Ceil#

Ceil - 13#

Version

  • name: Ceil (GitHub)

  • domain: main

  • since_version: 13

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 13.

Summary

Ceil takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the ceil is, y = ceil(x), is applied to the tensor elementwise.

Inputs

  • X (heterogeneous) - T: Input tensor

Outputs

  • Y (heterogeneous) - T: Output tensor

Type Constraints

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Examples

default

import numpy as np
import onnx

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

x = np.array([-1.5, 1.2]).astype(np.float32)
y = np.ceil(x)  # expected output [-1., 2.]
expect(node, inputs=[x], outputs=[y], name="test_ceil_example")

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.ceil(x)
expect(node, inputs=[x], outputs=[y], name="test_ceil")

Ceil - 6#

Version

  • name: Ceil (GitHub)

  • domain: main

  • since_version: 6

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 6.

Summary

Ceil takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the ceil is, y = ceil(x), is applied to the tensor elementwise.

Inputs

  • X (heterogeneous) - T: Input tensor

Outputs

  • Y (heterogeneous) - T: Output tensor

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Ceil - 1#

Version

  • name: Ceil (GitHub)

  • domain: main

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: False

This version of the operator has been available since version 1.

Summary

Ceil takes one input data (Tensor<T>) and produces one output data (Tensor<T>) where the ceil is, y = ceil(x), is applied to the tensor elementwise.

Attributes

  • consumed_inputs: legacy optimization attribute.

Inputs

  • X (heterogeneous) - T: Input tensor

Outputs

  • Y (heterogeneous) - T: Output tensor

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.