Min#
Min - 13#
Version
name: Min (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
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 Broadcasting in ONNX.
Inputs
Between 1 and 2147483647 inputs.
data_0 (variadic, heterogeneous) - T: List of tensors for min.
Outputs
min (heterogeneous) - T: Output tensor.
Type Constraints
T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input and output types to numeric tensors.
Examples
default
import numpy as np
import onnx
data_0 = np.array([3, 2, 1]).astype(np.float32)
data_1 = np.array([1, 4, 4]).astype(np.float32)
data_2 = np.array([2, 5, 0]).astype(np.float32)
result = np.array([1, 2, 0]).astype(np.float32)
node = onnx.helper.make_node(
"Min",
inputs=["data_0", "data_1", "data_2"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1, data_2],
outputs=[result],
name="test_min_example",
)
node = onnx.helper.make_node(
"Min",
inputs=["data_0"],
outputs=["result"],
)
expect(node, inputs=[data_0], outputs=[data_0], name="test_min_one_input")
result = np.minimum(data_0, data_1)
node = onnx.helper.make_node(
"Min",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node, inputs=[data_0, data_1], outputs=[result], name="test_min_two_inputs"
)
_min_all_numeric_types
import numpy as np
import onnx
for op_dtype in all_numeric_dtypes:
data_0 = np.array([3, 2, 1]).astype(op_dtype)
data_1 = np.array([1, 4, 4]).astype(op_dtype)
result = np.array([1, 2, 1]).astype(op_dtype)
node = onnx.helper.make_node(
"Min",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1],
outputs=[result],
name=f"test_min_{np.dtype(op_dtype).name}",
)
Min - 12#
Version
name: Min (GitHub)
domain: main
since_version: 12
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 12.
Summary
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 Broadcasting in ONNX.
Inputs
Between 1 and 2147483647 inputs.
data_0 (variadic, heterogeneous) - T: List of tensors for min.
Outputs
min (heterogeneous) - T: Output tensor.
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input and output types to numeric tensors.
Min - 8#
Version
name: Min (GitHub)
domain: main
since_version: 8
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 8.
Summary
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 Broadcasting in ONNX.
Inputs
Between 1 and 2147483647 inputs.
data_0 (variadic, heterogeneous) - T: List of tensors for min.
Outputs
min (heterogeneous) - T: Output tensor.
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.
Min - 6#
Version
name: Min (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
Element-wise min of each of the input tensors. All inputs and outputs must have the same shape and data type.
Inputs
Between 1 and 2147483647 inputs.
data_0 (variadic, heterogeneous) - T: List of tensors for Min
Outputs
min (heterogeneous) - T: Output tensor. Same dimension as inputs.
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.
Min - 1#
Version
name: Min (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
Element-wise min of each of the input tensors. All inputs and outputs must have the same shape and data type.
Attributes
consumed_inputs: legacy optimization attribute.
Inputs
Between 1 and 2147483647 inputs.
data_0 (variadic, heterogeneous) - T: List of tensors for Min
Outputs
min (heterogeneous) - T: Output tensor. Same dimension as inputs.
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.