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Add some conv1d test + bugfix using padding. (#349)

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This commit is contained in:
Laurent Mazare
2023-08-08 21:50:20 +02:00
committed by GitHub
parent 1e6dbeac01
commit 608b2358c6
3 changed files with 47 additions and 21 deletions
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26
candle-core/examples/basics.rs
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@ -8,25 +8,11 @@ use anyhow::Result;
use candle_core::{Device, Tensor};
fn main() -> Result<()> {
let a = Tensor::randn(0f32, 1., (2, 3), &Device::Cpu)?;
let b = Tensor::randn(0f32, 1., (3, 4), &Device::Cpu)?;
let c = a.matmul(&b)?;
println!("{a} {b} {c}");
let data = &[[3f32, 1., 4., 1., 5.], [2., 7., 1., 8., 2.]];
let t1 = Tensor::new(data, &Device::Cpu)?;
let data2 = &[[5f32, 5., 5., 5., 5.], [2., 7., 1., 8., 2.]];
let t2 = Tensor::new(data2, &Device::Cpu)?;
assert_eq!(
Tensor::cat(&[&t1.t()?, &t2.t()?], 1)?
.t()?
.to_vec2::<f32>()?,
[
[3.0, 1.0, 4.0, 1.0, 5.0],
[2.0, 7.0, 1.0, 8.0, 2.0],
[5.0, 5.0, 5.0, 5.0, 5.0],
[2.0, 7.0, 1.0, 8.0, 2.0]
]
);
let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?;
let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?;
let start = std::time::Instant::now();
let res = inp.conv2d(&w, 0, 1);
println!("{:?}", start.elapsed());
println!("{res:?}");
Ok(())
}

2
candle-core/src/cpu_backend.rs
View File

@ -1011,7 +1011,7 @@ impl<'a> Map2 for Conv1D<'a> {
let dst_idx = dst_idx + dst_l;
let mut d = T::zero();
for offset in 0..p.k_size {
let src_l_plus = p.stride * dst_l + offset;
let src_l_plus = p.stride * dst_l + offset + k_over_2 - p.padding;
// inp[bidx, src_c_idx, dst_l + offset - k//2] * k[dst_c_idx, src_c_idx, offset]
if k_over_2 <= src_l_plus && src_l_plus < k_over_2 + p.l_in {
let src_l = src_l_plus - k_over_2;

40
candle-core/tests/conv_tests.rs
View File

@ -6,6 +6,46 @@ use candle_core::{Device, Tensor};
import torch
torch.manual_seed(4242)
t = torch.randn((1, 4, 5))
w = torch.randn((2, 4, 3))
print(t.flatten())
print(w.flatten())
res = torch.nn.functional.conv1d(t, w)
print(res.flatten())
*/
#[test]
fn conv1d() -> Result<()> {
let dev = &Device::Cpu;
let t = Tensor::new(
&[
0.4056f32, -0.8689, -0.0773, -1.5630, 1.2279, -0.9287, -1.7030, 0.1370, 0.1866, 0.4145,
1.8025, -0.1536, 2.2013, -0.6836, 0.2477, 1.3127, -0.6957, 0.3278, -1.0124, 0.5599,
],
dev,
)?
.reshape((1, 4, 5))?;
let w = Tensor::new(
&[
-0.8404f32, -0.3490, 0.0130, 1.3123, 0.1763, -1.9249, 1.4270, 0.9421, 0.8670, -0.7181,
-1.1111, 0.8869, -1.2429, 1.8357, 1.6052, -1.3844, 0.3951, -1.2036, 0.6686, 1.6261,
-0.6451, -0.0840, -1.4247, 0.5512,
],
dev,
)?
.reshape((2, 4, 3))?;
let res = t.conv1d(&w, 0, 1)?;
assert_eq!(res.dims(), [1, 2, 3]);
assert_eq!(
test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
[2.6357, -1.3336, 4.1393, -1.1784, 3.5675, 0.5069]
);
Ok(())
}
/* This test is based on the following script.
import torch
torch.manual_seed(4242)
t = torch.randn((1, 4, 5, 5))
w = torch.randn((2, 4, 3, 3))
print(t.flatten())