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burn
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burn/backend-comparison/build.rs

282 lines
9.5 KiB
Rust
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use std::env;
use std::fs;
use std::path::Path;
use std::process::Command;
const MODELS_REPO: &str = "https://github.com/tracel-ai/models.git";
// Patch resnet code (remove pretrained feature code)
const PATCH: &str = r#"diff --git a/resnet-burn/resnet/src/resnet.rs b/resnet-burn/resnet/src/resnet.rs
index e7f8787..3967049 100644
--- a/resnet-burn/resnet/src/resnet.rs
+++ b/resnet-burn/resnet/src/resnet.rs
@@ -12,13 +12,6 @@ use burn::{
use super::block::{LayerBlock, LayerBlockConfig};
-#[cfg(feature = "pretrained")]
-use {
- super::weights::{self, WeightsMeta},
- burn::record::{FullPrecisionSettings, Recorder, RecorderError},
- burn_import::pytorch::{LoadArgs, PyTorchFileRecorder},
-};
-
// ResNet residual layer block configs
const RESNET18_BLOCKS: [usize; 4] = [2, 2, 2, 2];
const RESNET34_BLOCKS: [usize; 4] = [3, 4, 6, 3];
@@ -77,29 +70,6 @@ impl<B: Backend> ResNet<B> {
ResNetConfig::new(RESNET18_BLOCKS, num_classes, 1).init(device)
}
- /// ResNet-18 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385)
- /// with pre-trained weights.
- ///
- /// # Arguments
- ///
- /// * `weights`: Pre-trained weights to load.
- /// * `device` - Device to create the module on.
- ///
- /// # Returns
- ///
- /// A ResNet-18 module with pre-trained weights.
- #[cfg(feature = "pretrained")]
- pub fn resnet18_pretrained(
- weights: weights::ResNet18,
- device: &Device<B>,
- ) -> Result<Self, RecorderError> {
- let weights = weights.weights();
- let record = Self::load_weights_record(&weights, device)?;
- let model = ResNet::<B>::resnet18(weights.num_classes, device).load_record(record);
-
- Ok(model)
- }
-
/// ResNet-34 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385).
///
/// # Arguments
@@ -114,29 +84,6 @@ impl<B: Backend> ResNet<B> {
ResNetConfig::new(RESNET34_BLOCKS, num_classes, 1).init(device)
}
- /// ResNet-34 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385)
- /// with pre-trained weights.
- ///
- /// # Arguments
- ///
- /// * `weights`: Pre-trained weights to load.
- /// * `device` - Device to create the module on.
- ///
- /// # Returns
- ///
- /// A ResNet-34 module with pre-trained weights.
- #[cfg(feature = "pretrained")]
- pub fn resnet34_pretrained(
- weights: weights::ResNet34,
- device: &Device<B>,
- ) -> Result<Self, RecorderError> {
- let weights = weights.weights();
- let record = Self::load_weights_record(&weights, device)?;
- let model = ResNet::<B>::resnet34(weights.num_classes, device).load_record(record);
-
- Ok(model)
- }
-
/// ResNet-50 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385).
///
/// # Arguments
@@ -151,29 +98,6 @@ impl<B: Backend> ResNet<B> {
ResNetConfig::new(RESNET50_BLOCKS, num_classes, 4).init(device)
}
- /// ResNet-50 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385)
- /// with pre-trained weights.
- ///
- /// # Arguments
- ///
- /// * `weights`: Pre-trained weights to load.
- /// * `device` - Device to create the module on.
- ///
- /// # Returns
- ///
- /// A ResNet-50 module with pre-trained weights.
- #[cfg(feature = "pretrained")]
- pub fn resnet50_pretrained(
- weights: weights::ResNet50,
- device: &Device<B>,
- ) -> Result<Self, RecorderError> {
- let weights = weights.weights();
- let record = Self::load_weights_record(&weights, device)?;
- let model = ResNet::<B>::resnet50(weights.num_classes, device).load_record(record);
-
- Ok(model)
- }
-
/// ResNet-101 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385).
///
/// # Arguments
@@ -188,29 +112,6 @@ impl<B: Backend> ResNet<B> {
ResNetConfig::new(RESNET101_BLOCKS, num_classes, 4).init(device)
}
- /// ResNet-101 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385)
- /// with pre-trained weights.
- ///
- /// # Arguments
- ///
- /// * `weights`: Pre-trained weights to load.
- /// * `device` - Device to create the module on.
- ///
- /// # Returns
- ///
- /// A ResNet-101 module with pre-trained weights.
- #[cfg(feature = "pretrained")]
- pub fn resnet101_pretrained(
- weights: weights::ResNet101,
- device: &Device<B>,
- ) -> Result<Self, RecorderError> {
- let weights = weights.weights();
- let record = Self::load_weights_record(&weights, device)?;
- let model = ResNet::<B>::resnet101(weights.num_classes, device).load_record(record);
-
- Ok(model)
- }
-
/// ResNet-152 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385).
///
/// # Arguments
@@ -225,29 +126,6 @@ impl<B: Backend> ResNet<B> {
ResNetConfig::new(RESNET152_BLOCKS, num_classes, 4).init(device)
}
- /// ResNet-152 from [`Deep Residual Learning for Image Recognition`](https://arxiv.org/abs/1512.03385)
- /// with pre-trained weights.
- ///
- /// # Arguments
- ///
- /// * `weights`: Pre-trained weights to load.
- /// * `device` - Device to create the module on.
- ///
- /// # Returns
- ///
- /// A ResNet-152 module with pre-trained weights.
- #[cfg(feature = "pretrained")]
- pub fn resnet152_pretrained(
- weights: weights::ResNet152,
- device: &Device<B>,
- ) -> Result<Self, RecorderError> {
- let weights = weights.weights();
- let record = Self::load_weights_record(&weights, device)?;
- let model = ResNet::<B>::resnet152(weights.num_classes, device).load_record(record);
-
- Ok(model)
- }
-
/// Re-initialize the last layer with the specified number of output classes.
pub fn with_classes(mut self, num_classes: usize) -> Self {
let [d_input, _d_output] = self.fc.weight.dims();
@@ -256,32 +134,6 @@ impl<B: Backend> ResNet<B> {
}
}
-#[cfg(feature = "pretrained")]
-impl<B: Backend> ResNet<B> {
- /// Load specified pre-trained PyTorch weights as a record.
- fn load_weights_record(
- weights: &weights::Weights,
- device: &Device<B>,
- ) -> Result<ResNetRecord<B>, RecorderError> {
- // Download torch weights
- let torch_weights = weights.download().map_err(|err| {
- RecorderError::Unknown(format!("Could not download weights.\nError: {err}"))
- })?;
-
- // Load weights from torch state_dict
- let load_args = LoadArgs::new(torch_weights)
- // Map *.downsample.0.* -> *.downsample.conv.*
- .with_key_remap("(.+)\\.downsample\\.0\\.(.+)", "$1.downsample.conv.$2")
- // Map *.downsample.1.* -> *.downsample.bn.*
- .with_key_remap("(.+)\\.downsample\\.1\\.(.+)", "$1.downsample.bn.$2")
- // Map layer[i].[j].* -> layer[i].blocks.[j].*
- .with_key_remap("(layer[1-4])\\.([0-9]+)\\.(.+)", "$1.blocks.$2.$3");
- let record = PyTorchFileRecorder::<FullPrecisionSettings>::new().load(load_args, device)?;
-
- Ok(record)
- }
-}
-
/// [ResNet](ResNet) configuration.
struct ResNetConfig {
conv1: Conv2dConfig,
"#;
fn run<F>(name: &str, mut configure: F)
where
F: FnMut(&mut Command) -> &mut Command,
{
let mut command = Command::new(name);
let configured = configure(&mut command);
println!("Executing {:?}", configured);
if !configured.status().unwrap().success() {
panic!("failed to execute {:?}", configured);
}
println!("Command {:?} finished successfully", configured);
}
fn main() {
let models_dir = std::env::temp_dir().join("models");
let models_dir = models_dir.as_path();
// Checkout ResNet code from models repo
let models_dir = Path::new(models_dir);
if !models_dir.join(".git").exists() {
run("git", |command| {
command
.arg("clone")
.arg("--depth=1")
.arg("--no-checkout")
.arg(MODELS_REPO)
.arg(models_dir)
});
run("git", |command| {
command
.current_dir(models_dir)
.arg("sparse-checkout")
.arg("set")
.arg("resnet-burn")
});
run("git", |command| {
command.current_dir(models_dir).arg("checkout")
});
let patch_file = models_dir.join("benchmark.patch");
fs::write(&patch_file, PATCH).expect("should write to file successfully");
// Apply patch
run("git", |command| {
command
.current_dir(models_dir)
.arg("apply")
.arg(patch_file.to_str().unwrap())
});
}
// Copy contents to output dir
let out_dir = env::var("OUT_DIR").unwrap();
let source_path = models_dir.join("resnet-burn").join("resnet").join("src");
let dest_path = Path::new(&out_dir);
if let Ok(source_path) = fs::read_dir(source_path) {
for file in source_path {
let source_file = file.unwrap().path();
let dest_file = dest_path.join(source_file.file_name().unwrap());
fs::copy(source_file, dest_file).expect("should copy file successfully");
}
}
// Delete cloned repository contents
fs::remove_dir_all(models_dir.join(".git")).unwrap();
fs::remove_dir_all(models_dir).unwrap();
}
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