fix: grammar typos

tensorlayer/app/human_pose_estimation/common.py

@@ -251,7 +251,7 @@ def denormalize3D(self, data, which='scale'):
 
         if which == 'scale':
             data = data.reshape((-1, 17, 3)).copy()
-            # denormalize (x,y,z) coordiantes for results
+            # denormalize (x,y,z) coordinates for results
             for idx, item in enumerate(self.gt_testset):
                 camera_name = item['camera_param']['name']
                 if camera_name == '54138969' or camera_name == '60457274':
@@ -275,7 +275,7 @@ def denormalize2D(self, data, which='scale'):
 
         if which == 'scale':
             data = data.reshape((-1, 17, 2)).copy()
-            # denormalize (x,y,z) coordiantes for results
+            # denormalize (x,y,z) coordinates for results
             for idx, item in enumerate(self.gt_testset):
                 camera_name = item['camera_param']['name']
                 if camera_name == '54138969' or camera_name == '60457274':

tensorlayer/db.py

@@ -118,7 +118,7 @@ def save_model(self, network=None, model_name='model', **kwargs):
         model_name : str
             The name/key of model.
         kwargs : other events
-            Other events, such as name, accuracy, loss, step number and etc (optinal).
+            Other events, such as name, accuracy, loss, step number and etc (optional).
 
         Examples
         ---------
@@ -178,7 +178,7 @@ def find_top_model(self, sort=None, model_name='model', **kwargs):
         model_name : str or None
             The name/key of model.
         kwargs : other events
-            Other events, such as name, accuracy, loss, step number and etc (optinal).
+            Other events, such as name, accuracy, loss, step number and etc (optional).
 
         Examples
         ---------
@@ -265,7 +265,7 @@ def save_dataset(self, dataset=None, dataset_name=None, **kwargs):
         dataset_name : str
             The name of dataset.
         kwargs : other events
-            Other events, such as description, author and etc (optinal).
+            Other events, such as description, author and etc (optional).
 
         Examples
         ----------
@@ -309,7 +309,7 @@ def find_top_dataset(self, dataset_name=None, sort=None, **kwargs):
         sort : List of tuple
             PyMongo sort comment, search "PyMongo find one sorting" and `collection level operations <http://api.mongodb.com/python/current/api/pymongo/collection.html>`__ for more details.
         kwargs : other events
-            Other events, such as description, author and etc (optinal).
+            Other events, such as description, author and etc (optional).
 
         Examples
         ---------

tensorlayer/files/dataset_loaders/flickr_1M_dataset.py

@@ -32,7 +32,7 @@ def load_flickr1M_dataset(tag='sky', size=10, path="data", n_threads=50, printab
     n_threads : int
         The number of thread to read image.
     printable : boolean
-        Whether to print infomation when reading images, default is ``False``.
+        Whether to print information when reading images, default is ``False``.
 
     Examples
     ----------

tensorlayer/files/dataset_loaders/flickr_25k_dataset.py

@@ -30,7 +30,7 @@ def load_flickr25k_dataset(tag='sky', path="data", n_threads=50, printable=False
     n_threads : int
         The number of thread to read image.
     printable : boolean
-        Whether to print infomation when reading images, default is ``False``.
+        Whether to print information when reading images, default is ``False``.
 
     Examples
     -----------

tensorlayer/files/dataset_loaders/ptb_dataset.py

@@ -47,7 +47,7 @@ def load_ptb_dataset(path='data'):
     path = os.path.join(path, 'ptb')
     logging.info("Load or Download Penn TreeBank (PTB) dataset > {}".format(path))
 
-    #Maybe dowload and uncompress tar, or load exsisting files
+    # Maybe download and uncompress tar, or load existing files
     filename = 'simple-examples.tgz'
     url = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/'
     maybe_download_and_extract(filename, path, url, extract=True)

tensorlayer/files/utils.py

@@ -297,7 +297,7 @@ def static_graph2net(model_config):
 
 
 def load_hdf5_graph(filepath='model.hdf5', load_weights=False):
-    """Restore TL model archtecture from a a pickle file. Support loading model weights.
+    """Restore TL model architecture from a a pickle file. Support loading model weights.
 
