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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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5
candle-core/src/tensor.rs
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@ -497,10 +497,7 @@ impl Tensor {
let repeats = shape.into();
let repeats = repeats.dims();
let mut inp = if self.rank() < repeats.len() {
let mut shape = self.dims().to_vec();
while shape.len() < repeats.len() {
shape.push(1)
}
let shape = [vec![1; repeats.len() - self.rank()], self.dims().to_vec()].concat();
self.reshape(shape)?
} else {
self.clone()

21
candle-examples/examples/yolo-v3/darknet.rs
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@ -1,4 +1,4 @@
use candle::{Device, IndexOp, Result, Tensor};
use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{batch_norm, conv2d, conv2d_no_bias, Func, Module, VarBuilder};
use std::collections::BTreeMap;
use std::fs::File;
@ -145,11 +145,12 @@ fn conv(vb: VarBuilder, index: usize, p: usize, b: &Block) -> Result<(usize, Bl)
Some(bn) => bn.forward(&xs)?,
None => xs,
};
if leaky {
xs.maximum(&(&xs * 0.1)?)
let xs = if leaky {
xs.maximum(&(&xs * 0.1)?)?
} else {
Ok(xs)
}
xs
};
Ok(xs)
});
Ok((filters, Bl::Layer(Box::new(func))))
}
@ -225,12 +226,13 @@ fn detect(
let grid = Tensor::arange(0u32, grid_size as u32, &Device::Cpu)?;
let a = grid.repeat((grid_size, 1))?;
let b = a.t()?.contiguous()?;
let x_offset = a.unsqueeze(1)?;
let y_offset = b.unsqueeze(1)?;
let x_offset = a.flatten_all()?.unsqueeze(1)?;
let y_offset = b.flatten_all()?.unsqueeze(1)?;
let xy_offset = Tensor::cat(&[&x_offset, &y_offset], 1)?
.repeat((1, nanchors))?
.reshape((grid_size * grid_size * nanchors, 2))?
.unsqueeze(0)?;
.unsqueeze(0)?
.to_dtype(DType::F32)?;
let anchors: Vec<f32> = anchors
.iter()
.flat_map(|&(x, y)| vec![x as f32 / stride as f32, y as f32 / stride as f32].into_iter())
@ -245,7 +247,8 @@ fn detect(
let ys02 = (candle_nn::ops::sigmoid(&ys02)?.add(&xy_offset)? * stride as f64)?;
let ys24 = (ys24.exp()?.mul(&anchors)? * stride as f64)?;
let ys4 = candle_nn::ops::sigmoid(&ys4)?;
Tensor::cat(&[ys02, ys24, ys4], 2)
let ys = Tensor::cat(&[ys02, ys24, ys4], 2)?;
Ok(ys)
}
impl Darknet {

7
candle-examples/examples/yolo-v3/extract-weights.py Normal file
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@ -0,0 +1,7 @@
def remove_prefix(text, prefix):
return text[text.startswith(prefix) and len(prefix):]
nps = {}
for k, v in model.state_dict().items():
k = remove_prefix(k, 'module_list.')
nps[k] = v.detach().numpy()
np.savez('yolo-v3.ot', **nps)

