juice 0.3.0

Machine Learning Framework for Hackers
Documentation 
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//! Utility layer to give a tensor another shape.
//!
//! This layer should be used as in-place operation,
//! so the tensor that should be reshaped should be specified
//! as both input and output.
//!
//! Reshaping a tensor is required so that it becomes
//! usable for Layers that interpret meaning into the shape of
//! the tensor.
//!
//! A lot of layers interpret the last dimensions as NCHW,
//! where the letters stand for:
//!
//! - `N` : number of batch samples
//! - `C` : number of feature maps
//! - `H` : height
//! - `W` : width

use crate::capnp_util::*;
use crate::co::{IBackend, SharedTensor};
use crate::juice_capnp::reshape_config as capnp_config;
use crate::layer::*;
use crate::util::ArcLock;

#[derive(Debug, Clone)]
/// Reshape Utility Layer
pub struct Reshape {
    shape: Vec<usize>,
}

impl Reshape {
    /// Create a Reshape layer from a ReshapeConfig.
    pub fn from_config(config: &ReshapeConfig) -> Reshape {
        Reshape {
            shape: config.shape.clone(),
        }
    }
}

impl<B: IBackend> ILayer<B> for Reshape {
    fn compute_in_place(&self) -> bool {
        true
    }

    fn auto_output_blobs(&self) -> bool {
        false
    }

    fn reshape(
        &mut self,
        backend: ::std::rc::Rc<B>,
        input_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
        input_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
        weights_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
        weights_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
        output_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
        output_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
    ) {
        output_data[0].write().unwrap().resize(&self.shape).unwrap();
        output_gradient[0].write().unwrap().resize(&self.shape).unwrap();
    }
}

impl<B: IBackend> ComputeOutput<f32, B> for Reshape {
    fn compute_output(
        &self,
        backend: &B,
        _weights: &[&SharedTensor<f32>],
        input_data: &[&SharedTensor<f32>],
        output_data: &mut [&mut SharedTensor<f32>],
    ) {
    }
}

impl<B: IBackend> ComputeInputGradient<f32, B> for Reshape {
    fn compute_input_gradient(
        &self,
        backend: &B,
        weights_data: &[&SharedTensor<f32>],
        output_data: &[&SharedTensor<f32>],
        output_gradients: &[&SharedTensor<f32>],
        input_data: &[&SharedTensor<f32>],
        input_gradients: &mut [&mut SharedTensor<f32>],
    ) {
    }
}

impl<B: IBackend> ComputeParametersGradient<f32, B> for Reshape {}

#[derive(Debug, Clone)]
/// Specifies configuration parameters for a Reshape Layer.
pub struct ReshapeConfig {
    /// The target shape that the input should assume.
    ///
    /// Preceding dimensions are treated as independent inputs
    ///
    /// Defaults to `1`
    pub shape: Vec<usize>,
}

impl ReshapeConfig {
    /// Create a ReshapeConfig that describes a Reshape layer with a provided shape.
    pub fn of_shape(shape: &[usize]) -> ReshapeConfig {
        ReshapeConfig {
            shape: shape.to_owned(),
        }
    }
}

impl<'a> CapnpWrite<'a> for ReshapeConfig {
    type Builder = capnp_config::Builder<'a>;

    /// Write the ReshapeConfig into a capnp message.
    fn write_capnp(&self, builder: &mut Self::Builder) {
        let mut shape = builder.reborrow().init_shape(self.shape.len() as u32);
        for (i, dim) in self.shape.iter().enumerate() {
            shape.set(i as u32, *dim as u64);
        }
    }
}

impl<'a> CapnpRead<'a> for ReshapeConfig {
    type Reader = capnp_config::Reader<'a>;

    fn read_capnp(reader: Self::Reader) -> Self {
        let read_shape = reader.get_shape().unwrap();
        let mut shape = Vec::new();
        for i in 0..read_shape.len() {
            shape.push(read_shape.get(i) as usize)
        }

        ReshapeConfig { shape: shape }
    }
}

impl Into<LayerType> for ReshapeConfig {
    fn into(self) -> LayerType {
        LayerType::Reshape(self)
    }
}