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未验证 提交 b312cb89 编写于 作者: Z zhangchunle 提交者： GitHub
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remove high level api (#23854) (#23991)

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CMakeLists.txt
浏览文件 @ b312cb89
......@@ -83,7 +83,6 @@ option(WITH_CONTRIB "Compile the third-party contributation" OFF)
option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
option(WITH_INFERENCE_API_TEST "Test fluid inference C++ high-level api interface" OFF)
option(WITH_HIGH_LEVEL_API_TEST "Test fluid python high-level api interface" OFF)
option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VERSION})
option(WITH_DGC "Use DGC(Deep Gradient Compression) or not" ${WITH_DISTRIBUTE})
option(SANITIZER_TYPE "Choose the type of sanitizer, options are: Address, Leak, Memory, Thread, Undefined" OFF)
......
paddle/scripts/paddle_build.sh
浏览文件 @ b312cb89
......@@ -211,7 +211,6 @@ function cmake_base() {
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
-DWITH_CONTRIB=${WITH_CONTRIB:-ON}
-DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON}
-DWITH_HIGH_LEVEL_API_TEST=${WITH_HIGH_LEVEL_API_TEST:-OFF}
-DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR}
-DPY_VERSION=${PY_VERSION:-2.7}
-DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build}
......@@ -244,7 +243,6 @@ EOF
-DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
-DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON} \
-DWITH_HIGH_LEVEL_API_TEST=${WITH_HIGH_LEVEL_API_TEST:-OFF} \
-DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR} \
-DPY_VERSION=${PY_VERSION:-2.7} \
-DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build} \
-DWITH_GRPC=${grpc_flag} \
......
python/paddle/fluid/__init__.py
浏览文件 @ b312cb89
......@@ -47,7 +47,6 @@ from .dataset import *
from .data import *
from . import trainer_desc
from . import inferencer
from . import io
from . import evaluator
......@@ -92,7 +91,7 @@ from .dygraph.varbase_patch_methods import monkey_patch_varbase
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + \
trainer_desc.__all__ + inferencer.__all__ + transpiler.__all__ + \
trainer_desc.__all__ + transpiler.__all__ + \
parallel_executor.__all__ + lod_tensor.__all__ + \
data_feed_desc.__all__ + compiler.__all__ + backward.__all__ + [
'io',
......
python/paddle/fluid/contrib/inferencer.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
from ..wrapped_decorator import signature_safe_contextmanager
from .. import core
from .. import executor
from .. import framework
from .. import io
from .. import parallel_executor
from .. import unique_name
from .trainer import check_and_get_place
__all__ = ['Inferencer', ]
class Inferencer(object):
"""
Inferencer High Level API.
Args:
infer_func (Python func): Infer function that will return predict Variable
param_path (str): The path where the inference model is saved by fluid.io.save_params
place (Place): place to do the inference
parallel (bool): use parallel_executor to run the inference, it will use multi CPU/GPU.
Examples:
.. code-block:: python
def inference_program():
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
return y_predict
place = fluid.CPUPlace()
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path="/tmp/model", place=place)
"""
def __init__(self, infer_func, param_path, place=None, parallel=False):
self.param_path = param_path
self.scope = core.Scope()
self.parallel = parallel
self.place = check_and_get_place(place)
self.inference_program = framework.Program()
with framework.program_guard(self.inference_program):
with unique_name.guard():
self.predict_var = infer_func()
with self._prog_and_scope_guard():
# load params from param_path into scope
io.load_params(executor.Executor(self.place), param_path)
if parallel:
with self._prog_and_scope_guard():
self.exe = parallel_executor.ParallelExecutor(
use_cuda=isinstance(self.place, core.CUDAPlace),
loss_name=self.predict_var.name)
else:
self.exe = executor.Executor(self.place)
self.inference_program = self.inference_program.clone(for_test=True)
def infer(self, inputs, return_numpy=True):
"""
Do Inference for Inputs
Args:
inputs (map): a map of {"input_name": input_var} that will be feed into the inference program
return_numpy (bool): transform return value into numpy or not
Returns:
Tensor or Numpy: the predict value of the inference model for the inputs
Examples:
.. code-block:: python
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
"""
if not isinstance(inputs, dict):
raise ValueError(
"inputs should be a map of {'input_name': input_var}")
with self._prog_and_scope_guard():
results = self.exe.run(feed=inputs,
fetch_list=[self.predict_var.name],
return_numpy=return_numpy)
return results
@signature_safe_contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(main_program=self.inference_program):
with executor.scope_guard(self.scope):
yield
python/paddle/fluid/contrib/trainer.py 已删除 100644 → 0
浏览文件 @ e5985675
此差异已折叠。
python/paddle/fluid/inferencer.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
# NOTE: inferencer is moved into fluid.contrib.inferencer.
__all__ = []
python/paddle/fluid/tests/book/CMakeLists.txt
浏览文件 @ b312cb89
......@@ -5,7 +5,3 @@ string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
if(WITH_HIGH_LEVEL_API_TEST)
add_subdirectory(high-level-api)
endif()
python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ e5985675
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*_new_api.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# This test is buggy
# py_test(test_understand_sentiment_dynamic_rnn SRCS
# test_understand_sentiment_dynamic_rnn.py SERIAL)
LIST(REMOVE_ITEM TEST_OPS test_understand_sentiment_dynamic_rnn_new_api)
if(NOT APPLE)
# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
else()
foreach(src ${TEST_OPS})
if(${src} STREQUAL "test_image_classification_vgg_new_api")
message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
elseif(${src} STREQUAL "test_image_classification_resnet_new_api")
message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
elseif(${src} STREQUAL "test_recognize_digits_conv_new_api")
message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
elseif(${src} STREQUAL "test_recognize_digits_mlp_new_api")
message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
elseif()
py_test(${src} SRCS ${src}.py)
set_tests_properties(${src} PROPERTIES LABELS "RUN_TYPE=DIST")
endif()
endforeach()
endif()
python/paddle/fluid/tests/book/high-level-api/cifar10_small_test_set.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# 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.
"""
CIFAR dataset.
