fix useless string statement issues.

Author: luomai

example/tutorial_vgg16.py

@@ -37,10 +37,6 @@
 """
 
 import os
-import sys
-import time
-import numpy as np
-import tensorflow as tf
 import tensorlayer as tl
 from scipy.misc import imread, imresize
 from tensorlayer.layers import *
@@ -60,7 +56,8 @@ def conv_layers(net_in):
         """
         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
-    """ conv1 """
+
+    # conv1
     network = Conv2dLayer(
         net_in,
         act=tf.nn.relu,
@@ -76,7 +73,8 @@ def conv_layers(net_in):
         padding='SAME',
         name='conv1_2')
     network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool1')
-    """ conv2 """
+
+    # conv2
     network = Conv2dLayer(
         network,
         act=tf.nn.relu,
@@ -92,7 +90,8 @@ def conv_layers(net_in):
         padding='SAME',
         name='conv2_2')
     network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool2')
-    """ conv3 """
+
+    # conv3
     network = Conv2dLayer(
         network,
         act=tf.nn.relu,
@@ -115,7 +114,8 @@ def conv_layers(net_in):
         padding='SAME',
         name='conv3_3')
     network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool3')
-    """ conv4 """
+
+    # conv4
     network = Conv2dLayer(
         network,
         act=tf.nn.relu,
@@ -138,7 +138,8 @@ def conv_layers(net_in):
         padding='SAME',
         name='conv4_3')
     network = PoolLayer(network, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', pool=tf.nn.max_pool, name='pool4')
-    """ conv5 """
+
+    # conv5
     network = Conv2dLayer(
         network,
         act=tf.nn.relu,
@@ -173,25 +174,30 @@ def conv_layers_simple_api(net_in):
         """
         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
-    """ conv1 """
+
+    # conv1
     network = Conv2d(net_in, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_1')
     network = Conv2d(network, n_filter=64, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv1_2')
     network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool1')
-    """ conv2 """
+
+    # conv2
     network = Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_1')
     network = Conv2d(network, n_filter=128, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv2_2')
     network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool2')
-    """ conv3 """
+
+    # conv3
     network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_1')
     network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_2')
     network = Conv2d(network, n_filter=256, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv3_3')
     network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool3')
-    """ conv4 """
+
+    # conv4
     network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_1')
     network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_2')
     network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv4_3')
     network = MaxPool2d(network, filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool4')
-    """ conv5 """
+
+    # conv5
     network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_1')
     network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_2')
     network = Conv2d(network, n_filter=512, filter_size=(3, 3), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='conv5_3')
@@ -249,5 +255,3 @@ def fc_layers(net):
 preds = (np.argsort(prob)[::-1])[0:5]
 for p in preds:
     print(class_names[p], prob[p])
-
-#

example/tutorial_word2vec_basic.py

@@ -37,10 +37,6 @@
 
 """
 
-import collections
-import math
-import os
-import random
 import time
 
 import numpy as np
@@ -56,10 +52,9 @@
 def main_word2vec_basic():
     # sess = tf.InteractiveSession()
     sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
-    """ Step 1: Download the data, read the context into a list of strings.
-    Set hyperparameters.
-    """
 
+    # Step 1: Download the data, read the context into a list of strings.
+    # Set hyperparameters.
     words = tl.files.load_matt_mahoney_text8_dataset()
     data_size = len(words)
     print(data_size)  # 17005207
@@ -126,8 +121,8 @@ def main_word2vec_basic():
     print('%d Steps in a Epoch, total Epochs %d' % (int(data_size / batch_size), n_epoch))
     print('   learning_rate: %f' % learning_rate)
     print('   batch_size: %d' % batch_size)
-    """ Step 2: Build the dictionary and replace rare words with 'UNK' token.
-    """
+
+    # Step 2: Build the dictionary and replace rare words with 'UNK' token.
     print()
     if resume:
         print("Load existing data and dictionaries" + "!" * 10)
@@ -146,21 +141,21 @@ def main_word2vec_basic():
     ])  # [5243, 3081, 12, 6, 195, 2, 3135, 46, 59, 156] [b'anarchism', b'originated', b'as', b'a', b'term', b'of', b'abuse', b'first', b'used', b'against']
 
     del words  # Hint to reduce memory.
-    """ Step 3: Function to generate a training batch for the Skip-Gram model.
-    """
+
+    # Step 3: Function to generate a training batch for the Skip-Gram model.
     print()
-    data_index = 0
+
     batch, labels, data_index = tl.nlp.generate_skip_gram_batch(data=data, batch_size=8, num_skips=4, skip_window=2, data_index=0)
     for i in range(8):
         print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
 
