forked from mindspore-Ecosystem/mindspore
!45852 add CN docs for NonMaxSuppressionV3 and PadV3 and move Roll from inner…
Merge pull request !45852 from 李林杰/1122_add_a_few_CN_doc_master
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c0445d8a4e
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@ -80,6 +80,7 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支
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mindspore.ops.NuclearNorm
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mindspore.ops.Pad
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mindspore.ops.Padding
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mindspore.ops.PadV3
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mindspore.ops.ResizeNearestNeighbor
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mindspore.ops.ResizeBilinear
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mindspore.ops.UpsampleNearest3D
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@ -220,6 +221,7 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支
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mindspore.ops.IOU
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mindspore.ops.L2Normalize
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mindspore.ops.NMSWithMask
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mindspore.ops.NonMaxSuppressionV3
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mindspore.ops.NonMaxSuppressionWithOverlaps
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mindspore.ops.PSROIPooling
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mindspore.ops.RGBToHSV
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@ -543,6 +545,7 @@ Array操作
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mindspore.ops.ReverseSequence
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mindspore.ops.ReverseV2
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mindspore.ops.RightShift
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mindspore.ops.Roll
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mindspore.ops.ScatterAddWithAxis
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mindspore.ops.ScatterNd
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mindspore.ops.ScatterNdDiv
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@ -0,0 +1,35 @@
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mindspore.ops.NonMaxSuppressionV3
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==================================
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.. py:class:: mindspore.ops.NonMaxSuppressionV3
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贪婪选取一组按score降序排列后的边界框。
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.. warning::
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如果 `max_output_size` 小于0，将使用0代替。
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.. note::
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- 此算法与原点在坐标系中的位置无关。
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- 对于坐标系的正交变换和平移，该算法不受影响，因此坐标系的平移变换后算法会选择相同的框。
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输入：
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- **boxes** (Tensor) - 二维Tensor，shape为 :math:`(num_boxes, 4)` 。
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- **scores** (Tensor) - 一维Tensor，其shape为 :math:`(num_boxes)` 。表示对应每一行每个方框的score值， `scores` 和 `boxes` 的num_boxes必须相等。支持的数据类型为float32。
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- **max_output_size** (Union[Tensor, Number.Int]) - 选取最大的边框数，必须大于等于0，数据类型为int32。
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- **iou_threshold** (Union[Tensor, Number.Float]) - 边框重叠值阈值，重叠值大于此值说明重叠过大，其值必须大于等于0，小于等于1。支持的数据类型为float32。
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- **score_threshold** (Union[Tensor, Number.Float]) - 移除边框阈值，边框score值大于此值则移除相应边框。支持的数据类型为float32。
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输出：
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一维Tensor，表示被选中边框的index，其shape为 :math:`(M)` ，其中M <= `max_output_size`。
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异常：
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- **TypeError** - `boxes` 和 `scores` 的数据类型不一致。
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- **TypeError** - `iou_threshold` 和 `score_threshold` 的数据类型不一致。
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- **TypeError** - `boxes` 的数据类型不是float16或者float32。
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- **TypeError** - `scores` 的数据类型不是float16或者float32。
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- **TypeError** - `max_output_size` 不是Tensor或者Scalar或者其数据类型不是int32或int64。
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- **TypeError** - `iou_threshold` 不是Tesnor或者Scalar，或者其数据类型不是float16或float32。
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- **TypeError** - `score_threshold` 不是Tesnor或者Scalar，或者其数据类型不是float16或float32。
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- **ValueError** - `boxes` 的shape长度不是2或者第二维度的值不是4。
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- **ValueError** - `scores` shape长度不是1。
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- **ValueError** - `max_output_size` 、 `iou_threshold` 或 `score_threshold` 的shape长度不是0。
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@ -0,0 +1,32 @@
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mindspore.ops.PadV3
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====================
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.. py:class:: mindspore.ops.PadV3(mode="constant", paddings_contiguous=True)
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根据参数 `mode` 和 `paddings_contiguous` 对输入进行填充。
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参数：
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- **mode** (str，可选) - 填充模式，支持"constant" 、"reflect" 和 "edge"。默认值："constant"。
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- **paddings_contiguous** (bool，可选) - 是否连续填充。如果为True， `paddings` 格式为[begin0, end0, begin1, end1, ...]，如果为False，`paddings` 格式为[begin0, begin1, ..., end1, end2, ...]。默认值：True。
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输入：
