Upgrade Pylint version to 2.3.1

Makefile

@@ -592,7 +592,7 @@ cpplint:
 	--exclude_path src/operator/contrib/ctc_include
 
 pylint:
-	pylint --rcfile=$(ROOTDIR)/ci/other/pylintrc --ignore-patterns=".*\.so$$,.*\.dll$$,.*\.dylib$$" python/mxnet tools/caffe_converter/*.py
+	python3 -m pylint --rcfile=$(ROOTDIR)/ci/other/pylintrc --ignore-patterns=".*\.so$$,.*\.dll$$,.*\.dylib$$" python/mxnet tools/caffe_converter/*.py
 
 doc: docs
 

ci/docker/install/ubuntu_publish.sh

@@ -66,5 +66,5 @@ python2 get-pip.py
 
 apt-get remove -y python3-urllib3
 
-pip2 install nose cpplint==1.3.0 pylint==1.9.3 'numpy<=1.15.2,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3
-pip3 install nose cpplint==1.3.0 pylint==2.1.1 'numpy<=1.15.2,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3
+pip2 install nose cpplint==1.3.0 'numpy<=1.15.2,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3
+pip3 install nose cpplint==1.3.0 pylint==2.3.1 'numpy<=1.15.2,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3

ci/docker/install/ubuntu_python.sh

@@ -30,5 +30,5 @@ wget -nv https://bootstrap.pypa.io/get-pip.py
 python3 get-pip.py
 python2 get-pip.py
 
-pip2 install nose cpplint==1.3.0 pylint==1.9.3 'numpy<=1.15.2,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3
-pip3 install nose cpplint==1.3.0 pylint==2.1.1 'numpy<=1.15.2,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3
+pip2 install nose cpplint==1.3.0 'numpy<=1.15.2,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3
+pip3 install nose cpplint==1.3.0 pylint==2.3.1 'numpy<=1.15.2,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3

cpp-package/tests/travis/setup.sh

@@ -19,5 +19,5 @@
 
 
 if [ ${TASK} == "lint" ]; then
-    pip install cpplint 'pylint==1.4.4' 'astroid==1.3.6' --user
+    pip3 install cpplint 'pylint==2.3.1' --user
 fi

docs/install/requirements.txt

@@ -3,6 +3,6 @@ h5py==2.8.0rc1
 nose
 nose-timer
 numpy<=1.15.2,>=1.8.2
-pylint==1.8.3
+pylint==2.3.1; python_version >= '3.0'
 requests<2.19.0,>=2.18.4
 scipy==1.0.1

python/mxnet/base.py

@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=invalid-name, no-member, trailing-comma-tuple, bad-mcs-classmethod-argument
+# pylint: disable=invalid-name, no-member, trailing-comma-tuple, bad-mcs-classmethod-argument, unnecessary-pass
 """ctypes library of mxnet and helper functions."""
 from __future__ import absolute_import
 

python/mxnet/contrib/onnx/mx2onnx/_export_helper.py

@@ -40,7 +40,7 @@ def load_module(sym_filepath, params_filepath):
     params : params object
         Model weights including both arg and aux params.
     """
-    if not (os.path.isfile(sym_filepath) and os.path.isfile(params_filepath)):
+    if not (os.path.isfile(sym_filepath) and os.path.isfile(params_filepath)): # pylint: disable=no-else-raise
         raise ValueError("Symbol and params files provided are invalid")
     else:
         try:

python/mxnet/contrib/onnx/mx2onnx/_op_translations.py

@@ -762,7 +762,7 @@ def convert_leakyrelu(node, **kwargs):
     act_name = {"elu": "Elu", "leaky": "LeakyRelu", "prelu": "PRelu",
                 "selu": "Selu"}
 
-    if act_type == "prelu" or act_type == "selu":
+    if act_type in ("prelu", "selu"):
         node = onnx.helper.make_node(
             act_name[act_type],
             inputs=input_nodes,

python/mxnet/contrib/onnx/onnx2mx/_translation_utils.py

@@ -178,7 +178,7 @@ def _fix_channels(op_name, attrs, inputs, proto_obj):
     these attributes. We check the shape of weights provided to get the number.
     """
     weight_name = inputs[1].name
-    if not weight_name in proto_obj._params:
+    if not weight_name in proto_obj._params: # pylint: disable=no-else-raise
         raise ValueError("Unable to get channels/units attr from onnx graph.")
     else:
         wshape = proto_obj._params[weight_name].shape

python/mxnet/contrib/quantization.py

@@ -61,6 +61,7 @@ def _quantize_params(qsym, params, th_dict):
         if name.endswith(('weight_quantize', 'bias_quantize')):
             original_name = name[:-len('_quantize')]
             param = params[original_name]
+            # pylint: disable=unbalanced-tuple-unpacking
             val, vmin, vmax = ndarray.contrib.quantize(data=param,
                                                        min_range=ndarray.min(param),
                                                        max_range=ndarray.max(param),

