# Source code for jax._src.lax.other

```
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Optional, Sequence, Tuple, Union
from jax._src.numpy import lax_numpy as jnp
from jax._src.util import prod
from . import lax
DType = Any
[docs]def conv_general_dilated_patches(
lhs: lax.Array,
filter_shape: Sequence[int],
window_strides: Sequence[int],
padding: Union[str, Sequence[Tuple[int, int]]],
lhs_dilation: Sequence[int] = None,
rhs_dilation: Sequence[int] = None,
dimension_numbers: lax.ConvGeneralDilatedDimensionNumbers = None,
precision: lax.PrecisionType = None,
preferred_element_type: Optional[DType] = None,
) -> lax.Array:
"""Extract patches subject to the receptive field of `conv_general_dilated`.
Runs the input through a convolution with given parameters. The kernel of the
convolution is constructed such that the output channel dimension `"C"`
contains flattened image patches, so instead a single `"C"` dimension
represents, for example, three dimensions `"chw"` collapsed. The order of
these dimensions is `"c" + ''.join(c for c in rhs_spec if c not in 'OI')`,
where `rhs_spec == dimension_numbers[1]`, and the size of this `"C"`
dimension is therefore the size of each patch, i.e.
`np.prod(filter_shape) * lhs.shape[lhs_spec.index('C')]`, where
`lhs_spec == dimension_numbers[0]`.
Docstring below adapted from `jax.lax.conv_general_dilated`.
See Also:
https://www.tensorflow.org/xla/operation_semantics#conv_convolution
Args:
lhs: a rank `n+2` dimensional input array.
filter_shape: a sequence of `n` integers, representing the receptive window
spatial shape in the order as specified in
`rhs_spec = dimension_numbers[1]`.
window_strides: a sequence of `n` integers, representing the inter-window
strides.
padding: either the string `'SAME'`, the string `'VALID'`, or a sequence of
`n` `(low, high)` integer pairs that give the padding to apply before and
after each spatial dimension.
lhs_dilation: `None`, or a sequence of `n` integers, giving the
dilation factor to apply in each spatial dimension of `lhs`. LHS dilation
is also known as transposed convolution.
rhs_dilation: `None`, or a sequence of `n` integers, giving the
dilation factor to apply in each spatial dimension of `rhs`. RHS dilation
is also known as atrous convolution.
dimension_numbers: either `None`, or a 3-tuple
`(lhs_spec, rhs_spec, out_spec)`, where each element is a string
of length `n+2`. `None` defaults to `("NCHWD..., OIHWD..., NCHWD...")`.
precision: Optional. Either ``None``, which means the default precision for
the backend, or a ``lax.Precision`` enum value (``Precision.DEFAULT``,
``Precision.HIGH`` or ``Precision.HIGHEST``).
preferred_element_type: Optional. Either ``None``, which means the default
accumulation type for the input types, or a datatype, indicating to
accumulate results to and return a result with that datatype.
Returns:
A rank `n+2` array containing the flattened image patches in the output
channel (`"C"`) dimension. For example if
`dimension_numbers = ("NcHW", "OIwh", "CNHW")`, the output has dimension
numbers `"CNHW" = "{cwh}NHW"`, with the size of dimension `"C"` equal to
the size of each patch
(`np.prod(filter_shape) * lhs.shape[lhs_spec.index('C')]`).
"""
filter_shape = tuple(filter_shape)
dimension_numbers = lax.conv_dimension_numbers(
lhs.shape, (1, 1) + filter_shape, dimension_numbers)
lhs_spec, rhs_spec, out_spec = dimension_numbers
spatial_size = prod(filter_shape)
n_channels = lhs.shape[lhs_spec[1]]
# Move separate `lhs` spatial locations into separate `rhs` channels.
rhs = jnp.eye(spatial_size, dtype=lhs.dtype).reshape(filter_shape * 2)
rhs = rhs.reshape((spatial_size, 1) + filter_shape)
rhs = jnp.tile(rhs, (n_channels,) + (1,) * (rhs.ndim - 1))
rhs = jnp.moveaxis(rhs, (0, 1), (rhs_spec[0], rhs_spec[1]))
out = lax.conv_general_dilated(
lhs=lhs,
rhs=rhs,
window_strides=window_strides,
padding=padding,
lhs_dilation=lhs_dilation,
rhs_dilation=rhs_dilation,
dimension_numbers=dimension_numbers,
precision=None if precision is None else (precision,
lax.Precision.DEFAULT),
feature_group_count=n_channels,
preferred_element_type=preferred_element_type
)
return out
```