1 // Adapted from interp.cpp from Caffe util by Pauline Luc
2 // Originally developed by George Papandreou
3 #define TORCH_ASSERT_ONLY_METHOD_OPERATORS
4
5 #include <ATen/core/Tensor.h>
6 #include <ATen/TensorMeta.h>
7 #include <ATen/TensorUtils.h>
8 #include <ATen/native/UpSample.h>
9
10 #ifndef AT_PER_OPERATOR_HEADERS
11 #include <ATen/Functions.h>
12 #include <ATen/NativeFunctions.h>
13 #else
14 #include <ATen/ops/upsample_linear1d.h>
15 #include <ATen/ops/upsample_linear1d_backward.h>
16 #include <ATen/ops/upsample_linear1d_backward_native.h>
17 #include <ATen/ops/upsample_linear1d_native.h>
18 #endif
19
20 namespace at::meta {
21
TORCH_META_FUNC(upsample_linear1d)22 TORCH_META_FUNC(upsample_linear1d) (
23 const Tensor& input,
24 IntArrayRef output_size,
25 bool align_corners,
26 std::optional<double> scales
27 ) {
28 auto full_output_size = native::upsample_1d_common_check(input.sizes(), output_size);
29
30 // Allow for empty batch size but not other dimensions
31 TORCH_CHECK(
32 (input.size(1) != 0 && input.size(2) != 0) && input.dim() == 3,
33 "Non-empty 3D data tensor expected but got a tensor with sizes ",
34 input.sizes());
35
36 set_output_raw_strided(0, full_output_size, {}, input.options());
37 }
38
TORCH_META_FUNC(upsample_linear1d_backward)39 TORCH_META_FUNC(upsample_linear1d_backward) (
40 const Tensor& grad_output,
41 IntArrayRef output_size,
42 IntArrayRef input_size,
43 bool align_corners,
44 std::optional<double> scales
45 ) {
46 auto full_output_size = native::upsample_1d_common_check(input_size, output_size);
47
48 TORCH_CHECK(
49 input_size.size() == 3,
50 "It is expected input_size equals to 3, but got size ",
51 input_size.size());
52
53 check_dim_size(grad_output, 3, 0, full_output_size[0]);
54 check_dim_size(grad_output, 3, 1, full_output_size[1]);
55 check_dim_size(grad_output, 3, 2, full_output_size[2]);
56
57 set_output_raw_strided(0, input_size, {}, grad_output.options());
58 }
59
60 } // namespace at::meta
61
62 namespace at::native {
63
TORCH_IMPL_FUNC(upsample_linear1d_out_cpu)64 TORCH_IMPL_FUNC(upsample_linear1d_out_cpu) (
65 const Tensor& input,
66 IntArrayRef output_size,
67 bool align_corners,
68 std::optional<double> scales,
69 const Tensor& output
70 ) {
71 upsample_linear1d_kernel(kCPU, output, input, align_corners, scales);
72 }
73
TORCH_IMPL_FUNC(upsample_linear1d_backward_out_cpu)74 TORCH_IMPL_FUNC(upsample_linear1d_backward_out_cpu) (
75 const Tensor& grad_output,
76 IntArrayRef output_size,
77 IntArrayRef input_size,
78 bool align_corners,
79 std::optional<double> scales,
80 const Tensor& grad_input
81 ) {
82 grad_input.zero_();
83 upsample_linear1d_backward_kernel(kCPU, grad_input, grad_output, align_corners, scales);
84 }
85
86 // vec variants
87
88 using at::native::upsample::compute_output_size;
89 using at::native::upsample::get_scale_value;
90
upsample_linear1d(const Tensor & input,at::OptionalIntArrayRef output_size,bool align_corners,std::optional<ArrayRef<double>> scale_factors)91 Tensor upsample_linear1d(
92 const Tensor& input,
93 at::OptionalIntArrayRef output_size,
94 bool align_corners,
95 std::optional<ArrayRef<double>> scale_factors) {
96 auto osize = compute_output_size(input.sizes(), output_size, scale_factors);
97 auto scale_w = get_scale_value(scale_factors, 0);
98 return at::upsample_linear1d(input, osize, align_corners, scale_w);
99 }
100
101 DEFINE_DISPATCH(upsample_linear1d_kernel);
102 DEFINE_DISPATCH(upsample_linear1d_backward_kernel);
103
104 } // namespace at::native
105