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Paddle
提交 559d3632 编写于 作者: M minqiyang
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Apply 2to3 to current paddle main python code

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Showing with 805 addition and 745 deletion
paddle/fluid/pybind/pybind.cc
浏览文件 @ 559d3632
......@@ -394,8 +394,10 @@ All parameter, weight, gradient are variables in Paddle.
InferenceOptimize(*(origin.Proto()), &pruned_desc);
return new ProgramDesc(pruned_desc);
});
m.def("empty_var_name", []() { return framework::kEmptyVarName; });
m.def("grad_var_suffix", []() { return framework::kGradVarSuffix; });
m.def("empty_var_name",
[]() { return std::string(framework::kEmptyVarName); });
m.def("grad_var_suffix",
[]() { return std::string(framework::kGradVarSuffix); });
m.def_submodule(
"var_names",
"The module will return special predefined variable name in Paddle")
......
python/paddle/dataset/cifar.py
浏览文件 @ 559d3632
......@@ -28,7 +28,7 @@ images per class.
"""
import cPickle
import pickle
import itertools
import numpy
import paddle.dataset.common
......@@ -48,7 +48,7 @@ def reader_creator(filename, sub_name, cycle=False):
data = batch['data']
labels = batch.get('labels', batch.get('fine_labels', None))
assert labels is not None
for sample, label in itertools.izip(data, labels):
for sample, label in zip(data, labels):
yield (sample / 255.0).astype(numpy.float32), int(label)
def reader():
......@@ -58,7 +58,7 @@ def reader_creator(filename, sub_name, cycle=False):
while True:
for name in names:
batch = cPickle.load(f.extractfile(name))
batch = pickle.load(f.extractfile(name))
for item in read_batch(batch):
yield item
if not cycle:
......
python/paddle/dataset/common.py
浏览文件 @ 559d3632
......@@ -20,9 +20,9 @@ import shutil
import sys
import importlib
import paddle.dataset
import cPickle
import pickle
import glob
import cPickle as pickle
import pickle as pickle
__all__ = [
'DATA_HOME',
......@@ -75,13 +75,13 @@ def download(url, module_name, md5sum, save_name=None):
retry_limit = 3
while not (os.path.exists(filename) and md5file(filename) == md5sum):
if os.path.exists(filename):
print "file md5", md5file(filename), md5sum
print(("file md5", md5file(filename), md5sum))
if retry < retry_limit:
retry += 1
else:
raise RuntimeError("Cannot download {0} within retry limit {1}".
format(url, retry_limit))
print "Cache file %s not found, downloading %s" % (filename, url)
print(("Cache file %s not found, downloading %s" % (filename, url)))
r = requests.get(url, stream=True)
total_length = r.headers.get('content-length')
......@@ -104,8 +104,9 @@ def download(url, module_name, md5sum, save_name=None):
def fetch_all():
for module_name in filter(lambda x: not x.startswith("__"),
dir(paddle.dataset)):
for module_name in [
x for x in dir(paddle.dataset) if not x.startswith("__")
]:
if "fetch" in dir(
importlib.import_module("paddle.dataset.%s" % module_name)):
getattr(
......@@ -114,8 +115,9 @@ def fetch_all():
def fetch_all_recordio(path):
for module_name in filter(lambda x: not x.startswith("__"),
dir(paddle.dataset)):
for module_name in [
x for x in dir(paddle.dataset) if not x.startswith("__")
]:
if "convert" in dir(
importlib.import_module("paddle.dataset.%s" % module_name)) and \
not module_name == "common":
......@@ -126,7 +128,7 @@ def fetch_all_recordio(path):
"convert")(ds_path)
def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
def split(reader, line_count, suffix="%05d.pickle", dumper=pickle.dump):
"""
you can call the function as:
......@@ -167,7 +169,7 @@ def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
def cluster_files_reader(files_pattern,
trainer_count,
trainer_id,
loader=cPickle.load):
loader=pickle.load):
"""
Create a reader that yield element from the given files, select
a file set according trainer count and trainer_id
......@@ -188,7 +190,7 @@ def cluster_files_reader(files_pattern,
my_file_list = []
for idx, fn in enumerate(file_list):
if idx % trainer_count == trainer_id:
print "append file: %s" % fn
print(("append file: %s" % fn))
my_file_list.append(fn)
for fn in my_file_list:
with open(fn, "r") as f:
......@@ -221,7 +223,7 @@ def convert(output_path, reader, line_count, name_prefix):
for l in lines:
# FIXME(Yancey1989):
# dumps with protocol: pickle.HIGHEST_PROTOCOL
writer.write(cPickle.dumps(l))
writer.write(pickle.dumps(l))
writer.close()
lines = []
......
python/paddle/dataset/conll05.py
浏览文件 @ 559d3632
......@@ -87,12 +87,12 @@ def corpus_reader(data_path, words_name, props_name):
sentences = []
labels = []
one_seg = []
for word, label in itertools.izip(words_file, props_file):
for word, label in zip(words_file, props_file):
word = word.strip()
label = label.strip().split()
if len(label) == 0: # end of sentence
for i in xrange(len(one_seg[0])):
for i in range(len(one_seg[0])):
a_kind_lable = [x[i] for x in one_seg]
labels.append(a_kind_lable)
......
python/paddle/dataset/flowers.py
浏览文件 @ 559d3632
......@@ -28,10 +28,10 @@ Graphics and Image Processing (2008)
http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
"""
import cPickle
import pickle
import itertools
import functools
from common import download
from .common import download
import tarfile
import scipy.io as scio
from paddle.dataset.image import *
......@@ -116,10 +116,10 @@ def reader_creator(data_file,
file = file.strip()
batch = None
with open(file, 'r') as f:
batch = cPickle.load(f)
batch = pickle.load(f)
data = batch['data']
labels = batch['label']
for sample, label in itertools.izip(data, batch['label']):
for sample, label in zip(data, batch['label']):
yield sample, int(label) - 1
if not cycle:
break
......
python/paddle/dataset/image.py
浏览文件 @ 559d3632
......@@ -36,7 +36,7 @@ except ImportError:
cv2 = None
import os
import tarfile
import cPickle
import pickle
__all__ = [
"load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
......@@ -86,10 +86,10 @@ def batch_images_from_tar(data_file,
output = {}
output['label'] = labels
output['data'] = data
cPickle.dump(
pickle.dump(
output,
open('%s/batch_%d' % (out_path, file_id), 'w'),
protocol=cPickle.HIGHEST_PROTOCOL)
protocol=pickle.HIGHEST_PROTOCOL)
file_id += 1
data = []
labels = []
......@@ -97,10 +97,10 @@ def batch_images_from_tar(data_file,
output = {}
output['label'] = labels
output['data'] = data
cPickle.dump(
pickle.dump(
output,
open('%s/batch_%d' % (out_path, file_id), 'w'),
protocol=cPickle.HIGHEST_PROTOCOL)
protocol=pickle.HIGHEST_PROTOCOL)
with open(meta_file, 'a') as meta:
for file in os.listdir(out_path):
......
python/paddle/dataset/imdb.py
浏览文件 @ 559d3632
......@@ -42,13 +42,13 @@ def tokenize(pattern):
# sequential access of member files, other than
# tarfile.extractfile, which does random access and might
# destroy hard disks.
tf = tarf.next()
tf = next(tarf)
while tf != None:
if bool(pattern.match(tf.name)):
# newline and punctuations removal and ad-hoc tokenization.
yield tarf.extractfile(tf).read().rstrip("\n\r").translate(
None, string.punctuation).lower().split()
tf = tarf.next()
tf = next(tarf)
def build_dict(pattern, cutoff):
......@@ -62,11 +62,11 @@ def build_dict(pattern, cutoff):
word_freq[word] += 1
# Not sure if we should prune less-frequent words here.
word_freq = filter(lambda x: x[1] > cutoff, word_freq.items())
word_freq = [x for x in list(word_freq.items()) if x[1] > cutoff]
dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
words, _ = list(zip(*dictionary))
word_idx = dict(zip(words, xrange(len(words))))
word_idx = dict(list(zip(words, list(range(len(words))))))
word_idx['<unk>'] = len(words)
return word_idx
......
python/paddle/dataset/imikolov.py
浏览文件 @ 559d3632
......@@ -64,11 +64,11 @@ def build_dict(min_word_freq=50):
# remove <unk> for now, since we will set it as last index
del word_freq['<unk>']
word_freq = filter(lambda x: x[1] > min_word_freq, word_freq.items())
word_freq = [x for x in list(word_freq.items()) if x[1] > min_word_freq]
word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
words, _ = list(zip(*word_freq_sorted))
word_idx = dict(zip(words, xrange(len(words))))
word_idx = dict(list(zip(words, list(range(len(words))))))
word_idx['<unk>'] = len(words)
return word_idx
......
python/paddle/dataset/mnist.py
浏览文件 @ 559d3632
......@@ -65,7 +65,7 @@ def reader_creator(image_filename, label_filename, buffer_size):
images = images / 255.0 * 2.0 - 1.0
for i in xrange(buffer_size):
for i in range(buffer_size):
yield images[i, :], int(labels[i])
finally:
try:
......
python/paddle/dataset/movielens.py
浏览文件 @ 559d3632
......@@ -187,7 +187,7 @@ def max_movie_id():
Get the maximum value of movie id.
"""
__initialize_meta_info__()
return reduce(__max_index_info__, MOVIE_INFO.viewvalues()).index
return reduce(__max_index_info__, list(MOVIE_INFO.values())).index
def max_user_id():
......@@ -195,7 +195,7 @@ def max_user_id():
Get the maximum value of user id.
"""
__initialize_meta_info__()
return reduce(__max_index_info__, USER_INFO.viewvalues()).index
return reduce(__max_index_info__, list(USER_INFO.values())).index
def __max_job_id_impl__(a, b):
......@@ -210,7 +210,7 @@ def max_job_id():
Get the maximum value of job id.
"""
__initialize_meta_info__()
return reduce(__max_job_id_impl__, USER_INFO.viewvalues()).job_id
return reduce(__max_job_id_impl__, list(USER_INFO.values())).job_id
def movie_categories():
......@@ -243,7 +243,7 @@ def unittest():
for test_count, _ in enumerate(test()()):
pass
print train_count, test_count
print((train_count, test_count))
def fetch():
......
python/paddle/dataset/mq2007.py
浏览文件 @ 559d3632
......@@ -26,7 +26,7 @@ http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ20
import os
import functools
import rarfile
from common import download
from .common import download
import numpy as np
# URL = "http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar"
......@@ -330,4 +330,4 @@ if __name__ == "__main__":
mytest = functools.partial(
__reader__, filepath="MQ2007/MQ2007/Fold1/sample", format="listwise")
for label, query in mytest():
print label, query
print((label, query))
python/paddle/dataset/sentiment.py
浏览文件 @ 559d3632
......@@ -43,11 +43,11 @@ def download_data_if_not_yet():
nltk.data.path.append(paddle.dataset.common.DATA_HOME)
movie_reviews.categories()
except LookupError:
print "Downloading movie_reviews data set, please wait....."
print("Downloading movie_reviews data set, please wait.....")
nltk.download(
'movie_reviews', download_dir=paddle.dataset.common.DATA_HOME)
print "Download data set success....."
print "Path is " + nltk.data.find('corpora/movie_reviews').path
print("Download data set success.....")
print(("Path is " + nltk.data.find('corpora/movie_reviews').path))
def get_word_dict():
......@@ -64,7 +64,7 @@ def get_word_dict():
for field in movie_reviews.fileids(category):
for words in movie_reviews.words(field):
word_freq_dict[words] += 1
words_sort_list = word_freq_dict.items()
words_sort_list = list(word_freq_dict.items())
words_sort_list.sort(cmp=lambda a, b: b[1] - a[1])
for index, word in enumerate(words_sort_list):
words_freq_sorted.append((word[0], index))
......@@ -80,7 +80,8 @@ def sort_files():
files_list = list()
neg_file_list = movie_reviews.fileids('neg')
pos_file_list = movie_reviews.fileids('pos')
files_list = list(chain.from_iterable(zip(neg_file_list, pos_file_list)))
files_list = list(
chain.from_iterable(list(zip(neg_file_list, pos_file_list))))
return files_list
......
python/paddle/dataset/tests/common_test.py
浏览文件 @ 559d3632
......@@ -36,7 +36,7 @@ class TestCommon(unittest.TestCase):
def test_split(self):
def test_reader():
def reader():
for x in xrange(10):
for x in range(10):
yield x
return reader
......@@ -49,7 +49,7 @@ class TestCommon(unittest.TestCase):
def test_cluster_file_reader(self):
_, temp_path = tempfile.mkstemp()
for x in xrange(5):
for x in range(5):
with open(temp_path + '/%05d.test' % x) as f:
f.write('%d\n' % x)
reader = paddle.dataset.common.cluster_files_reader(
......@@ -63,7 +63,7 @@ class TestCommon(unittest.TestCase):
def test_reader():
def reader():
for x in xrange(record_num):
for x in range(record_num):
yield x
return reader
......
