST(TensorFlowFrontend, load_model_withio) -{ - moco::tf::Frontend frontend; - moco::tf::ModelSignature signature; - - imemstream mempb(basic_pbtxtdata, std::strlen(basic_pbtxtdata)); - - signature.add_input(moco::tf::TensorName("Placeholder", 0)); - signature.add_output(moco::tf::TensorName("output/identity", 0)); - - std::unique_ptr graph = - frontend.load(signature, &mempb, moco::tf::Frontend::FileType::Text); - - loco::Graph::InputContext *inputs = graph->inputs(); - ASSERT_EQ(inputs->size(), 1); - loco::GraphInput *input = inputs->at(0); - loco::Pull *pull = input->node(); - ASSERT_EQ(pull->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(pull->rank(), 4); - loco::Dimension dim1 = loco::make_dimension(1); - loco::Dimension dim2 = loco::make_dimension(2); - ASSERT_EQ(pull->dim(0).value(), dim1.value()); - ASSERT_EQ(pull->dim(1).value(), dim2.value()); - ASSERT_EQ(pull->dim(2).value(), dim1.value()); - ASSERT_EQ(pull->dim(3).value(), dim2.value()); - - loco::Graph::OutputContext *outputs = graph->outputs(); - ASSERT_EQ(outputs->size(), 1); - loco::GraphOutput *output = outputs->at(0); - loco::Push *push = output->node(); - // Currently we don't know the shape of output node(s) - // ASSERT_EQ(push->rank(), 4); - // ASSERT_EQ(push->dim(0).value(), dim1.value()); - // ASSERT_EQ(push->dim(1).value(), dim2.value()); - // ASSERT_EQ(push->dim(2).value(), dim1.value()); - // ASSERT_EQ(push->dim(3).value(), dim2.value()); - - loco::Graph::NodeContext *nodes = graph->nodes(); - ASSERT_EQ(nodes->size(), 3); - loco::Pull *node0 = dynamic_cast(nodes->at(0)); - ASSERT_EQ(node0, pull); - loco::Push *node2 = dynamic_cast(nodes->at(2)); - ASSERT_EQ(node2, push); - loco::Forward *node1 = dynamic_cast(nodes->at(1)); - ASSERT_NE(node1, nullptr); -}