fix pylint issues

tensorlayer/layers/convolution.py

@@ -508,7 +508,7 @@ def __init__(
         else:
             raise Exception("Donot support shape %s" % self.inputs.get_shape())
         logging.info("UpSampling2dLayer %s: is_scale:%s size:%s method:%d align_corners:%s" % (name, is_scale, size, method, align_corners))
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             try:
                 self.outputs = tf.image.resize_images(self.inputs, size=size, method=method, align_corners=align_corners)
             except Exception:  # for TF 0.10
@@ -568,7 +568,7 @@ def __init__(
         else:
             raise Exception("Donot support shape %s" % self.inputs.get_shape())
         logging.info("DownSampling2dLayer %s: is_scale:%s size:%s method:%d, align_corners:%s" % (name, is_scale, size, method, align_corners))
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             try:
                 self.outputs = tf.image.resize_images(self.inputs, size=size, method=method, align_corners=align_corners)
             except Exception:  # for TF 0.10
@@ -797,7 +797,7 @@ def _tf_batch_map_offsets(inputs, offsets, grid_offset):
             logging.info("[warnings] unknow input channels, set to 1")
         shape = (filter_size[0], filter_size[1], pre_channel, n_filter)
 
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             offset = self.offset_layer.outputs
             assert offset.get_shape()[-1] == 2 * shape[0] * shape[1]
 
@@ -987,7 +987,7 @@ def __init__(self,
         if act is None:
             act = tf.identity
         logging.info("AtrousConv2dLayer %s: n_filter:%d filter_size:%s rate:%d pad:%s act:%s" % (self.name, n_filter, filter_size, rate, padding, act.__name__))
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             shape = [filter_size[0], filter_size[1], int(self.inputs.get_shape()[-1]), n_filter]
             filters = tf.get_variable(name='filter', shape=shape, initializer=W_init, dtype=D_TYPE, **W_init_args)
             if b_init:
@@ -1627,7 +1627,7 @@ def __init__(
 
         assert len(strides) == 4, "len(strides) should be 4."
 
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             W = tf.get_variable(
                 name='W_sepconv2d', shape=shape, initializer=W_init, dtype=D_TYPE,
                 **W_init_args)  # [filter_height, filter_width, in_channels, channel_multiplier]

tensorlayer/layers/core.py

@@ -1323,10 +1323,10 @@ def __init__(
         self.n_units = n_units
         logging.info("DropconnectDenseLayer %s: %d %s" % (self.name, self.n_units, act.__name__))
 
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=D_TYPE, **W_init_args)
             b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, dtype=D_TYPE, **b_init_args)
-            self.outputs = act(tf.matmul(self.inputs, W) + b)  #, name=name)    # 1.2
+            self.outputs = act(tf.matmul(self.inputs, W) + b)
 
         set_keep[name] = tf.placeholder(tf.float32)
         W_dropcon = tf.nn.dropout(W, set_keep[name])

tensorlayer/layers/recurrent.py

@@ -1062,7 +1062,7 @@ def __init__(
 
         # Apply dropout
         if dropout:
-            if type(dropout) in [tuple, list]:
+            if isinstance(dropout, (tuple, list)):
                 in_keep_prob = dropout[0]
                 out_keep_prob = dropout[1]
             elif isinstance(dropout, float):
@@ -1311,7 +1311,7 @@ def __init__(
 
             # Apply dropout
             if dropout:
-                if type(dropout) in [tuple, list]:
+                if isinstance(dropout, (tuple, list)):
                     in_keep_prob = dropout[0]
                     out_keep_prob = dropout[1]
                 elif isinstance(dropout, float):
@@ -1445,7 +1445,7 @@ class Seq2Seq(Layer):
         A TensorFlow core RNN cell
             - see `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`__
             - Note TF1.0+ and TF1.0- are different
-    cell_init_args : dictionary
+    cell_init_args : dictionary or None
         The arguments for the cell initializer.
     n_hidden : int
         The number of hidden units in the layer.
@@ -1540,7 +1540,7 @@ def __init__(
             net_encode_in,
             net_decode_in,
             cell_fn,  #tf.nn.rnn_cell.LSTMCell,
-            cell_init_args={'state_is_tuple': True},
+            cell_init_args=None,
             n_hidden=256,
             initializer=tf.random_uniform_initializer(-0.1, 0.1),
             encode_sequence_length=None,
@@ -1552,6 +1552,9 @@ def __init__(
             return_seq_2d=False,
             name='seq2seq',
     ):
+        if cell_init_args is None:
+            cell_init_args = {'state_is_tuple': True}
+
         Layer.__init__(self, name=name)
         if cell_fn is None:
             raise Exception("Please put in cell_fn")

tensorlayer/prepro.py

@@ -2651,7 +2651,7 @@ def _get_coord(coord):
 
     coords_new = list()
     classes_new = list()
-    for i in range(len(coords)):
+    for i, _ in enumerate(coords):
         coord = coords[i]
         assert len(coord) == 4, "coordinate should be 4 values : [x, y, w, h]"
         if is_rescale:
@@ -2794,7 +2794,7 @@ def _get_coord(coord):
 
     coords_new = list()
     classes_new = list()
-    for i in range(len(coords)):
+    for i, _ in enumerate(coords):
         coord = coords[i]
         assert len(coord) == 4, "coordinate should be 4 values : [x, y, w, h]"
         if is_rescale:
@@ -3010,7 +3010,7 @@ def sequences_add_start_id(sequences, start_id=0, remove_last=False):
 
     """
     sequences_out = [[] for _ in range(len(sequences))]  #[[]] * len(sequences)
-    for i in range(len(sequences)):
+    for i, _ in enumerate(sequences):
         if remove_last:
             sequences_out[i] = [start_id] + sequences[i][:-1]
         else:
@@ -3041,7 +3041,7 @@ def sequences_add_end_id(sequences, end_id=888):
 
     """
     sequences_out = [[] for _ in range(len(sequences))]  #[[]] * len(sequences)
-    for i in range(len(sequences)):
+    for i, _ in enumerate(sequences):
         sequences_out[i] = sequences[i] + [end_id]
     return sequences_out