97
candle-examples/examples/yolo-v3/main.rs
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@ -11,22 +11,22 @@ use anyhow::Result;
use candle::{DType, Device, Tensor};
use candle_nn::{Module, VarBuilder};
use clap::Parser;
use image::{DynamicImage, ImageBuffer};
const CONFIG_NAME: &str = "candle-examples/examples/yolo/yolo-v3.cfg";
const CONFIDENCE_THRESHOLD: f64 = 0.5;
const NMS_THRESHOLD: f64 = 0.4;
const CONFIDENCE_THRESHOLD: f32 = 0.5;
const NMS_THRESHOLD: f32 = 0.4;
#[derive(Debug, Clone, Copy)]
struct Bbox {
xmin: f64,
ymin: f64,
xmax: f64,
ymax: f64,
confidence: f64,
xmin: f32,
ymin: f32,
xmax: f32,
ymax: f32,
confidence: f32,
}
// Intersection over union of two bounding boxes.
fn iou(b1: &Bbox, b2: &Bbox) -> f64 {
fn iou(b1: &Bbox, b2: &Bbox) -> f32 {
let b1_area = (b1.xmax - b1.xmin + 1.) * (b1.ymax - b1.ymin + 1.);
let b2_area = (b2.xmax - b2.xmin + 1.) * (b2.ymax - b2.ymin + 1.);
let i_xmin = b1.xmin.max(b2.xmin);
@ -38,18 +38,35 @@ fn iou(b1: &Bbox, b2: &Bbox) -> f64 {
}
// Assumes x1 <= x2 and y1 <= y2
pub fn draw_rect(_: &mut Tensor, _x1: usize, _x2: usize, _y1: usize, _y2: usize) {
todo!()
pub fn draw_rect(
img: &mut ImageBuffer<image::Rgb<u8>, Vec<u8>>,
x1: u32,
x2: u32,
y1: u32,
y2: u32,
) {
for x in x1..=x2 {
let pixel = img.get_pixel_mut(x, y1);
*pixel = image::Rgb([255, 0, 0]);
let pixel = img.get_pixel_mut(x, y2);
*pixel = image::Rgb([255, 0, 0]);
}
for y in y1..=y2 {
let pixel = img.get_pixel_mut(x1, y);
*pixel = image::Rgb([255, 0, 0]);
let pixel = img.get_pixel_mut(x2, y);
*pixel = image::Rgb([255, 0, 0]);
}
}
pub fn report(pred: &Tensor, img: &Tensor, w: usize, h: usize) -> Result<Tensor> {
pub fn report(pred: &Tensor, img: DynamicImage, w: usize, h: usize) -> Result<DynamicImage> {
let (npreds, pred_size) = pred.dims2()?;
let nclasses = pred_size - 5;
// The bounding boxes grouped by (maximum) class index.
let mut bboxes: Vec<Vec<Bbox>> = (0..nclasses).map(|_| vec![]).collect();
// Extract the bounding boxes for which confidence is above the threshold.
for index in 0..npreds {
let pred = Vec::<f64>::try_from(pred.get(index)?)?;
let pred = Vec::<f32>::try_from(pred.get(index)?)?;
let confidence = pred[4];
if confidence > CONFIDENCE_THRESHOLD {
let mut class_index = 0;
@ -91,24 +108,21 @@ pub fn report(pred: &Tensor, img: &Tensor, w: usize, h: usize) -> Result<Tensor>
bboxes_for_class.truncate(current_index);
}
// Annotate the original image and print boxes information.
let (_, initial_h, initial_w) = img.dims3()?;
let mut img = (img.to_dtype(DType::F32)? * (1. / 255.))?;
let w_ratio = initial_w as f64 / w as f64;
let h_ratio = initial_h as f64 / h as f64;
let (initial_h, initial_w) = (img.height(), img.width());
let w_ratio = initial_w as f32 / w as f32;
let h_ratio = initial_h as f32 / h as f32;
let mut img = img.to_rgb8();
for (class_index, bboxes_for_class) in bboxes.iter().enumerate() {
for b in bboxes_for_class.iter() {
println!("{}: {:?}", coco_classes::NAMES[class_index], b);
let xmin = ((b.xmin * w_ratio) as usize).clamp(0, initial_w - 1);
let ymin = ((b.ymin * h_ratio) as usize).clamp(0, initial_h - 1);
let xmax = ((b.xmax * w_ratio) as usize).clamp(0, initial_w - 1);
let ymax = ((b.ymax * h_ratio) as usize).clamp(0, initial_h - 1);
draw_rect(&mut img, xmin, xmax, ymin, ymax.min(ymin + 2));
draw_rect(&mut img, xmin, xmax, ymin.max(ymax - 2), ymax);
draw_rect(&mut img, xmin, xmax.min(xmin + 2), ymin, ymax);
draw_rect(&mut img, xmin.max(xmax - 2), xmax, ymin, ymax);
let xmin = ((b.xmin * w_ratio) as u32).clamp(0, initial_w - 1);
let ymin = ((b.ymin * h_ratio) as u32).clamp(0, initial_h - 1);
let xmax = ((b.xmax * w_ratio) as u32).clamp(0, initial_w - 1);
let ymax = ((b.ymax * h_ratio) as u32).clamp(0, initial_h - 1);
draw_rect(&mut img, xmin, xmax, ymin, ymax);
}
}
Ok((img * 255.)?.to_dtype(DType::U8)?)
Ok(DynamicImage::ImageRgb8(img))
}
#[derive(Parser, Debug)]
@ -118,6 +132,9 @@ struct Args {
#[arg(long)]
model: String,
#[arg(long)]
config: String,
images: Vec<String>,
}
@ -128,18 +145,36 @@ pub fn main() -> Result<()> {
let weights = unsafe { candle::safetensors::MmapedFile::new(&args.model)? };
let weights = weights.deserialize()?;
let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &Device::Cpu);
let darknet = darknet::parse_config(CONFIG_NAME)?;
let darknet = darknet::parse_config(&args.config)?;
let model = darknet.build_model(vb)?;
for image in args.images.iter() {
for image_name in args.images.iter() {
println!("processing {image_name}");
let mut image_name = std::path::PathBuf::from(image_name);
// Load the image file and resize it.
let net_width = darknet.width()?;
let net_height = darknet.height()?;
let image = candle_examples::load_image_and_resize(image, net_width, net_height)?;
let original_image = image::io::Reader::open(&image_name)?
.decode()
.map_err(candle::Error::wrap)?;
let image = {
let data = original_image
.resize_exact(
net_width as u32,
net_height as u32,
image::imageops::FilterType::Triangle,
)
.to_rgb8()
.into_raw();
Tensor::from_vec(data, (net_width, net_height, 3), &Device::Cpu)?.permute((2, 0, 1))?
};
let image = (image.unsqueeze(0)?.to_dtype(DType::F32)? * (1. / 255.))?;
let predictions = model.forward(&image)?.squeeze(0)?;
let _image = report(&predictions, &image, net_width, net_height)?;
println!("converted {image}");
let image = report(&predictions, original_image, net_width, net_height)?;
image_name.set_extension("pp.jpg");
println!("writing {image_name:?}");
image.save(image_name)?
}
Ok(())
}

