未验证 提交 4b62fcd0 编写于 作者: K kexinzhao 提交者: GitHub

Add Inference example and unit test for understand sentiment (#8251)

* initial commit

* fix bug

* end of file fix

* address comments
上级 159b7722
...@@ -30,3 +30,4 @@ inference_test(image_classification ARGS vgg resnet) ...@@ -30,3 +30,4 @@ inference_test(image_classification ARGS vgg resnet)
inference_test(label_semantic_roles) inference_test(label_semantic_roles)
inference_test(rnn_encoder_decoder) inference_test(rnn_encoder_decoder)
inference_test(recommender_system) inference_test(recommender_system)
inference_test(understand_sentiment)
/* 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, understand_sentiment) {
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 words;
paddle::framework::LoD lod{{0, 4, 10}};
SetupLoDTensor(words, lod, static_cast<int64_t>(0), static_cast<int64_t>(10));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&words);
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.lod();
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.lod();
LOG(INFO) << output2.dims();
CheckError<float>(output1, output2);
#endif
}
...@@ -17,6 +17,7 @@ import paddle.v2.fluid as fluid ...@@ -17,6 +17,7 @@ import paddle.v2.fluid as fluid
import paddle.v2 as paddle import paddle.v2 as paddle
import contextlib import contextlib
import math import math
import numpy as np
import sys import sys
...@@ -43,7 +44,7 @@ def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32, ...@@ -43,7 +44,7 @@ def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002) adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost) adam_optimizer.minimize(avg_cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label) accuracy = fluid.layers.accuracy(input=prediction, label=label)
return avg_cost, accuracy return avg_cost, accuracy, prediction
def stacked_lstm_net(data, def stacked_lstm_net(data,
...@@ -81,13 +82,18 @@ def stacked_lstm_net(data, ...@@ -81,13 +82,18 @@ def stacked_lstm_net(data,
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002) adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost) adam_optimizer.minimize(avg_cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label) accuracy = fluid.layers.accuracy(input=prediction, label=label)
return avg_cost, accuracy return avg_cost, accuracy, prediction
def main(word_dict, net_method, use_cuda): def create_random_lodtensor(lod, place, low, high):
if use_cuda and not fluid.core.is_compiled_with_cuda(): data = np.random.random_integers(low, high, [lod[-1], 1]).astype("int64")
return res = fluid.LoDTensor()
res.set(data, place)
res.set_lod([lod])
return res
def train(word_dict, net_method, use_cuda, save_dirname=None):
BATCH_SIZE = 128 BATCH_SIZE = 128
PASS_NUM = 5 PASS_NUM = 5
dict_dim = len(word_dict) dict_dim = len(word_dict)
...@@ -96,7 +102,7 @@ def main(word_dict, net_method, use_cuda): ...@@ -96,7 +102,7 @@ def main(word_dict, net_method, use_cuda):
data = fluid.layers.data( data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1) name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64") label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost, acc_out = net_method( cost, acc_out, prediction = net_method(
data, label, input_dim=dict_dim, class_dim=class_dim) data, label, input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch( train_data = paddle.batch(
...@@ -116,6 +122,9 @@ def main(word_dict, net_method, use_cuda): ...@@ -116,6 +122,9 @@ def main(word_dict, net_method, use_cuda):
fetch_list=[cost, acc_out]) fetch_list=[cost, acc_out])
print("cost=" + str(cost_val) + " acc=" + str(acc_val)) print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if cost_val < 0.4 and acc_val > 0.8: if cost_val < 0.4 and acc_val > 0.8:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ["words"],
prediction, exe)
return return
if math.isnan(float(cost_val)): if math.isnan(float(cost_val)):
sys.exit("got NaN loss, training failed.") sys.exit("got NaN loss, training failed.")
...@@ -123,6 +132,49 @@ def main(word_dict, net_method, use_cuda): ...@@ -123,6 +132,49 @@ def main(word_dict, net_method, use_cuda):
net_method.__name__)) net_method.__name__))
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)
lod = [0, 4, 10]
word_dict = paddle.dataset.imdb.word_dict()
tensor_words = create_random_lodtensor(
lod, place, low=0, high=len(word_dict) - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == "words"
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_words},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
def main(word_dict, net_method, use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "understand_sentiment.inference.model"
train(word_dict, net_method, use_cuda, save_dirname)
infer(use_cuda, save_dirname)
class TestUnderstandSentiment(unittest.TestCase): class TestUnderstandSentiment(unittest.TestCase):
@classmethod @classmethod
def setUpClass(cls): def setUpClass(cls):
......
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