Flatten

Flatten - 13

Version

• name: Flatten (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

Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, … d_n) then the output will have shape (d_0 X d_1 … d_(axis-1), d_axis X d_(axis+1) … X dn).

Attributes

• axis: Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 … d_n), where the shape of the input tensor is (d_0, d_1, … d_n). Default value is 1.

Inputs

• input (heterogeneous) - T: A tensor of rank >= axis.

Outputs

• output (heterogeneous) - T: A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.

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 to all tensor types.

Examples

flatten_with_default_axis

node = onnx.helper.make_node(
    'Flatten',
    inputs=['a'],
    outputs=['b'],  # Default value for axis: axis=1
)

shape = (5, 4, 3, 2)
a = np.random.random_sample(shape).astype(np.float32)
new_shape = (5, 24)
b = np.reshape(a, new_shape)
expect(node, inputs=[a], outputs=[b],
       name='test_flatten_default_axis')

flatten_negative_axis

shape = (2, 3, 4, 5)
a = np.random.random_sample(shape).astype(np.float32)

for i in range(-len(shape), 0):
    node = onnx.helper.make_node(
        'Flatten',
        inputs=['a'],
        outputs=['b'],
        axis=i,
    )

    new_shape = (np.prod(shape[0:i]).astype(int), -1)
    b = np.reshape(a, new_shape)
    expect(node, inputs=[a], outputs=[b],
           name='test_flatten_negative_axis' + str(abs(i)))

Differences

0	0	Flattens the input tensor into a 2D matrix. If input tensor has shape	Flattens the input tensor into a 2D matrix. If input tensor has shape
1	1	(d_0, d_1, ... d_n) then the output will have shape	(d_0, d_1, ... d_n) then the output will have shape
2	2	(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).	(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
3	3
4	4	**Attributes**	**Attributes**
5	5
6	6	* **axis**:	* **axis**:
7	7	Indicate up to which input dimensions (exclusive) should be	Indicate up to which input dimensions (exclusive) should be
8	8	flattened to the outer dimension of the output. The value for axis	flattened to the outer dimension of the output. The value for axis
9	9	must be in the range [-r, r], where r is the rank of the input	must be in the range [-r, r], where r is the rank of the input
10	10	tensor. Negative value means counting dimensions from the back. When	tensor. Negative value means counting dimensions from the back. When
11	11	axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n),	axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n),
12	12	where the shape of the input tensor is (d_0, d_1, ... d_n). Default value is 1.	where the shape of the input tensor is (d_0, d_1, ... d_n). Default value is 1.
13	13
14	14	**Inputs**	**Inputs**
15	15
16	16	* **input** (heterogeneous) - **T**:	* **input** (heterogeneous) - **T**:
17	17	A tensor of rank >= axis.	A tensor of rank >= axis.
18	18
19	19	**Outputs**	**Outputs**
20	20
21	21	* **output** (heterogeneous) - **T**:	* **output** (heterogeneous) - **T**:
22	22	A 2D tensor with the contents of the input tensor, with input	A 2D tensor with the contents of the input tensor, with input
23	23	dimensions up to axis flattened to the outer dimension of the output	dimensions up to axis flattened to the outer dimension of the output
24	24	and remaining input dimensions flattened into the inner dimension of	and remaining input dimensions flattened into the inner dimension of
25	25	the output.	the output.
26	26
27	27	**Type Constraints**	**Type Constraints**
28	28
29	29	* **T** in (	* **T** in (
	30		tensor(bfloat16),
30	31	tensor(bool),	tensor(bool),
31	32	tensor(complex128),	tensor(complex128),
32	33	tensor(complex64),	tensor(complex64),
33	34	tensor(double),	tensor(double),
34	35	tensor(float),	tensor(float),
35	36	tensor(float16),	tensor(float16),
36	37	tensor(int16),	tensor(int16),
37	38	tensor(int32),	tensor(int32),
38	39	tensor(int64),	tensor(int64),
39	40	tensor(int8),	tensor(int8),
40	41	tensor(string),	tensor(string),
41	42	tensor(uint16),	tensor(uint16),
42	43	tensor(uint32),	tensor(uint32),
43	44	tensor(uint64),	tensor(uint64),
44	45	tensor(uint8)	tensor(uint8)
45	46	):	):
46	47	Constrain input and output to all tensor types.	Constrain input and output to all tensor types.

Flatten - 11

Version

• name: Flatten (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

Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, … d_n) then the output will have shape (d_0 X d_1 … d_(axis-1), d_axis X d_(axis+1) … X dn).

Attributes

• axis: Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 … d_n), where the shape of the input tensor is (d_0, d_1, … d_n). Default value is 1.

Inputs

• input (heterogeneous) - T: A tensor of rank >= axis.

Outputs

• output (heterogeneous) - T: A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.

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 to all tensor types.