This module will download dataset from
https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into
paddle reader creators.
The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test
images.
The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes
containing 600 images each. There are 500 training images and 100 testing
images per class.
"""
from __future__ import print_function
import itertools
import numpy
import paddle.dataset.common
import tarfile
import six
from six.moves import cPickle as pickle
__all__ = ['train10']
URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/'
CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
def reader_creator(filename, sub_name, batch_size=None):
def read_batch(batch):
data = batch[six.b('data')]
labels = batch.get(
six.b('labels'), batch.get(six.b('fine_labels'), None))
assert labels is not None
for sample, label in six.moves.zip(data, labels):
yield (sample / 255.0).astype(numpy.float32), int(label)
def reader():
with tarfile.open(filename, mode='r') as f:
names = [
each_item.name for each_item in f if sub_name in each_item.name
]
batch_count = 0
for name in names:
if six.PY2:
batch = pickle.load(f.extractfile(name))
else:
batch = pickle.load(f.extractfile(name), encoding='bytes')
for item in read_batch(batch):
if isinstance(batch_size, int) and batch_count > batch_size:
break
batch_count += 1
yield item
return reader
def train10(batch_size=None):
"""
CIFAR-10 training set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Training reader creator
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
'data_batch',
batch_size=batch_size)
def test10(batch_size=None):
"""
CIFAR-10 test set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Test reader creator.
:rtype: callable
"""
return reader_creator(
paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
'test_batch',
batch_size=batch_size)
python/paddle/fluid/tests/book/high-level-api/test_fit_a_line_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import sys
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import contextlib
import numpy
import unittest
# train reader
BATCH_SIZE = 20
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.test(), buf_size=500),
batch_size=BATCH_SIZE)
def inference_program():
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
return y_predict
def train_program():
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = inference_program()
loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss)
return [avg_loss, y_predict]
def optimizer_func():
return fluid.optimizer.SGD(learning_rate=0.001)
def train(use_cuda, train_program, params_dirname, inference_model_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = Trainer(
train_func=train_program, place=place, optimizer_func=optimizer_func)
def event_handler(event):
if isinstance(event, EndStepEvent):
if event.step == 10:
test_metrics = trainer.test(
reader=test_reader, feed_order=['x', 'y'])
print(test_metrics)
'''
...
['25.768919467926025']
['15.343549569447836']
...
'''
if params_dirname is not None:
trainer.save_params(params_dirname)
trainer.save_inference_model(inference_model_dirname,
['x'], [1])
trainer.stop()
trainer.train(
reader=train_reader,
num_epochs=100,
event_handler=event_handler,
feed_order=['x', 'y'])
# infer
def infer(use_cuda, inference_program, params_dirname=None):
if params_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(
infer_func=inference_program, param_path=params_dirname, place=place)
batch_size = 10
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
print("infer results: ", results[0])
def infer_by_saved_model(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)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be fed
# 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
test_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=batch_size)
test_data = next(test_reader())
test_feat = numpy.array(
[data[0] for data in test_data]).astype("float32")
test_label = numpy.array(
[data[1] for data in test_data]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: numpy.array(test_feat)},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
print("ground truth: ", test_label)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
params_dirname = "fit_a_line.model"
inference_model_dirname = "fit_a_line.inference_model"
train(use_cuda, train_program, params_dirname, inference_model_dirname)
infer(use_cuda, inference_program, params_dirname)
infer_by_saved_model(use_cuda, inference_model_dirname)
class TestFitALine(unittest.TestCase):
def test_cpu(self):
with self.program_scope_guard():
with fluid.unique_name.guard():
main(use_cuda=False)
def test_cuda(self):
with self.program_scope_guard():
with fluid.unique_name.guard():
main(use_cuda=True)
@contextlib.contextmanager
def program_scope_guard(self):
prog = fluid.Program()
startup_prog = fluid.Program()
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
yield
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/book/high-level-api/test_image_classification_resnet_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import sys
import paddle
import paddle.fluid as fluid
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import paddle.fluid.core as core
import numpy
import os
import cifar10_small_test_set
def resnet_cifar10(input, depth=32):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride):
if ch_in != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0, None)
else:
return input
def basicblock(input, ch_in, ch_out, stride):
tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True)
short = shortcut(input, ch_in, ch_out, stride)
return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
def layer_warp(block_func, input, ch_in, ch_out, count, stride):
tmp = block_func(input, ch_in, ch_out, stride)
for i in range(1, count):
tmp = block_func(tmp, ch_out, ch_out, 1)
return tmp
assert (depth - 2) % 6 == 0
n = (depth - 2) // 6
conv1 = conv_bn_layer(
input=input, ch_out=16, filter_size=3, stride=1, padding=1)
res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
pool = fluid.layers.pool2d(
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
predict = fluid.layers.fc(input=pool, size=10, act='softmax')
return predict
def inference_network():
data_shape = [3, 32, 32]
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
predict = resnet_cifar10(images, 32)
return predict
def train_network():
predict = inference_network()
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, accuracy]
def optimizer_func():
return fluid.optimizer.Adam(learning_rate=0.001)
def train(use_cuda, train_program, parallel, params_dirname):
BATCH_SIZE = 128
EPOCH_NUM = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
batch_size=BATCH_SIZE,
drop_last=False)
test_reader = paddle.batch(
paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE, drop_last=False)
def event_handler(event):
if isinstance(event, EndStepEvent):
avg_cost, accuracy = trainer.test(
reader=test_reader, feed_order=['pixel', 'label'])
print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
if accuracy > 0.01: # Low threshold for speeding up CI
if params_dirname is not None:
trainer.save_params(params_dirname)
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = Trainer(
train_func=train_program,
optimizer_func=optimizer_func,
place=place,
parallel=parallel)
trainer.train(
reader=train_reader,
num_epochs=EPOCH_NUM,
event_handler=event_handler,
feed_order=['pixel', 'label'])
def infer(use_cuda, inference_program, parallel, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(
