Upgrade string literals to raw string (#28989)

* upgrade comment string to raw string

* fix string in

* fix string with ' '

* revert update on comments

* upgrade only necessary

* fix sample code checker

* fix comments with '''

paddle/scripts/conda_build.py

@@ -1,4 +1,19 @@
 #!/bin/python
+
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 #
 import platform
 from sys import argv
@@ -120,7 +135,7 @@ def __init__(self):
         self.py_str = ["py27", "py35", "py36", "py37"]
         self.pip_end = ".whl --no-deps"
         self.pip_prefix_linux = "pip install /package/paddlepaddle"
-        self.pip_prefix_windows = "pip install C:\package\paddlepaddle"
+        self.pip_prefix_windows = r"pip install C:\package\paddlepaddle"
         self.pip_gpu = "_gpu-"
         self.pip_cpu = "-"
         self.mac_pip = [
@@ -216,15 +231,15 @@ def meta_build_windows(var,
     - matplotlib"""
     if not (cuda_str == None):
         meta_str = meta_str + cuda_str
-    
+
     blt_str = var.blt_const + blt_var
     if (python_str == var.python27):
         blt_str = blt_str + """
     pip install C:\package\opencv_python-4.2.0.32-cp27-cp27m-win_amd64.whl"""
     else:
         meta_str = meta_str + """
     - opencv>=3.4.2"""
-    
+
     meta_str = meta_str + var.test + var.about
     meta_filename = "meta.yaml"
     build_filename = "bld.bat"

python/paddle/dataset/imdb.py

@@ -116,8 +116,8 @@ def train(word_idx):
     :rtype: callable
     """
     return reader_creator(
-        re.compile("aclImdb/train/pos/.*\.txt$"),
-        re.compile("aclImdb/train/neg/.*\.txt$"), word_idx)
+        re.compile(r"aclImdb/train/pos/.*\.txt$"),
+        re.compile(r"aclImdb/train/neg/.*\.txt$"), word_idx)
 
 
 @deprecated(
@@ -137,8 +137,8 @@ def test(word_idx):
     :rtype: callable
     """
     return reader_creator(
-        re.compile("aclImdb/test/pos/.*\.txt$"),
-        re.compile("aclImdb/test/neg/.*\.txt$"), word_idx)
+        re.compile(r"aclImdb/test/pos/.*\.txt$"),
+        re.compile(r"aclImdb/test/neg/.*\.txt$"), word_idx)
 
 
 @deprecated(
@@ -153,7 +153,7 @@ def word_dict():
     :rtype: dict
     """
     return build_dict(
-        re.compile("aclImdb/((train)|(test))/((pos)|(neg))/.*\.txt$"), 150)
+        re.compile(r"aclImdb/((train)|(test))/((pos)|(neg))/.*\.txt$"), 150)
 
 
 @deprecated(

python/paddle/dataset/tests/imdb_test.py

@@ -18,13 +18,13 @@
 import unittest
 import re
 
-TRAIN_POS_PATTERN = re.compile("aclImdb/train/pos/.*\.txt$")
-TRAIN_NEG_PATTERN = re.compile("aclImdb/train/neg/.*\.txt$")
-TRAIN_PATTERN = re.compile("aclImdb/train/.*\.txt$")
+TRAIN_POS_PATTERN = re.compile(r"aclImdb/train/pos/.*\.txt$")
+TRAIN_NEG_PATTERN = re.compile(r"aclImdb/train/neg/.*\.txt$")
+TRAIN_PATTERN = re.compile(r"aclImdb/train/.*\.txt$")
 
-TEST_POS_PATTERN = re.compile("aclImdb/test/pos/.*\.txt$")
-TEST_NEG_PATTERN = re.compile("aclImdb/test/neg/.*\.txt$")
-TEST_PATTERN = re.compile("aclImdb/test/.*\.txt$")
+TEST_POS_PATTERN = re.compile(r"aclImdb/test/pos/.*\.txt$")
+TEST_NEG_PATTERN = re.compile(r"aclImdb/test/neg/.*\.txt$")
+TEST_PATTERN = re.compile(r"aclImdb/test/.*\.txt$")
 
 
 class TestIMDB(unittest.TestCase):

python/paddle/distributed/fleet/base/distributed_strategy.py

@@ -862,7 +862,7 @@ def dgc(self, flag):
 
     @property
     def dgc_configs(self):
-        """
+        r"""
         Set Deep Gradient Compression training configurations. In general, dgc has serveral configurable
         settings that can be configured through a dict.
 