Differences

0	0	Flattens the input tensor into a 2D matrix. If input tensor has shape	Flattens the input tensor into a 2D matrix. If input tensor has shape
1	1	(d_0, d_1, ... d_n) then the output will have shape	(d_0, d_1, ... d_n) then the output will have shape
2	2	(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).	(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
3	3
4	4	**Attributes**	**Attributes**
5	5
6	6	* **axis**:	* **axis**:
7	7	Indicate up to which input dimensions (exclusive) should be	Indicate up to which input dimensions (exclusive) should be
8	8	flattened to the outer dimension of the output. The value for axis	flattened to the outer dimension of the output. The value for axis
9	9	must be in the range [0, R], where R is the rank of the input	must be in the range [-r, r], where r is the rank of the input
	10		tensor. Negative value means counting dimensions from the back. When
10	11	tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X	axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n),
11	12	d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ...	where the shape of the input tensor is (d_0, d_1, ... d_n). Default value is 1.
12		d_n). Default value is 1.
13	13
14	14	**Inputs**	**Inputs**
15	15
16	16	* **input** (heterogeneous) - **T**:	* **input** (heterogeneous) - **T**:
17	17	A tensor of rank >= axis.	A tensor of rank >= axis.
18	18
19	19	**Outputs**	**Outputs**
20	20
21	21	* **output** (heterogeneous) - **T**:	* **output** (heterogeneous) - **T**:
22	22	A 2D tensor with the contents of the input tensor, with input	A 2D tensor with the contents of the input tensor, with input
23	23	dimensions up to axis flattened to the outer dimension of the output	dimensions up to axis flattened to the outer dimension of the output
24	24	and remaining input dimensions flattened into the inner dimension of	and remaining input dimensions flattened into the inner dimension of
25	25	the output.	the output.
26	26
27	27	**Type Constraints**	**Type Constraints**
28	28
29	29	* **T** in (	* **T** in (
30	30	tensor(bool),	tensor(bool),
31	31	tensor(complex128),	tensor(complex128),
32	32	tensor(complex64),	tensor(complex64),
33	33	tensor(double),	tensor(double),
34	34	tensor(float),	tensor(float),
35	35	tensor(float16),	tensor(float16),
36	36	tensor(int16),	tensor(int16),
37	37	tensor(int32),	tensor(int32),
38	38	tensor(int64),	tensor(int64),
39	39	tensor(int8),	tensor(int8),
40	40	tensor(string),	tensor(string),
41	41	tensor(uint16),	tensor(uint16),
42	42	tensor(uint32),	tensor(uint32),
43	43	tensor(uint64),	tensor(uint64),
44	44	tensor(uint8)	tensor(uint8)
45	45	):	):
46	46	Constrain input and output to all tensor types.	Constrain input and output to all tensor types.

Flatten - 9

Version

• name: Flatten (GitHub)

• domain: main

• since_version: 9

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 9.

Summary

Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, … d_n) then the output will have shape (d_0 X d_1 … d_(axis-1), d_axis X d_(axis+1) … X dn).

Attributes

• axis: Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 … d_n), where the shape of the input tensor is (d_0, d_1, … d_n). Default value is 1.

Inputs

• input (heterogeneous) - T: A tensor of rank >= axis.

Outputs

• output (heterogeneous) - T: A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.

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 to all tensor types.

Differences

0	0	Flattens the input tensor into a 2D matrix. If input tensor has shape	Flattens the input tensor into a 2D matrix. If input tensor has shape
1	1	(d_0, d_1, ... d_n) then the output will have shape	(d_0, d_1, ... d_n) then the output will have shape
2	2	(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).	(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn).
3	3
4	4	**Attributes**	**Attributes**
5	5
6	6	* **axis**:	* **axis**:
7	7	Indicate up to which input dimensions (exclusive) should be	Indicate up to which input dimensions (exclusive) should be
8	8	flattened to the outer dimension of the output. The value for axis	flattened to the outer dimension of the output. The value for axis
9	9	must be in the range [0, R], where R is the rank of the input	must be in the range [0, R], where R is the rank of the input
10	10	tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X	tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X
11	11	d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ...	d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ...
12	12	d_n). Default value is 1.	d_n). Default value is 1.
13	13
14	14	**Inputs**	**Inputs**
15	15
16	16	* **input** (heterogeneous) - **T**:	* **input** (heterogeneous) - **T**:
17	17	A tensor of rank >= axis.	A tensor of rank >= axis.
18	18
19	19	**Outputs**	**Outputs**
20	20
21	21	* **output** (heterogeneous) - **T**:	* **output** (heterogeneous) - **T**:
22	22	A 2D tensor with the contents of the input tensor, with input	A 2D tensor with the contents of the input tensor, with input
23	23	dimensions up to axis flattened to the outer dimension of the output	dimensions up to axis flattened to the outer dimension of the output
24	24	and remaining input dimensions flattened into the inner dimension of	and remaining input dimensions flattened into the inner dimension of
25	25	the output.	the output.
26	26
27	27	**Type Constraints**	**Type Constraints**
28	28
29	29	* **T** in (	* **T** in (
	30		tensor(bool),
	31		tensor(complex128),
	32		tensor(complex64),
30	33	tensor(double),	tensor(double),
31	34	tensor(float),	tensor(float),
32	35	tensor(float16)	tensor(float16),
	36		tensor(int16),
	37		tensor(int32),
	38		tensor(int64),
	39		tensor(int8),
	40		tensor(string),
	41		tensor(uint16),
	42		tensor(uint32),
	43		tensor(uint64),
	44		tensor(uint8)
33	45	):	):
34	46	Constrain input and output types to float tensors.	Constrain input and output to all tensor types.

Flatten - 1

Version

• name: Flatten (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

Flattens the input tensor into a 2D matrix. If input tensor has shape (d_0, d_1, … d_n) then the output will have shape (d_0 X d_1 … d_(axis-1), d_axis X d_(axis+1) … X dn).

Attributes

• axis: Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 … d_n), where the shape of the input tensor is (d_0, d_1, … d_n). Default value is 1.

Inputs

• input (heterogeneous) - T: A tensor of rank >= axis.

Outputs

• output (heterogeneous) - T: A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.

Type Constraints

• T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

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