1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141

//! Applies the nonlinear Rectified Linear Unit.
//!
//! Non-linearity activation function: y = max(0, x)
//!
//! This is generally the preferred choice over Sigmod or TanH.
//! The max function used in ReLU is usually faster to compute than the exponentiation
//! needed in a Sigmoid layer.

use co::{IBackend,SharedTensor};
use conn::Relu;
#[cfg(all(feature="cuda", not(feature="native")))]
use conn::ReluPointwise;
use layer::*;
use util::ArcLock;

#[derive(Debug, Clone)]
#[allow(missing_copy_implementations)]
/// ReLU Activation Layer
pub struct ReLU;

//
// ReLU + ReLUPointwise
// Only on CUDA
//
#[cfg(all(feature="cuda", not(feature="native")))]
impl<B: IBackend + Relu<f32> + ReluPointwise<f32>> ILayer<B> for ReLU {
    impl_ilayer_activation!();

    fn compute_in_place(&self) -> bool {
        true
    }

    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>>>) {
        if let Some(inp) = input_data.get(0) {
            let read_inp = inp.read().unwrap();
            let input_desc = read_inp.desc();
            input_gradient[0].write().unwrap().resize(input_desc).unwrap();
            output_data[0].write().unwrap().resize(input_desc).unwrap();
            output_gradient[0].write().unwrap().resize(input_desc).unwrap();
        }
    }
}

#[cfg(all(feature="cuda", not(feature="native")))]
impl<B: IBackend + Relu<f32> + ReluPointwise<f32>> ComputeOutput<f32, B> for ReLU {
    fn compute_output(&self,
                      backend: &B,
                      _weights: &[&SharedTensor<f32>],
                      input_data: &[&SharedTensor<f32>],
                      output_data: &mut [&mut SharedTensor<f32>]) {
        match input_data.get(0) {
            Some(input) => backend.relu_plain(input, output_data[0]).unwrap(),
            None => backend.relu_pointwise_plain(output_data[0]).unwrap(),
        }
    }
}

#[cfg(all(feature="cuda", not(feature="native")))]
impl<B: IBackend + Relu<f32> + ReluPointwise<f32>> ComputeInputGradient<f32, B> for ReLU {
    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>]) {
        match output_data.get(0) {
            Some(_) => backend.relu_grad_plain(output_data[0], output_gradients[0], input_data[0], input_gradients[0]).unwrap(),
            None => backend.relu_pointwise_grad_plain(input_data[0], input_gradients[0]).unwrap(),
        }
    }
}

#[cfg(all(feature="cuda", not(feature="native")))]
impl<B: IBackend + Relu<f32> + ReluPointwise<f32>> ComputeParametersGradient<f32, B> for ReLU {}

//
// ReLU without ReLUPointwise
// Only on CUDA
//
#[cfg(feature="native")]
impl<B: IBackend + Relu<f32>> ILayer<B> for ReLU {
    impl_ilayer_activation!();

    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>>>) {
        if let Some(inp) = input_data.get(0) {
            let read_inp = inp.read().unwrap();
            let input_desc = read_inp.desc();
            input_gradient[0].write().unwrap().resize(input_desc).unwrap();
            output_data[0].write().unwrap().resize(input_desc).unwrap();
            output_gradient[0].write().unwrap().resize(input_desc).unwrap();
        }
    }
}

#[cfg(feature="native")]
impl<B: IBackend + Relu<f32>> ComputeOutput<f32, B> for ReLU {
    fn compute_output(&self,
                      backend: &B,
                      _weights: &[&SharedTensor<f32>],
                      input_data: &[&SharedTensor<f32>],
                      output_data: &mut [&mut SharedTensor<f32>]) {
        match input_data.get(0) {
            Some(input) => backend.relu_plain(input, output_data[0]).unwrap(),
            None => panic!("No input provided for ReLU layer."),
        }
    }
}

#[cfg(feature="native")]
impl<B: IBackend + Relu<f32>> ComputeInputGradient<f32, B> for ReLU {
    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>]) {
        match output_data.get(0) {
            Some(_) => backend.relu_grad_plain(output_data[0], output_gradients[0], input_data[0], input_gradients[0]).unwrap(),
            None => panic!("No output_data provided for ReLU layer backward."),
        }
    }
}

#[cfg(feature="native")]
impl<B: IBackend + Relu<f32>> ComputeParametersGradient<f32, B> for ReLU {}