Col2Im#
Col2Im - 18#
Version
name: Col2Im (GitHub)
domain: main
since_version: 18
function:
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 18.
Summary
Attributes
dilations - INTS : 1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.
pads - INTS : 1-dimensional tensor with padding value 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 is the number of pixels added at the beginning of axis i and xi_end is the number of pixels added at the end of axis i. If not present, the padding defaults to 0 along start and end of each spatial axis.
strides - INTS : 1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
Inputs
input (heterogeneous) - T:
image_shape (heterogeneous) - tensor(int64):
block_shape (heterogeneous) - tensor(int64):
Outputs
output (heterogeneous) - T:
Type Constraints
T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input and output types to all numeric tensor types.
Examples
default
import numpy as np
import onnx
input = np.array(
[
[
[1.0, 6.0, 11.0, 16.0, 21.0], # (1, 5, 5)
[2.0, 7.0, 12.0, 17.0, 22.0],
[3.0, 8.0, 13.0, 18.0, 23.0],
[4.0, 9.0, 14.0, 19.0, 24.0],
[5.0, 0.0, 15.0, 20.0, 25.0],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([1, 5]).astype(np.int64)
node = onnx.helper.make_node(
"Col2Im", ["input", "image_shape", "block_shape"], ["output"]
)
output = np.array(
[
[
[
[1.0, 2.0, 3.0, 4.0, 5.0], # (1, 1, 5, 5)
[6.0, 7.0, 8.0, 9.0, 0.0],
[11.0, 12.0, 13.0, 14.0, 15.0],
[16.0, 17.0, 18.0, 19.0, 20.0],
[21.0, 22.0, 23.0, 24.0, 25.0],
]
]
]
).astype(np.float32)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im",
)
_col2im_strides
import numpy as np
import onnx
input = np.array(
[
[
[0.0, 0.0, 0.0, 0.0], # (1, 9, 4)
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([3, 3]).astype(np.int64)
output = np.array(
[
[
[
[0.0, 1.0, 1.0, 1.0, 1.0], # (1, 1, 5, 5)
[1.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 2.0, 1.0, 2.0, 1.0],
[1.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 1.0, 0.0],
]
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"Col2Im",
["input", "image_shape", "block_shape"],
["output"],
strides=[2, 2],
)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im_strides",
)
_col2im_pads
import numpy as np
import onnx
input = np.array(
[
[
[
1.0,
6.0,
11.0,
16.0,
21.0,
26,
31,
36,
41,
46,
51,
56,
61,
66,
71,
], # (1, 5, 15)
[
2.0,
7.0,
12.0,
17.0,
22.0,
27,
32,
37,
42,
47,
52,
57,
62,
67,
72,
],
[
3.0,
8.0,
13.0,
18.0,
23.0,
28,
33,
38,
43,
48,
53,
58,
63,
68,
73,
],
[
4.0,
9.0,
14.0,
19.0,
24.0,
29,
34,
39,
44,
49,
54,
59,
64,
69,
74,
],
[
5.0,
10.0,
15.0,
20.0,
25.0,
30,
35,
40,
45,
50,
55,
60,
65,
70,
75,
],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([1, 5]).astype(np.int64)
output = np.array(
[
[
[
[8.0, 21.0, 24.0, 27.0, 24.0], # (1, 1, 5, 5)
[38.0, 66.0, 69.0, 72.0, 54.0],
[68.0, 111.0, 114.0, 117.0, 84.0],
[98.0, 156.0, 159.0, 162.0, 114.0],
[128.0, 201.0, 204.0, 207.0, 144.0],
]
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"Col2Im",
["input", "image_shape", "block_shape"],
["output"],
pads=[0, 1, 0, 1],
)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im_pads",
)
_col2im_dilations
import numpy as np
import onnx
input = np.array(
[
[
[1.0, 5.0, 9.0, 13.0, 17], # (1, 4, 5)
[2.0, 6.0, 10.0, 14.0, 18],
[3.0, 7.0, 11.0, 15.0, 19],
[4.0, 8.0, 12.0, 16.0, 20],
]
]
).astype(np.float32)
image_shape = np.array([6, 6]).astype(np.int64)
block_shape = np.array([2, 2]).astype(np.int64)
output = np.array(
[
[
[
[1.0, 0.0, 0.0, 0.0, 0.0, 2.0], # (1, 1, 6, 6)
[8.0, 0.0, 0.0, 0.0, 0.0, 10.0],
[16.0, 0.0, 0.0, 0.0, 0.0, 18.0],
[24.0, 0.0, 0.0, 0.0, 0.0, 26.0],
[32.0, 0.0, 0.0, 0.0, 0.0, 34.0],
[19.0, 0.0, 0.0, 0.0, 0.0, 20.0],
]
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"Col2Im",
["input", "image_shape", "block_shape"],
["output"],
dilations=[1, 5],
)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im_dilations",
)
_col2im_5d
import numpy as np
import onnx
input = np.array(
[
[
[1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56], # (1, 10, 12)
[2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, 57],
[3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, 58],
[4, 9, 14, 19, 24, 29, 34, 39, 44, 49, 54, 59],
[5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60],
[61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116],
[62, 67, 72, 77, 82, 87, 92, 97, 102, 107, 112, 117],
[63, 68, 73, 78, 83, 88, 93, 98, 103, 108, 113, 118],
[64, 69, 74, 79, 84, 89, 94, 99, 104, 109, 114, 119],
[65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120],
]
]
).astype(np.float32)
image_shape = np.array([3, 4, 5]).astype(np.int64)
block_shape = np.array([1, 1, 5]).astype(np.int64)
output = np.array(
[
[
[
[
[1, 2, 3, 4, 5], # (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],
[26, 27, 28, 29, 30],
[31, 32, 33, 34, 35],
[36, 37, 38, 39, 40],
],
[
[41, 42, 43, 44, 45],
[46, 47, 48, 49, 50],
[51, 52, 53, 54, 55],
[56, 57, 58, 59, 60],
],
],
[
[
[61, 62, 63, 64, 65],
[66, 67, 68, 69, 70],
[71, 72, 73, 74, 75],
[76, 77, 78, 79, 80],
],
[
[81, 82, 83, 84, 85],
[86, 87, 88, 89, 90],
[91, 92, 93, 94, 95],
[96, 97, 98, 99, 100],
],
[
[101, 102, 103, 104, 105],
[106, 107, 108, 109, 110],
[111, 112, 113, 114, 115],
[116, 117, 118, 119, 120],
],
],
]
]
).astype(np.float32)
node = onnx.helper.make_node(
"Col2Im", ["input", "image_shape", "block_shape"], ["output"]
)
expect(
node,
inputs=[input, image_shape, block_shape],
outputs=[output],
name="test_col2im_5d",
)