Size#

Size - 13 #

Version

name: Size (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

Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.

Inputs

data (heterogeneous) - T: An input tensor.

Outputs

size (heterogeneous) - T1: Total number of elements of the input tensor

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) ): Input tensor can be of arbitrary type.
T1 in ( tensor(int64) ): Constrain output to int64 tensor, which should be a scalar though.

Examples

default

import numpy as np
import onnx

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

x = np.array(
    [
        [1, 2, 3],
        [4, 5, 6],
    ]
).astype(np.float32)
y = np.array(6).astype(np.int64)

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

x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.array(x.size).astype(np.int64)

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

Size - 1 vs 13

Size - 1 #