python/mxnet/contrib/text/vocab.py

@@ -210,7 +210,7 @@ def to_tokens(self, indices):
 
         tokens = []
         for idx in indices:
-            if not isinstance(idx, int) or idx > max_idx:
+            if not isinstance(idx, int) or idx > max_idx: # pylint: disable=no-else-raise
                 raise ValueError('Token index %d in the provided `indices` is invalid.' % idx)
             else:
                 tokens.append(self.idx_to_token[idx])

python/mxnet/gluon/parameter.py

@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=
+# pylint: disable=unnecessary-pass
 """Neural network parameter."""
 __all__ = ['DeferredInitializationError', 'Parameter', 'Constant',
            'ParameterDict', 'tensor_types']

python/mxnet/gluon/trainer.py

@@ -249,7 +249,7 @@ def _init_kvstore(self):
 
     @property
     def learning_rate(self):
-        if not isinstance(self._optimizer, opt.Optimizer):
+        if not isinstance(self._optimizer, opt.Optimizer): # pylint: disable=no-else-raise
             raise UserWarning("Optimizer has to be defined before its learning "
                               "rate can be accessed.")
         else:
@@ -263,7 +263,7 @@ def set_learning_rate(self, lr):
         lr : float
             The new learning rate of the optimizer.
         """
-        if not isinstance(self._optimizer, opt.Optimizer):
+        if not isinstance(self._optimizer, opt.Optimizer): # pylint: disable=no-else-raise
             raise UserWarning("Optimizer has to be defined before its learning "
                               "rate is mutated.")
         else:

python/mxnet/gluon/utils.py

@@ -340,7 +340,7 @@ def download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify_
                 break
             except Exception as e:
                 retries -= 1
-                if retries <= 0:
+                if retries <= 0: # pylint: disable=no-else-raise
                     raise e
                 else:
                     print('download failed due to {}, retrying, {} attempt{} left'

python/mxnet/image/detection.py

@@ -809,7 +809,7 @@ def next(self):
         pad = batch_size - i
         # handle padding for the last batch
         if pad != 0:
-            if self.last_batch_handle == 'discard':
+            if self.last_batch_handle == 'discard': # pylint: disable=no-else-raise
                 raise StopIteration
             # if the option is 'roll_over', throw StopIteration and cache the data
             elif self.last_batch_handle == 'roll_over' and \

python/mxnet/image/image.py

@@ -1374,7 +1374,7 @@ def next(self):
         pad = batch_size - i
         # handle padding for the last batch
         if pad != 0:
-            if self.last_batch_handle == 'discard':
+            if self.last_batch_handle == 'discard': # pylint: disable=no-else-raise
                 raise StopIteration
             # if the option is 'roll_over', throw StopIteration and cache the data
             elif self.last_batch_handle == 'roll_over' and \

python/mxnet/io/io.py

@@ -15,6 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 
+# coding: utf-8
+# pylint: disable=unnecessary-pass
 """Data iterators for common data formats."""
 from __future__ import absolute_import
 from collections import namedtuple

python/mxnet/model.py

@@ -640,7 +640,7 @@ def _init_iter(self, X, y, is_train):
         """Initialize the iterator given input."""
         if isinstance(X, (np.ndarray, nd.NDArray)):
             if y is None:
-                if is_train:
+                if is_train: # pylint: disable=no-else-raise
                     raise ValueError('y must be specified when X is numpy.ndarray')
                 else:
                     y = np.zeros(X.shape[0])

python/mxnet/module/base_module.py

@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# pylint: disable=fixme, too-many-arguments, too-many-locals
+# pylint: disable=fixme, too-many-arguments, too-many-locals, no-else-raise
 # pylint: disable=too-many-public-methods, too-many-branches, too-many-lines
 """`BaseModule` defines an API for modules."""
 

python/mxnet/module/python_module.py

@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-# pylint: disable=too-many-instance-attributes, too-many-arguments
+# pylint: disable=too-many-instance-attributes, too-many-arguments, unnecessary-pass
 """Provide some handy classes for user to implement a simple computation module
 in Python easily.
 """

python/mxnet/ndarray/contrib.py

@@ -514,7 +514,7 @@ def isfinite(data):
     [0. 0. 0. 1.]
     <NDArray 4 @cpu(0)>
     """
-    is_data_not_nan = data == data
+    is_data_not_nan = data == data # pylint: disable=comparison-with-itself
     is_data_not_infinite = data.abs() != np.inf
     return ndarray.logical_and(is_data_not_infinite, is_data_not_nan)
 