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- **x** (Tensor) - Pad的输入，任意维度的Tensor。
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- **paddings** (Tensor) - Pad的输入，任意维度的Tensor。
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- **constant_value** (Tensor) - Pad的输入，任意维度的Tensor。
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输出：
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填充后的Tensor。
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异常：
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- **TypeError** - `x` 或 `paddings` 不是Tensor。
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- **TypeError** - `padding_contiguous` bool。
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- **ValueError** - `mode` 不是string类型或者不在支持的列表里。
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- **ValueError** - `mode` 是"constant"的同时 `paddings` 元素个数不是偶数。
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- **ValueError** - `mode` 是"constant"的同时 `paddings` 元素个数大于输入维度乘以2。
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- **ValueError** - `mode` 是"edge"或"reflect"的同时 `paddings` 元素个数不是2、4或6。
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- **ValueError** - `mode` 是"edge"或"reflect"， `x` 的维度是3， `paddings` 元素个数是2。
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- **ValueError** - `mode` 是"edge"或"reflect"， `x` 的维度是4， `paddings` 元素个数是4。
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- **ValueError** - `mode` 是"edge"或"reflect"的同时 `x` 的维度小于3。
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- **ValueError** - `mode` 是"edge"的同时 `x` 的维度大于5。
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- **ValueError** - `mode` 是"reflect"的同时填充值大于对应 `x` 的维度。
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- **ValueError** - 填充之后，输出shape数不大于零。
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@ -0,0 +1,8 @@
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mindspore.ops.Roll
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===================
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.. py:class:: mindspore.ops.Roll(shift, axis)
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沿轴移动Tensor的元素。
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更多参考详见 :func:`mindspore.ops.roll`。
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@ -80,6 +80,7 @@ Neural Network
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mindspore.ops.Pad
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mindspore.ops.EmbeddingLookup
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mindspore.ops.Padding
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mindspore.ops.PadV3
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mindspore.ops.ResizeNearestNeighbor
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mindspore.ops.ResizeBilinear
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mindspore.ops.UpsampleNearest3D
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@ -219,6 +220,7 @@ Image Processing
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mindspore.ops.IOU
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mindspore.ops.L2Normalize
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mindspore.ops.NMSWithMask
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mindspore.ops.NonMaxSuppressionV3
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mindspore.ops.NonMaxSuppressionWithOverlaps
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mindspore.ops.PSROIPooling
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mindspore.ops.RGBToHSV
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@ -542,6 +544,7 @@ Array Operation
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mindspore.ops.ReverseSequence
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mindspore.ops.ReverseV2
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mindspore.ops.RightShift
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mindspore.ops.Roll
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mindspore.ops.ScatterAddWithAxis
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mindspore.ops.ScatterNd
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mindspore.ops.ScatterNdDiv
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@ -1656,7 +1656,7 @@ class Roll(Cell):
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Validator.check_is_int(s_axis, "axis", "Roll")
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for s_shift in self.shift:
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Validator.check_is_int(s_shift, "shift", "Roll")
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self.roll = inner.Roll(self.shift, self.axis)
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self.roll = P.Roll(self.shift, self.axis)
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self.gpu = True
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if len(self.shift) != len(self.axis):
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raise ValueError(f"For '{self.cls_name}', the shape of 'shift' and the shape of 'axis' must be "
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@ -1664,14 +1664,14 @@ class Roll(Cell):
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f"and the length of 'axis' {len(self.axis)}.")
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else:
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if not isinstance(self.axis, (list, tuple)):
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self.op_list.append((inner.Roll(shift=self.shift, axis=0), self.axis))
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self.op_list.append((P.Roll(shift=self.shift, axis=0), self.axis))
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else:
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if len(self.shift) != len(self.axis):
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raise ValueError(f"For '{self.cls_name}', the shape of 'shift' and the shape of 'axis' must be "
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f"the same, but got the length of 'shift' {len(self.shift)} "
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f"and the length of 'axis' {len(self.axis)}.")
