Upsample the input tensor.
The width and height of the output tensor are:
output_width = floor(input_width * width_scale),
output_height = floor(input_height * height_scale).
Example:
Given data tensor, width_scale, height_scale, mode,
Upsample the input 4-D tensor in nearest mode:
data = [[[
[1, 2],
[3, 4]
]]]
width_scale = 2
height_scale = 2
mode = "nearest"
output = [[[
[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]
]]]
No versioning maintained for experimental ops.
- height_scale : float (required)
- The scale along height dimension. It takes value greater than or equal to 1.
- mode : string (default is nearest)
- Two interpolation modes: nearest(default), bilinear
- width_scale : float (required)
- The scale along width dimension. It takes value greater than or equal to 1.
- X : T
- 4-D tensor, [N,C,H,W]
- Y : T
- 4-D tensor after resizing, [N,C,H,W]
- T : tensor(bool), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double)
- Constrain output types to bool, int32, int64, float16, float, double tensors.
Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale).
This version of the operator has been available since version 7 of the default ONNX operator set.
- mode : string (default is nearest)
- Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)
- scales : list of floats (required)
- The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.
- X : T
- N-D tensor
- Y : T
- N-D tensor after resizing
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input and output types to all tensor types.
Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale).
This version of the operator has been available since version 9 of the default ONNX operator set.
- mode : string (default is nearest)
- Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)
- X : T
- N-D tensor
- scales : tensor(float)
- The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.
- Y : T
- N-D tensor after resizing
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input 'X' and output 'Y' to all tensor types.
Upsample the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale).
This version of the operator has been deprecated since version 10 of the default ONNX operator set.
Resize the input tensor. Each dimension value of the output tensor is: output_dimension = floor(input_dimension * scale).
This version of the operator has been available since version 10 of the default ONNX operator set.
- mode : string (default is nearest)
- Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)
- X : T
- N-D tensor
- scales : tensor(float)
- The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.
- Y : T
- N-D tensor after resizing
- T : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)
- Constrain input 'X' and output 'Y' to all tensor types.