fix many pylint issues.

Author: luomai

example/tutorial_bipedalwalker_a3c_continuous_action.py

@@ -210,7 +210,7 @@ def work(self):
                         self.AC.v_target: buffer_v_target,
                     }
                     # update gradients on global network
-                    test = self.AC.update_global(feed_dict)
+                    self.AC.update_global(feed_dict)
                     buffer_s, buffer_a, buffer_r = [], [], []
 
                     # update local network from global network

example/tutorial_imagenet_inceptionV3_distributed.py

@@ -288,12 +288,12 @@ def calculate_metrics(predicted_batch, real_batch, threshold=0.5, is_training=Fa
 def run_evaluator(task_spec, checkpoints_path, batch_size=32):
     with tf.Graph().as_default():
         # load dataset
-        images_input, one_hot_classes, num_classes, dataset_size = \
+        images_input, one_hot_classes, num_classes, _dataset_size = \
             load_data(file=VAL_FILE,
                       task_spec=task_spec,
                       batch_size=batch_size,
                       epochs=1)
-        network, predictions = build_network(images_input, num_classes=num_classes, is_training=False)
+        _network, predictions = build_network(images_input, num_classes=num_classes, is_training=False)
         saver = tf.train.Saver()
         # metrics
         metrics_init_ops, _, metrics_ops = \

example/tutorial_vgg16.py

@@ -48,7 +48,7 @@
 
 
 def conv_layers(net_in):
-    with tf.name_scope('preprocess') as scope:
+    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

tensorlayer/db.py

@@ -407,8 +407,8 @@ def find_one_job(self, args=None):
 
     def push_job(self, margs, wargs, dargs, epoch):
 
-        ms, mid = self.load_model_architecture(margs)
-        weight, wid = self.find_one_params(wargs)
+        _ms, mid = self.load_model_architecture(margs)
+        _weight, wid = self.find_one_params(wargs)
         args = {"weight": wid, "model": mid, "dargs": dargs, "epoch": epoch, "time": datetime.utcnow(), "Running": False}
         self.__autofill(args)
         self.db.JOBS.insert_one(args)

tensorlayer/files.py

@@ -1171,8 +1171,8 @@ def save_npz(save_list=None, name='model.npz', sess=None):
         save_list_var = sess.run(save_list)
     else:
         try:
-            for k, value in enumerate(save_list):
-                save_list_var.append(value.eval())
+            for _k, v in enumerate(save_list):
+                save_list_var.append(v.eval())
         except Exception:
             logging.info(" Fail to save model, Hint: pass the session into this function, tl.files.save_npz(network.all_params, name='model.npz', sess=sess)")
     np.savez(name, params=save_list_var)
@@ -1351,60 +1351,6 @@ def load_and_assign_npz_dict(name='model.npz', sess=None):
     logging.info("[*] Model restored from npz_dict %s" % name)
 
 
-# def save_npz_dict(save_list=[], name='model.npz', sess=None):
-#     """Input parameters and the file name, save parameters as a dictionary into .npz file. Use tl.utils.load_npz_dict() to restore.
-#
-#     Parameters
-#     ----------
-#     save_list : a list
-#         Parameters want to be saved.
-#     name : a string or None
-#         The name of the .npz file.
-#     sess : None or Session
-#
-#     Notes
-#     -----
-#     This function tries to avoid a potential broadcasting error raised by numpy.
-#
-#     """
-#     ## save params into a list
-#     save_list_var = []
-#     if sess:
-#         save_list_var = sess.run(save_list)
-#     else:
-#         try:
-#             for k, value in enumerate(save_list):
-#                 save_list_var.append(value.eval())
-#         except:
-#             logging.info(" Fail to save model, Hint: pass the session into this function, save_npz_dict(network.all_params, name='model.npz', sess=sess)")
-#     save_var_dict = {str(idx):val for idx, val in enumerate(save_list_var)}
-#     np.savez(name, **save_var_dict)
-#     save_list_var = None
-#     save_var_dict = None
-#     del save_list_var
-#     del save_var_dict
-#     logging.info("[*] %s saved" % name)
-#
-# def load_npz_dict(path='', name='model.npz'):
-#     """Load the parameters of a Model saved by tl.files.save_npz_dict().
-#
-#     Parameters
-#     ----------
-#     path : a string
-#         Folder path to .npz file.
-#     name : a string or None
-#         The name of the .npz file.
-#
-#     Returns
-#     --------
-#     params : list
-#         A list of parameters in order.
-#     """
-#     d = np.load( path+name )
-#     saved_list_var = [val[1] for val in sorted(d.items(), key=lambda tup: int(tup[0]))]
-#     return saved_list_var
-
-
 def save_ckpt(sess=None, mode_name='model.ckpt', save_dir='checkpoint', var_list=None, global_step=None, printable=False):
     """Save parameters into `ckpt` file.
 

