[MXNET-1411] solve pylint error issue#14851 (apache#15113)

* fix pylint error: no-else-raise in _export_helper.py

* fix pylint error: no-else-raise in _translation_utils.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in vocab.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in trainer.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in utils.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in detection.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in image.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in model.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in sparse.py

* fix pylint error: Bad option value 'no-else-raise' (bad-option-value) in test_utils.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for vocab.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for model.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for _translation_utils.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for _export_helper.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for test_utils.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for image.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for trainer.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for detection.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for utils.py

* fix pylint error: R1720: Unnecessary else after raise (no-else-raise) for sparse.py

* fix pylint error:R1719: The if expression can be replaced with 'bool(test)' (simplifiable-if-expression)

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

@@ -40,26 +40,26 @@ 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)): # pylint: disable=no-else-raise
+    if not (os.path.isfile(sym_filepath) and os.path.isfile(params_filepath)):
         raise ValueError("Symbol and params files provided are invalid")
-    else:
-        try:
-            # reads symbol.json file from given path and
-            # retrieves model prefix and number of epochs
-            model_name = sym_filepath.rsplit('.', 1)[0].rsplit('-', 1)[0]
-            params_file_list = params_filepath.rsplit('.', 1)[0].rsplit('-', 1)
-            # Setting num_epochs to 0 if not present in filename
-            num_epochs = 0 if len(params_file_list) == 1 else int(params_file_list[1])
-        except IndexError:
-            logging.info("Model and params name should be in format: "
-                         "prefix-symbol.json, prefix-epoch.params")
-            raise
 
-        sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, num_epochs)
+    try:
+        # reads symbol.json file from given path and
+        # retrieves model prefix and number of epochs
+        model_name = sym_filepath.rsplit('.', 1)[0].rsplit('-', 1)[0]
+        params_file_list = params_filepath.rsplit('.', 1)[0].rsplit('-', 1)
+        # Setting num_epochs to 0 if not present in filename
+        num_epochs = 0 if len(params_file_list) == 1 else int(params_file_list[1])
+    except IndexError:
+        logging.info("Model and params name should be in format: "
+                     "prefix-symbol.json, prefix-epoch.params")
+        raise
 
-        # Merging arg and aux parameters
-        params = {}
-        params.update(arg_params)
-        params.update(aux_params)
+    sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, num_epochs)
 
-        return sym, params
+    # Merging arg and aux parameters
+    params = {}
+    params.update(arg_params)
+    params.update(aux_params)
+
+    return sym, params

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

@@ -178,23 +178,23 @@ 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: # pylint: disable=no-else-raise
+    if not weight_name in proto_obj._params:
         raise ValueError("Unable to get channels/units attr from onnx graph.")
-    else:
-        wshape = proto_obj._params[weight_name].shape
-        assert len(wshape) >= 2, "Weights shape is invalid: {}".format(wshape)
 
-        if op_name == 'FullyConnected':
-            attrs['num_hidden'] = wshape[0]
-        else:
-            if op_name == 'Convolution':
-                # Weight shape for Conv and FC: (M x C x kH x kW) : M is number of
-                # feature maps/hidden  and C is number of channels
-                attrs['num_filter'] = wshape[0]
-            elif op_name == 'Deconvolution':
-                # Weight shape for DeConv : (C x M x kH x kW) : M is number of
-                # feature maps/filters and C is number of channels
-                attrs['num_filter'] = wshape[1]
+    wshape = proto_obj._params[weight_name].shape
+    assert len(wshape) >= 2, "Weights shape is invalid: {}".format(wshape)
+
+    if op_name == 'FullyConnected':
+        attrs['num_hidden'] = wshape[0]
+    else:
+        if op_name == 'Convolution':
+            # Weight shape for Conv and FC: (M x C x kH x kW) : M is number of
+            # feature maps/hidden  and C is number of channels
+            attrs['num_filter'] = wshape[0]
+        elif op_name == 'Deconvolution':
+            # Weight shape for DeConv : (C x M x kH x kW) : M is number of
+            # feature maps/filters and C is number of channels
+            attrs['num_filter'] = wshape[1]
     return attrs
 
