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91 lines
2.8 KiB
91 lines
2.8 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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 <fstream>
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#include "gflags/gflags.h"
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#include "gtest/gtest.h"
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#include "paddle/fluid/framework/executor.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/program_desc.h"
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/inference/io.h"
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#include "paddle/fluid/platform/device_context.h"
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#include "paddle/fluid/platform/init.h"
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#include "paddle/fluid/platform/place.h"
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DEFINE_string(dirname, "", "Directory of the train model.");
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namespace paddle {
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void Train() {
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CHECK(!FLAGS_dirname.empty());
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framework::InitDevices(false);
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const auto cpu_place = platform::CPUPlace();
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framework::Executor executor(cpu_place);
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framework::Scope scope;
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auto train_program = inference::Load(
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&executor, &scope, FLAGS_dirname + "__model_combined__.main_program",
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FLAGS_dirname + "__params_combined__");
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std::string loss_name = "";
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for (auto op_desc : train_program->Block(0).AllOps()) {
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if (op_desc->Type() == "mean") {
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loss_name = op_desc->Output("Out")[0];
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break;
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}
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}
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PADDLE_ENFORCE_NE(loss_name, "", "loss not found");
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// prepare data
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auto x_var = scope.Var("img");
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auto x_tensor = x_var->GetMutable<framework::LoDTensor>();
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x_tensor->Resize({64, 1, 28, 28});
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auto x_data = x_tensor->mutable_data<float>(cpu_place);
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for (int i = 0; i < 64 * 28 * 28; ++i) {
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x_data[i] = 1.0;
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}
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auto y_var = scope.Var("label");
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auto y_tensor = y_var->GetMutable<framework::LoDTensor>();
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y_tensor->Resize({64, 1});
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auto y_data = y_tensor->mutable_data<int64_t>(cpu_place);
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for (int i = 0; i < 64 * 1; ++i) {
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y_data[i] = static_cast<int64_t>(1);
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}
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auto loss_var = scope.Var(loss_name);
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float first_loss = 0.0;
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float last_loss = 0.0;
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for (int i = 0; i < 100; ++i) {
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executor.Run(*train_program, &scope, 0, false, true,
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{loss_name, "img", "label"});
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if (i == 0) {
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first_loss = loss_var->Get<framework::LoDTensor>().data<float>()[0];
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} else if (i == 99) {
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last_loss = loss_var->Get<framework::LoDTensor>().data<float>()[0];
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}
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}
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EXPECT_LT(last_loss, first_loss);
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}
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TEST(train, recognize_digits) { Train(); }
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} // namespace paddle
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