tensorlayer/layers/convolution.py

@@ -180,7 +180,7 @@ def __init__(
             act = tf.identity
         logging.info("Conv2dLayer %s: shape:%s strides:%s pad:%s act:%s" % (self.name, str(shape), str(strides), padding, act.__name__))
 
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             W = tf.get_variable(name='W_conv2d', shape=shape, initializer=W_init, dtype=D_TYPE, **W_init_args)
             if b_init:
                 b = tf.get_variable(name='b_conv2d', shape=(shape[-1]), initializer=b_init, dtype=D_TYPE, **b_init_args)
@@ -511,7 +511,7 @@ def __init__(
         with tf.variable_scope(name) as vs:
             try:
                 self.outputs = tf.image.resize_images(self.inputs, size=size, method=method, align_corners=align_corners)
-            except:  # for TF 0.10
+            except Exception:  # for TF 0.10
                 self.outputs = tf.image.resize_images(self.inputs, new_height=size[0], new_width=size[1], method=method, align_corners=align_corners)
 
         self.all_layers = list(layer.all_layers)
@@ -571,7 +571,7 @@ def __init__(
         with tf.variable_scope(name) as vs:
             try:
                 self.outputs = tf.image.resize_images(self.inputs, size=size, method=method, align_corners=align_corners)
-            except:  # for TF 0.10
+            except Exception:  # for TF 0.10
                 self.outputs = tf.image.resize_images(self.inputs, new_height=size[0], new_width=size[1], method=method, align_corners=align_corners)
 
         self.all_layers = list(layer.all_layers)
@@ -792,7 +792,7 @@ def _tf_batch_map_offsets(inputs, offsets, grid_offset):
 
         try:
             pre_channel = int(layer.outputs.get_shape()[-1])
-        except:  # if pre_channel is ?, it happens when using Spatial Transformer Net
+        except Exception:  # if pre_channel is ?, it happens when using Spatial Transformer Net
             pre_channel = 1
             logging.info("[warnings] unknow input channels, set to 1")
         shape = (filter_size[0], filter_size[1], pre_channel, n_filter)
@@ -1351,7 +1351,7 @@ def conv2d(
 
     try:
         pre_channel = int(layer.outputs.get_shape()[-1])
-    except:  # if pre_channel is ?, it happens when using Spatial Transformer Net
+    except Exception:  # if pre_channel is ?, it happens when using Spatial Transformer Net
         pre_channel = 1
         logging.info("[warnings] unknow input channels, set to 1")
     return Conv2dLayer(
@@ -1616,7 +1616,7 @@ def __init__(
         logging.info("DepthwiseConv2d %s: shape:%s strides:%s pad:%s act:%s" % (self.name, str(shape), str(strides), padding, act.__name__))
         try:
             pre_channel = int(layer.outputs.get_shape()[-1])
-        except:  # if pre_channel is ?, it happens when using Spatial Transformer Net
+        except Exception:  # if pre_channel is ?, it happens when using Spatial Transformer Net
             pre_channel = 1
             logging.info("[warnings] unknow input channels, set to 1")
 

tensorlayer/layers/core.py

@@ -221,7 +221,7 @@ def get_variables_with_name(name=None, train_only=True, printable=False):
     else:
         try:  # TF1.0+
             t_vars = tf.global_variables()
-        except:  # TF0.12
+        except Exception:  # TF0.12
             t_vars = tf.all_variables()
 
