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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.
Args | |
|---|---|
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].
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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].
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padding
|
A string from: "SAME", "VALID".
The type of padding algorithm to use.
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name
|
A name for the operation (optional). |
Returns | |
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A Tensor. Has the same type as input.
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