infer_func=inference_program,
param_path=params_dirname,
place=place,
parallel=parallel)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range
# [0, 1.0].
tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
results = inferencer.infer({'pixel': tensor_img})
print("infer results: ", results)
def main(use_cuda, parallel):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_path = "image_classification_resnet.inference.model"
os.environ['CPU_NUM'] = str(4)
train(
use_cuda=use_cuda,
train_program=train_network,
params_dirname=save_path,
parallel=parallel)
# FIXME(zcd): in the inference stage, the number of
# input data is one, it is not appropriate to use parallel.
if parallel and use_cuda:
return
os.environ['CPU_NUM'] = str(1)
infer(
use_cuda=use_cuda,
inference_program=inference_network,
params_dirname=save_path,
parallel=parallel)
if __name__ == '__main__':
on_ci = bool(int(os.environ.get("SKIP_UNSTABLE_CI", '0')))
if not on_ci:
for use_cuda in (False, True):
for parallel in (False, True):
if use_cuda and not core.is_compiled_with_cuda():
continue
main(use_cuda=use_cuda, parallel=parallel)
python/paddle/fluid/tests/book/high-level-api/test_image_classification_vgg_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import sys
import paddle
import paddle.fluid as fluid
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import paddle.fluid.core as core
import numpy
import os
import cifar10_small_test_set
def vgg16_bn_drop(input):
def conv_block(input, num_filter, groups, dropouts):
return fluid.nets.img_conv_group(
input=input,
pool_size=2,
pool_stride=2,
conv_num_filter=[num_filter] * groups,
conv_filter_size=3,
conv_act='relu',
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type='max')
conv1 = conv_block(input, 64, 2, [0.3, 0])
conv2 = conv_block(conv1, 128, 2, [0.4, 0])
conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
bn = fluid.layers.batch_norm(input=fc1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
predict = fluid.layers.fc(input=fc2, size=10, act='softmax')
return predict
def inference_network():
data_shape = [3, 32, 32]
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
predict = vgg16_bn_drop(images)
return predict
def train_network():
predict = inference_network()
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, accuracy]
def optimizer_func():
return fluid.optimizer.Adam(learning_rate=0.001)
def train(use_cuda, train_program, parallel, params_dirname):
BATCH_SIZE = 128
train_reader = paddle.batch(
paddle.reader.shuffle(
cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
batch_size=BATCH_SIZE,
drop_last=False)
# Use only part of the test set data validation program
test_reader = paddle.batch(
cifar10_small_test_set.test10(BATCH_SIZE),
batch_size=BATCH_SIZE,
drop_last=False)
def event_handler(event):
if isinstance(event, EndStepEvent):
avg_cost, accuracy = trainer.test(
reader=test_reader, feed_order=['pixel', 'label'])
print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
if accuracy > 0.01: # Low threshold for speeding up CI
if params_dirname is not None:
trainer.save_params(params_dirname)
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = Trainer(
train_func=train_program,
place=place,
optimizer_func=optimizer_func,
parallel=parallel)
trainer.train(
reader=train_reader,
num_epochs=1,
event_handler=event_handler,
feed_order=['pixel', 'label'])
def infer(use_cuda, inference_program, parallel, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(
infer_func=inference_program,
param_path=params_dirname,
place=place,
parallel=parallel)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range
# [0, 1.0].
tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
results = inferencer.infer({'pixel': tensor_img})
print("infer results: ", results)
def main(use_cuda, parallel):
save_path = "image_classification_vgg.inference.model"
os.environ['CPU_NUM'] = str(4)
train(
use_cuda=use_cuda,
train_program=train_network,
params_dirname=save_path,
parallel=parallel)
# FIXME(zcd): in the inference stage, the number of
# input data is one, it is not appropriate to use parallel.
if parallel and use_cuda:
return
os.environ['CPU_NUM'] = str(1)
infer(
use_cuda=use_cuda,
inference_program=inference_network,
params_dirname=save_path,
parallel=parallel)
if __name__ == '__main__':
for use_cuda in (False, True):
for parallel in (False, True):
if use_cuda and not core.is_compiled_with_cuda():
continue
main(use_cuda=use_cuda, parallel=parallel)
python/paddle/fluid/tests/book/high-level-api/test_label_semantic_roles_new_api.py 已删除 100755 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import sys
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import numpy as np
WORD_DICT, VERB_DICT, LABEL_DICT = paddle.dataset.conll05.get_dict()
WORD_DICT_LEN = len(WORD_DICT)
LABEL_DICT_LEN = len(LABEL_DICT)
PRED_DICT_LEN = len(VERB_DICT)
MARK_DICT_LEN = 2
IS_SPARSE = True
BATCH_SIZE = 10
EMBEDDING_NAME = 'emb'
def lstm_net():
WORD_DIM = 32
MARK_DIM = 5
HIDDEN_DIM = 512
DEPTH = 8
# Data definitions
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
predicate = fluid.layers.data(
name='verb_data', shape=[1], dtype='int64', lod_level=1)
ctx_n2 = fluid.layers.data(
name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
ctx_n1 = fluid.layers.data(
name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
ctx_0 = fluid.layers.data(
name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
ctx_p1 = fluid.layers.data(
name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
ctx_p2 = fluid.layers.data(
name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
mark = fluid.layers.data(
name='mark_data', shape=[1], dtype='int64', lod_level=1)
# 8 features
predicate_embedding = fluid.layers.embedding(
input=predicate,
size=[PRED_DICT_LEN, WORD_DIM],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='vemb')
mark_embedding = fluid.layers.embedding(
input=mark,
size=[MARK_DICT_LEN, MARK_DIM],
dtype='float32',
is_sparse=IS_SPARSE)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
fluid.layers.embedding(
size=[WORD_DICT_LEN, WORD_DIM],
input=x,
param_attr=fluid.ParamAttr(name=EMBEDDING_NAME))
for x in word_input
#name=EMBEDDING_NAME, trainable=False)) for x in word_input
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
hidden_0_layers = [
fluid.layers.fc(input=emb, size=HIDDEN_DIM, act='tanh')
for emb in emb_layers
]
hidden_0 = fluid.layers.sums(input=hidden_0_layers)
lstm_0 = fluid.layers.dynamic_lstm(
input=hidden_0,
size=HIDDEN_DIM,
candidate_activation='relu',
gate_activation='sigmoid',
cell_activation='sigmoid')
# stack L-LSTM and R-LSTM with direct edges
input_tmp = [hidden_0, lstm_0]
for i in range(1, DEPTH):
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=HIDDEN_DIM, act='tanh'),
fluid.layers.fc(input=input_tmp[1], size=HIDDEN_DIM, act='tanh')
])
lstm = fluid.layers.dynamic_lstm(
input=mix_hidden,
size=HIDDEN_DIM,
candidate_activation='relu',
gate_activation='sigmoid',
cell_activation='sigmoid',
is_reverse=((i % 2) == 1))
input_tmp = [mix_hidden, lstm]
feature_out = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=LABEL_DICT_LEN, act='tanh'),