python/paddle/distributed/fleet/launch.py

@@ -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.
-"""
+r"""
 fleetrun is a module that spawns multiple distributed
 process on each training node for gpu training and cpu training.
 Usage:

python/paddle/distributed/fleet/meta_optimizers/parameter_server_optimizer.py

@@ -158,13 +158,13 @@ def get_sys_free_mem():
                     ['vm_stat'], stdout=subprocess.PIPE).communicate()[0]
                 # Process vm_stat
                 vmLines = vm.split('\n')
-                sep = re.compile(':[\s]+')
+                sep = re.compile(r':[\s]+')
                 vmStats = {}
                 for row in range(1, len(vmLines) - 2):
                     rowText = vmLines[row].strip()
                     rowElements = sep.split(rowText)
                     vmStats[(rowElements[0]
-                             )] = int(rowElements[1].strip('\.')) * 4096
+                             )] = int(rowElements[1].strip(r'\.')) * 4096
                 return vmStats["Pages free"]
             elif platform.system() == "Linux":
                 mems = {}

python/paddle/distributed/launch.py

@@ -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.
-"""
+r"""
 paddle.distributed.launch is a module that spawns multiple distributed 
 process on each training node for gpu training.
 Usage:

python/paddle/distribution.py

@@ -166,7 +166,7 @@ def _check_values_dtype_in_probs(self, param, value):
 
 
 class Uniform(Distribution):
-    """Uniform distribution with `low` and `high` parameters.
+    r"""Uniform distribution with `low` and `high` parameters.
 
     Mathematical Details
 
@@ -374,7 +374,7 @@ def probs(self, value):
         return elementwise_div((lb * ub), (self.high - self.low), name=name)
 
     def entropy(self):
-        """Shannon entropy in nats.
+        r"""Shannon entropy in nats.
 
         The entropy is
 
@@ -391,7 +391,7 @@ def entropy(self):
 
 
 class Normal(Distribution):
-    """The Normal distribution with location `loc` and `scale` parameters.
+    r"""The Normal distribution with location `loc` and `scale` parameters.
 
     Mathematical details
 
@@ -534,7 +534,7 @@ def sample(self, shape, seed=0):
                 return output
 
     def entropy(self):
-        """Shannon entropy in nats.
+        r"""Shannon entropy in nats.
 
         The entropy is
 
@@ -599,7 +599,7 @@ def probs(self, value):
             name=name)
 
     def kl_divergence(self, other):
-        """The KL-divergence between two normal distributions.
+        r"""The KL-divergence between two normal distributions.
 
         The probability density function (pdf) is
 
@@ -644,7 +644,7 @@ def kl_divergence(self, other):
 
 
 class Categorical(Distribution):
-    """
+    r"""
     Categorical distribution is a discrete probability distribution that 
     describes the possible results of a random variable that can take on 
     one of K possible categories, with the probability of each category 

python/paddle/fluid/clip.py

@@ -40,7 +40,7 @@ def _append_clip_op(self, block, grad_name):
 
 
 class ErrorClipByValue(BaseErrorClipAttr):
-    """
+    r"""
     Clips tensor values to the range [min, max].
 
     Given a tensor ``t`` (see Examples below), this operation clips its value \
@@ -241,7 +241,7 @@ def _create_operators(self, param, grad):
 
 
 class ClipGradByNorm(ClipGradBase):
-    """
+    r"""
     Limit the l2 norm of multi-dimensional Tensor :math:`X` to ``clip_norm`` .
     
     - If the l2 norm of :math:`X` is greater than ``clip_norm`` , :math:`X` will be compressed by a ratio.
@@ -343,7 +343,7 @@ def _create_operators(self, param, grad):
 
 
 class ClipGradByGlobalNorm(ClipGradBase):
-    """
+    r"""
     Given a list of Tensor :math:`t\_list` , calculate the global norm for the elements of all tensors in 
     :math:`t\_list` , and limit it to ``clip_norm`` .
     

python/paddle/fluid/contrib/layers/nn.py

@@ -137,7 +137,7 @@ def var_conv_2d(input,
                 act=None,
                 dtype='float32',
                 name=None):
-    """
+    r"""
     The var_conv_2d layer calculates the output base on the :attr:`input` with variable length,
     row, col, input channel, filter size and strides. Both :attr:`input`, :attr:`row`,
     and :attr:`col` are 1-level LodTensor. The convolution operation is same as conv2d layer with
@@ -477,7 +477,7 @@ def fused_embedding_seq_pool(input,
                              combiner='sum',
                              param_attr=None,
                              dtype='float32'):
-    """
+    r"""
     **Embedding Sequence pool**
 
     This layer is the fusion of lookup table and sequence_pool.
@@ -1442,7 +1442,7 @@ def batch_fc(input, param_size, param_attr, bias_size, bias_attr, act=None):
 
 
 def _pull_box_extended_sparse(input, size, extend_size=64, dtype='float32'):
-    """
+    r"""
     **Pull Box Extended Sparse Layer**
     This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
     BoxPS lookup table. The result of this lookup is the embedding of each ID in the
@@ -1640,7 +1640,7 @@ def fused_bn_add_act(x,
                      moving_variance_name=None,
                      act=None,
                      name=None):
-    """
+    r"""
     This Op performs batch norm on input x, and adds the result to input y. Then
     it performs activation on the sum. The data format of inputs must be NHWC
     `[batch, in_height, in_width, in_channels]`.

python/paddle/fluid/contrib/layers/rnn_impl.py

@@ -175,7 +175,7 @@ def basic_gru(input,
               activation=None,
               dtype='float32',
               name='basic_gru'):
-    """
+    r"""
     GRU implementation using basic operator, supports multiple layers and bidirectional gru.
 