python/paddle/dataset/tests/imikolov_test.py
浏览文件 @ 559d3632
......@@ -59,7 +59,7 @@ class TestMikolov(unittest.TestCase):
self.assertEqual(first_line, read_line)
def test_total(self):
_, idx = zip(*WORD_DICT.items())
_, idx = list(zip(*list(WORD_DICT.items())))
self.assertEqual(sorted(idx)[-1], len(WORD_DICT) - 1)
......
python/paddle/dataset/tests/test_sentiment.py
浏览文件 @ 559d3632
......@@ -24,9 +24,8 @@ from nltk.corpus import movie_reviews
class TestSentimentMethods(unittest.TestCase):
def test_get_word_dict(self):
word_dict = st.get_word_dict()[0:10]
test_word_list = [(u',', 0), (u'the', 1), (u'.', 2), (u'a', 3),
(u'and', 4), (u'of', 5), (u'to', 6), (u"'", 7),
(u'is', 8), (u'in', 9)]
test_word_list = [(',', 0), ('the', 1), ('.', 2), ('a', 3), ('and', 4),
('of', 5), ('to', 6), ("'", 7), ('is', 8), ('in', 9)]
for idx, each in enumerate(word_dict):
self.assertEqual(each, test_word_list[idx])
self.assertTrue("/root/.cache/paddle/dataset" in nltk.data.path)
......
python/paddle/dataset/uci_housing.py
浏览文件 @ 559d3632
......@@ -49,9 +49,12 @@ def feature_range(maximums, minimums):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
feature_num = len(maximums)
ax.bar(range(feature_num), maximums - minimums, color='r', align='center')
ax.bar(list(range(feature_num)),
maximums - minimums,
color='r',
align='center')
ax.set_title('feature scale')
plt.xticks(range(feature_num), feature_names)
plt.xticks(list(range(feature_num)), feature_names)
plt.xlim([-1, feature_num])
fig.set_figheight(6)
fig.set_figwidth(10)
......@@ -71,7 +74,7 @@ def load_data(filename, feature_num=14, ratio=0.8):
maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(
axis=0) / data.shape[0]
feature_range(maximums[:-1], minimums[:-1])
for i in xrange(feature_num - 1):
for i in range(feature_num - 1):
data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i])
offset = int(data.shape[0] * ratio)
UCI_TRAIN_DATA = data[:offset]
......
python/paddle/dataset/wmt14.py
浏览文件 @ 559d3632
......@@ -154,8 +154,8 @@ def get_dict(dict_size, reverse=True):
tar_file = paddle.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN)
src_dict, trg_dict = __read_to_dict(tar_file, dict_size)
if reverse:
src_dict = {v: k for k, v in src_dict.items()}
trg_dict = {v: k for k, v in trg_dict.items()}
src_dict = {v: k for k, v in list(src_dict.items())}
trg_dict = {v: k for k, v in list(trg_dict.items())}
return src_dict, trg_dict
......
python/paddle/dataset/wmt16.py
浏览文件 @ 559d3632
......@@ -70,7 +70,9 @@ def __build_dict(tar_file, dict_size, save_path, lang):
fout.write("%s\n%s\n%s\n" % (START_MARK, END_MARK, UNK_MARK))
for idx, word in enumerate(
sorted(
word_dict.iteritems(), key=lambda x: x[1], reverse=True)):
iter(list(word_dict.items())),
key=lambda x: x[1],
reverse=True)):
if idx + 3 == dict_size: break
fout.write("%s\n" % (word[0]))
......
python/paddle/fluid/__init__.py
浏览文件 @ 559d3632
......@@ -14,49 +14,49 @@
from __future__ import print_function
# import all class inside framework into fluid module
import framework
from framework import *
from . import framework
from .framework import *
# import all class inside executor into fluid module
import executor
from executor import *
import trainer
from trainer import Trainer
from trainer import BeginEpochEvent
from trainer import EndEpochEvent
from trainer import BeginStepEvent
from trainer import EndStepEvent
from trainer import CheckpointConfig
import inferencer
from inferencer import Inferencer
import io
import evaluator
import initializer
import layers
import contrib
import nets
import optimizer
import backward
import regularizer
import average
import metrics
import transpiler
from param_attr import ParamAttr, WeightNormParamAttr
from data_feeder import DataFeeder
from core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
from transpiler import DistributeTranspiler, InferenceTranspiler, \
from . import executor
from .executor import *
from . import trainer
from .trainer import Trainer
from .trainer import BeginEpochEvent
from .trainer import EndEpochEvent
from .trainer import BeginStepEvent
from .trainer import EndStepEvent
from .trainer import CheckpointConfig
from . import inferencer
from .inferencer import Inferencer
from . import io
from . import evaluator
from . import initializer
from . import layers
from . import contrib
from . import nets
from . import optimizer
from . import backward
from . import regularizer
from . import average
from . import metrics
from . import transpiler
from .param_attr import ParamAttr, WeightNormParamAttr
from .data_feeder import DataFeeder
from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
from .transpiler import DistributeTranspiler, InferenceTranspiler, \
memory_optimize, release_memory, DistributeTranspilerConfig
from concurrency import (Go, make_channel, channel_send, channel_recv,
from .concurrency import (Go, make_channel, channel_send, channel_recv,
channel_close, Select)
from lod_tensor import create_lod_tensor, create_random_int_lodtensor
import clip
import profiler
import unique_name
import recordio_writer
import parallel_executor
from parallel_executor import *
from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
from . import clip
from . import profiler
from . import unique_name
from . import recordio_writer
from . import parallel_executor
from .parallel_executor import *
from paddle.fluid.layers.math_op_patch import monkey_patch_variable
Tensor = LoDTensor
......@@ -99,8 +99,8 @@ def __bootstrap__():
None
"""
import sys
import core
import os
from . import core
in_test = 'unittest' in sys.modules
......
python/paddle/fluid/annotations.py
浏览文件 @ 559d3632
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import functools
import sys
......@@ -28,7 +29,7 @@ def deprecated(since, instead, extra_message=""):
@functools.wraps(func)
def wrapper(*args, **kwargs):
print >> sys.stderr, err_msg
print(err_msg, file=sys.stderr)
return func(*args, **kwargs)
wrapper.__doc__ += "\n "
......
python/paddle/fluid/backward.py
浏览文件 @ 559d3632
......@@ -16,7 +16,8 @@ from paddle.fluid import framework as framework
from . import core
import collections
import copy
import unique_name
import six
from . import unique_name
__all__ = ['append_backward']
......@@ -44,17 +45,25 @@ def _create_op_desc_(op_type, inputs, outputs, attrs):
"""
op_desc = core.OpDesc()
op_desc.set_type(op_type)
for para, args in inputs.iteritems():
op_desc.set_input(para, args)
for para, args in outputs.iteritems():
op_desc.set_output(para, args)
for para, args in list(inputs.items()):
op_desc.set_input(
para,
list(
map(lambda arg: arg.decode() if isinstance(arg, six.binary_type) else arg,
args)))
for para, args in list(outputs.items()):
op_desc.set_output(
para,
list(
map(lambda arg: arg.decode() if isinstance(arg, six.binary_type) else arg,
args)))
op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName()
if op_role_attr_name not in attrs:
attrs[
op_role_attr_name] = core.op_proto_and_checker_maker.OpRole.Backward
for name, val in attrs.iteritems():
for name, val in list(attrs.items()):
if isinstance(val, framework.Block):
op_desc.set_block_attr(name, val.desc)
else:
......@@ -105,7 +114,9 @@ def _strip_grad_suffix_(name):
e.g. x@GRAD ==> x
y@GRAD@RENAME@1 ==> y
"""
pos = name.find(core.grad_var_suffix())
if isinstance(name, six.text_type):
name = name.encode()
pos = name.find(six.b(core.grad_var_suffix()))
return name[:pos] if pos != -1 else name
......@@ -114,7 +125,9 @@ def _append_grad_suffix_(name):
Append grad suffix to the given variable name
e.g. x ==> x@GRAD
"""
return name + core.grad_var_suffix()
if isinstance(name, six.text_type):
name = name.encode()
return name + six.b(core.grad_var_suffix())
def _addup_repetitive_outputs_(op_descs):
......@@ -174,7 +187,7 @@ def _addup_repetitive_outputs_(op_descs):
op_desc.set_output(param_name, arg_names)
renamed_vars[var_name].append(new_name)
for var_name, inputs in renamed_vars.iteritems():
for var_name, inputs in list(renamed_vars.items()):
if len(inputs) > 1:
pending_sum_ops.append(
(_create_op_desc_("sum", {"X": inputs}, {"Out": [var_name]},
......@@ -198,16 +211,19 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
out_arg_names = op_desc.output_arg_names()
if len(out_arg_names) == 0 or _all_in_set_(out_arg_names, no_grad_set):
return True
if _all_in_set_(
filter(lambda name: name.find(core.grad_var_suffix()) != -1,
op_desc.input_arg_names()), no_grad_set):
if _all_in_set_([
name for name in op_desc.input_arg_names()
if name.find(core.grad_var_suffix()) != -1
], no_grad_set):
no_grad_set.update(out_arg_names)
return True
return False
# Remove ops whose outputs are all in no_grad_dict
op_descs = filter(
lambda op_desc: not _op_can_be_removed_(op_desc, no_grad_set), op_descs)
op_descs = [
op_desc for op_desc in op_descs
if not _op_can_be_removed_(op_desc, no_grad_set)
]
# Insert fill_zeros_like_op
to_insert = []
for idx, op_desc in enumerate(op_descs):
......@@ -217,12 +233,12 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
"X": [_strip_grad_suffix_(arg)]
}, {"Out": [arg]}, {}), idx))
map(lambda p: op_descs.insert(p[1], p[0]), reversed(to_insert))
list([op_descs.insert(p[1], p[0]) for p in reversed(to_insert)])
return op_descs
import proto.framework_pb2 as framework_pb2
from .proto import framework_pb2
def serialize_op_decs(op_desc):
......@@ -244,8 +260,10 @@ def _callback_lookup_(op):
if op.type == 'parallel_do' and op.attr('use_nccl'):
all_vars = op.block.vars
param_names = set(op.input('parameters'))
param_names = filter(lambda name: all_vars[name].stop_gradient is False,
param_names)
param_names = [
name for name in param_names
if all_vars[name].stop_gradient is False
]
param_grad_names = [n + "@GRAD" for n in param_names]
class ParallelDoCallBack(object):
......@@ -399,7 +417,7 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
continue
block.desc.var(grad_var_name)
new_vars.add(grad_var_name)
if not grad_to_var.has_key(grad_var_name):
if grad_var_name not in grad_to_var:
continue
grad_info_map[grad_to_var[grad_var_name]] = (grad_var_name, block)
# infer_shape and infer_type
......@@ -427,7 +445,7 @@ def _rename_grad_(block, start_op_idx, grad_to_var, target_grad_map):
op_desc.rename_output(name, new_name)
var_map[name] = new_name
for g, ng in var_map.iteritems():
for g, ng in list(var_map.items()):
if g in grad_to_var:
grad_to_var[ng] = grad_to_var[g]
grad_to_var.pop(g)
......@@ -439,7 +457,7 @@ def _get_stop_gradients_(program):
for block in program.blocks:
assert isinstance(block, framework.Block)
block_no_grad_set = set()
for var in block.vars.itervalues():
for var in list(block.vars.values()):
assert isinstance(var, framework.Variable)
if var.stop_gradient:
block_no_grad_set.add(_append_grad_suffix_(var.name))
......@@ -535,7 +553,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
no_grad_set = set()
no_grad_set = copy.copy(no_grad_set)
no_grad_dict = _get_stop_gradients_(program)
no_grad_dict[0].update(map(_append_grad_suffix_, no_grad_set))
no_grad_dict[0].update(list(map(_append_grad_suffix_, no_grad_set)))
grad_info_map = dict()
root_block = program.block(0)
......@@ -558,7 +576,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0]))
op_path = _find_op_path_(root_block, [loss], [], block_no_grad_set)
no_grad_dict[0].update(map(_append_grad_suffix_, block_no_grad_set))
no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set)))
_append_backward_ops_(root_block, op_path, root_block, no_grad_dict,
grad_to_var, callbacks)
......@@ -699,7 +717,7 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None):
no_grad_set = set()
no_grad_set = copy.copy(no_grad_set)
no_grad_dict = _get_stop_gradients_(prog)
no_grad_dict[0].update(map(_append_grad_suffix_, no_grad_set))
no_grad_dict[0].update(list(map(_append_grad_suffix_, no_grad_set)))
fwd_op_num = block.desc.op_size()
......@@ -733,7 +751,7 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None):
block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0]))
op_path = _find_op_path_(block, targets, inputs, block_no_grad_set)
no_grad_dict[0].update(map(_append_grad_suffix_, block_no_grad_set))
no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set)))
grad_to_var = dict()
grad_info_map = dict()
_append_backward_ops_(block, op_path, block, no_grad_dict, grad_to_var)
......
