Dependency and YAPF Style Update (#808)

* Pin yapf to latest version 0.23.0

* Update requirements_test.txt

* Update CHANGELOG.md

* YAPF Cleaning

Author: pyup-bot

CHANGELOG.md

@@ -134,13 +134,12 @@ To release a new version, please update the changelog as followed:
 
 - tensorflow>=1.8,<1.9 => tensorflow>=1.6,<1.11 (PR #739 and PR #798)
 - tensorflow-gpu>=1.8,<1.9 => tensorflow-gpu>=1.6,<1.11 (PR #739 and PR #798)
-- pymongo>=3.6,<3.7 => pymongo>=3.6,<3.8 (PR #750)
 - numpy>=1.14,<1.15 => numpy>=1.14,<1.16 (PR #754)
-- tqdm>=4.23,<4.24 => tqdm>=4.23,<4.25 (PR #798)
+- pymongo>=3.6,<3.7 => pymongo>=3.6,<3.8 (PR #750)
 - pytest>=3.6,<3.7 => tqdm>=3.6,<3.8 (PR #798)
-- yapf>=0.21,<0.22 => yapf>=0.21,<0.23 (PR #798)
-- tqdm>=4.23,<4.25 => tqdm>=4.23,<4.26 (PR #798)
 - pytest-xdist>=1.22,<1.23 => pytest-xdist>=1.22,<1.24 (PR #805 and #806)
+- tqdm>=4.23,<4.25 => tqdm>=4.23,<4.26 (PR #798)
+- yapf>=0.21,<0.22 => yapf>=0.22,<0.24 (PR #798 #808)
 
 ### Contributors
 

example/tutorial_binarynet_cifar10_tfrecord.py

@@ -106,7 +106,7 @@ def read_and_decode(filename, is_train=None):
     img = tf.decode_raw(features['img_raw'], tf.float32)
     img = tf.reshape(img, [32, 32, 3])
     # img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
-    if is_train ==True:
+    if is_train == True:
         # 1. Randomly crop a [height, width] section of the image.
         img = tf.random_crop(img, [24, 24, 3])
 

example/tutorial_bipedalwalker_a3c_continuous_action.py

@@ -194,7 +194,7 @@ def work(self):
                 s_, r, done, _info = self.env.step(a)
 
                 # set robot falls reward to -2 instead of -100
-                if r == -100: r = -2
+                if r== -100: r = -2
 
                 ep_r += r
                 buffer_s.append(s)
@@ -211,7 +211,7 @@ def work(self):
                     buffer_v_target = []
 
                     for r in buffer_r[::-1]:  # reverse buffer r
-                        v_s_ = r + GAMMA * v_s_
+                        v_s_ = r+ GAMMA * v_s_
                         buffer_v_target.append(v_s_)
                     buffer_v_target.reverse()
 

example/tutorial_cartpole_ac.py

@@ -133,7 +133,7 @@ def __init__(self, sess, n_features, lr=0.01):
 
         with tf.variable_scope('squared_TD_error'):
             # TD_error = r + lambd * V(newS) - V(S)
-            self.td_error = self.r + LAMBDA * self.v_ - self.v
+            self.td_error = self.r+ LAMBDA * self.v_ - self.v
             self.loss = tf.square(self.td_error)
 
         with tf.variable_scope('train'):

example/tutorial_cifar10_tfrecord.py

@@ -107,7 +107,7 @@ def read_and_decode(filename, is_train=None):
     img = tf.decode_raw(features['img_raw'], tf.float32)
     img = tf.reshape(img, [32, 32, 3])
     # img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
-    if is_train ==True:
+    if is_train == True:
         # 1. Randomly crop a [height, width] section of the image.
         img = tf.random_crop(img, [24, 24, 3])
 

example/tutorial_dorefanet_cifar10_tfrecord.py

@@ -106,7 +106,7 @@ def read_and_decode(filename, is_train=None):
     img = tf.decode_raw(features['img_raw'], tf.float32)
     img = tf.reshape(img, [32, 32, 3])
     # img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
-    if is_train ==True:
+    if is_train == True:
         # 1. Randomly crop a [height, width] section of the image.
         img = tf.random_crop(img, [24, 24, 3])
 

example/tutorial_frozenlake_dqn.py

@@ -79,7 +79,7 @@ def to_one_hot(i, n_classes=None):
             ## Choose an action by greedily (with e chance of random action) from the Q-network
             a, allQ = sess.run([predict, y], feed_dict={inputs: [to_one_hot(s, 16)]})
             ## e-Greedy Exploration !!! sample random action
-            if np.random.rand(1) < e:
+            if np.random.rand(1) <e:
                 a[0] = env.action_space.sample()
             ## Get new state and reward from environment
             s1, r, d, _ = env.step(a[0])
@@ -88,7 +88,7 @@ def to_one_hot(i, n_classes=None):
             ## Obtain maxQ' and set our target value for chosen action.
             maxQ1 = np.max(Q1)  # in Q-Learning, policy is greedy, so we use "max" to select the next action.
             targetQ = allQ
-            targetQ[0, a[0]] = r + lambd * maxQ1
+            targetQ[0, a[0]] = r+ lambd * maxQ1
             ## Train network using target and predicted Q values
             # it is not real target Q value, it is just an estimation,
             # but check the Q-Learning update formula:
@@ -99,7 +99,7 @@ def to_one_hot(i, n_classes=None):
             rAll += r
             s = s1
             ## Reduce chance of random action if an episode is done.
-            if d ==True:
+            if d == True:
                 e = 1. / ((i / 50) + 10)  # reduce e, GLIE: Greey in the limit with infinite Exploration
                 break
 

