Min#

Domain: ai.onnx
Since version: 13

Element-wise min of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type.

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.

Inputs

data_0 (T): List of tensors for min.

Outputs

min (T): Output tensor.

Type Constraints

T: Constrain input and output types to numeric tensors. Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8).

Examples#

test_cc_min_bcast

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(2, 2), dtype=float32
    [[1., 2.],
     [3., 4.]]
  data_1: shape=(), dtype=float32
    2.5

Outputs:
  result: shape=(2, 2), dtype=float32
    [[1. , 2. ],
     [2.5, 2.5]]

test_min_example

Node:
  Min(data_0, data_1, data_2) -> (result)

Inputs:
  data_0: shape=(3,), dtype=float32
    [3., 2., 1.]
  data_1: shape=(3,), dtype=float32
    [1., 4., 4.]
  data_2: shape=(3,), dtype=float32
    [2., 5., 0.]

Outputs:
  result: shape=(3,), dtype=float32
    [1., 2., 0.]

test_min_float16

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=float16
    [1., 4., 3.]
  data_1: shape=(3,), dtype=float16
    [3., 2., 5.]

Outputs:
  result: shape=(3,), dtype=float16
    [1., 2., 3.]

test_min_float32

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=float32
    [3., 2., 1.]
  data_1: shape=(3,), dtype=float32
    [1., 4., 4.]

Outputs:
  result: shape=(3,), dtype=float32
    [1., 2., 1.]

test_min_float64

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=float64
    [3., 2., 1.]
  data_1: shape=(3,), dtype=float64
    [1., 4., 4.]

Outputs:
  result: shape=(3,), dtype=float64
    [1., 2., 1.]

test_min_int16

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=int16
    [3, 2, 1]
  data_1: shape=(3,), dtype=int16
    [1, 4, 4]

Outputs:
  result: shape=(3,), dtype=int16
    [1, 2, 1]

test_min_int32

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=int32
    [3, 2, 1]
  data_1: shape=(3,), dtype=int32
    [1, 4, 4]

Outputs:
  result: shape=(3,), dtype=int32
    [1, 2, 1]

test_min_int64

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=int64
    [3, 2, 1]
  data_1: shape=(3,), dtype=int64
    [1, 4, 4]

Outputs:
  result: shape=(3,), dtype=int64
    [1, 2, 1]

test_min_int8

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=int8
    [3, 2, 1]
  data_1: shape=(3,), dtype=int8
    [1, 4, 4]

Outputs:
  result: shape=(3,), dtype=int8
    [1, 2, 1]

test_min_one_input

Node:
  Min(data_0) -> (result)

Inputs:
  data_0: shape=(3,), dtype=float32
    [3., 2., 1.]

Outputs:
  result: shape=(3,), dtype=float32
    [3., 2., 1.]

test_min_two_inputs

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=float32
    [3., 2., 1.]
  data_1: shape=(3,), dtype=float32
    [1., 4., 4.]

Outputs:
  result: shape=(3,), dtype=float32
    [1., 2., 1.]

test_min_uint16

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=uint16
    [3, 2, 1]
  data_1: shape=(3,), dtype=uint16
    [1, 4, 4]

Outputs:
  result: shape=(3,), dtype=uint16
    [1, 2, 1]

test_min_uint32

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=uint32
    [3, 2, 1]
  data_1: shape=(3,), dtype=uint32
    [1, 4, 4]

Outputs:
  result: shape=(3,), dtype=uint32
    [1, 2, 1]

test_min_uint64

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=uint64
    [3, 2, 1]
  data_1: shape=(3,), dtype=uint64
    [1, 4, 4]

Outputs:
  result: shape=(3,), dtype=uint64
    [1, 2, 1]

test_min_uint8

Node:
  Min(data_0, data_1) -> (result)

Inputs:
  data_0: shape=(3,), dtype=uint8
    [3, 2, 1]
  data_1: shape=(3,), dtype=uint8
    [1, 4, 4]

Outputs:
  result: shape=(3,), dtype=uint8
    [1, 2, 1]

Differences with previous version (12)#

SchemaDiff: Min (domain 'ai.onnx')

old version: 12
new version: 13
breaking: no

Type constraints:

changed ‘T’: added types: [‘tensor(bfloat16)’]

Min#

Examples#

Differences with previous version (12)#

Version History#