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Fixed typos in image_ops_impl.py #40780

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merged 1 commit into from Jun 26, 2020
Merged

Fixed typos in image_ops_impl.py #40780

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12 changes: 6 additions & 6 deletions tensorflow/python/ops/image_ops_impl.py
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Expand Up @@ -4578,16 +4578,16 @@ def non_max_suppression_padded(boxes,
sorted_input: a boolean indicating whether the input boxes and scores
are sorted in descending order by the score.
canonicalized_coordinates: if box coordinates are given as
`[y_min, x_min, y_max, x_max]`, settign to True eliminate redundant
`[y_min, x_min, y_max, x_max]`, setting to True eliminate redundant
computation to canonicalize box coordinates.
tile_size: an integer representing the number of boxes in a tile, i.e.,
the maximum number of boxes per image that can be used to suppress other
boxes in parallel; larger tile_size means larger parallelism and
potentially more redundant work.
Returns:
idx: a tensor with a shape of [..., num_boxes] representing the
indices selected by non-max suppression. The leadign dimensions
are the batch dimensions of the input boxes. All numbers are are within
indices selected by non-max suppression. The leading dimensions
are the batch dimensions of the input boxes. All numbers are within
[0, num_boxes). For each image (i.e., idx[i]), only the first num_valid[i]
indices (i.e., idx[i][:num_valid[i]]) are valid.
num_valid: a tensor of rank 0 or higher with a shape of [...]
Expand Down Expand Up @@ -4703,16 +4703,16 @@ def non_max_suppression_padded_v2(boxes,
sorted_input: a boolean indicating whether the input boxes and scores
are sorted in descending order by the score.
canonicalized_coordinates: if box coordinates are given as
`[y_min, x_min, y_max, x_max]`, settign to True eliminate redundant
`[y_min, x_min, y_max, x_max]`, setting to True eliminate redundant
computation to canonicalize box coordinates.
tile_size: an integer representing the number of boxes in a tile, i.e.,
the maximum number of boxes per image that can be used to suppress other
boxes in parallel; larger tile_size means larger parallelism and
potentially more redundant work.
Returns:
idx: a tensor with a shape of [..., num_boxes] representing the
indices selected by non-max suppression. The leadign dimensions
are the batch dimensions of the input boxes. All numbers are are within
indices selected by non-max suppression. The leading dimensions
are the batch dimensions of the input boxes. All numbers are within
[0, num_boxes). For each image (i.e., idx[i]), only the first num_valid[i]
indices (i.e., idx[i][:num_valid[i]]) are valid.
num_valid: a tensor of rank 0 or higher with a shape of [...]
Expand Down