From e2f80cf5dce36b4ad69379fb57caa1b15a722b06 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 18:35:42 +0000
Subject: [PATCH 01/56] feat: add dynamic-range-preserving intensity transforms

Add a shared, stateless transform module (asinh and generalized Anscombe)
that maps raw counts to a bounded, network-friendly domain and back without
a hard brightness clip, so bright neurites keep distinct, invertible values
instead of being flattened by a percentile clip. Includes offset calibration
helpers and a config-driven factory, with unit tests covering round-trip,
monotonicity, boundedness, and the documented Anscombe inverse behavior.

This is Part A of the denoising/compression normalization overhaul; it is not
yet wired into training or inference.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../machine_learning/transforms.py            | 362 ++++++++++++++++++
 tests/test_transforms.py                      | 148 +++++++
 2 files changed, 510 insertions(+)
 create mode 100644 src/aind_exaspim_image_compression/machine_learning/transforms.py
 create mode 100644 tests/test_transforms.py

diff --git a/src/aind_exaspim_image_compression/machine_learning/transforms.py b/src/aind_exaspim_image_compression/machine_learning/transforms.py
new file mode 100644
index 0000000..75a409a
--- /dev/null
+++ b/src/aind_exaspim_image_compression/machine_learning/transforms.py
@@ -0,0 +1,362 @@
+"""
+Dynamic-range-preserving intensity transforms for exaSPIM denoising and
+compression.
+
+Provides fixed, stateless forward/inverse transforms that map raw uint16
+counts to a bounded, network-friendly domain and back. Two families are
+implemented:
+
+    * ``AsinhTransform``   - HDR-style asinh compression (log-like tail).
+    * ``AnscombeTransform`` - generalized Anscombe variance-stabilizing
+      transform for Poisson-Gaussian noise (sqrt-like tail).
+
+The same transform object is meant to be used identically during training,
+validation, and inference. Neither transform applies a hard brightness clip
+below the physical sensor maximum, so bright structure keeps a distinct,
+invertible value instead of being flattened by a percentile clip.
+
+"""
+
+import numpy as np
+
+
+class IntensityTransform:
+    """
+    Abstract base class for count <-> normalized intensity transforms.
+    """
+
+    def forward(self, x):
+        """
+        Maps raw counts to the normalized (approximately [0, 1]) domain.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Image in raw count units.
+
+        Returns
+        -------
+        numpy.ndarray
+            Image in the normalized domain.
+        """
+        raise NotImplementedError
+
+    def inverse(self, y):
+        """
+        Maps normalized values back to raw uint16 counts.
+
+        Parameters
+        ----------
+        y : numpy.ndarray
+            Image in the normalized domain.
+
+        Returns
+        -------
+        numpy.ndarray
+            Image in raw count units, clipped to the physical range.
+        """
+        raise NotImplementedError
+
+
+class AsinhTransform(IntensityTransform):
+    """
+    HDR-style asinh intensity transform.
+
+    The transform is approximately linear for ``(x - offset) << scale`` and
+    approximately logarithmic for ``(x - offset) >> scale``, so it is
+    monotonic and invertible over the whole range with no plateau.
+
+    ``scale`` is the dynamic-range knob: larger values stay linear longer
+    (more faithful absolute range, less headroom); smaller values compress
+    the bright tail harder (more headroom).
+
+    The normalized output is only *approximately* [0, 1]: sub-background
+    voxels (``x < offset``) map to small negative values. This is intentional
+    (it preserves noise-floor symmetry) and harmless downstream. The only
+    hard bound is the physical clamp applied in ``inverse``.
+
+    Attributes
+    ----------
+    offset : float
+        Background / black-point in counts.
+    scale : float
+        Count scale of the linear-to-log knee.
+    max_count : float
+        Physical sensor maximum used as the normalization reference.
+    """
+
+    def __init__(self, offset=0.0, scale=32.0, max_count=65535.0):
+        """
+        Instantiates an AsinhTransform.
+
+        Parameters
+        ----------
+        offset : float, optional
+            Background / black-point in counts. Default is 0.0.
+        scale : float, optional
+            Count scale of the linear-to-log knee. Default is 32.0.
+        max_count : float, optional
+            Physical sensor maximum used as the normalization reference.
+            Default is 65535.0.
+        """
+        self.offset = float(offset)
+        self.scale = float(scale)
+        self.max_count = float(max_count)
+        self._norm = float(
+            np.arcsinh((self.max_count - self.offset) / self.scale)
+        )
+
+    def forward(self, x):
+        """
+        Maps raw counts to the normalized asinh domain.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Image in raw count units.
+
+        Returns
+        -------
+        numpy.ndarray
+            Normalized image (approximately [0, 1]), float32.
+        """
+        x = np.asarray(x, dtype=np.float32)
+        y = np.arcsinh((x - self.offset) / self.scale) / self._norm
+        return y.astype(np.float32)
+
+    def inverse(self, y):
+        """
+        Maps normalized asinh values back to raw uint16 counts.
+
+        Parameters
+        ----------
+        y : numpy.ndarray
+            Image in the normalized asinh domain.
+
+        Returns
+        -------
+        numpy.ndarray
+            Image in raw counts, clipped to [0, max_count], uint16.
+        """
+        y = np.asarray(y, dtype=np.float32)
+        counts = self.offset + self.scale * np.sinh(y * self._norm)
+        counts = np.clip(counts, 0, self.max_count)
+        return np.rint(counts).astype(np.uint16)
+
+
+class AnscombeTransform(IntensityTransform):
+    """
+    Generalized Anscombe variance-stabilizing transform.
+
+    Models the data as ``x = gain * Poisson + Normal(offset, read_noise^2)``
+    (Makitalo & Foi). The transform is sqrt-like, so it compresses the bright
+    tail more gently than asinh while making the noise approximately
+    homoscedastic. It reduces to the standard Anscombe transform
+    ``2 * sqrt(x + 3/8)`` when ``gain=1``, ``read_noise=0``, ``offset=0``.
