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fix(train): repair tch-backend bit-rot — gated path compiles and tests run again

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Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from
(+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() /
divide_scalar_mode(floor) — the latter fixed a silent true-division bug in
heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar
overflow panic); petgraph EdgeRef import; missing EvalMetrics and
verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test
uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11
Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged.
Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof
seed race under parallel threads — documented, deliberate follow-ups.

Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
2026-06-10 20:13:47 -04:00
parent e04751b763
commit cc50e28a29
6 changed files with 62 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files
v2/crates/wifi-densepose-train/src/losses.rs
+5 -4
View File
@@ -118,7 +118,7 @@ impl WiFiDensePoseLoss {
// Normalise by number of visible joints in the batch.
let n_visible = visibility.sum(Kind::Float);
// Guard against division by zero (entire batch may have no labels).
let safe_n = n_visible.clamp(1.0, f64::MAX);
let safe_n = n_visible.clamp_min(1.0);
masked.sum(Kind::Float) / safe_n
}
@@ -165,7 +165,7 @@ impl WiFiDensePoseLoss {
let masked_target_uv = target_uv * &fg_mask_f;
// Count foreground pixels × 48 channels to normalise.
let n_fg = fg_mask_f.sum(Kind::Float).clamp(1.0, f64::MAX);
let n_fg = fg_mask_f.sum(Kind::Float).clamp_min(1.0);
// Smooth-L1 with beta=1.0, reduction=Sum then divide by fg count.
let uv_loss_sum = masked_pred_uv.smooth_l1_loss(&masked_target_uv, Reduction::Sum, 1.0);
@@ -234,7 +234,7 @@ impl WiFiDensePoseLoss {
// UV loss (foreground masked)
let fg_mask = target_int.not_equal(0_i64);
let fg_mask_f = fg_mask.unsqueeze(1).expand_as(pu).to_kind(Kind::Float);
let n_fg = fg_mask_f.sum(Kind::Float).clamp(1.0, f64::MAX);
let n_fg = fg_mask_f.sum(Kind::Float).clamp_min(1.0);
let uv_loss =
(pu * &fg_mask_f).smooth_l1_loss(&(tu * &fg_mask_f), Reduction::Sum, 1.0)
/ n_fg;
@@ -743,10 +743,11 @@ mod tests {
}
// Visible batch (index 1) should have non-zero heatmaps.
let heatmaps_ref = &heatmaps;
let batch1_sum: f32 = (0..num_joints)
.map(|j| {
(0..size)
.flat_map(|r| (0..size).map(move |c| heatmaps[[1, j, r, c]]))
.flat_map(|r| (0..size).map(move |c| heatmaps_ref[[1, j, r, c]]))
.sum::<f32>()
})
.sum();
v2/crates/wifi-densepose-train/src/metrics.rs
+19
View File
@@ -19,6 +19,7 @@
use ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use petgraph::graph::{DiGraph, NodeIndex};
use petgraph::visit::EdgeRef;
use ruvector_mincut::{DynamicMinCut, MinCutBuilder};
use std::collections::VecDeque;
@@ -106,6 +107,24 @@ impl Default for MetricsResult {
}
}
// ---------------------------------------------------------------------------
// EvalMetrics
// ---------------------------------------------------------------------------
/// Per-evaluation pose metrics.
///
/// Plain value container produced by evaluation runs: lower `mpjpe`/`gps`
/// and higher `pck_at_05` indicate better predictions.
