TensorFlow 2.0 Beta is available

tf.nn.depthwise_conv2d_native

Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.

Aliases:

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

Defined in generated file: `python/ops/gen_nn_ops.py`

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]`.

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`: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use.
• `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).

Returns:

A `Tensor`. Has the same type as `input`.