com.ms.internal.nhwc - MaxPool#
MaxPool - 11#
Version
name: MaxPool (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 the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.
ceil_mode - INT : Whether to use ceil or floor (default) to compute the output shape.
dilations - INTS : Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.
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.
storage_order - INT : The storage order of the tensor. 0 is row major, and 1 is column major.
strides - INTS : Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs
X (heterogeneous) - T:
Outputs
Between 1 and 2 outputs.
Y (heterogeneous) - T:
Indices (optional, heterogeneous) - I:
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.
I in ( tensor(int64) ): Constrain index tensor to int64
Examples
_maxpool_2d_uint8
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(
"MaxPool",
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.uint8)
y = np.array(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.uint8)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_uint8")
_maxpool_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(
"MaxPool",
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(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_pads")
_maxpool_with_argmax_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(
"MaxPool",
inputs=["x"],
outputs=["y", "z"],
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(
[
[
[
[13, 14, 15, 15, 15],
[18, 19, 20, 20, 20],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
[23, 24, 25, 25, 25],
]
]
]
).astype(np.float32)
z = np.array(
[
[
[
[12, 13, 14, 14, 14],
[17, 18, 19, 19, 19],
[22, 23, 24, 24, 24],
[22, 23, 24, 24, 24],
[22, 23, 24, 24, 24],
]
]
]
).astype(np.int64)
expect(
node,
inputs=[x],
outputs=[y, z],
name="test_maxpool_with_argmax_2d_precomputed_pads",
)
_maxpool_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(
"MaxPool", 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([[[[7, 9], [17, 19]]]]).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_strides"
)
_maxpool_with_argmax_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(
"MaxPool",
inputs=["x"],
outputs=["y", "z"],
kernel_shape=[2, 2],
strides=[2, 2],
storage_order=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([[[[7, 9], [17, 19]]]]).astype(np.float32)
z = np.array([[[[6, 16], [8, 18]]]]).astype(np.int64)
expect(
node,
inputs=[x],
outputs=[y, z],
name="test_maxpool_with_argmax_2d_precomputed_strides",
)
_maxpool_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(
"MaxPool",
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([[[[7, 9, 10], [17, 19, 20], [22, 24, 25]]]]).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_maxpool_2d_precomputed_same_upper"
)
_maxpool_1d_default
import numpy as np
import onnx
"""
input_shape: [1, 3, 32]
output_shape: [1, 3, 31]
"""
node = onnx.helper.make_node(
"MaxPool",
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], "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_1d_default")
_maxpool_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(
"MaxPool",
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), "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_default")
_maxpool_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(
"MaxPool",
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], "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_3d_default")
_maxpool_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(
"MaxPool",
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, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_upper")
_maxpool_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(
"MaxPool",
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, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_same_lower")
_maxpool_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(
"MaxPool",
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 = pad_top = pad_right = 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, "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_pads")
_maxpool_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(
"MaxPool", 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), "MAX")
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_strides")
_maxpool_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(
"MaxPool",
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([[[[11, 12], [15, 16]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_ceil")
_maxpool_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(
"MaxPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[1, 1],
dilations=[2, 2],
)
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([[[[11, 12], [15, 16]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_maxpool_2d_dilations")