提交 d60116db 编写于 作者: D dangqingqing

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into srl_api_v2

import paddle.v2 as paddle
import mnist_util
def train_reader():
train_file = './data/raw_data/train'
generator = mnist_util.read_from_mnist(train_file)
for item in generator:
yield item
def main():
paddle.init(use_gpu=False, trainer_count=1)
......@@ -40,11 +31,13 @@ def main():
trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
trainer.train(
train_data_reader=train_reader,
reader=paddle.reader.batched(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=32),
cost=cost,
parameters=parameters,
event_handler=event_handler,
batch_size=32, # batch size should be refactor in Data reader
reader_dict={images.name: 0,
label.name: 1})
......
......@@ -92,7 +92,6 @@ void CosSimForward<DEVICE_TYPE_GPU>(GpuMatrix& out_mat,
CHECK(in1_mat.useGpu_ == true && in2_mat.useGpu_ == true)
<< "Matrix type are not GPU";
size_t num_samples = out_mat.getHeight();
size_t dim = in1_mat.getWidth();
real* out = out_mat.getData();
const real* x = in1_mat.getData();
......
......@@ -4,7 +4,7 @@ set(OUTPUT_DIR
file(GLOB TRAINER_PY_FILES . ./paddle/trainer/*.py)
file(GLOB HELPERS_PY_FILES . ./paddle/trainer_config_helpers/*.py)
file(GLOB UTILS_PY_FILES . ./paddle/utils/*.py)
file(GLOB V2_PY_FILES . ./paddle/v2/*.py)
file(GLOB_RECURSE V2_PY_FILES ./paddle/v2/ *.py)
set(PY_FILES paddle/__init__.py
${TRAINER_PY_FILES}
......@@ -24,7 +24,7 @@ add_custom_target(paddle_python ALL DEPENDS
${OUTPUT_DIR}/.timestamp)
add_subdirectory(paddle/trainer_config_helpers/tests)
add_subdirectory(paddle/reader/tests)
add_subdirectory(paddle/v2/reader/tests)
add_subdirectory(paddle/v2/tests)
install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/
......
add_test(NAME reader_decorator_test
COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/decorator_test.py
WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)
add_test(NAME reader_creator_test
COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/creator_test.py
WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)
......@@ -20,13 +20,16 @@ import event
import data_type
import topology
import data_feeder
from . import dataset
from . import reader
import attr
import pooling
import py_paddle.swig_paddle as api
__all__ = [
'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
'event', 'data_type', 'attr', 'pooling', 'data_feeder', 'topology'
'event', 'data_type', 'attr', 'pooling', 'data_feeder', 'dataset', 'reader',
'topology'
]
......
"""
MNIST dataset.
"""
import numpy
import paddle.v2.dataset.common
import subprocess
import numpy
import platform
__all__ = ['train', 'test']
URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/'
......@@ -20,12 +20,19 @@ TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
def reader_creator(image_filename, label_filename, buffer_size):
def reader():
if platform.system() == 'Darwin':
zcat_cmd = 'gzcat'
elif platform.system() == 'Linux':
zcat_cmd = 'zcat'
else:
raise NotImplementedError()
# According to http://stackoverflow.com/a/38061619/724872, we
# cannot use standard package gzip here.
m = subprocess.Popen(["zcat", image_filename], stdout=subprocess.PIPE)
m = subprocess.Popen([zcat_cmd, image_filename], stdout=subprocess.PIPE)
m.stdout.read(16) # skip some magic bytes
l = subprocess.Popen(["zcat", label_filename], stdout=subprocess.PIPE)
l = subprocess.Popen([zcat_cmd, label_filename], stdout=subprocess.PIPE)
l.stdout.read(8) # skip some magic bytes
while True:
......
......@@ -14,7 +14,7 @@
__all__ = [
'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
'ComposeNotAligned'
'ComposeNotAligned', 'batched'
]
from Queue import Queue
......@@ -191,3 +191,25 @@ def buffered(reader, size):
e = q.get()
return data_reader
def batched(reader, batch_size):
"""
Create a batched reader.
:param reader: the data reader to read from.
:param batch_size: batch_size
:return: the batched reader.
