spellcheck config.py

mrcnn/model.py

@@ -1661,7 +1661,7 @@ def data_generator(dataset, config, shuffle=True, augment=False, augmentation=No
         defined in the Dataset class.
 
     Returns a Python generator. Upon calling next() on it, the
-    generator returns two lists, inputs and outputs. The containtes
+    generator returns two lists, inputs and outputs. The contents
     of the lists differs depending on the received arguments:
     inputs list:
     - images: [batch, H, W, C]
@@ -1693,7 +1693,7 @@ def data_generator(dataset, config, shuffle=True, augment=False, augmentation=No
                                              config.BACKBONE_STRIDES,
                                              config.RPN_ANCHOR_STRIDE)
 
-    # Keras requires a generator to run indefinately.
+    # Keras requires a generator to run indefinitely.
     while True:
         try:
             # Increment index to pick next image. Shuffle if at the start of an epoch.
@@ -2101,10 +2101,10 @@ def find_last(self):
         return checkpoint
 
     def load_weights(self, filepath, by_name=False, exclude=None):
-        """Modified version of the correspoding Keras function with
+        """Modified version of the corresponding Keras function with
         the addition of multi-GPU support and the ability to exclude
         some layers from loading.
-        exlude: list of layer names to excluce
+        exclude: list of layer names to exclude
         """
         import h5py
         # Conditional import to support versions of Keras before 2.2
@@ -2238,7 +2238,7 @@ def set_trainable(self, layer_regex, keras_model=None, indent=0, verbose=1):
                 layer.layer.trainable = trainable
             else:
                 layer.trainable = trainable
-            # Print trainble layer names
+            # Print trainable layer names
             if trainable and verbose > 0:
                 log("{}{:20}   ({})".format(" " * indent, layer.name,
                                             layer.__class__.__name__))
@@ -2306,7 +2306,7 @@ def train(self, train_dataset, val_dataset, learning_rate, epochs, layers,
             flips images right/left 50% of the time. You can pass complex
             augmentations as well. This augmentation applies 50% of the
             time, and when it does it flips images right/left half the time
-            and adds a Gausssian blur with a random sigma in range 0 to 5.
+            and adds a Gaussian blur with a random sigma in range 0 to 5.
 
                 augmentation = imgaug.augmenters.Sometimes(0.5, [
                     imgaug.augmenters.Fliplr(0.5),
@@ -2385,10 +2385,10 @@ def train(self, train_dataset, val_dataset, learning_rate, epochs, layers,
     def mold_inputs(self, images):
         """Takes a list of images and modifies them to the format expected
         as an input to the neural network.
-        images: List of image matricies [height,width,depth]. Images can have
+        images: List of image matrices [height,width,depth]. Images can have
             different sizes.
 
-        Returns 3 Numpy matricies:
+        Returns 3 Numpy matrices:
         molded_images: [N, h, w, 3]. Images resized and normalized.
         image_metas: [N, length of meta data]. Details about each image.
         windows: [N, (y1, x1, y2, x2)]. The portion of the image that has the
@@ -2550,7 +2550,7 @@ def detect_molded(self, molded_images, image_metas, verbose=0):
         the model.
 
         molded_images: List of images loaded using load_image_gt()
-        image_metas: image meta data, also retruned by load_image_gt()
+        image_metas: image meta data, also returned by load_image_gt()
 
         Returns a list of dicts, one dict per image. The dict contains:
         rois: [N, (y1, x1, y2, x2)] detection bounding boxes
@@ -2678,7 +2678,7 @@ def run_graph(self, images, outputs, image_metas=None):
         outputs.
 
         image_metas: If provided, the images are assumed to be already
-            molded (i.e. resized, padded, and noramlized)
+            molded (i.e. resized, padded, and normalized)
 
         outputs: List of tuples (name, tensor) to compute. The tensors are
             symbolic TensorFlow tensors and the names are for easy tracking.
@@ -2803,7 +2803,7 @@ def parse_image_meta_graph(meta):
 
 
 def mold_image(images, config):
-    """Expects an RGB image (or array of images) and subtraces
+    """Expects an RGB image (or array of images) and subtracts
     the mean pixel and converts it to float. Expects image
     colors in RGB order.
     """
@@ -2820,7 +2820,7 @@ def unmold_image(normalized_images, config):
 ############################################################
 
 def trim_zeros_graph(boxes, name=None):
-    """Often boxes are represented with matricies of shape [N, 4] and
+    """Often boxes are represented with matrices of shape [N, 4] and
     are padded with zeros. This removes zero boxes.
 
     boxes: [N, 4] matrix of boxes.