tract_gpu/ops/
resize.rs

1use crate::tensor::{DeviceTensor, DeviceTensorExt, IntoDevice};
2use derive_new::new;
3use tract_core::internal::*;
4
5/// Resamples one axis: `output[.., x, ..] = sum_k weights[x, k] * input[.., indices[x, k], ..]`,
6/// with `indices` already clamped into the axis by the host-built plan.
7pub type DispatchResizeAxisFn = fn(
8    input: &DeviceTensor,
9    axis: usize,
10    indices: &DeviceTensor,
11    weights: &DeviceTensor,
12    window: usize,
13    output: &DeviceTensor,
14) -> TractResult<()>;
15
16/// Resize against a plan baked at translation time, one dispatch per resampled
17/// axis. The plan makes the op independent of the interpolator: nearest, linear
18/// and cubic differ only in window size and weights, so translation is limited
19/// to nodes whose shapes and scales are known then. The scales/sizes input is
20/// kept for arity but no longer read.
21#[derive(Clone, new)]
22pub struct GpuResize {
23    pub axes: TVec<usize>,
24    pub windows: TVec<usize>,
25    pub plans: TVec<(Arc<Tensor>, Arc<Tensor>)>,
26    pub output_shape: TVec<usize>,
27    pub backend_name: &'static str,
28    pub dispatch: DispatchResizeAxisFn,
29}
30
31impl std::fmt::Debug for GpuResize {
32    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
33        write!(f, "{}Resize", self.backend_name)
34    }
35}
36
37impl PartialEq for GpuResize {
38    fn eq(&self, other: &Self) -> bool {
39        self.backend_name == other.backend_name
40            && self.axes == other.axes
41            && self.windows == other.windows
42            && self.output_shape == other.output_shape
43    }
44}
45impl Eq for GpuResize {}
46
47impl std::hash::Hash for GpuResize {
48    fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
49        self.backend_name.hash(state);
50        self.axes.hash(state);
51        self.windows.hash(state);
52        self.output_shape.hash(state);
53    }
54}
55
56impl Op for GpuResize {
57    fn name(&self) -> StaticName {
58        format!("{}Resize", self.backend_name).into()
59    }
60    fn info(&self) -> TractResult<Vec<String>> {
61        Ok(vec![format!("axes={:?} windows={:?}", self.axes, self.windows)])
62    }
63    op_as_typed_op!();
64}
65
66impl EvalOp for GpuResize {
67    op_out_of_plan!();
68
69    fn eval(&self, ctx: &EvalContext, inputs: TVec<TValue>) -> TractResult<TVec<TValue>> {
70        let data = inputs[0].to_device_tensor()?;
71        let dt = data.datum_type();
72        let mut shape: TVec<usize> = data.shape().into();
73        let mut current = data.clone();
74        for (step, (&axis, &window)) in self.axes.iter().zip(&self.windows).enumerate() {
75            let (indices, weights) = &self.plans[step];
76            let indices = indices.as_ref().clone().into_device()?;
77            let weights = weights.as_ref().clone().into_device()?;
78            shape[axis] = self.output_shape[axis];
79            let last = step + 1 == self.axes.len();
80            let output = if last {
81                crate::turn_handler::make_tensor_for_node(ctx, dt, &shape)?
82            } else {
83                DeviceTensor::uninitialized_dt(dt, &shape)?
84            };
85            (self.dispatch)(&current, axis, &indices, &weights, window, &output)?;
86            current = output;
87        }
88        Ok(tvec!(current.into_tensor().into_tvalue()))
89    }
90}
91
92impl TypedOp for GpuResize {
93    fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
94        crate::utils::facts_to_device_facts(inputs, |facts| {
95            ensure!(facts.len() == 1);
96            let shape: TVec<TDim> = self.output_shape.iter().map(|d| d.to_dim()).collect();
97            Ok(tvec!(facts[0].datum_type.fact(&shape)))
98        })
99        .with_context(|| format!("Error while computing facts for {:?}", self.name()))
100    }
101    as_op!();
102}
tract_gpu/ops/resize.rs

tract_gpu/ops/
resize.rs