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for idx, _ in enumerate(self.axis):
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self.op_list.append((inner.Roll(shift=self.shift[idx], axis=0), self.axis[idx]))
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self.op_list.append((P.Roll(shift=self.shift[idx], axis=0), self.axis[idx]))
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def construct(self, input_x):
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dim = len(self.shape_op(input_x))
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@ -16,7 +16,6 @@
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"""inner_ops"""
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from __future__ import absolute_import
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from mindspore import context
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from mindspore.ops.operations.comm_ops import _VirtualPipelineEnd
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from mindspore.ops._grad.grad_base import bprop_getters
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from mindspore.ops.operations import _inner_ops as inner
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@ -40,27 +39,6 @@ def get_bprop_tensor_copy_slices(self):
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return bprop
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@bprop_getters.register(inner.Roll)
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def get_bprop_roll(self):
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"""Generate bprop for Roll"""
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if context.get_context("device_target") == "GPU":
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shift = []
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axis = self.axis
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for tmp in enumerate(self.shift):
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shift.append(-tmp[1])
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roll_grad = inner.Roll(shift, axis)
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else:
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shift = self.shift
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axis = self.axis
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roll_grad = inner.Roll(-shift, axis)
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def bprop(x_input, out, dout):
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dx = roll_grad(dout)
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return (dx,)
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return bprop
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@bprop_getters.register(_VirtualPipelineEnd)
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def get_bprop_virtual_pipeline_end(self):
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"""Backpropagator for _VirtualPipelineEnd."""
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@ -17,6 +17,7 @@
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import numpy as np
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import mindspore.numpy as mnp
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from mindspore import context
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from mindspore.common import dtype as mstype
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from mindspore.nn import LGamma
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from mindspore.ops import functional as F
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@ -129,6 +130,27 @@ def get_bprop_logit(self):
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return bprop
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@bprop_getters.register(P.Roll)
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def get_bprop_roll(self):
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"""Generate bprop for Roll"""
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if context.get_context("device_target") == "GPU":
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shift = []
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axis = self.axis
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for tmp in enumerate(self.shift):
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shift.append(-tmp[1])
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roll_grad = P.Roll(shift, axis)
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else:
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shift = self.shift
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axis = self.axis
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roll_grad = P.Roll(-shift, axis)
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def bprop(x_input, out, dout):
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dx = roll_grad(dout)
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return (dx,)
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return bprop
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@bprop_getters.register(P.Cdist)
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def get_bprop_cdist(self):
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"""Generate bprop for Cdist"""
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@ -29,7 +29,7 @@ from mindspore.ops.operations.math_ops import STFT
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from mindspore.ops.operations.math_ops import ReduceStd
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from mindspore.ops.operations.math_ops import Logit
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from mindspore.ops.operations.math_ops import LuUnpack
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from mindspore.ops.operations._inner_ops import Roll
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from mindspore.ops.operations.math_ops import Roll
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from mindspore.nn import layer
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from mindspore._checkparam import check_is_number
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from mindspore._checkparam import Rel
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@ -28,7 +28,7 @@ from mindspore.ops.operations import _grad_ops, _csr_ops, _inner_ops, linalg_ops
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from mindspore.ops.operations.math_ops import Median
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from mindspore.ops.operations.array_ops import UniqueConsecutive, Triu
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from mindspore.ops.operations.nn_ops import AdaptiveMaxPool2D
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from mindspore.ops.operations._inner_ops import Roll
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from mindspore.ops.operations.math_ops import Roll
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from mindspore.ops.composite.array_ops import repeat_interleave
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from mindspore.ops.composite.math_ops import mm
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@ -85,7 +85,8 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
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FFTWithSize, Heaviside, Histogram, Hypot, Lcm, LuUnpack, MatrixExp,
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MatrixLogarithm, MatrixPower, MatrixSolve, MatrixTriangularSolve, ReduceStd, STFT,
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NextAfter, Orgqr, Qr, RaggedRange, Digamma, Eig, EuclideanNorm, CompareAndBitpack, ComplexAbs,
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CumulativeLogsumexp, Gcd, Trace, TridiagonalMatMul, TrilIndices, TriuIndices, Zeta)
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CumulativeLogsumexp, Gcd, Trace, TridiagonalMatMul, TrilIndices, TriuIndices, Zeta,
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Roll)
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from .nn_ops import (LSTM, SGD, Adam, AdamWeightDecay, FusedSparseAdam, FusedSparseLazyAdam, AdamNoUpdateParam,
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ApplyMomentum, BatchNorm, BiasAdd, Conv2D, Conv3D, Conv2DTranspose, Conv3DTranspose,
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DepthwiseConv2dNative,
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@ -112,7 +113,7 @@ from .nn_ops import (LSTM, SGD, Adam, AdamWeightDecay, FusedSparseAdam, FusedSpa
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GridSampler3D, MaxPool3DWithArgmax, MaxUnpool2D, NuclearNorm, NthElement, MultilabelMarginLoss,
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PSROIPooling, Dilation2D, DataFormatVecPermute, DeformableOffsets, FractionalAvgPool,
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FractionalMaxPool, FractionalMaxPool3DWithFixedKsize, FractionalMaxPoolWithFixedKsize,
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GridSampler2D, TripletMarginLoss, UpsampleNearest3D, UpsampleTrilinear3D)
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GridSampler2D, TripletMarginLoss, UpsampleNearest3D, UpsampleTrilinear3D, PadV3)
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from .other_ops import (Assign, IOU, BoundingBoxDecode, BoundingBoxEncode,
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ConfusionMatrix, UpdateState, Load, StopGradient,
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CheckValid, Partial, Depend, identity, Push, Pull, PyFunc, _DynamicLossScale,
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@ -640,6 +641,8 @@ __all__ = [
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"UniqueConsecutive",
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"UnravelIndex",
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"Zeta",
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"PadV3",
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"Roll",
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]
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__custom__ = [
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@ -1281,69 +1281,6 @@ class TensorCopySlices(Primitive):
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self.init_prim_io_names(inputs=['x', 'value', 'begin', 'end', 'strides'], outputs=['y'])
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class Roll(Primitive):
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"""
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Rolls the elements of a tensor along an axis.
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The elements are shifted positively (towards larger indices) by the offset of `shift` along the dimension of `axis`.
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Negative `shift` values will shift elements in the opposite direction. Elements that roll passed the last position
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will wrap around to the first and vice versa. Multiple shifts along multiple axes may be specified.
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Note:
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This inner operation is valid only if the axis is equal to 0. If the shift and the axis are tuples or lists,
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this inner operation is valid only for the first pair of elements.
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Args:
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shift (Union[list(int), tuple(int), int]): Specifies the number of places by which elements are shifted
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positively (towards larger indices) along the specified dimension. Negative shifts will roll the elements
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in the opposite direction.
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axis (Union[list(int), tuple(int), int]): Specifies the dimension indexes of shape to be rolled. The value is
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forced to be zero in this operation.
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Inputs:
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- **input_x** (Tensor) - Input tensor.
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Outputs:
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Tensor, has the same shape and type as `input_x`.
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Raises:
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TypeError: If `shift` is not an int, a tuple or a list.
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TypeError: If `axis` is not an int, a tuple or a list.
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Supported Platforms:
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``Ascend`` ``GPU``
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Examples:
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>>> from mindspore.ops.operations import _inner_ops as inner
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>>> input_x = Tensor(np.array([0, 1, 2, 3, 4]).astype(np.float32))
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>>> op = inner.Roll(shift=2, axis=0)
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>>> output = op(input_x)
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>>> print(output)
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[3. 4. 0. 1. 2.]
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>>> input_x = Tensor(np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]).astype(np.float32))
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>>> op = inner.Roll(shift=-1, axis=0)
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>>> output = op(input_x)
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>>> print(output)
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[[5. 6. 7. 8. 9.]
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[0. 1. 2. 3. 4.]]
