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::LPPool2d;
impl Module for LPPool2d {
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);
let output_height = if self.ceil_mode {
((input_shape[2] - self.kernel_size.0) as f32 / stride.0 as f32).ceil() as usize + 1
} else {
(input_shape[2] - self.kernel_size.0) / stride.0 + 1
};
let output_width = if self.ceil_mode {
((input_shape[3] - self.kernel_size.1) as f32 / stride.1 as f32).ceil() as usize + 1
} else {
(input_shape[3] - self.kernel_size.1) / stride.1 + 1
};
let output_shape = [input_shape[0], input_shape[1], 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 LPPool2d {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
f.debug_struct("LPPool2d")
.field("norm_type", &self.norm_type)
.field("kernel_size", &self.kernel_size)
.field("stride", &self.stride)
.finish()
}
}