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tests/test_matcher.py

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+import torch
+
+from models.matcher import hungarian_match
+
+
+def test_matcher_recovers_perfect_assignment():
+    """If predicted curves exactly equal GT curves (in different query order),
+    the matcher should recover the correct pred<->gt pairing."""
+    torch.manual_seed(0)
+    B, N, S = 1, 4, 10
+    sample_ys = torch.linspace(0, 1, S)
+
+    gt_xs = torch.rand(B, N, S)
+    target_valid_mask = torch.ones(B, N, S, dtype=torch.bool)
+    target_lane_valid = torch.tensor([[True, True, False, False]])
+
+    # Build predictions as a *permuted* copy of the (valid) GT curves via exact
+    # per-point fit isn't needed here -- construct predicted curve coeffs that
+    # exactly reproduce gt_xs at the two valid GT rows, placed at query indices [2, 0].
+    curve_coeffs = torch.zeros(B, N, 4)
+    cls_logits = torch.zeros(B, N, 2)
+
+    for q_idx, gt_idx in [(2, 0), (0, 1)]:
+        # fit a cubic through the GT points exactly (deg-3 fit on 10 pts, small residual)
+        ys_np = sample_ys.numpy()
+        xs_np = gt_xs[0, gt_idx].numpy()
+        coeffs = torch.tensor([0.0, 0.0, 0.0, 0.0])
+        import numpy as np
+        fit = np.polyfit(ys_np, xs_np, deg=3)
+        coeffs = torch.tensor(fit, dtype=torch.float32)
+        curve_coeffs[0, q_idx] = coeffs
+        cls_logits[0, q_idx] = torch.tensor([-5.0, 5.0])  # confident "lane"
+
+    matches = hungarian_match(cls_logits, curve_coeffs, gt_xs, target_valid_mask,
+                               target_lane_valid, sample_ys)
+    pred_idx, gt_idx = matches[0]
+    pairs = set(zip(pred_idx.tolist(), gt_idx.tolist()))
+    assert (2, 0) in pairs
+    assert (0, 1) in pairs
+
+
+def test_matcher_handles_no_gt_lanes():
+    B, N, S = 1, 4, 10
+    sample_ys = torch.linspace(0, 1, S)
+    cls_logits = torch.zeros(B, N, 2)
+    curve_coeffs = torch.zeros(B, N, 4)
+    target_xs = torch.zeros(B, N, S)
+    target_valid_mask = torch.zeros(B, N, S, dtype=torch.bool)
+    target_lane_valid = torch.zeros(B, N, dtype=torch.bool)
+
+    matches = hungarian_match(cls_logits, curve_coeffs, target_xs, target_valid_mask,
+                               target_lane_valid, sample_ys)
+    pred_idx, gt_idx = matches[0]
+    assert pred_idx.numel() == 0 and gt_idx.numel() == 0