     Parameters
     -----------
@@ -353,7 +353,7 @@ def load_hdf5_graph(filepath='model.hdf5', load_weights=False):
 
 
 # def load_pkl_graph(name='model.pkl'):
-#     """Restore TL model archtecture from a a pickle file. No parameters be restored.
+#     """Restore TL model architecture from a a pickle file. No parameters be restored.
 #
 #     Parameters
 #     -----------
@@ -778,7 +778,7 @@ def load_ptb_dataset(path='data'):
     path = os.path.join(path, 'ptb')
     logging.info("Load or Download Penn TreeBank (PTB) dataset > {}".format(path))
 
-    # Maybe dowload and uncompress tar, or load exsisting files
+    # Maybe download and uncompress tar, or load existing files
     filename = 'simple-examples.tgz'
     url = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/'
     maybe_download_and_extract(filename, path, url, extract=True)
@@ -1063,7 +1063,7 @@ def load_flickr25k_dataset(tag='sky', path="data", n_threads=50, printable=False
     n_threads : int
         The number of thread to read image.
     printable : boolean
-        Whether to print infomation when reading images, default is ``False``.
+        Whether to print information when reading images, default is ``False``.
 
     Examples
     -----------
@@ -1135,7 +1135,7 @@ def load_flickr1M_dataset(tag='sky', size=10, path="data", n_threads=50, printab
     n_threads : int
         The number of thread to read image.
     printable : boolean
-        Whether to print infomation when reading images, default is ``False``.
+        Whether to print information when reading images, default is ``False``.
 
     Examples
     ----------
@@ -2104,7 +2104,7 @@ def load_and_assign_npz_dict(name='model.npz', network=None, skip=False):
                 logging.warning("Weights named '%s' not found in network. Skip it." % key)
             else:
                 raise RuntimeError(
-                    "Weights named '%s' not found in network. Hint: set argument skip=Ture "
+                    "Weights named '%s' not found in network. Hint: set argument skip=True "
                     "if you want to skip redundant or mismatch weights." % key
                 )
         else:
@@ -2332,7 +2332,7 @@ def load_file_list(path=None, regx='\.jpg', printable=True, keep_prefix=False):
     regx : str
         The regx of file name.
     printable : boolean
-        Whether to print the files infomation.
+        Whether to print the files information.
     keep_prefix : boolean
         Whether to keep path in the file name.
 
@@ -2404,19 +2404,19 @@ def exists_or_mkdir(path, verbose=True):
 
 
 def maybe_download_and_extract(filename, working_directory, url_source, extract=False, expected_bytes=None):
-    """Checks if file exists in working_directory otherwise tries to dowload the file,
+    """Checks if file exists in working_directory otherwise tries to download the file,
     and optionally also tries to extract the file if format is ".zip" or ".tar"
 
     Parameters
     -----------
     filename : str
         The name of the (to be) dowloaded file.
     working_directory : str
-        A folder path to search for the file in and dowload the file to
+        A folder path to search for the file in and download the file to
     url : str
         The URL to download the file from
     extract : boolean
-        If True, tries to uncompress the dowloaded file is ".tar.gz/.tar.bz2" or ".zip" file, default is False.
+        If True, tries to uncompress the downloaded file is ".tar.gz/.tar.bz2" or ".zip" file, default is False.
     expected_bytes : int or None
         If set tries to verify that the downloaded file is of the specified size, otherwise raises an Exception, defaults is None which corresponds to no check being performed.
 

tensorlayer/layers/embedding.py

@@ -28,9 +28,9 @@ class OneHot(Layer):
     depth : None or int
         If the input indices is rank N, the output will have rank N+1. The new axis is created at dimension `axis` (default: the new axis is appended at the end).
     on_value : None or number
-        The value to represnt `ON`. If None, it will default to the value 1.
+        The value to represent `ON`. If None, it will default to the value 1.
     off_value : None or number
-        The value to represnt `OFF`. If None, it will default to the value 0.
+        The value to represent `OFF`. If None, it will default to the value 0.
     axis : None or int
         The axis.
     dtype : None or TensorFlow dtype

