jengamon/burn_learn - Change T4JMISYCRZ3BODA2PEOXKJ2RSDIYK37FH5B6DHGRKVT47ABPIJUQC

feat: get to burn training

Created by Jengamon on March 21, 2024

T4JMISYCRZ3BODA2PEOXKJ2RSDIYK37FH5B6DHGRKVT47ABPIJUQC

Dependencies

[2] IX7A7EAPFCO4A6UZCFKPT7GC2RGP2VE4EVXEN3TUN2QFKKETBO4AC

In channels

main

Change contents

File addition: training.rs (----------)

[2.15]

use burn::{
    config::Config,
    data::{dataloader::DataLoaderBuilder, dataset::vision::MNISTDataset},
    module::Module,
    nn::loss::CrossEntropyLossConfig,
    optim::AdamConfig,
    record::CompactRecorder,
    tensor::{
        backend::{AutodiffBackend, Backend},
        Int, Tensor,
    },
    train::{
        metric::{AccuracyMetric, LossMetric},
        ClassificationOutput, LearnerBuilder, TrainOutput, TrainStep, ValidStep,
    },
};
use crate::{
    data::{MNISTBatch, MNISTBatcher},
    model::{Model, ModelConfig},
};
impl<B: Backend> Model<B> {
    pub fn forward_classification(
        &self,
        images: Tensor<B, 3>,
        targets: Tensor<B, 1, Int>,
    ) -> ClassificationOutput<B> {
        let output = self.forward(images);
        let loss = CrossEntropyLossConfig::new()
            .init(&output.device())
            .forward(output.clone(), targets.clone());
        ClassificationOutput::new(loss, output, targets)
    }
}
impl<B: AutodiffBackend> TrainStep<MNISTBatch<B>, ClassificationOutput<B>> for Model<B> {
    fn step(&self, batch: MNISTBatch<B>) -> TrainOutput<ClassificationOutput<B>> {
        let item = self.forward_classification(batch.images, batch.targets);
        TrainOutput::new(self, item.loss.backward(), item)
    }
}
impl<B: Backend> ValidStep<MNISTBatch<B>, ClassificationOutput<B>> for Model<B> {
    fn step(&self, batch: MNISTBatch<B>) -> ClassificationOutput<B> {
        self.forward_classification(batch.images, batch.targets)
    }
}
#[derive(Config)]
pub struct TrainingConfig {
    pub model: ModelConfig,
    pub optimizer: AdamConfig,
    #[config(default = 10)]
    pub num_epochs: usize,
    #[config(default = 64)]
    pub batch_size: usize,
    #[config(default = 4)]
    pub num_workers: usize,
    #[config(default = 42)]
    pub seed: u64,
    #[config(default = 1.0e-4)]
    pub learning_rate: f64,
}
pub fn train<B: AutodiffBackend>(artifact_dir: &str, config: TrainingConfig, device: B::Device) {
    std::fs::create_dir_all(artifact_dir).ok();
    config
        .save(format!("{artifact_dir}/config.json"))
        .expect("config not saved correctly");
    B::seed(config.seed);
    let batcher_train = MNISTBatcher::<B>::new(device.clone());
    let batcher_valid = MNISTBatcher::<B::InnerBackend>::new(device.clone());
    let dataloader_train = DataLoaderBuilder::new(batcher_train)
        .batch_size(config.batch_size)
        .shuffle(config.seed)
        .num_workers(config.num_workers)
        .build(MNISTDataset::train());
    let dataloader_test = DataLoaderBuilder::new(batcher_valid)
        .batch_size(config.batch_size)
        .shuffle(config.seed)
        .num_workers(config.num_workers)
        .build(MNISTDataset::test());
    let model = config.model.init::<B>(&device);
    let learner = LearnerBuilder::new(artifact_dir)
        .metric_train_numeric(AccuracyMetric::new())
        .metric_valid_numeric(AccuracyMetric::new())
        .metric_train_numeric(LossMetric::new())
        .metric_valid_numeric(LossMetric::new())
        .with_file_checkpointer(CompactRecorder::new())
        .devices(vec![device])
        .num_epochs(config.num_epochs)
        .build(model, config.optimizer.init(), config.learning_rate);
    let model_trained = learner.fit(dataloader_train, dataloader_test);
    model_trained
        .save_file(format!("{artifact_dir}/model"), &CompactRecorder::new())
        .expect("Trained model should be saved successfully");
}

File addition: model.rs (----------)

[2.15]

