(l-onnx-doc-Det)=

# Det


(l-onnx-op-det-22)=

## Det - 22

### Version

- **name**: [Det (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Det)
- **domain**: `main`
- **since_version**: `22`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 22**.

### Summary

Det calculates determinant of a square matrix or batches of square matrices.
Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions,
and the inner-most 2 dimensions form square matrices.
The output is a tensor of shape `[*]`, containing the determinants of all input submatrices.
e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`).

### Inputs

- **X** (heterogeneous) - **T**:

  Input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  Output tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to floating-point tensors.

### Examples

#### _2d

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "Det",
    inputs=["x"],
    outputs=["y"],
)

x = np.arange(4).reshape(2, 2).astype(np.float32)
y = np.linalg.det(x)  # expect -2
expect(node, inputs=[x], outputs=[y], name="test_det_2d")
```

#### _nd

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "Det",
    inputs=["x"],
    outputs=["y"],
)

x = np.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]).astype(
    np.float32
)
y = np.linalg.det(x)  # expect array([-2., -3., -8.])
expect(node, inputs=[x], outputs=[y], name="test_det_nd")
```

```{toctree}
text_diff_Det_11_22
```

(l-onnx-op-det-11)=

## Det - 11

### Version

- **name**: [Det (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Det)
- **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

Det calculates determinant of a square matrix or batches of square matrices.
Det takes one input tensor of shape `[*, M, M]`, where `*` is zero or more batch dimensions,
and the inner-most 2 dimensions form square matrices.
The output is a tensor of shape `[*]`, containing the determinants of all input submatrices.
e.g., When the input is 2-D, the output is a scalar(shape is empty: `[]`).

### Inputs

- **X** (heterogeneous) - **T**:

  Input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  Output tensor

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to floating-point tensors.