#[derive(Debug, Clone, Copy, Default, PartialEq)]
pub struct EvalMetrics {
/// Mean Per-Joint Position Error (normalised units).
pub mpjpe: f64,
/// Percentage of Correct Keypoints at threshold 0.05 (0-1 scale).
pub pck_at_05: f64,
/// Geodesic Point Similarity error for DensePose surface predictions.
pub gps: f64,
}
// ---------------------------------------------------------------------------
// MetricsAccumulator
// ---------------------------------------------------------------------------
v2/crates/wifi-densepose-train/src/model.rs
+13 -3
View File
@@ -182,7 +182,7 @@ impl WiFiDensePoseModel {
self.vs
.trainable_variables()
.iter()
.map(|t| t.numel())
.map(|t| t.numel() as i64)
.sum()
}
@@ -297,7 +297,12 @@ fn apply_antenna_attention(x: &Tensor, lambda: f32) -> Tensor {
let xi = x.select(0, bi as i64); // [n_ant, n_sc]
// Move to CPU and convert to f32 for the pure-Rust attention kernel.
let flat: Vec<f32> = Vec::from(xi.to_kind(Kind::Float).to_device(Device::Cpu).contiguous());
let flat: Vec<f32> = Vec::<f32>::try_from(
xi.to_kind(Kind::Float)
.to_device(Device::Cpu)
.flatten(0, -1),
)
.expect("antenna tensor to vec");
// Q = K = V = the antenna features (self-attention over antenna paths).
let out = attn_mincut(
@@ -350,7 +355,12 @@ fn apply_spatial_attention(x: &Tensor) -> Tensor {
for bi in 0..b {
// Extract [C, H*W] and transpose to [H*W, C].
let xi = x.select(0, bi).reshape([c, h * w]).transpose(0, 1); // [H*W, C]
let flat: Vec<f32> = Vec::from(xi.to_kind(Kind::Float).to_device(Device::Cpu).contiguous());
let flat: Vec<f32> = Vec::<f32>::try_from(
xi.to_kind(Kind::Float)
.to_device(Device::Cpu)
.flatten(0, -1),
)
.expect("spatial tensor to vec");
// Build token slices — one per spatial position.
let tokens: Vec<&[f32]> = (0..n_spatial).map(|i| &flat[i * d..(i + 1) * d]).collect();
v2/crates/wifi-densepose-train/src/proof.rs
+12 -3
View File
@@ -153,11 +153,11 @@ pub fn run_proof(proof_dir: &Path) -> Result<ProofResult, Box<dyn std::error::Er
let num_kp = kp.size()[1] as usize;
let hm_size = cfg.heatmap_size;
let kp_vec: Vec<f32> = Vec::<f64>::from(kp.to_kind(Kind::Double).flatten(0, -1))
let kp_vec: Vec<f32> = Vec::<f64>::try_from(kp.to_kind(Kind::Double).flatten(0, -1))?
.iter()
.map(|&x| x as f32)
.collect();
let vis_vec: Vec<f32> = Vec::<f64>::from(vis.to_kind(Kind::Double).flatten(0, -1))
let vis_vec: Vec<f32> = Vec::<f64>::try_from(vis.to_kind(Kind::Double).flatten(0, -1))?
.iter()
.map(|&x| x as f32)
.collect();
@@ -261,7 +261,7 @@ pub fn hash_model_weights(model: &WiFiDensePoseModel) -> String {
.flatten(0, -1)
.to_kind(Kind::Float)
.to_device(Device::Cpu);
let values: Vec<f32> = Vec::<f32>::from(&flat);
let values: Vec<f32> = Vec::<f32>::try_from(&flat).expect("param tensor to vec");
let mut buf = vec![0u8; values.len() * 4];
for (i, v) in values.iter().enumerate() {
let bytes = v.to_le_bytes();
@@ -292,6 +292,15 @@ pub fn load_expected_hash(proof_dir: &Path) -> Result<Option<String>, std::io::E
Ok(if hash.is_empty() { None } else { Some(hash) })
}
/// Verify that `path` is a valid checkpoint directory.
///
/// Returns `true` only when the path exists and is a directory. Deterministic
/// and side-effect free — repeated calls always return the same result for an
/// unchanged filesystem.
pub fn verify_checkpoint_dir(path: &Path) -> bool {
path.is_dir()
}
/// Save the expected model hash to `<proof_dir>/expected_proof.sha256`.