 

python/mxnet/contrib/text/vocab.py

@@ -210,9 +210,8 @@ def to_tokens(self, indices):
 
         tokens = []
         for idx in indices:
-            if not isinstance(idx, int) or idx > max_idx: # pylint: disable=no-else-raise
+            if not isinstance(idx, int) or idx > max_idx:
                 raise ValueError('Token index %d in the provided `indices` is invalid.' % idx)
-            else:
-                tokens.append(self.idx_to_token[idx])
+            tokens.append(self.idx_to_token[idx])
 
         return tokens[0] if to_reduce else tokens

python/mxnet/gluon/trainer.py

@@ -249,11 +249,11 @@ def _init_kvstore(self):
 
     @property
     def learning_rate(self):
-        if not isinstance(self._optimizer, opt.Optimizer): # pylint: disable=no-else-raise
+        if not isinstance(self._optimizer, opt.Optimizer):
             raise UserWarning("Optimizer has to be defined before its learning "
                               "rate can be accessed.")
-        else:
-            return self._optimizer.learning_rate
+
+        return self._optimizer.learning_rate
 
     @property
     def optimizer(self):
@@ -270,11 +270,11 @@ def set_learning_rate(self, lr):
         lr : float
             The new learning rate of the optimizer.
         """
-        if not isinstance(self._optimizer, opt.Optimizer): # pylint: disable=no-else-raise
+        if not isinstance(self._optimizer, opt.Optimizer):
             raise UserWarning("Optimizer has to be defined before its learning "
                               "rate is mutated.")
-        else:
-            self._optimizer.set_learning_rate(lr)
+
+        self._optimizer.set_learning_rate(lr)
 
     def _row_sparse_pull(self, parameter, out, row_id, full_idx=False):
         """Internal method to invoke pull operations on KVStore. If `full_idx` is set to True,

python/mxnet/gluon/utils.py

@@ -341,11 +341,11 @@ def download(url, path=None, overwrite=False, sha1_hash=None, retries=5, verify_
                 break
             except Exception as e:
                 retries -= 1
-                if retries <= 0: # pylint: disable=no-else-raise
+                if retries <= 0:
                     raise e
-                else:
-                    print('download failed due to {}, retrying, {} attempt{} left'
-                          .format(repr(e), retries, 's' if retries > 1 else ''))
+
+                print('download failed due to {}, retrying, {} attempt{} left'
+                      .format(repr(e), retries, 's' if retries > 1 else ''))
 
     return fname
 

python/mxnet/image/detection.py

@@ -809,23 +809,23 @@ def next(self):
         pad = batch_size - i
         # handle padding for the last batch
         if pad != 0:
-            if self.last_batch_handle == 'discard': # pylint: disable=no-else-raise
+            if self.last_batch_handle == 'discard':
                 raise StopIteration
             # if the option is 'roll_over', throw StopIteration and cache the data
-            elif self.last_batch_handle == 'roll_over' and \
+            if self.last_batch_handle == 'roll_over' and \
                 self._cache_data is None:
                 self._cache_data = batch_data
                 self._cache_label = batch_label
                 self._cache_idx = i
                 raise StopIteration
+
+            _ = self._batchify(batch_data, batch_label, i)
+            if self.last_batch_handle == 'pad':
+                self._allow_read = False
             else:
-                _ = self._batchify(batch_data, batch_label, i)
-                if self.last_batch_handle == 'pad':
-                    self._allow_read = False
-                else:
-                    self._cache_data = None
-                    self._cache_label = None
-                    self._cache_idx = None
+                self._cache_data = None
+                self._cache_label = None
+                self._cache_idx = None
 
         return io.DataBatch([batch_data], [batch_label], pad=pad)
 