fluid.layers.fc(input=input_tmp[1], size=LABEL_DICT_LEN, act='tanh')
])
return feature_out
def inference_program():
predict = lstm_net()
return predict
def train_program():
MIX_HIDDEN_LR = 1e-3
predict = lstm_net()
target = fluid.layers.data(
name='target', shape=[1], dtype='int64', lod_level=1)
crf_cost = fluid.layers.linear_chain_crf(
input=predict,
label=target,
param_attr=fluid.ParamAttr(
name='crfw', learning_rate=MIX_HIDDEN_LR))
avg_cost = fluid.layers.mean(crf_cost)
return [avg_cost]
def optimize_func():
return fluid.optimizer.SGD(learning_rate=fluid.layers.exponential_decay(
learning_rate=0.01, decay_steps=100000, decay_rate=0.5, staircase=True))
def train(use_cuda, train_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = Trainer(
train_func=train_program, place=place, optimizer_func=optimize_func)
feed_order = [
'word_data', 'ctx_n2_data', 'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'verb_data', 'mark_data', 'target'
]
#embedding_param = fluid.global_scope().find_var(
# EMBEDDING_NAME).get_tensor()
#embedding_param.set(
# load_parameter(conll05.get_embedding(), WORD_DICT_LEN, WORD_DIM),
# place)
def event_handler(event):
if isinstance(event, EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.conll05.test(), batch_size=BATCH_SIZE)
avg_cost_set = trainer.test(
reader=test_reader, feed_order=feed_order)
# get avg cost
avg_cost = np.array(avg_cost_set).mean()
print("avg_cost: %s" % avg_cost)
if float(avg_cost) < 100.0: # Large value to increase CI speed
trainer.save_params(params_dirname)
else:
print(
('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
float(avg_cost))))
if math.isnan(float(avg_cost)):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, list(map(np.array, event.metrics))))
if event.step == 1: # Run 2 iterations to speed CI
trainer.save_params(params_dirname)
trainer.stop()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=BATCH_SIZE)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=feed_order)
def infer(use_cuda, inference_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(
inference_program, param_path=params_dirname, place=place)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_n2 = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_n1 = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_0 = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_p1 = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
ctx_p2 = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
pred = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=PRED_DICT_LEN - 1)
mark = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=MARK_DICT_LEN - 1)
results = inferencer.infer(
{
'word_data': word,
'ctx_n2_data': ctx_n2,
'ctx_n1_data': ctx_n1,
'ctx_0_data': ctx_0,
'ctx_p1_data': ctx_p1,
'ctx_p2_data': ctx_p2,
'verb_data': pred,
'mark_data': mark
},
return_numpy=False)
print("infer results: ", np.array(results[0]).shape)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
params_dirname = "label_semantic_roles.inference.model"
train(use_cuda, train_program, params_dirname)
infer(use_cuda, inference_program, params_dirname)
if __name__ == '__main__':
for use_cuda in (False, True):
main(use_cuda=use_cuda)
python/paddle/fluid/tests/book/high-level-api/test_machine_translation_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import contextlib
import sys
import numpy as np
import paddle
import paddle.fluid as fluid
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import paddle.fluid.framework as framework
import paddle.fluid.layers as pd
from paddle.fluid.executor import Executor
from functools import partial
import unittest
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
hidden_dim = 32
word_dim = 16
batch_size = 2
max_length = 8
topk_size = 50
trg_dic_size = 10000
beam_size = 2
decoder_size = hidden_dim
def encoder(is_sparse):
# encoder
src_word_id = pd.data(
name="src_word_id", shape=[1], dtype='int64', lod_level=1)
src_embedding = pd.embedding(
input=src_word_id,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=is_sparse,
param_attr=fluid.ParamAttr(name='vemb'))
fc1 = pd.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
lstm_hidden0, lstm_0 = pd.dynamic_lstm(input=fc1, size=hidden_dim * 4)
encoder_out = pd.sequence_last_step(input=lstm_hidden0)
return encoder_out
def train_decoder(context, is_sparse):
# decoder
trg_language_word = pd.data(
name="target_language_word", shape=[1], dtype='int64', lod_level=1)
trg_embedding = pd.embedding(
input=trg_language_word,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=is_sparse,
param_attr=fluid.ParamAttr(name='vemb'))
rnn = pd.DynamicRNN()
with rnn.block():
current_word = rnn.step_input(trg_embedding)
pre_state = rnn.memory(init=context)
current_state = pd.fc(input=[current_word, pre_state],
size=decoder_size,
act='tanh')
current_score = pd.fc(input=current_state,
size=target_dict_dim,
act='softmax')
rnn.update_memory(pre_state, current_state)
rnn.output(current_score)
return rnn()
def decode(context, is_sparse):
init_state = context
array_len = pd.fill_constant(shape=[1], dtype='int64', value=max_length)
counter = pd.zeros(shape=[1], dtype='int64', force_cpu=True)
# fill the first element with init_state
state_array = pd.create_array('float32')
pd.array_write(init_state, array=state_array, i=counter)
# ids, scores as memory
ids_array = pd.create_array('int64')
scores_array = pd.create_array('float32')
init_ids = pd.data(name="init_ids", shape=[1], dtype="int64", lod_level=2)
init_scores = pd.data(
name="init_scores", shape=[1], dtype="float32", lod_level=2)
pd.array_write(init_ids, array=ids_array, i=counter)
pd.array_write(init_scores, array=scores_array, i=counter)
cond = pd.less_than(x=counter, y=array_len)
while_op = pd.While(cond=cond)
with while_op.block():
pre_ids = pd.array_read(array=ids_array, i=counter)
pre_state = pd.array_read(array=state_array, i=counter)
pre_score = pd.array_read(array=scores_array, i=counter)
# expand the lod of pre_state to be the same with pre_score
pre_state_expanded = pd.sequence_expand(pre_state, pre_score)
pre_ids_emb = pd.embedding(
input=pre_ids,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=is_sparse)
# use rnn unit to update rnn
current_state = pd.fc(input=[pre_state_expanded, pre_ids_emb],
size=decoder_size,
act='tanh')
current_state_with_lod = pd.lod_reset(x=current_state, y=pre_score)
# use score to do beam search
current_score = pd.fc(input=current_state_with_lod,
size=target_dict_dim,
act='softmax')
topk_scores, topk_indices = pd.topk(current_score, k=beam_size)
# calculate accumulated scores after topk to reduce computation cost
accu_scores = pd.elementwise_add(
x=pd.log(topk_scores), y=pd.reshape(
pre_score, shape=[-1]), axis=0)
selected_ids, selected_scores = pd.beam_search(
pre_ids,
pre_score,
topk_indices,
accu_scores,
beam_size,
end_id=10,
level=0)
pd.increment(x=counter, value=1, in_place=True)
# update the memories
pd.array_write(current_state, array=state_array, i=counter)
pd.array_write(selected_ids, array=ids_array, i=counter)
pd.array_write(selected_scores, array=scores_array, i=counter)
# update the break condition: up to the max length or all candidates of
# source sentences have ended.
length_cond = pd.less_than(x=counter, y=array_len)
finish_cond = pd.logical_not(pd.is_empty(x=selected_ids))
pd.logical_and(x=length_cond, y=finish_cond, out=cond)
translation_ids, translation_scores = pd.beam_search_decode(
ids=ids_array, scores=scores_array, beam_size=beam_size, end_id=10)
# return init_ids, init_scores
return translation_ids, translation_scores
def train_program(is_sparse):
context = encoder(is_sparse)
rnn_out = train_decoder(context, is_sparse)
label = pd.data(
name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
cost = pd.cross_entropy(input=rnn_out, label=label)
avg_cost = pd.mean(cost)
return avg_cost
def optimizer_func():
return fluid.optimizer.Adagrad(
learning_rate=1e-4,
regularization=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.1))
def train(use_cuda, is_sparse, is_local=True):
EPOCH_NUM = 1
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=batch_size)
feed_order = [
'src_word_id', 'target_language_word', 'target_language_next_word'
]
def event_handler(event):
if isinstance(event, EndStepEvent):
print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))
if event.step == 10:
trainer.stop()
trainer = Trainer(
train_func=partial(train_program, is_sparse),
place=place,
optimizer_func=optimizer_func)
trainer.train(
reader=train_reader,
num_epochs=EPOCH_NUM,
event_handler=event_handler,
feed_order=feed_order)
def decode_main(use_cuda, is_sparse):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
context = encoder(is_sparse)
translation_ids, translation_scores = decode(context, is_sparse)
exe = Executor(place)
exe.run(framework.default_startup_program())
init_ids_data = np.array([1 for _ in range(batch_size)], dtype='int64')
init_scores_data = np.array(
[1. for _ in range(batch_size)], dtype='float32')
init_ids_data = init_ids_data.reshape((batch_size, 1))
init_scores_data = init_scores_data.reshape((batch_size, 1))
init_recursive_seq_lens = [1] * batch_size
init_recursive_seq_lens = [init_recursive_seq_lens, init_recursive_seq_lens]
init_ids = fluid.create_lod_tensor(init_ids_data, init_recursive_seq_lens,
place)
init_scores = fluid.create_lod_tensor(init_scores_data,
init_recursive_seq_lens, place)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=batch_size)
feed_order = ['src_word_id']
feed_list = [
framework.default_main_program().global_block().var(var_name)
for var_name in feed_order
]
feeder = fluid.DataFeeder(feed_list, place)
for data in train_data():
feed_dict = feeder.feed([[x[0]] for x in data])
feed_dict['init_ids'] = init_ids
feed_dict['init_scores'] = init_scores
result_ids, result_scores = exe.run(
framework.default_main_program(),
feed=feed_dict,
fetch_list=[translation_ids, translation_scores],
return_numpy=False)
print(result_ids.recursive_sequence_lengths())
break
class TestMachineTranslation(unittest.TestCase):
pass
@contextlib.contextmanager
def scope_prog_guard():
prog = fluid.Program()
startup_prog = fluid.Program()
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
yield
def inject_test_train(use_cuda, is_sparse):
f_name = 'test_{0}_{1}_train'.format('cuda' if use_cuda else 'cpu', 'sparse'
if is_sparse else 'dense')
def f(*args):
with scope_prog_guard():
train(use_cuda, is_sparse)
setattr(TestMachineTranslation, f_name, f)
def inject_test_decode(use_cuda, is_sparse, decorator=None):
f_name = 'test_{0}_{1}_decode'.format('cuda'
if use_cuda else 'cpu', 'sparse'
if is_sparse else 'dense')
def f(*args):
with scope_prog_guard():
decode_main(use_cuda, is_sparse)
if decorator is not None:
f = decorator(f)
setattr(TestMachineTranslation, f_name, f)
for _use_cuda_ in (False, True):
for _is_sparse_ in (False, True):
inject_test_train(_use_cuda_, _is_sparse_)
for _use_cuda_ in (False, True):
for _is_sparse_ in (False, True):
_decorator_ = None
if _use_cuda_:
_decorator_ = unittest.skip(
reason='Beam Search does not support CUDA!')
