Gather#
Gather - 13#
Version
name: Gather (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
Given data tensor of rank r >= 1, and indices tensor of rank q, gather entries of the axis dimension of data (by default outer-most one as axis=0) indexed by indices, and concatenates them in an output tensor of rank q + (r - 1).
axis = 0 :
Let k = indices[i_{0}, …, i_{q-1}] Then output[i_{0}, …, i_{q-1}, j_{0}, …, j_{r-2}] = input[k , j_{0}, …, j_{r-2}]
data = [
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
indices = [
[0, 1],
[1, 2],
]
output = [
[
[1.0, 1.2],
[2.3, 3.4],
],
[
[2.3, 3.4],
[4.5, 5.7],
],
]
axis = 1 :
Let k = indices[i_{0}, …, i_{q-1}] Then output[j_{0}, i_{0}, …, i_{q-1}, j_{1}, …, j_{r-2}] = input[j_{0}, k, j_{1}, …, j_{r-2}]
data = [
[1.0, 1.2, 1.9],
[2.3, 3.4, 3.9],
[4.5, 5.7, 5.9],
]
indices = [
[0, 2],
]
axis = 1,
output = [
[[1.0, 1.9]],
[[2.3, 3.9]],
[[4.5, 5.9]],
]
Attributes
axis: Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
Inputs
data (heterogeneous) - T: Tensor of rank r >= 1.
indices (heterogeneous) - Tind: Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
Outputs
output (heterogeneous) - T: Tensor of rank q + (r - 1).
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 any tensor type.
Tind in ( tensor(int32), tensor(int64) ): Constrain indices to integer types
Examples
_gather_0
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=0,
)
data = np.random.randn(5, 4, 3, 2).astype(np.float32)
indices = np.array([0, 1, 3])
y = np.take(data, indices, axis=0)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_0",
)
_gather_1
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=1,
)
data = np.random.randn(5, 4, 3, 2).astype(np.float32)
indices = np.array([0, 1, 3])
y = np.take(data, indices, axis=1)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_1",
)
_gather_2d_indices
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=1,
)
data = np.random.randn(3, 3).astype(np.float32)
indices = np.array([[0, 2]])
y = np.take(data, indices, axis=1)
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_2d_indices",
)
_gather_negative_indices
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gather",
inputs=["data", "indices"],
outputs=["y"],
axis=0,
)
data = np.arange(10).astype(np.float32)
indices = np.array([0, -9, -10])
y = np.take(data, indices, axis=0)
# print(y)
# [0. 1. 0.]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_negative_indices",
)
Gather - 11#
Version
name: Gather (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
Given data tensor of rank r >= 1, and indices tensor of rank q, gather entries of the axis dimension of data (by default outer-most one as axis=0) indexed by indices, and concatenates them in an output tensor of rank q + (r - 1).
axis = 0 :
Let k = indices[i_{0}, …, i_{q-1}] Then output[i_{0}, …, i_{q-1}, j_{0}, …, j_{r-2}] = input[k , j_{0}, …, j_{r-2}]
data = [
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
indices = [
[0, 1],
[1, 2],
]
output = [
[
[1.0, 1.2],
[2.3, 3.4],
],
[
[2.3, 3.4],
[4.5, 5.7],
],
]
axis = 1 :
Let k = indices[i_{0}, …, i_{q-1}] Then output[i_{0}, …, i_{q-1}, j_{0}, …, j_{r-2}] = input[j_{0}, k, j_{1}, …, j_{r-2}]
data = [
[1.0, 1.2, 1.9],
[2.3, 3.4, 3.9],
[4.5, 5.7, 5.9],
]
indices = [
[0, 2],
]
axis = 1,
output = [
[
[1.0, 1.9],
[2.3, 3.9],
[4.5, 5.9],
],
]
Attributes
axis: Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
Inputs
data (heterogeneous) - T: Tensor of rank r >= 1.
indices (heterogeneous) - Tind: Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
Outputs
output (heterogeneous) - T: Tensor of rank q + (r - 1).
Type Constraints
T in ( 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 any tensor type.
Tind in ( tensor(int32), tensor(int64) ): Constrain indices to integer types
Gather - 1#
Version
name: Gather (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
Given data tensor of rank r >= 1, and indices tensor of rank q, gather entries of the axis dimension of data (by default outer-most one as axis=0) indexed by indices, and concatenates them in an output tensor of rank q + (r - 1). Example 1:
data = [
[1.0, 1.2],
[2.3, 3.4],
[4.5, 5.7],
]
indices = [
[0, 1],
[1, 2],
]
output = [
[
[1.0, 1.2],
[2.3, 3.4],
],
[
[2.3, 3.4],
[4.5, 5.7],
],
]
Example 2:
data = [
[1.0, 1.2, 1.9],
[2.3, 3.4, 3.9],
[4.5, 5.7, 5.9],
]
indices = [
[0, 2],
]
axis = 1,
output = [
[
[1.0, 1.9],
[2.3, 3.9],
[4.5, 5.9],
],
]
Attributes
axis: Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]
Inputs
data (heterogeneous) - T: Tensor of rank r >= 1.
indices (heterogeneous) - Tind: Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.
Outputs
output (heterogeneous) - T: Tensor of rank q + (r - 1).
Type Constraints
T in ( 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 any tensor type.
Tind in ( tensor(int32), tensor(int64) ): Constrain indices to integer types