tensorlayer/layers/normalization.py

@@ -98,7 +98,7 @@ def _bias_scale(x, b, data_format):
 
 
 def _bias_add(x, b, data_format):
-    """Alternative implementation of tf.nn.bias_add which is compatiable with tensorRT."""
+    """Alternative implementation of tf.nn.bias_add which is compatible with tensorRT."""
     if data_format == 'NHWC':
         return tf.add(x, b)
     elif data_format == 'NCHW':

tensorlayer/layers/recurrent.py

@@ -813,7 +813,7 @@ class BasicConvLSTMCell(ConvRNNCell):
         The bias added to forget gates (see above).
     input_size : int
         Deprecated and unused.
-    state_is_tuple : boolen
+    state_is_tuple : boolean
         If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`.
         If False, they are concatenated along the column axis. The latter behavior will soon be deprecated.
     act : activation function

tensorlayer/layers/utils.py

@@ -112,7 +112,7 @@ def get_layers_with_name(net, name="", verbose=False):
     >>> layers = tl.layers.get_layers_with_name(net, "CNN", True)
 
     """
-    logging.info("  [*] geting layers with %s" % name)
+    logging.info("  [*] getting layers with %s" % name)
 
     layers = []
     i = 0
@@ -157,7 +157,7 @@ def get_variables_with_name(name=None, train_only=True, verbose=False):
     name : str
         Get the variables that contain this name.
     train_only : boolean
-        If Ture, only get the trainable variables.
+        If True, only get the trainable variables.
     verbose : boolean
         If True, print the information of all variables.
 
@@ -175,7 +175,7 @@ def get_variables_with_name(name=None, train_only=True, verbose=False):
     if name is None:
         raise Exception("please input a name")
 
-    logging.info("  [*] geting variables with %s" % name)
+    logging.info("  [*] getting variables with %s" % name)
 
     # tvar = tf.trainable_variables() if train_only else tf.all_variables()
     if train_only:

tensorlayer/models/core.py

@@ -41,9 +41,9 @@ class Model(object):
     __init__(self, inputs=None, outputs=None, name=None)
         Initializing the Model.
     inputs()
-        Get input tensors to this network (only avaiable for static model).
+        Get input tensors to this network (only available for static model).
     outputs()
-        Get output tensors to this network (only avaiable for static model).
+        Get output tensors to this network (only available for static model).
     __call__(inputs, is_train=None, **kwargs)
         Forward input tensors through this network.
     all_layers()

tensorlayer/prepro.py

@@ -572,7 +572,7 @@ def affine_transform_cv2(x, transform_matrix, flags=None, border_mode='constant'
     elif border_mode is 'replicate':
         border_mode = cv2.BORDER_REPLICATE
     else:
-        raise Exception("unsupport border_mode, check cv.BORDER_ for more details.")
+        raise Exception("unsupported border_mode, check cv.BORDER_ for more details.")
     return cv2.warpAffine(x, transform_matrix[0:2,:], \
             (cols,rows), flags=flags, borderMode=border_mode)
 
@@ -2613,7 +2613,7 @@ def parse_darknet_ann_str_to_list(annotations):
     Parameters
     -----------
     annotations : str
-        The annotations in darkent format "class, x, y, w, h ...." seperated by "\\n".
+        The annotations in darkent format "class, x, y, w, h ...." separated by "\\n".
 
     Returns
     -------
@@ -3762,7 +3762,7 @@ def resize_image(image, annos, mask, target_width, target_height):
             new_keypoints = []
             for keypoints in people:
 
-                # case orginal points are not usable
+                # case original points are not usable
                 if keypoints[1] >= crop_range_y and keypoints[1] <= crop_range_y + _target_height - 1:
                     pts = (int(keypoints[0]), int(keypoints[1] - crop_range_y))
                 else:
@@ -3783,7 +3783,7 @@ def resize_image(image, annos, mask, target_width, target_height):
             new_keypoints = []
             for keypoints in people:
 
-                # case orginal points are not usable
+                # case original points are not usable
                 if keypoints[0] >= crop_range_x and keypoints[0] <= crop_range_x + _target_width - 1:
                     pts = (int(keypoints[0] - crop_range_x), int(keypoints[1]))
                 else: