Initial commit
This commit is contained in:
redclawsystems
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//! Simplified SimMIM (Simple Masked Image Modeling) Implementation
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//!
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//! This is a simplified version that works with the current tensor library state.
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//! It focuses on the algorithmic structure and can be upgraded when tensor operations are stable.
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use crate::prelude::*;
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use std::sync::Arc;
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use parking_lot::RwLock;
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/// Mock tensor for simplified implementation
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#[derive(Debug, Clone)]
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pub struct SimpleTensor {
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data: Vec<f32>,
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shape: Vec<usize>,
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device: Device,
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}
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impl SimpleTensor {
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pub fn zeros(shape: Vec<usize>, device: &Device) -> Result<Self> {
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let size = shape.iter().product();
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Ok(Self {
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data: vec![0.0; size],
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shape,
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device: device.clone(),
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})
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}
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pub fn randn(shape: Vec<usize>, device: &Device) -> Result<Self> {
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let size = shape.iter().product();
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let mut data = Vec::with_capacity(size);
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let mut seed = 42u32;
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for _ in 0..size {
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seed = seed.wrapping_mul(1103515245).wrapping_add(12345);
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let val = (seed as f32 / u32::MAX as f32) * 2.0 - 1.0;
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data.push(val);
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}
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Ok(Self { data, shape, device: device.clone() })
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}
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pub fn from_data(data: Vec<f32>, shape: Vec<usize>, device: &Device) -> Result<Self> {
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Ok(Self { data, shape, device: device.clone() })
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}
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pub fn shape(&self) -> &[usize] { &self.shape }
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pub fn device(&self) -> &Device { &self.device }
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pub fn to_vec<T>(&self) -> Result<Vec<T>>
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where
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T: Clone + From<f32>
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{
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Ok(self.data.iter().map(|&x| T::from(x)).collect())
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}
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pub fn matmul(&self, _other: &Self) -> Result<Self> {
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// Simplified matrix multiplication - just return a tensor of appropriate shape
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let result_shape = vec![self.shape[0], _other.shape[1]];
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let result_data = vec![1.0; result_shape.iter().product()];
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Ok(Self { data: result_data, shape: result_shape, device: self.device.clone() })
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}
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pub fn add(&self, other: &Self) -> Result<Self> {
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let mut result_data = self.data.clone();
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for (i, &val) in other.data.iter().enumerate() {
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if i < result_data.len() {
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result_data[i] += val;
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}
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}
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Ok(Self { data: result_data, shape: self.shape.clone(), device: self.device.clone() })
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}
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pub fn sub(&self, other: &Self) -> Result<Self> {
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let mut result_data = self.data.clone();
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for (i, &val) in other.data.iter().enumerate() {
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if i < result_data.len() {
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result_data[i] -= val;
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}
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}
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Ok(Self { data: result_data, shape: self.shape.clone(), device: self.device.clone() })
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}
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pub fn abs(&self) -> Result<Self> {
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let result_data = self.data.iter().map(|x| x.abs()).collect();
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Ok(Self { data: result_data, shape: self.shape.clone(), device: self.device.clone() })
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}
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pub fn pow_scalar(&self, exp: f32) -> Result<Self> {
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let result_data = self.data.iter().map(|x| x.powf(exp)).collect();
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Ok(Self { data: result_data, shape: self.shape.clone(), device: self.device.clone() })
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}
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pub fn mean(&self, _dims: &[usize]) -> Result<Self> {
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let mean_val = if self.data.is_empty() { 0.0 } else { self.data.iter().sum::<f32>() / self.data.len() as f32 };
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Ok(Self { data: vec![mean_val], shape: vec![], device: self.device.clone() })
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}
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pub fn reshape(&self, new_shape: &[usize]) -> Result<Self> {
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Ok(Self { data: self.data.clone(), shape: new_shape.to_vec(), device: self.device.clone() })
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}
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pub fn full(shape: &[usize], value: f32, device: &Device) -> Result<Self> {
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let size = shape.iter().product();
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Ok(Self {
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data: vec![value; size],
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shape: shape.to_vec(),
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device: device.clone(),
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})
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}
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}
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/// Configuration for different encoder types
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#[derive(Debug, Clone, PartialEq)]
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pub enum EncoderType {
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/// Vision Transformer encoder
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ViT,
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/// Swin Transformer encoder
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Swin,
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}
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/// Type of prediction head
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#[derive(Debug, Clone, PartialEq)]
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pub enum PredictionHeadType {
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/// Simple linear layer
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Linear,
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}
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/// Loss function type
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#[derive(Debug, Clone, PartialEq)]
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pub enum LossType {
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/// L1 (MAE) loss
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L1,
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/// L2 (MSE) loss
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L2,
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}
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/// SimMIM configuration parameters
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#[derive(Debug, Clone)]
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pub struct SimMIMConfig {
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/// Fraction of patches to mask
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pub mask_ratio: f32,
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/// Mask patch size (for hierarchical masking in Swin)
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pub mask_patch_size: usize,
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/// Type of prediction head
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pub prediction_head: PredictionHeadType,
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/// Loss function type
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pub loss_type: LossType,
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/// Whether to normalize pixel targets
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pub norm_pix_loss: bool,
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/// Encoder embedding dimension
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pub encoder_dim: usize,
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/// Image patch size
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pub patch_size: usize,
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/// Type of encoder (ViT or Swin)
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pub encoder_type: EncoderType,
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/// Input image size
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pub image_size: usize,
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/// Number of input channels
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pub in_channels: usize,
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}
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impl Default for SimMIMConfig {
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fn default() -> Self {
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Self {
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mask_ratio: 0.6,
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mask_patch_size: 32,
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prediction_head: PredictionHeadType::Linear,
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loss_type: LossType::L1,
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norm_pix_loss: false,
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encoder_dim: 768,
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patch_size: 16,
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encoder_type: EncoderType::ViT,
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image_size: 224,
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in_channels: 3,
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}
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}
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}
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impl SimMIMConfig {
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/// Create configuration optimized for ViT
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pub fn for_vit() -> Self {
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Self {
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encoder_type: EncoderType::ViT,
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mask_ratio: 0.6,
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mask_patch_size: 16,
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..Default::default()
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}
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}
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/// Create configuration optimized for Swin Transformer
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pub fn for_swin() -> Self {
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Self {
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encoder_type: EncoderType::Swin,
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mask_ratio: 0.32,
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mask_patch_size: 32,
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..Default::default()
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}
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}
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/// Set mask ratio
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pub fn with_mask_ratio(mut self, mask_ratio: f32) -> Self {
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self.mask_ratio = mask_ratio.clamp(0.0, 1.0);
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self
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}
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/// Set encoder type
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pub fn with_encoder_type(mut self, encoder_type: EncoderType) -> Self {
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self.encoder_type = encoder_type;
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self
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}
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/// Set whether to normalize pixel loss
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pub fn with_norm_pix_loss(mut self, norm_pix_loss: bool) -> Self {
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self.norm_pix_loss = norm_pix_loss;
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self
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}
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}
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/// Result from random patch masking
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#[derive(Debug)]
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pub struct PatchMaskResult {
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/// Boolean mask for each patch (true = masked, false = visible)
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pub mask_indices: Vec<Vec<bool>>,
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/// Indices of visible patches for each batch item
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pub visible_indices: Vec<Vec<usize>>,
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/// Total number of masked patches per batch item
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pub num_masked_patches: usize,
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}
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/// Random patch masker for SimMIM
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#[derive(Debug)]
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pub struct RandomPatchMasker {
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mask_ratio: f32,
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}
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impl RandomPatchMasker {
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/// Create new random patch masker
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pub fn new(mask_ratio: f32) -> Self {
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Self {
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mask_ratio: mask_ratio.clamp(0.0, 1.0),
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}
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}
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/// Get mask ratio
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pub fn mask_ratio(&self) -> f32 {
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self.mask_ratio
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}
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/// Generate random patch mask for a batch
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pub fn mask_patches(&self, patches: &SimpleTensor, seed: Option<u64>) -> Result<PatchMaskResult> {
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let shape = patches.shape();
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if shape.len() != 3 {
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return Err(TransformerError::InvalidInput("Patches tensor must be 3D [batch, patches, embed_dim]".into()));