python/paddle/fluid/clip.py
浏览文件 @ 559d3632
......@@ -15,8 +15,8 @@
import copy
import functools
import layers
import framework
from . import layers
from . import framework
from . import core
__all__ = [
......@@ -80,8 +80,7 @@ def error_clip_callback(block, context):
# the context is a grad_to_var map
grad_to_var = context
op_desc = block.desc.op(block.desc.op_size() - 1)
for grad_n in filter(lambda n: grad_to_var.has_key(n),
op_desc.output_arg_names()):
for grad_n in [n for n in op_desc.output_arg_names() if n in grad_to_var]:
fwd_var = block._var_recursive(grad_to_var[grad_n])
error_clip = getattr(fwd_var, "error_clip", None)
if not (error_clip is None or isinstance(error_clip,
......@@ -247,7 +246,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
"""
def __init__(self, clip_norm, group_name="default_group"):
if not isinstance(group_name, basestring):
if not isinstance(group_name, str):
raise TypeError("'group_name' must be a basestring.")
self.clip_norm = clip_norm
......@@ -284,7 +283,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
x=clip_var,
y=layers.elementwise_max(
x=clip_var, y=group_norm_var))
assert group_scale_var.shape == (1L, )
assert group_scale_var.shape == (1, )
self.context[group_scale_name] = group_scale_var
new_grad = layers.elementwise_mul(
......@@ -313,7 +312,7 @@ def set_gradient_clip(clip, param_list=None, program=None):
program = framework.default_main_program()
if param_list is None:
param_list = program.block(0).all_parameters()
if all(isinstance(elem, basestring) for elem in param_list):
if all(isinstance(elem, str) for elem in param_list):
param_list = [program.block(0).var(elem) for elem in param_list]
if not all(isinstance(elem, framework.Parameter) for elem in param_list):
raise TypeError(
......
python/paddle/fluid/concurrency.py
浏览文件 @ 559d3632
......@@ -12,11 +12,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from layers.control_flow import BlockGuard, equal
from .layers.control_flow import BlockGuard, equal
from .framework import Operator
from layer_helper import LayerHelper, unique_name
from layers import fill_constant
import core
from .layer_helper import LayerHelper, unique_name
from .layers import fill_constant
from . import core
__all__ = [
'Go', 'make_channel', 'channel_send', 'channel_recv', 'channel_close',
......
python/paddle/fluid/data_feeder.py
浏览文件 @ 559d3632
......@@ -12,14 +12,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import core
from . import core
import numpy
import os
import six.moves as six
import multiprocessing
from framework import Variable, default_main_program
from .framework import Variable, default_main_program
__all__ = ['DataFeeder']
......@@ -142,7 +141,7 @@ class DataFeeder(object):
if program is None:
program = default_main_program()
for each_var in feed_list:
if isinstance(each_var, basestring):
if isinstance(each_var, str):
each_var = program.block(0).var(each_var)
if not isinstance(each_var, Variable):
raise TypeError("Feed list should contain a list of variable")
......
python/paddle/fluid/debugger.py
浏览文件 @ 559d3632
......@@ -14,8 +14,8 @@
import sys
import re
from graphviz import GraphPreviewGenerator
import proto.framework_pb2 as framework_pb2
from .graphviz import GraphPreviewGenerator
from .proto import framework_pb2
from google.protobuf import text_format
_vartype2str_ = [
......
python/paddle/fluid/evaluator.py
浏览文件 @ 559d3632
......@@ -15,11 +15,11 @@
import warnings
import numpy as np
import layers
from framework import Program, Variable, program_guard
import unique_name
from layer_helper import LayerHelper
from initializer import Constant
from . import layers
from .framework import Program, Variable, program_guard
from . import unique_name
from .layer_helper import LayerHelper
from .initializer import Constant
__all__ = [
'ChunkEvaluator',
......
python/paddle/fluid/executor.py
浏览文件 @ 559d3632
......@@ -14,7 +14,7 @@
import numpy as np
import contextlib
from framework import Program, default_main_program, Variable
from .framework import Program, default_main_program, Variable
from . import core
__all__ = [
......@@ -204,19 +204,19 @@ def fetch_var(name, scope=None, return_numpy=True):
def _get_program_cache_key(feed, fetch_list):
feed_var_names = feed.keys()
feed_var_names = list(feed.keys())
def to_name_str(var):
if isinstance(var, Variable):
return var.desc.name()
elif isinstance(var, str):
return var
elif isinstance(var, basestring):
elif isinstance(var, str):
return str(var)
else:
raise TypeError(str(var) + " should be Variable or str")
fetch_var_names = map(to_name_str, fetch_list)
fetch_var_names = list(map(to_name_str, fetch_list))
return str(feed_var_names + fetch_var_names)
......@@ -345,7 +345,7 @@ class Executor(object):
def _fetch_data(self, fetch_list, fetch_var_name, scope):
outs = [
core.get_fetch_variable(scope, fetch_var_name, i)
for i in xrange(len(fetch_list))
for i in range(len(fetch_list))
]
return outs
......
python/paddle/fluid/framework.py
浏览文件 @ 559d3632
......@@ -15,21 +15,22 @@
import collections
import contextlib
import re
import six
import numpy as np
import proto.framework_pb2 as framework_pb2
from .proto import framework_pb2
try:
from . import core
except ImportError, e:
except ImportError as e:
raise ImportError(
"""NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
if you encounters \"libmkldnn.so not found\" errors. If you have python
installed in other directory, replace \"/usr/local/lib\" with your own
directory. The original error is: \n""" + e.message)
except Exception, e:
except Exception as e:
raise e
import unique_name
from . import unique_name
__all__ = [
'Program',
......@@ -86,7 +87,7 @@ def convert_np_dtype_to_dtype_(np_dtype):
elif dtype == np.uint8:
return core.VarDesc.VarType.UINT8
else:
raise ValueError("Not supported numpy dtype " + str(dtype))
raise ValueError("Not supported numpy dtype " + six.binary_type(dtype))
def dtype_is_floating(dtype):
......@@ -197,6 +198,7 @@ class Variable(object):
if name is None:
name = unique_name.generate('_generated_var')
is_new_var = False
name = name if isinstance(name, six.binary_type) else name.encode()
self.desc = self.block.desc.find_var(name)
if self.desc is None:
......@@ -290,13 +292,13 @@ class Variable(object):
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
protostr = self.desc.serialize_to_string()
proto = framework_pb2.VarDesc.FromString(str(protostr))
proto = framework_pb2.VarDesc.FromString(six.binary_type(protostr))
res_str = _debug_string_(proto, throw_on_error)
if with_details:
additional_attr = ("error_clip", "stop_gradient")
for attr_name in additional_attr:
res_str += "%s: %s\n" % (attr_name,
str(getattr(self, attr_name)))
res_str += "%s: %s\n" % (
attr_name, six.binary_type(getattr(self, attr_name)))
return res_str
__repr__ = __str__
......@@ -369,7 +371,7 @@ def get_all_op_protos():
protostrs = core.get_all_op_protos()
ret_values = []
for pbstr in protostrs:
op_proto = framework_pb2.OpProto.FromString(str(pbstr))
op_proto = framework_pb2.OpProto.FromString(six.binary_type(pbstr))
ret_values.append(op_proto)
return ret_values
......@@ -472,7 +474,6 @@ class Operator(object):
inputs=None,
outputs=None,
attrs=None):
self.block = block
self.desc = desc
self.attrs = attrs
......@@ -523,10 +524,15 @@ class Operator(object):
% (in_proto.name, len(in_args)))
in_arg_names = []
for arg in in_args:
if isinstance(arg, basestring):
if issubclass(arg.__class__, six.string_types):
in_arg_names.append(arg)
elif isinstance(arg, six.binary_type):
in_arg_names.append(arg.decode())
else:
if issubclass(arg.name.__class__, six.string_types):
in_arg_names.append(arg.name)
elif isinstance(arg.name, six.binary_type):
in_arg_names.append(arg.name.decode())
self.desc.set_input(in_proto.name, in_arg_names)
else:
self.desc.set_input(in_proto.name, [])
......@@ -541,8 +547,9 @@ class Operator(object):
if not given == need:
raise ValueError(("Incorrect setting for output(s) of "
"operator \"%s\". Need: [%s] Given: [%s]") %
(type, ", ".join(str(e) for e in need),
", ".join(str(e) for e in given)))
(type,
", ".join(six.binary_type(e) for e in need),
", ".join(six.binary_type(e) for e in given)))
for out_proto in proto.outputs:
out_args = outputs[out_proto.name]
......@@ -554,7 +561,12 @@ class Operator(object):
(out_proto.name, len(out_args)))
out_arg_names = []
for arg in out_args:
if issubclass(arg.name.__class__, six.string_types):
out_arg_names.append(arg.name)
elif isinstance(arg.name, six.binary_type):
out_arg_names.append(arg.name.decode())
else:
out_arg_names.append(six.u(arg.name))
arg.op = self
self.desc.set_output(out_proto.name, out_arg_names)
......@@ -590,7 +602,7 @@ class Operator(object):
"""
protostr = self.desc.serialize_to_string()
proto = framework_pb2.OpDesc.FromString(str(protostr))
proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr))
return _debug_string_(proto, throw_on_error)
def __str__(self):
......@@ -845,7 +857,7 @@ class Block(object):
re_add_indent = re.compile(r"\n(.)")
res_str = "blocks {\n idx: %d\n parent_idx: %d" % (
self.idx, self.parent_idx)
for var in self.vars.itervalues():
for var in list(self.vars.values()):
res_str += "\n vars {\n %s }" % re_add_indent.sub(
r"\n \1", var.to_string(throw_on_error, with_details))
for op in self.ops:
......@@ -854,7 +866,8 @@ class Block(object):
res_str += "\n}"
else:
protostr = self.desc.serialize_to_string()
proto = framework_pb2.BlockDesc.FromString(str(protostr))
proto = framework_pb2.BlockDesc.FromString(
six.binary_type(protostr))
res_str = _debug_string_(proto, throw_on_error)
return res_str
......@@ -898,7 +911,8 @@ class Block(object):
Returns:
Variable: the Variable with the giving name.
"""
if not isinstance(name, basestring):
if not issubclass(name.__class__, six.string_types):
if not isinstance(name, six.binary_type):
raise TypeError(
"var require string as parameter, but get %s instead." %
(type(name)))
......@@ -949,10 +963,10 @@ class Block(object):
raise ValueError("Var {0} is not found recursively".format(name))
def all_parameters(self):
return list(self._iter_parameters())
return list(self.iter_parameters())
def _iter_parameters(self):
return (item[1] for item in self.vars.iteritems()
def iter_parameters(self):
return (item[1] for item in list(self.vars.items())
if isinstance(item[1], Parameter))
def create_var(self, *args, **kwargs):
......@@ -1113,7 +1127,7 @@ class Block(object):
self.create_var(name=var.name(), desc=var, type=var.type())
# sync variables removed from c++ end
for var in self.vars.keys():
for var in list(self.vars.keys()):
if not self.desc.find_var(var):
self.vars.pop(var)
......@@ -1185,7 +1199,7 @@ class Block(object):
if not isinstance(other, Block):
raise TypeError(
"_copy_param_info_from should be invoked with Block")
for p in other._iter_parameters():
for p in other.iter_parameters():
assert isinstance(p, Parameter)
v = self.vars.get(p.name, None)
if v is None:
......@@ -1384,7 +1398,8 @@ class Program(object):
res_str += block.to_string(throw_on_error, with_details)
else:
protostr = self.desc.serialize_to_string()
proto = framework_pb2.ProgramDesc.FromString(str(protostr))
proto = framework_pb2.ProgramDesc.FromString(
six.binary_type(protostr))
res_str = _debug_string_(proto, throw_on_error)
return res_str
......@@ -1482,7 +1497,7 @@ class Program(object):
else:
p = Program()
p.desc = core.ProgramDesc(self.desc)
p.blocks = [Block(p, i) for i in xrange(self.desc.num_blocks())]
p.blocks = [Block(p, i) for i in range(self.desc.num_blocks())]
p._sync_with_cpp()
p._copy_param_info_from(self)
......@@ -1534,7 +1549,7 @@ class Program(object):
targets_idx.append([t.block.idx, t.idx])
res = Program()
res.desc = core.prune(self.desc, targets_idx)
res.blocks = [Block(res, i) for i in xrange(res.desc.num_blocks())]
res.blocks = [Block(res, i) for i in range(res.desc.num_blocks())]
res._sync_with_cpp()
return res
......@@ -1554,13 +1569,13 @@ class Program(object):
# core.inference_optimize being fixed.
res = Program()
res.desc = core.ProgramDesc(self.desc)
for i in xrange(res.desc.num_blocks()):
for i in range(res.desc.num_blocks()):
block = res.desc.block(i)
for j in xrange(block.op_size()):
for j in range(block.op_size()):
op = block.op(j)
if op.has_attr('is_test'):
op.set_attr('is_test', True)
res.blocks = [Block(res, i) for i in xrange(res.desc.num_blocks())]
res.blocks = [Block(res, i) for i in range(res.desc.num_blocks())]
res._sync_with_cpp()
return res
......@@ -1573,14 +1588,14 @@ class Program(object):
and deserialization.
Args:
binary_str(str): The binary prootbuf string.
binary_str_type(str): The binary prootbuf string.
Returns:
Program: A deserialized program desc.