     .. math::
@@ -418,7 +418,7 @@ def basic_lstm(input,
                forget_bias=1.0,
                dtype='float32',
                name='basic_lstm'):
-    """
+    r"""
     LSTM implementation using basic operators, supports multiple layers and bidirectional LSTM.
 
     .. math::
@@ -697,7 +697,7 @@ def get_single_direction_output(rnn_input,
 
 
 class BasicLSTMUnit(Layer):
-    """
+    r"""
     ****
     BasicLSTMUnit class, Using basic operator to build LSTM
     The algorithm can be described as the code below.

python/paddle/fluid/contrib/memory_usage_calc.py

@@ -44,7 +44,7 @@
 
 
 def memory_usage(program, batch_size):
-    """
+    r"""
     Get the estimate memory usage of program with input batch size.
 
     Args:

python/paddle/fluid/contrib/slim/quantization/imperative/qat.py

@@ -64,7 +64,7 @@ def __init__(self,
                  act_preprocess_layer=None,
                  weight_quantize_layer=None,
                  act_quantize_layer=None):
-        """
+        r"""
         The constructor for ImperativeQuantAware.
 
         Args:

python/paddle/fluid/contrib/slim/quantization/imperative/quant_nn.py

@@ -30,7 +30,7 @@
 
 
 class FakeQuantMovingAverage(layers.Layer):
-    """
+    r"""
     FakeQuantMovingAverage layer does the moving_average_abs_max quant and then dequant.
     Its computational formula is described as below:
 
@@ -128,7 +128,7 @@ def forward(self, input):
 
 
 class FakeQuantAbsMax(layers.Layer):
-    """
+    r"""
     FakeQuantAbsMax layer does the abs_max quant and then dequant.
     Its computational formula is described as below:
 
@@ -545,7 +545,7 @@ def forward(self, input):
 
 class MovingAverageAbsMaxScale(layers.Layer):
     def __init__(self, name=None, moving_rate=0.9, dtype='float32'):
-        """
+        r"""
         MovingAverageMaxScale layer is used to calculating the output quantization scale of Layer.
         Its computational formula is described as below:
 

python/paddle/fluid/contrib/slim/quantization/quant_int8_mkldnn_pass.py

@@ -37,7 +37,7 @@ class QuantInt8MkldnnPass(object):
     """
 
     def __init__(self, _scope=None, _place=None):
-        """
+        r"""
         Args:
             scope(fluid.Scope): scope is used to initialize the new parameters.
             place(fluid.CPUPlace): place is used to initialize the new parameters.

python/paddle/fluid/contrib/slim/quantization/quantization_pass.py

@@ -239,7 +239,7 @@ def __init__(self,
                  act_preprocess_func=None,
                  optimizer_func=None,
                  executor=None):
-        """
+        r"""
         Constructor.
 
         Args:

python/paddle/fluid/contrib/utils/hdfs_utils.py

@@ -33,7 +33,7 @@
 
 
 class HDFSClient(object):
-    """
+    r"""
     A tool of HDFS 
 
     Args:
@@ -376,7 +376,7 @@ def ls(self, hdfs_path):
             _logger.info("HDFS list path: {} successfully".format(hdfs_path))
 
             ret_lines = []
-            regex = re.compile('\s+')
+            regex = re.compile(r'\s+')
             out_lines = output.strip().split("\n")
             for line in out_lines:
                 re_line = regex.split(line)
@@ -418,7 +418,7 @@ def sort_by_time(v1, v2):
             _logger.info("HDFS list all files: {} successfully".format(
                 hdfs_path))
             lines = []
-            regex = re.compile('\s+')
+            regex = re.compile(r'\s+')
             out_lines = output.strip().split("\n")
             for line in out_lines:
                 re_line = regex.split(line)

python/paddle/fluid/core.py

@@ -224,7 +224,7 @@ def less_than_ver(a, b):
     import operator
 
     def to_list(s):
-        s = re.sub('(\.0+)+$', '', s)
+        s = re.sub(r'(\.0+)+$', '', s)
         return [int(x) for x in s.split('.')]
 
     return operator.lt(to_list(a), to_list(b))