GridSample#
GridSample - 16#
Version
name: GridSample (GitHub)
domain: main
since_version: 16
function:
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 16.
Summary
Attributes
align_corners - INT : If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input’s corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input’s corner pixels, making the sampling more resolution agnostic.
mode - STRING : Three interpolation modes: bilinear (default), nearest and bicubic.
padding_mode - STRING : Support padding modes for outside grid values: zeros`(default), `border, reflection. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x’ = 1.5, then reflects by border 1 and becomes x’’ = 0.5.
Inputs
X (heterogeneous) - T1:
grid (heterogeneous) - T2:
Outputs
Y (heterogeneous) - T1:
Type Constraints
T1 in ( 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) ): Constrain input X and output Y types to all tensor types.
T2 in ( tensor(double), tensor(float), tensor(float16) ): Constrain grid types to float tensors.
Examples
_gridsample
import numpy as np
import onnx
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="bilinear",
padding_mode="zeros",
align_corners=0,
)
# X shape, [N, C, H, W] - [1, 1, 4, 4]
X = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0],
[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0],
[12.0, 13.0, 14.0, 15.0],
]
]
],
dtype=np.float32,
)
# Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2]
Grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.6000, -1.0000],
[-0.2000, -1.0000],
[0.2000, -1.0000],
[0.6000, -1.0000],
[1.0000, -1.0000],
],
[
[-1.0000, -0.6000],
[-0.6000, -0.6000],
[-0.2000, -0.6000],
[0.2000, -0.6000],
[0.6000, -0.6000],
[1.0000, -0.6000],
],
[
[-1.0000, -0.2000],
[-0.6000, -0.2000],
[-0.2000, -0.2000],
[0.2000, -0.2000],
[0.6000, -0.2000],
[1.0000, -0.2000],
],
[
[-1.0000, 0.2000],
[-0.6000, 0.2000],
[-0.2000, 0.2000],
[0.2000, 0.2000],
[0.6000, 0.2000],
[1.0000, 0.2000],
],
[
[-1.0000, 0.6000],
[-0.6000, 0.6000],
[-0.2000, 0.6000],
[0.2000, 0.6000],
[0.6000, 0.6000],
[1.0000, 0.6000],
],
[
[-1.0000, 1.0000],
[-0.6000, 1.0000],
[-0.2000, 1.0000],
[0.2000, 1.0000],
[0.6000, 1.0000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6]
Y = np.array(
[
[
[
[0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500],
[0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000],
[2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000],
[3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000],
[5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000],
[3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500],
]
]
],
dtype=np.float32,
)
expect(node, inputs=[X, Grid], outputs=[Y], name="test_gridsample")
_gridsample_paddingmode
import numpy as np
import onnx
# X shape, [N, C, H, W] - [1, 1, 3, 2]
X = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
Grid = np.array(
[
[
[
[-10.0000, -10.0000],
[-5.0000, -5.0000],
[-0.2000, -0.2000],
[10.0000, 10.0000],
],
[
[10.0000, 10.0000],
[-0.2000, -0.2000],
[5.0000, 5.0000],
[10.0000, 10.0000],
],
]
],
dtype=np.float32,
)
# setting padding_mode = 'zeros'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
padding_mode="zeros",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_zeros = np.array(
[[[[0.0000, 0.0000, 1.7000, 0.0000], [0.0000, 1.7000, 0.0000, 0.0000]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_zeros],
name="test_gridsample_zeros_padding",
)
# setting padding_mode = 'border'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
padding_mode="border",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_border = np.array(
[[[[0.0000, 0.0000, 1.7000, 5.0000], [5.0000, 1.7000, 5.0000, 5.0000]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_border],
name="test_gridsample_border_padding",
)
# setting padding_mode = 'reflection'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
padding_mode="reflection",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_reflection = np.array(
[[[[2.5000, 0.0000, 1.7000, 2.5000], [2.5000, 1.7000, 5.0000, 2.5000]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_reflection],
name="test_gridsample_reflection_padding",
)
_gridsample_mode_aligncorners
import numpy as np
import onnx
# X shape, [N, C, H, W] - [1, 1, 3, 2]
X = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
# Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2]
Grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.5000, -0.5000],
[-0.2000, -0.2000],
[0.0000, 0.0000],
],
[
[0.0000, 0.0000],
[-0.2000, -0.2000],
[0.5000, 0.5000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
# setting mode = 'bilinear', default align_corners = 0
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="bilinear",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_bilinear = np.array(
[[[[0.0000, 0.5000, 1.7000, 2.5000], [2.5000, 1.7000, 4.5000, 1.2500]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_bilinear],
name="test_gridsample_bilinear",
)
# setting mode = 'bilinear', align_corners = 1
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="bilinear",
align_corners=1,
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_align_corners = np.array(
[[[[0.0000, 1.2500, 2.0000, 2.5000], [2.5000, 2.0000, 3.7500, 5.0000]]]],
dtype=np.float32,
)
expect(
node,
inputs=[X, Grid],
outputs=[Y_align_corners],
name="test_gridsample_aligncorners_true",
)
# setting mode = 'nearest'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="nearest",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_nearest = np.array(
[[[[0.0, 0.0, 2.0, 2.0], [2.0, 2.0, 5.0, 0.0]]]],
dtype=np.float32,
)
expect(
node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest"
)
# setting mode = 'bicubic'
node = onnx.helper.make_node(
"GridSample",
inputs=["X", "Grid"],
outputs=["Y"],
mode="bicubic",
)
# Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4]
Y_bicubic = np.array(
[[[[-0.1406, 0.3828, 1.7556, 2.9688], [2.9688, 1.7556, 5.1445, 1.3906]]]],
dtype=np.float32,
)
expect(
node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic"
)
"""
For someone who want to test by script. Comment it cause github ONNX CI
do not have the torch python package.