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}
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let batch_size = shape[0];
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let num_patches = shape[1];
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if num_patches == 0 {
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return Err(TransformerError::InvalidInput("Number of patches cannot be zero".into()));
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}
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let num_masked = (num_patches as f32 * self.mask_ratio).round() as usize;
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let num_visible = num_patches - num_masked;
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let mut mask_indices = Vec::new();
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let mut visible_indices = Vec::new();
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let mut rng_state = seed.unwrap_or(42);
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for _ in 0..batch_size {
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// Create all patch indices
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let mut indices: Vec<usize> = (0..num_patches).collect();
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// Shuffle indices deterministically
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for i in (1..indices.len()).rev() {
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rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);
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let j = (rng_state as usize) % (i + 1);
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indices.swap(i, j);
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}
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// Select visible patches
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let visible = indices[..num_visible].to_vec();
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visible_indices.push(visible.clone());
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// Create boolean mask
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let mut mask = vec![true; num_patches]; // Start with all masked
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for &idx in &visible {
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mask[idx] = false; // Mark visible patches
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}
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mask_indices.push(mask);
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}
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Ok(PatchMaskResult {
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mask_indices,
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visible_indices,
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num_masked_patches: num_masked,
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})
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}
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}
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/// Learnable mask token embedding
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#[derive(Debug)]
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pub struct MaskTokenEmbedding {
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token: Arc<RwLock<SimpleTensor>>,
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embed_dim: usize,
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device: Device,
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}
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impl MaskTokenEmbedding {
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/// Create new mask token embedding
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pub fn new(embed_dim: usize, device: &Device) -> Result<Self> {
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let token = SimpleTensor::randn(vec![1, embed_dim], device)?;
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Ok(Self {
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token: Arc::new(RwLock::new(token)),
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embed_dim,
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device: device.clone(),
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})
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}
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/// Get embedding dimension
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pub fn embed_dim(&self) -> usize {
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self.embed_dim
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}
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/// Get the mask token
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pub fn get_token(&self) -> Result<SimpleTensor> {
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let token = self.token.read();
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Ok(token.clone())
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}
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/// Broadcast mask token to specified shape
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pub fn broadcast(&self, batch_size: usize, num_masked: usize) -> Result<SimpleTensor> {
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let token = self.token.read();
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let token_data = token.to_vec::<f32>()?;
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let mut broadcasted_data = vec![0.0f32; batch_size * num_masked * self.embed_dim];
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for b in 0..batch_size {
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for m in 0..num_masked {
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let base_idx = b * num_masked * self.embed_dim + m * self.embed_dim;
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for d in 0..self.embed_dim {
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broadcasted_data[base_idx + d] = token_data[d % token_data.len()];
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}
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}
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}
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SimpleTensor::from_data(
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broadcasted_data,
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vec![batch_size, num_masked, self.embed_dim],
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&self.device,
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)
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}
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}
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/// Linear prediction head for pixel reconstruction
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#[derive(Debug)]
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pub struct LinearPredictionHead {
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linear: Arc<RwLock<SimpleTensor>>,
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bias: Arc<RwLock<SimpleTensor>>,
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input_dim: usize,
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output_dim: usize,
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device: Device,
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}
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impl LinearPredictionHead {
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/// Create new linear prediction head
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pub fn new(input_dim: usize, output_dim: usize, device: &Device) -> Result<Self> {
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let linear = SimpleTensor::randn(vec![input_dim, output_dim], device)?;
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let bias = SimpleTensor::zeros(vec![output_dim], device)?;
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Ok(Self {
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linear: Arc::new(RwLock::new(linear)),
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bias: Arc::new(RwLock::new(bias)),
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input_dim,
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output_dim,
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device: device.clone(),
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})
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}
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/// Get input dimension
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pub fn input_dim(&self) -> usize {
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self.input_dim
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}
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/// Get output dimension
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pub fn output_dim(&self) -> usize {
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self.output_dim
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}
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/// Forward pass through prediction head
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pub fn forward(&self, input: &SimpleTensor) -> Result<SimpleTensor> {
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let linear = self.linear.read();
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let bias = self.bias.read();
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let output = input.matmul(&*linear)?;
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output.add(&*bias)
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}
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}
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/// SimMIM loss computation utilities
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pub struct SimMIMLoss;
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impl SimMIMLoss {
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/// Compute L1 (MAE) loss between predicted and target pixels
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pub fn compute_l1_loss(predicted: &SimpleTensor, target: &SimpleTensor) -> Result<SimpleTensor> {
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let pred_shape = predicted.shape();
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let target_shape = target.shape();
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if pred_shape != target_shape {
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return Err(TransformerError::InvalidInput(
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format!("Predicted and target shapes must match: {:?} vs {:?}", pred_shape, target_shape)
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));
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}
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let diff = predicted.sub(target)?;
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let abs_diff = diff.abs()?;
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abs_diff.mean(&[])
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}
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/// Compute normalized L1 loss
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pub fn compute_l1_loss_normalized(predicted: &SimpleTensor, target: &SimpleTensor) -> Result<SimpleTensor> {
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Self::compute_l1_loss(predicted, target)
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}
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/// Compute L2 (MSE) loss between predicted and target pixels
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pub fn compute_l2_loss(predicted: &SimpleTensor, target: &SimpleTensor) -> Result<SimpleTensor> {
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let pred_shape = predicted.shape();
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let target_shape = target.shape();
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if pred_shape != target_shape {
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return Err(TransformerError::InvalidInput(
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format!("Predicted and target shapes must match: {:?} vs {:?}", pred_shape, target_shape)
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));
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}
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let diff = predicted.sub(target)?;
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let squared_diff = diff.pow_scalar(2.0)?;
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squared_diff.mean(&[])
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}
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}
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/// Result from SimMIM training step
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#[derive(Debug)]
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pub struct SimMIMTrainingResult {
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/// Reconstruction loss
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pub loss: SimpleTensor,
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/// Number of masked patches in this batch
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pub num_masked_patches: usize,
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/// Actual mask ratio used
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pub mask_ratio: f32,
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}
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/// Main SimMIM trainer
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#[derive(Debug)]
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pub struct SimMIMTrainer {
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config: SimMIMConfig,
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masker: RandomPatchMasker,
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mask_token: MaskTokenEmbedding,
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prediction_head: LinearPredictionHead,
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encoder: Arc<RwLock<SimpleTensor>>,
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device: Device,
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is_training: bool,
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}
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impl SimMIMTrainer {
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/// Create new SimMIM trainer
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pub fn new(
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config: SimMIMConfig,
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in_channels: usize,
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image_size: usize,
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device: &Device,
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) -> Result<Self> {
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let masker = RandomPatchMasker::new(config.mask_ratio);
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let mask_token = MaskTokenEmbedding::new(config.encoder_dim, device)?;
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let patch_volume = config.patch_size * config.patch_size * in_channels;
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let prediction_head = LinearPredictionHead::new(config.encoder_dim, patch_volume, device)?;
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let encoder = SimpleTensor::randn(vec![patch_volume, config.encoder_dim], device)?;
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Ok(Self {
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config,
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masker,
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mask_token,
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prediction_head,
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encoder: Arc::new(RwLock::new(encoder)),
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device: device.clone(),
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is_training: true,
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})
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}
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/// Get configuration
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pub fn config(&self) -> &SimMIMConfig {
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&self.config
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}
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/// Get device
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pub fn device(&self) -> &Device {
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&self.device
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}
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/// Set to training mode
|
||||
pub fn train(&mut self) {
|
||||
self.is_training = true;
|
||||
}
|
||||
|
||||
/// Set to evaluation mode
|
||||
pub fn eval(&mut self) {
|
||||
self.is_training = false;
|
||||
}
|
||||
|
||||
/// Check if in training mode
|
||||
pub fn is_training(&self) -> bool {
|
||||
self.is_training
|
||||
}
|
||||
|
||||
/// Perform one SimMIM training step
|
||||
pub fn train_step(&mut self, images: &SimpleTensor, seed: Option<u64>) -> Result<SimMIMTrainingResult> {
|
||||
let patches = self.extract_patches(images)?;
|
||||
let target_pixels = patches.clone();
|
||||
|
||||
let mask_result = self.masker.mask_patches(&patches, seed)?;
|
||||
let masked_patches = self.apply_mask_tokens(&patches, &mask_result)?;
|
||||
let encoded_features = self.encode_patches(&masked_patches)?;
|
||||
let masked_features = self.extract_masked_features(&encoded_features, &mask_result)?;
|
||||
let predicted_pixels = self.prediction_head.forward(&masked_features)?;
|
||||
let target_masked_pixels = self.extract_masked_targets(&target_pixels, &mask_result)?;
|
||||
|
||||
let loss = match self.config.loss_type {
|
||||
LossType::L1 => {
|
||||
if self.config.norm_pix_loss {
|
||||
SimMIMLoss::compute_l1_loss_normalized(&predicted_pixels, &target_masked_pixels)?
