ArgMin#

ArgMin - 13 #

Version

name: ArgMin (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

Computes the indices of the min elements of the input tensor’s element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the min is selected if the min appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.

Attributes

axis: The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims: Keep the reduced dimension or not, default 1 means keep reduced dimension.
select_last_index: Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).

Inputs

data (heterogeneous) - T: An input tensor.

Outputs

reduced (heterogeneous) - tensor(int64): Reduced output tensor with integer data type.

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 all numeric tensors.

Examples

_no_keepdims

import numpy as np
import onnx

data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
node = onnx.helper.make_node(
    "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1, 0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_no_keepdims_example",
)

data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 4]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
    node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random"
)

_keepdims

import numpy as np
import onnx

data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
node = onnx.helper.make_node(
    "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1], [0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
    node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example"
)

data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 1, 4]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
    node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random"
)

_default_axes_keepdims

import numpy as np
import onnx

data = np.array([[2, 1], [3, 10]], dtype=np.float32)
keepdims = 1
node = onnx.helper.make_node(
    "ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims
)

# The content of result is : [[0], [0]]
result = argmin_use_numpy(data, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_default_axis_example",
)

data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [1, 3, 4]
result = argmin_use_numpy(data, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_default_axis_random",
)

_negative_axis_keepdims

import numpy as np
import onnx

data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
node = onnx.helper.make_node(
    "ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1], [0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_negative_axis_keepdims_example",
)

data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 3, 1]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_negative_axis_keepdims_random",
)

_no_keepdims_select_last_index

import numpy as np
import onnx

data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
node = onnx.helper.make_node(
    "ArgMin",
    inputs=["data"],
    outputs=["result"],
    axis=axis,
    keepdims=keepdims,
    select_last_index=True,
)
# result: [[1, 0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_no_keepdims_example_select_last_index",
)

data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 4]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_no_keepdims_random_select_last_index",
)

_keepdims_select_last_index

import numpy as np
import onnx

data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
node = onnx.helper.make_node(
    "ArgMin",
    inputs=["data"],
    outputs=["result"],
    axis=axis,
    keepdims=keepdims,
    select_last_index=True,
)
# result: [[1], [0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_keepdims_example_select_last_index",
)

data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 1, 4]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_keepdims_random_select_last_index",
)

_default_axes_keepdims_select_last_index

import numpy as np
import onnx

data = np.array([[2, 2], [3, 10]], dtype=np.float32)
keepdims = 1
node = onnx.helper.make_node(
    "ArgMin",
    inputs=["data"],
    outputs=["result"],
    keepdims=keepdims,
    select_last_index=True,
)

# result: [[0, 0]]
result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_default_axis_example_select_last_index",
)

data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [1, 3, 4]
result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_default_axis_random_select_last_index",
)

_negative_axis_keepdims_select_last_index

import numpy as np
import onnx

data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
node = onnx.helper.make_node(
    "ArgMin",
    inputs=["data"],
    outputs=["result"],
    axis=axis,
    keepdims=keepdims,
    select_last_index=True,
)
# result: [[1], [0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_negative_axis_keepdims_example_select_last_index",
)

data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 3, 1]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
    node,
    inputs=[data],
    outputs=[result],
    name="test_argmin_negative_axis_keepdims_random_select_last_index",
)

ArgMin - 12 vs 13

ArgMin - 12 #

Version

name: ArgMin (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

Computes the indices of the min elements of the input tensor’s element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the min is selected if the min appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.

Attributes

axis: The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims: Keep the reduced dimension or not, default 1 means keep reduced dimension.
select_last_index: Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).

Inputs

data (heterogeneous) - T: An input tensor.

Outputs

reduced (heterogeneous) - tensor(int64): Reduced output tensor with integer data type.

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 all numeric tensors.

ArgMin - 11 #

Version

name: ArgMin (GitHub)
domain: main
since_version: 11
function: False
support_level: SupportType.COMMON
shape inference: True

This version of the operator has been available since version 11.

Summary

Attributes

axis: The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).
keepdims: Keep the reduced dimension or not, default 1 means keep reduced dimension.

Inputs

data (heterogeneous) - T: An input tensor.

Outputs

reduced (heterogeneous) - tensor(int64): Reduced output tensor with integer data type.

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 all numeric tensors.

ArgMin - 1 #

Version

name: ArgMin (GitHub)
domain: main
since_version: 1
function: False
support_level: SupportType.COMMON
shape inference: True

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

Summary

Attributes

axis: The axis in which to compute the arg indices.
keepdims: Keep the reduced dimension or not, default 1 means keep reduced dimension.

Inputs

data (heterogeneous) - T: An input tensor.

Outputs

reduced (heterogeneous) - tensor(int64): Reduced output tensor with integer data type.

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 all numeric tensors.

ArgMin#

ArgMin - 13#

ArgMin - 12#

ArgMin - 11#

ArgMin - 1#

ArgMin - 13 #

ArgMin - 12 #

ArgMin - 11 #

ArgMin - 1 #