+
+    The inverse is a closed form whose constant depends on
+    ``unbiased_inverse``: the algebraic inverse (3/8) exactly round-trips the
+    forward transform, while the asymptotically unbiased inverse (1/8) is
+    appropriate for inverting denoised (expectation) values and therefore
+    does not round-trip exactly.
+
+    Attributes
+    ----------
+    gain : float
+        Detector gain in counts per photo-electron.
+    read_noise : float
+        Gaussian read-noise standard deviation in counts.
+    offset : float
+        Dark / pedestal offset in counts.
+    max_count : float
+        Physical sensor maximum used as the normalization reference.
+    unbiased_inverse : bool
+        Whether the inverse uses the asymptotically unbiased constant.
+    """
+
+    def __init__(
+        self,
+        gain=1.0,
+        read_noise=0.0,
+        offset=0.0,
+        max_count=65535.0,
+        unbiased_inverse=True,
+    ):
+        """
+        Instantiates an AnscombeTransform.
+
+        Parameters
+        ----------
+        gain : float, optional
+            Detector gain in counts per photo-electron. Default is 1.0.
+        read_noise : float, optional
+            Gaussian read-noise standard deviation in counts. Default is 0.0.
+        offset : float, optional
+            Dark / pedestal offset in counts. Default is 0.0.
+        max_count : float, optional
+            Physical sensor maximum used as the normalization reference.
+            Default is 65535.0.
+        unbiased_inverse : bool, optional
+            If True, use the asymptotically unbiased inverse (constant 1/8),
+            appropriate for inverting denoised values. If False, use the
+            exact algebraic inverse (constant 3/8), which round-trips the
+            forward transform. Default is True.
+        """
+        self.gain = float(gain)
+        self.read_noise = float(read_noise)
+        self.offset = float(offset)
+        self.max_count = float(max_count)
+        self.unbiased_inverse = bool(unbiased_inverse)
+        self._c_inv = 1.0 / 8.0 if unbiased_inverse else 3.0 / 8.0
+        self._norm = float(
+            self._gat(np.asarray(self.max_count, dtype=np.float32))
+        )
+
+    def _gat(self, x):
+        """
+        Evaluates the unnormalized generalized Anscombe transform.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Image in raw count units.
+
+        Returns
+        -------
+        numpy.ndarray
+            Variance-stabilized values (before normalization).
+        """
+        arg = (
+            self.gain * (x - self.offset)
+            + (3.0 / 8.0) * self.gain ** 2
+            + self.read_noise ** 2
+        )
+        return (2.0 / self.gain) * np.sqrt(np.maximum(arg, 0.0))
+
+    def forward(self, x):
+        """
+        Maps raw counts to the normalized Anscombe domain.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Image in raw count units.
+
+        Returns
+        -------
+        numpy.ndarray
+            Normalized image (approximately [0, 1]), float32.
+        """
+        gat = self._gat(np.asarray(x, dtype=np.float32))
+        return (gat / self._norm).astype(np.float32)
+
+    def inverse(self, y):
+        """
+        Maps normalized Anscombe values back to raw uint16 counts.
+
+        Parameters
+        ----------
+        y : numpy.ndarray
+            Image in the normalized Anscombe domain.
+
+        Returns
+        -------
+        numpy.ndarray
+            Image in raw counts, clipped to [0, max_count], uint16.
+        """
+        d = np.clip(np.asarray(y, dtype=np.float32), 0.0, None) * self._norm
+        arg = (d * self.gain / 2.0) ** 2
+        counts = self.offset + (
+            arg - self._c_inv * self.gain ** 2 - self.read_noise ** 2
+        ) / self.gain
+        counts = np.clip(counts, 0, self.max_count)
+        return np.rint(counts).astype(np.uint16)
+
+
+def estimate_offset(sample, percentile=1.0):
+    """
+    Estimates a robust global background / black-point (counts).
+
+    Compute this once over a representative sample of the training set, then
+    freeze it into the transform config. Do not recompute per patch or per
+    inference volume.
+
+    Parameters
+    ----------
+    sample : numpy.ndarray
+        Representative sample of raw counts.
+    percentile : float, optional
+        Low percentile used as the background estimate. Default is 1.0.
+
+    Returns
+    -------
+    float
+        Estimated background offset in counts.
+    """
+    sample = np.asarray(sample, dtype=np.float32)
+    return float(np.percentile(sample, percentile))
+
+
+def build_transform(cfg):
+    """
+    Builds an intensity transform from a config dict.
+
+    Params are treated as frozen constants; any data-calibrated value must
+    already be baked into ``cfg`` (see ``calibrate_transform``) so that
+    training and inference construct the identical transform.
+
+    Parameters
+    ----------
+    cfg : dict
+        Config of the form ``{"kind": "asinh" | "anscombe",
+        "params": {...}}``.
+
+    Returns
+    -------
+    IntensityTransform
+        The constructed transform.
+
+    Raises
+    ------
+    ValueError
+        If ``cfg["kind"]`` is not a recognized transform kind.
+    """
+    kind = cfg["kind"]
+    params = cfg.get("params", {})
+    if kind == "asinh":
+        return AsinhTransform(**params)
+    if kind == "anscombe":
+        return AnscombeTransform(**params)
+    raise ValueError(f"Unknown transform kind: {kind}")
+
+
+def calibrate_transform(cfg, sample):
+    """
+    Freezes data-driven params into a transform config, once, globally.
+
+    Only the black-point ``offset`` is calibrated, from a low percentile of
+    the sample; the scale/knee (asinh) and gain/read_noise (Anscombe) are not
+    taken from high signal percentiles. The input ``cfg`` is not mutated; the
+    returned cfg is what should be serialized with the model and reused
+    verbatim at inference.
+
+    Parameters
+    ----------
+    cfg : dict
+        Transform config, optionally containing a ``"calibrate"`` block of
+        the form ``{"offset": bool, "offset_percentile": float}``.
+    sample : numpy.ndarray
+        Representative sample of raw counts used for calibration.
+
+    Returns
+    -------
+    dict
+        A new config with calibrated params frozen in.
