edit loss desc

mrcnn/model.py

 """
 Mask R-CNN
-The main Mask R-CNN model implemenetation.
+The main Mask R-CNN model implementation.
 
 Copyright (c) 2017 Matterport, Inc.
 Licensed under the MIT License (see LICENSE for details)
@@ -63,7 +63,7 @@ class BatchNorm(KL.BatchNormalization):
         Note about training values:
             None: Train BN layers. This is the normal mode
             False: Freeze BN layers. Good when batch size is small
-            True: (don't use). Set layer in training mode even when inferencing
+            True: (don't use). Set layer in training mode even when making inferences
         """
         return super(self.__class__, self).call(inputs, training=training)
 
@@ -97,12 +97,12 @@ def identity_block(input_tensor, kernel_size, filters, stage, block,
     """The identity_block is the block that has no conv layer at shortcut
     # Arguments
         input_tensor: input tensor
-        kernel_size: defualt 3, the kernel size of middle conv layer at main path
+        kernel_size: default 3, the kernel size of middle conv layer at main path
         filters: list of integers, the nb_filters of 3 conv layer at main path
         stage: integer, current stage label, used for generating layer names
         block: 'a','b'..., current block label, used for generating layer names
         use_bias: Boolean. To use or not use a bias in conv layers.
-        train_bn: Boolean. Train or freeze Batch Norm layres
+        train_bn: Boolean. Train or freeze Batch Norm layers
     """
     nb_filter1, nb_filter2, nb_filter3 = filters
     conv_name_base = 'res' + str(stage) + block + '_branch'
@@ -132,12 +132,12 @@ def conv_block(input_tensor, kernel_size, filters, stage, block,
     """conv_block is the block that has a conv layer at shortcut
     # Arguments
         input_tensor: input tensor
-        kernel_size: defualt 3, the kernel size of middle conv layer at main path
+        kernel_size: default 3, the kernel size of middle conv layer at main path
         filters: list of integers, the nb_filters of 3 conv layer at main path
         stage: integer, current stage label, used for generating layer names
         block: 'a','b'..., current block label, used for generating layer names
         use_bias: Boolean. To use or not use a bias in conv layers.
-        train_bn: Boolean. Train or freeze Batch Norm layres
+        train_bn: Boolean. Train or freeze Batch Norm layers
     Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
     And the shortcut should have subsample=(2,2) as well
     """
@@ -172,7 +172,7 @@ def resnet_graph(input_image, architecture, stage5=False, train_bn=True):
     """Build a ResNet graph.
         architecture: Can be resnet50 or resnet101
         stage5: Boolean. If False, stage5 of the network is not created
-        train_bn: Boolean. Train or freeze Batch Norm layres
+        train_bn: Boolean. Train or freeze Batch Norm layers
     """
     assert architecture in ["resnet50", "resnet101"]
     # Stage 1
@@ -337,7 +337,7 @@ class ProposalLayer(KE.Layer):
 ############################################################
 
 def log2_graph(x):
-    """Implementatin of Log2. TF doesn't have a native implemenation."""
+    """Implementation of Log2. TF doesn't have a native implementation."""
     return tf.log(x) / tf.log(2.0)
 
 
@@ -399,7 +399,7 @@ class PyramidROIAlign(KE.Layer):
             ix = tf.where(tf.equal(roi_level, level))
             level_boxes = tf.gather_nd(boxes, ix)
 
-            # Box indicies for crop_and_resize.
+            # Box indices for crop_and_resize.
             box_indices = tf.cast(ix[:, 0], tf.int32)
 
             # Keep track of which box is mapped to which level
@@ -457,9 +457,9 @@ def overlaps_graph(boxes1, boxes2):
     """Computes IoU overlaps between two sets of boxes.
     boxes1, boxes2: [N, (y1, x1, y2, x2)].
     """
-    # 1. Tile boxes2 and repeate boxes1. This allows us to compare
+    # 1. Tile boxes2 and repeat boxes1. This allows us to compare
     # every boxes1 against every boxes2 without loops.
-    # TF doesn't have an equivalent to np.repeate() so simulate it
+    # TF doesn't have an equivalent to np.repeat() so simulate it
     # using tf.tile() and tf.reshape.
     b1 = tf.reshape(tf.tile(tf.expand_dims(boxes1, 1),
                             [1, 1, tf.shape(boxes2)[0]]), [-1, 4])
@@ -539,7 +539,7 @@ def detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config)
     crowd_iou_max = tf.reduce_max(crowd_overlaps, axis=1)
     no_crowd_bool = (crowd_iou_max < 0.001)
 
