Fix codacy issues (continue)

example/tutorial_tfrecord3.py

@@ -15,15 +15,12 @@
 
 """
 
-import io
 import json
 import os
-import time
 import numpy as np
 import tensorflow as tf
 import tensorlayer as tl
 from PIL import Image
-from tensorlayer.layers import set_keep
 
 
 def _int64_feature(value):
@@ -326,7 +323,7 @@ def prefetch_input_data(reader,
 try:
     # for TensorFlow 0.11
     img = tf.image.resize_images(img, size=(resize_height, resize_width), method=tf.image.ResizeMethod.BILINEAR)
-except:
+except Exception:
     # for TensorFlow 0.10
     img = tf.image.resize_images(img, new_height=resize_height, new_width=resize_width, method=tf.image.ResizeMethod.BILINEAR)
 # Crop to final dimensions.

tensorlayer/layers/convolution.py

@@ -834,9 +834,9 @@ def _tf_batch_map_offsets(inputs, offsets, grid_offset):
         offset_params = [osparam for osparam in offset_layer.all_params if osparam not in layer.all_params]
         offset_layers = [oslayer for oslayer in offset_layer.all_layers if oslayer not in layer.all_layers]
 
-        self.all_params.extend(offset_params)
-        self.all_layers.extend(offset_layers)
-        self.all_drop.update(offset_layer.all_drop)
+        self.all_params.extend(list(offset_params))
+        self.all_layers.extend(list(offset_layers))
+        self.all_drop.update(dict(offset_layer.all_drop))
 
         # this layer
         self.all_layers.extend([self.outputs])

tensorlayer/prepro.py

@@ -100,8 +100,10 @@ def apply_fn(results, i, data, kwargs):
     if thread_count is None:
         results = [None] * len(data)
         threads = []
-        for i in range(len(data)):
-            t = threading.Thread(name='threading_and_return', target=apply_fn, args=(results, i, data[i], kwargs))
+        # for i in range(len(data)):
+        #     t = threading.Thread(name='threading_and_return', target=apply_fn, args=(results, i, data[i], kwargs))
+        for i, d in enumerate(data):
+            t = threading.Thread(name='threading_and_return', target=apply_fn, args=(results, i, d, kwargs))
             t.start()
             threads.append(t)
     else:
@@ -120,7 +122,7 @@ def apply_fn(results, i, data, kwargs):
     if thread_count is None:
         try:
             return np.asarray(results)
-        except:
+        except Exception:
             return results
     else:
         return np.concatenate(results)

tensorlayer/rein.py

@@ -6,7 +6,7 @@
 from six.moves import xrange
 
 
-def discount_episode_rewards(rewards=[], gamma=0.99, mode=0):
+def discount_episode_rewards(rewards=None, gamma=0.99, mode=0):
     """Take 1D float array of rewards and compute discounted rewards for an
     episode. When encount a non-zero value, consider as the end a of an episode.
 
@@ -40,6 +40,8 @@ def discount_episode_rewards(rewards=[], gamma=0.99, mode=0):
     ... 1.49048996  1.65610003  0.72899997  0.81        0.89999998  1.        ]
 
     """
+    if rewards is None:
+        raise Exception("rewards should be a list")
     discounted_r = np.zeros_like(rewards, dtype=np.float32)
     running_add = 0
     for t in reversed(xrange(0, rewards.size)):
@@ -84,13 +86,13 @@ def cross_entropy_reward_loss(logits, actions, rewards, name=None):
     """
     try:  # TF 1.0+
         cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=actions, logits=logits, name=name)
-    except:
+    except Exception:
         cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, targets=actions)
         # cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, actions)
 
     try:  ## TF1.0+
         loss = tf.reduce_sum(tf.multiply(cross_entropy, rewards))
-    except:  ## TF0.12
+    except Exception:  ## TF0.12
         loss = tf.reduce_sum(tf.mul(cross_entropy, rewards))  # element-wise mul
     return loss
 
@@ -153,5 +155,6 @@ def choice_action_by_probs(probs=[0.5, 0.5], action_list=None):
         n_action = len(probs)
         action_list = np.arange(n_action)
     else:
-        assert len(action_list) == len(probs), "Number of actions should equal to number of probabilities."
+        if len(action_list) != len(probs):
+            raise Exception("number of actions should equal to number of probabilities.")
     return np.random.choice(action_list, p=probs)