1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
|
### `tf.nn.dilation2d(input, filter, strides, rates, padding, name=None)` {#dilation2d}
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:
* <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
4-D with shape `[batch, in_height, in_width, depth]`.
* <b>`filter`</b>: A `Tensor`. Must have the same type as `input`.
3-D with shape `[filter_height, filter_width, depth]`.
* <b>`strides`</b>: 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]`.
* <b>`rates`</b>: A list of `ints` that has length `>= 4`.
The input stride for atrous morphological dilation. Must be:
`[1, rate_height, rate_width, 1]`.
* <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
The type of padding algorithm to use.
* <b>`name`</b>: A name for the operation (optional).
##### Returns:
A `Tensor`. Has the same type as `input`.
4-D with shape `[batch, out_height, out_width, depth]`.
|