-    # Determine postive and negative ROIs
+    # Determine positive and negative ROIs
     roi_iou_max = tf.reduce_max(overlaps, axis=1)
     # 1. Positive ROIs are those with >= 0.5 IoU with a GT box
     positive_roi_bool = (roi_iou_max >= 0.5)
@@ -584,7 +584,7 @@ def detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config)
     # Compute mask targets
     boxes = positive_rois
     if config.USE_MINI_MASK:
-        # Transform ROI corrdinates from normalized image space
+        # Transform ROI coordinates from normalized image space
         # to normalized mini-mask space.
         y1, x1, y2, x2 = tf.split(positive_rois, 4, axis=1)
         gt_y1, gt_x1, gt_y2, gt_x2 = tf.split(roi_gt_boxes, 4, axis=1)
@@ -741,7 +741,7 @@ def refine_detections_graph(rois, probs, deltas, window, config):
                 tf.gather(pre_nms_scores, ixs),
                 max_output_size=config.DETECTION_MAX_INSTANCES,
                 iou_threshold=config.DETECTION_NMS_THRESHOLD)
-        # Map indicies
+        # Map indices
         class_keep = tf.gather(keep, tf.gather(ixs, class_keep))
         # Pad with -1 so returned tensors have the same shape
         gap = config.DETECTION_MAX_INSTANCES - tf.shape(class_keep)[0]
@@ -844,8 +844,8 @@ def rpn_graph(feature_map, anchors_per_location, anchor_stride):
         rpn_bbox: [batch, H, W, (dy, dx, log(dh), log(dw))] Deltas to be
                   applied to anchors.
     """
-    # TODO: check if stride of 2 causes alignment issues if the featuremap
-    #       is not even.
+    # TODO: check if stride of 2 causes alignment issues if the feature map
+    # is not even.
     # Shared convolutional base of the RPN
     shared = KL.Conv2D(512, (3, 3), padding='same', activation='relu',
                        strides=anchor_stride,
@@ -908,12 +908,12 @@ def fpn_classifier_graph(rois, feature_maps, image_meta,
 
     rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized
           coordinates.
-    feature_maps: List of feature maps from diffent layers of the pyramid,
+    feature_maps: List of feature maps from different layers of the pyramid,
                   [P2, P3, P4, P5]. Each has a different resolution.
     - image_meta: [batch, (meta data)] Image details. See compose_image_meta()
     pool_size: The width of the square feature map generated from ROI Pooling.
     num_classes: number of classes, which determines the depth of the results
-    train_bn: Boolean. Train or freeze Batch Norm layres
+    train_bn: Boolean. Train or freeze Batch Norm layers
     fc_layers_size: Size of the 2 FC layers
 
     Returns:
@@ -962,12 +962,12 @@ def build_fpn_mask_graph(rois, feature_maps, image_meta,
 
     rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized
           coordinates.
-    feature_maps: List of feature maps from diffent layers of the pyramid,
+    feature_maps: List of feature maps from different layers of the pyramid,
                   [P2, P3, P4, P5]. Each has a different resolution.
     image_meta: [batch, (meta data)] Image details. See compose_image_meta()
     pool_size: The width of the square feature map generated from ROI Pooling.
     num_classes: number of classes, which determines the depth of the results
-    train_bn: Boolean. Train or freeze Batch Norm layres
+    train_bn: Boolean. Train or freeze Batch Norm layers
 
     Returns: Masks [batch, roi_count, height, width, num_classes]
     """
@@ -1014,7 +1014,7 @@ def build_fpn_mask_graph(rois, feature_maps, image_meta,
 
 def smooth_l1_loss(y_true, y_pred):
     """Implements Smooth-L1 loss.
-    y_true and y_pred are typicallly: [N, 4], but could be any shape.
+    y_true and y_pred are typically: [N, 4], but could be any shape.
     """
     diff = K.abs(y_true - y_pred)
     less_than_one = K.cast(K.less(diff, 1.0), "float32")
@@ -1039,7 +1039,7 @@ def rpn_class_loss_graph(rpn_match, rpn_class_logits):
     # Pick rows that contribute to the loss and filter out the rest.
     rpn_class_logits = tf.gather_nd(rpn_class_logits, indices)
     anchor_class = tf.gather_nd(anchor_class, indices)
-    # Crossentropy loss
+    # Cross entropy loss
     loss = K.sparse_categorical_crossentropy(target=anchor_class,
                                              output=rpn_class_logits,
                                              from_logits=True)
@@ -1129,7 +1129,7 @@ def mrcnn_bbox_loss_graph(target_bbox, target_class_ids, pred_bbox):
     pred_bbox = K.reshape(pred_bbox, (-1, K.int_shape(pred_bbox)[2], 4))
 
     # Only positive ROIs contribute to the loss. And only
-    # the right class_id of each ROI. Get their indicies.
+    # the right class_id of each ROI. Get their indices.
     positive_roi_ix = tf.where(target_class_ids > 0)[:, 0]
     positive_roi_class_ids = tf.cast(
         tf.gather(target_class_ids, positive_roi_ix), tf.int64)
@@ -1194,7 +1194,7 @@ def load_image_gt(dataset, config, image_id, augment=False, augmentation=None,
                   use_mini_mask=False):
     """Load and return ground truth data for an image (image, mask, bounding boxes).
 