     batch, labels, data_index = tl.nlp.generate_skip_gram_batch(data=data, batch_size=8, num_skips=2, skip_window=1, data_index=0)
     for i in range(8):
         print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
-    # exit()
-    """ Step 4: Build a Skip-Gram model.
-    """
+
+    # Step 4: Build a Skip-Gram model.
     print()
+
     # We pick a random validation set to sample nearest neighbors. Here we limit the
     # validation samples to the words that have a low numeric ID, which by
     # construction are also the most frequent.
@@ -208,9 +203,10 @@ def main_word2vec_basic():
     similarity = tf.matmul(valid_embed, normalized_embeddings, transpose_b=True)
     # multiply all valid word vector with all word vector.
     # transpose_b=True, normalized_embeddings is transposed before multiplication.
-    """ Step 5: Start training.
-    """
+
+    # Step 5: Start training.
     print()
+
     tl.layers.initialize_global_variables(sess)
     if resume:
         print("Load existing model" + "!" * 10)
@@ -229,7 +225,7 @@ def main_word2vec_basic():
     average_loss = 0
     step = 0
     print_freq = 2000
-    while (step < num_steps):
+    while step < num_steps:
         start_time = time.time()
         batch_inputs, batch_labels, data_index = tl.nlp.generate_skip_gram_batch(
             data=data, batch_size=batch_size, num_skips=num_skips, skip_window=skip_window, data_index=data_index)
@@ -279,16 +275,17 @@ def main_word2vec_basic():
         #         learning_rate = float(input("Input new learning rate: "))
         #         train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
         step += 1
-    """ Step 6: Visualize the normalized embedding matrix by t-SNE.
-    """
+
+    # Step 6: Visualize the normalized embedding matrix by t-SNE.
     print()
+
     final_embeddings = sess.run(normalized_embeddings)  #.eval()
     tl.visualize.tsne_embedding(final_embeddings, reverse_dictionary, plot_only=500, second=5, saveable=False, name='word2vec_basic')
-    """ Step 7: Evaluate by analogy questions.
-        see tensorflow/models/embedding/word2vec_optimized.py
-    """
+
+    # Step 7: Evaluate by analogy questions. see tensorflow/models/embedding/word2vec_optimized.py
     print()
-    #   from tensorflow/models/embedding/word2vec.py
+
+    #  from tensorflow/models/embedding/word2vec.py
     analogy_questions = tl.nlp.read_analogies_file( \
                 eval_file='questions-words.txt', word2id=dictionary)
     # The eval feeds three vectors of word ids for a, b, c, each of
@@ -352,5 +349,3 @@ def predict(analogy):
 
 if __name__ == '__main__':
     main_word2vec_basic()
-
-#

tensorlayer/db.py

@@ -335,7 +335,6 @@ def load_model_architecture(self, args=None):
             return False, False
         try:
             archs = self.archfs.get(fid).read()
-            '''print("[TensorDB] Find one params SUCCESS, {} took: {}s".format(args, round(time.time()-s, 2)))'''
             return archs, fid
         except Exception as e:
             print("exception")

tensorlayer/layers/super_resolution.py

@@ -72,14 +72,13 @@ def subpixel_conv2d(net, scale=2, n_out_channel=None, act=tf.identity, name='sub
     def _PS(X, r, n_out_channels):
         if n_out_channels >= 1:
             assert int(X.get_shape()[-1]) == (r**2) * n_out_channels, _err_log
-            """
-            bsize, a, b, c = X.get_shape().as_list()
-            bsize = tf.shape(X)[0] # Handling Dimension(None) type for undefined batch dim
-            Xs=tf.split(X,r,3) #b*h*w*r*r
-            Xr=tf.concat(Xs,2) #b*h*(r*w)*r
-            X=tf.reshape(Xr,(bsize,r*a,r*b,n_out_channel)) # b*(r*h)*(r*w)*c
-
-            """
+
+            # bsize, a, b, c = X.get_shape().as_list()
+            # bsize = tf.shape(X)[0] # Handling Dimension(None) type for undefined batch dim
+            # Xs=tf.split(X,r,3) #b*h*w*r*r
+            # Xr=tf.concat(Xs,2) #b*h*(r*w)*r
+            # X=tf.reshape(Xr,(bsize,r*a,r*b,n_out_channel)) # b*(r*h)*(r*w)*c
+
             X = tf.depth_to_space(X, r)
         else:
             logging.info(_err_log)

tensorlayer/prepro.py

@@ -2738,7 +2738,7 @@ def _get_coord(coord):
         if is_center:
             coord = obj_box_coord_centroid_to_upleft(coord)
 
-        ##======= pixel unit format and upleft, w, h ==========##
+        # ======= pixel unit format and upleft, w, h ==========
         x = (coord[0] - im.shape[1] / 2) / zy + im.shape[1] / 2  # only change this
         y = (coord[1] - im.shape[0] / 2) / zx + im.shape[0] / 2  # only change this
         w = coord[2] / zy  # only change this
@@ -2776,7 +2776,7 @@ def _get_coord(coord):
 
         coord = [x, y, w, h]
 
-        ## convert back if input format is center.
+        # convert back if input format is center.
         if is_center:
             coord = obj_box_coord_upleft_to_centroid(coord)
 
@@ -2788,7 +2788,7 @@ def _get_coord(coord):
         coord = coords[i]
         assert len(coord) == 4, "coordinate should be 4 values : [x, y, w, h]"
         if is_rescale:
-            """ for scaled coord, upscaled before process and scale back in the end. """
+            # for scaled coord, upscaled before process and scale back in the end.
             coord = obj_box_coord_scale_to_pixelunit(coord, im.shape)
             coord = _get_coord(coord)
             if coord is not None:
@@ -2803,7 +2803,6 @@ def _get_coord(coord):
     return im_new, classes_new, coords_new
 
 
-## Sequence
 def pad_sequences(sequences, maxlen=None, dtype='int32', padding='post', truncating='pre', value=0.):
     """Pads each sequence to the same length:
     the length of the longest sequence.