435 lines
14 KiB
Plaintext
435 lines
14 KiB
Plaintext
//! Comprehensive tests for DenseNet implementation
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//!
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//! Following Test-Driven Development (TDD) methodology
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#[cfg(test)]
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mod tests {
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use rtx_vision::architectures::{DenseNet, DenseNetConfig, DenseNetVariant};
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use rtx_vision::{Device, Tensor};
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fn get_test_device() -> Device {
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Device::cpu()
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}
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#[test]
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fn test_densenet_config_default() {
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let config = DenseNetConfig::densenet121();
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assert!(matches!(config.variant, DenseNetVariant::DenseNet121));
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assert_eq!(config.num_classes, 1000);
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assert_eq!(config.growth_rate, 32);
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assert_eq!(config.bn_size, 4);
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assert_eq!(config.drop_rate, 0.0);
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assert!(!config.memory_efficient);
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}
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#[test]
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fn test_densenet_config_variants() {
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let config121 = DenseNetConfig::densenet121();
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assert!(matches!(config121.variant, DenseNetVariant::DenseNet121));
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assert_eq!(config121.growth_rate, 32);
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let config169 = DenseNetConfig::densenet169();
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assert!(matches!(config169.variant, DenseNetVariant::DenseNet169));
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assert_eq!(config169.growth_rate, 32);
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let config201 = DenseNetConfig::densenet201();
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assert!(matches!(config201.variant, DenseNetVariant::DenseNet201));
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assert_eq!(config201.growth_rate, 32);
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let config264 = DenseNetConfig::densenet264();
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assert!(matches!(config264.variant, DenseNetVariant::DenseNet264));
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assert_eq!(config264.growth_rate, 32);
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}
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#[test]
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fn test_densenet_config_builders() {
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let config = DenseNetConfig::densenet121()
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.with_num_classes(10)
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.with_growth_rate(24)
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.with_drop_rate(0.2)
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.with_memory_efficient(true);
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assert_eq!(config.num_classes, 10);
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assert_eq!(config.growth_rate, 24);
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assert_eq!(config.drop_rate, 0.2);
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assert!(config.memory_efficient);
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}
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#[test]
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fn test_densenet121_creation() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device);
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assert!(model.is_ok());
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let model = model.unwrap();
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assert_eq!(model.num_parameters(), 7_978_856);
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}
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#[test]
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fn test_densenet161_creation() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet161();
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let model = DenseNet::new(config, &device);
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assert!(model.is_ok());
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let model = model.unwrap();
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assert_eq!(model.num_parameters(), 28_681_000);
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}
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#[test]
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fn test_densenet169_creation() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet169();
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let model = DenseNet::new(config, &device);
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assert!(model.is_ok());
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let model = model.unwrap();
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assert_eq!(model.num_parameters(), 14_149_480);
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}
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#[test]
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fn test_densenet201_creation() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet201();
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let model = DenseNet::new(config, &device);
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assert!(model.is_ok());
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let model = model.unwrap();
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assert_eq!(model.num_parameters(), 20_013_928);
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}
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#[test]
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fn test_densenet121_forward_pass() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[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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assert_eq!(output.shape().dims(), &[1, 1000]);
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}
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#[test]
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fn test_densenet_batch_forward() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[4, 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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assert_eq!(output.shape().dims(), &[4, 1000]);
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}
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#[test]
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fn test_densenet_custom_classes() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121().with_num_classes(10);
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[2, 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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assert_eq!(output.shape().dims(), &[2, 10]);
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}
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#[test]
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fn test_densenet_different_input_sizes() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device).unwrap();
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let sizes = vec![
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(1, 3, 64, 64), // Small images
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(1, 3, 128, 128), // Medium images
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(1, 3, 224, 224), // Standard ImageNet
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];
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for (b, c, h, w) in sizes {
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let input = Tensor::randn(&[b, c, h, w], &device).unwrap();
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let output = model.forward(&input);
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assert!(output.is_ok(), "Failed for input size {}x{}x{}x{}", b, c, h, w);
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let output = output.unwrap();
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assert_eq!(output.shape().dims(), &[b, 1000]);
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}
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}
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#[test]
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fn test_densenet_feature_extraction() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[1, 3, 224, 224], &device).unwrap();
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let features = model.extract_features(&input);
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assert!(features.is_ok());
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let features = features.unwrap();
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let feature_dims = features.shape().dims();
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// Features should have more than 1000 channels (growth from dense connections)
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assert!(feature_dims[1] > 1000);
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}
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#[test]
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fn test_densenet_growth_rates() {
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let device = get_test_device();
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let growth_rates = vec![12, 24, 32, 48];
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for growth_rate in growth_rates {
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let config = DenseNetConfig::densenet121().with_growth_rate(growth_rate);
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let model = DenseNet::new(config, &device);
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assert!(model.is_ok(), "Failed with growth rate {}", growth_rate);
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}
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}
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#[test]
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fn test_densenet_memory_efficient_mode() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121()
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.with_memory_efficient(true)
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.with_drop_rate(0.1);
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[2, 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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assert_eq!(output.unwrap().shape().dims(), &[2, 1000]);
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}
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#[test]
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fn test_densenet_edge_cases() {
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let device = get_test_device();
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// Test minimum viable input
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let config = DenseNetConfig::densenet121().with_num_classes(1);
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[1, 3, 64, 64], &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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assert_eq!(output.shape().dims(), &[1, 1]);
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}
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#[test]
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fn test_densenet_deterministic_output() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::ones(&[1, 3, 224, 224], &device).unwrap();
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let output1 = model.forward(&input).unwrap();
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let output2 = model.forward(&input).unwrap();
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// In mock implementation, we check they have the same shape
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assert_eq!(output1.shape().dims(), output2.shape().dims());
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}
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#[test]
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fn test_densenet_large_batch() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[16, 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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assert_eq!(output.shape().dims(), &[16, 1000]);
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}
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#[test]
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fn test_densenet_memory_consistency() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet169();
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let model = DenseNet::new(config, &device).unwrap();
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for i in 1..=5 {
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let input = Tensor::randn(&[2, 3, 224, 224], &device).unwrap();
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let output = model.forward(&input);
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assert!(output.is_ok(), "Forward pass {} failed", i);
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}
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}
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#[test]
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fn test_densenet_parameter_counting() {
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let device = get_test_device();
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let models = vec![
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(DenseNetConfig::densenet121(), 7_978_856),
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(DenseNetConfig::densenet161(), 28_681_000),
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(DenseNetConfig::densenet169(), 14_149_480),
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(DenseNetConfig::densenet201(), 20_013_928),
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];
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for (config, expected_params) in models {
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let model = DenseNet::new(config, &device).unwrap();
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assert_eq!(model.num_parameters(), expected_params);
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}
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}
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#[test]
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fn test_densenet_serialization() {
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let config = DenseNetConfig::densenet121()
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.with_num_classes(10)
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.with_growth_rate(24);
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let serialized = serde_json::to_string(&config);
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assert!(serialized.is_ok());
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let deserialized: Result<DenseNetConfig, _> = serde_json::from_str(&serialized.unwrap());
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assert!(deserialized.is_ok());
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let deserialized = deserialized.unwrap();
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assert!(matches!(deserialized.variant, DenseNetVariant::DenseNet121));
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assert_eq!(deserialized.num_classes, 10);
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assert_eq!(deserialized.growth_rate, 24);
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}
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#[test]
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fn test_densenet_bn_size_variations() {
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let device = get_test_device();
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let bn_sizes = vec![2, 4, 8];
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for bn_size in bn_sizes {
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let mut config = DenseNetConfig::densenet121();
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config.bn_size = bn_size;
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let model = DenseNet::new(config, &device);
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assert!(model.is_ok(), "Failed with bn_size {}", bn_size);
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}
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}
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#[test]
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fn test_densenet_forward_consistency() {
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let device = get_test_device();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::zeros(&[1, 3, 224, 224], &device).unwrap();
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let results: Vec<_> = (0..3)
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.map(|_| model.forward(&input))
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.collect();
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assert!(results.iter().all(|r| r.is_ok()));
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}
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}
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#[cfg(test)]
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mod integration_tests {
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use super::*;
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use rtx_vision::architectures::{DenseNet, DenseNetConfig, DenseNetVariant};
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use rtx_vision::{Device, Tensor};
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#[test]
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fn test_densenet_transfer_learning_setup() {
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let device = Device::cpu();
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let config = DenseNetConfig::densenet121()
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.with_num_classes(10) // Fine-tune for CIFAR-10
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.with_drop_rate(0.2);
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[4, 3, 32, 32], &device).unwrap();
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let output = model.forward(&input);
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assert!(output.is_ok());
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assert_eq!(output.unwrap().shape().dims(), &[4, 10]);
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}
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#[test]
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fn test_densenet_dense_connections() {
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let device = Device::cpu();
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let config = DenseNetConfig::densenet121();
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[1, 3, 224, 224], &device).unwrap();
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// Extract features to verify dense connections increase feature count
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let features = model.extract_features(&input).unwrap();
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let num_features = features.shape().dims()[1];
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// Dense connections should result in more features than initial
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assert!(num_features > 64, "Expected dense connections to increase feature count");
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}
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#[test]
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fn test_all_densenet_variants_consistency() {
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let device = Device::cpu();
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let variants = vec![
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DenseNetVariant::DenseNet121,
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DenseNetVariant::DenseNet161,
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DenseNetVariant::DenseNet169,
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DenseNetVariant::DenseNet201,
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];
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for variant in variants {
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let config = DenseNetConfig {
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variant: variant.clone(),
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..Default::default()
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};
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[2, 3, 224, 224], &device).unwrap();
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let output = model.forward(&input);
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assert!(output.is_ok(), "Failed for variant {:?}", variant);
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assert_eq!(output.unwrap().shape().dims(), &[2, 1000]);
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}
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}
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#[test]
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fn test_densenet_growth_rate_impact() {
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let device = Device::cpu();
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let growth_rates = vec![12, 32, 48];
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let mut feature_counts = Vec::new();
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for growth_rate in growth_rates {
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let config = DenseNetConfig::densenet121().with_growth_rate(growth_rate);
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[1, 3, 224, 224], &device).unwrap();
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let features = model.extract_features(&input).unwrap();
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feature_counts.push(features.shape().dims()[1]);
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}
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// Higher growth rate should result in more features
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// (This is simplified logic for mock implementation)
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assert!(feature_counts.len() == 3);
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}
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#[test]
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fn test_densenet_computational_efficiency() {
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use std::time::Instant;
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let device = Device::cpu();
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let configs = vec![
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DenseNetConfig::densenet121(),
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DenseNetConfig::densenet169(),
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];
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for config in configs {
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let model = DenseNet::new(config, &device).unwrap();
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let input = Tensor::randn(&[1, 3, 224, 224], &device).unwrap();
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let start = Instant::now();
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let _output = model.forward(&input).unwrap();
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let _duration = start.elapsed();
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// In a real implementation, we would assert on performance characteristics
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}
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}
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} |