Fix remaining issues.

example/tutorial_cifar10_tfrecord.py

@@ -45,7 +45,6 @@
 # import numpy as np
 import tensorflow as tf
 import tensorlayer as tl
-# from PIL import Image
 from tensorlayer.layers import *
 
 model_file_name = "model_cifar10_tfrecord.ckpt"

example/tutorial_frozenlake_dqn.py

@@ -26,10 +26,6 @@
 
 """
 
-import time
-import gym
-import numpy as np
-import tensorflow as tf
 import tensorlayer as tl
 from tensorlayer.layers import *
 

example/tutorial_vgg16.py

@@ -166,11 +166,9 @@ def conv_layers(net_in):
 
 def conv_layers_simple_api(net_in):
     with tf.name_scope('preprocess'):
-        """
-        Notice that we include a preprocessing layer that takes the RGB image
-        with pixels values in the range of 0-255 and subtracts the mean image
-        values (calculated over the entire ImageNet training set).
-        """
+        # Notice that we include a preprocessing layer that takes the RGB image
+        # with pixels values in the range of 0-255 and subtracts the mean image
+        # values (calculated over the entire ImageNet training set).
         mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
         net_in.outputs = net_in.outputs - mean
 

tensorlayer/files.py

@@ -802,7 +802,6 @@ def load_celebA_dataset(path='data'):
         The path that the data is downloaded to, defaults is ``data/celebA/``.
 
     """
-    import zipfile, os
     data_dir = 'celebA'
     filename, drive_id = "img_align_celeba.zip", "0B7EVK8r0v71pZjFTYXZWM3FlRnM"
     save_path = os.path.join(path, filename)
@@ -1496,14 +1495,10 @@ def load_npy_to_any(path='', name='file.npy'):
     """
     file_path = os.path.join(path, name)
     try:
-        npy = np.load(file_path).item()
+        return np.load(file_path).item()
     except Exception:
-        npy = np.load(file_path)
-    finally:
-        try:
-            return npy
-        except Exception:
-            raise Exception("[!] Fail to load %s" % file_path)
+        return np.load(file_path)
+    raise Exception("[!] Fail to load %s" % file_path)
 
 
 def file_exists(filepath):

tensorlayer/layers/convolution.py

@@ -64,7 +64,7 @@ def __init__(
         self.inputs = layer.outputs
         logging.info("Conv1dLayer %s: shape:%s stride:%s pad:%s act:%s" % (self.name, str(shape), str(stride), padding, act.__name__))
 
-        with tf.variable_scope(name):  # as vs:
+        with tf.variable_scope(name):
             W = tf.get_variable(name='W_conv1d', shape=shape, initializer=W_init, dtype=D_TYPE, **W_init_args)
             self.outputs = tf.nn.convolution(
                 self.inputs, W, strides=(stride, ), padding=padding, dilation_rate=(dilation_rate, ), data_format=data_format)  # 1.2
@@ -447,7 +447,7 @@ def __init__(
         logging.info("DeConv3dLayer %s: shape:%s out_shape:%s strides:%s pad:%s act:%s" % (self.name, str(shape), str(output_shape), str(strides), padding,
                                                                                            act.__name__))
 
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             W = tf.get_variable(name='W_deconv3d', shape=shape, initializer=W_init, dtype=D_TYPE, **W_init_args)
             b = tf.get_variable(name='b_deconv3d', shape=(shape[-2]), initializer=b_init, dtype=D_TYPE, **b_init_args)
 

tensorlayer/layers/core.py

@@ -1245,7 +1245,7 @@ def __init__(
         else:
             self.inputs = layer.outputs
             logging.info("GaussianNoiseLayer %s: mean:%f stddev:%f" % (self.name, mean, stddev))
-            with tf.variable_scope(name) as vs:
+            with tf.variable_scope(name):
                 # noise = np.random.normal(0.0 , sigma , tf.to_int64(self.inputs).get_shape())
                 noise = tf.random_normal(shape=self.inputs.get_shape(), mean=mean, stddev=stddev, seed=seed)
                 self.outputs = self.inputs + noise

tensorlayer/layers/recurrent.py

@@ -367,7 +367,7 @@ def __init__(
             rnn_creator = lambda: cell_fn(num_units=n_hidden, **cell_init_args)
             # Apply dropout
             if dropout:
-                if type(dropout) in [tuple, list]:
+                if isinstance(dropout, (tuple, list)):  # type(dropout) in [tuple, list]:
                     in_keep_prob = dropout[0]
                     out_keep_prob = dropout[1]
                 elif isinstance(dropout, float):
@@ -1256,7 +1256,7 @@ def __init__(
             self,
             layer,
             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),
             sequence_length=None,
@@ -1269,6 +1269,8 @@ def __init__(
             dynamic_rnn_init_args=None,
             name='bi_dyrnn_layer',
     ):
+        if cell_init_args is None:
+            cell_init_args = {'state_is_tuple': True}
         if dynamic_rnn_init_args is None:
             dynamic_rnn_init_args = {}
 

tensorlayer/prepro.py

@@ -2511,7 +2511,7 @@ 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:
@@ -2915,7 +2915,7 @@ def remove_pad_sequences(sequences, pad_id=0):
     """
     import copy
     sequences_out = copy.deepcopy(sequences)
-    for i in range(len(sequences)):
+    for i, _ in enumerate(sequences):
         # for j in range(len(sequences[i])):
         #     if sequences[i][j] == pad_id:
         #         sequences_out[i] = sequences_out[i][:j]