-    augment: (Depricated. Use augmentation instead). If true, apply random
+    augment: (deprecated. Use augmentation instead). If true, apply random
         image augmentation. Currently, only horizontal flipping is offered.
     augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation.
         For example, passing imgaug.augmenters.Fliplr(0.5) flips images
@@ -1229,7 +1229,7 @@ def load_image_gt(dataset, config, image_id, augment=False, augmentation=None,
     # Random horizontal flips.
     # TODO: will be removed in a future update in favor of augmentation
     if augment:
-        logging.warning("'augment' is depricated. Use 'augmentation' instead.")
+        logging.warning("'augment' is deprecated. Use 'augmentation' instead.")
         if random.randint(0, 1):
             image = np.fliplr(image)
             mask = np.fliplr(mask)
@@ -1239,7 +1239,7 @@ def load_image_gt(dataset, config, image_id, augment=False, augmentation=None,
     if augmentation:
         import imgaug
 
-        # Augmentors that are safe to apply to masks
+        # Augmenters that are safe to apply to masks
         # Some, such as Affine, have settings that make them unsafe, so always
         # test your augmentation on masks
         MASK_AUGMENTERS = ["Sequential", "SomeOf", "OneOf", "Sometimes",
@@ -1248,7 +1248,7 @@ def load_image_gt(dataset, config, image_id, augment=False, augmentation=None,
 
         def hook(images, augmenter, parents, default):
             """Determines which augmenters to apply to masks."""
-            return (augmenter.__class__.__name__ in MASK_AUGMENTERS)
+            return augmenter.__class__.__name__ in MASK_AUGMENTERS
 
         # Store shapes before augmentation to compare
         image_shape = image.shape
@@ -1302,7 +1302,7 @@ def build_detection_targets(rpn_rois, gt_class_ids, gt_boxes, gt_masks, config):
     rpn_rois: [N, (y1, x1, y2, x2)] proposal boxes.
     gt_class_ids: [instance count] Integer class IDs
     gt_boxes: [instance count, (y1, x1, y2, x2)]
-    gt_masks: [height, width, instance count] Grund truth masks. Can be full
+    gt_masks: [height, width, instance count] Ground truth masks. Can be full
               size or mini-masks.
 
     Returns:
@@ -1357,7 +1357,7 @@ def build_detection_targets(rpn_rois, gt_class_ids, gt_boxes, gt_masks, config):
 
     # Negative ROIs are those with max IoU 0.1-0.5 (hard example mining)
     # TODO: To hard example mine or not to hard example mine, that's the question
-#     bg_ids = np.where((rpn_roi_iou_max >= 0.1) & (rpn_roi_iou_max < 0.5))[0]
+    # bg_ids = np.where((rpn_roi_iou_max >= 0.1) & (rpn_roi_iou_max < 0.5))[0]
     bg_ids = np.where(rpn_roi_iou_max < 0.5)[0]
 
     # Subsample ROIs. Aim for 33% foreground.
@@ -1373,7 +1373,7 @@ def build_detection_targets(rpn_rois, gt_class_ids, gt_boxes, gt_masks, config):
         keep_bg_ids = np.random.choice(bg_ids, remaining, replace=False)
     else:
         keep_bg_ids = bg_ids
-    # Combine indicies of ROIs to keep
+    # Combine indices of ROIs to keep
     keep = np.concatenate([keep_fg_ids, keep_bg_ids])
     # Need more?
     remaining = config.TRAIN_ROIS_PER_IMAGE - keep.shape[0]
@@ -1644,7 +1644,7 @@ def data_generator(dataset, config, shuffle=True, augment=False, augmentation=No
     dataset: The Dataset object to pick data from
     config: The model config object
     shuffle: If True, shuffles the samples before every epoch
-    augment: (Depricated. Use augmentation instead). If true, apply random
+    augment: (deprecated. Use augmentation instead). If true, apply random
         image augmentation. Currently, only horizontal flipping is offered.
     augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation.
         For example, passing imgaug.augmenters.Fliplr(0.5) flips images