"""
def batched_reader():
r = reader()
batch = []
for instance in r:
batch.append(instance)
if len(batch) == batch_size:
yield batch
batch = []
if batch:
yield batch
return batched_reader
add_test(NAME reader_tests
COMMAND bash ${PROJ_ROOT}/python/paddle/v2/reader/tests/run_tests.sh
${PYTHON_EXECUTABLE})
......@@ -11,17 +11,19 @@
# 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.
import os
import unittest
import paddle.reader.creator
import numpy as np
import os
import paddle.v2.reader.creator
class TestNumpyArray(unittest.TestCase):
def test_numpy_array(self):
l = [[1, 2, 3], [4, 5, 6]]
x = np.array(l, np.int32)
reader = paddle.reader.creator.np_array(x)
reader = paddle.v2.reader.creator.np_array(x)
for idx, e in enumerate(reader()):
self.assertItemsEqual(e, l[idx])
......@@ -29,7 +31,7 @@ class TestNumpyArray(unittest.TestCase):
class TestTextFile(unittest.TestCase):
def test_text_file(self):
path = os.path.join(os.path.dirname(__file__), "test_data_creator.txt")
reader = paddle.reader.creator.text_file(path)
reader = paddle.v2.reader.creator.text_file(path)
for idx, e in enumerate(reader()):
self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1))
......
......@@ -11,9 +11,10 @@
# 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.
import unittest
import paddle.reader
import time
import unittest
import paddle.v2.reader
def reader_creator_10(dur):
......@@ -37,7 +38,7 @@ class TestMap(unittest.TestCase):
yield "h"
yield "i"
r = paddle.reader.map_readers(tokenize, read)
r = paddle.v2.reader.map_readers(tokenize, read)
for i, e in enumerate(r()):
self.assertEqual(e, i)
......@@ -45,7 +46,7 @@ class TestMap(unittest.TestCase):
class TestBuffered(unittest.TestCase):
def test_read(self):
for size in range(20):
b = paddle.reader.buffered(reader_creator_10(0), size)
b = paddle.v2.reader.buffered(reader_creator_10(0), size)
c = 0
for i in b():
self.assertEqual(i, c)
......@@ -54,7 +55,7 @@ class TestBuffered(unittest.TestCase):
def test_buffering(self):
# read have 30ms delay.
b = paddle.reader.buffered(reader_creator_10(0.03), 10)
b = paddle.v2.reader.buffered(reader_creator_10(0.03), 10)
last_time = time.time()
for idx, i in enumerate(b()):
elapsed_time = time.time() - last_time
......@@ -68,17 +69,17 @@ class TestBuffered(unittest.TestCase):
class TestCompose(unittest.TestCase):
def test_compse(self):
reader = paddle.reader.compose(
reader = paddle.v2.reader.compose(
reader_creator_10(0), reader_creator_10(0))
for idx, e in enumerate(reader()):
self.assertEqual(e, (idx, idx))
def test_compose_not_aligned(self):
total = 0
reader = paddle.reader.compose(
paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)),
reader = paddle.v2.reader.compose(
paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0)),
reader_creator_10(0))
with self.assertRaises(paddle.reader.ComposeNotAligned):
with self.assertRaises(paddle.v2.reader.ComposeNotAligned):
for e in reader():
total += 1
# expecting 10, not 20
......@@ -86,8 +87,8 @@ class TestCompose(unittest.TestCase):
def test_compose_not_aligned_no_check(self):
total = 0
reader = paddle.reader.compose(
paddle.reader.chain(reader_creator_10(0), reader_creator_10(0)),
reader = paddle.v2.reader.compose(
paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0)),
reader_creator_10(0),
check_alignment=False)
for e in reader():
......@@ -98,7 +99,7 @@ class TestCompose(unittest.TestCase):
class TestChain(unittest.TestCase):
def test_chain(self):
c = paddle.reader.chain(reader_creator_10(0), reader_creator_10(0))
c = paddle.v2.reader.chain(reader_creator_10(0), reader_creator_10(0))
idx = 0
for e in c():
self.assertEqual(e, idx % 10)
......@@ -111,7 +112,7 @@ class TestShuffle(unittest.TestCase):
case = [(0, True), (1, True), (10, False), (100, False)]
a = reader_creator_10(0)
for size, checkEq in case:
s = paddle.reader.shuffle(a, size)
s = paddle.v2.reader.shuffle(a, size)
total = 0
for idx, e in enumerate(s()):
if checkEq:
......
