Merge pull request #7874 from Xreki/core_add_inference_unittest
Change the inference example to an unittestemailweixu-patch-1
Tao Luo
7 years ago
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GitHub
commit
455639b205
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <time.h>
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#include <iostream>
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#include "gflags/gflags.h"
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#include "paddle/framework/init.h"
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#include "paddle/framework/lod_tensor.h"
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#include "paddle/inference/io.h"
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DEFINE_string(dirname, "", "Directory of the inference model.");
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int main(int argc, char** argv) {
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google::ParseCommandLineFlags(&argc, &argv, true);
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if (FLAGS_dirname.empty()) {
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// Example:
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// ./example --dirname=recognize_digits_mlp.inference.model
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std::cout << "Usage: ./example --dirname=path/to/your/model" << std::endl;
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exit(1);
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}
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// 1. Define place, executor, scope
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auto place = paddle::platform::CPUPlace();
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paddle::framework::InitDevices();
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auto* executor = new paddle::framework::Executor(place);
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auto* scope = new paddle::framework::Scope();
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std::cout << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
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std::string dirname = FLAGS_dirname;
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// 2. Initialize the inference program
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auto inference_program = paddle::inference::Load(*executor, *scope, dirname);
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// 3. Optional: perform optimization on the inference_program
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// 4. Get the feed_target_names and fetch_target_names
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const std::vector<std::string>& feed_target_names =
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inference_program->GetFeedTargetNames();
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const std::vector<std::string>& fetch_target_names =
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inference_program->GetFetchTargetNames();
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// 5. Generate input
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paddle::framework::LoDTensor input;
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srand(time(0));
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float* input_ptr =
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input.mutable_data<float>({1, 784}, paddle::platform::CPUPlace());
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for (int i = 0; i < 784; ++i) {
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input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
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}
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std::vector<paddle::framework::LoDTensor> feeds;
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feeds.push_back(input);
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std::vector<paddle::framework::LoDTensor> fetchs;
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// Set up maps for feed and fetch targets
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std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
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std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
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// set_feed_variable
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for (size_t i = 0; i < feed_target_names.size(); ++i) {
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feed_targets[feed_target_names[i]] = &feeds[i];
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}
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// get_fetch_variable
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fetchs.resize(fetch_target_names.size());
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for (size_t i = 0; i < fetch_target_names.size(); ++i) {
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fetch_targets[fetch_target_names[i]] = &fetchs[i];
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}
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// Run the inference program
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executor->Run(*inference_program, scope, feed_targets, fetch_targets);
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// Get outputs
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for (size_t i = 0; i < fetchs.size(); ++i) {
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auto dims_i = fetchs[i].dims();
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std::cout << "dims_i:";
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for (int j = 0; j < dims_i.size(); ++j) {
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std::cout << " " << dims_i[j];
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}
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std::cout << std::endl;
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std::cout << "result:";
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float* output_ptr = fetchs[i].data<float>();
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for (int j = 0; j < paddle::framework::product(dims_i); ++j) {
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std::cout << " " << output_ptr[j];
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}
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std::cout << std::endl;
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}
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delete scope;
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delete executor;
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return 0;
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}
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@ -0,0 +1,7 @@
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set(PYTHON_TESTS_DIR ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/tests)
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cc_test(test_inference_recognize_digits_mlp
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SRCS test_inference_recognize_digits.cc
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DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
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ARGS --dirname=${PYTHON_TESTS_DIR}/book/recognize_digits_mlp.inference.model)
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set_tests_properties(test_inference_recognize_digits_mlp
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PROPERTIES DEPENDS test_recognize_digits_mlp_cpu)
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@ -0,0 +1,113 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <gtest/gtest.h>
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#include <time.h>
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#include <sstream>
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#include "gflags/gflags.h"
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#include "paddle/framework/lod_tensor.h"
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#include "paddle/inference/io.h"
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DEFINE_string(dirname, "", "Directory of the inference model.");
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template <typename Place, typename T>
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void TestInference(const std::string& dirname,
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const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
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std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
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// 1. Define place, executor and scope
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auto place = Place();
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auto executor = paddle::framework::Executor(place);
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auto* scope = new paddle::framework::Scope();
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// 2. Initialize the inference_program and load all parameters from file
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auto inference_program = paddle::inference::Load(executor, *scope, dirname);
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// 3. Get the feed_target_names and fetch_target_names
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const std::vector<std::string>& feed_target_names =
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inference_program->GetFeedTargetNames();
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const std::vector<std::string>& fetch_target_names =
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inference_program->GetFetchTargetNames();
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// 4. Prepare inputs: set up maps for feed targets
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std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
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for (size_t i = 0; i < feed_target_names.size(); ++i) {
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// Please make sure that cpu_feeds[i] is right for feed_target_names[i]
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feed_targets[feed_target_names[i]] = cpu_feeds[i];
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}
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// 5. Define Tensor to get the outputs: set up maps for fetch targets
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std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
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for (size_t i = 0; i < fetch_target_names.size(); ++i) {
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fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
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}
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// 6. Run the inference program
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executor.Run(*inference_program, scope, feed_targets, fetch_targets);
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delete scope;
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}
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TEST(inference, recognize_digits) {
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if (FLAGS_dirname.empty()) {
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LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
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}
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LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
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std::string dirname = FLAGS_dirname;
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// 0. Call `paddle::framework::InitDevices()` initialize all the devices
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// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
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paddle::framework::LoDTensor input;
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srand(time(0));
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float* input_ptr =
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input.mutable_data<float>({1, 28, 28}, paddle::platform::CPUPlace());
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for (int i = 0; i < 784; ++i) {
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input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
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}
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std::vector<paddle::framework::LoDTensor*> cpu_feeds;
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cpu_feeds.push_back(&input);
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paddle::framework::LoDTensor output1;
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std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
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cpu_fetchs1.push_back(&output1);
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// Run inference on CPU
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TestInference<paddle::platform::CPUPlace, float>(
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dirname, cpu_feeds, cpu_fetchs1);
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LOG(INFO) << output1.dims();
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#ifdef PADDLE_WITH_CUDA
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paddle::framework::LoDTensor output2;
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std::vector<paddle::framework::LoDTensor*> cpu_fetchs2;
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cpu_fetchs2.push_back(&output2);
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// Run inference on CUDA GPU
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TestInference<paddle::platform::CUDAPlace, float>(
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dirname, cpu_feeds, cpu_fetchs2);
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LOG(INFO) << output2.dims();
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EXPECT_EQ(output1.dims(), output2.dims());
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EXPECT_EQ(output1.numel(), output2.numel());
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float err = 1E-3;
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int count = 0;
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for (int64_t i = 0; i < output1.numel(); ++i) {
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if (fabs(output1.data<float>()[i] - output2.data<float>()[i]) > err) {
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count++;
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}
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}
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EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
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#endif
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}
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