com.ms.internal.nhwc - AveragePool#
AveragePool - 11#
Version
name: AveragePool (GitHub)
domain: com.ms.internal.nhwc
since_version: 11
function:
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 11 of domain com.ms.internal.nhwc.
Summary
Attributes
activation - STRING :
activation_params - FLOATS :
auto_pad - STRING : auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that output_shape[i] = ceil(input_shape[i] / strides[i]) for each axis i. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.
ceil_mode - INT : Whether to use ceil or floor (default) to compute the output shape.
count_include_pad - INT : Whether include pad pixels when calculating values for the edges. Default is 0, doesn’t count include pad.
kernel_shape - INTS (required) : The size of the kernel along each axis.
pads - INTS : Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. pads format should be as follow [x1_begin, x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added at the beginning of axis i and xi_end, the number of pixels added at the end of axis i. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides - INTS : Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs
X (heterogeneous) - T:
Outputs
Y (heterogeneous) - T:
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.
Examples
_averagepool_2d_precomputed_pads
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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
import numpy as np
import onnx
"""
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")
_averagepool_2d_dilations
import numpy as np
import onnx
"""
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=[1, 1],
dilations=[2, 2],
ceil_mode=True,
)
x = (np.arange(16) + 1).astype(np.float32).reshape((1, 1, 4, 4))
y = np.array([[[[6, 7], [10, 11]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_dilations")