+    """
+    cfg = {**cfg, "params": dict(cfg.get("params", {}))}
+    calib = cfg.get("calibrate", {})
+    if calib.get("offset", False):
+        cfg["params"]["offset"] = estimate_offset(
+            sample, percentile=calib.get("offset_percentile", 1.0)
+        )
+    return cfg
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
new file mode 100644
index 0000000..8bee73b
--- /dev/null
+++ b/tests/test_transforms.py
@@ -0,0 +1,148 @@
+"""Tests for the intensity-transform module."""
+
+import unittest
+
+import numpy as np
+
+from aind_exaspim_image_compression.machine_learning.transforms import (
+    AnscombeTransform,
+    AsinhTransform,
+    IntensityTransform,
+    build_transform,
+    calibrate_transform,
+    estimate_offset,
+)
+
+
+class AsinhTransformTest(unittest.TestCase):
+    """Tests for AsinhTransform."""
+
+    def test_round_trip(self):
+        """Inverse recovers counts across the range, with no plateau."""
+        t = AsinhTransform(offset=35, scale=32)
+        vals = np.array([0, 100, 1000, 10000, 60000, 65535], dtype=np.float32)
+        rec = t.inverse(t.forward(vals)).astype(np.float64)
+        np.testing.assert_allclose(rec, vals, rtol=1e-2, atol=3)
+
+    def test_no_bright_plateau(self):
+        """Distinct bright counts map to distinct recovered counts."""
+        t = AsinhTransform(offset=35, scale=32)
+        vals = np.array([2500, 10000, 60000], dtype=np.float32)
+        rec = t.inverse(t.forward(vals)).astype(np.float64)
+        self.assertTrue(np.all(np.diff(rec) > 1000))
+
+    def test_monotonic(self):
+        """Forward is strictly increasing (no plateau)."""
+        t = AsinhTransform(offset=35, scale=32)
+        xs = np.linspace(0, 65535, 500).astype(np.float32)
+        ys = t.forward(xs)
+        self.assertTrue(np.all(np.diff(ys) > 0))
+
+    def test_bounded(self):
+        """Max maps to 1, background near 0, sub-background negative."""
+        t = AsinhTransform(offset=35, scale=32)
+        self.assertAlmostEqual(
+            float(t.forward(np.array(65535.0))), 1.0, places=4
+        )
+        self.assertLess(abs(float(t.forward(np.array(35.0)))), 0.05)
+        self.assertLess(float(t.forward(np.array(0.0))), 0.0)
+
+
+class AnscombeTransformTest(unittest.TestCase):
+    """Tests for AnscombeTransform."""
+
+    def test_round_trip_algebraic(self):
+        """Algebraic inverse (3/8) round-trips the forward transform."""
+        t = AnscombeTransform(
+            gain=8, read_noise=5, offset=100, unbiased_inverse=False
+        )
+        vals = np.array([100, 500, 2000, 20000, 65535], dtype=np.float32)
+        rec = t.inverse(t.forward(vals)).astype(np.float64)
+        np.testing.assert_allclose(rec, vals, rtol=5e-3, atol=3)
+
+    def test_unbiased_inverse_has_expected_bias(self):
+        """Unbiased inverse exceeds algebraic by ~gain/4 (no exact id)."""
+        t_alg = AnscombeTransform(gain=8, unbiased_inverse=False)
+        t_unb = AnscombeTransform(gain=8, unbiased_inverse=True)
+        vals = np.array([1000, 20000, 60000], dtype=np.float32)
+        y = t_alg.forward(vals)
+        rec_alg = t_alg.inverse(y).astype(np.float64)
+        rec_unb = t_unb.inverse(y).astype(np.float64)
+        np.testing.assert_allclose(rec_unb - rec_alg, 2.0, atol=1.0)
+
+    def test_monotonic(self):
+        """Forward is strictly increasing (no plateau)."""
+        t = AnscombeTransform(gain=8, read_noise=5, offset=0)
+        xs = np.linspace(0, 65535, 500).astype(np.float32)
+        ys = t.forward(xs)
+        self.assertTrue(np.all(np.diff(ys) > 0))
+
+    def test_bounded(self):
+        """Max maps to 1."""
+        t = AnscombeTransform(gain=8, read_noise=5, offset=100)
+        self.assertAlmostEqual(
+            float(t.forward(np.array(65535.0))), 1.0, places=4
+        )
+
+    def test_reduces_to_standard_anscombe(self):
+        """Default params match the standard 2*sqrt(x + 3/8)."""
+        t = AnscombeTransform(gain=1, read_noise=0, offset=0)
+        x = np.array([0, 1, 10, 100, 1000], dtype=np.float32)
+        expected = 2.0 * np.sqrt(x + 3.0 / 8.0)
+        np.testing.assert_allclose(t._gat(x), expected, rtol=1e-5)
+
+
+class HelperTest(unittest.TestCase):
+    """Tests for module-level helpers."""
+
+    def test_estimate_offset(self):
+        """Offset estimate matches the requested percentile."""
+        sample = np.arange(0, 101, dtype=np.float32)
+        self.assertAlmostEqual(estimate_offset(sample, percentile=0), 0.0)
+        self.assertAlmostEqual(estimate_offset(sample, percentile=50), 50.0)
+        self.assertAlmostEqual(estimate_offset(sample, percentile=100), 100.0)
+
+    def test_build_transform(self):
+        """Factory builds each kind and rejects unknown kinds."""
+        t = build_transform({"kind": "asinh"})
+        self.assertIsInstance(t, AsinhTransform)
+
+        t = build_transform({"kind": "anscombe", "params": {"gain": 8}})
+        self.assertIsInstance(t, AnscombeTransform)
+        self.assertEqual(t.gain, 8.0)
+
+        with self.assertRaises(ValueError):
+            build_transform({"kind": "nope"})
+
+    def test_calibrate_transform_sets_offset(self):
+        """Calibration freezes the offset without mutating the input."""
+        sample = np.arange(0, 1000, dtype=np.float32)
+        cfg = {
+            "kind": "asinh",
+            "calibrate": {"offset": True, "offset_percentile": 10.0},
+        }
+        out = calibrate_transform(cfg, sample)
+        self.assertAlmostEqual(
+            out["params"]["offset"],
+            float(np.percentile(sample, 10.0)),
+            places=4,
+        )
+        self.assertNotIn("params", cfg)
+
+    def test_calibrate_transform_noop(self):
+        """Without a calibrate block, params pass through unchanged."""
+        cfg = {"kind": "anscombe", "params": {"gain": 2}}
+        out = calibrate_transform(cfg, np.zeros(10, dtype=np.float32))
+        self.assertEqual(out["params"], {"gain": 2})
+
+    def test_base_class_not_implemented(self):
+        """The abstract base raises for both directions."""
+        t = IntensityTransform()
+        with self.assertRaises(NotImplementedError):
+            t.forward(np.zeros(1))
+        with self.assertRaises(NotImplementedError):
+            t.inverse(np.zeros(1))
+
+
+if __name__ == "__main__":
+    unittest.main()

From 93bc2edb73fb5073bfa988ff093f04ec1f34dd2a Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 18:50:37 +0000
Subject: [PATCH 02/56] fix: symmetric skeleton sampling, validation sigma,
 device threading

Part D quick fixes ahead of the normalization overhaul:

- sample_skeleton_voxel jittered the patch center with a positive-only
  shift (0..15), biasing the skeleton into one octant off-center; use a
  symmetric +/- patch_shape//4 shift so the neurite is centered.
- init_datasets built ValidateDataset with default args, so a non-default
  training sigma_bm4d silently diverged from validation; pass it through.
- forward_pass moved tensors to a hard-coded "cuda" despite Trainer.device,
  and the inference tensor helpers forced "cuda"; use the model's device.

evaluate.py's device hard-coding is left for the Part B repair (that file
is already broken against current inference and will be rewritten there).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../inference.py                              | 27 +++++++++++--------
 .../machine_learning/data_handling.py         |  5 ++--
 .../machine_learning/train.py                 |  4 +--
 3 files changed, 21 insertions(+), 15 deletions(-)

diff --git a/src/aind_exaspim_image_compression/inference.py b/src/aind_exaspim_image_compression/inference.py
index 4a74dad..77eeac0 100644
--- a/src/aind_exaspim_image_compression/inference.py
+++ b/src/aind_exaspim_image_compression/inference.py
@@ -136,7 +136,7 @@ def predict_patch(patch, model, normalization_percentiles=(0.5, 99.9)):
         patch = patch[np.newaxis, ...]
 
     # Run model
-    patch = to_tensor(patch)
+    patch = to_tensor(patch, device=next(model.parameters()).device)
     with torch.no_grad():
         pred = model(patch)
 
@@ -164,7 +164,7 @@ def get_patch(idx):
     inputs = np.stack([r[1] for r in results], axis=0)
 
     # Run model
-    inputs = batch_to_tensor(inputs)
+    inputs = batch_to_tensor(inputs, device=next(model.parameters()).device)
     with torch.no_grad():
         outputs = model(inputs).cpu().squeeze(1).numpy()
     return outputs[:, trim:-trim, trim:-trim, trim:-trim]
@@ -271,39 +271,44 @@ def load_model(path, device="cuda"):
     return model
 
 
-def to_tensor(arr):
+def to_tensor(arr, device="cuda"):
     """
-    Converts a NumPy array containing to a PyTorch tensor and moves it to the
-    GPU.
+    Converts a NumPy array to a PyTorch tensor and moves it to the given
+    device.
 
     Parameters
     ----------
     arr : numpy.ndarray
         Array to be converted.
+    device : str or torch.device, optional
+        Device to move the tensor to. Default is "cuda".
 
     Returns
     -------
     torch.Tensor
-        Tensor on GPU with shape (1, 1, depth, height, width).
+        Tensor on the given device with shape (1, 1, depth, height, width).
     """
     while (len(arr.shape)) < 5:
         arr = arr[np.newaxis, ...]
-    return torch.tensor(arr).to("cuda", dtype=torch.float)
+    return torch.tensor(arr).to(device, dtype=torch.float)
 
 
-def batch_to_tensor(arr):
+def batch_to_tensor(arr, device="cuda"):
     """
     Converts a NumPy array containing a batch of inputs to a PyTorch tensor
-    and moves it to the GPU.
+    and moves it to the given device.
 
     Parameters
     ----------
     arr : numpy.ndarray
         Array to be converted, with shape (batch_size, depth, height, width).
+    device : str or torch.device, optional
+        Device to move the tensor to. Default is "cuda".
 
     Returns
     -------
     torch.Tensor
-        Tensor on GPU with shape (batch_size, 1, depth, height, width).
+        Tensor on the given device with shape
+        (batch_size, 1, depth, height, width).
     """
-    return to_tensor(arr[:, np.newaxis, ...])
+    return to_tensor(arr[:, np.newaxis, ...], device=device)
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 59509b9..4240cc2 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -267,7 +267,8 @@ def sample_skeleton_voxel(self, brain_id):
             Voxel coordinate near a skeleton point.
         """
         idx = random.randint(0, len(self.skeletons[brain_id]) - 1)
-        shift = np.random.randint(0, 16, size=3)
+        radius = np.array(self.patch_shape) // 4
+        shift = np.array([np.random.randint(-r, r + 1) for r in radius])
         return tuple(self.skeletons[brain_id][idx] + shift)
 
     def sample_segmentation_voxel(self, brain_id):
@@ -705,7 +706,7 @@ def init_datasets(
         n_examples_per_epoch=n_train_examples_per_epoch,
         sigma_bm4d=sigma_bm4d
     )
-    val_dataset = ValidateDataset(patch_shape)
+    val_dataset = ValidateDataset(patch_shape, sigma_bm4d=sigma_bm4d)
 
     # Read segmentation path lookup (if applicable)
     if segmentation_prefixes_path:
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index f3bd3ae..a0e9c4c 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -212,8 +212,8 @@ def forward_pass(self, x, y):
             Computed loss value.
         """
         with self.autocast:
-            x = x.to("cuda")
-            y = y.to("cuda")
+            x = x.to(self.device)
+            y = y.to(self.device)
             hat_y = self.model(x)
             loss = self.criterion(hat_y, y)
             return hat_y, loss

From f88ed48720fa11a4f57f48e2650fb67d8ffc1e22 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 19:23:17 +0000
Subject: [PATCH 03/56] feat: wire intensity transform through train/inference,
 drop clip

Part B of the normalization overhaul. Replace the per-patch percentile
normalize-and-clip with one fixed, stateless intensity transform used
identically in training, validation, and inference, eliminating the
train/inference mismatch and the bright-tail plateau.

- transforms.py: add LinearClipTransform (frozen linear+clip baseline for
  A/B) and stamp the frozen cfg onto instances via build_transform.
- data_handling.py: TrainDataset/ValidateDataset take a transform, forward()
  raw and BM4D target (clipped to the physical range first), and return
  (x, y); drop mn/mx threading and the normalize() helpers. init_datasets
  resolves one transform from transform_cfg (optionally calibrating the
  offset from a global sample) and shares it across both datasets.
- train.py: iterate (x, y); compute_cratios inverts via the transform;
  save_model bundles {model, transform cfg}; load_pretrained_weights reads
  either format.
- inference.py: predict/predict_patch take a transform and use
  forward()/inverse() (no more hard-coded 0..5 clip); load_model returns
  (model, transform) and reads new or legacy checkpoints.
- evaluate.py: repair to the new predict API (drop nonexistent stitch,
  single-array return), read_patch instead of get_patch, thread transform
  and device, handle both checkpoint formats.
- img_util.py: add compute_mae and compute_lmax (used by evaluate and the
  Part C metrics); evaluate now uses the existing ssim3D.

Verified: 17 transform tests pass; all modules import (evaluate.py was
previously unimportable); functional smoke test covers predict,
predict_patch, both checkpoint formats, compute_cratios, and
ValidateDataset end to end on CPU.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../evaluate.py                               |  57 ++++--
 .../inference.py                              |  63 ++++---
 .../machine_learning/data_handling.py         | 171 +++++++-----------
 .../machine_learning/train.py                 |  27 ++-
 .../machine_learning/transforms.py            | 101 ++++++++++-
 .../utils/img_util.py                         |  42 +++++
 tests/test_transforms.py                      |  30 +++
 7 files changed, 327 insertions(+), 164 deletions(-)

diff --git a/src/aind_exaspim_image_compression/evaluate.py b/src/aind_exaspim_image_compression/evaluate.py
index 9602602..acec1e7 100644
--- a/src/aind_exaspim_image_compression/evaluate.py
+++ b/src/aind_exaspim_image_compression/evaluate.py
@@ -18,23 +18,28 @@
 import pandas as pd
 import torch
 
+from aind_exaspim_image_compression.inference import predict, predict_patch
 from aind_exaspim_image_compression.machine_learning import data_handling
-from aind_exaspim_image_compression.inference import (
-    predict, predict_patch, stitch
+from aind_exaspim_image_compression.machine_learning.transforms import (
+    build_transform,
 )
 from aind_exaspim_image_compression.utils import img_util, util
 from aind_exaspim_image_compression.utils.img_util import (
-    compute_cratio, compute_ssim3D, compute_mae, compute_lmax
+    compute_cratio, compute_lmax, compute_mae, ssim3D
 )
 
 
 class SupervisedEvaluator:
-    def __init__(self, img_paths, model, output_dir):
+    def __init__(
+        self, img_paths, model, output_dir, transform=None, device="cuda"
+    ):
         # Instance attributes
         self.codec = blosc.Blosc(cname="zstd", clevel=6, shuffle=blosc.SHUFFLE)
+        self.device = device
         self.img_paths = img_paths
         self.model = model
-        self.model.eval().to("cuda")
+        self.model.eval().to(device)
+        self.transform = transform or build_transform({"kind": "asinh"})
 
         # Initialize output directory
         self.output_dir = output_dir
@@ -63,7 +68,13 @@ def load_images(self):
     # --- Main ---
     def run(self, model_path):
         # Initializations
-        self.model.load_state_dict(torch.load(model_path))
+        ckpt = torch.load(model_path, map_location=self.device)
+        if isinstance(ckpt, dict) and "model" in ckpt:
+            self.model.load_state_dict(ckpt["model"])
+            if ckpt.get("transform"):
+                self.transform = build_transform(ckpt["transform"])
+        else:
+            self.model.load_state_dict(ckpt)
         model_name = os.path.basename(model_path)
         results_dir = os.path.join(self.output_dir, model_name)
         util.mkdir(results_dir)
@@ -74,15 +85,14 @@ def run(self, model_path):
         desc = "Denoise Blocks"
         for block_id, noise in tqdm(self.noise_imgs.items(), desc=desc):
             # Run model
-            coords, preds = predict(noise, self.model, verbose=False)
-            denoised = stitch(noise, coords, preds)
+            denoised = predict(
+                noise, self.model, self.transform, verbose=False
+            )
 
             # Compute metrics
             df.loc[block_id, "cratio"] = compute_cratio(denoised, self.codec)
-            df.loc[block_id, "ssim"] = compute_ssim3D(
-                noise[0, 0, ...],
-                denoised[0, 0, ...],
-                data_range=np.max(noise),
+            df.loc[block_id, "ssim"] = ssim3D(
+                noise[0], denoised, data_range=np.max(noise),
             )
 
             # Save MIPs
@@ -103,7 +113,9 @@ def find_img_name(self, img_path):
 
 
 class UnsupervisedEvaluator:
-    def __init__(self, root_dir, model, img_paths_json, patch_shape):
+    def __init__(
+        self, root_dir, model, img_paths_json, patch_shape, transform=None
+    ):
         # Class attributes
         self.codec = blosc.Blosc(cname="zstd", clevel=6, shuffle=blosc.SHUFFLE)
         self.img_paths_json = img_paths_json
@@ -112,6 +124,7 @@ def __init__(self, root_dir, model, img_paths_json, patch_shape):
         self.root_dir = root_dir
         self.data_dir = os.path.join(root_dir, "data")
         self.result_dir = os.path.join(root_dir, "models")
+        self.transform = transform or build_transform({"kind": "asinh"})
 
