pub fn collect(p: &Pyramid, q: &[i32], preds: &[Pred]) -> Vec<Sample>Expand description
Collect (features, residual) for every coefficient of q, in coding
order (the features the coder will see).
Examples found in repository?
examples/pretrain.rs (line 58)
16fn main() {
17 let a: Vec<String> = std::env::args().skip(1).collect();
18 let out = &a[0];
19 let mut dims = vec![64, 128, 128];
20 let mut psnrs = vec![40.0, 60.0, 80.0, 100.0];
21 let mut cfg = TrainConfig::pretrain();
22 let mut per = 150_000usize;
23 let mut files = Vec::new();
24 let mut i = 1;
25 while i < a.len() {
26 match a[i].as_str() {
27 "--dims" => dims = a[i + 1].split('x').map(|s| s.parse().unwrap()).collect(),
28 "--psnr" => psnrs = a[i + 1].split(',').map(|s| s.parse().unwrap()).collect(),
29 "--hidden" => cfg.hidden = a[i + 1].split(',').map(|s| s.parse().unwrap()).collect(),
30 "--per" => per = a[i + 1].parse().unwrap(),
31 "--epochs" => cfg.epochs = a[i + 1].parse().unwrap(),
32 "--per-epoch" => cfg.per_epoch = a[i + 1].parse().unwrap(),
33 f => {
34 files.push(f.to_string());
35 i += 1;
36 continue;
37 }
38 }
39 i += 2;
40 }
41 let mut samples: Vec<Sample> = Vec::new();
42 let mut seed = 12345u64;
43 for f in &files {
44 let b = std::fs::read(f).unwrap();
45 let x: Vec<f32> = b
46 .as_chunks::<4>()
47 .0
48 .iter()
49 .map(|&c| f32::from_le_bytes(c))
50 .collect();
51 let xd: Vec<f64> = x.iter().map(|&v| v as f64).collect();
52 let co = Coeffs::new(&xd, &dims);
53 for &p in &psnrs {
54 let wc = WavConfig::new(ErrorBound::Psnr(p));
55 let (_, st) = encode_f32(&x, &dims, &wc);
56 let (q, _) = quantize(&co, st.delta, wc.rho);
57 let preds = fit_predictors(&co.p, &q);
58 let mut s = collect(&co.p, &q, &preds);
59 // seeded partial Fisher–Yates subsample
60 let k = per.min(s.len());
61 for j in 0..k {
62 seed = seed
63 .wrapping_mul(6364136223846793005)
64 .wrapping_add(1442695040888963407);
65 let r = j + (seed >> 11) as usize % (s.len() - j);
66 s.swap(j, r);
67 }
68 s.truncate(k);
69 eprintln!(
70 "{f} psnr {p}: delta {:.4e}, hand {:.4} bpv, {} samples",
71 st.delta,
72 st.bits_per_value,
73 s.len()
74 );
75 samples.extend(s);
76 }
77 }
78 let t = std::time::Instant::now();
79 let net = train(&samples, &cfg);
80 eprintln!(
81 "trained {} params on {} samples in {:.1}s: {:.4} bits/sample (float view)",
82 net.num_params(),
83 samples.len(),
84 t.elapsed().as_secs_f64(),
85 mean_bits_float(&net, &samples)
86 );
87 std::fs::write(out, Arc::new(net).to_bytes()).unwrap();
88 let _ = Net::from_bytes(&std::fs::read(out).unwrap()).unwrap();
89}