CenterCropPad#

CenterCropPad - 18 #

Version

name: CenterCropPad (GitHub)
domain: main
since_version: 18
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 18.

Summary

Center crop or pad an input to given dimensions.

The crop/pad dimensions can be specified for a subset of the axes. Non-specified dimensions will not be cropped or padded.

If the input dimensions are bigger than the crop shape, a centered cropping window is extracted from the input. If the input dimensions are smaller than the crop shape, the input is padded on each side equally, so that the input is centered in the output.

Attributes

axes: If provided, it specifies a subset of axes that ‘shape’ refer to. If not provided, all axes are assumed [0, 1, …, r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.

Inputs

input_data (heterogeneous) - T: Input to extract the centered crop from.
shape (heterogeneous) - Tind: 1-D tensor representing the cropping window dimensions.

Outputs

output_data (heterogeneous) - T: Output data.

Type Constraints

T in ( 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) ): Constrain input and output types to all tensor types.
Tind in ( tensor(int32), tensor(int64) ): Constrain indices to integer types

Examples

_center_crop_pad_crop

import numpy as np
import onnx

node = onnx.helper.make_node(
    "CenterCropPad",
    inputs=["x", "shape"],
    outputs=["y"],
)

# First dim is even diff, second is uneven
x = np.random.randn(20, 10, 3).astype(np.float32)
shape = np.array([10, 7, 3], dtype=np.int64)
y = x[5:15, 1:8, :]

expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop")

_center_crop_pad_pad

import numpy as np
import onnx

node = onnx.helper.make_node(
    "CenterCropPad",
    inputs=["x", "shape"],
    outputs=["y"],
)

# First dim is even diff, second is uneven
x = np.random.randn(10, 7, 3).astype(np.float32)
shape = np.array([20, 10, 3], dtype=np.int64)
y = np.zeros([20, 10, 3], dtype=np.float32)
y[5:15, 1:8, :] = x

expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_pad")

_center_crop_pad_crop_and_pad

import numpy as np
import onnx

node = onnx.helper.make_node(
    "CenterCropPad",
    inputs=["x", "shape"],
    outputs=["y"],
)

# Cropping on first dim, padding on second, third stays the same
x = np.random.randn(20, 8, 3).astype(np.float32)
shape = np.array([10, 10, 3], dtype=np.int64)
y = np.zeros([10, 10, 3], dtype=np.float32)
y[:, 1:9, :] = x[5:15, :, :]

expect(
    node,
    inputs=[x, shape],
    outputs=[y],
    name="test_center_crop_pad_crop_and_pad",
)

_center_crop_pad_crop_axes_hwc

import numpy as np
import onnx

node = onnx.helper.make_node(
    "CenterCropPad",
    inputs=["x", "shape"],
    outputs=["y"],
    axes=[0, 1],
)

# Cropping on first dim, padding on second, third stays the same
x = np.random.randn(20, 8, 3).astype(np.float32)
shape = np.array([10, 9], dtype=np.int64)
y = np.zeros([10, 9, 3], dtype=np.float32)
y[:, :8, :] = x[5:15, :, :]

expect(
    node,
    inputs=[x, shape],
    outputs=[y],
    name="test_center_crop_pad_crop_axes_hwc",
)

_center_crop_pad_crop_axes_chw

import numpy as np
import onnx

node = onnx.helper.make_node(
    "CenterCropPad",
    inputs=["x", "shape"],
    outputs=["y"],
    axes=[1, 2],
)

# Cropping on second dim, padding on third, first stays the same
x = np.random.randn(3, 20, 8).astype(np.float32)
shape = np.array([10, 9], dtype=np.int64)
y = np.zeros([3, 10, 9], dtype=np.float32)
y[:, :, :8] = x[:, 5:15, :]

expect(
    node,
    inputs=[x, shape],
    outputs=[y],
    name="test_center_crop_pad_crop_axes_chw",
)

CenterCropPad#

CenterCropPad - 18#

CenterCropPad - 18 #