example/tutorial_frozenlake_q_table.py

@@ -43,13 +43,13 @@
         ## Get new state and reward from environment
         s1, r, d, _ = env.step(a)
         ## Update Q-Table with new knowledge
-        Q[s, a] = Q[s, a] + lr * (r + lambd * np.max(Q[s1, :]) - Q[s, a])
+        Q[s, a] = Q[s, a] + lr * (r+ lambd * np.max(Q[s1, :]) - Q[s, a])
         rAll += r
         s = s1
-        if d ==True:
+        if d == True:
             break
     rList.append(rAll)
-    running_reward = r if running_reward is None else running_reward * 0.99 + r * 0.01
+    running_reward = r if running_reward is None else running_reward * 0.99 +r* 0.01
     print("Episode [%d/%d] sum reward: %f running reward: %f took: %.5fs %s" % \
         (i, num_episodes, rAll, running_reward, time.time() - episode_time, '' if rAll == 0 else ' !!!!!!!!'))
 

example/tutorial_ternaryweight_cifar10_tfrecord.py

@@ -105,7 +105,7 @@ def read_and_decode(filename, is_train=None):
     img = tf.decode_raw(features['img_raw'], tf.float32)
     img = tf.reshape(img, [32, 32, 3])
     # img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
-    if is_train ==True:
+    if is_train == True:
         # 1. Randomly crop a [height, width] section of the image.
         img = tf.random_crop(img, [24, 24, 3])
 

requirements/requirements_test.txt

@@ -6,4 +6,4 @@ pytest-cache>=1.0,<1.1
 pytest-cov>=2.5,<2.6
 pytest-xdist>=1.22,<1.24
 sphinx>=1.7,<1.8
-yapf>=0.21,<0.23
+yapf>=0.22,<0.24

tensorlayer/cost.py

@@ -259,7 +259,7 @@ def dice_coe(output, target, loss_type='jaccard', axis=(1, 2, 3), smooth=1e-5):
     # epsilon = 1e-5
     # dice = tf.clip_by_value(dice, 0, 1.0-epsilon) # if all empty, dice = 1
     # new haodong
-    dice = (2. * inse + smooth) / (l + r + smooth)
+    dice = (2. * inse + smooth) / (l +r+ smooth)
     ##
     dice = tf.reduce_mean(dice, name='dice_coe')
     return dice
@@ -298,7 +298,7 @@ def dice_hard_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5):
     # epsilon = 1e-5
     # hard_dice = tf.clip_by_value(hard_dice, 0, 1.0-epsilon)
     # new haodong
-    hard_dice = (2. * inse + smooth) / (l + r + smooth)
+    hard_dice = (2. * inse + smooth) / (l +r+ smooth)
     ##
     hard_dice = tf.reduce_mean(hard_dice, name='hard_dice')
     return hard_dice

tensorlayer/files/dataset_loaders/mpii_dataset.py

@@ -157,7 +157,7 @@ def save_joints():
                             vis = None
 
                         # if len(joint_pos) == 16:
-                        if ((is_16_pos_only ==True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
+                        if ((is_16_pos_only == True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
                             # only use image with 16 key points / or use all
                             data = {
                                 'filename': img_fn,

tensorlayer/files/utils.py

@@ -1461,7 +1461,7 @@ def save_joints():
                             vis = None
 
                         # if len(joint_pos) == 16:
-                        if ((is_16_pos_only ==True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
+                        if ((is_16_pos_only == True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
                             # only use image with 16 key points / or use all
                             data = {
                                 'filename': img_fn,

tensorlayer/iterate.py

@@ -65,7 +65,7 @@ def minibatches(inputs=None, targets=None, batch_size=None, allow_dynamic_batch_
             excerpt = indices[start_idx:end_idx]
         else:
             excerpt = slice(start_idx, end_idx)
-        if (isinstance(inputs, list) or isinstance(targets, list)) and (shuffle ==True):
+        if (isinstance(inputs, list) or isinstance(targets, list)) and (shuffle == True):
             # zsdonghao: for list indexing when shuffle==True
             yield [inputs[i] for i in excerpt], [targets[i] for i in excerpt]
         else:

tensorlayer/prepro.py

@@ -1228,10 +1228,10 @@ def rgb_to_hsv(rgb):
     rc = np.zeros_like(r)
     gc = np.zeros_like(g)
     bc = np.zeros_like(b)
-    rc[mask] = (maxc - r)[mask] / (maxc - minc)[mask]
+    rc[mask] = (maxc -r)[mask] / (maxc - minc)[mask]
     gc[mask] = (maxc - g)[mask] / (maxc - minc)[mask]
     bc[mask] = (maxc - b)[mask] / (maxc - minc)[mask]
-    hsv[..., 0] = np.select([r == maxc, g == maxc], [bc - gc, 2.0 + rc - bc], default=4.0 + gc - rc)
+    hsv[..., 0] = np.select([r== maxc, g == maxc], [bc - gc, 2.0 + rc - bc], default=4.0 + gc - rc)
     hsv[..., 0] = (hsv[..., 0] / 6.0) % 1.0
     return hsv
 
@@ -3114,7 +3114,7 @@ def process_sequences(sequences, end_id=0, pad_val=0, is_shorten=True, remain_en
                     max_length = i_w
                 if remain_end_id is False:
                     seq[i_w] = pad_val  # set end_id to pad_val
-            elif is_end ==True:
+            elif is_end == True:
                 seq[i_w] = pad_val
 
     if remain_end_id is True: