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Some fixes for yolo-v3. (#529)

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* Some fixes for yolo-v3.

* Use the running stats for inference in the batch-norm layer.

* Get some proper predictions for yolo.

* Avoid the quadratic insertion.
This commit is contained in:
Laurent Mazare
2023-08-20 23:19:15 +01:00
committed by GitHub
parent a1812f934f
commit 11c7e7bd67
6 changed files with 144 additions and 53 deletions
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14
candle-nn/tests/batch_norm.rs
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@ -7,8 +7,8 @@ extern crate accelerate_src;
mod test_utils;
use anyhow::Result;
use candle::{Device, Tensor};
use candle_nn::{BatchNorm, Module};
use candle::{DType, Device, Tensor};
use candle_nn::BatchNorm;
/* The test below has been generated using the following PyTorch code:
import torch
@ -21,7 +21,9 @@ print(output.flatten())
*/
#[test]
fn batch_norm() -> Result<()> {
let bn = BatchNorm::new_no_bias(5, 1e-8)?;
let running_mean = Tensor::zeros(5, DType::F32, &Device::Cpu)?;
let running_var = Tensor::ones(5, DType::F32, &Device::Cpu)?;
let bn = BatchNorm::new_no_bias(5, running_mean.clone(), running_var.clone(), 1e-8)?;
let input: [f32; 120] = [
-0.7493, -1.0410, 1.6977, -0.6579, 1.7982, -0.0087, 0.2812, -0.1190, 0.2908, -0.5975,
-0.0278, -0.2138, -1.3130, -1.6048, -2.2028, 0.9452, 0.4002, 0.0831, 1.0004, 0.1860,
@ -37,7 +39,7 @@ fn batch_norm() -> Result<()> {
1.4252, -0.9115, -0.1093, -0.3100, -0.6734, -1.4357, 0.9205,
];
let input = Tensor::new(&input, &Device::Cpu)?.reshape((2, 5, 3, 4))?;
let output = bn.forward(&input)?;
let output = bn.forward_learning(&input)?;
assert_eq!(output.dims(), &[2, 5, 3, 4]);
let output = output.flatten_all()?;
assert_eq!(
@ -59,11 +61,13 @@ fn batch_norm() -> Result<()> {
);
let bn2 = BatchNorm::new(
5,
running_mean.clone(),
running_var.clone(),
Tensor::new(&[0.5f32], &Device::Cpu)?.broadcast_as(5)?,
Tensor::new(&[-1.5f32], &Device::Cpu)?.broadcast_as(5)?,
1e-8,
)?;
let output2 = bn2.forward(&input)?;
let output2 = bn2.forward_learning(&input)?;
assert_eq!(output2.dims(), &[2, 5, 3, 4]);
let output2 = output2.flatten_all()?;
let diff2 = ((output2 - (output * 0.5)?)? + 1.5)?.sqr()?;