DepthToSpace - 1 vs 13#

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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. DepthToSpace1 → DepthToSpace13 +1 -26
DepthToSpace1 → DepthToSpace13 RENAMED
@@ -1 +1 @@
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  DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
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  This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
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  the input tensor where values from the depth dimension are moved in spatial blocks to the height
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+ and width dimensions.
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- and width dimensions. By default, mode = DCR.
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- In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
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- following order: depth, column, and then row. The output y is computed from the input x as below:
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-
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- b, c, h, w = x.shape
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-
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- tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
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-
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- tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
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-
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- y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
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-
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- In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the
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- following order: column, row, and the depth. The output y is computed from the input x as below:
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-
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- b, c, h, w = x.shape
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-
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- tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
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-
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- tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
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-
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- y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
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  **Attributes**
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  * **blocksize** (required):
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  Blocks of [blocksize, blocksize] are moved.
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- * **mode**:
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- DCR (default) for depth-column-row order re-arrangement. Use CRD for
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- column-row-depth order.
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  **Inputs**
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  * **input** (heterogeneous) - **T**:
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  Input tensor of [N,C,H,W], where N is the batch axis, C is the
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  channel or depth, H is the height and W is the width.
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  **Outputs**
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  * **output** (heterogeneous) - **T**:
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  Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W *
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  blocksize].
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  **Type Constraints**
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  * **T** in (
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- tensor(bfloat16),
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  tensor(bool),
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  tensor(complex128),
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  tensor(complex64),
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  tensor(double),
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  tensor(float),
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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(string),
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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 types to all tensor types.