All the tests are passing! I haven't verified if the tests are useful, but at least I have a unit and integration test suite, and they are passing!

Author: saugatkandel

PROJECT_STATUS.md

@@ -51,16 +51,15 @@ We have implemented a 2D prototype for refining organelle segmentations using a
 ## Validation Status
 - **Synthetic Data**: Basic functionality works, but recent changes (template models, masking) have not been rigorously verified.
 - **Real Data**: The new template models and B-Spline refiner have **not yet been tested** on real data.
-- **Tests**: Major failures in loss and optimization tests. Investigation required.
+- **Tests**: Fixed 6 failing tests. The root causes were an incorrect gradient assertion in `test_loss`, a path type mismatch in `test_trainer`, a missing attribute access guard in `PerPointTemplateModel`, and an unstable learning rate for the `NeuralField` optimization test. All tests now pass.
 
 ## Design Decisions
 - **No Explicit Remeshing**: We avoid remeshing during the optimization loop to maintain differentiability. Instead, we rely on `EdgeLengthConsistencyLoss` and `LaplacianSmoothingLoss` to maintain mesh quality.
 - **B-Spline Regularization**: For the B-spline refiner, we apply Laplacian and Edge Length regularization to the **control points** to ensure a uniform parameterization and prevent control point bunching, even though the spline curve itself is inherently smooth.
 - **Implicit Regularization**: We prefer B-Spline or Neural Field parameterizations for template parameters to enforce smoothness implicitly, rather than relying solely on explicit smoothness losses.
 
 ## In Progress / Next Steps
-1.  **Investigate Test Failures**: Debug and fix the failing loss and optimization tests.
-2.  **2D Optimization Validation**: Run `train_2d.ipynb` notebook to verify the full training loop on synthetic data.
+1.  **2D Optimization Validation**: Run `train_2d.ipynb` notebook to verify the full training loop on synthetic data with the various refiner and template combinations.
 3.  **Real Data Validation**: Run `BSplineContourRefiner` with `BSplineTemplateModel` on the real data slice (`data/20289/...`).
 4.  **Visualization**: Use the new visualization tools to inspect the learned template parameters on real data.
 5.  **Port to 3D**: (Paused until 2D is fully robust).

diffmeshopt/opt2d/template.py

@@ -240,10 +240,9 @@ def get_params(
 
     def get_regularization_loss(self) -> dict[str, torch.Tensor]:
         # Regularization: Gaussian prior on log(sigma) centered at initialization
-        prior = (self.log_sigma - self.sigma_init.log()).pow(2).mean() + (
-            self.log_sigma_ratio - self.sigma_ratio_init.log()
-        ).pow(2).mean()
-
+        prior = (self.log_sigma - self.sigma_init.log()).pow(2).mean()
+        if not self.props.symmetric:
+            prior = prior + (self.log_sigma_ratio - self.sigma_ratio_init.log()).pow(2).mean()
         # Reconstruct log_peak_dist for smoothness
         sigma = self.log_sigma.exp()
         peak_dist = sigma * (self.props.min_peak_ratio + self.log_excess.exp())

tests/opt2d/test_loss.py

@@ -45,11 +45,12 @@ def test_laplacian_smoothing_loss(simple_contour):
     loss_for_grad.backward()
 
     assert perturbed_contour_grad.grad is not None
-    # The gradient for the perturbed point (p0) should point inwards, opposite to the perturbation.
+    # The gradient points in the direction of steepest ascent (increasing loss).
     # p0 was at (10, 0) and was moved to (12, 0).
-    # The gradient vector should be approximately in the (-1, 0) direction.
+    # Moving the point further out increases the Laplacian loss, so the gradient is positive (outwards).
+    # The gradient vector should be approximately in the (+1, 0) direction.
     grad_p0 = perturbed_contour_grad.grad[0]
-    assert grad_p0[0] < 0  # x-component should be negative
+    assert grad_p0[0] > 0  # x-component should be positive
     assert torch.isclose(grad_p0[1], torch.tensor(0.0), atol=1e-4)  # y-component should be ~0
 
 

tests/opt2d/test_optimize.py

@@ -60,6 +60,9 @@ def test_refiner_template_combinations(
         )
     elif "neural" in template_name:
         template_props = NeuralFieldTemplateProps(**template_props.__dict__, neural_hidden_dim=16)
+        # Neural fields can be sensitive to high learning rates in short tests
+        if hasattr(props, "learning_rate"):
+            props.learning_rate = 0.01
     elif "grid" in template_name:
         template_props = GridTemplateProps(**template_props.__dict__, grid_size=8)
     elif "splat" in template_name:

tests/opt2d/test_trainer.py

@@ -64,7 +64,7 @@ def test_trainer_checkpointing(mock_refiner, temp_output_dir):
         (cb for cb in trainer.trainer.callbacks if isinstance(cb, ModelCheckpoint)), None
     )
     assert checkpoint_cb is not None
-    assert checkpoint_cb.dirpath == trainer.output_dir
+    assert Path(checkpoint_cb.dirpath) == trainer.output_dir
 
     # Check for saved files
     # The test doesn't actually run, so we can't check for files.