concrete.ml.onnx.onnx_impl_utils.md
module concrete.ml.onnx.onnx_impl_utils
concrete.ml.onnx.onnx_impl_utils
Utility functions for onnx operator implementations.
function numpy_onnx_pad
numpy_onnx_pad
Pad a tensor according to ONNX spec, using an optional custom pad value.
Args:
x
(numpy.ndarray): input tensor to padpads
(List[int]): padding values according to ONNX specpad_value
(Optional[Union[float, int]]): value used to fill in padding, default 0int_only
(bool): set to True to generate integer only code with Concrete
Returns:
res
(numpy.ndarray): the input tensor with padding applied
function compute_conv_output_dims
compute_conv_output_dims
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 Wkernel_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 Wstrides
(Tuple[int, ...]): strides for each dimensionceil_mode
(int): set to 1 to use theceil
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
function compute_onnx_pool_padding
compute_onnx_pool_padding
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 Wkernel_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 Wstrides
(Tuple[int, ...]): strides for each dimensionceil_mode
(int): set to 1 to use theceil
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
function onnx_avgpool_compute_norm_const
onnx_avgpool_compute_norm_const
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 Wkernel_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 Wstrides
(Tuple[int, ...]): strides for each dimensionceil_mode
(int): set to 1 to use theceil
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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