Resize

Resize an input tensor into an output tensor. The output shape is either set directly using shape or calculated by the scales parameters.

Attributes

resize_mode Controls what kind of resize is used, defaults to NEAREST.

  • NEAREST Nearest neighbor resizing on the innermost m dimensions of an input of rank N where \(0 \leq m \leq min(3,N)\).

  • LINEAR Linear resizing on the innermost m dimensions of an input of rank N where \(0 \leq m \leq min(3,N)\).

  • CUBIC Cubic resizing innermost 2 dimensions of N-D, N >= 2.

shape The output shape. The number of elements must be the number of dimensions in input.

scales List of resize scales. Must have the same number of dimensions as the input.

coordinate_transformation Controls how to map the resized coordinate back to the original coordinate, defaults to ASYMMETRIC.

  • ALIGN_CORNERS In this mode: \(x_{original}=x_{resized} \cdot \frac{length_{original}-1}{length_{resized}-1}\).

  • ASYMMETRIC In this mode: \(x_{original} = x_{resized} \cdot \frac{length_{original}}{length_{resized}}\).

  • HALF_PIXEL In this mode: \(x_{original} = (x_{resized} + 0.5) \cdot \frac{length_{original}}{length_{resized}} -0.5\).

selector_for_single_pixel The coordinate selector when resize to single pixel output, defaults to FORMULA.

  • FORMULA Use formula to map the original index.

  • UPPER Select the upper left pixel.

nearest_rounding The rounding mode for nearest neighbor resize, defaults to FLOOR.

  • HALF_UP Round half up.

  • HALF_DOWN Round half down.

  • FLOOR Round to floor.

  • CEIL Round to ceil.

cubic_coeff The cubic coefficient to use when cubic resize is used.

Inputs

input0: tensor of type T1.

input1: optional tensor of type Int32 or Int64 with shape.

Outputs

output: tensor of type T1.

Data Types

T1: int8, float16, float32

Shape Information

input0 is a tensor with a shape of \([a_0,...,a_n]\).

input1 has shape \([n]\).

output is a tensor with a shape of \([b_0,...,b_n]\), where:

  • When shape is set, \(b_i = shape_i\).

  • When scales is set, \(b_i = \lfloor a_i \cdot scales_i \rfloor\).

DLA Support

DLA FP16 and DLA INT8 are supported.

Supported combination of parameters are:

when resize_mode is NEAREST:

coordinate_transformation

selector_for_single_pixel

nearest_rounding

ASYMMETRIC

FORMULA

FLOOR

HALF_PIXEL

FORMULA

HALF_DOWN

HALF_PIXEL

FORMULA

HALF_UP

when resize_mode is LINEAR:

coordinate_transformation

selector_for_single_pixel

HALF_PIXEL

FORMULA

HALF_PIXEL

UPPER

Examples

Linear Resize
input = network.add_input("input", dtype=trt.float32, shape=(1, 1, 3, 3))
layer = network.add_resize(input)
layer.resize_mode = trt.InterpolationMode.LINEAR
layer.shape = (1, 1, 5, 5)
layer.coordinate_transformation = trt.ResizeCoordinateTransformation.ALIGN_CORNERS
network.mark_output(layer.get_output(0))

inputs[input.name] = np.array([[[[0, 1, 2], [3, 4, 5], [6, 7, 8]]]])

outputs[layer.get_output(0).name] = layer.get_output(0).shape
expected[layer.get_output(0).name] = np.array(
    [
        [
            [0, 0.5, 1, 1.5, 2],
            [1.5, 2, 2.5, 3, 3.5],
            [3, 3.5, 4, 4.5, 5],
            [4.5, 5, 5.5, 6, 6.5],
            [6, 6.5, 7, 7.5, 8],
        ]
    ]
)
Nearest Resize, Shape Is Calculated By Using Scales
input = network.add_input("input", dtype=trt.float32, shape=(1, 1, 3, 3))
layer = network.add_resize(input)
layer.resize_mode = trt.InterpolationMode.NEAREST
layer.scales = (1, 1, 2, 2)
layer.coordinate_transformation = trt.ResizeCoordinateTransformation.ALIGN_CORNERS
network.mark_output(layer.get_output(0))

inputs[input.name] = np.array([[[[0, 1, 2], [3, 4, 5], [6, 7, 8]]]])

outputs[layer.get_output(0).name] = layer.get_output(0).shape
expected[layer.get_output(0).name] = np.array(
    [
        [
            [
                [0.0, 0.0, 0.0, 1.0, 1.0, 2.0],
                [0.0, 0.0, 0.0, 1.0, 1.0, 2.0],
                [0.0, 0.0, 0.0, 1.0, 1.0, 2.0],
                [3.0, 3.0, 3.0, 4.0, 4.0, 5.0],
                [3.0, 3.0, 3.0, 4.0, 4.0, 5.0],
                [6.0, 6.0, 6.0, 7.0, 7.0, 8.0],
            ]
        ]
    ]
)

C++ API

For more information about the C++ IResizeLayer operator, refer to the C++ IResizeLayer documentation

Python API

For more information about the Python IResizeLayer operator, refer to the Python IResizeLayer documentation