         # Initialize directories
         util.mkdir(self.result_dir)
@@ -160,18 +173,20 @@ def compute_metrics(
         # Run evaluation
         for voxel in tqdm(voxels, desc=brain_id):
             # Get images
-            input_noise = dataset.get_patch(brain_id, voxel)
+            input_noise = np.asarray(dataset.read_patch(brain_id, voxel))
             noise = input_noise[5:-5, 5:-5, 5:-5]
             denoised_gt = np.maximum(bm4d(noise, 10), 0).astype(int)
-            denoised = predict_patch(input_noise, self.model)[5:-5, 5:-5, 5:-5]
+            denoised = predict_patch(
+                input_noise, self.model, self.transform
+            )[5:-5, 5:-5, 5:-5]
 
             # Compute metrics
             metrics["cratio"].append(compute_cratio(denoised, self.codec))
             metrics["cratio_noise"].append(compute_cratio(noise, self.codec))
             metrics["cratio_gt"].append(compute_cratio(denoised_gt, self.codec))
 
-            metrics["ssim_noise"].append(compute_ssim3D(noise, denoised))
-            metrics["ssim_gt"].append(compute_ssim3D(denoised_gt, denoised))
+            metrics["ssim_noise"].append(ssim3D(noise, denoised))
+            metrics["ssim_gt"].append(ssim3D(denoised_gt, denoised))
 
             metrics["l1_gt"].append(compute_mae(denoised_gt, denoised))
             metrics["lmax_gt"].append(compute_lmax(denoised_gt, denoised))
@@ -188,7 +203,13 @@ def init_dataset(self, brain_ids_dict):
         return dataset
 
     def ingest_model(self, model_path):
-        self.model.load_state_dict(torch.load(model_path))
+        ckpt = torch.load(model_path)
+        if isinstance(ckpt, dict) and "model" in ckpt:
+            self.model.load_state_dict(ckpt["model"])
+            if ckpt.get("transform"):
+                self.transform = build_transform(ckpt["transform"])
+        else:
+            self.model.load_state_dict(ckpt)
         model_name = os.path.basename(model_path)
         util.mkdir(os.path.join(self.result_dir, model_name))
 
diff --git a/src/aind_exaspim_image_compression/inference.py b/src/aind_exaspim_image_compression/inference.py
index 77eeac0..7e0dd71 100644
--- a/src/aind_exaspim_image_compression/inference.py
+++ b/src/aind_exaspim_image_compression/inference.py
@@ -17,15 +17,17 @@
 import numpy as np
 import torch
 
+from aind_exaspim_image_compression.machine_learning.transforms import (
+    build_transform,
+)
 from aind_exaspim_image_compression.machine_learning.unet3d import UNet
 
 
 def predict(
     img,
     model,
+    transform,
     batch_size=32,
-    normalization_percentiles=(0.5, 99.9),
-    normalized_brightness_clip=7,
     patch_size=64,
     overlap=12,
     trim=5,
@@ -41,14 +43,11 @@ def predict(
         Input 3D image of shape (1, 1, depth, height, width).
     model : torch.nn.Module
         PyTorch model to perform prediction on patches.
+    transform : IntensityTransform
+        Transform mapping raw counts to the normalized domain and back. Must
+        be the same transform the model was trained with.
     batch_size : int, optional
         Number of patches to process in a batch. Default is 32.
-    normalization_percentiles : Tuple[int], optional
-        Lower and upper percentiles used for normalization. Default is
-        (0.5, 99.9).
-    normalized_brightness_clip : float, optional
-        Brightness value used as an upper limit that normalized intensities
-        are clipped to. Default is 10.
     patch_size : int, optional
         Size of the cubic patch extracted from the image. Default is 64.
     overlap : int, optional
@@ -62,12 +61,10 @@ def predict(
     Returns
     -------
     denoised : numpy.ndarray
-        Denoised image.
+        Denoised image in raw counts (uint16).
     """
     # Preprocess image
-    mn, mx = np.percentile(img, normalization_percentiles)
-    img = (img - mn) / (mx - mn + 1e-8)
-    img = np.clip(img, 0, 5)
+    img = transform.forward(img)
     while len(img.shape) < 5:
         img = img[np.newaxis, ...]
 
@@ -105,11 +102,10 @@ def predict(
 
     # Postprocess prediction
     denoised = accum_pred[:, ...] / (accum_wgt + 1e-8)
-    denoised = np.clip(denoised * (mx - mn) + mn, 0, 2**16 - 1)
-    return denoised.astype(np.uint16)
+    return transform.inverse(denoised)
 
 
-def predict_patch(patch, model, normalization_percentiles=(0.5, 99.9)):
+def predict_patch(patch, model, transform):
     """
     Denoises a single 3D patch using the provided model.
 
