CenterCropPad

CenterCropPad - 18

Version

• name: CenterCropPad (GitHub)

• domain: main

• since_version: 18

• function:

• support_level: SupportType.COMMON

• shape inference: True

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

Summary

Attributes

• axes - INTS : 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:

• shape (heterogeneous) - Tind:

Outputs

• output_data (heterogeneous) - T:

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",
)