# tf.compat.v1.nn.depthwise_conv2d_native

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/nn_ops.py#L2976-L3048) |

Computes a 2-D depthwise convolution.

    tf.compat.v1.nn.depthwise_conv2d_native(
        input,
        filter,
        strides,
        padding,
        data_format='NHWC',
        dilations=[1, 1, 1, 1],
        name=None
    )

Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
and a filter / kernel tensor of shape
`[filter_height, filter_width, in_channels, channel_multiplier]`, containing
`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
a different filter to each input channel (expanding from 1 channel to
`channel_multiplier` channels for each), then concatenates the results
together. Thus, the output has `in_channels * channel_multiplier` channels.

    for k in 0..in_channels-1
      for q in 0..channel_multiplier-1
        output[b, i, j, k * channel_multiplier + q] =
          sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
                            filter[di, dj, k, q]

Must have `strides[0] = strides[3] = 1`. For the most common case of the same
horizontal and vertices strides, `strides = [1, stride, stride, 1]`.

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| ## Args ||
|---|---|
| `input` | A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. |
| `filter` | A `Tensor`. Must have the same type as `input`. |
| `strides` | A list of `ints`. 1-D of length 4. The stride of the sliding window for each dimension of `input`. |
| `padding` | Controls how to pad the image before applying the convolution. Can be the string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`. |
| `data_format` | An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: \[batch, height, width, channels\]. Alternatively, the format could be "NCHW", the data storage order of: \[batch, channels, height, width\]. |
| `dilations` | An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k \> 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
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| A `Tensor`. Has the same type as `input`. ||

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