#!/bin/bash
# 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.
pushd `dirname $0` > /dev/null
SCRIPTPATH=$PWD
popd > /dev/null
cd $SCRIPTPATH
$1 -m pip install ../../../../../paddle/dist/*.whl
test_list="creator_test.py decorator_test.py"
export PYTHONPATH=$PWD/../../../../../python/
for fn in $test_list
do
echo "test $fn"
$1 $fn
if [ $? -ne 0 ]; then
exit 1
fi
done
......@@ -27,19 +27,13 @@ class ITrainer(object):
The interface of Trainer. The only exposed method is `train`.
"""
def train(self,
train_data_reader,
cost,
parameters,
test_data_reader=None,
event_handler=None):
def train(self, reader, topology, parameters, event_handler=None):
"""
train method.
:param train_data_reader:
:param cost:
:param reader:
:param topology:
:param parameters:
:param test_data_reader:
:param event_handler:
:return:
"""
......@@ -61,26 +55,22 @@ class SGD(ITrainer):
self.__optimizer__ = update_equation
def train(self,
train_data_reader,
reader,
cost,
parameters,
num_passes=1,
test_data_reader=None,
event_handler=None,
batch_size=32,
reader_dict=None):
"""
Training method. Will train num_passes of input data.
:param train_data_reader:
:param cost: cost layers, to be optimized.
:param reader:
:param topology: Network Topology, use one or more Layers to represent it.
:param parameters: The parameter pools.
:param num_passes: The total train passes.
:param test_data_reader:
:param event_handler: Event handler. A method will be invoked when event
occurred.
:type event_handler: (BaseEvent) => None
:param batch_size: Not important, will be removed after data refactor.
:return:
"""
if event_handler is None:
......@@ -112,9 +102,9 @@ class SGD(ITrainer):
event_handler(v2_event.BeginPass(pass_id))
pass_evaluator.start()
updater.startPass()
for batch_id, data_batch in enumerate(
__data_reader_to_batch__(train_data_reader, batch_size,
topology)):
for batch_id, data_batch in enumerate(reader()):
pass_type = updater.startBatch(len(data_batch))
gm.forwardBackward(feeder(data_batch), out_args, pass_type)
batch_evaluator.start()
event_handler(
v2_event.BeginIteration(
......@@ -144,56 +134,19 @@ class SGD(ITrainer):
gm.finish()
def __data_reader_to_batch__(reader, batch_size, topology):
"""
This function is not important, and will be removed when data refactored.
"""
def input_reorder(func):
for item in func():
retv = []
for __layer_name__ in topology.proto().input_layer_names:
retv.append(item[__layer_name__])
yield retv
return __generator_to_batch__(input_reorder(reader), batch_size=batch_size)
def __generator_to_batch__(generator, batch_size):
"""
This function is not important, and will be removed when data refactored.
"""
ret_val = list()
for each_item in generator:
ret_val.append(each_item)
if len(ret_val) == batch_size:
yield ret_val
ret_val = list()
if len(ret_val) != 0:
yield ret_val
def __check_train_args__(train_data_reader, topology, parameters,
test_data_reader, event_handler, **kwargs):
def __check_train_args__(reader, topology, parameters, event_handler, **kwargs):
"""
Check train function's argument types
"""
if not callable(train_data_reader) or not isinstance(train_data_reader(),
collections.Iterator):
raise ValueError('train_data_reader should be a function, '
'which can return a iterator')
if test_data_reader is not None:
if not callable(test_data_reader) or not isinstance(
test_data_reader(), collections.Iterator):
raise ValueError('test_data_reader should be a function, which can '
'return a iterator')
if not callable(reader) or not isinstance(reader(), collections.Iterator):
raise TypeError('train_data_reader should be a function, '
'which can return a iterator')
if not isinstance(topology, Topology):
raise ValueError('topology should be a model config')
raise TypeError('topology should be a model config')
if not isinstance(parameters, v2_parameters.Parameters):
raise ValueError('parameters should be a parameter pool')
raise TypeError('parameters should be a parameter pool')
if not callable(event_handler):
raise ValueError('event handler should be a function')
raise TypeError('event handler should be a function')
......@@ -5,7 +5,9 @@ packages=['paddle',
'paddle.trainer',
'paddle.trainer_config_helpers',
'paddle.utils',
'paddle.v2']
'paddle.v2',
'paddle.v2.dataset',
'paddle.v2.reader']
setup(name='paddle',
version='${PADDLE_VERSION}',
......
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