tf.compat.v1.nn.dilation2d | TensorFlow v2.16.1 (original) (raw)
tf.compat.v1.nn.dilation2d
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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 thefilter
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]. |
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). |
Returns |
---|
A Tensor. Has the same type as input. |