convnext_example/
convnext_example.rs

1use ndarray::Array;
2use scirs2_neural::{
3    error::Result,
4    models::architectures::{ConvNeXt, ConvNeXtConfig, ConvNeXtVariant},
5    prelude::*,
6};
7
8fn main() -> Result<()> {
9    println!("ConvNeXt Example");
10    println!("----------------");
11
12    // Create a random input tensor (batch_size=1, channels=3, height=224, width=224)
13    let input_shape = [1, 3, 224, 224];
14    let mut input = Array::<f32, _>::zeros(input_shape).into_dyn();
15
16    // Fill with random values between 0 and 1
17    for elem in input.iter_mut() {
18        *elem = rand::random::<f32>();
19    }
20
21    // Create ConvNeXt-Tiny with default configuration
22    println!("\nConvNeXt-Tiny:");
23    let convnext_tiny = ConvNeXt::convnext_tiny(1000, true)?;
24    let output_tiny = convnext_tiny.forward(&input)?;
25    println!("Output shape: {:?}", output_tiny.shape());
26
27    // Create ConvNeXt-Small with default configuration
28    println!("\nConvNeXt-Small:");
29    let convnext_small = ConvNeXt::convnext_small(1000, true)?;
30    let output_small = convnext_small.forward(&input)?;
31    println!("Output shape: {:?}", output_small.shape());
32
33    // Create ConvNeXt-Base with default configuration
34    println!("\nConvNeXt-Base:");
35    let convnext_base = ConvNeXt::convnext_base(1000, true)?;
36    let output_base = convnext_base.forward(&input)?;
37    println!("Output shape: {:?}", output_base.shape());
38
39    // Create ConvNeXt-Large with default configuration
40    println!("\nConvNeXt-Large:");
41    let convnext_large = ConvNeXt::convnext_large(1000, true)?;
42    let output_large = convnext_large.forward(&input)?;
43    println!("Output shape: {:?}", output_large.shape());
44
45    // Custom ConvNeXt with specific configuration
46    println!("\nCustom ConvNeXt:");
47    let custom_config = ConvNeXtConfig {
48        variant: ConvNeXtVariant::Tiny,
49        input_channels: 3,
50        depths: vec![3, 3, 9, 3],
51        dims: vec![96, 192, 384, 768],
52        num_classes: 10,
53        dropout_rate: Some(0.2),
54        layer_scale_init_value: 1e-6,
55        include_top: true,
56    };
57
58    let custom_convnext = ConvNeXt::new(custom_config)?;
59    let output_custom = custom_convnext.forward(&input)?;
60    println!("Output shape: {:?}", output_custom.shape());
61
62    // Example of inference
63    println!("\nInference example with ConvNeXt-Tiny:");
64    let inference_input = Array::<f32, _>::zeros(input_shape).into_dyn();
65    let inference_output = convnext_tiny.forward(&inference_input)?;
66
67    // Get top prediction (normally you'd have class labels)
68    let mut max_val = f32::MIN;
69    let mut max_idx = 0;
70
71    for (i, &val) in inference_output.iter().enumerate() {
72        if val > max_val {
73            max_val = val;
74            max_idx = i;
75        }
76    }
77
78    println!(
79        "Predicted class: {} with confidence: {:.4}",
80        max_idx, max_val
81    );
82
83    Ok(())
84}
convnext_example/convnext_example.rs

convnext_example/
convnext_example.rs