"""
p = Program()
p.desc = core.ProgramDesc(binary_str)
p.blocks = [Block(p, i) for i in xrange(p.desc.num_blocks())]
p.blocks = [Block(p, i) for i in range(p.desc.num_blocks())]
p._sync_with_cpp()
return p
......@@ -1608,7 +1623,7 @@ class Program(object):
self._seed = seed
def __repr__(self):
return str(self)
return self.__str__()
def global_block(self):
"""
......@@ -1719,7 +1734,7 @@ class Program(object):
if len(self.blocks) != len(other.blocks):
raise ValueError("_copy_param_info_from should be invoked with two "
"program, with represent the same topology")
for var in other.global_block().vars.itervalues():
for var in list(other.global_block().vars.values()):
if var.is_data:
self.global_block().var(var.name).is_data = True
......@@ -1731,7 +1746,7 @@ class Program(object):
iterable: The generator will yield every variable in this program.
"""
for each_block in self.blocks:
for each_var in each_block.vars.itervalues():
for each_var in list(each_block.vars.values()):
yield each_var
......@@ -1805,8 +1820,8 @@ class Parameter(Variable):
additional_attr = ("trainable", "optimize_attr", "regularizer",
"gradient_clip_attr", "do_model_average")
for attr_name in additional_attr:
res_str += "%s: %s\n" % (attr_name,
str(getattr(self, attr_name)))
res_str += "%s: %s\n" % (
attr_name, six.binary_type(getattr(self, attr_name)))
else:
res_str = Variable.to_string(self, throw_on_error, False)
return res_str
......
python/paddle/fluid/graphviz.py
浏览文件 @ 559d3632
......@@ -19,7 +19,7 @@ import logging
def crepr(v):
if type(v) is str or type(v) is unicode:
if type(v) is str or type(v) is str:
return '"%s"' % v
return str(v)
......@@ -104,7 +104,7 @@ class Graph(object):
def _rank_repr(self):
ranks = sorted(
self.rank_groups.items(),
list(self.rank_groups.items()),
cmp=lambda a, b: a[1].priority > b[1].priority)
repr = []
for x in ranks:
......@@ -148,7 +148,7 @@ class Node(object):
name=self.name,
label=self.label,
extra=',' + ','.join("%s=%s" % (key, crepr(value))
for key, value in self.attrs.items())
for key, value in list(self.attrs.items()))
if self.attrs else "")
return reprs
......@@ -172,7 +172,7 @@ class Edge(object):
target=self.target.name,
extra="" if not self.attrs else
"[" + ','.join("{}={}".format(attr[0], crepr(attr[1]))
for attr in self.attrs.items()) + "]")
for attr in list(self.attrs.items())) + "]")
return repr
......
python/paddle/fluid/inferencer.py
浏览文件 @ 559d3632
......@@ -14,14 +14,14 @@
import contextlib
import core
import executor
import framework
import io
import parallel_executor
import unique_name
from trainer import check_and_get_place
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', ]
......
python/paddle/fluid/initializer.py
浏览文件 @ 559d3632
......@@ -12,11 +12,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import framework
from . import framework
import numpy as np
import contextlib
from framework import convert_np_dtype_to_dtype_
from core import VarDesc
from .framework import convert_np_dtype_to_dtype_
from .core import VarDesc
__all__ = [
'Constant', 'Uniform', 'Normal', 'Xavier', 'Bilinear', 'MSRA',
......
python/paddle/fluid/io.py
浏览文件 @ 559d3632
......@@ -16,6 +16,7 @@ import os
import errno
import time
import shutil
import six
from paddle.fluid.evaluator import Evaluator
from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable
......@@ -163,7 +164,7 @@ def save_vars(executor,
save_vars(
executor,
dirname=dirname,
vars=filter(predicate, main_program.list_vars()),
vars=list(filter(predicate, main_program.list_vars())),
filename=filename)
else:
save_program = Program()
......@@ -369,7 +370,7 @@ def load_vars(executor,
load_vars(
executor,
dirname=dirname,
vars=filter(predicate, main_program.list_vars()),
vars=list(filter(predicate, main_program.list_vars())),
filename=filename)
else:
load_prog = Program()
......@@ -599,13 +600,27 @@ def save_inference_model(dirname,
# "./infer_model".
"""
if isinstance(feeded_var_names, basestring):
if isinstance(feeded_var_names, six.binary_type):
feeded_var_names = [feeded_var_names]
elif isinstance(feeded_var_names, six.text_type):
feeded_var_names = [feeded_var_names.encode()]
else:
if len(feeded_var_names) > 0:
if not (bool(feeded_var_names) and all(
isinstance(name, basestring) for name in feeded_var_names)):
raise ValueError("'feed_var_names' should be a list of str.")
isinstance(name, six.binary_type)
for name in feeded_var_names)):
if not (all(
isinstance(name, six.text_type)
for name in feeded_var_names)):
import sys
print([type(name) for name in feeded_var_names])
sys.stdout.flush()
raise ValueError(
"'feed_var_names' should be a list of str.")
else:
feeded_var_names = [
name.encode() for name in feeded_var_names
]
if isinstance(target_vars, Variable):
target_vars = [target_vars]
......
python/paddle/fluid/layer_helper.py
浏览文件 @ 559d3632
......@@ -15,11 +15,11 @@
import copy
import itertools
from framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
import unique_name
from .framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
from . import unique_name
from paddle.fluid.initializer import Constant, Xavier
from param_attr import ParamAttr, WeightNormParamAttr
import core
from .param_attr import ParamAttr, WeightNormParamAttr
from . import core
class LayerHelper(object):
......@@ -83,7 +83,7 @@ class LayerHelper(object):
raise ValueError("parameter number mismatch")
elif len(param_attr) == 1 and length != 1:
tmp = [None] * length
for i in xrange(length):
for i in range(length):
tmp[i] = copy.deepcopy(param_attr[0])
param_attr = tmp
return param_attr
......@@ -91,7 +91,7 @@ class LayerHelper(object):
def iter_inputs_and_params(self, input_param_name='input'):
inputs = self.multiple_input(input_param_name)
param_attrs = self.multiple_param_attr(len(inputs))
for ipt, param_attr in itertools.izip(inputs, param_attrs):
for ipt, param_attr in zip(inputs, param_attrs):
yield ipt, param_attr
def input_dtype(self, input_param_name='input'):
......@@ -218,7 +218,7 @@ class LayerHelper(object):
norm = __norm_op(reshape, dim=0, block=block)
__reshape_op(norm, out=out, shape=out_shape, block=block)
else:
perm = range(len(x.shape))
perm = list(range(len(x.shape)))
perm[0], perm[dim] = dim, 0
transpose = __transpose_op(x, perm, block=block)
norm = __norm_op(transpose, dim=0, block=block)
......@@ -397,7 +397,7 @@ class LayerHelper(object):
act = self.kwargs.get('act', None)
if act is None:
return input_var
if isinstance(act, basestring):
if isinstance(act, str):
act = {'type': act}
if 'use_cudnn' in self.kwargs and self.kwargs.get('use_cudnn'):
......
python/paddle/fluid/layers/__init__.py
浏览文件 @ 559d3632
......@@ -12,25 +12,25 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import ops
from ops import *
import nn
from nn import *
import io
from io import *
import tensor
from tensor import *
import control_flow
from control_flow import *
import device
from device import *
import math_op_patch
from math_op_patch import *
import detection
from detection import *
import metric_op
from metric_op import *
from learning_rate_scheduler import *
from . import ops
from .ops import *
from . import nn
from .nn import *
from . import io
from .io import *
from . import tensor
from .tensor import *
from . import control_flow
from .control_flow import *
from . import device
from .device import *
from . import math_op_patch
from .math_op_patch import *
from . import detection
from .detection import *
from . import metric_op
from .metric_op import *
from .learning_rate_scheduler import *
__all__ = []
__all__ += nn.__all__
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 559d3632
......@@ -13,14 +13,15 @@
# limitations under the License.
import contextlib
from layer_function_generator import autodoc, templatedoc
from tensor import assign, fill_constant
from .layer_function_generator import autodoc, templatedoc
from .tensor import assign, fill_constant
from .. import core
from ..framework import Program, Variable, Operator
from ..layer_helper import LayerHelper, unique_name
from ..initializer import force_init_on_cpu
from ops import logical_and, logical_not, logical_or
from .ops import logical_and, logical_not, logical_or
import numpy
from functools import reduce
__all__ = [
'While',
......@@ -597,7 +598,7 @@ class StaticRNN(object):
boot_memories = []
pre_memories = []
memories = []
for _, mem in self.memories.iteritems():
for _, mem in list(self.memories.items()):
boot_memories.append(mem.init)
pre_memories.append(mem.pre_mem.name)
mem_var = rnn_block.var(mem.mem.name)
......@@ -1508,7 +1509,7 @@ class IfElse(object):
def __call__(self):
if self.status != self.OUT_IF_ELSE_BLOCKS:
raise ValueError("IfElse::__call__ must be out of sub-block")
false_len, true_len = map(len, self.output_table)
false_len, true_len = list(map(len, self.output_table))
if false_len == 0 and true_len == 0:
raise ValueError("Must invoke true_block/false_block before "
"__call__")
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 559d3632
......@@ -15,12 +15,13 @@
All layers just related to the detection neural network.
"""
from layer_function_generator import generate_layer_fn
from layer_function_generator import autodoc, templatedoc
from .layer_function_generator import generate_layer_fn
from .layer_function_generator import autodoc, templatedoc
from ..layer_helper import LayerHelper
import tensor
import nn
from . import tensor
from . import nn
import math
from functools import reduce
__all__ = [
'prior_box',
......@@ -1031,7 +1032,7 @@ def multi_box_head(inputs,
min_sizes = []
max_sizes = []
step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
for ratio in xrange(min_ratio, max_ratio + 1, step):
for ratio in range(min_ratio, max_ratio + 1, step):
min_sizes.append(base_size * ratio / 100.)
max_sizes.append(base_size * (ratio + step) / 100.)
min_sizes = [base_size * .10] + min_sizes
......
python/paddle/fluid/layers/device.py
浏览文件 @ 559d3632
......@@ -15,7 +15,7 @@
All util layers.
"""
from layer_function_generator import autodoc
from .layer_function_generator import autodoc
from ..framework import unique_name
from ..layer_helper import LayerHelper
from ..annotations import deprecated
......
python/paddle/fluid/layers/io.py
浏览文件 @ 559d3632
......@@ -16,8 +16,8 @@ import multiprocessing
import threading
from ..data_feeder import DataFeeder
from control_flow import BlockGuard
from layer_function_generator import templatedoc
from .control_flow import BlockGuard
from .layer_function_generator import templatedoc
from .. import core
from ..executor import global_scope
from ..framework import convert_np_dtype_to_dtype_, default_main_program, \
......@@ -69,7 +69,7 @@ def data(name,
"""
helper = LayerHelper('data', **locals())
shape = list(shape)
for i in xrange(len(shape)):
for i in range(len(shape)):
if shape[i] is None:
shape[i] = -1
append_batch_size = False
......@@ -1008,7 +1008,7 @@ class Preprocessor(object):
source_lod_levels = self.underlying_reader.desc.lod_levels()
self.source_var_names = [
unique_name("preprocessor_source")
for _ in xrange(len(source_shapes))
for _ in range(len(source_shapes))
]
source_vars = []
for var_name, shape, dtype, lod_level in zip(
......
python/paddle/fluid/layers/layer_function_generator.py
浏览文件 @ 559d3632
......@@ -12,11 +12,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import cStringIO
import functools
import warnings
import string
from six.moves import cStringIO
from ..proto import framework_pb2
from ..framework import OpProtoHolder, Variable
from ..layer_helper import LayerHelper
......@@ -70,7 +70,7 @@ def _generate_doc_string_(op_proto):
if not isinstance(op_proto, framework_pb2.OpProto):
raise TypeError("OpProto should be `framework_pb2.OpProto`")
buf = cStringIO.StringIO()
buf = cStringIO()
buf.write(escape_math(op_proto.comment))
buf.write('\nArgs:\n')
for each_input in op_proto.inputs:
......@@ -119,9 +119,9 @@ def generate_layer_fn(op_type):
"""
op_proto = OpProtoHolder.instance().get_op_proto(op_type)
not_intermediate_outputs = \
filter(lambda output: not output.intermediate, op_proto.outputs)
[output for output in op_proto.outputs if not output.intermediate]
intermediate_outputs = \
filter(lambda output: output.intermediate, op_proto.outputs)
[output for output in op_proto.outputs if output.intermediate]
if len(not_intermediate_outputs) != 1:
raise ValueError("Only one non intermediate output operator can be",
......
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @ 559d3632
......@@ -20,10 +20,10 @@ User can also implement their own learning_rate_decay
strategy according to this module.
