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add function api of BoundingboxEncode,BoundingboxDecode.

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LV 2022-10-10 11:08:26 +08:00
parent 2ea5414a93
commit a65653c65e
8 changed files with 132 additions and 65 deletions
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2
docs/api/api_python/mindspore.ops.function.rst
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@ -22,6 +22,8 @@ mindspore.ops.function
mindspore.ops.avg_pool3d
mindspore.ops.batch_norm
mindspore.ops.bias_add
mindspore.ops.boundingbox_encode
mindspore.ops.boundingbox_decode
mindspore.ops.ctc_greedy_decoder
mindspore.ops.conv2d
mindspore.ops.conv3d

18
docs/api/api_python/ops/mindspore.ops.BoundingBoxDecode.rst
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@ -7,20 +7,4 @@ mindspore.ops.BoundingBoxDecode
算子的功能是计算偏移量，此算子将偏移量转换为Bbox，用于在后续图像中标记目标等。
参数：
- **means** (tuple) - 计算 `deltas` 的均值。默认值：（0.0, 0.0, 0.0, 0.0, 0.0）。
- **stds** (tuple) - 计算 `deltas` 的标准差。默认值：（1.0, 1.0, 1.0, 1.0）。
- **max_shape** (tuple) - 解码框计算的上限值。
- **wh_ratio_clip** (float) - 解码框计算的宽高比限制。默认值：0.016。
输入：
- **anchor_box** (Tensor) - 锚框。锚框的shape必须为 :math:`(n,4)` 。
- **deltas** (Tensor) - 框的增量。它的shape与 `anchor_box` 相同。
输出：
Tensor，解码框。它的数据类型和shape与 `anchor_box` 相同。
异常：
- **TypeError** - 如果 `means` 、 `stds` 或 `max_shape` 不是tuple。
- **TypeError** - 如果 `wh_ratio_clip` 不是float。
- **TypeError** - 如果 `anchor_box` 或 `deltas` 不是Tensor。
更多细节详见 :func:`mindspore.ops.boundingbox_decode`。

15
docs/api/api_python/ops/mindspore.ops.BoundingBoxEncode.rst
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@ -7,17 +7,4 @@ mindspore.ops.BoundingBoxEncode
算子的功能是计算预测边界框和真实边界框之间的偏移，并将此偏移作为损失变量。
参数：
- **means** (tuple) - 计算编码边界框的均值。默认值：（0.0, 0.0, 0.0, 0.0, 0.0）。
- **stds** (tuple) - 计算增量的标准偏差。默认值：（1.0、1.0、1.0、1.0）。
输入：
- **anchor_box** (Tensor) - 锚框。锚框的shape必须为 :math:`(n,4)` 。
- **groundtruth_box** (Tensor) - 真实边界框。它的shape与锚框相同。
输出：
Tensor，编码边界框。数据类型和shape与输入 `anchor_box` 相同。
异常：
- **TypeError** - 如果 `means` 或 `stds` 不是tuple。
- **TypeError** - 如果 `anchor_box` 或 `groundtruth_box` 不是Tensor。
更多细节详见 :func:`mindspore.ops.boundingbox_encode`。

24
docs/api/api_python/ops/mindspore.ops.func_boundingbox_decode.rst Normal file
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@ -0,0 +1,24 @@
mindspore.ops.boundingbox_decode
================================
.. py:function:: mindspore.ops.boundingbox_decode(max_shape, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), wh_ratio_clip=0.016)
解码边界框位置信息。
算子的功能是计算偏移量，此算子将偏移量转换为Bbox，用于在后续图像中标记目标等。
参数：
- **anchor_box** (Tensor) - 锚框。锚框的shape必须为 :math:`(n,4)` 。
- **deltas** (Tensor) - 框的增量。它的shape与 `anchor_box` 相同。
- **means** (tuple) - 计算 `deltas` 的均值。默认值：（0.0, 0.0, 0.0, 0.0, 0.0）。
- **stds** (tuple) - 计算 `deltas` 的标准差。默认值：（1.0, 1.0, 1.0, 1.0）。
- **max_shape** (tuple) - 解码框计算的上限值。
- **wh_ratio_clip** (float) - 解码框计算的宽高比限制。默认值：0.016。
返回：
Tensor，解码框。它的数据类型和shape与 `anchor_box` 相同。
异常：
- **TypeError** - 如果 `means` 、 `stds` 或 `max_shape` 不是tuple。
- **TypeError** - 如果 `wh_ratio_clip` 不是float。
- **TypeError** - 如果 `anchor_box` 或 `deltas` 不是Tensor。