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"""
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@prim_attr_register
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def __init__(self, shift, axis):
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"""Initialize Roll"""
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if context.get_context("device_target") == "GPU":
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validator.check_value_type("shift", shift, [int, tuple, list], self.name)
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validator.check_value_type("axis", axis, [int, tuple, list], self.name)
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else:
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if isinstance(shift, (tuple, list)) and isinstance(axis, (tuple, list)):
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validator.check_equal_int(len(shift), 1, "shift size", self.name)
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validator.check_equal_int(len(axis), 1, "shift size", self.name)
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validator.check_equal_int(axis[0], 0, "axis", self.name)
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elif isinstance(shift, int) and isinstance(axis, int):
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validator.check_equal_int(axis, 0, "axis", self.name)
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self.init_prim_io_names(inputs=['input_x'], outputs=['output'])
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class DSDMatmul(PrimitiveWithInfer):
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"""
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The definition of the CusSquare primitive.
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@ -355,23 +355,23 @@ class NonMaxSuppressionV3(Primitive):
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Greedily selects a subset of bounding boxes in descending order of score.
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.. warning::
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When input "max_output_size" is negative, it will be treated as 0.
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When input `max_output_size` is negative, it will be treated as 0.
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Note:
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This algorithm is agnostic to where the origin is in the coordinate system.
|
||||
This algorithm is invariant to orthogonal transformations and translations of the coordinate system;
|
||||
thus translating or reflections of the coordinate system result in the same boxes being
|
||||
selected by the algorithm.
|
||||
- This algorithm is agnostic to where the origin is in the coordinate system.
|
||||
- This algorithm is invariant to orthogonal transformations and translations of the coordinate system,
|
||||
thus translating or reflections of the coordinate system result in the same boxes being
|
||||
selected by the algorithm.
|
||||
|
||||
Inputs:
|
||||
- **boxes** (Tensor) - A 2-D Tensor of shape [num_boxes, 4].
|
||||
- **scores** (Tensor) - A 1-D Tensor of shape [num_boxes] representing a single score
|
||||
corresponding to each box (each row of boxes), the num_boxes of "scores" must be equal to
|
||||
the num_boxes of "boxes".
|
||||
- **boxes** (Tensor) - A 2-D Tensor of shape :math:`(num_boxes, 4)`.
|
||||
- **scores** (Tensor) - A 1-D Tensor of shape :math:`(num_boxes)` representing a single score
|
||||
corresponding to each box (each row of boxes), the num_boxes of `scores` must be equal to
|
||||
the num_boxes of `boxes`.
|
||||
- **max_output_size** (Union[Tensor, Number.Int]) - A scalar integer Tensor representing the maximum
|
||||
number of boxes to be selected by non max suppression.
|
||||
- **iou_threshold** (Union[Tensor, Number.Float]) - A 0-D float tensor representing the threshold for
|
||||
deciding whether boxes overlap too much with respect to IOU, and iou_threshold must be equal or greater
|
||||
deciding whether boxes overlap too much with respect to IOU, and `iou_threshold` must be equal or greater
|
||||
than 0 and be equal or smaller than 1.
|
||||
- **score_threshold** (Union[Tensor, Number.Float]) - A 0-D float tensor representing the threshold for
|
||||
deciding when to remove boxes based on score.
|
||||
|
|
@ -381,16 +381,17 @@ class NonMaxSuppressionV3(Primitive):
|
|||
where M <= max_output_size.
|
||||
|
||||
Raises:
|
||||
TypeError: If the dtype of `boxes` and `scores` is different.
|
||||
TypeError: If the dtype of `iou_threshold` and `score_threshold` is different.
|
||||
TypeError: If the dtype of `boxes` and `scores` are different.
|
||||
TypeError: If the dtype of `iou_threshold` and `score_threshold` are different.
|
||||
TypeError: If `boxes` is not tensor or its dtype is not float16 or float32.
|
||||
TypeEroor: If `scores` is not tensor or its dtype is not float16 or float32.
|
||||
TypeError: If `max_output_size` is not tensor or scalar.If `max_output_size` is not int32 or int64.
|
||||
TypeError: If `iou_threshold` is not tensor or scalar. If its type is not float16 or float32.
|
||||
TypeError: If `score_threshold` is not tensor or scalar. If its type is not float16 or float32.
|
||||
TypeError: If `max_output_size` is not tensor or scalar or its date type is not int32 or int64.
