515 lines
15 KiB
Rust
515 lines
15 KiB
Rust
#!/usr/bin/env rust-script
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//! # CoAtNet TDD Standalone Test
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//!
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//! This is a standalone test to verify CoAtNet implementation in strict TDD mode.
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//! Running the red phase - tests should FAIL initially to validate TDD approach.
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use std::fmt;
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// Mock error and tensor types for standalone testing
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#[derive(Debug)]
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pub enum VisionError {
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InvalidConfig(String),
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TensorError(String),
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ComputationError(String),
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}
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impl std::fmt::Display for VisionError {
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fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
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match self {
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VisionError::InvalidConfig(msg) => write!(f, "Invalid config: {}", msg),
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VisionError::TensorError(msg) => write!(f, "Tensor error: {}", msg),
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VisionError::ComputationError(msg) => write!(f, "Computation error: {}", msg),
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}
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}
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}
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impl std::error::Error for VisionError {}
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pub type Result<T> = std::result::Result<T, VisionError>;
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// Mock device
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#[derive(Debug, Clone, PartialEq)]
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pub struct Device;
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impl Device {
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pub fn cpu() -> Self { Self }
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}
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// Mock tensor
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#[derive(Debug, Clone)]
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pub struct Tensor {
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shape: Vec<usize>,
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data: Vec<f32>,
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}
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impl Tensor {
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pub fn zeros(shape: Vec<usize>, _device: &Device) -> Result<Self> {
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let numel = shape.iter().product();
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Ok(Self { shape, data: vec![0.0; numel] })
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}
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pub fn randn(shape: Vec<usize>, _device: &Device) -> Result<Self> {
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let numel = shape.iter().product();
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Ok(Self { shape, data: vec![1.0; numel] })
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}
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pub fn shape(&self) -> &[usize] { &self.shape }
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pub fn mul_scalar(&self, scalar: f32) -> Result<Self> {
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let data = self.data.iter().map(|x| x * scalar).collect();
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Ok(Self { shape: self.shape.clone(), data })
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}
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pub fn add_scalar(&self, scalar: f32) -> Result<Self> {
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let data = self.data.iter().map(|x| x + scalar).collect();
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Ok(Self { shape: self.shape.clone(), data })
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}
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pub fn mean_dim(&self, _dims: &[usize], _keepdim: bool) -> Result<Self> {
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if self.shape.len() >= 4 {
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// For 4D tensor [B, C, H, W], mean over spatial dims gives [B, C]
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let batch_size = self.shape[0];
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let channels = self.shape[1];
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Ok(Self {
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shape: vec![batch_size, channels],
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data: vec![0.5; batch_size * channels]
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})
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} else {
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Ok(Self { shape: vec![], data: vec![0.5] })
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}
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}
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pub fn matmul(&self, other: &Self) -> Result<Self> {
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let result_shape = vec![self.shape[0], other.shape[1]];
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let numel = result_shape.iter().product();
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Ok(Self { shape: result_shape, data: vec![1.0; numel] })
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}
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}
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// CoAtNet types and implementations
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#[derive(Debug, Clone, Copy, PartialEq, Eq)]
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pub enum CoAtNetVariant {
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CoAtNet0, CoAtNet1, CoAtNet2, CoAtNet3, CoAtNet4,
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}
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#[derive(Debug, Clone)]
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pub struct CoAtNetConfig {
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pub variant: CoAtNetVariant,
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pub num_classes: usize,
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pub image_size: usize,
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pub channels: Vec<usize>,
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pub depths: Vec<usize>,
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pub stem_channels: usize,
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pub drop_path_rate: f32,
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pub dropout_rate: f32,
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}
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impl CoAtNetConfig {
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pub fn coatnet0() -> Self {
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Self {
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variant: CoAtNetVariant::CoAtNet0,
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num_classes: 1000,
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image_size: 224,
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channels: vec![64, 96, 192, 384, 768],
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depths: vec![2, 3, 5, 2],
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stem_channels: 64,
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drop_path_rate: 0.1,
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dropout_rate: 0.1,
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}
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}
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pub fn coatnet2() -> Self {
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Self {
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variant: CoAtNetVariant::CoAtNet2,
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num_classes: 1000,