21
docs/api/api_python/ops/mindspore.ops.func_boundingbox_encode.rst Normal file
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@ -0,0 +1,21 @@
mindspore.ops.boundingbox_encode
================================
.. py:function:: mindspore.ops.boundingbox_encode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
编码边界框位置信息。
算子的功能是计算预测边界框和真实边界框之间的偏移，并将此偏移作为损失变量。
参数：
- **anchor_box** (Tensor) - 锚框。锚框的shape必须为 :math:`(n,4)` 。
- **groundtruth_box** (Tensor) - 真实边界框。它的shape与锚框相同。
- **means** (tuple) - 计算编码边界框的均值。默认值：（0.0, 0.0, 0.0, 0.0, 0.0）。
- **stds** (tuple) - 计算增量的标准偏差。默认值：（1.0、1.0、1.0、1.0）。
返回：
Tensor，编码边界框。数据类型和shape与输入 `anchor_box` 相同。
异常：
- **TypeError** - 如果 `means` 或 `stds` 不是tuple。
- **TypeError** - 如果 `anchor_box` 或 `groundtruth_box` 不是Tensor。

2
mindspore/python/mindspore/ops/function/__init__.py
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@ -325,6 +325,8 @@ from .nn_func import (
glu,
multi_margin_loss,
multi_label_margin_loss,
boundingbox_decode,
boundingbox_encode
)
from .linalg_func import (
svd,

80
mindspore/python/mindspore/ops/function/nn_func.py
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@ -3286,6 +3286,82 @@ def multi_label_margin_loss(inputs, target, reduction='mean'):
return outputs
def boundingbox_encode(anchor_box, groundtruth_box, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
"""
Encodes bounding boxes locations.
This operator will calculate the offset between the predicted bounding boxes and the real bounding boxes,
and this offset will be used as a variable for the loss.
Args:
anchor_box(Tensor): Anchor boxes. The shape of anchor_box must be (n, 4).
groundtruth_box(Tensor): Ground truth boxes. Which has the same shape with anchor_box.
means (tuple): Means for encoding bounding boxes calculation. Default: (0.0, 0.0, 0.0, 0.0).
stds (tuple): The standard deviations of deltas calculation. Default: (1.0, 1.0, 1.0, 1.0).
Returns:
Tensor, encoded bounding boxes. It has the same data type and shape as input `anchor_box`.
Raises:
TypeError: If `means` or `stds` is not a tuple.
TypeError: If `anchor_box` or `groundtruth_box` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> anchor_box = Tensor([[2, 2, 2, 3], [2, 2, 2, 3]], mindspore.float32)
>>> groundtruth_box = Tensor([[1, 2, 1, 4], [1, 2, 1, 4]], mindspore.float32)
>>> output = ops.boundingbox_encode(anchor_box, groundtruth_box, means=(0.0, 0.0, 0.0, 0.0),
... stds=(1.0, 1.0, 1.0, 1.0))
>>> print(output)
[[ -1. 0.25 0. 0.40551758]
[ -1. 0.25 0. 0.40551758]]
"""
boundingbox_encode_op = _get_cache_prim(P.BoundingBoxEncode)(means, stds)
return boundingbox_encode_op(anchor_box, groundtruth_box)
def boundingbox_decode(anchor_box, deltas, max_shape, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
wh_ratio_clip=0.016):
"""
Decodes bounding boxes locations.
The function of the operator is to calculate the offset, and this operator converts the offset into a Bbox,
which is used to mark the target in the subsequent images, etc.
Args:
anchor_box** (Tensor): Anchor boxes. The shape of `anchor_box` must be (n, 4).
deltas** (Tensor): Delta of boxes. Which has the same shape with `anchor_box`.
means (tuple): The means of deltas calculation. Default: (0.0, 0.0, 0.0, 0.0).
stds (tuple): The standard deviations of deltas calculation. Default: (1.0, 1.0, 1.0, 1.0).
max_shape (tuple): The max size limit for decoding box calculation.
wh_ratio_clip (float): The limit of width and height ratio for decoding box calculation. Default: 0.016.
Returns:
Tensor, decoded boxes. It has the same data type and shape as `anchor_box`.
Raises:
TypeError: If `means`, `stds` or `max_shape` is not a tuple.
TypeError: If `wh_ratio_clip` is not a float.
TypeError: If `anchor_box` or `deltas` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> anchor_box = Tensor([[4, 1, 2, 1], [2, 2, 2, 3]], mindspore.float32)
>>> deltas = Tensor([[3, 1, 2, 2], [1, 2, 1, 4]], mindspore.float32)
>>> output = ops.boundingbox_decode(anchor_box, deltas, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0),
... max_shape=(768, 1280), wh_ratio_clip=0.016)
>>> print(output)
[[ 4.1953125 0. 0. 5.1953125]
[ 2.140625 0. 3.859375 60.59375 ]]
"""
boundingbox_decode_op = _get_cache_prim(P.BoundingBoxDecode)(max_shape, means, stds, wh_ratio_clip)
return boundingbox_decode_op(anchor_box, deltas)
__all__ = [
'adaptive_avg_pool1d',
'adaptive_avg_pool2d',
@ -3336,6 +3412,8 @@ __all__ = [
'conv3d',
'glu',
'multi_margin_loss',
'multi_label_margin_loss'
'multi_label_margin_loss',
'boundingbox_decode',
'boundingbox_encode'
]
__all__.sort()