     d_vars = [var for var in t_vars if name in var.name]
@@ -418,13 +418,13 @@ def print_layers(self):
     def count_params(self):
         """Return the number of parameters in the network"""
         n_params = 0
-        for i, p in enumerate(self.all_params):
+        for _i, p in enumerate(self.all_params):
             n = 1
             # for s in p.eval().shape:
             for s in p.get_shape():
                 try:
                     s = int(s)
-                except:
+                except Exception:
                     s = 1
                 if s:
                     n = n * s
@@ -610,14 +610,15 @@ def __init__(
         Layer.__init__(self, name=name)
         self.inputs = inputs
         logging.info("Word2vecEmbeddingInputlayer %s: (%d, %d)" % (self.name, vocabulary_size, embedding_size))
+
         # Look up embeddings for inputs.
         # Note: a row of 'embeddings' is the vector representation of a word.
         # for the sake of speed, it is better to slice the embedding matrix
         # instead of transfering a word id to one-hot-format vector and then
         # multiply by the embedding matrix.
         # embed is the outputs of the hidden layer (embedding layer), it is a
         # row vector with 'embedding_size' values.
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             embeddings = tf.get_variable(name='embeddings', shape=(vocabulary_size, embedding_size), initializer=E_init, dtype=D_TYPE, **E_init_args)
             embed = tf.nn.embedding_lookup(embeddings, self.inputs)
             # Construct the variables for the NCE loss (i.e. negative sampling)
@@ -878,7 +879,7 @@ def __init__(
             if b_init is not None:
                 try:
                     b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, dtype=D_TYPE, **b_init_args)
-                except:  # If initializer is a constant, do not specify shape.
+                except Exception:  # If initializer is a constant, do not specify shape.
                     b = tf.get_variable(name='b', initializer=b_init, dtype=D_TYPE, **b_init_args)
                 self.outputs = act(tf.matmul(self.inputs, W) + b)
             else:
@@ -982,10 +983,11 @@ def __init__(
         L2_w = tf.contrib.layers.l2_regularizer(lambda_l2_w)(self.train_params[0]) \
                 + tf.contrib.layers.l2_regularizer(lambda_l2_w)(self.train_params[2])           # faster than the code below
         # L2_w = lambda_l2_w * tf.reduce_mean(tf.square(self.train_params[0])) + lambda_l2_w * tf.reduce_mean( tf.square(self.train_params[2]))
+
         # DropNeuro
-        P_o = cost.lo_regularizer(0.03)(
-            self.train_params[0])  # + cost.lo_regularizer(0.5)(self.train_params[2])    # <haodong>: if add lo on decoder, no neuron will be broken
-        P_i = cost.li_regularizer(0.03)(self.train_params[0])  # + cost.li_regularizer(0.001)(self.train_params[2])
+        # P_o = cost.lo_regularizer(0.03)(
+        #     self.train_params[0])  # + cost.lo_regularizer(0.5)(self.train_params[2])    # <haodong>: if add lo on decoder, no neuron will be broken
+        # P_i = cost.li_regularizer(0.03)(self.train_params[0])  # + cost.li_regularizer(0.001)(self.train_params[2])
 
         # L1 of activation outputs
         activation_out = self.all_layers[-2]
@@ -1082,7 +1084,7 @@ def pretrain(self, sess, x, X_train, X_val, denoise_name=None, n_epoch=100, batc
                         visualize.draw_weights(
                             self.train_params[0].eval(), second=10, saveable=True, shape=[28, 28], name=save_name + str(epoch + 1), fig_idx=2012)
                         files.save_npz([self.all_params[0]], name=save_name + str(epoch + 1) + '.npz')
-                    except:
+                    except Exception:
                         raise Exception(
                             "You should change the visualize.W() in ReconLayer.pretrain(), if you want to save the feature images for different dataset")
 

tensorlayer/layers/extend.py

@@ -29,7 +29,7 @@ def __init__(
         self.inputs = layer.outputs
 
         logging.info("ExpandDimsLayer  %s: axis:%d" % (self.name, axis))
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             try:  # TF12 TF1.0
                 self.outputs = tf.expand_dims(self.inputs, axis=axis)
             except Exception:  # TF11

tensorlayer/layers/normalization.py

@@ -99,9 +99,8 @@ def __init__(
         params_shape = x_shape[-1:]
 
         from tensorflow.python.training import moving_averages
-        from tensorflow.python.ops import control_flow_ops
 
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             axis = list(range(len(x_shape) - 1))
 
             # 1. beta, gamma