"""
import control_flow
import nn
import ops
import tensor
from . import control_flow
from . import nn
from . import ops
from . import tensor
from ..initializer import init_on_cpu
from ..framework import default_main_program, Parameter
......
python/paddle/fluid/layers/math_op_patch.py
浏览文件 @ 559d3632
......@@ -13,7 +13,7 @@
# limitations under the License.
from ..framework import Variable, unique_name
from layer_function_generator import OpProtoHolder
from .layer_function_generator import OpProtoHolder
from ..initializer import force_init_on_cpu
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ 559d3632
......@@ -20,7 +20,7 @@ from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
from ..param_attr import ParamAttr
import nn
from . import nn
__all__ = ['accuracy', 'auc']
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 559d3632
......@@ -33,11 +33,12 @@ from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
from ..param_attr import ParamAttr
from layer_function_generator import autodoc, templatedoc
from tensor import concat
import utils
from .layer_function_generator import autodoc, templatedoc
from .tensor import concat
from . import utils
import random
from .. import unique_name
from functools import reduce
__all__ = [
'fc',
......@@ -4843,7 +4844,7 @@ def dice_loss(input, label, epsilon=0.00001):
loss = fluid.layers.dice_loss(input=predictions, label=label, 2)
"""
label = one_hot(label, depth=input.shape[-1])
reduce_dim = range(1, len(input.shape))
reduce_dim = list(range(1, len(input.shape)))
inse = reduce_sum(input * label, dim=reduce_dim)
dice_denominator = reduce_sum(
input, dim=reduce_dim) + reduce_sum(
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 559d3632
......@@ -11,7 +11,7 @@
# 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 layer_function_generator import generate_layer_fn
from .layer_function_generator import generate_layer_fn
__activations__ = [
'sigmoid',
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 559d3632
......@@ -18,7 +18,7 @@ from ..framework import convert_np_dtype_to_dtype_
from ..framework import Variable
from ..initializer import Constant, force_init_on_cpu
from ..core import VarDesc
from layer_function_generator import templatedoc
from .layer_function_generator import templatedoc
import numpy
__all__ = [
......
python/paddle/fluid/lod_tensor.py
浏览文件 @ 559d3632
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import core
from . import core
import numpy as np
__all__ = ['create_lod_tensor', 'create_random_int_lodtensor']
......
python/paddle/fluid/metrics.py
浏览文件 @ 559d3632
......@@ -79,10 +79,10 @@ class MetricBase(object):
"""
states = {
attr: value
for attr, value in self.__dict__.iteritems()
for attr, value in list(self.__dict__.items())
if not attr.startswith("_")
}
for attr, value in states.iteritems():
for attr, value in list(states.items()):
if isinstance(value, int):
setattr(self, attr, 0)
elif isinstance(value, float):
......@@ -105,7 +105,7 @@ class MetricBase(object):
"""
states = {
attr: value
for attr, value in self.__dict__.iteritems()
for attr, value in list(self.__dict__.items())
if not attr.startswith("_")
}
config = {}
......
python/paddle/fluid/net_drawer.py
浏览文件 @ 559d3632
......@@ -24,7 +24,7 @@ logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
try:
from graphviz import Digraph
from .graphviz import Digraph
except ImportError:
logger.info(
'Cannot import graphviz, which is required for drawing a network. This '
......@@ -77,7 +77,7 @@ def parse_graph(program, graph, var_dict, **kwargs):
# fill the known variables
for block in program.blocks:
for var in block.vars:
if not var_dict.has_key(var):
if var not in var_dict:
var_dict[var] = "Feed"
temp_id = 0
......@@ -93,17 +93,17 @@ def parse_graph(program, graph, var_dict, **kwargs):
var_dict[arg] = op.type
for e in op.inputs:
for arg in e.arguments:
if var_dict.has_key(arg):
if arg in var_dict:
graph.edge(**draw_edge(var_dict, op, e, arg))
break # only plot the first block
def draw_graph(startup_program, main_program, **kwargs):
if kwargs.has_key("graph_attr"):
if "graph_attr" in kwargs:
GRAPH_STYLE.update(kwargs[graph_attr])
if kwargs.has_key("node_attr"):
if "node_attr" in kwargs:
OP_STYLE.update(kwargs[node_attr])
if kwargs.has_key("edge_attr"):
if "edge_attr" in kwargs:
VAR_STYLE.update(kwargs[edge_attr])
graph_id = unique_id()
......
python/paddle/fluid/nets.py
浏览文件 @ 559d3632
......@@ -11,7 +11,7 @@
# 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 layers
from . import layers
__all__ = [
"simple_img_conv_pool",
......@@ -210,7 +210,7 @@ def img_conv_group(input,
conv_with_batchnorm = __extend_list__(conv_with_batchnorm)
conv_batchnorm_drop_rate = __extend_list__(conv_batchnorm_drop_rate)
for i in xrange(len(conv_num_filter)):
for i in range(len(conv_num_filter)):
local_conv_act = conv_act
if conv_with_batchnorm[i]:
local_conv_act = None
......@@ -488,10 +488,11 @@ def scaled_dot_product_attention(queries,
trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
return layers.reshape(
x=trans_x,
shape=map(int, [
trans_x.shape[0], trans_x.shape[1],
trans_x.shape[2] * trans_x.shape[3]
]))
shape=list(
map(int, [
trans_x.shape[0], trans_x.shape[1], trans_x.shape[2] *
trans_x.shape[3]
])))
q, k, v = __compute_qkv(queries, keys, values, num_heads)
......
python/paddle/fluid/op.py
浏览文件 @ 559d3632
......@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import six
import paddle.fluid.core as core
import paddle.fluid.proto.framework_pb2 as framework_pb2
......@@ -24,13 +26,13 @@ def get_all_op_protos():
protostrs = core.get_all_op_protos()
ret_values = []
for pbstr in protostrs:
op_proto = framework_pb2.OpProto.FromString(str(pbstr))
op_proto = framework_pb2.OpProto.FromString(six.binary_type(pbstr))
ret_values.append(op_proto)
return ret_values
def is_str(s):
return isinstance(s, str) or isinstance(s, unicode)
return isinstance(s, str) or isinstance(s, str)
class OpDescCreationMethod(object):
......@@ -189,7 +191,7 @@ class OperatorFactory(object):
return self.get_op_info(t).method(**kwargs)
def types(self):
return self.op_methods.keys()
return list(self.op_methods.keys())
def get_op_info(self, t):
if t not in self.op_methods:
......@@ -197,13 +199,13 @@ class OperatorFactory(object):
return self.op_methods.get(t)
def get_op_input_names(self, type):
return map(lambda x: x[0], self.get_op_info(type).inputs)
return [x[0] for x in self.get_op_info(type).inputs]
def get_op_inputs(self, type):
return self.get_op_info(type).inputs
def get_op_output_names(self, type):
return map(lambda x: x[0], self.get_op_info(type).outputs)
return [x[0] for x in self.get_op_info(type).outputs]
def get_op_outputs(self, type):
return self.get_op_info(type).outputs
......
python/paddle/fluid/optimizer.py
浏览文件 @ 559d3632
......@@ -14,15 +14,15 @@
import re
from collections import defaultdict
from paddle.fluid.framework import Program, Variable
import framework
import layers
from backward import append_backward
from framework import program_guard
import unique_name
from initializer import Constant
from layer_helper import LayerHelper
from regularizer import append_regularization_ops
from clip import append_gradient_clip_ops, error_clip_callback
from . import framework
from . import layers
from .backward import append_backward
from .framework import program_guard
from . import unique_name
from .initializer import Constant
from .layer_helper import LayerHelper
from .regularizer import append_regularization_ops
from .clip import append_gradient_clip_ops, error_clip_callback
from contextlib import contextmanager
__all__ = [
......@@ -106,7 +106,7 @@ class Optimizer(object):
param_lr = param.optimize_attr['learning_rate']
if type(param_lr) == Variable:
# param learning rate has been updated (LARS)
print("returns updated param lr ", param_lr)
print(("returns updated param lr ", param_lr))
return param_lr
else:
if param_lr == 1.0:
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 559d3632
......@@ -12,10 +12,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import core
from __future__ import print_function
import multiprocessing
import framework
import executor
from . import core
from . import framework
from . import executor
import warnings
import sys
import os
......@@ -94,7 +95,7 @@ class ParallelExecutor(object):
self._places = []
self._act_places = []
if use_cuda:
for i in xrange(core.get_cuda_device_count()):
for i in range(core.get_cuda_device_count()):
p = core.Place()
self._act_places.append(core.CUDAPlace(i))
p.set_place(self._act_places[-1])
......@@ -102,7 +103,7 @@ class ParallelExecutor(object):
else:
cpu_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
for i in xrange(cpu_num):
for i in range(cpu_num):
p = core.Place()
self._act_places.append(core.CPUPlace())
p.set_place(self._act_places[-1])
......@@ -143,16 +144,16 @@ class ParallelExecutor(object):
) if share_vars_from else []
self.persistable_vars = [
v.name
for v in filter(
lambda var: var.persistable and var.type != core.VarDesc.VarType.RAW,
main.list_vars())
v.name for v in [
var for var in main.list_vars()
if var.persistable and var.type != core.VarDesc.VarType.RAW
]
]
self.executor = core.ParallelExecutor(
self._places,
set([
p.name for p in main.global_block()._iter_parameters()
p.name for p in main.global_block().iter_parameters()
if not p.stop_gradient
]),
set(self.persistable_vars), main.desc, loss_name
......@@ -227,7 +228,9 @@ class ParallelExecutor(object):
"""
if feed is None and feed_dict is not None:
feed = feed_dict
print >> sys.stderr, "`feed_dict` is deprecated. Please use `feed=`"
print(
"`feed_dict` is deprecated. Please use `feed=`",
file=sys.stderr)
if isinstance(feed, dict):
feed_tensor_dict = dict()
......
python/paddle/fluid/param_attr.py
浏览文件 @ 559d3632
......@@ -12,8 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from initializer import Initializer, Xavier, Constant
from regularizer import WeightDecayRegularizer
from .initializer import Initializer, Xavier, Constant
from .regularizer import WeightDecayRegularizer
__all__ = [
'ParamAttr',
......@@ -134,7 +134,7 @@ class ParamAttr(object):
return [ParamAttr._to_attr(a) for a in arg]
elif isinstance(arg, ParamAttr):
return arg
elif isinstance(arg, str) or isinstance(arg, unicode):
elif isinstance(arg, str) or isinstance(arg, str):
return ParamAttr(name=arg)
elif isinstance(arg, Initializer):
return ParamAttr(initializer=arg)
......
python/paddle/fluid/profiler.py
浏览文件 @ 559d3632
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import core
from . import core
from contextlib import contextmanager
import os
......
python/paddle/fluid/recordio_writer.py
浏览文件 @ 559d3632
......@@ -13,8 +13,8 @@
# limitations under the License.
import os
import core
import contextlib
from . import core
__all__ = [
'convert_reader_to_recordio_file', 'convert_reader_to_recordio_files'
]
......
python/paddle/fluid/regularizer.py
浏览文件 @ 559d3632
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import framework
from . import framework
from . import core
__all__ = ['L1Decay', 'L2Decay', 'L1DecayRegularizer', 'L2DecayRegularizer']
......
python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py
浏览文件 @ 559d3632
......@@ -63,7 +63,7 @@ def train(use_cuda, train_program, params_dirname):
if event.step == 10:
test_metrics = trainer.test(
reader=test_reader, feed_order=['x', 'y'])
print test_metrics
print(test_metrics)
'''
...
['25.768919467926025']
......@@ -94,7 +94,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
print("infer results: ", results[0])
print(("infer results: ", results[0]))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/image_classification/cifar10_small_test_set.py
浏览文件 @ 559d3632
......@@ -28,7 +28,7 @@ images per class.
"""
import cPickle
import pickle
import itertools
import numpy
import paddle.v2.dataset.common
......@@ -46,7 +46,7 @@ def reader_creator(filename, sub_name, batch_size=None):
data = batch['data']
labels = batch.get('labels', batch.get('fine_labels', None))
assert labels is not None
for sample, label in itertools.izip(data, labels):
for sample, label in zip(data, labels):
yield (sample / 255.0).astype(numpy.float32), int(label)
def reader():
......@@ -56,7 +56,7 @@ def reader_creator(filename, sub_name, batch_size=None):
batch_count = 0
for name in names:
batch = cPickle.load(f.extractfile(name))
batch = pickle.load(f.extractfile(name))
for item in read_batch(batch):
if isinstance(batch_size, int) and batch_count > batch_size:
break
......