35
mindspore/python/mindspore/ops/operations/other_ops.py
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@ -113,20 +113,7 @@ class BoundingBoxEncode(PrimitiveWithInfer):
This operator will calculate the offset between the predicted bounding boxes and the real bounding boxes,
and this offset will be used as a variable for the loss.
Args:
means (tuple): Means for encoding bounding boxes calculation. Default: (0.0, 0.0, 0.0, 0.0).
stds (tuple): The standard deviations of deltas calculation. Default: (1.0, 1.0, 1.0, 1.0).
Inputs:
- **anchor_box** (Tensor) - Anchor boxes. The shape of anchor_box must be (n, 4).
- **groundtruth_box** (Tensor) - Ground truth boxes. Which has the same shape with anchor_box.
Outputs:
Tensor, encoded bounding boxes. It has the same data type and shape as input `anchor_box`.
Raises:
TypeError: If `means` or `stds` is not a tuple.
TypeError: If `anchor_box` or `groundtruth_box` is not a Tensor.
Refer to :func:`mindspore.ops.boundingbox_encode` for more detail.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -140,7 +127,6 @@ class BoundingBoxEncode(PrimitiveWithInfer):
[[ -1. 0.25 0. 0.40551758]
[ -1. 0.25 0. 0.40551758]]
"""
@prim_attr_register
def __init__(self, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
"""Initialize BoundingBoxEncode."""
@ -223,23 +209,7 @@ class BoundingBoxDecode(Primitive):
The function of the operator is to calculate the offset, and this operator converts the offset into a Bbox,
which is used to mark the target in the subsequent images, etc.
Args:
means (tuple): The means of deltas calculation. Default: (0.0, 0.0, 0.0, 0.0).
stds (tuple): The standard deviations of deltas calculation. Default: (1.0, 1.0, 1.0, 1.0).
max_shape (tuple): The max size limit for decoding box calculation.
wh_ratio_clip (float): The limit of width and height ratio for decoding box calculation. Default: 0.016.
Inputs:
- **anchor_box** (Tensor) - Anchor boxes. The shape of `anchor_box` must be (n, 4).
- **deltas** (Tensor) - Delta of boxes. Which has the same shape with `anchor_box`.
Outputs:
Tensor, decoded boxes. It has the same data type and shape as `anchor_box`.
Raises:
TypeError: If `means`, `stds` or `max_shape` is not a tuple.
TypeError: If `wh_ratio_clip` is not a float.
TypeError: If `anchor_box` or `deltas` is not a Tensor.
Refer to :func:`mindspore.ops.boundingbox_decode` for more detail.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -255,7 +225,6 @@ class BoundingBoxDecode(Primitive):
[ 2.140625 0. 3.859375 60.59375 ]]
"""
@prim_attr_register
def __init__(self, max_shape, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), wh_ratio_clip=0.016):
"""Initialize BoundingBoxDecode."""