SpaceToDepth rearranges blocks of spatial data into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension.
Args
tensor(@Tensor<T>) - The input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.
blocksize(usize) - The size of the blocks to move along [blocksize, blocksize].
Returns
A Tensor<T> of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].
Examples
use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor};
use orion::operators::tensor::{I8Tensor, I8TensorAdd};
use orion::numbers::NumberTrait;
use orion::operators::nn::NNTrait;
use orion::operators::nn::I8NN;
use orion::numbers::FixedTrait;
fn space_to_depth_example() -> Tensor<i8> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(1);
shape.append(2);
shape.append(2);
shape.append(4);
let mut data = ArrayTrait::new();
data.append(-3);
data.append(0);
data.append(0);
data.append(0);
data.append(-1);
data.append(1);
data.append(-2);
data.append(-3);
data.append(2);
data.append(-2);
data.append(-3);
data.append(-3);
data.append(-1);
data.append(0);
data.append(1);
data.append(-3);
let tensor = TensorTrait::new(shape.span(), data.span());
return NNTrait::space_to_depth(@tensor, 2);
}
>>> [[[[-3, 0]], [[2, -3]], [[0, 0]], [[-2, -3]], [[-1, -2]], [[-1, 1]], [[1, -3]], [[0, -3]]]]