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py
浏览文件 @ 559d3632
......@@ -12,8 +12,6 @@
# 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 numpy
......@@ -107,7 +105,7 @@ def train(use_cuda, train_program, params_dirname):
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))
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:
......@@ -136,7 +134,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
results = inferencer.infer({'pixel': tensor_img})
print("infer results: ", results)
print(("infer results: ", results))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py
浏览文件 @ 559d3632
......@@ -12,8 +12,6 @@
# 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 numpy
......@@ -84,7 +82,7 @@ def train(use_cuda, train_program, params_dirname):
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))
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:
......@@ -113,7 +111,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
results = inferencer.infer({'pixel': tensor_img})
print("infer results: ", results)
print(("infer results: ", results))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py
浏览文件 @ 559d3632
......@@ -12,8 +12,6 @@
# 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 numpy as np
......@@ -173,19 +171,20 @@ def train(use_cuda, train_program, params_dirname):
# get avg cost
avg_cost = np.array(avg_cost_set).mean()
print("avg_cost: %s" % avg_cost)
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)))
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, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(np.array, event.metrics)))
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()
......@@ -249,7 +248,7 @@ def infer(use_cuda, inference_program, params_dirname):
},
return_numpy=False)
print("infer results: ", np.array(results[0]).shape)
print(("infer results: ", np.array(results[0]).shape))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py
浏览文件 @ 559d3632
......@@ -197,7 +197,7 @@ def train(use_cuda, is_sparse, is_local=True):
def event_handler(event):
if isinstance(event, fluid.EndStepEvent):
print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))
print(('pass_id=' + str(event.epoch) + ' batch=' + str(event.step)))
if event.step == 10:
trainer.stop()
......@@ -250,7 +250,7 @@ def decode_main(use_cuda, is_sparse):
feeder = fluid.DataFeeder(feed_list, place)
for data in train_data():
feed_dict = feeder.feed(map(lambda x: [x[0]], data))
feed_dict = feeder.feed([[x[0]] for x in data])
feed_dict['init_ids'] = init_ids
feed_dict['init_scores'] = init_scores
......@@ -259,7 +259,7 @@ def decode_main(use_cuda, is_sparse):
feed=feed_dict,
fetch_list=[translation_ids, translation_scores],
return_numpy=False)
print result_ids.recursive_sequence_lengths()
print((result_ids.recursive_sequence_lengths()))
break
......
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py
浏览文件 @ 559d3632
......@@ -11,7 +11,7 @@
# 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 argparse
import paddle.fluid as fluid
import paddle.fluid.core as core
......@@ -78,19 +78,21 @@ def train(use_cuda, train_program, params_dirname):
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['img', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
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))
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, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(numpy.array, event.metrics)))
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(
......@@ -116,7 +118,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
results = inferencer.infer({'img': tensor_img})
print("infer results: ", results[0])
print(("infer results: ", results[0]))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py
浏览文件 @ 559d3632
......@@ -11,7 +11,7 @@
# 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 argparse
import paddle.fluid as fluid
import paddle
......@@ -61,14 +61,14 @@ def train(use_cuda, train_program, params_dirname):
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['img', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
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))
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.")
......@@ -96,7 +96,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
results = inferencer.infer({'img': tensor_img})
print("infer results: ", results[0])
print(("infer results: ", results[0]))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py
浏览文件 @ 559d3632
......@@ -180,14 +180,15 @@ def train(use_cuda, train_program, params_dirname):
# get avg cost
avg_cost = np.array(avg_cost_set).mean()
print("avg_cost: %s" % avg_cost)
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)))
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.")
......@@ -239,7 +240,7 @@ def infer(use_cuda, inference_program, params_dirname):
},
return_numpy=False)
print("infer results: ", np.array(results[0]))
print(("infer results: ", np.array(results[0])))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py
浏览文件 @ 559d3632
......@@ -12,8 +12,6 @@
# 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
from functools import partial
......@@ -84,21 +82,21 @@ def train(use_cuda, train_program, params_dirname):
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
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))
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, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(np.array, event.metrics)))
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()
......@@ -140,7 +138,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
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)
print(("infer results: ", results))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py
浏览文件 @ 559d3632
......@@ -12,8 +12,6 @@
# 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
from functools import partial
......@@ -99,21 +97,21 @@ def train(use_cuda, train_program, params_dirname):
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
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))
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, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(np.array, event.metrics)))
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()
......@@ -155,7 +153,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
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)
print(("infer results: ", results))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py
浏览文件 @ 559d3632
......@@ -12,8 +12,6 @@
# 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
from functools import partial
......@@ -93,21 +91,21 @@ def train(use_cuda, train_program, params_dirname):
avg_cost, acc = trainer.test(
reader=test_reader, feed_order=['words', 'label'])
print("avg_cost: %s" % avg_cost)
print("acc : %s" % acc)
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))
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, fluid.EndStepEvent):
print("Step {0}, Epoch {1} Metrics {2}".format(
event.step, event.epoch, map(np.array, event.metrics)))
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()
......@@ -150,7 +148,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
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)
print(("infer results: ", results))
def main(use_cuda):
......
python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py
浏览文件 @ 559d3632
......@@ -98,7 +98,7 @@ def train(use_cuda, train_program, params_dirname):
reader=test_reader,
feed_order=['firstw', 'secondw', 'thirdw', 'forthw', 'nextw'])
avg_cost = outs[0]
print("loss= ", avg_cost)
print(("loss= ", avg_cost))
if avg_cost < 10.0:
trainer.save_params(params_dirname)
......@@ -149,7 +149,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
'forthw': fourth_word
},
return_numpy=False)
print(np.array(result[0]))
print((np.array(result[0])))
def main(use_cuda, is_sparse):
......
python/paddle/fluid/tests/book/notest_understand_sentiment.py
浏览文件 @ 559d3632
......@@ -11,7 +11,7 @@
# 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 paddle.fluid.layers.device import get_places
import unittest
import paddle.fluid as fluid
......@@ -175,12 +175,12 @@ def train(word_dict,
def train_loop(main_program):
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
for pass_id in range(PASS_NUM):
for data in train_data():
cost_val, acc_val = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
print(("cost=" + str(cost_val) + " acc=" + str(acc_val)))
if cost_val < 0.4 and acc_val > 0.8:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ["words"],
......@@ -261,10 +261,10 @@ def infer(word_dict, use_cuda, save_dirname=None):
feed={feed_target_names[0]: tensor_words},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].recursive_sequence_lengths())
print((results[0].recursive_sequence_lengths()))
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
print(("Inference Shape: ", np_data.shape))
print(("Inference results: ", np_data))
def main(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
......
python/paddle/fluid/tests/book/test_fit_a_line.py
浏览文件 @ 559d3632
......@@ -114,7 +114,7 @@ def infer(use_cuda, save_dirname=None):
test_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=batch_size)
test_data = test_reader().next()
test_data = next(test_reader())
test_feat = numpy.array(
[data[0] for data in test_data]).astype("float32")
test_label = numpy.array(
......@@ -124,9 +124,9 @@ def infer(use_cuda, save_dirname=None):
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)
print(("infer shape: ", results[0].shape))
print(("infer results: ", results[0]))
print(("ground truth: ", test_label))
def main(use_cuda, is_local=True):
......
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @ 559d3632
......@@ -12,8 +12,6 @@
# 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 contextlib
......@@ -165,10 +163,10 @@ def train(net_type, use_cuda, save_dirname, is_local):
acc_value = numpy.array(acc_list).mean()
avg_loss_value = numpy.array(avg_loss_list).mean()
print(
print((
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_value), float(acc_value)))
float(avg_loss_value), float(acc_value))))
if acc_value > 0.01: # Low threshold for speeding up CI
fluid.io.save_inference_model(save_dirname, ["pixel"],
......@@ -241,7 +239,7 @@ def infer(use_cuda, save_dirname=None):
np.testing.assert_almost_equal(
results[0][i], transpiler_results[0][i], decimal=5)
print("infer results: ", results[0])
print(("infer results: ", results[0]))
fluid.io.save_inference_model(save_dirname, feed_target_names,
fetch_targets, exe,
......
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ 559d3632
......@@ -181,7 +181,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
start_time = time.time()
batch_id = 0
for pass_id in xrange(PASS_NUM):
for pass_id in range(PASS_NUM):
for data in train_data():
cost = exe.run(main_program,
feed=feeder.feed(data),
......@@ -189,10 +189,10 @@ def train(use_cuda, save_dirname=None, is_local=True):
cost = cost[0]
if batch_id % 10 == 0:
print("avg_cost:" + str(cost))
print(("avg_cost:" + str(cost)))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
print(("second per batch: " + str(
(time.time() - start_time) / batch_id)))
# Set the threshold low to speed up the CI test
if float(cost) < 60.0:
if save_dirname is not None:
......@@ -333,9 +333,9 @@ def infer(use_cuda, save_dirname=None):
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].recursive_sequence_lengths())
print((results[0].recursive_sequence_lengths()))
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print(("Inference Shape: ", np_data.shape))
def main(use_cuda, is_local=True):
......
python/paddle/fluid/tests/book/test_machine_translation.py
浏览文件 @ 559d3632
......@@ -199,14 +199,14 @@ def train_main(use_cuda, is_sparse, is_local=True):
feeder = fluid.DataFeeder(feed_list, place)
batch_id = 0
for pass_id in xrange(1):
for pass_id in range(1):
for data in train_data():
outs = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
print(('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val)))
if batch_id > 3:
break
batch_id += 1
......@@ -273,7 +273,7 @@ def decode_main(use_cuda, is_sparse):
feeder = fluid.DataFeeder(feed_list, place)
for data in train_data():
feed_dict = feeder.feed(map(lambda x: [x[0]], data))
feed_dict = feeder.feed([[x[0]] for x in data])
feed_dict['init_ids'] = init_ids
feed_dict['init_scores'] = init_scores
......@@ -282,7 +282,7 @@ def decode_main(use_cuda, is_sparse):
feed=feed_dict,
fetch_list=[translation_ids, translation_scores],
return_numpy=False)
print result_ids.recursive_sequence_lengths()
print((result_ids.recursive_sequence_lengths()))
break
......
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @ 559d3632
......@@ -11,7 +11,6 @@
# 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.fluid.core as core
import math
......@@ -143,10 +142,10 @@ def train(nn_type,
params_filename=params_filename)
return
else:
print(
print((
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_val), float(acc_val)))
float(avg_loss_val), float(acc_val))))
if math.isnan(float(avg_loss_val)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Loss of recognize digits is too large")
......@@ -207,7 +206,7 @@ def infer(use_cuda,
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
print(("infer results: ", results[0]))
def main(use_cuda, parallel, nn_type, combine):
......
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @ 559d3632
......@@ -304,7 +304,7 @@ def infer(use_cuda, save_dirname=None):
},
fetch_list=fetch_targets,
return_numpy=False)
print("inferred score: ", np.array(results[0]))
print(("inferred score: ", np.array(results[0])))
def main(use_cuda):
......
python/paddle/fluid/tests/book/test_rnn_encoder_decoder.py
浏览文件 @ 559d3632
......@@ -175,15 +175,15 @@ def train(use_cuda, save_dirname=None):
feeder = fluid.DataFeeder(feed_list, place)
batch_id = 0
for pass_id in xrange(2):
for pass_id in range(2):
for data in train_data():
outs = exe.run(framework.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
print(('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val)))
if math.isnan(float(avg_cost_val[0])):
sys.exit("got NaN loss, training failed.")
if batch_id > 3:
......@@ -241,10 +241,10 @@ def infer(use_cuda, save_dirname=None):
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].recursive_sequence_lengths())
print((results[0].recursive_sequence_lengths()))
np_data = np.array(results[0])
print("Inference shape: ", np_data.shape)
print("Inference results: ", np_data)
print(("Inference shape: ", np_data.shape))
print(("Inference results: ", np_data))
def main(use_cuda):
......
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @ 559d3632
......@@ -85,9 +85,11 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
pd = fluid.layers.ParallelDo(places)
with pd.do():
avg_cost, predict_word = __network__(
list(
map(pd.read_input, [
first_word, second_word, third_word, forth_word, next_word
]))
first_word, second_word, third_word, forth_word,
next_word
])))
pd.write_output(avg_cost)
avg_cost = fluid.layers.mean(pd())
......@@ -202,9 +204,9 @@ def infer(use_cuda, save_dirname=None):
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].recursive_sequence_lengths())
print((results[0].recursive_sequence_lengths()))
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print(("Inference Shape: ", np_data.shape))
def main(use_cuda, is_sparse, is_parallel):
......
python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
浏览文件 @ 559d3632
......@@ -78,7 +78,7 @@ for pass_id in range(PASS_NUM):
if avg_loss_value[0] < 10.0:
exit(0) # if avg cost less than 10.0, we think our code is good.
print avg_loss_value[0]
print((avg_loss_value[0]))
if math.isnan(float(avg_loss_value)):
sys.exit("got NaN loss, training failed.")
exit(1)
python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
浏览文件 @ 559d3632
......@@ -12,8 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import sys
import paddle
......@@ -157,8 +155,8 @@ for pass_id in range(PASS_NUM):
fetch_list=[avg_cost, batch_acc, batch_size])
accuracy.add(value=acc, weight=weight)
pass_acc = accuracy.eval()
print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
print(("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" +
str(pass_acc)))
# this model is slow, so if we can train two mini batch, we think it works properly.
if i > 0:
exit(0)
......
python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py
浏览文件 @ 559d3632
......@@ -118,14 +118,14 @@ def main():
feeder = fluid.DataFeeder(feed_list, place)
batch_id = 0
for pass_id in xrange(10):
for pass_id in range(10):
for data in train_data():
outs = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
print(('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val)))
if batch_id > 2:
exit(0)
if math.isnan(float(avg_cost_val)):
......
