提交 720994b4 编写于 作者: K kexinzhao 提交者: Yiqun Liu

Add inference example and unit-test for fit-a-line book chapter (#8208)

* initial commit

* minor fix

* remove redundency

* address comments
上级 cd10cede
...@@ -24,6 +24,7 @@ function(inference_test TARGET_NAME) ...@@ -24,6 +24,7 @@ function(inference_test TARGET_NAME)
endforeach() endforeach()
endfunction(inference_test) endfunction(inference_test)
inference_test(fit_a_line)
inference_test(recognize_digits ARGS mlp) inference_test(recognize_digits ARGS mlp)
inference_test(image_classification ARGS vgg resnet) inference_test(image_classification ARGS vgg resnet)
inference_test(label_semantic_roles) inference_test(label_semantic_roles)
......
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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 <gtest/gtest.h>
#include "gflags/gflags.h"
#include "test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
TEST(inference, fit_a_line) {
if (FLAGS_dirname.empty()) {
LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
}
LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
std::string dirname = FLAGS_dirname;
// 0. Call `paddle::framework::InitDevices()` initialize all the devices
// In unittests, this is done in paddle/testing/paddle_gtest_main.cc
paddle::framework::LoDTensor input;
// The second dim of the input tensor should be 13
// The input data should be >= 0
int64_t batch_size = 10;
SetupTensor<float>(
input, {batch_size, 13}, static_cast<float>(0), static_cast<float>(10));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&input);
paddle::framework::LoDTensor output1;
std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
// Run inference on CPU
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1);
LOG(INFO) << output1.dims();
#ifdef PADDLE_WITH_CUDA
paddle::framework::LoDTensor output2;
std::vector<paddle::framework::LoDTensor*> cpu_fetchs2;
cpu_fetchs2.push_back(&output2);
// Run inference on CUDA GPU
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2);
LOG(INFO) << output2.dims();
CheckError<float>(output1, output2);
#endif
}
...@@ -15,15 +15,13 @@ ...@@ -15,15 +15,13 @@
import paddle.v2 as paddle import paddle.v2 as paddle
import paddle.v2.fluid as fluid import paddle.v2.fluid as fluid
import contextlib import contextlib
import numpy
import unittest import unittest
import math import math
import sys import sys
def main(use_cuda): def train(use_cuda, save_dirname):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
x = fluid.layers.data(name='x', shape=[13], dtype='float32') x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None) y_predict = fluid.layers.fc(input=x, size=1, act=None)
...@@ -51,14 +49,15 @@ def main(use_cuda): ...@@ -51,14 +49,15 @@ def main(use_cuda):
PASS_NUM = 100 PASS_NUM = 100
for pass_id in range(PASS_NUM): for pass_id in range(PASS_NUM):
fluid.io.save_persistables(exe, "./fit_a_line.model/")
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader(): for data in train_reader():
avg_loss_value, = exe.run(fluid.default_main_program(), avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data), feed=feeder.feed(data),
fetch_list=[avg_cost]) fetch_list=[avg_cost])
print(avg_loss_value) print(avg_loss_value)
if avg_loss_value[0] < 10.0: if avg_loss_value[0] < 10.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ['x'],
[y_predict], exe)
return return
if math.isnan(float(avg_loss_value)): if math.isnan(float(avg_loss_value)):
sys.exit("got NaN loss, training failed.") sys.exit("got NaN loss, training failed.")
...@@ -66,6 +65,43 @@ def main(use_cuda): ...@@ -66,6 +65,43 @@ def main(use_cuda):
avg_loss_value[0])) avg_loss_value[0]))
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_x},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "fit_a_line.inference.model"
train(use_cuda, save_dirname)
infer(use_cuda, save_dirname)
class TestFitALine(unittest.TestCase): class TestFitALine(unittest.TestCase):
def test_cpu(self): def test_cpu(self):
with self.program_scope_guard(): with self.program_scope_guard():
......
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