use burn::{
    config::Config,
    module::Module,
    nn::{
        conv::{Conv2d, Conv2dConfig},
        pool::{AdaptiveAvgPool2d, AdaptiveAvgPool2dConfig},
        Dropout, DropoutConfig, Linear, LinearConfig, ReLU,
    },
    tensor::{backend::Backend, Tensor},
};
#[derive(Module, Debug)]
pub struct Model<B: Backend> {
    conv1: Conv2d<B>,
    conv2: Conv2d<B>,
    pool: AdaptiveAvgPool2d,
    dropout: Dropout,
    linear1: Linear<B>,
    linear2: Linear<B>,
    activation: ReLU,
}
impl<B: Backend> Model<B> {
    /// # Shapes
    /// - Images [batch_size, height, width]
    /// - Output [batch_size, num_classes]
    pub fn forward(&self, images: Tensor<B, 3>) -> Tensor<B, 2> {
        let [batch_size, height, width] = images.dims();
        // Create channel at second dim
        let x = images.reshape([batch_size, 1, height, width]);
        let x = self.conv1.forward(x); // [batch_size, 8, _, _]
        let x = self.dropout.forward(x);
        let x = self.conv2.forward(x); // [batch_size, 16, _, _]
        let x = self.dropout.forward(x);
        let x = self.activation.forward(x);
        let x = self.pool.forward(x); // [batch_size, 16, 8, 8]
        let x = x.reshape([batch_size, 16 * 8 * 8]);
        let x = self.linear1.forward(x);
        let x = self.dropout.forward(x);
        let x = self.activation.forward(x);
        self.linear2.forward(x)
    }
}
#[derive(Config, Debug)]
pub struct ModelConfig {
    num_classes: usize,
    hidden_size: usize,
    #[config(default = "0.5")]
    dropout: f64,
}
impl ModelConfig {
    pub fn init<B: Backend>(&self, device: &B::Device) -> Model<B> {
        Model {
            conv1: Conv2dConfig::new([1, 8], [3, 3]).init(device),
            conv2: Conv2dConfig::new([8, 16], [3, 3]).init(device),
            pool: AdaptiveAvgPool2dConfig::new([8, 8]).init(),
            activation: ReLU::new(),
            linear1: LinearConfig::new(16 * 8 * 8, self.hidden_size).init(device),
            linear2: LinearConfig::new(self.hidden_size, self.num_classes).init(device),
            dropout: DropoutConfig::new(self.dropout).init(),
        }
    }
}

Insertion in src/main.rs at line 1 [2.49]

[2.49]

[2.50]

mod data;
mod model;
mod training;
use burn::{
    backend::{
        wgpu::{AutoGraphicsApi, WgpuDevice},
        Autodiff, Wgpu,
    },
    optim::AdamConfig,
};
use crate::model::ModelConfig;

Replacement in src/main.rs at line 16 [2.49]

B:BD[2.62] → [2.62:93]

    println!("Hello, world!");

[2.62]

[2.93]

    type Backend = Wgpu<AutoGraphicsApi, f32, i32>;
    type AutodiffBackend = Autodiff<Backend>;
    let device = WgpuDevice::default();
    training::train::<AutodiffBackend>(
        "/tmp/guide",
        training::TrainingConfig::new(ModelConfig::new(10, 512), AdamConfig::new()),
        device,
    );

File addition: data.rs (----------)

[2.15]

use burn::{
    data::{dataloader::batcher::Batcher, dataset::vision::MNISTItem},
    tensor::{backend::Backend, Data, ElementConversion, Int, Tensor},
};
pub struct MNISTBatcher<B: Backend> {
    device: B::Device,
}
impl<B: Backend> MNISTBatcher<B> {
    pub fn new(device: B::Device) -> Self {
        Self { device }
    }
}
#[derive(Clone, Debug)]
pub struct MNISTBatch<B: Backend> {
    pub images: Tensor<B, 3>,
    pub targets: Tensor<B, 1, Int>,
}
impl<B: Backend> Batcher<MNISTItem, MNISTBatch<B>> for MNISTBatcher<B> {
    fn batch(&self, items: Vec<MNISTItem>) -> MNISTBatch<B> {
        let images = items
            .iter()
            .map(|item| Data::<f32, 2>::from(item.image))
            .map(|data| Tensor::<B, 2>::from_data(data.convert(), &self.device))
            .map(|tensor| tensor.reshape([1, 28, 28]))
            // Normalize: make between [0,1] and make the mean=0 and std=1
            // values mean=0.1307,std=0.3081 are from the PyTorch MNIST example
            // https://github.com/pytorch/examples/blob/54f4572509891883a947411fd7239237dd2a39c3/mnist/main.py#L122
            .map(|tensor| ((tensor / 255) - 0.1307) / 0.3081)
            .collect();
        let targets = items
            .iter()
            .map(|item| {
                Tensor::<B, 1, Int>::from_data(
                    Data::from([(item.label as i64).elem()]),
                    &self.device,
                )
            })
            .collect();
        let images = Tensor::cat(images, 0).to_device(&self.device);
        let targets = Tensor::cat(targets, 0).to_device(&self.device);
        MNISTBatch { images, targets }
    }
}

Replacement in justfile at line 2 [2.129]
B:BD[2.135] → [2.135:151]
```
    cargo build
```
[2.135]
[2.151]
```
    cargo run
```

Replacement in Cargo.toml at line 9 [2.3279]

B:BD[2.3459] → [2.3459:3510]

burn = { version = "0.12.1", features = ["wgpu"] }

[2.3459]

burn = { version = "0.12.1", features = ["train", "wgpu", "vision"] }

Insertion in Cargo.lock at line 270 [2.3546]
[2.10467]
[2.10467]
```
 "burn-autodiff",
```
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[2.10986]
```
 "flate2",
```
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```
 "indicatif",
```
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```
 "reqwest",
```
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```
 "tokio",
```

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[[package]]

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[[package]]
name = "foreign-types"

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```
 "foreign-types-shared",
```
[2.26632]
[2.26657]
```
 "foreign-types-shared 0.3.1",
```

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[[package]]
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dependencies = [
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 "portable-atomic",
 "unicode-width",
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dependencies = [
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Insertion in Cargo.lock at line 1646 [2.3546]

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[[package]]

Replacement in Cargo.lock at line 1860 [2.3546]
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```
 "foreign-types",
```
[2.43652]
[2.43670]
```
 "foreign-types 0.5.0",
```

Insertion in Cargo.lock at line 1867 [2.3546]

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 "tempfile",
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dependencies = [
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 "vcpkg",
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dependencies = [
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 "encoding_rs",
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 "url",
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 "web-sys",
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feat: get to burn training

Dependencies

In channels

Change contents

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