In signal processing, cross-correlation is a measure of similarity of
two waveforms as a function of a time-lag applied to one of them. This
is also known as a sliding dot product or sliding inner-product.
Our Conv3D implements a form of cross-correlation.
Args
input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
Shape [batch, in_depth, in_height, in_width, in_channels].
filter
A Tensor. Must have the same type as input.
Shape [filter_depth, filter_height, filter_width, in_channels,
out_channels]. in_channels must match between input and filter.
strides
A list of ints that has length >= 5.
1-D tensor of length 5. The stride of the sliding window for each
dimension of input. Must have strides[0] = strides[4] = 1.
padding
A string from: "SAME", "VALID".
The type of padding algorithm to use.
data_format
An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC".
The data format of the input and output data. With the
default format "NDHWC", the data is stored in the order of:
[batch, in_depth, in_height, in_width, in_channels].
Alternatively, the format could be "NCDHW", the data storage order is:
[batch, in_channels, in_depth, in_height, in_width].
dilations
An optional list of ints. Defaults to [1, 1, 1, 1, 1].
1-D tensor of length 5. The dilation factor for each dimension of
input. If set to k > 1, there will be k-1 skipped cells between each
filter element on that dimension. The dimension order is determined by the
value of data_format, see above for details. Dilations in the batch and
depth dimensions must be 1.