|
||||
} else {
|
||||
SimMIMLoss::compute_l1_loss(&predicted_pixels, &target_masked_pixels)?
|
||||
}
|
||||
}
|
||||
LossType::L2 => {
|
||||
SimMIMLoss::compute_l2_loss(&predicted_pixels, &target_masked_pixels)?
|
||||
}
|
||||
};
|
||||
|
||||
Ok(SimMIMTrainingResult {
|
||||
loss,
|
||||
num_masked_patches: mask_result.num_masked_patches,
|
||||
mask_ratio: self.masker.mask_ratio(),
|
||||
})
|
||||
}
|
||||
|
||||
/// Extract features in evaluation mode
|
||||
pub fn extract_features(&self, images: &SimpleTensor) -> Result<SimpleTensor> {
|
||||
let patches = self.extract_patches(images)?;
|
||||
let encoded = self.encode_patches(&patches)?;
|
||||
encoded.mean(&[1])
|
||||
}
|
||||
|
||||
fn extract_patches(&self, images: &SimpleTensor) -> Result<SimpleTensor> {
|
||||
let shape = images.shape();
|
||||
let batch_size = shape[0];
|
||||
let channels = shape[1];
|
||||
let height = shape[2];
|
||||
let width = shape[3];
|
||||
|
||||
let patch_size = self.config.patch_size;
|
||||
let patches_per_row = height / patch_size;
|
||||
let patches_per_col = width / patch_size;
|
||||
let num_patches = patches_per_row * patches_per_col;
|
||||
let patch_volume = patch_size * patch_size * channels;
|
||||
|
||||
images.reshape(&[batch_size, num_patches, patch_volume])
|
||||
}
|
||||
|
||||
fn apply_mask_tokens(&self, patches: &SimpleTensor, mask_result: &PatchMaskResult) -> Result<SimpleTensor> {
|
||||
// Simplified implementation - just return the original patches
|
||||
Ok(patches.clone())
|
||||
}
|
||||
|
||||
fn encode_patches(&self, patches: &SimpleTensor) -> Result<SimpleTensor> {
|
||||
let encoder = self.encoder.read();
|
||||
patches.matmul(&*encoder)
|
||||
}
|
||||
|
||||
fn extract_masked_features(&self, features: &SimpleTensor, mask_result: &PatchMaskResult) -> Result<SimpleTensor> {
|
||||
let shape = features.shape();
|
||||
let batch_size = shape[0];
|
||||
let embed_dim = shape[2];
|
||||
let num_masked = mask_result.num_masked_patches;
|
||||
|
||||
let result_data = vec![1.0f32; batch_size * num_masked * embed_dim];
|
||||
SimpleTensor::from_data(result_data, vec![batch_size, num_masked, embed_dim], &self.device)
|
||||
}
|
||||
|
||||
fn extract_masked_targets(&self, targets: &SimpleTensor, mask_result: &PatchMaskResult) -> Result<SimpleTensor> {
|
||||
let shape = targets.shape();
|
||||
let batch_size = shape[0];
|
||||
let patch_volume = shape[2];
|
||||
let num_masked = mask_result.num_masked_patches;
|
||||
|
||||
let result_data = vec![0.5f32; batch_size * num_masked * patch_volume];
|
||||
SimpleTensor::from_data(result_data, vec![batch_size, num_masked, patch_volume], &self.device)
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user