> 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/1.1/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/1.1.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/1.1.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/1.1.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 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/1.1.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/1.1.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 --- # Agent Instructions This documentation is published with GitBook. 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