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image_size: 224,
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channels: vec![128, 128, 256, 512, 1024],
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depths: vec![2, 6, 14, 2],
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stem_channels: 128,
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drop_path_rate: 0.2,
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dropout_rate: 0.1,
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}
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}
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pub fn coatnet4() -> Self {
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Self {
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variant: CoAtNetVariant::CoAtNet4,
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num_classes: 1000,
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image_size: 384,
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channels: vec![192, 256, 512, 1024, 2048],
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depths: vec![3, 15, 40, 4],
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stem_channels: 192,
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drop_path_rate: 0.3,
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dropout_rate: 0.2,
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}
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}
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}
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#[derive(Debug, Clone)]
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pub struct MBConvConfig {
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pub in_channels: usize,
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pub out_channels: usize,
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pub expansion_ratio: usize,
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pub se_ratio: f32,
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}
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pub struct MBConvBlock {
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config: MBConvConfig,
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}
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impl MBConvBlock {
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pub fn new(config: MBConvConfig, _device: &Device) -> Result<Self> {
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Ok(Self { config })
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}
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pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
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let expanded = x.mul_scalar(self.config.expansion_ratio as f32)?;
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expanded.add_scalar(0.1)
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}
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}
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#[derive(Debug, Clone)]
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pub struct RelativeAttentionConfig {
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pub dim: usize,
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pub num_heads: usize,
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}
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pub struct RelativeAttention {
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config: RelativeAttentionConfig,
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}
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impl RelativeAttention {
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pub fn new(config: RelativeAttentionConfig, _device: &Device) -> Result<Self> {
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Ok(Self { config })
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}
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pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
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x.mul_scalar(0.99)
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}
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}
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pub struct TransformerBlock {
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attention: RelativeAttention,
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}
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impl TransformerBlock {
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pub fn new(dim: usize, num_heads: usize, device: &Device) -> Result<Self> {
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let attn_config = RelativeAttentionConfig { dim, num_heads };
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let attention = RelativeAttention::new(attn_config, device)?;
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Ok(Self { attention })
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}
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pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
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let attended = self.attention.forward(x)?;
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attended.add_scalar(0.01)
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}
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}
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pub struct CoAtNet {
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config: CoAtNetConfig,
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stem: MBConvBlock,
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classifier: Tensor,
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}
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impl CoAtNet {
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pub fn new(config: CoAtNetConfig, device: &Device) -> Result<Self> {
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let stem_config = MBConvConfig {
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in_channels: 3,
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out_channels: config.stem_channels,
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expansion_ratio: 1,
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se_ratio: 0.25,
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};
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let stem = MBConvBlock::new(stem_config, device)?;
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let classifier = Tensor::zeros(vec![config.channels[4], config.num_classes], device)?;
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Ok(Self { config, stem, classifier })
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}
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pub fn coatnet0(num_classes: usize, device: &Device) -> Result<Self> {
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let mut config = CoAtNetConfig::coatnet0();
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config.num_classes = num_classes;
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Self::new(config, device)
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}
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pub fn coatnet2(num_classes: usize, device: &Device) -> Result<Self> {
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let mut config = CoAtNetConfig::coatnet2();
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config.num_classes = num_classes;
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Self::new(config, device)
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}
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pub fn coatnet4(num_classes: usize, device: &Device) -> Result<Self> {
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let mut config = CoAtNetConfig::coatnet4();
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config.num_classes = num_classes;
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Self::new(config, device)
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}
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pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
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let features = self.stem.forward(x)?;
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let pooled = features.mean_dim(&[2, 3], false)?;
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pooled.matmul(&self.classifier)
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}
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pub fn config(&self) -> &CoAtNetConfig { &self.config }
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}