python/paddle/fluid/tests/demo/fc_gan.py
浏览文件 @ 559d3632
......@@ -137,7 +137,7 @@ def main():
generated_img = exe.run(g_program,
feed={'noise': n},
fetch_list={g_img})[0]
real_data = numpy.array(map(lambda x: x[0], data)).astype('float32')
real_data = numpy.array([x[0] for x in data]).astype('float32')
real_data = real_data.reshape(num_true, 784)
total_data = numpy.concatenate([real_data, generated_img])
total_label = numpy.concatenate([
......@@ -150,7 +150,7 @@ def main():
feed={'img': total_data,
'label': total_label},
fetch_list={d_loss})[0]
for _ in xrange(NUM_TRAIN_TIMES_OF_DG):
for _ in range(NUM_TRAIN_TIMES_OF_DG):
n = numpy.random.uniform(
low=-1.0, high=1.0,
size=[2 * num_true * NOISE_SIZE]).astype('float32').reshape(
......@@ -158,8 +158,8 @@ def main():
dg_loss_np = exe.run(dg_program,
feed={'noise': n},
fetch_list={dg_loss})[0]
print("Pass ID={0}, Batch ID={1}, D-Loss={2}, DG-Loss={3}".format(
pass_id, batch_id, d_loss_np, dg_loss_np))
print(("Pass ID={0}, Batch ID={1}, D-Loss={2}, DG-Loss={3}".format(
pass_id, batch_id, d_loss_np, dg_loss_np)))
# generate image each batch
fig = plot(generated_img)
plt.savefig(
......
python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py
浏览文件 @ 559d3632
......@@ -36,7 +36,7 @@ if len(sys.argv) == 1:
else:
word_dict = load_vocab(sys.argv[1])
word_dict["<unk>"] = len(word_dict)
print "Dict dim = ", len(word_dict)
print("Dict dim = ", len(word_dict))
# input text data
data = fluid.layers.data(name="words", shape=[1], dtype="int64", lod_level=1)
......
python/paddle/fluid/tests/no_test_concurrency.py
浏览文件 @ 559d3632
......@@ -194,7 +194,7 @@ class TestRoutineOp(unittest.TestCase):
quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
for i in xrange(10):
for i in range(10):
fluid.channel_recv(ch1, result)
Print(result)
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 559d3632
......@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.framework import Program, program_guard
......@@ -47,7 +46,7 @@ class TestDetection(unittest.TestCase):
scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
self.assertIsNotNone(out)
self.assertEqual(out.shape[-1], 6)
print(str(program))
print((str(program)))
def test_detection_api(self):
program = Program()
......@@ -82,7 +81,7 @@ class TestDetection(unittest.TestCase):
self.assertIsNotNone(trg)
self.assertIsNotNone(trg_weight)
print(str(program))
print((str(program)))
def test_ssd_loss(self):
program = Program()
......@@ -106,7 +105,7 @@ class TestDetection(unittest.TestCase):
loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
self.assertIsNotNone(loss)
self.assertEqual(loss.shape[-1], 1)
print(str(program))
print((str(program)))
class TestPriorBox(unittest.TestCase):
......@@ -197,7 +196,7 @@ class TestDetectionMAP(unittest.TestCase):
map_out = layers.detection_map(detect_res, label, 21)
self.assertIsNotNone(map_out)
self.assertEqual(map_out.shape, (1, ))
print(str(program))
print((str(program)))
if __name__ == '__main__':
......
python/paddle/fluid/tests/test_error_clip.py
浏览文件 @ 559d3632
......@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import paddle
import paddle.fluid as fluid
......
python/paddle/fluid/tests/test_if_else_op.py
浏览文件 @ 559d3632
......@@ -76,15 +76,15 @@ class TestMNISTIfElseOp(unittest.TestCase):
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
x_data = np.array([x[0] for x in data]).astype("float32")
y_data = np.array([x[1] for x in data]).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
outs = exe.run(prog,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print outs[0]
print((outs[0]))
if outs[0] < 1.0:
return
self.assertFalse(True)
......@@ -131,15 +131,15 @@ class TestMNISTIfElseOp(unittest.TestCase):
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
x_data = np.array([x[0] for x in data]).astype("float32")
y_data = np.array([x[1] for x in data]).astype("int64")
y_data = y_data.reshape((y_data.shape[0], 1))
outs = exe.run(prog,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print outs[0]
print((outs[0]))
if outs[0] < 1.0:
return
self.assertFalse(True)
......
python/paddle/fluid/tests/unittests/benchmark.py
浏览文件 @ 559d3632
......@@ -20,7 +20,7 @@ import itertools
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from op_test import OpTest
from .op_test import OpTest
class BenchmarkSuite(OpTest):
......@@ -40,8 +40,7 @@ class BenchmarkSuite(OpTest):
expect_t = np.array(item_cpu_out)
actual = item_gpu_out
actual_t = np.array(item_gpu_out)
var_name = variable if isinstance(variable,
basestring) else variable.name
var_name = variable if isinstance(variable, str) else variable.name
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol),
......@@ -53,7 +52,7 @@ class BenchmarkSuite(OpTest):
def _get_input_names(self):
inputs = []
for name, value in self.inputs.iteritems():
for name, value in list(self.inputs.items()):
if isinstance(value, list):
inputs.extend([sub_name for sub_name, _ in value])
inputs.append(name)
......@@ -61,7 +60,7 @@ class BenchmarkSuite(OpTest):
def _get_output_names(self):
outputs = []
for var_name, var in self.outputs.iteritems():
for var_name, var in list(self.outputs.items()):
if isinstance(var, list):
for sub_var_name, sub_var in var:
outputs.append(sub_var_name)
......@@ -89,8 +88,8 @@ class BenchmarkSuite(OpTest):
for place in places:
elapses.append(self.timeit_output_with_place(place, iters))
for place, elapse in zip(places, elapses):
print("One pass of ({2}_op) at {0} cost {1}".format(
str(place), elapse, self.op_type))
print(("One pass of ({2}_op) at {0} cost {1}".format(
str(place), elapse, self.op_type)))
def timeit_grad_with_place(self, place, iters=100):
inputs_to_check = self._get_input_names()
......@@ -109,5 +108,5 @@ class BenchmarkSuite(OpTest):
for place in places:
elapses.append(self.timeit_grad_with_place(place, iters))
for place, elapse in zip(places, elapses):
print("One pass of ({2}_grad_op) at {0} cost {1}".format(
str(place), elapse, self.op_type))
print(("One pass of ({2}_grad_op) at {0} cost {1}".format(
str(place), elapse, self.op_type)))
python/paddle/fluid/tests/unittests/benchmark_sum_op.py
浏览文件 @ 559d3632
......@@ -16,8 +16,8 @@ import unittest
import numpy as np
import paddle.fluid as fluid
from benchmark import BenchmarkSuite
from op_test import OpTest
from .benchmark import BenchmarkSuite
from .op_test import OpTest
# This is a demo op test case for operator benchmarking and high resolution number stability alignment.
......
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @ 559d3632
......@@ -91,7 +91,7 @@ class TestParallelExecutorBase(unittest.TestCase):
first_loss, = run_executor(
exe=exe, feed=feed_dict, fetch_list=[loss.name])
for i in xrange(iter):
for i in range(iter):
run_executor(exe=exe, feed=feed_dict, fetch_list=[])
last_loss, = run_executor(
......@@ -99,8 +99,8 @@ class TestParallelExecutorBase(unittest.TestCase):
end = time.time()
if batch_size is not None:
print "%.4f Instance per second" % (
(batch_size * iter + 2) / (end - begin))
print(("%.4f Instance per second" % (
(batch_size * iter + 2) / (end - begin))))
avg_last_loss_val = np.array(last_loss).mean()
avg_first_loss_val = np.array(first_loss).mean()
......@@ -108,6 +108,6 @@ class TestParallelExecutorBase(unittest.TestCase):
float(avg_first_loss_val)):
sys.exit("got NaN loss, training failed.")
print first_loss, last_loss
print((first_loss, last_loss))
# self.assertGreater(first_loss[0], last_loss[0])
return first_loss, last_loss
python/paddle/fluid/tests/unittests/testsuite.py
浏览文件 @ 559d3632
......@@ -142,7 +142,7 @@ def append_input_output(block, op_proto, np_list, is_input, dtype):
def append_loss_ops(block, output_names):
mean_inputs = map(block.var, output_names)
mean_inputs = list(map(block.var, output_names))
# for item in mean_inputs:
# print(item)
# print("Item", item.dtype)
......
python/paddle/fluid/tests/unittests/transformer_model.py
浏览文件 @ 559d3632
......@@ -118,8 +118,9 @@ def multi_head_attention(queries,
# FIXME(guosheng): Decouple the program desc with batch_size.
return layers.reshape(
x=trans_x,
shape=map(int,
[batch_size, -1, trans_x.shape[2] * trans_x.shape[3]]))
shape=list(
map(int, [batch_size, -1, trans_x.shape[2] * trans_x.shape[3]
])))
def scaled_dot_product_attention(q, k, v, attn_bias, d_model, dropout_rate):
"""
......
python/paddle/fluid/trainer.py
浏览文件 @ 559d3632
......@@ -18,16 +18,15 @@ import errno
import shutil
import time
import core
import data_feeder
import executor
import framework
import io
from . import core
from . import data_feeder
from . import executor
from . import framework
from . import io
# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
import optimizer as opt_module
import parallel_executor
from transpiler import distribute_transpiler
from . import optimizer as opt_module
from . import parallel_executor
from .transpiler import distribute_transpiler
__all__ = [
'Trainer', 'BeginEpochEvent', 'EndEpochEvent', 'BeginStepEvent',
......@@ -614,11 +613,12 @@ def build_feed_var_list(program, feed_order):
if not isinstance(feed_order, dict):
raise TypeError(
"The 'feed_order' should be either None, list or dict.")
if not sorted(feed_order.values()) == range(len(feed_order)):
if not sorted(feed_order.values()) == list(range(len(feed_order))):
raise ValueError(
"The values of 'feed_order' should be a permutation of [0, len(feed_order))"
)
sorted_pair_list = sorted(feed_order.items(), key=lambda item: item[1])
sorted_pair_list = sorted(
list(feed_order.items()), key=lambda item: item[1])
feed_var_list = [
program.global_block().var(pair[0]) for pair in sorted_pair_list
]
......@@ -1036,7 +1036,7 @@ def _save_trainer_args(dirname, trainer_id, trainer_args):
cur_dir = _get_trainer_dir(dirname, trainer_id)
for name, value in trainer_args.iteritems():
for name, value in list(trainer_args.items()):
args_file = os.path.join(cur_dir, name)
with open(args_file, 'w') as f:
f.write(str(value))
......@@ -1168,10 +1168,10 @@ def _scroll_delete(dirname, max_num_checkpoints=3):
serial_num = _get_dir_serial(serial)
serial_map[serial_num] = serial
if len(serial_map.keys()) <= max_num_checkpoints:
if len(list(serial_map.keys())) <= max_num_checkpoints:
return
serials = serial_map.keys()
serials = list(serial_map.keys())
serials.sort(reverse=True)
serials = serials[max_num_checkpoints:]
for serial in serials:
......
python/paddle/fluid/transpiler/__init__.py
浏览文件 @ 559d3632
......@@ -12,10 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from distribute_transpiler import DistributeTranspiler, DistributeTranspilerConfig
from inference_transpiler import InferenceTranspiler
from memory_optimization_transpiler import memory_optimize, release_memory
from ps_dispatcher import HashName, RoundRobin
from .distribute_transpiler import DistributeTranspiler, DistributeTranspilerConfig
from .inference_transpiler import InferenceTranspiler
from .memory_optimization_transpiler import memory_optimize, release_memory
from .ps_dispatcher import HashName, RoundRobin
__all__ = [
"DistributeTranspiler", "InferenceTranspiler", "memory_optimize",
......
