Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into ssd_target_assign
dangqingqing
7 years ago
commit
53b6ee1954
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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 "paddle/framework/lod_tensor.h"
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#include "paddle/inference/io.h"
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template <typename T>
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void SetupTensor(paddle::framework::LoDTensor& input,
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paddle::framework::DDim dims,
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T lower,
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T upper) {
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srand(time(0));
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T* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
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for (int i = 0; i < input.numel(); ++i) {
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input_ptr[i] =
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(static_cast<T>(rand()) / static_cast<T>(RAND_MAX)) * (upper - lower) +
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lower;
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}
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}
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template <typename T>
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void SetupLoDTensor(paddle::framework::LoDTensor& input,
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paddle::framework::LoD& lod,
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T lower,
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T upper) {
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input.set_lod(lod);
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int dim = lod[0][lod[0].size() - 1];
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SetupTensor(input, {dim, 1}, lower, upper);
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}
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template <typename T>
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void CheckError(paddle::framework::LoDTensor& output1,
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paddle::framework::LoDTensor& output2) {
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// Check lod information
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EXPECT_EQ(output1.lod(), output2.lod());
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EXPECT_EQ(output1.dims(), output2.dims());
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EXPECT_EQ(output1.numel(), output2.numel());
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T err = static_cast<T>(0);
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if (typeid(T) == typeid(float)) {
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err = 1E-3;
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} else if (typeid(T) == typeid(double)) {
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err = 1E-6;
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} else {
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err = 0;
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}
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size_t count = 0;
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for (int64_t i = 0; i < output1.numel(); ++i) {
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if (fabs(output1.data<T>()[i] - output2.data<T>()[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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}
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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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@ -0,0 +1,81 @@
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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 "test_helper.h"
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DEFINE_string(dirname, "", "Directory of the inference model.");
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TEST(inference, label_semantic_roles) {
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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 word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1,
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ctx_p2, mark;
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paddle::framework::LoD lod{{0, 4, 10}};
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SetupLoDTensor(word, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
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SetupLoDTensor(
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predicate, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
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SetupLoDTensor(ctx_n2, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
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SetupLoDTensor(ctx_n1, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
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SetupLoDTensor(ctx_0, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
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SetupLoDTensor(ctx_p1, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
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SetupLoDTensor(ctx_p2, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
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SetupLoDTensor(mark, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
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std::vector<paddle::framework::LoDTensor*> cpu_feeds;
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cpu_feeds.push_back(&word);
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cpu_feeds.push_back(&predicate);
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cpu_feeds.push_back(&ctx_n2);
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cpu_feeds.push_back(&ctx_n1);
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cpu_feeds.push_back(&ctx_0);
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cpu_feeds.push_back(&ctx_p1);
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cpu_feeds.push_back(&ctx_p2);
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cpu_feeds.push_back(&mark);
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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.lod();
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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.lod();
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LOG(INFO) << output2.dims();
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CheckError<float>(output1, output2);
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#endif
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}
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import unittest
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import paddle.v2.fluid.core as core
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import paddle.v2.fluid.layers as layers
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import paddle.v2.fluid.framework as framework
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from paddle.v2.fluid.executor import Executor
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from paddle.v2.fluid.framework import default_startup_program
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class TestSwitch(unittest.TestCase):
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def check_switch(self, value):
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x = layers.fill_constant(shape=[1], dtype='float32', value=value)
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zero_var = layers.fill_constant(shape=[1], dtype='float32', value=0.0)
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one_var = layers.fill_constant(shape=[1], dtype='float32', value=1.0)
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two_var = layers.fill_constant(shape=[1], dtype='float32', value=2.0)
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three_var = layers.fill_constant(shape=[1], dtype='float32', value=3.0)
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result = layers.create_global_var(
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shape=[1], value=-1.0, dtype='float32', persistable=True)
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with layers.Switch() as switch:
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with switch.case(layers.less_than(x, zero_var)):
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layers.assign(zero_var, result)
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with switch.case(layers.less_than(x, one_var)):
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layers.assign(one_var, result)
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with switch.case(layers.less_than(x, two_var)):
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layers.assign(two_var, result)
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with switch.default():
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layers.assign(three_var, result)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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exe.run(default_startup_program())
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out = exe.run(feed={}, fetch_list=[result])[0][0]
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return out
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def test_switch(self):
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test_data = {(-0.1, 0), (0.1, 1), (1.1, 2), (2.1, 3)}
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for x, expected_result in test_data:
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main_program = framework.Program()
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startup_program = framework.Program()
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with framework.program_guard(main_program, startup_program):
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result = self.check_switch(x)
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self.assertEqual(result, expected_result)
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if __name__ == '__main__':
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unittest.main()
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