tract_core/ops/downsample/
mod.rs1use crate::internal::*;
2use crate::ops;
3use ndarray::prelude::*;
4
5use super::identity::Identity;
6
7mod array;
8mod conv;
9mod scan;
10
11#[derive(Debug, Clone, new, Default, PartialEq, Eq, Hash)]
12pub struct Downsample {
13 pub axis: usize,
14 pub stride: isize,
15 pub modulo: usize,
16}
17
18impl Downsample {
19 pub(crate) fn transform_dim(&self, input_dim: &TDim) -> TDim {
20 (input_dim.clone() - self.modulo).div_ceil(self.stride.unsigned_abs() as u64)
21 }
22
23 pub(crate) fn transform_fact(&self, input_fact: &TypedFact) -> TractResult<TypedFact> {
24 let mut downed = input_fact.clone();
25 let down_len = self.transform_dim(&input_fact.shape[self.axis]);
26 downed.shape.set(self.axis, down_len);
27 if let Some(k) = downed.konst {
28 let mut outputs = self.eval(tvec!(k.into_tvalue()))?;
29 downed.konst = Some(outputs.remove(0).into_arc_tensor())
30 }
31 if cfg!(debug_assertions) {
32 downed.consistent()?;
33 }
34 Ok(downed)
35 }
36}
37
38impl Op for Downsample {
39 fn name(&self) -> Cow<str> {
40 "Downsample".into()
41 }
42
43 fn info(&self) -> TractResult<Vec<String>> {
44 Ok(vec![format!("axis:{} stride:{} modulo:{}", self.axis, self.stride, self.modulo)])
45 }
46
47 impl_op_same_as!();
48 op_as_typed_op!();
49}
50
51impl EvalOp for Downsample {
52 fn is_stateless(&self) -> bool {
53 true
54 }
55
56 fn eval(&self, inputs: TVec<TValue>) -> TractResult<TVec<TValue>> {
57 let input = args_1!(inputs);
58 unsafe {
59 let t = if self.modulo > input.shape()[self.axis] {
60 let mut shape: TVec<usize> = input.shape().into();
61 shape[self.axis] = 0;
62 Tensor::uninitialized_dt(input.datum_type(), &shape)?
63 } else {
64 let slice = ndarray::Slice::new(self.modulo as isize, None, self.stride);
65 unsafe fn do_slice<T: Datum>(
66 t: &Tensor,
67 axis: usize,
68 slice: ndarray::Slice,
69 ) -> Tensor {
70 let dt = t.datum_type();
71 let mut t2 = t
72 .to_array_view_unchecked::<T>()
73 .slice_axis(Axis(axis), slice)
74 .into_owned()
75 .into_tensor();
76 t2.set_datum_type(dt);
77 t2
78 }
79 dispatch_datum_by_size!(do_slice(input.datum_type())(&*input, self.axis, slice))
80 };
81 Ok(tvec!(t.into_tvalue()))
82 }
83 }
84}
85
86impl TypedOp for Downsample {
87 fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
88 ensure!(self.axis < inputs[0].rank());
89 ensure!(self.modulo == 0 || self.stride > 0, "non-zero modulo is only defined with forward strides");
90 let mut downed = inputs[0].clone();
91 let down_len = self.transform_dim(&downed.shape[self.axis]);
92 downed.shape.set(self.axis, down_len);
93 Ok(tvec!(downed))
94 }
95
96 fn declutter(
97 &self,
98 model: &TypedModel,
99 node: &TypedNode,
100 ) -> TractResult<Option<TypedModelPatch>> {
101 if self.stride == 1 {
102 return Ok(Some(TypedModelPatch::replace_single_op(
103 model,
104 node,
105 &node.inputs,
106 Identity,
107 )?));
108 }
109 pull_downsample_up(model, node)
110 .with_context(|| format!("Pulling {} over {}", node, model.node(node.inputs[0].node)))
111 }
112
113 as_op!();
114}
115
116fn pull_downsample_up(
117 model: &TypedModel,
118 down_node: &TypedNode,
119) -> TractResult<Option<TypedModelPatch>> {
120 model.check_consistency()?;
121 let down_op = down_node.op_as::<Downsample>().unwrap();
122 if let Some(prec) = model.single_prec(down_node.id)? {
123 let (input_facts, output_facts) = model.node_facts(prec.id)?;
124 let axes_mapping = prec.op.axes_mapping(&input_facts, &output_facts)?;
125 debug!("Consider pull {:?} over {:?} (invariants: {:?})", down_op, prec, axes_mapping);
126 if let Some(slice_op) = prec.op_as::<ops::array::Slice>() {
127 if let Some(p) =
128 array::pull_downsample_over_slice(model, prec, slice_op, down_node, down_op)?
129 {
130 return Ok(Some(p));
131 }
132 } else if let Some(other_op) = prec.op_as::<AxisOp>() {
133 return array::pull_downsample_over_axis_op(model, prec, other_op, down_node, down_op);
134 } else if let Some(conv_op) = prec.op_as::<ops::cnn::conv::Conv>() {
135 return conv::fuse_downsample_into_conv(model, prec, conv_op, down_node, down_op);
136 } else if let Some(other_op) = prec.op_as::<ops::scan::Scan>() {
137 return scan::pull_downsample_over_scan(model, prec, other_op, down_node, down_op);
138 }
139 if prec.outputs.len() > 1 || prec.inputs.len() == 0 {
140 return Ok(None);
141 }
142 let axis_info = axes_mapping.axis((InOut::Out(0), down_op.axis))?;
143 let mut patch = TypedModelPatch::default();
144 let mut inputs = vec![];
145 for (ix, (outlet, axis_info)) in prec.inputs.iter().zip(&axis_info.inputs).enumerate() {
146 let mut wire = patch.tap_model(model, *outlet)?;
147 if let &[axis] = &**axis_info {
148 if !patch.outlet_fact(wire)?.shape[axis].is_one() {
149 let mut op = down_op.clone();
150 op.axis = axis;
151 wire = patch.wire_node(
152 format!("{}.{}-{}", down_node.name, prec.name, ix),
153 op,
154 &[wire],
155 )?[0];
156 }
157 } else {
158 return Ok(None);
159 }
160 inputs.push(wire);
161 }
162 let other = patch.wire_node(&prec.name, prec.op.clone(), &inputs)?;
163 patch.shunt_outside(model, OutletId::new(down_node.id, 0), other[0])?;
164 return Ok(Some(patch));
165 }
166 Ok(None)
167}