(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.

```{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.