python/paddle/fluid/transpiler/details/__init__.py
浏览文件 @ 559d3632
......@@ -12,5 +12,5 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from program_utils import *
from ufind import *
from .program_utils import *
from .ufind import *
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ 559d3632
......@@ -17,8 +17,8 @@ def delete_ops(block, ops):
try:
start = list(block.ops).index(ops[0])
end = list(block.ops).index(ops[-1])
[block._remove_op(start) for _ in xrange(end - start + 1)]
except Exception, e:
[block._remove_op(start) for _ in range(end - start + 1)]
except Exception as e:
raise e
block.program._sync_with_cpp()
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 559d3632
......@@ -28,18 +28,17 @@ Steps to transpile pserver:
5. add listen_and_serv op
"""
from __future__ import print_function
import math
import random
import numpy as np
from ps_dispatcher import RoundRobin, HashName, PSDispatcher
from .ps_dispatcher import RoundRobin, HashName, PSDispatcher
from .. import core, framework
from ..framework import Program, default_main_program, \
default_startup_program, Block, \
Parameter, grad_var_name
from details import *
from .details import *
from functools import reduce
LOOKUP_TABLE_TYPE = "lookup_table"
LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
......@@ -102,7 +101,7 @@ def slice_variable(var_list, slice_count, min_block_size):
block_size += dim1 - remains
# update split_count after aligning
split_count = int(math.ceil(var_numel / float(block_size)))
for block_id in xrange(split_count):
for block_id in range(split_count):
curr_block_size = min(block_size, var_numel - (
(block_id) * block_size))
block = VarBlock(var.name, block_id, curr_block_size)
......@@ -218,7 +217,7 @@ class DistributeTranspiler(object):
# fc_w@GRAD_trainer_0, fc_w@GRAD_trainer_1 --> pserver1
# fc_b@GRAD_trainer_0, fc_b@GRAD_trainer_1 --> pserver2
# shuffle the map will avoid the uneven distribution above
grad_var_mapping_items = self.grad_var_mapping.items()
grad_var_mapping_items = list(self.grad_var_mapping.items())
if not self.config.slice_var_up:
random.seed(self.trainer_num)
random.shuffle(grad_var_mapping_items)
......@@ -278,7 +277,7 @@ class DistributeTranspiler(object):
self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])
# step4: Concat the parameters splits together after recv.
for varname, splited_var in self.param_var_mapping.iteritems():
for varname, splited_var in list(self.param_var_mapping.items()):
eps = []
for var in splited_var:
index = [v.name for v in recv_vars].index(var.name)
......@@ -302,7 +301,7 @@ class DistributeTranspiler(object):
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
})
for varname, splited_var in self.param_var_mapping.iteritems():
for varname, splited_var in list(self.param_var_mapping.items()):
if len(splited_var) <= 1:
continue
orig_param = program.global_block().vars[varname]
......@@ -371,7 +370,7 @@ class DistributeTranspiler(object):
dtype=v.dtype,
shape=v.shape)
if self.sync_mode and self.trainer_num > 1:
for trainer_id in xrange(self.trainer_num):
for trainer_id in range(self.trainer_num):
var = pserver_program.global_block().create_var(
name="%s.trainer_%d" % (orig_var_name, trainer_id),
persistable=False,
......@@ -556,7 +555,7 @@ class DistributeTranspiler(object):
# 1. create vars in pserver program to startup program
pserver_vars = pserver_program.global_block().vars
created_var_map = dict()
for _, var in pserver_vars.iteritems():
for _, var in list(pserver_vars.items()):
tmpvar = s_prog.global_block()._clone_variable(var)
created_var_map[var.name] = tmpvar
......@@ -989,11 +988,11 @@ class DistributeTranspiler(object):
var_mapping = dict()
for block_str in block_list:
varname, offset, size = block_str.split(":")
if not block_map.has_key(varname):
if varname not in block_map:
block_map[varname] = []
block_map[varname].append((long(offset), long(size)))
block_map[varname].append((int(offset), int(size)))
for varname, splited in block_map.iteritems():
for varname, splited in list(block_map.items()):
orig_var = program.global_block().var(varname)
if len(splited) == 1:
if self.sync_mode and add_trainer_suffix:
......@@ -1156,7 +1155,7 @@ class DistributeTranspiler(object):
grad_to_block_id.append(merged_var.name + ":" + str(optimize_block.idx))
if self.sync_mode and self.trainer_num > 1:
vars2merge = []
for i in xrange(self.trainer_num):
for i in range(self.trainer_num):
per_trainer_name = "%s.trainer_%d" % \
(merged_var_name, i)
vars2merge.append(pserver_block.vars[per_trainer_name])
......@@ -1204,7 +1203,7 @@ class DistributeTranspiler(object):
# learning rate variable has already be created by non-optimize op,
# don't create it once again.
lr_varname = opt_op.input(key)[0]
if pserver_block.vars.has_key(lr_varname):
if lr_varname in pserver_block.vars:
new_inputs[key] = pserver_block.vars[opt_op.input(key)[0]]
else:
origin_var = origin_program.global_block().vars[lr_varname]
......@@ -1244,7 +1243,7 @@ class DistributeTranspiler(object):
def _is_splited_grad_var(self, var, var_dict):
grad_block = None
for _, g in var_dict.iteritems():
for _, g in list(var_dict.items()):
if self._orig_varname(g.name) == self._orig_varname(var.name):
if g.name.find(".trainer_") == -1:
grad_block = g
......@@ -1254,7 +1253,7 @@ class DistributeTranspiler(object):
def _clone_lr_op(self, program, block, op):
inputs = self._get_input_map_from_op(
self.origin_program.global_block().vars, op)
for key, varlist in inputs.iteritems():
for key, varlist in list(inputs.items()):
if not isinstance(varlist, list):
varlist = [varlist]
for var in varlist:
......@@ -1263,7 +1262,7 @@ class DistributeTranspiler(object):
outputs = self._get_output_map_from_op(
self.origin_program.global_block().vars, op)
for key, varlist in outputs.iteritems():
for key, varlist in list(outputs.items()):
if not isinstance(varlist, list):
varlist = [varlist]
for var in varlist:
......@@ -1278,7 +1277,7 @@ class DistributeTranspiler(object):
# Append the ops for parameters that do not need to be optimized/updated
inputs = self._get_input_map_from_op(
self.origin_program.global_block().vars, opt_op)
for key, varlist in inputs.iteritems():
for key, varlist in list(inputs.items()):
if not isinstance(varlist, list):
varlist = [varlist]
for var in varlist:
......@@ -1288,7 +1287,7 @@ class DistributeTranspiler(object):
var, program.global_block().vars)
if grad_block:
inputs[key] = grad_block
elif not program.global_block().vars.has_key(var.name):
elif var.name not in program.global_block().vars:
program.global_block().create_var(
name=var.name,
persistable=var.persistable,
......@@ -1297,7 +1296,7 @@ class DistributeTranspiler(object):
outputs = self._get_output_map_from_op(
self.origin_program.global_block().vars, opt_op)
for key, varlist in outputs.iteritems():
for key, varlist in list(outputs.items()):
if not isinstance(varlist, list):
varlist = [varlist]
for var in varlist:
......@@ -1305,7 +1304,7 @@ class DistributeTranspiler(object):
var, program.global_block().vars)
if grad_block:
outputs[key] = grad_block
elif not program.global_block().vars.has_key(var.name):
elif var.name not in program.global_block().vars:
program.global_block()._clone_variable(var)
return optimize_block.append_op(
......@@ -1326,8 +1325,8 @@ class DistributeTranspiler(object):
def _create_ufind(self, optimize_ops):
# Create a unit find data struct by optimize ops
ufind = UnionFind(optimize_ops)
for i in xrange(len(optimize_ops)):
for j in xrange(i, len(optimize_ops)):
for i in range(len(optimize_ops)):
for j in range(i, len(optimize_ops)):
op1 = optimize_ops[i]
op2 = optimize_ops[j]
if self._is_op_connected(op1, op2):
......
python/paddle/fluid/transpiler/inference_transpiler.py
浏览文件 @ 559d3632
......@@ -305,6 +305,6 @@ class InferenceTranspiler(object):
args += current_op.output_arg_names
args = list(set(args)) # unique the input and output arguments
for var in self.block.vars.keys():
for var in list(self.block.vars.keys()):
if var not in args:
self.block._remove_var(var)
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ 559d3632
......@@ -16,6 +16,7 @@ from collections import defaultdict
from .. import core
from ..framework import Program, default_main_program, Parameter
from ..backward import _rename_arg_
from functools import reduce
dtype_to_size = {
core.VarDesc.VarType.FP16: 2,
......@@ -107,7 +108,7 @@ class ControlFlowGraph(object):
# Repeatedly apply liveness updates until the algorithm stablize
# on a complete set live input vars and live output vars.
while True:
for i in reversed(range(self.op_size)):
for i in reversed(list(range(self.op_size))):
live_in[i] = set(self._live_in[i])
live_out[i] = set(self._live_out[i])
for s in self._successors[i]:
......@@ -172,9 +173,10 @@ class ControlFlowGraph(object):
is_forward = i < self._forward_num
in_diff, out_diff = self._get_diff(self._live_in[i],
self._live_out[i])
can_optimize = filter(
lambda x: self._check_var_validity(block_desc, x, is_forward),
in_diff)
can_optimize = [
x for x in in_diff
if self._check_var_validity(block_desc, x, is_forward)
]
if can_optimize:
index = i + fwd_id + 1 if is_forward else i - self._forward_num + bwd_id + 1
delete_op = block_desc._insert_op(index)
......@@ -213,9 +215,10 @@ class ControlFlowGraph(object):
block_desc = op.block()
is_forward = i < self._forward_num
if self.pool:
defs_can_optimize = filter(
lambda x: self._check_var_validity(block_desc, x, is_forward),
self._defs[i])
defs_can_optimize = [
x for x in self._defs[i]
if self._check_var_validity(block_desc, x, is_forward)
]
out_pair = [
(x, self._find_var(block_desc, x, is_forward).shape())
for x in defs_can_optimize
......@@ -243,11 +246,11 @@ class ControlFlowGraph(object):
continue
if PRINT_LOG:
print(("Hit Cache !!!! cache pool index "
print((("Hit Cache !!!! cache pool index "
"is %d, var name is %s, "
"cached var name is %s, "
"var shape is %s ") % (index, x, cache_var,
str(cache_shape)))
str(cache_shape))))
self.pool.pop(index)
if x == cache_var:
break
......@@ -261,9 +264,10 @@ class ControlFlowGraph(object):
break
in_diff, _ = self._get_diff(self._live_in[i], self._live_out[i])
can_optimize = filter(
lambda x: self._check_var_validity(block_desc, x, is_forward),
in_diff)
can_optimize = [
x for x in in_diff
if self._check_var_validity(block_desc, x, is_forward)
]
if can_optimize:
for var_name in can_optimize:
self.pool.append((var_name, self._find_var(
......
python/paddle/fluid/unique_name.py
浏览文件 @ 559d3632
......@@ -67,7 +67,7 @@ def switch(new_generator=None):
@contextlib.contextmanager
def guard(new_generator=None):
if isinstance(new_generator, basestring):
if isinstance(new_generator, str):
new_generator = UniqueNameGenerator(new_generator)
old = switch(new_generator)
yield
......
python/paddle/reader/creator.py
浏览文件 @ 559d3632
......@@ -67,10 +67,10 @@ def recordio(paths, buf_size=100):
import recordio as rec
import paddle.reader.decorator as dec
import cPickle as pickle
import pickle as pickle
def reader():
if isinstance(paths, basestring):
if isinstance(paths, str):
path = paths
else:
path = ",".join(paths)
......
python/paddle/reader/decorator.py
浏览文件 @ 559d3632
......@@ -21,6 +21,7 @@ from threading import Thread
import subprocess
from six.moves.queue import Queue
from six.moves import zip_longest
import itertools
import random
import zlib
......@@ -42,7 +43,7 @@ def map_readers(func, *readers):
rs = []
for r in readers:
rs.append(r())
for e in itertools.imap(func, *rs):
for e in map(func, *rs):
yield e
return reader
......@@ -148,16 +149,16 @@ def compose(*readers, **kwargs):
for r in readers:
rs.append(r())
if not check_alignment:
for outputs in itertools.izip(*rs):
yield sum(map(make_tuple, outputs), ())
for outputs in zip(*rs):
yield sum(list(map(make_tuple, outputs)), ())
else:
for outputs in itertools.izip_longest(*rs):
for outputs in zip_longest(*rs):
for o in outputs:
if o is None:
# None will be not be present if compose is aligned
raise ComposeNotAligned(
"outputs of readers are not aligned.")
yield sum(map(make_tuple, outputs), ())
yield sum(list(map(make_tuple, outputs)), ())
return reader
......@@ -306,7 +307,7 @@ def xmap_readers(mapper, reader, process_num, buffer_size, order=False):
args = (in_queue, out_queue, mapper, out_order) if order else (
in_queue, out_queue, mapper)
workers = []
for i in xrange(process_num):
for i in range(process_num):
worker = Thread(target=target, args=args)
worker.daemon = True
workers.append(worker)
......
python/paddle/reader/tests/decorator_test.py
浏览文件 @ 559d3632
......@@ -136,7 +136,7 @@ class TestXmap(unittest.TestCase):
reader = paddle.reader.xmap_readers(mapper,
reader_creator_10(0),
tNum, size, order)
for n in xrange(3):
for n in range(3):
result = []
for i in reader():
result.append(i)
......@@ -156,7 +156,7 @@ class TestPipeReader(unittest.TestCase):
import tempfile
records = [str(i) for i in xrange(5)]
records = [str(i) for i in range(5)]
temp = tempfile.NamedTemporaryFile()
try:
with open(temp.name, 'w') as f:
......
tools/test_runner.py
浏览文件 @ 559d3632
......@@ -12,19 +12,20 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import os
import sys
import paddle.fluid as fluid
import importlib
import cStringIO
from six.moves import cStringIO
def main():
sys.path.append(os.getcwd())
some_test_failed = False
for module_name in sys.argv[1:]:
buffer = cStringIO.StringIO()
buffer = cStringIO()
main = fluid.Program()
startup = fluid.Program()
scope = fluid.core.Scope()
......@@ -37,8 +38,11 @@ def main():
res = unittest.TextTestRunner(stream=buffer).run(tests)
if not res.wasSuccessful():
some_test_failed = True
print >> sys.stderr, module_name, 'failed\n', buffer.getvalue(
)
print(
module_name,
'failed\n',
buffer.getvalue(),
file=sys.stderr)
if some_test_failed:
exit(1)
......
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