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More testing for avg-pool2d. (#366)

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* More testing for avg-pool2d.

* Another fix.

* Add a max-pool test with non-divisible kernel sizes.
This commit is contained in:
Laurent Mazare
2023-08-09 17:12:23 +02:00
committed by GitHub
parent dece0b8a76
commit cd225bd3b1
2 changed files with 37 additions and 5 deletions
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4
candle-core/src/cpu_backend.rs
View File

@ -660,8 +660,8 @@ impl Map1 for AvgPool2D {
let mut sum = T::zero(); let mut sum = T::zero();
for m in 0..k_h { for m in 0..k_h {
for n in 0..k_w { for n in 0..k_w {
let m = k_h * h_idx + m; let m = s_h * h_idx + m;
let n = k_w * w_idx + n; let n = s_w * w_idx + n;
sum += src[src_index + m * stride_h + n * stride_w] sum += src[src_index + m * stride_h + n * stride_w]
} }
} }

38
candle-core/tests/pool_tests.rs
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@ -4,14 +4,46 @@ use candle_core::{Device, Tensor};
// https://github.com/huggingface/candle/issues/364 // https://github.com/huggingface/candle/issues/364
#[test] #[test]
fn avg_pool2d() -> anyhow::Result<()> { fn avg_pool2d() -> anyhow::Result<()> {
let device = Device::Cpu;
let data: Vec<f32> = vec![ let data: Vec<f32> = vec![
1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
]; ];
let t = Tensor::from_vec(data, (1, 1, 4, 4), &device)?; let t = Tensor::from_vec(data, (1, 1, 4, 4), &Device::Cpu)?;
let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?.squeeze(0)?; let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?.squeeze(0)?;
assert_eq!(pool.to_vec2::<f32>()?, [[0.5f32, 1.], [1., 1.]]); assert_eq!(pool.to_vec2::<f32>()?, [[0.5f32, 1.], [1., 1.]]);
Ok(()) Ok(())
} }
/* This test corresponds to the following PyTorch script.
import torch
torch.manual_seed(4242)
t = torch.randn((1, 2, 4, 4))
print(t.flatten())
res = torch.nn.functional.avg_pool2d(t, 2)
print(res)
*/
#[test]
fn avg_pool2d_pytorch() -> anyhow::Result<()> {
let t = Tensor::new(
&[
0.4056f32, -0.8689, -0.0773, -1.5630, -2.8012, -1.5059, 0.3972, 1.0852, 0.4997, 3.0616,
1.6541, 0.0964, -0.8338, -1.6523, -0.8323, -0.1699, 0.0823, 0.3526, 0.6843, 0.2395,
1.2279, -0.9287, -1.7030, 0.1370, 0.6047, 0.3770, -0.6266, 0.3529, 2.2013, -0.6836,
0.2477, 1.3127,
],
&Device::Cpu,
)?
.reshape((1, 2, 4, 4))?;
let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?;
assert_eq!(
test_utils::to_vec3_round(pool, 4)?,
[
[[-1.1926, -0.0395], [0.2688, 0.1871]],
[[0.1835, -0.1606], [0.6249, 0.3217]]
]
);
let pool = t.avg_pool2d((3, 3), (3, 3))?.squeeze(0)?;
assert_eq!(test_utils::to_vec3_round(pool, 4)?, [[[0.085]], [[0.0078]]]);
Ok(())
}