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
|
#include <gtest/gtest.h>
#include <torch/script.h>
#include <torch/torch.h>
// FIXME: the include path differs from OSS due to the extra csrc
#include <torchvision/csrc/ops/nms.h>
TEST(test_custom_operators, nms) {
// make sure that the torchvision ops are visible to the jit interpreter
auto& ops = torch::jit::getAllOperatorsFor(
torch::jit::Symbol::fromQualString("torchvision::nms"));
ASSERT_EQ(ops.size(), 1);
auto& op = ops.front();
ASSERT_EQ(op->schema().name(), "torchvision::nms");
torch::jit::Stack stack;
at::Tensor boxes = at::rand({50, 4}), scores = at::rand({50});
double thresh = 0.7;
torch::jit::push(stack, boxes, scores, thresh);
op->getOperation()(stack);
at::Tensor output_jit;
torch::jit::pop(stack, output_jit);
at::Tensor output = vision::ops::nms(boxes, scores, thresh);
ASSERT_TRUE(output_jit.allclose(output));
}
TEST(test_custom_operators, roi_align_visible) {
// make sure that the torchvision ops are visible to the jit interpreter even
// if not explicitly included
auto& ops = torch::jit::getAllOperatorsFor(
torch::jit::Symbol::fromQualString("torchvision::roi_align"));
ASSERT_EQ(ops.size(), 1);
auto& op = ops.front();
ASSERT_EQ(op->schema().name(), "torchvision::roi_align");
torch::jit::Stack stack;
float roi_data[] = {0., 0., 0., 5., 5., 0., 5., 5., 10., 10.};
at::Tensor input = at::rand({1, 2, 10, 10}),
rois = at::from_blob(roi_data, {2, 5});
double spatial_scale = 1.0;
int64_t pooled_height = 3, pooled_width = 3, sampling_ratio = -1;
bool aligned = true;
torch::jit::push(
stack,
input,
rois,
spatial_scale,
pooled_height,
pooled_width,
sampling_ratio,
aligned);
op->getOperation()(stack);
at::Tensor output_jit;
torch::jit::pop(stack, output_jit);
ASSERT_EQ(output_jit.sizes()[0], 2);
ASSERT_EQ(output_jit.sizes()[1], 2);
ASSERT_EQ(output_jit.sizes()[2], 3);
ASSERT_EQ(output_jit.sizes()[3], 3);
}
|
