rustyml 0.15.0

A high-performance machine learning & deep learning library in pure Rust, offering ML algorithms and neural network support
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//! 2D cropping layer that removes rows and columns at the edges of an image

use crate::error::Error;
use crate::neural_network::Tensor;
use crate::neural_network::layers::TrainingParameters;
use crate::neural_network::layers::border::Border2D;
use crate::neural_network::layers::border::pad_crop_engine::{
    crop_backward, crop_forward, crop_summary,
};
use crate::neural_network::layers::layer_weight::LayerWeight;
use crate::neural_network::layers::no_trainable_parameters_layer_functions;
use crate::neural_network::traits::Layer;

/// Removes rows and columns at the edges of a rank-4 tensor
///
/// The input shape is `[batch_size, height, width, channels]`. The output shape is
/// `[batch_size, height - top - bottom, width - left - right, channels]`. The batch axis and
/// the channel axis pass through unchanged
///
/// The layer holds no parameter. Each spatial axis must keep at least 1 position, so the
/// forward pass fails when the 2 amounts on an axis together reach its extent
///
/// A common use is a decoder that upsamples past the wanted size. The layer then trims the
/// output to the size of the matching encoder feature map
///
/// [`ZeroPadding2D`](crate::neural_network::layers::border::ZeroPadding2D) is the inverse
/// layer, and it is also this layer's backward pass
///
/// # Examples
///
/// ```rust
/// use rustyml::neural_network::sequential::Sequential;
/// use rustyml::neural_network::layers::*;
/// use rustyml::neural_network::optimizers::*;
/// use rustyml::neural_network::losses::*;
/// use ndarray::Array4;
///
/// // A rank-4 input: 2 samples, 8x8 pixels, 3 channels
/// let x = Array4::ones((2, 8, 8, 3)).into_dyn();
///
/// let mut model = Sequential::new();
/// model
///     .add(Cropping2D::new(((1, 1), (2, 0))))
///     .compile(SGD::new(0.01, 0.0, false, 0.0).unwrap(), MeanSquaredError::new());
///
/// // View model structure
/// model.summary();
///
/// let cropped = model.predict(&x).unwrap();
///
/// // 1 row off the top and bottom, and 2 columns off the left only
/// assert_eq!(cropped.shape(), &[2, 6, 6, 3]);
/// ```
#[derive(Debug)]
pub struct Cropping2D {
    /// Rows and columns to remove at each of the 4 edges
    cropping: Border2D,
    /// Shape of the most recent forward input. The backward pass needs it to size the gradient
    input_shape: Option<Vec<usize>>,
}

impl Cropping2D {
    /// Creates a new Cropping2D layer
    ///
    /// # Parameters
    ///
    /// - `cropping` - Rows and columns to remove. An integer gives the same amount at all 4
    ///   edges. A `(height, width)` pair gives 1 amount per axis. A pair of pairs
    ///   `((top, bottom), (left, right))` names all 4 edges. See [`Border2D`]
    ///
    /// # Returns
    ///
    /// - `Self` - New `Cropping2D` layer instance
    pub fn new(cropping: impl Into<Border2D>) -> Self {
        Cropping2D {
            cropping: cropping.into(),
            input_shape: None,
        }
    }
}

impl Layer for Cropping2D {
    fn forward(&mut self, input: &Tensor) -> Result<Tensor, Error> {
        let output = crop_forward(input, &self.cropping.0, 4, "Cropping2D")?;
        self.input_shape = Some(input.shape().to_vec());
        Ok(output)
    }

    /// Inference forward (eval mode, writes no caches). See [`Layer::predict`]
    fn predict(&self, input: &Tensor) -> Result<Tensor, Error> {
        crop_forward(input, &self.cropping.0, 4, "Cropping2D")
    }

    fn backward(&mut self, grad_output: &Tensor) -> Result<Tensor, Error> {
        crop_backward(
            grad_output,
            self.input_shape.as_deref(),
            &self.cropping.0,
            "Cropping2D",
        )
    }

    fn layer_type(&self) -> &str {
        "Cropping2D"
    }

    fn output_shape(&self) -> String {
        crop_summary(self.input_shape.as_deref(), &self.cropping.0)
    }

    no_trainable_parameters_layer_functions!();
}