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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,101 @@ | ||
| use anyhow::Result; | ||
| use candle_core::{DType, Device, Tensor, Var}; | ||
| use candle_nn::{ | ||
| conv2d_no_bias, linear, linear_no_bias, loss::cross_entropy, Conv2d, Conv2dConfig, Linear, | ||
| Module, Optimizer, VarBuilder, VarMap, SGD, | ||
| }; | ||
|
|
||
| pub struct LeNet5Classifier { | ||
| device: Device, | ||
| dtype: DType, | ||
| model: LeNet5, | ||
| optimizer: Option<SGD>, | ||
| varmap: VarMap, | ||
| _var_builder: VarBuilder<'static>, | ||
| } | ||
|
|
||
| impl LeNet5Classifier { | ||
| pub fn new(device: Device, dtype: DType) -> Result<Self> { | ||
| let varmap = VarMap::new(); | ||
| let var_builder = VarBuilder::from_varmap(&varmap, dtype, &device); | ||
| let model = LeNet5::new(&var_builder)?; | ||
| Ok(Self { | ||
| device, | ||
| dtype, | ||
| model, | ||
| optimizer: None, | ||
| varmap, | ||
| _var_builder: var_builder, | ||
| }) | ||
| } | ||
| pub fn with_sgd(self, momentum: bool) -> Result<Self> { | ||
| if momentum { | ||
| anyhow::bail!("Momentum not supported by candle!"); | ||
| } | ||
| /* | ||
| let momentum = if momentum { 0.01 } else { 0.0 }; | ||
| */ | ||
| let learning_rate = 0.01; | ||
| let optimizer = SGD::new(self.varmap.all_vars(), learning_rate)?; | ||
| Ok(Self { | ||
| optimizer: Some(optimizer), | ||
| ..self | ||
| }) | ||
| } | ||
| pub fn infer(&self, batch_size: usize) -> Result<()> { | ||
| let x = Tensor::zeros((batch_size, 1, 28, 28), self.dtype, &self.device)?; | ||
| let _y = self.model.forward(&x)?; | ||
| Ok(()) | ||
| } | ||
| pub fn train(&mut self, batch_size: usize) -> Result<()> { | ||
| let x = Var::zeros((batch_size, 1, 28, 28), self.dtype, &self.device)?; | ||
| let t = Tensor::zeros(batch_size, DType::U32, &self.device)?; | ||
| let y = self.model.forward(&x)?; | ||
| let loss = cross_entropy(&y, &t)?; | ||
| self.optimizer.as_mut().unwrap().backward_step(&loss)?; | ||
| Ok(()) | ||
| } | ||
| } | ||
|
|
||
| #[derive(Debug)] | ||
| struct LeNet5 { | ||
| conv1: Conv2d, | ||
| conv2: Conv2d, | ||
| dense1: Linear, | ||
| dense2: Linear, | ||
| dense3: Linear, | ||
| } | ||
|
|
||
| impl LeNet5 { | ||
| fn new(var_builder: &VarBuilder) -> Result<Self> { | ||
| let conv1 = conv2d_no_bias(1, 6, 5, Conv2dConfig::default(), var_builder.pp("conv1"))?; | ||
| let conv2 = conv2d_no_bias(6, 16, 5, Conv2dConfig::default(), var_builder.pp("conv2"))?; | ||
| let dense1 = linear_no_bias(16 * 4 * 4, 128, var_builder.pp("dense1"))?; | ||
| let dense2 = linear_no_bias(128, 84, var_builder.pp("dense2"))?; | ||
| let dense3 = linear(84, 10, var_builder.pp("dense3"))?; | ||
| Ok(Self { | ||
| conv1, | ||
| conv2, | ||
| dense1, | ||
| dense2, | ||
| dense3, | ||
| }) | ||
| } | ||
| } | ||
|
|
||
| impl Module for LeNet5 { | ||
| fn forward(&self, xs: &Tensor) -> Result<Tensor, candle_core::error::Error> { | ||
| let Self { | ||
| conv1, | ||
| conv2, | ||
| dense1, | ||
| dense2, | ||
| dense3, | ||
| } = self; | ||
| let x = conv1.forward(xs)?.relu()?.max_pool2d(2)?; | ||
| let x = conv2.forward(&x)?.relu()?.max_pool2d(2)?.flatten_from(1)?; | ||
| let x = dense1.forward(&x)?.relu()?; | ||
| let x = dense2.forward(&x)?.relu()?; | ||
| dense3.forward(&x) | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,6 @@ | ||
| use autograph::half; | ||
| pub mod autograph_backend; | ||
| #[cfg(feature = "candle")] | ||
| pub mod candle_backend; | ||
| #[cfg(feature = "tch")] | ||
| pub mod tch_backend; |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,5 @@ | ||
| use crate::half::bf16; | ||
| use anyhow::Result; | ||
| use autograph::half::bf16; | ||
| use tch::{ | ||
| kind::Kind, | ||
| nn::{ | ||
|
|
||