huggingface/candle
1
0
Fork 0
mirror of https://github.com/huggingface/candle.git synced 2025-06-22 12:28:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add a yolo-v3 example. (#528)

Browse Source 
* Add a couple functions required for yolo.

* Add the yolo-v3 example.

* Add minimum and maximum.

* Use the newly introduced maximum.

* Cuda support for min/max + add some testing.

* Allow for more tests to work with accelerate.

* Fix a typo.
This commit is contained in:
Laurent Mazare
2023-08-20 18:19:37 +01:00
committed by GitHub
parent e3d2786ffb
commit a1812f934f
24 changed files with 1497 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
candle-nn/examples/basic_optimizer.rs
View File

@ -1,3 +1,9 @@
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, Device, Result, Tensor};
use candle_nn::{linear, AdamW, Linear, Module, ParamsAdamW, VarBuilder, VarMap};

3
candle-nn/tests/batch_norm.rs
View File

@ -1,6 +1,9 @@
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
mod test_utils;
use anyhow::Result;

3
candle-nn/tests/group_norm.rs
View File

@ -21,6 +21,9 @@ print(group_norm(t, num_groups=3))
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use candle::{Device, Tensor};
use candle_nn::{GroupNorm, Module};

17
candle-nn/tests/layer_norm.rs
View File

@ -1,10 +1,15 @@
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use candle::{Device, Tensor};
use candle_nn::{LayerNorm, Module};
mod test_utils;
#[test]
fn layer_norm() -> Result<()> {
let device = &Device::Cpu;
@ -23,11 +28,11 @@ fn layer_norm() -> Result<()> {
let inp = Tensor::new(&[[[1f32, 2., 3.], [4., 5., 6.], [9., 8., 7.]]], device)?;
let res = ln.forward(&inp)?;
assert_eq!(
res.to_vec3::<f32>()?,
test_utils::to_vec3_round(res.clone(), 4)?,
[[
[-3.1742344, 0.5, 4.1742344],
[-3.1742344, 0.5, 4.1742344],
[4.1742344, 0.5, -3.1742344]
[-3.1742, 0.5, 4.1742],
[-3.1742, 0.5, 4.1742],
[4.1742, 0.5, -3.1742]
]]
);
let mean = (res.sum_keepdim(2)? / 3.0)?;
@ -36,8 +41,8 @@ fn layer_norm() -> Result<()> {
let std = (res.broadcast_sub(&mean)?.sqr()?.sum_keepdim(2)?.sqrt()? / 3.0)?;
// The standard deviation should be sqrt(`w`).
assert_eq!(
std.to_vec3::<f32>()?,
[[[1.7320508], [1.7320508], [1.7320508]]]
test_utils::to_vec3_round(std, 4)?,
[[[1.7321], [1.7321], [1.7321]]]
);
Ok(())
}

6
candle-nn/tests/loss.rs
View File

@ -1,3 +1,9 @@
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{Device, Result, Tensor};
mod test_utils;
use test_utils::to_vec0_round;

3
candle-nn/tests/ops.rs
View File

@ -1,6 +1,9 @@
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
mod test_utils;
use test_utils::to_vec3_round;

3
candle-nn/tests/optim.rs
View File

@ -1,6 +1,9 @@
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
mod test_utils;
use test_utils::{to_vec0_round, to_vec2_round};