///
/// Creates `proof_dir` if it does not already exist.
v2/crates/wifi-densepose-train/src/trainer.rs
+10 -6
View File
@@ -582,11 +582,13 @@ fn kp_to_heatmap_tensor(
let num_kp = kp_tensor.size()[1] as usize;
// Convert to ndarray for generate_target_heatmaps.
let kp_vec: Vec<f32> = Vec::<f64>::from(kp_tensor.to_kind(Kind::Double).flatten(0, -1))
let kp_vec: Vec<f32> = Vec::<f64>::try_from(kp_tensor.to_kind(Kind::Double).flatten(0, -1))
.expect("kp tensor to vec")
.iter()
.map(|&x| x as f32)
.collect();
let vis_vec: Vec<f32> = Vec::<f64>::from(vis_tensor.to_kind(Kind::Double).flatten(0, -1))
let vis_vec: Vec<f32> = Vec::<f64>::try_from(vis_tensor.to_kind(Kind::Double).flatten(0, -1))
.expect("vis tensor to vec")
.iter()
.map(|&x| x as f32)
.collect();
@@ -622,8 +624,8 @@ fn heatmap_to_keypoints(heatmaps: &Tensor) -> Tensor {
let arg = flat.argmax(-1, false);
// Decompose linear index into (row, col).
let row = (&arg / w).to_kind(Kind::Float); // [B, 17]
let col = (&arg % w).to_kind(Kind::Float); // [B, 17]
let row = arg.divide_scalar_mode(w, "floor").to_kind(Kind::Float); // [B, 17]
let col = arg.remainder(w).to_kind(Kind::Float); // [B, 17]
// Normalize to [0, 1]
let x = col / (w - 1) as f64;
@@ -639,7 +641,8 @@ fn heatmap_to_keypoints(heatmaps: &Tensor) -> Tensor {
fn extract_kp_ndarray(kp_tensor: &Tensor, batch_idx: usize) -> Array2<f32> {
let num_kp = kp_tensor.size()[1] as usize;
let row = kp_tensor.select(0, batch_idx as i64);
let data: Vec<f32> = Vec::<f64>::from(row.to_kind(Kind::Double).flatten(0, -1))
let data: Vec<f32> = Vec::<f64>::try_from(row.to_kind(Kind::Double).flatten(0, -1))
.expect("kp tensor to vec")
.iter()
.map(|&v| v as f32)
.collect();
@@ -652,7 +655,8 @@ fn extract_kp_ndarray(kp_tensor: &Tensor, batch_idx: usize) -> Array2<f32> {
fn extract_vis_ndarray(vis_tensor: &Tensor, batch_idx: usize) -> Array1<f32> {
let num_kp = vis_tensor.size()[1] as usize;
let row = vis_tensor.select(0, batch_idx as i64);
let data: Vec<f32> = Vec::<f64>::from(row.to_kind(Kind::Double))
let data: Vec<f32> = Vec::<f64>::try_from(row.to_kind(Kind::Double))
.expect("vis tensor to vec")
.iter()
.map(|&v| v as f32)
.collect();
v2/crates/wifi-densepose-train/src/wiflow_std/model.rs
+3 -1
View File
@@ -283,7 +283,9 @@ mod tests {
let cfg = WiFlowStdConfig::default();
let mut model = WiFlowStdModel::new(&cfg, Device::Cpu).expect("build");
let tmp = tempdir().expect("tempdir");
let path = tmp.path().join("wiflow_std.pt");
// safetensors, not .pt: this torch build's _save_parameters/_load_parameters
// .pt roundtrip is broken on Windows (GenericDict internal assert)
let path = tmp.path().join("wiflow_std.safetensors");
model.save(&path).expect("save");
model.load(&path).expect("load");
let out = model.forward_inference(&random_csi(&cfg, 1));