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Computes the grayscale dilation of 4-D input
and 3-D filter
tensors.
tf.compat.v1.nn.dilation2d(
input, filter=None, strides=None, rates=None, padding=None, name=None,
filters=None, dilations=None
)
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
.
input
: A Tensor
. 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
: A Tensor
. Must have the same type as input
.
3-D with shape [filter_height, filter_width, depth]
.strides
: A list of ints
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 of ints
that has length >= 4
.
The input stride for atrous morphological dilation. Must be:
[1, rate_height, rate_width, 1]
.padding
: A string
from: "SAME", "VALID"
.
The type of padding algorithm to use.name
: A name for the operation (optional).A Tensor
. Has the same type as input
.