DepthToSpace#
Domain:
ai.onnxSince version: 28
DepthToSpace rearranges (permutes) data from depth into blocks of spatial data.
This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of
the input tensor where values from the depth dimension are moved in spatial blocks to the height
and width dimensions. By default, mode = DCR.
In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the
following order: depth, column, and then row. The output y is computed from the input x as below:
b, c, h, w = x.shape
tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below:
b, c, h, w = x.shape
tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])
tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])
y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])
Inputs
input (T): Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
Outputs
output (T): Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].
Attributes
blocksize (int): Blocks of [blocksize, blocksize] are moved.
mode (string): DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.
Type Constraints
T: Constrain input and output types to all tensor types. Allowed types: tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8).
Differences with previous version (13)#
SchemaDiff: DepthToSpace (domain 'ai.onnx')
old version: 13
new version: 28
breaking: no