Pad - 2 vs 11#

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.

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  1. Pad2 → Pad11 +21 -88
Pad2 → Pad11 RENAMED
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+ Given data tensor, pads, mode, and value.
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+ Example:
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- Given a tensor containing the data to be padded (data), a tensor containing the number of start and end pad values for axis (pads), (optionally) a mode, and (optionally) constant_value,
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- a padded tensor (output) is generated.
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-
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- The three supported modes are (similar to corresponding modes supported by numpy.pad):
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-
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- 1) constant(default) - pads with a given constant value as specified by constant_value (which defaults to 0)
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-
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- 2) reflect - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis
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-
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- 3) edge - pads with the edge values of array
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-
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- Example 1 (constant mode):
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  Insert 0 pads to the beginning of the second dimension.
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-
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- data =
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+ data = [
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- [
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  [1.0, 1.2],
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  [2.3, 3.4],
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  [4.5, 5.7],
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  ]
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-
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  pads = [0, 2, 0, 0]
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-
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- mode = 'constant'
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-
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- constant_value = 0.0
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-
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- output =
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+ output = [
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- [
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+ [
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- [0.0, 0.0, 1.0, 1.2],
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+ [0.0, 0.0, 1.0, 1.2],
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- [0.0, 0.0, 2.3, 3.4],
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+ [0.0, 0.0, 2.3, 3.4],
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- [0.0, 0.0, 4.5, 5.7],
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+ [0.0, 0.0, 4.5, 5.7],
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+ ],
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- ]
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-
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- Example 2 (reflect mode):
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- data =
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- [
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- [1.0, 1.2],
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- [2.3, 3.4],
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- [4.5, 5.7],
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- ]
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-
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- pads = [0, 2, 0, 0]
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-
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- mode = 'reflect'
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-
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- output =
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- [
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- [1.0, 1.2, 1.0, 1.2],
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- [2.3, 3.4, 2.3, 3.4],
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- [4.5, 5.7, 4.5, 5.7],
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- ]
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-
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- Example 3 (edge mode):
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- data =
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- [
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- [1.0, 1.2],
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- [2.3, 3.4],
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- [4.5, 5.7],
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- ]
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-
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- pads = [0, 2, 0, 0]
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-
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- mode = 'edge'
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-
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- output =
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- [
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- [1.0, 1.0, 1.0, 1.2],
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- [2.3, 2.3, 2.3, 3.4],
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- [4.5, 4.5, 4.5, 5.7],
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  ]
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  **Attributes**
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  * **mode**:
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- Supported modes: constant(default), reflect, edge
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+ Three modes: constant(default), reflect, edge
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+ * **pads** (required):
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+ List of integers indicating the number of padding elements to add or
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+ remove (if negative) at the beginning and end of each axis. For 2D
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+ it is the number of pixels. pads rank should be double of the
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+ input's rank. pads format should be as follow [x1_begin,
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+ x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels
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+ added at the beginning of axis i and xi_end, the number of pixels
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+ added at the end of axis i.
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+ * **value**:
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+ One float, indicates the value to be filled.
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  **Inputs**
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- Between 2 and 3 inputs.
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-
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  * **data** (heterogeneous) - **T**:
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  Input tensor.
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- * **pads** (heterogeneous) - **tensor(int64)**:
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- Tensor of integers indicating the number of padding elements to add
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- or remove (if negative) at the beginning and end of each axis. For
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- 2D input tensor, it is the number of pixels. pads should be a 1D
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- tensor of shape [2 * input_rank]. pads format should be:
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- [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the
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- number of pad values added at the beginning of axis i and xi_end,
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- the number of pad values added at the end of axis i.
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- * **constant_value** (optional, heterogeneous) - **T**:
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- (Optional) A scalar value to be used if the mode chosen is
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- constant (by default it is 0).
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  **Outputs**
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  * **output** (heterogeneous) - **T**:
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  Tensor after padding.
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  **Type Constraints**
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  * **T** in (
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  tensor(double),
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  tensor(float),
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- tensor(float16),
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+ tensor(float16)
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- tensor(int16),
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- tensor(int32),
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- tensor(int64),
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- tensor(int8),
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- tensor(uint16),
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- tensor(uint32),
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- tensor(uint64),
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- tensor(uint8)
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  ):
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- Constrain input and output to only numeric types.+ Constrain input and output types to float tensors.