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// TDD Tests - RED PHASE (These should ALL FAIL initially)
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_coatnet_variant_types() {
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assert_eq!(CoAtNetVariant::CoAtNet0, CoAtNetVariant::CoAtNet0);
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assert_ne!(CoAtNetVariant::CoAtNet0, CoAtNetVariant::CoAtNet4);
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let variants = vec![
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CoAtNetVariant::CoAtNet0,
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CoAtNetVariant::CoAtNet1,
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CoAtNetVariant::CoAtNet2,
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CoAtNetVariant::CoAtNet3,
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CoAtNetVariant::CoAtNet4,
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];
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assert_eq!(variants.len(), 5);
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}
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#[test]
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fn test_coatnet_config_creation() {
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let config0 = CoAtNetConfig::coatnet0();
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assert_eq!(config0.variant, CoAtNetVariant::CoAtNet0);
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assert_eq!(config0.channels, vec![64, 96, 192, 384, 768]);
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assert_eq!(config0.depths, vec![2, 3, 5, 2]);
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assert_eq!(config0.image_size, 224);
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assert_eq!(config0.drop_path_rate, 0.1);
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let config2 = CoAtNetConfig::coatnet2();
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assert_eq!(config2.variant, CoAtNetVariant::CoAtNet2);
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assert_eq!(config2.channels, vec![128, 128, 256, 512, 1024]);
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assert_eq!(config2.depths, vec![2, 6, 14, 2]);
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assert_eq!(config2.drop_path_rate, 0.2);
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let config4 = CoAtNetConfig::coatnet4();
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assert_eq!(config4.variant, CoAtNetVariant::CoAtNet4);
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assert_eq!(config4.image_size, 384);
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assert_eq!(config4.drop_path_rate, 0.3);
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}
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#[test]
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fn test_mbconv_config_validation() {
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let config = MBConvConfig {
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in_channels: 64,
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out_channels: 128,
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expansion_ratio: 4,
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se_ratio: 0.25,
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};
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assert_eq!(config.in_channels, 64);
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assert_eq!(config.out_channels, 128);
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assert_eq!(config.expansion_ratio, 4);
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assert_eq!(config.se_ratio, 0.25);
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// SE ratio should be between 0 and 1
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assert!(config.se_ratio > 0.0 && config.se_ratio <= 1.0);
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// Expansion ratio should be positive
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assert!(config.expansion_ratio > 0);
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}
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#[test]
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fn test_relative_attention_config() {
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let config = RelativeAttentionConfig {
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dim: 256,
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num_heads: 8,
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};
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assert_eq!(config.dim, 256);
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assert_eq!(config.num_heads, 8);
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// Dimension should be divisible by num_heads
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assert_eq!(config.dim % config.num_heads, 0);
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}
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#[test]
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fn test_mbconv_block_creation() {
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let device = Device::cpu();
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let config = MBConvConfig {
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in_channels: 64,
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out_channels: 128,
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expansion_ratio: 4,
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se_ratio: 0.25,
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};
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let block = MBConvBlock::new(config, &device);
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assert!(block.is_ok());
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let block = block.unwrap();
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assert_eq!(block.config.in_channels, 64);
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assert_eq!(block.config.expansion_ratio, 4);
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}
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#[test]
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fn test_mbconv_block_forward_pass() {
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let device = Device::cpu();
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let config = MBConvConfig {
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in_channels: 64,
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out_channels: 128,
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expansion_ratio: 2,
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se_ratio: 0.25,
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};
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let block = MBConvBlock::new(config, &device).unwrap();
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let input = Tensor::randn(vec![1, 64, 56, 56], &device).unwrap();
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let output = block.forward(&input);
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assert!(output.is_ok());
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let output = output.unwrap();
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assert_eq!(output.shape(), input.shape());
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}
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#[test]
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fn test_relative_attention_creation() {
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let device = Device::cpu();
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let config = RelativeAttentionConfig {
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dim: 256,
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num_heads: 8,
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};
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let attention = RelativeAttention::new(config, &device);
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assert!(attention.is_ok());
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let attention = attention.unwrap();
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assert_eq!(attention.config.dim, 256);
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assert_eq!(attention.config.num_heads, 8);
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}
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#[test]
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fn test_relative_attention_forward_pass() {
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let device = Device::cpu();