53
candle-nn/src/batch_norm.rs
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@ -40,6 +40,8 @@ impl From<f64> for BatchNormConfig {
#[derive(Debug)]
pub struct BatchNorm {
running_mean: Tensor,
running_var: Tensor,
weight_and_bias: Option<(Tensor, Tensor)>,
remove_mean: bool,
eps: f64,
@ -47,7 +49,14 @@ pub struct BatchNorm {
}
impl BatchNorm {
pub fn new(num_features: usize, weight: Tensor, bias: Tensor, eps: f64) -> Result<Self> {
pub fn new(
num_features: usize,
running_mean: Tensor,
running_var: Tensor,
weight: Tensor,
bias: Tensor,
eps: f64,
) -> Result<Self> {
if eps < 0. {
candle::bail!("batch-norm eps cannot be negative {eps}")
}
@ -64,6 +73,8 @@ impl BatchNorm {
)
}
Ok(Self {
running_mean,
running_var,
weight_and_bias: Some((weight, bias)),
remove_mean: true,
eps,
@ -71,11 +82,18 @@ impl BatchNorm {
})
}
pub fn new_no_bias(num_features: usize, eps: f64) -> Result<Self> {
pub fn new_no_bias(
num_features: usize,
running_mean: Tensor,
running_var: Tensor,
eps: f64,
) -> Result<Self> {
if eps < 0. {
candle::bail!("batch-norm eps cannot be negative {eps}")
}
Ok(Self {
running_mean,
running_var,
weight_and_bias: None,
remove_mean: true,
eps,
@ -84,8 +102,8 @@ impl BatchNorm {
}
}
impl crate::Module for BatchNorm {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
impl BatchNorm {
pub fn forward_learning(&self, x: &Tensor) -> Result<Tensor> {
let x_dtype = x.dtype();
let internal_dtype = match x_dtype {
DType::F16 | DType::BF16 => DType::F32,
@ -129,6 +147,29 @@ impl crate::Module for BatchNorm {
}
}
impl crate::Module for BatchNorm {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let target_shape: Vec<usize> = x
.dims()
.iter()
.enumerate()
.map(|(idx, v)| if idx == 1 { *v } else { 1 })
.collect();
let target_shape = target_shape.as_slice();
let x = x
.broadcast_sub(&self.running_mean.reshape(target_shape)?)?
.broadcast_div(&(self.running_var.reshape(target_shape)? + self.eps)?.sqrt()?)?;
match &self.weight_and_bias {
None => Ok(x),
Some((weight, bias)) => {
let weight = weight.reshape(target_shape)?;
let bias = bias.reshape(target_shape)?;
x.broadcast_mul(&weight)?.broadcast_add(&bias)
}
}
}
}
pub fn batch_norm<C: Into<BatchNormConfig>>(
num_features: usize,
config: C,
@ -138,6 +179,8 @@ pub fn batch_norm<C: Into<BatchNormConfig>>(
if config.eps < 0. {
candle::bail!("batch-norm eps cannot be negative {}", config.eps)
}
let running_mean = vb.get_or_init(num_features, "running_mean", crate::Init::Const(0.))?;
let running_var = vb.get_or_init(num_features, "running_var", crate::Init::Const(1.))?;
let weight_and_bias = if config.affine {
let weight = vb.get_or_init(num_features, "weight", crate::Init::Const(1.))?;
let bias = vb.get_or_init(num_features, "bias", crate::Init::Const(0.))?;
@ -146,6 +189,8 @@ pub fn batch_norm<C: Into<BatchNormConfig>>(
None
};
Ok(BatchNorm {
running_mean,
running_var,
weight_and_bias,
remove_mean: config.remove_mean,
eps: config.eps,

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()?;