|
||||
TypeError: If `iou_threshold` is not tensor or scalar or its type is neither float16 or float32.
|
||||
TypeError: If `score_threshold` is not tensor or scalar or its type is neither float16 or float32.
|
||||
ValueError: If the size of shape of `boxes` is not 2 or the second value of its shape is not 4.
|
||||
ValueError: If the size of shape of `scores` is not 1.
|
||||
ValueError: If each of the size of shape of `max_output_size`, `iou_threshold`, `score_threshold` is not 0.
|
||||
ValueError: If any of the size of shape of `max_output_size`,
|
||||
`iou_threshold`, `score_threshold` is not 0.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend``
|
||||
|
|
|
|||
|
|
@ -7505,7 +7505,7 @@ class TriuIndices(Primitive):
|
|||
row (int): number of rows in the 2-D matrix.
|
||||
col (int): number of columns in the 2-D matrix.
|
||||
offset (int, optional): diagonal offset from the main diagonal. Default: 0.
|
||||
dtype (:class:`mindspore.dtype`): The specified type of output tensor.
|
||||
dtype (:class:`mindspore.dtype`, optional): The specified type of output tensor.
|
||||
An optional data type of `mindspore.int32` and `mindspore.int64`. Default: `mindspore.int32`.
|
||||
|
||||
Outputs:
|
||||
|
|
@ -7782,3 +7782,63 @@ class Ormqr(Primitive):
|
|||
self.transpose = validator.check_value_type('transpose', transpose, [bool], self.name)
|
||||
self.add_prim_attr('left', self.left)
|
||||
self.add_prim_attr('transpose', self.transpose)
|
||||
|
||||
|
||||
class Roll(Primitive):
|
||||
"""
|
||||
Rolls the elements of a tensor along an axis.
|
||||
|
||||
The elements are shifted positively (towards larger indices) by the offset of `shift` along the dimension of `axis`.
|
||||
Negative `shift` values will shift elements in the opposite direction. Elements that roll passed the last position
|
||||
will wrap around to the first and vice versa. Multiple shifts along multiple axes may be specified.
|
||||
|
||||
Note:
|
||||
This inner operation is valid only if the axis is equal to 0. If the shift and the axis are tuples or lists,
|
||||
this inner operation is valid only for the first pair of elements.
|
||||
|
||||
Args:
|
||||
shift (Union[list(int), tuple(int), int]): Specifies the number of places by which elements are shifted
|
||||
positively (towards larger indices) along the specified dimension. Negative shifts will roll the elements
|
||||
in the opposite direction.
|
||||
axis (Union[list(int), tuple(int), int]): Specifies the dimension indexes of shape to be rolled. The value is
|
||||
forced to be zero in this operation.
|
||||
|
||||
Inputs:
|
||||
- **input_x** (Tensor) - Input tensor.
|
||||
|
||||
Outputs:
|
||||
Tensor, has the same shape and type as `input_x`.
|
||||
|
||||
Raises:
|
||||
TypeError: If `shift` is not an int, a tuple or a list.
|
||||
TypeError: If `axis` is not an int, a tuple or a list.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend`` ``GPU``
|
||||
|
||||
Examples:
|
||||
>>> input_x = Tensor(np.array([0, 1, 2, 3, 4]).astype(np.float32))
|
||||
>>> op = ops.Roll(shift=2, axis=0)
|
||||
>>> output = op(input_x)
|
||||
>>> print(output)
|
||||
[3. 4. 0. 1. 2.]
