tf.nn.dilation2d(
input,
filter,
strides,
rates,
padding,
name=None
)
Defined in generated file: tensorflow/python/ops/gen_nn_ops.py
.
Computes the grayscale dilation of 4-D input
and 3-D filter
tensors.
The input
tensor has shape [batch, in_height, in_width, depth]
and the
filter
tensor has shape [filter_height, filter_width, depth]
, i.e., each
input channel is processed independently of the others with its own structuring
function. The output
tensor has shape
[batch, out_height, out_width, depth]
. The spatial dimensions of the output
tensor depend on the padding
algorithm. We currently only support the default
"NHWC" data_format
.
In detail, the grayscale morphological 2-D dilation is the max-sum correlation
(for consistency with conv2d
, we use unmirrored filters):
output[b, y, x, c] =
max_{dy, dx} input[b,
strides[1] * y + rates[1] * dy,
strides[2] * x + rates[2] * dx,
c] +
filter[dy, dx, c]
Max-pooling is a special case when the filter has size equal to the pooling kernel size and contains all zeros.
Note on duality: The dilation of input
by the filter
is equal to the
negation of the erosion of -input
by the reflected filter
.
Args:
input
: ATensor
. Must be one of the following types:float32
,float64
,int32
,uint8
,int16
,int8
,int64
,bfloat16
,uint16
,half
,uint32
,uint64
. 4-D with shape[batch, in_height, in_width, depth]
.filter
: ATensor
. Must have the same type asinput
. 3-D with shape[filter_height, filter_width, depth]
.strides
: A list ofints
that has length>= 4
. The stride of the sliding window for each dimension of the input tensor. Must be:[1, stride_height, stride_width, 1]
.rates
: A list ofints
that has length>= 4
. The input stride for atrous morphological dilation. Must be:[1, rate_height, rate_width, 1]
.padding
: Astring
from:"SAME", "VALID"
. The type of padding algorithm to use.name
: A name for the operation (optional).
Returns:
A Tensor
. Has the same type as input
.