inject_test_decode(
is_sparse=_is_sparse_, use_cuda=_use_cuda_, decorator=_decorator_)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/book/high-level-api/test_recognize_digits_conv_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import sys
import paddle.fluid as fluid
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import paddle.fluid.core as core
import paddle
import numpy
import math
import sys
import os
BATCH_SIZE = 64
def inference_program():
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
return prediction
def train_program():
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
predict = inference_program()
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
acc = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, acc]
def optimizer_func():
return fluid.optimizer.Adam(learning_rate=0.001)
def train(use_cuda, train_program, parallel, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = Trainer(
train_func=train_program,
place=place,
optimizer_func=optimizer_func,
parallel=parallel)
def event_handler(event):
if isinstance(event, EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['img', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(params_dirname)
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, EndStepEvent):
print(
("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch,
list(map(numpy.array, event.metrics)))))
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['img', 'label'])
def infer(use_cuda, inference_program, parallel, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(
infer_func=inference_program,
param_path=params_dirname,
place=place,
parallel=parallel)
batch_size = 1
tensor_img = numpy.random.uniform(-1.0, 1.0,
[batch_size, 1, 28, 28]).astype("float32")
results = inferencer.infer({'img': tensor_img})
print("infer results: ", results[0])
def main(use_cuda, parallel):
params_dirname = "recognize_digits_conv.inference.model"
# call train() with is_local argument to run distributed train
os.environ['CPU_NUM'] = str(4)
train(
use_cuda=use_cuda,
train_program=train_program,
params_dirname=params_dirname,
parallel=parallel)
# FIXME(zcd): in the inference stage, the number of
# input data is one, it is not appropriate to use parallel.
if parallel and use_cuda:
return
os.environ['CPU_NUM'] = str(1)
infer(
use_cuda=use_cuda,
inference_program=inference_program,
params_dirname=params_dirname,
parallel=parallel)
if __name__ == '__main__':
for use_cuda in (False, True):
for parallel in (False, True):
if use_cuda and not core.is_compiled_with_cuda():
continue
main(use_cuda=use_cuda, parallel=parallel)
python/paddle/fluid/tests/book/high-level-api/test_recognize_digits_mlp_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import sys
import paddle.fluid as fluid
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import paddle.fluid.core as core
import paddle
import numpy
import math
import sys
import os
BATCH_SIZE = 64
def inference_program():
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
hidden = fluid.layers.fc(input=img, size=200, act='tanh')
hidden = fluid.layers.fc(input=hidden, size=200, act='tanh')
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
return prediction
def train_program():
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
predict = inference_program()
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
acc = fluid.layers.accuracy(input=predict, label=label)
return [avg_cost, acc]
def optimizer_func():
return fluid.optimizer.Adam(learning_rate=0.001)
def train(use_cuda, train_program, params_dirname, parallel):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = Trainer(
train_func=train_program,
place=place,
optimizer_func=optimizer_func,
parallel=parallel)
def event_handler(event):
if isinstance(event, EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['img', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(params_dirname)
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['img', 'label'])
def infer(use_cuda, inference_program, parallel, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(
infer_func=inference_program,
param_path=params_dirname,
place=place,
parallel=parallel)
batch_size = 1
tensor_img = numpy.random.uniform(-1.0, 1.0,
[batch_size, 1, 28, 28]).astype("float32")
results = inferencer.infer({'img': tensor_img})
print("infer results: ", results[0])
def main(use_cuda, parallel):
params_dirname = "recognize_digits_mlp.inference.model"
# call train() with is_local argument to run distributed train
os.environ['CPU_NUM'] = str(4)
train(
use_cuda=use_cuda,
train_program=train_program,
params_dirname=params_dirname,
parallel=parallel)
# FIXME(zcd): in the inference stage, the number of
# input data is one, it is not appropriate to use parallel.
if parallel and use_cuda:
return
os.environ['CPU_NUM'] = str(1)
infer(
use_cuda=use_cuda,
inference_program=inference_program,
params_dirname=params_dirname,
parallel=parallel)
if __name__ == '__main__':
for use_cuda in (False, True):
for parallel in (False, True):
if use_cuda and not core.is_compiled_with_cuda():
continue
main(use_cuda=use_cuda, parallel=parallel)
python/paddle/fluid/tests/book/high-level-api/test_recommender_system_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import math
import sys
import numpy as np
import paddle
import paddle.fluid as fluid
import sys
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import paddle.fluid.layers as layers
import paddle.fluid.nets as nets
IS_SPARSE = True
USE_GPU = False
BATCH_SIZE = 256
def get_usr_combined_features():
# FIXME(dzh) : old API integer_value(10) may have range check.
# currently we don't have user configurated check.
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
param_attr='user_table',
is_sparse=IS_SPARSE)
usr_fc = layers.fc(input=usr_emb, size=32)
USR_GENDER_DICT_SIZE = 2
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
is_sparse=IS_SPARSE)
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
param_attr='age_table')
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
is_sparse=IS_SPARSE)
usr_job_fc = layers.fc(input=usr_job_emb, size=16)
concat_embed = layers.concat(
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return usr_combined_features
def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
param_attr='movie_table',
is_sparse=IS_SPARSE)
mov_fc = layers.fc(input=mov_emb, size=32)
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
input=mov_categories_emb, pool_type="sum")
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
input=mov_title_emb,
num_filters=32,
filter_size=3,
act="tanh",
pool_type="sum")
concat_embed = layers.concat(
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
# FIXME(dzh) : need tanh operator
mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return mov_combined_features
def inference_program():
usr_combined_features = get_usr_combined_features()
mov_combined_features = get_mov_combined_features()
inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
scale_infer = layers.scale(x=inference, scale=5.0)
return scale_infer
def train_program():
scale_infer = inference_program()
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(square_cost)
return [avg_cost, scale_infer]
def optimizer_func():
return fluid.optimizer.SGD(learning_rate=0.2)
def train(use_cuda, train_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = Trainer(
train_func=train_program, place=place, optimizer_func=optimizer_func)
feed_order = [
'user_id', 'gender_id', 'age_id', 'job_id', 'movie_id', 'category_id',
'movie_title', 'score'
]
def event_handler(event):
if isinstance(event, EndStepEvent):
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
avg_cost_set = trainer.test(
reader=test_reader, feed_order=feed_order)
# get avg cost
avg_cost = np.array(avg_cost_set).mean()
print("avg_cost: %s" % avg_cost)
if float(avg_cost) < 4: # Smaller value to increase CI speed
trainer.save_params(params_dirname)
trainer.stop()
else:
print(
('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
float(avg_cost))))
if math.isnan(float(avg_cost)):
sys.exit("got NaN loss, training failed.")
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=feed_order)
def infer(use_cuda, inference_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(
inference_program, param_path=params_dirname, place=place)
# Use the first data from paddle.dataset.movielens.test() as input.
# Use create_lod_tensor(data, recursive_sequence_lengths, place) API
# to generate LoD Tensor where `data` is a list of sequences of index
# numbers, `recursive_sequence_lengths` is the length-based level of detail
# (lod) info associated with `data`.
# For example, data = [[10, 2, 3], [2, 3]] means that it contains
# two sequences of indexes, of length 3 and 2, respectively.
# Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
# level of detail info, indicating that `data` consists of two sequences
# of length 3 and 2, respectively.
user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)
category_id = fluid.create_lod_tensor(
[np.array(
[10, 8, 9], dtype='int64')], [[3]], place)
movie_title = fluid.create_lod_tensor(
[np.array(
[1069, 4140, 2923, 710, 988], dtype='int64')], [[5]], place)
results = inferencer.infer(
{
'user_id': user_id,
'gender_id': gender_id,
'age_id': age_id,
'job_id': job_id,
'movie_id': movie_id,
'category_id': category_id,
'movie_title': movie_title
},
return_numpy=False)
print("infer results: ", np.array(results[0]))
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
params_dirname = "recommender_system.inference.model"
train(
use_cuda=use_cuda,
train_program=train_program,
params_dirname=params_dirname)
infer(
use_cuda=use_cuda,
inference_program=inference_program,
params_dirname=params_dirname)
if __name__ == '__main__':
main(USE_GPU)
python/paddle/fluid/tests/book/high-level-api/test_understand_sentiment_conv_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import sys
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
from functools import partial
import numpy as np
CLASS_DIM = 2
EMB_DIM = 128
HID_DIM = 512
BATCH_SIZE = 128
def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
return prediction
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
return net
def train_program(word_dict):
prediction = inference_program(word_dict)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
return [avg_cost, accuracy]
def optimizer_func():
return fluid.optimizer.Adagrad(learning_rate=0.002)
def train(use_cuda, train_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
trainer = Trainer(
train_func=partial(train_program, word_dict),
place=place,
optimizer_func=optimizer_func)
def event_handler(event):
if isinstance(event, EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(params_dirname)
trainer.stop()
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, list(map(np.array, event.metrics))))
if event.step == 1: # Run 2 iterations to speed CI
trainer.save_params(params_dirname)
trainer.stop()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['words', 'label'])
def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
inferencer = Inferencer(
infer_func=partial(inference_program, word_dict),
param_path=params_dirname,
place=place)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
tensor_words = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=len(word_dict) - 1)
results = inferencer.infer({'words': tensor_words})
print("infer results: ", results)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
params_dirname = "understand_sentiment_conv.inference.model"
train(use_cuda, train_program, params_dirname)
infer(use_cuda, inference_program, params_dirname)
if __name__ == '__main__':
for use_cuda in (False, True):
main(use_cuda=use_cuda)
python/paddle/fluid/tests/book/high-level-api/test_understand_sentiment_dynamic_rnn_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import sys
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
from functools import partial
import numpy as np
CLASS_DIM = 2
EMB_DIM = 128
BATCH_SIZE = 128
LSTM_SIZE = 128
def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size):
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
sentence = fluid.layers.fc(input=emb, size=lstm_size, act='tanh')
rnn = fluid.layers.DynamicRNN()
with rnn.block():
word = rnn.step_input(sentence)
prev_hidden = rnn.memory(value=0.0, shape=[lstm_size])
prev_cell = rnn.memory(value=0.0, shape=[lstm_size])
def gate_common(ipt, hidden, size):
gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True)
gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
return gate0 + gate1
forget_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
lstm_size))
input_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
lstm_size))
output_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
lstm_size))
cell_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
lstm_size))
cell = forget_gate * prev_cell + input_gate * cell_gate
hidden = output_gate * fluid.layers.tanh(x=cell)
rnn.update_memory(prev_cell, cell)
rnn.update_memory(prev_hidden, hidden)
rnn.output(hidden)
last = fluid.layers.sequence_last_step(rnn())
prediction = fluid.layers.fc(input=last, size=class_dim, act="softmax")
return prediction
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
pred = dynamic_rnn_lstm(data, dict_dim, CLASS_DIM, EMB_DIM, LSTM_SIZE)
return pred
def train_program(word_dict):
prediction = inference_program(word_dict)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
return [avg_cost, accuracy]
def optimizer_func():
return fluid.optimizer.Adagrad(learning_rate=0.002)
def train(use_cuda, train_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
trainer = Trainer(
train_func=partial(train_program, word_dict),
place=place,
optimizer_func=optimizer_func)
def event_handler(event):
if isinstance(event, EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(params_dirname)
trainer.stop()
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, list(map(np.array, event.metrics))))
if event.step == 1: # Run 2 iterations to speed CI
trainer.save_params(params_dirname)
trainer.stop()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['words', 'label'])
def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
inferencer = Inferencer(
infer_func=partial(inference_program, word_dict),
param_path=params_dirname,
place=place)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
tensor_words = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=len(word_dict) - 1)
results = inferencer.infer({'words': tensor_words})
print("infer results: ", results)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
params_dirname = "understand_sentiment_conv.inference.model"
train(use_cuda, train_program, params_dirname)
infer(use_cuda, inference_program, params_dirname)
if __name__ == '__main__':
for use_cuda in (False, True):
main(use_cuda=use_cuda)
python/paddle/fluid/tests/book/high-level-api/test_understand_sentiment_stacked_lstm_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import sys
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
from functools import partial
import numpy as np
CLASS_DIM = 2
EMB_DIM = 128
HID_DIM = 512
STACKED_NUM = 3
BATCH_SIZE = 128
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
assert stacked_num % 2 == 1
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = fluid.layers.fc(input=inputs, size=hid_dim)
lstm, cell = fluid.layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
inputs = [fc, lstm]
fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
prediction = fluid.layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
return prediction
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM,
STACKED_NUM)
return net
def train_program(word_dict):
prediction = inference_program(word_dict)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
return [avg_cost, accuracy]
def optimizer_func():
return fluid.optimizer.Adagrad(learning_rate=0.002)
def train(use_cuda, train_program, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
trainer = Trainer(
train_func=partial(train_program, word_dict),
place=place,
optimizer_func=optimizer_func)
def event_handler(event):
if isinstance(event, EndEpochEvent):
test_reader = paddle.batch(
paddle.dataset.imdb.test(word_dict),
batch_size=BATCH_SIZE,
drop_last=False)
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
if acc > 0.2: # Smaller value to increase CI speed
trainer.save_params(params_dirname)
trainer.stop()
else:
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
event.epoch + 1, avg_cost, acc))
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
elif isinstance(event, EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, list(map(np.array, event.metrics))))
if event.step == 1: # Run 2 iterations to speed CI
trainer.save_params(params_dirname)
trainer.stop()
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE,
drop_last=False)
trainer.train(
num_epochs=1,
event_handler=event_handler,
reader=train_reader,
feed_order=['words', 'label'])
def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
word_dict = paddle.dataset.imdb.word_dict()
inferencer = Inferencer(
infer_func=partial(inference_program, word_dict),
param_path=params_dirname,
place=place)
# Setup input by creating LoDTensor to represent sequence of words.
# Here each word is the basic element of the LoDTensor and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
# which has only one level of detail. Then the created LoDTensor will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[3, 4, 2]]
base_shape = [1]
# The range of random integers is [low, high]
tensor_words = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=len(word_dict) - 1)
results = inferencer.infer({'words': tensor_words})
print("infer results: ", results)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
params_dirname = "understand_sentiment_stacked_lstm.inference.model"
train(use_cuda, train_program, params_dirname)
infer(use_cuda, inference_program, params_dirname)
if __name__ == '__main__':
for use_cuda in (False, True):
main(use_cuda=use_cuda)
python/paddle/fluid/tests/book/high-level-api/test_word2vec_new_api.py 已删除 100644 → 0
浏览文件 @ e5985675
# 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.
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import sys
try:
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *
except ImportError:
print(
"In the fluid 1.0, the trainer and inferencer are moving to paddle.fluid.contrib",
file=sys.stderr)
from paddle.fluid.trainer import *
from paddle.fluid.inferencer import *
import numpy as np
import math
import sys
from functools import partial
PASS_NUM = 100
EMBED_SIZE = 32
HIDDEN_SIZE = 256
N = 5
BATCH_SIZE = 32
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
def inference_program(is_sparse):
first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
embed_first = fluid.layers.embedding(
input=first_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=is_sparse,
param_attr='shared_w')
embed_second = fluid.layers.embedding(
input=second_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=is_sparse,
param_attr='shared_w')
embed_third = fluid.layers.embedding(
input=third_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=is_sparse,
param_attr='shared_w')
embed_forth = fluid.layers.embedding(
input=forth_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=is_sparse,
param_attr='shared_w')
concat_embed = fluid.layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
hidden1 = fluid.layers.fc(input=concat_embed,
size=HIDDEN_SIZE,
act='sigmoid')
predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
return predict_word
def train_program(is_sparse):
# The declaration of 'next_word' must be after the invoking of inference_program,
# or the data input order of train program would be [next_word, firstw, secondw,
# thirdw, forthw], which is not correct.
predict_word = inference_program(is_sparse)
next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = fluid.layers.mean(cost)
return avg_cost
def optimizer_func():
return fluid.optimizer.SGD(learning_rate=0.001)
def train(use_cuda, train_program, params_dirname):
train_reader = paddle.batch(
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
def event_handler(event):
if isinstance(event, EndStepEvent):
outs = trainer.test(
reader=test_reader,
feed_order=['firstw', 'secondw', 'thirdw', 'forthw', 'nextw'])
avg_cost = outs[0]
print("loss= ", avg_cost)
if avg_cost < 10.0:
trainer.save_params(params_dirname)
trainer.stop()
if math.isnan(avg_cost):
sys.exit("got NaN loss, training failed.")
trainer = Trainer(
train_func=train_program, optimizer_func=optimizer_func, place=place)
trainer.train(
reader=train_reader,
num_epochs=1,
event_handler=event_handler,
feed_order=['firstw', 'secondw', 'thirdw', 'forthw', 'nextw'])
def infer(use_cuda, inference_program, params_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
inferencer = Inferencer(
infer_func=inference_program, param_path=params_dirname, place=place)
# Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
# is simply an index to look up for the corresponding word vector and hence
# the shape of word (base_shape) should be [1]. The recursive_sequence_lengths,
# which is length-based level of detail (lod) of each LoDTensor, should be [[1]]
# meaning there is only one level of detail and there is only one sequence of
# one word on this level.
# Note that recursive_sequence_lengths should be a list of lists.
recursive_seq_lens = [[1]]
base_shape = [1]
# The range of random integers is [low, high]
first_word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
second_word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
third_word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
fourth_word = fluid.create_random_int_lodtensor(
recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
result = inferencer.infer(
{
'firstw': first_word,
'secondw': second_word,
'thirdw': third_word,
'forthw': fourth_word
},
return_numpy=False)
print(np.array(result[0]))
def main(use_cuda, is_sparse):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
params_dirname = "word2vec.inference.model"
train(
use_cuda=use_cuda,
train_program=partial(train_program, is_sparse),
params_dirname=params_dirname)
infer(
use_cuda=use_cuda,
inference_program=partial(inference_program, is_sparse),
params_dirname=params_dirname)
if __name__ == '__main__':
for use_cuda in (False, True):
for is_sparse in (False, True):
main(use_cuda=use_cuda, is_sparse=is_sparse)
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