|
||||
>>> input_x = Tensor(np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]).astype(np.float32))
|
||||
>>> op = ops.Roll(shift=-1, axis=0)
|
||||
>>> output = op(input_x)
|
||||
>>> print(output)
|
||||
"""
|
||||
|
||||
@prim_attr_register
|
||||
def __init__(self, shift, axis):
|
||||
"""Initialize Roll"""
|
||||
if context.get_context("device_target") == "GPU":
|
||||
validator.check_value_type("shift", shift, [int, tuple, list], self.name)
|
||||
validator.check_value_type("axis", axis, [int, tuple, list], self.name)
|
||||
else:
|
||||
if isinstance(shift, (tuple, list)) and isinstance(axis, (tuple, list)):
|
||||
validator.check_equal_int(len(shift), 1, "shift size", self.name)
|
||||
validator.check_equal_int(len(axis), 1, "shift size", self.name)
|
||||
validator.check_equal_int(axis[0], 0, "axis", self.name)
|
||||
elif isinstance(shift, int) and isinstance(axis, int):
|
||||
validator.check_equal_int(axis, 0, "axis", self.name)
|
||||
self.init_prim_io_names(inputs=['input_x'], outputs=['output'])
|
||||
|
|
|
|||
|
|
@ -4359,14 +4359,15 @@ class Pad(Primitive):
|
|||
|
||||
class PadV3(Primitive):
|
||||
"""
|
||||
Pads the input tensor according to the paddings, mode and paddings_contiguous.
|
||||
Pads the input tensor according to the paddings, `mode` and `paddings_contiguous`.
|
||||
|
||||
Args:
|
||||
mode (str): An optional string, Defaults to "constant", indicates padding mode,
|
||||
support "constant", "reflect", "edge", Defaults to "constant".
|
||||
paddings_contiguous (bool): An optional bool value, Defaults to True.
|
||||
mode (str, optional): An optional string indicates padding mode,
|
||||
support "constant", "reflect", "edge". Default: "constant".
|
||||
paddings_contiguous (bool, optional): An optional bool value indicates if the padding is paddings_contiguous.
|
||||
If true, paddings is arranged as [begin0, end0, begin1, end1, ...]
|
||||
If false, paddings is arranged as [begin0, begin1, ..., end1, end2, ...]
|
||||
Default:True.
|
||||
|
||||
Inputs:
|
||||
- **x** (Tensor) - Tensor of shape :math:`(N, *)`, where :math:`*` means, any number of
|
||||
|
|
@ -4381,16 +4382,18 @@ class PadV3(Primitive):
|
|||
TypeError: If `x` or `paddings` is not a Tensor.
|
||||
TypeError: If `padding_contiguous` is not a bool.
|
||||
ValueError: If `mode` is not a str or not in support modes.
|
||||
ValueError: If `mode` is constant, the element's number of paddings not be even.
|
||||
ValueError: If `mode` is constant, the element's number of paddings large than input dim * 2.
|
||||
ValueError: If `mode` is edge or reflect, the element's number of paddings is not 2, 4 or 6.
|
||||
ValueError: If `mode` is edge or reflect, x dims equal 3, the element's number of paddings is 2.
|
||||
ValueError: If `mode` is edge or reflect, x dims equal 4, the element's number of paddings is 4.
|
||||
ValueError: If `mode` is edge or reflect, x dims smaller than 3.
|
||||
ValueError: If `mode` is edge, x dims bigger than 5.
|
||||
ValueError: If `mode` is reflect, x dims bigger than 4.
|
||||
ValueError: If `mode` is reflect, padding size bigger than the corresponding x dimension.
|
||||
ValueError: After padding, output's shape number must be greater than 0.
|
||||
ValueError: If `mode` is "constant", the element's number of `paddings` not be even.
|
||||
ValueError: If `mode` is "constant", the element's number of `paddings` large than input dim * 2.
|
||||
ValueError: If `mode` is "edge" or "reflect", the element's number of `paddings` is not 2, 4 or 6.
|
||||
ValueError: If `mode` is "edge" or "reflect", `x` dims equals 3,
|
||||
the element's number of `paddings` is 2.
|
||||
ValueError: If `mode` is "edge" or "reflect", `x` dims equals 4,
|
||||
the element's number of `paddings` is 4.
|
||||
ValueError: If `mode` is "edge" or "reflect", `x` dims smaller than 3.
|
||||
ValueError: If `mode` is "edge", x dims bigger than 5.
|
||||
ValueError: If `mode` is "reflect", x dims bigger than 4.
|
||||
ValueError: If `mode` is "reflect", padding size bigger than the corresponding `x` dimension.
|
||||
ValueError: After padding, output's shape number is not greater than 0.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend`` ``GPU`` ``CPU``
|
||||
|
|
|
|||
Loading…
Reference in New Issue