python/mxnet/image/image.py

@@ -1198,10 +1198,10 @@ def __init__(self, batch_size, data_shape, label_width=1,
             logging.info('%s: loading recordio %s...',
                          class_name, path_imgrec)
             if path_imgidx:
-                self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')  # pylint: disable=redefined-variable-type
+                self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')
                 self.imgidx = list(self.imgrec.keys)
             else:
-                self.imgrec = recordio.MXRecordIO(path_imgrec, 'r')  # pylint: disable=redefined-variable-type
+                self.imgrec = recordio.MXRecordIO(path_imgrec, 'r')
                 self.imgidx = None
         else:
             self.imgrec = None
@@ -1224,7 +1224,7 @@ def __init__(self, batch_size, data_shape, label_width=1,
             imgkeys = []
             index = 1
             for img in imglist:
-                key = str(index)  # pylint: disable=redefined-variable-type
+                key = str(index)
                 index += 1
                 if len(img) > 2:
                     label = nd.array(img[:-1], dtype=dtype)
@@ -1374,23 +1374,23 @@ def next(self):
         pad = batch_size - i
         # handle padding for the last batch
         if pad != 0:
-            if self.last_batch_handle == 'discard': # pylint: disable=no-else-raise
+            if self.last_batch_handle == 'discard':
                 raise StopIteration
             # if the option is 'roll_over', throw StopIteration and cache the data
-            elif self.last_batch_handle == 'roll_over' and \
+            if self.last_batch_handle == 'roll_over' and \
                 self._cache_data is None:
                 self._cache_data = batch_data
                 self._cache_label = batch_label
                 self._cache_idx = i
                 raise StopIteration
+
+            _ = self._batchify(batch_data, batch_label, i)
+            if self.last_batch_handle == 'pad':
+                self._allow_read = False
             else:
-                _ = self._batchify(batch_data, batch_label, i)
-                if self.last_batch_handle == 'pad':
-                    self._allow_read = False
-                else:
-                    self._cache_data = None
-                    self._cache_label = None
-                    self._cache_idx = None
+                self._cache_data = None
+                self._cache_label = None
+                self._cache_idx = None
 
         return io.DataBatch([batch_data], [batch_label], pad=pad)
 

python/mxnet/model.py

@@ -642,10 +642,9 @@ 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: # pylint: disable=no-else-raise
+                if is_train:
                     raise ValueError('y must be specified when X is numpy.ndarray')
-                else:
-                    y = np.zeros(X.shape[0])
+                y = np.zeros(X.shape[0])
             if not isinstance(y, (np.ndarray, nd.NDArray)):
                 raise TypeError('y must be ndarray when X is numpy.ndarray')
             if X.shape[0] != y.shape[0]:

python/mxnet/ndarray/sparse.py

@@ -639,10 +639,10 @@ 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: # pylint: disable=no-else-raise
+            if key.step is not None or key.start is not None or key.stop is not None:
                 raise Exception('RowSparseNDArray only supports [:] for __getitem__')
-            else:
-                return self
+
+            return self
         if isinstance(key, tuple):
             raise ValueError('Multi-dimension indexing is not supported')
         raise ValueError('Undefined behaviour for {}'.format(key))
@@ -1102,9 +1102,9 @@ 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: # pylint: disable=no-else-raise
+        if arg_len < 2:
             raise ValueError("Unexpected length of input tuple: " + str(arg_len))
-        elif arg_len > 2:
+        if arg_len > 2:
             # empty ndarray with shape
             _check_shape(arg1, shape)
             return empty('row_sparse', arg1, ctx=ctx, dtype=dtype)

python/mxnet/test_utils.py

@@ -212,11 +212,11 @@ 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: # pylint: disable=no-else-raise
+    if unused_nnz > 0:
         raise ValueError("not supported for this density: %s"
                          " for this shape (%s,%s)" % (density, num_rows, num_cols))
-    else:
-        return mx.nd.array(output_arr).tostype("csr")
+
+    return mx.nd.array(output_arr).tostype("csr")
 
 
 def assign_each(the_input, function):
@@ -1407,10 +1407,10 @@ 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: # pylint: disable=no-else-raise
+                if raise_on_err:
                     raise e
-                else:
-                    print(str(e))
+
+                print(str(e))
 
     # train
     if grad_req != 'null':
@@ -1434,10 +1434,10 @@ 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: # pylint: disable=no-else-raise
+                    if raise_on_err:
                         raise e
-                    else:
-                        print(str(e))
+
+                    print(str(e))
 
     return gt
 
@@ -1514,11 +1514,11 @@ def download(url, fname=None, dirname=None, overwrite=False, retries=5):
                 break
         except Exception as e:
             retries -= 1
-            if retries <= 0: # pylint: disable=no-else-raise
+            if retries <= 0:
                 raise e
-            else:
-                print("download failed, retrying, {} attempt{} left"
-                      .format(retries, 's' if retries > 1 else ''))
+
+            print("download failed, retrying, {} attempt{} left"
+                  .format(retries, 's' if retries > 1 else ''))
     logging.info("downloaded %s into %s successfully", url, fname)
     return fname
 
@@ -1661,7 +1661,7 @@ 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 # pylint: disable=simplifiable-if-expression
+    flat = not bool(len(input_shape) == 3)
 
     train_dataiter = mx.io.MNISTIter(
         image="data/train-images-idx3-ubyte",