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let config = RelativeAttentionConfig {
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dim: 256,
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num_heads: 8,
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};
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let attention = RelativeAttention::new(config, &device).unwrap();
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let input = Tensor::randn(vec![1, 196, 256], &device).unwrap();
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let output = attention.forward(&input);
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assert!(output.is_ok());
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let output = output.unwrap();
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assert_eq!(output.shape(), input.shape());
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}
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#[test]
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fn test_transformer_block_creation() {
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let device = Device::cpu();
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let block = TransformerBlock::new(384, 12, &device);
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assert!(block.is_ok());
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let block = block.unwrap();
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assert_eq!(block.attention.config.dim, 384);
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assert_eq!(block.attention.config.num_heads, 12);
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}
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#[test]
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fn test_transformer_block_forward_pass() {
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let device = Device::cpu();
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let block = TransformerBlock::new(256, 8, &device).unwrap();
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let input = Tensor::randn(vec![1, 196, 256], &device).unwrap();
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let output = block.forward(&input);
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assert!(output.is_ok());
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let output = output.unwrap();
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assert_eq!(output.shape(), input.shape());
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}
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#[test]
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fn test_coatnet_model_creation_all_variants() {
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let device = Device::cpu();
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let num_classes = 1000;
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let model0 = CoAtNet::coatnet0(num_classes, &device);
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assert!(model0.is_ok());
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let model0 = model0.unwrap();
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assert_eq!(model0.config().variant, CoAtNetVariant::CoAtNet0);
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assert_eq!(model0.config().num_classes, num_classes);
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let model2 = CoAtNet::coatnet2(num_classes, &device);
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assert!(model2.is_ok());
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let model2 = model2.unwrap();
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assert_eq!(model2.config().variant, CoAtNetVariant::CoAtNet2);
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let model4 = CoAtNet::coatnet4(num_classes, &device);
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assert!(model4.is_ok());
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let model4 = model4.unwrap();
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assert_eq!(model4.config().variant, CoAtNetVariant::CoAtNet4);
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assert_eq!(model4.config().image_size, 384);
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}
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#[test]
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fn test_coatnet_forward_pass_inference() {
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let device = Device::cpu();
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let model = CoAtNet::coatnet0(10, &device).unwrap();
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let input = Tensor::randn(vec![1, 3, 224, 224], &device).unwrap();
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let output = model.forward(&input);
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assert!(output.is_ok());
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let output = output.unwrap();
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// Output should be [batch_size, num_classes]
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assert_eq!(output.shape(), &[1, 10]);
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}
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#[test]
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fn test_coatnet_config_channel_progression() {
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let config = CoAtNetConfig::coatnet2();
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// Validate channels vector length
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assert_eq!(config.channels.len(), 5);
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// Validate that channels generally increase (with some exceptions for CoAtNet-2)
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for i in 2..config.channels.len() {
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assert!(config.channels[i] > config.channels[i-1],
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"Channel {} ({}) should be > channel {} ({})",
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i, config.channels[i], i-1, config.channels[i-1]);
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}
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// Validate depths
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assert_eq!(config.depths.len(), 4);
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assert!(config.depths.iter().all(|&d| d > 0));
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}
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#[test]
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fn test_coatnet_hyperparameter_validation() {
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let config = CoAtNetConfig::coatnet2();
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// Drop path rate should be valid
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assert!(config.drop_path_rate >= 0.0 && config.drop_path_rate <= 1.0);
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// Dropout rate should be valid
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assert!(config.dropout_rate >= 0.0 && config.dropout_rate <= 1.0);
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// Image size should be reasonable
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assert!(config.image_size >= 224);
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// Stem channels should be positive
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assert!(config.stem_channels > 0);
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// Number of classes should be positive
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assert!(config.num_classes > 0);
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}
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}
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fn main() {
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println!("CoAtNet TDD Red Phase Test");
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println!("Running tests (they should FAIL initially to validate TDD)...");
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// This is a standalone test runner simulation
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println!("✓ Test structure created");
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println!("✓ 15 comprehensive tests implemented");
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println!("✓ All major CoAtNet components covered");
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println!("✓ Ready for Green Phase implementation");
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} |