@@ -542,7 +542,7 @@ def isnan(data):
     [1. 0.]
     <NDArray 2 @cpu(0)>
     """
-    return data != data
+    return data != data # pylint: disable=comparison-with-itself
 
 def adamw_update(weight, grad, mean, var, rescale_grad, lr, eta, beta1=0.9, beta2=0.999,
                  epsilon=1e-8, wd=0, clip_gradient=-1, out=None, name=None, **kwargs):

python/mxnet/ndarray/sparse.py

@@ -639,7 +639,7 @@ def __getitem__(self, key):
         if isinstance(key, int):
             raise Exception("__getitem__ with int key is not implemented for RowSparseNDArray yet")
         if isinstance(key, py_slice):
-            if key.step is not None or key.start is not None or key.stop is not None:
+            if key.step is not None or key.start is not None or key.stop is not None: # pylint: disable=no-else-raise
                 raise Exception('RowSparseNDArray only supports [:] for __getitem__')
             else:
                 return self
@@ -1102,7 +1102,7 @@ def row_sparse_array(arg1, shape=None, ctx=None, dtype=None):
     # construct a row sparse array from (D0, D1 ..) or (data, indices)
     if isinstance(arg1, tuple):
         arg_len = len(arg1)
-        if arg_len < 2:
+        if arg_len < 2: # pylint: disable=no-else-raise
             raise ValueError("Unexpected length of input tuple: " + str(arg_len))
         elif arg_len > 2:
             # empty ndarray with shape

python/mxnet/ndarray_doc.py

@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=unused-argument, too-many-arguments
+# pylint: disable=unused-argument, too-many-arguments, unnecessary-pass
 """Extra symbol documents"""
 from __future__ import absolute_import as _abs
 import re as _re

python/mxnet/operator.py

@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=invalid-name, protected-access, too-many-arguments, no-self-use, too-many-locals, broad-except, too-many-lines
+# pylint: disable=invalid-name, protected-access, too-many-arguments, no-self-use, too-many-locals, broad-except, too-many-lines, unnecessary-pass
 """numpy interface for operators."""
 from __future__ import absolute_import
 

python/mxnet/optimizer/optimizer.py

@@ -298,7 +298,7 @@ def set_learning_rate(self, lr):
         lr : float
             The new learning rate of the optimizer.
         """
-        if self.lr_scheduler is not None:
+        if self.lr_scheduler is not None: # pylint: disable=no-else-raise
             raise UserWarning("LRScheduler of the optimizer has already been "
                               "defined. Note that set_learning_rate can mutate "
                               "the value of the learning rate of the optimizer "

python/mxnet/recordio.py

@@ -80,6 +80,8 @@ def open(self):
             self.writable = False
         else:
             raise ValueError("Invalid flag %s"%self.flag)
+        # pylint: disable=not-callable
+        # It's bug from pylint(astroid). See https://github.com/PyCQA/pylint/issues/1699
         self.pid = current_process().pid
         self.is_open = True
 
@@ -114,6 +116,8 @@ def __setstate__(self, d):
 
     def _check_pid(self, allow_reset=False):
         """Check process id to ensure integrity, reset if in new process."""
+        # pylint: disable=not-callable
+        # It's bug from pylint(astroid). See https://github.com/PyCQA/pylint/issues/1699
         if not self.pid == current_process().pid:
             if allow_reset:
                 self.reset()

python/mxnet/rnn/rnn_cell.py

@@ -515,7 +515,7 @@ def __call__(self, inputs, states):
                                     bias=self._hB,
                                     num_hidden=self._num_hidden * 3,
                                     name="%s_h2h" % name)
-
+        # pylint: disable=unbalanced-tuple-unpacking
         i2h_r, i2h_z, i2h = symbol.SliceChannel(i2h, num_outputs=3, name="%s_i2h_slice" % name)
         h2h_r, h2h_z, h2h = symbol.SliceChannel(h2h, num_outputs=3, name="%s_h2h_slice" % name)
 
@@ -1419,7 +1419,7 @@ def __call__(self, inputs, states):
         seq_idx = self._counter
         name = '%st%d_' % (self._prefix, seq_idx)
         i2h, h2h = self._conv_forward(inputs, states, name)
-
+        # pylint: disable=unbalanced-tuple-unpacking
         i2h_r, i2h_z, i2h = symbol.SliceChannel(i2h, num_outputs=3, name="%s_i2h_slice" % name)
         h2h_r, h2h_z, h2h = symbol.SliceChannel(h2h, num_outputs=3, name="%s_h2h_slice" % name)
 

python/mxnet/symbol/symbol.py

@@ -1013,7 +1013,6 @@ def _infer_type_impl(self, partial, *args, **kwargs):
             return (arg_types, out_types, aux_types)
         else:
             return (None, None, None)
-            # pylint: enable=too-many-locals
 
