Ben_Kosytorz
3a67c0979c
- Updated stablediffusion crate path from "../stable-diffusion-burn" to "./crates/stable-diffusion-burn" for proper workspace resolution - Enhanced .gitignore to include generated model files (.mpk, .pt, .bin, .safetensors, .ckpt) and user_data directory - Added Cargo.lock to gitignore with appropriate comment - Reorganized IDE files section in gitignore for better clarity - Added newline at end of file for proper formatting
119 lines
3.5 KiB
Rust
119 lines
3.5 KiB
Rust
#![warn(missing_docs)]
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#![cfg_attr(docsrs, feature(doc_cfg))]
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//! A library for training neural networks using the burn crate.
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#[macro_use]
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extern crate derive_new;
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/// The checkpoint module.
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pub mod checkpoint;
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pub(crate) mod components;
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/// Renderer modules to display metrics and training information.
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pub mod renderer;
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/// The logger module.
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pub mod logger;
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/// The metric module.
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pub mod metric;
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pub use metric::processor::*;
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mod learner;
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pub use learner::*;
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mod evaluator;
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pub use evaluator::*;
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pub use components::*;
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#[cfg(test)]
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pub(crate) type TestBackend = burn_ndarray::NdArray<f32>;
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#[cfg(test)]
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pub(crate) mod tests {
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use crate::TestBackend;
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use burn_core::{prelude::Tensor, tensor::Bool};
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use std::default::Default;
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pub type TestAutodiffBackend = burn_autodiff::Autodiff<TestBackend>;
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/// Probability of tp before adding errors
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pub const THRESHOLD: f64 = 0.5;
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#[derive(Debug, Default)]
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pub enum ClassificationType {
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#[default]
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Binary,
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Multiclass,
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Multilabel,
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}
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/// Sample x Class shaped matrix for use in
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/// classification metrics testing
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pub fn dummy_classification_input(
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classification_type: &ClassificationType,
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) -> (Tensor<TestBackend, 2>, Tensor<TestBackend, 2, Bool>) {
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match classification_type {
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ClassificationType::Binary => {
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(
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Tensor::from_data([[0.3], [0.2], [0.7], [0.1], [0.55]], &Default::default()),
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// targets
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Tensor::from_data([[0], [1], [0], [0], [1]], &Default::default()),
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// predictions @ threshold=0.5
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// [[0], [0], [1], [0], [1]]
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)
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}
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ClassificationType::Multiclass => {
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(
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Tensor::from_data(
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[
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[0.2, 0.8, 0.0],
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[0.3, 0.6, 0.1],
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[0.7, 0.25, 0.05],
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[0.1, 0.15, 0.8],
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[0.9, 0.03, 0.07],
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],
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&Default::default(),
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),
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Tensor::from_data(
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// targets
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[[0, 1, 0], [1, 0, 0], [0, 0, 1], [0, 0, 1], [1, 0, 0]],
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// predictions @ top_k=1
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// [[0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0]]
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// predictions @ top_k=2
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// [[1, 1, 0], [1, 1, 0], [1, 1, 0], [0, 1, 1], [1, 0, 1]]
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&Default::default(),
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),
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)
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}
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ClassificationType::Multilabel => {
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(
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Tensor::from_data(
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[
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[0.1, 0.7, 0.6],
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[0.3, 0.9, 0.05],
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[0.8, 0.9, 0.4],
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[0.7, 0.5, 0.9],
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[1.0, 0.3, 0.2],
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],
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&Default::default(),
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),
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// targets
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Tensor::from_data(
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[[1, 1, 0], [1, 0, 1], [1, 1, 1], [0, 0, 1], [1, 0, 0]],
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// predictions @ threshold=0.5
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// [[0, 1, 1], [0, 1, 0], [1, 1, 0], [1, 0, 1], [1, 0, 0]]
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&Default::default(),
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),
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)
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}
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}
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}
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}
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