> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete-ml/1.2/developer-guide/api/concrete.ml.onnx.onnx_impl_utils.md).

# concrete.ml.onnx.onnx\_impl\_utils.md

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/onnx/onnx_impl_utils.py#L0)

## module `concrete.ml.onnx.onnx_impl_utils`

Utility functions for onnx operator implementations.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/onnx/onnx_impl_utils.py#L12)

### function `numpy_onnx_pad`

```python
numpy_onnx_pad(
    x: ndarray,
    pads: Tuple[int, ],
    pad_value: Union[float, int, ndarray] = 0,
    int_only: bool = False
) → ndarray
```

Pad a tensor according to ONNX spec, using an optional custom pad value.

**Args:**

* `x` (numpy.ndarray): input tensor to pad
* `pads` (List\[int]): padding values according to ONNX spec
* `pad_value` (Optional\[Union\[float, int]]): value used to fill in padding, default 0
* `int_only` (bool): set to True to generate integer only code with Concrete

**Returns:**

* `res` (numpy.ndarray): the input tensor with padding applied

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/onnx/onnx_impl_utils.py#L66)

### function `compute_conv_output_dims`

```python
compute_conv_output_dims(
    input_shape: Tuple[int, ],
    kernel_shape: Tuple[int, ],
    pads: Tuple[int, ],
    strides: Tuple[int, ],
    ceil_mode: int
) → Tuple[int, ]
```

Compute the output shape of a pool or conv operation.

See <https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html> for details on the computation of the output shape.

**Args:**

* `input_shape` (Tuple\[int, ...]): shape of the input to be padded as N x C x H x W
* `kernel_shape` (Tuple\[int, ...]): shape of the conv or pool kernel, as Kh x Kw (or n-d)
* `pads` (Tuple\[int, ...]): padding values following ONNX spec: dim1\_start, dim2\_start, .. dimN\_start, dim1\_end, dim2\_end, ... dimN\_end where in the 2-d case dim1 is H, dim2 is W
* `strides` (Tuple\[int, ...]): strides for each dimension
* `ceil_mode` (int): set to 1 to use the `ceil` function to compute the output shape, as described in the PyTorch doc

**Returns:**

* `res` (Tuple\[int, ...]): shape of the output of a conv or pool operator with given parameters

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/onnx/onnx_impl_utils.py#L110)

### function `compute_onnx_pool_padding`

```python
compute_onnx_pool_padding(
    input_shape: Tuple[int, ],
    kernel_shape: Tuple[int, ],
    pads: Tuple[int, ],
    strides: Tuple[int, ],
    ceil_mode: int
) → Tuple[int, ]
```

Compute any additional padding needed to compute pooling layers.

The ONNX standard uses ceil\_mode=1 to match TensorFlow style pooling output computation. In this setting, the kernel can be placed at a valid position even though it contains values outside of the input shape including padding. The ceil\_mode parameter controls whether this mode is enabled. If the mode is not enabled, the output shape follows PyTorch rules.

**Args:**

* `input_shape` (Tuple\[int, ...]): shape of the input to be padded as N x C x H x W
* `kernel_shape` (Tuple\[int, ...]): shape of the conv or pool kernel, as Kh x Kw (or n-d)
* `pads` (Tuple\[int, ...]): padding values following ONNX spec: dim1\_start, dim2\_start, .. dimN\_start, dim1\_end, dim2\_end, ... dimN\_end where in the 2-d case dim1 is H, dim2 is W
* `strides` (Tuple\[int, ...]): strides for each dimension
* `ceil_mode` (int): set to 1 to use the `ceil` function to compute the output shape, as described in the PyTorch doc

**Returns:**

* `res` (Tuple\[int, ...]): shape of the output of a conv or pool operator with given parameters

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/onnx/onnx_impl_utils.py#L156)

### function `onnx_avgpool_compute_norm_const`

```python
onnx_avgpool_compute_norm_const(
    input_shape: Tuple[int, ],
    kernel_shape: Tuple[int, ],
    pads: Tuple[int, ],
    strides: Tuple[int, ],
    ceil_mode: int
) → Union[ndarray, float]
```

Compute the average pooling normalization constant.

This constant can be a tensor of the same shape as the input or a scalar.

**Args:**

* `input_shape` (Tuple\[int, ...]): shape of the input to be padded as N x C x H x W
* `kernel_shape` (Tuple\[int, ...]): shape of the conv or pool kernel, as Kh x Kw (or n-d)
* `pads` (Tuple\[int, ...]): padding values following ONNX spec: dim1\_start, dim2\_start, .. dimN\_start, dim1\_end, dim2\_end, ... dimN\_end where in the 2-d case dim1 is H, dim2 is W
* `strides` (Tuple\[int, ...]): strides for each dimension
* `ceil_mode` (int): set to 1 to use the `ceil` function to compute the output shape, as described in the PyTorch doc

**Returns:**

* `res` (float): tensor or scalar, corresponding to normalization factors to apply for the average pool computation for each valid kernel position


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