forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathLerp.cpp
111 lines (100 loc) · 4.2 KB
/
Lerp.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
#include <ATen/ATen.h>
#include <ATen/CPUApplyUtils.h>
#include <ATen/NativeFunctions.h>
#include <ATen/Dispatch.h>
#include <ATen/ExpandUtils.h>
namespace {
template <typename scalar_t>
void lerp_cpu(at::Tensor& ret, const at::Tensor& self, const at::Tensor& end, const at::Tensor& weight) {
at::CPU_tensor_apply4<scalar_t, scalar_t, scalar_t, scalar_t>(
ret, self, end, weight,
[](scalar_t& ret_val,
const scalar_t& self_val,
const scalar_t& end_val,
const scalar_t& weight_val) {
ret_val = (weight_val < 0.5) ?
self_val + weight_val * (end_val - self_val) : end_val - (end_val - self_val) * (1 - weight_val);
});
}
template <typename scalar_t>
void lerp_cpu(at::Tensor& ret, const at::Tensor& self, const at::Tensor& end, scalar_t weight_val) {
at::CPU_tensor_apply3<scalar_t, scalar_t, scalar_t>(
ret, self, end,
[=](scalar_t& ret_val,
const scalar_t& self_val,
const scalar_t& end_val) {
ret_val = (weight_val < 0.5) ?
self_val + weight_val * (end_val - self_val) : end_val - (end_val - self_val) * (1 - weight_val);
});
}
} // namespace
namespace at {
namespace native {
Tensor& lerp_cpu_tensor_out(Tensor& result, const Tensor& self,
const Tensor& end, const Tensor& weight) {
Tensor b_self, b_end, b_weight;
AT_CHECK(weight.dim() <= std::max(self.dim(), end.dim()),
"weight should be of dimension max(self.dim(), end.dim()) or lesser");
std::tie(b_self, b_end, b_weight) = expand_outplace(self, end, weight, "lerp_out_cpu");
result.resize_as_(b_self);
AT_DISPATCH_FLOATING_TYPES(self.scalar_type(), "lerp_out_cpu", [&]{
lerp_cpu<scalar_t>(result, b_self, b_end, b_weight);
});
return result;
}
Tensor& lerp_cpu_scalar_out(Tensor& result, const Tensor& self,
const Tensor& end, Scalar weight) {
Tensor b_self, b_end;
std::tie(b_self, b_end) = expand_outplace(self, end, "lerp_out_cpu");
result.resize_as_(b_self);
AT_DISPATCH_FLOATING_TYPES(self.scalar_type(), "lerp_out_cpu", [&]{
lerp_cpu<scalar_t>(result, b_self, b_end, weight.to<scalar_t>());
});
return result;
}
Tensor& lerp_cpu_tensor_(Tensor& self, const Tensor& end, const Tensor& weight) {
Tensor b_self, b_end, b_weight;
std::tie(b_self, b_end, b_weight) = expand_outplace(self, end, weight, "lerp__cpu");
AT_CHECK(b_self.sizes() == self.sizes(),
"output with shape ", self.sizes(),
" doesn't match the broadcast shape ", b_self.sizes());
AT_CHECK(weight.dim() <= std::max(self.dim(), end.dim()),
"weight should be of dimension max(self.dim(), end.dim()) or lesser");
AT_DISPATCH_FLOATING_TYPES(self.scalar_type(), "lerp__cpu", [&]{
lerp_cpu<scalar_t>(self, b_self, b_end, b_weight);
});
return self;
}
Tensor& lerp_cpu_scalar_(Tensor& self, const Tensor& end, Scalar weight) {
Tensor b_self, b_end;
std::tie(b_self, b_end) = expand_outplace(self, end, "lerp__cpu");
AT_CHECK(b_self.sizes() == self.sizes(),
"output with shape ", self.sizes(),
" doesn't match the broadcast shape ", b_self.sizes());
AT_DISPATCH_FLOATING_TYPES(self.scalar_type(), "lerp__cpu", [&]{
lerp_cpu<scalar_t>(self, b_self, b_end, weight.to<scalar_t>());
});
return self;
}
Tensor lerp_cpu_tensor(const Tensor& self, const Tensor& end, const Tensor& weight) {
Tensor b_self, b_end, b_weight;
AT_CHECK(weight.dim() <= std::max(self.dim(), end.dim()),
"weight should be of dimension max(self.dim(), end.dim()) or lesser");
std::tie(b_self, b_end, b_weight) = expand_outplace(self, end, weight, "lerp_cpu");
Tensor result = at::empty_like(b_self);
AT_DISPATCH_FLOATING_TYPES(result.scalar_type(), "lerp_cpu", [&]{
lerp_cpu<scalar_t>(result, b_self, b_end, b_weight);
});
return result;
}
Tensor lerp_cpu_scalar(const Tensor& self, const Tensor& end, Scalar weight) {
Tensor b_self, b_end;
std::tie(b_self, b_end) = expand_outplace(self, end, "lerp_cpu");
Tensor result = at::empty_like(b_self);
AT_DISPATCH_FLOATING_TYPES(result.scalar_type(), "lerp_cpu", [&]{
lerp_cpu<scalar_t>(result, b_self, b_end, weight.to<scalar_t>());
});
return result;
}
} // namespace native
} // namespace at