com.microsoft.nchwc - AveragePool

AveragePool - 1 (com.microsoft.nchwc)

Version

• name: AveragePool (GitHub)

• domain: com.microsoft.nchwc

• since_version: 1

• function:

• support_level:

• shape inference:

This version of the operator has been available since version 1 of domain com.microsoft.nchwc.

Summary

For internal use.

Attributes

• auto_pad:

Default value is ?.

• ceil_mode:

Default value is ?.

• count_include_pad:

Default value is ?.

• dilations:

Default value is ?.

• kernel_shape (required):

Default value is ?.

• pads:

Default value is ?.

• strides:

Default value is ?.

Inputs

• X (heterogeneous) - T:

Outputs

• Y (heterogeneous) - T:

Examples

_averagepool_2d_precomputed_pads

"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[5, 5],
    pads=[2, 2, 2, 2],
)
x = np.array(
    [
        [
            [
                [1, 2, 3, 4, 5],
                [6, 7, 8, 9, 10],
                [11, 12, 13, 14, 15],
                [16, 17, 18, 19, 20],
                [21, 22, 23, 24, 25],
            ]
        ]
    ]
).astype(np.float32)
y = np.array(
    [
        [
            [
                [7, 7.5, 8, 8.5, 9],
                [9.5, 10, 10.5, 11, 11.5],
                [12, 12.5, 13, 13.5, 14],
                [14.5, 15, 15.5, 16, 16.5],
                [17, 17.5, 18, 18.5, 19],
            ]
        ]
    ]
).astype(np.float32)

expect(
    node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads"
)

_averagepool_2d_precomputed_pads_count_include_pad

"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[5, 5],
    pads=[2, 2, 2, 2],
    count_include_pad=1,
)
x = np.array(
    [
        [
            [
                [1, 2, 3, 4, 5],
                [6, 7, 8, 9, 10],
                [11, 12, 13, 14, 15],
                [16, 17, 18, 19, 20],
                [21, 22, 23, 24, 25],
            ]
        ]
    ]
).astype(np.float32)
y = np.array(
    [
        [
            [
                [2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
                [4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
                [7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
                [6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
                [6.1200, 8.4000, 10.8000, 8.8800, 6.8400],
            ]
        ]
    ]
).astype(np.float32)

expect(
    node,
    inputs=[x],
    outputs=[y],
    name="test_averagepool_2d_precomputed_pads_count_include_pad",
)

_averagepool_2d_precomputed_strides

"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[2, 2],
    strides=[2, 2],
)
x = np.array(
    [
        [
            [
                [1, 2, 3, 4, 5],
                [6, 7, 8, 9, 10],
                [11, 12, 13, 14, 15],
                [16, 17, 18, 19, 20],
                [21, 22, 23, 24, 25],
            ]
        ]
    ]
).astype(np.float32)
y = np.array([[[[4, 6], [14, 16]]]]).astype(np.float32)

expect(
    node,
    inputs=[x],
    outputs=[y],
    name="test_averagepool_2d_precomputed_strides",
)

_averagepool_2d_precomputed_same_upper

"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 3, 3]
pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[3, 3],
    strides=[2, 2],
    auto_pad="SAME_UPPER",
)
x = np.array(
    [
        [
            [
                [1, 2, 3, 4, 5],
                [6, 7, 8, 9, 10],
                [11, 12, 13, 14, 15],
                [16, 17, 18, 19, 20],
                [21, 22, 23, 24, 25],
            ]
        ]
    ]
).astype(np.float32)
y = np.array([[[[4, 5.5, 7], [11.5, 13, 14.5], [19, 20.5, 22]]]]).astype(
    np.float32
)

expect(
    node,
    inputs=[x],
    outputs=[y],
    name="test_averagepool_2d_precomputed_same_upper",
)

_averagepool_1d_default

"""
input_shape: [1, 3, 32]
output_shape: [1, 3, 31]
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[2],
)
x = np.random.randn(1, 3, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = [2]
strides = [1]
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], "AVG")

expect(node, inputs=[x], outputs=[y], name="test_averagepool_1d_default")

_averagepool_2d_default

"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 31, 31]
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[2, 2],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), "AVG")

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_default")

_averagepool_3d_default

"""
input_shape: [1, 3, 32, 32, 32]
output_shape: [1, 3, 31, 31, 31]
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[2, 2, 2],
)
x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = [2, 2, 2]
strides = [1, 1, 1]
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0, 0, 0], "AVG")

expect(node, inputs=[x], outputs=[y], name="test_averagepool_3d_default")

_averagepool_2d_same_upper

"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[2, 2],
    auto_pad="SAME_UPPER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("SAME_UPPER", x_shape[2:], kernel_shape, strides)
pad_shape = get_pad_shape(
    "SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_top = pad_shape[0] // 2
pad_bottom = pad_shape[0] - pad_top
pad_left = pad_shape[1] // 2
pad_right = pad_shape[1] - pad_left
padded = np.pad(
    x,
    ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
    mode="constant",
    constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "AVG")

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper")

_averagepool_2d_same_lower

"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[2, 2],
    auto_pad="SAME_LOWER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape("SAME_LOWER", x_shape[2:], kernel_shape, strides)
pad_shape = get_pad_shape(
    "SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_bottom = pad_shape[0] // 2
pad_top = pad_shape[0] - pad_bottom
pad_right = pad_shape[1] // 2
pad_left = pad_shape[1] - pad_right
padded = np.pad(
    x,
    ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
    mode="constant",
    constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "AVG")

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower")

_averagepool_2d_pads

"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[3, 3],
    pads=[2, 2, 2, 2],
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = 2
pad_top = 2
pad_right = 2
pad_left = 2
pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
out_shape = get_output_shape(
    "VALID", np.add(x_shape[2:], pad_shape), kernel_shape, strides
)
padded = np.pad(
    x,
    ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
    mode="constant",
    constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, pad_shape, "AVG")

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads")

_averagepool_2d_pads_count_include_pad

"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[3, 3],
    pads=[2, 2, 2, 2],
    count_include_pad=1,
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = 2
pad_top = 2
pad_right = 2
pad_left = 2
pad_shape = [pad_top + pad_bottom, pad_left + pad_right]
out_shape = get_output_shape(
    "VALID", np.add(x_shape[2:], pad_shape), kernel_shape, strides
)
padded = np.pad(
    x,
    ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
    mode="constant",
    constant_values=0,
)
y = pool(
    padded,
    x_shape,
    kernel_shape,
    strides,
    out_shape,
    pad_shape,
    "AVG",
    count_include_pad=1,
)

expect(
    node,
    inputs=[x],
    outputs=[y],
    name="test_averagepool_2d_pads_count_include_pad",
)

_averagepool_2d_strides

"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 10, 10]
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[5, 5],
    strides=[3, 3],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (5, 5)
strides = (3, 3)
out_shape = get_output_shape("VALID", x_shape[2:], kernel_shape, strides)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, (0, 0), "AVG")

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides")

_averagepool_2d_ceil

"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
    "AveragePool",
    inputs=["x"],
    outputs=["y"],
    kernel_shape=[3, 3],
    strides=[2, 2],
    ceil_mode=True,
)
x = np.array(
    [
        [
            [
                [1, 2, 3, 4],
                [5, 6, 7, 8],
                [9, 10, 11, 12],
                [13, 14, 15, 16],
            ]
        ]
    ]
).astype(np.float32)
y = np.array([[[[6, 7.5], [12, 13.5]]]]).astype(np.float32)

expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil")