torsh-nn 0.1.2

Neural network modules for ToRSh with PyTorch-compatible API
Documentation 
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

//! # MaxPool3d - Trait Implementations
//!
//! This module contains trait implementations for `MaxPool3d`.
//!
//! ## Implemented Traits
//!
//! - `Module`
//! - `Debug`
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use crate::{Module, Parameter};
#[cfg(not(feature = "std"))]
use hashbrown::HashMap;
#[cfg(feature = "std")]
use std::collections::HashMap;
use torsh_core::device::DeviceType;
use torsh_core::error::Result;
use torsh_tensor::{creation::*, Tensor};

use super::types::MaxPool3d;

impl Module for MaxPool3d {
    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let binding = input.shape();
        let input_shape = binding.dims();
        let stride = self.stride.unwrap_or(self.kernel_size);
        if input_shape.len() != 5 {
            return Err(torsh_core::TorshError::InvalidArgument(
                "MaxPool3d expects 5D input [N, C, D, H, W]".to_string(),
            ));
        }
        let output_depth = if self.ceil_mode {
            ((input_shape[2] + 2 * self.padding.0 - self.dilation.0 * (self.kernel_size.0 - 1) - 1)
                as f32
                / stride.0 as f32)
                .ceil() as usize
                + 1
        } else {
            (input_shape[2] + 2 * self.padding.0 - self.dilation.0 * (self.kernel_size.0 - 1) - 1)
                / stride.0
                + 1
        };
        let output_height = if self.ceil_mode {
            ((input_shape[3] + 2 * self.padding.1 - self.dilation.1 * (self.kernel_size.1 - 1) - 1)
                as f32
                / stride.1 as f32)
                .ceil() as usize
                + 1
        } else {
            (input_shape[3] + 2 * self.padding.1 - self.dilation.1 * (self.kernel_size.1 - 1) - 1)
                / stride.1
                + 1
        };
        let output_width = if self.ceil_mode {
            ((input_shape[4] + 2 * self.padding.2 - self.dilation.2 * (self.kernel_size.2 - 1) - 1)
                as f32
                / stride.2 as f32)
                .ceil() as usize
                + 1
        } else {
            (input_shape[4] + 2 * self.padding.2 - self.dilation.2 * (self.kernel_size.2 - 1) - 1)
                / stride.2
                + 1
        };
        let output_shape = [
            input_shape[0],
            input_shape[1],
            output_depth,
            output_height,
            output_width,
        ];
        let output = zeros(&output_shape)?;
        Ok(output)
    }
    fn parameters(&self) -> HashMap<String, Parameter> {
        self.base.parameters.clone()
    }
    fn training(&self) -> bool {
        self.base.training()
    }
    fn train(&mut self) {
        self.base.set_training(true);
    }
    fn eval(&mut self) {
        self.base.set_training(false);
    }
    fn set_training(&mut self, training: bool) {
        self.base.set_training(training);
    }
    fn to_device(&mut self, device: DeviceType) -> Result<()> {
        self.base.to_device(device)
    }
    fn named_parameters(&self) -> HashMap<String, Parameter> {
        self.base.named_parameters()
    }
}

impl std::fmt::Debug for MaxPool3d {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("MaxPool3d")
            .field("kernel_size", &self.kernel_size)
            .field("stride", &self.stride)
            .field("padding", &self.padding)
            .field("dilation", &self.dilation)
            .field("ceil_mode", &self.ceil_mode)
            .finish()
    }
}