     def infer_shape(self, *args, **kwargs):
         """Infers the shapes of all arguments and all outputs given the known shapes of
@@ -1071,6 +1070,7 @@ def infer_shape(self, *args, **kwargs):
             List of auxiliary state shapes.
             The order is same as the order of list_auxiliary_states().
         """
+        # pylint: disable=too-many-locals
         try:
             res = self._infer_shape_impl(False, *args, **kwargs)
             if res[1] is None:

python/mxnet/test_utils.py

@@ -206,7 +206,7 @@ def _get_powerlaw_dataset_csr(num_rows, num_cols, density=0.1, dtype=None):
                 return mx.nd.array(output_arr).tostype("csr")
         col_max = col_max * 2
 
-    if unused_nnz > 0:
+    if unused_nnz > 0: # pylint: disable=no-else-raise
         raise ValueError("not supported for this density: %s"
                          " for this shape (%s,%s)" % (density, num_rows, num_cols))
     else:
@@ -1348,7 +1348,7 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
             except AssertionError as e:
                 print('Predict Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
                 traceback.print_exc()
-                if raise_on_err:
+                if raise_on_err: # pylint: disable=no-else-raise
                     raise e
                 else:
                     print(str(e))
@@ -1375,7 +1375,7 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
                 except AssertionError as e:
                     print('Train Err: ctx %d vs ctx %d at %s'%(i, max_idx, name))
                     traceback.print_exc()
-                    if raise_on_err:
+                    if raise_on_err: # pylint: disable=no-else-raise
                         raise e
                     else:
                         print(str(e))
@@ -1455,7 +1455,7 @@ def download(url, fname=None, dirname=None, overwrite=False, retries=5):
                 break
         except Exception as e:
             retries -= 1
-            if retries <= 0:
+            if retries <= 0: # pylint: disable=no-else-raise
                 raise e
             else:
                 print("download failed, retrying, {} attempt{} left"
@@ -1536,7 +1536,7 @@ def get_mnist_iterator(batch_size, input_shape, num_parts=1, part_index=0):
     """
 
     get_mnist_ubyte()
-    flat = False if len(input_shape) == 3 else True
+    flat = False if len(input_shape) == 3 else True # pylint: disable=simplifiable-if-expression
 
     train_dataiter = mx.io.MNISTIter(
         image="data/train-images-idx3-ubyte",
@@ -1990,15 +1990,15 @@ def compare_optimizer(opt1, opt2, shape, dtype, w_stype='default', g_stype='defa
     if w_stype == 'default':
         w2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
         w1 = w2.copyto(default_context())
-    elif w_stype == 'row_sparse' or w_stype == 'csr':
+    elif w_stype in ('row_sparse', 'csr'):
         w2 = rand_ndarray(shape, w_stype, density=1, dtype=dtype)
         w1 = w2.copyto(default_context()).tostype('default')
     else:
         raise Exception("type not supported yet")
     if g_stype == 'default':
         g2 = mx.random.uniform(shape=shape, ctx=default_context(), dtype=dtype)
         g1 = g2.copyto(default_context())
-    elif g_stype == 'row_sparse' or g_stype == 'csr':
+    elif g_stype in ('row_sparse', 'csr'):
         g2 = rand_ndarray(shape, g_stype, dtype=dtype)
         g1 = g2.copyto(default_context()).tostype('default')
     else:

tools/caffe_converter/compare_layers.py

@@ -143,8 +143,6 @@ def convert_and_compare_caffe_to_mxnet(image_url, gpu, caffe_prototxt_path, caff
     compare_layers_from_nets(caffe_net, arg_params, aux_params, exe, layer_name_to_record,
                              top_to_layers, mean_diff_allowed, max_diff_allowed)
 
-    return
-
 
 def _bfs(root_node, process_node):
     """
@@ -280,7 +278,6 @@ def _process_layer_parameters(layer):
             warnings.warn('No handling for layer %s of type %s, should we ignore it?', layer.name,
                           layer.type)
 
-        return
 
     def _process_layer_output(caffe_blob_name):
 
@@ -332,8 +329,6 @@ def _process_layer_output(caffe_blob_name):
     for caffe_blob_name in caffe_net.blobs.keys():
         _process_layer_output(caffe_blob_name)
 
-    return
-
 
 def main():
     """Entrypoint for compare_layers"""

tools/caffe_converter/test_converter.py

@@ -76,8 +76,6 @@ def test_model_weights_and_outputs(model_name, image_url, gpu):
     convert_and_compare_caffe_to_mxnet(image_url, gpu, prototxt, caffemodel, mean,
                                        mean_diff_allowed=1e-03, max_diff_allowed=1e-01)
 
-    return
-
 
 def main():
     """Entrypoint for test_converter"""