# tf.nn.atrous_conv2d

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_ops.py#L1785-L1933) |

Atrous convolution (a.k.a. convolution with holes or dilated convolution).

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.nn.atrous_conv2d`](https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d)

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    tf.nn.atrous_conv2d(
        value, filters, rate, padding, name=None
    )

This function is a simpler wrapper around the more general
[`tf.nn.convolution`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/nn/convolution), and exists only for backwards compatibility. You can
use [`tf.nn.convolution`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/nn/convolution) to perform 1-D, 2-D, or 3-D atrous convolution.

Computes a 2-D atrous convolution, also known as convolution with holes or
dilated convolution, given 4-D `value` and `filters` tensors. If the `rate`
parameter is equal to one, it performs regular 2-D convolution. If the `rate`
parameter is greater than one, it performs convolution with holes, sampling
the input values every `rate` pixels in the `height` and `width` dimensions.
This is equivalent to convolving the input with a set of upsampled filters,
produced by inserting `rate - 1` zeros between two consecutive values of the
filters along the `height` and `width` dimensions, hence the name atrous
convolution or convolution with holes (the French word trous means holes in
English).

#### More specifically:

    output[batch, height, width, out_channel] =
        sum_{dheight, dwidth, in_channel} (
            filters[dheight, dwidth, in_channel, out_channel] *
            value[batch, height + rate*dheight, width + rate*dwidth, in_channel]
        )

Atrous convolution allows us to explicitly control how densely to compute
feature responses in fully convolutional networks. Used in conjunction with
bilinear interpolation, it offers an alternative to `conv2d_transpose` in
dense prediction tasks such as semantic image segmentation, optical flow
computation, or depth estimation. It also allows us to effectively enlarge
the field of view of filters without increasing the number of parameters or
the amount of computation.

For a description of atrous convolution and how it can be used for dense
feature extraction, please see: (Chen et al., 2015). The same operation is
investigated further in (Yu et al., 2016). Previous works that effectively
use atrous convolution in different ways are, among others,
(Sermanet et al., 2014) and (Giusti et al., 2013).
Atrous convolution is also closely related to the so-called noble identities
in multi-rate signal processing.

There are many different ways to implement atrous convolution (see the refs
above). The implementation here reduces

    atrous_conv2d(value, filters, rate, padding=padding)

to the following three operations:

    paddings = ...
    net = space_to_batch(value, paddings, block_size=rate)
    net = conv2d(net, filters, strides=[1, 1, 1, 1], padding="VALID")
    crops = ...
    net = batch_to_space(net, crops, block_size=rate)

Advanced usage. Note the following optimization: A sequence of `atrous_conv2d`
operations with identical `rate` parameters, 'SAME' `padding`, and filters
with odd heights/ widths:

    net = atrous_conv2d(net, filters1, rate, padding="SAME")
    net = atrous_conv2d(net, filters2, rate, padding="SAME")
    ...
    net = atrous_conv2d(net, filtersK, rate, padding="SAME")

can be equivalently performed cheaper in terms of computation and memory as:

    pad = ...  # padding so that the input dims are multiples of rate
    net = space_to_batch(net, paddings=pad, block_size=rate)
    net = conv2d(net, filters1, strides=[1, 1, 1, 1], padding="SAME")
    net = conv2d(net, filters2, strides=[1, 1, 1, 1], padding="SAME")
    ...
    net = conv2d(net, filtersK, strides=[1, 1, 1, 1], padding="SAME")
    net = batch_to_space(net, crops=pad, block_size=rate)

because a pair of consecutive `space_to_batch` and `batch_to_space` ops with
the same `block_size` cancel out when their respective `paddings` and `crops`
inputs are identical.

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| ## Args ||
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| `value` | A 4-D `Tensor` of type `float`. It needs to be in the default "NHWC" format. Its shape is `[batch, in_height, in_width, in_channels]`. |
| `filters` | A 4-D `Tensor` with the same type as `value` and shape `[filter_height, filter_width, in_channels, out_channels]`. `filters`' `in_channels` dimension must match that of `value`. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height `filter_height + (filter_height - 1) * (rate - 1)` and effective width `filter_width + (filter_width - 1) * (rate - 1)`, produced by inserting `rate - 1` zeros along consecutive elements across the `filters`' spatial dimensions. |
| `rate` | A positive int32. The stride with which we sample input values across the `height` and `width` dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the `height` and `width` dimensions. In the literature, the same parameter is sometimes called `input stride` or `dilation`. |
| `padding` | A string, either `'VALID'` or `'SAME'`. The padding algorithm. See [here](https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2) for more information. |
| `name` | Optional name for the returned tensor. |

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| ## Returns ||
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| A `Tensor` with the same type as `value`. Output shape with `'VALID'` padding is: <br /> [batch, height - rate * (filter_width - 1), width - rate * (filter_height - 1), out_channels]. Output shape with `'SAME'` padding is: [batch, height, width, out_channels]. <br /> ||

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| ## Raises ||
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| `ValueError` | If input/output depth does not match `filters`' shape, or if padding is other than `'VALID'` or `'SAME'`. |

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| ## References ||
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| Multi-Scale Context Aggregation by Dilated Convolutions: [Yu et al., 2016](https://arxiv.org/abs/1511.07122) ([pdf](https://arxiv.org/pdf/1511.07122.pdf)) Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs: [Chen et al., 2015](http://arxiv.org/abs/1412.7062) ([pdf](https://arxiv.org/pdf/1412.7062)) OverFeat - Integrated Recognition, Localization and Detection using Convolutional Networks: [Sermanet et al., 2014](https://arxiv.org/abs/1312.6229) ([pdf](https://arxiv.org/pdf/1312.6229.pdf)) Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks: [Giusti et al., 2013](https://ieeexplore.ieee.org/abstract/document/6738831) ([pdf](https://arxiv.org/pdf/1302.1700.pdf)) ||

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