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Add Mobilenet v4 (#2325)

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* Support different resolutions in load_image()

* Added MobilenetV4 model.

* Add MobileNetv4 to README
This commit is contained in:
Jani Monoses
2024-07-09 14:52:20 +03:00
committed by GitHub
parent 25960676ca
commit a226a9736b
6 changed files with 939 additions and 18 deletions
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30
candle-examples/src/imagenet.rs
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@ -1,15 +1,16 @@
use candle::{Device, Result, Tensor};
/// Loads an image from disk using the image crate, this returns a tensor with shape
/// (3, 224, 224). imagenet normalization is applied.
pub fn load_image224<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
/// Loads an image from disk using the image crate at the requested resolution.
// This returns a tensor with shape (3, res, res). imagenet normalization is applied.
pub fn load_image<P: AsRef<std::path::Path>>(p: P, res: u32) -> Result<Tensor> {
let img = image::io::Reader::open(p)?
.decode()
.map_err(candle::Error::wrap)?
.resize_to_fill(224, 224, image::imageops::FilterType::Triangle);
.resize_to_fill(res, res, image::imageops::FilterType::Triangle);
let img = img.to_rgb8();
let data = img.into_raw();
let data = Tensor::from_vec(data, (224, 224, 3), &Device::Cpu)?.permute((2, 0, 1))?;
let data = Tensor::from_vec(data, (res as usize, res as usize, 3), &Device::Cpu)?
.permute((2, 0, 1))?;
let mean = Tensor::new(&[0.485f32, 0.456, 0.406], &Device::Cpu)?.reshape((3, 1, 1))?;
let std = Tensor::new(&[0.229f32, 0.224, 0.225], &Device::Cpu)?.reshape((3, 1, 1))?;
(data.to_dtype(candle::DType::F32)? / 255.)?
@ -17,22 +18,17 @@ pub fn load_image224<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
.broadcast_div(&std)
}
/// Loads an image from disk using the image crate, this returns a tensor with shape
/// (3, 224, 224). imagenet normalization is applied.
pub fn load_image224<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
load_image(p, 224)
}
/// Loads an image from disk using the image crate, this returns a tensor with shape
/// (3, 518, 518). imagenet normalization is applied.
/// The model dinov2 reg4 analyzes images with dimensions 3x518x518 (resulting in 37x37 transformer tokens).
pub fn load_image518<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
let img = image::io::Reader::open(p)?
.decode()
.map_err(candle::Error::wrap)?
.resize_to_fill(518, 518, image::imageops::FilterType::Triangle);
let img = img.to_rgb8();
let data = img.into_raw();
let data = Tensor::from_vec(data, (518, 518, 3), &Device::Cpu)?.permute((2, 0, 1))?;
let mean = Tensor::new(&[0.485f32, 0.456, 0.406], &Device::Cpu)?.reshape((3, 1, 1))?;
let std = Tensor::new(&[0.229f32, 0.224, 0.225], &Device::Cpu)?.reshape((3, 1, 1))?;
(data.to_dtype(candle::DType::F32)? / 255.)?
.broadcast_sub(&mean)?
.broadcast_div(&std)
load_image(p, 518)
}
pub const CLASS_COUNT: i64 = 1000;