flodl 0.1.3

floDl — a flow-graph deep learning framework built on libtorch
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
142
143
144
145
146
147
148

//! Pooling layers.

use crate::autograd::{self, Variable};
use crate::tensor::Result;

use super::Module;
use super::parameter::Parameter;

/// 2D max pooling layer.
///
/// Applies max pooling over a 4D input `[B, C, H, W]`, equivalent to
/// PyTorch's `nn.MaxPool2d`.
///
/// ```ignore
/// let pool = MaxPool2d::new(2);  // kernel_size=2, stride=2
/// let y = pool.forward(&x)?;    // [B, C, H/2, W/2]
/// ```
pub struct MaxPool2d {
    kernel_size: [i64; 2],
    stride: [i64; 2],
    padding: [i64; 2],
    dilation: [i64; 2],
    ceil_mode: bool,
}

impl MaxPool2d {
    /// Create a MaxPool2d with the given kernel size. Stride defaults to kernel size.
    pub fn new(kernel_size: i64) -> Self {
        Self {
            kernel_size: [kernel_size, kernel_size],
            stride: [kernel_size, kernel_size],
            padding: [0, 0],
            dilation: [1, 1],
            ceil_mode: false,
        }
    }

    /// Create with explicit stride.
    pub fn with_stride(kernel_size: i64, stride: i64) -> Self {
        Self {
            kernel_size: [kernel_size, kernel_size],
            stride: [stride, stride],
            padding: [0, 0],
            dilation: [1, 1],
            ceil_mode: false,
        }
    }

    /// Set padding (applied symmetrically).
    pub fn padding(mut self, padding: i64) -> Self {
        self.padding = [padding, padding];
        self
    }

    /// Set dilation.
    pub fn dilation(mut self, dilation: i64) -> Self {
        self.dilation = [dilation, dilation];
        self
    }

    /// Enable ceiling mode for output size computation.
    pub fn ceil_mode(mut self, ceil_mode: bool) -> Self {
        self.ceil_mode = ceil_mode;
        self
    }
}

impl Module for MaxPool2d {
    fn name(&self) -> &str { "maxpool2d" }

    fn forward(&self, input: &Variable) -> Result<Variable> {
        autograd::max_pool2d(input, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode)
    }

    fn parameters(&self) -> Vec<Parameter> {
        vec![] // pooling has no learnable parameters
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::tensor::Tensor;

    #[test]
    fn test_max_pool2d_basic() {
        let opts = crate::tensor::test_opts();
        // [1, 1, 4, 4] → kernel=2, stride=2 → [1, 1, 2, 2]
        let x = Variable::new(
            Tensor::randn(&[1, 1, 4, 4], opts).unwrap(),
            false,
        );
        let pool = MaxPool2d::new(2);
        let y = pool.forward(&x).unwrap();
        assert_eq!(y.shape(), vec![1, 1, 2, 2]);
    }

    #[test]
    fn test_max_pool2d_with_padding() {
        let opts = crate::tensor::test_opts();
        // [2, 3, 8, 8] → kernel=3, stride=2, padding=1 → [2, 3, 4, 4]
        let x = Variable::new(
            Tensor::randn(&[2, 3, 8, 8], opts).unwrap(),
            false,
        );
        let pool = MaxPool2d::with_stride(3, 2).padding(1);
        let y = pool.forward(&x).unwrap();
        assert_eq!(y.shape(), vec![2, 3, 4, 4]);
    }

    #[test]
    fn test_max_pool2d_gradient() {
        let opts = crate::tensor::test_opts();
        let x = Variable::new(
            Tensor::randn(&[2, 1, 4, 4], opts).unwrap(),
            true,
        );
        let pool = MaxPool2d::new(2);
        let y = pool.forward(&x).unwrap();
        let loss = y.sum().unwrap();
        loss.backward().unwrap();

        let grad = x.grad().unwrap();
        assert_eq!(grad.shape(), vec![2, 1, 4, 4]);
        // Gradient should be sparse: only max elements get gradient
    }

    #[test]
    fn test_max_pool2d_values() {
        let device = crate::tensor::test_device();
        // Manual check: 2x2 max pool on a known 4x4 input
        let data = vec![
            1.0_f32, 2.0, 3.0, 4.0,
            5.0, 6.0, 7.0, 8.0,
            9.0, 10.0, 11.0, 12.0,
            13.0, 14.0, 15.0, 16.0,
        ];
        let x = Variable::new(
            Tensor::from_f32(&data, &[1, 1, 4, 4], device).unwrap(),
            false,
        );
        let pool = MaxPool2d::new(2);
        let y = pool.forward(&x).unwrap();
        let y_data = y.data().to_f32_vec().unwrap();
        // Each 2x2 block → max: [6, 8, 14, 16]
        assert_eq!(y_data, vec![6.0, 8.0, 14.0, 16.0]);
    }
}