@@ -119,19 +115,17 @@ def predict_patch(patch, model, normalization_percentiles=(0.5, 99.9)):
         3D input patch to denoise.
     model : torch.nn.Module
         PyTorch model used for prediction.
-    normalization_percentiles : Tuple[int], optional
-        Lower and upper percentiles used for normalization. Default is
-        (0.5, 99.9).
+    transform : IntensityTransform
+        Transform mapping raw counts to the normalized domain and back. Must
+        be the same transform the model was trained with.
 
     Returns
     -------
     pred : numpy.ndarray
-        Denoised 3D patch with the same shape as input patch.
+        Denoised 3D patch (uint16) with the same shape as the input patch.
     """
     # Preprocess image
-    mn, mx = np.percentile(patch, normalization_percentiles)
-    patch = (patch - mn) / (mx - mn + 1e-8)
-    patch = np.clip(patch, 0, 5)
+    patch = transform.forward(patch)
     while len(patch.shape) < 5:
         patch = patch[np.newaxis, ...]
 
@@ -142,8 +136,7 @@ def predict_patch(patch, model, normalization_percentiles=(0.5, 99.9)):
 
     # Process output
     pred = np.array(pred.cpu())
-    pred = np.clip(pred[0, 0, ...] * (mx - mn) + mn, 0, 2**16 - 1)
-    return pred.astype(np.uint16)
+    return transform.inverse(pred[0, 0, ...])
 
 
 def _predict_batch(img, model, starts, patch_size, trim=5):
@@ -250,12 +243,16 @@ def count_patches(img, patch_size, overlap):
 
 def load_model(path, device="cuda"):
     """
-    Loads a pretrained UNet model from a file.
+    Loads a pretrained UNet model and its intensity transform from a file.
+
+    Supports both the current checkpoint format (a dict with "model" and
+    "transform" keys) and a bare state_dict (legacy), in which case the
+    transform defaults to asinh.
 
     Parameters
     ----------
     path : str
-        Path to the saved model weights (e.g., .pt or .pth file).
+        Path to the saved checkpoint (e.g., .pt or .pth file).
     device : str, optional
         Device to load the model onto. Default is "cuda".
 
@@ -263,12 +260,22 @@ def load_model(path, device="cuda"):
     -------
     model : torch.nn.Module
         UNet model loaded with weights and set to evaluation mode.
+    transform : IntensityTransform
+        The intensity transform the model was trained with.
     """
+    ckpt = torch.load(path, map_location=device)
+    if isinstance(ckpt, dict) and "model" in ckpt:
+        state_dict = ckpt["model"]
+        transform_cfg = ckpt.get("transform") or {"kind": "asinh"}
+    else:
+        state_dict = ckpt
+        transform_cfg = {"kind": "asinh"}
+
     model = UNet()
-    model.load_state_dict(torch.load(path, map_location=device))
+    model.load_state_dict(state_dict)
     model.to(device)
     model.eval()
-    return model
+    return model, build_transform(transform_cfg)
 
 
 def to_tensor(arr, device="cuda"):
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 4240cc2..6a2323e 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -23,6 +23,10 @@
 import tensorstore as ts
 import torch
 
+from aind_exaspim_image_compression.machine_learning.transforms import (
+    build_transform,
+    calibrate_transform,
+)
 from aind_exaspim_image_compression.utils import img_util, util
 from aind_exaspim_image_compression.utils.swc_util import Reader
 
@@ -46,10 +50,9 @@ def __init__(
         boundary_buffer=5000,
         foreground_sampling_rate=0.5,
         n_examples_per_epoch=300,
-        normalization_percentiles=(0.5, 99.9),
-        normalized_brightness_clip=8,
         prefetch_foreground_sampling=16,
         sigma_bm4d=16,
+        transform=None,
     ):
         # Call parent class
         super(TrainDataset, self).__init__()
@@ -59,11 +62,10 @@ def __init__(
         self.boundary_buffer = boundary_buffer
         self.foreground_sampling_rate = foreground_sampling_rate
         self.n_examples_per_epoch = n_examples_per_epoch
-        self.normalization_percentiles = normalization_percentiles
-        self.normalized_brightness_clip = normalized_brightness_clip
         self.patch_shape = patch_shape
         self.prefetch_foreground_sampling = prefetch_foreground_sampling
         self.sigma_bm4d = sigma_bm4d
+        self.transform = transform or build_transform({"kind": "asinh"})
         self.swc_reader = Reader()
 
         # Data structures
@@ -147,8 +149,7 @@ def _load_swcs(self, brain_id, swc_pointer):
     # --- Sample Image Patches ---
     def __getitem__(self, dummy_input):
         """
-        Returns a pair of noisy and BM4D-denoised image patches, normalized
-        according to percentile-based scaling.
+        Returns a pair of transformed noisy and BM4D-denoised image patches.
 
         Parameters
         ----------
@@ -158,25 +159,22 @@ def __getitem__(self, dummy_input):
 
         Returns
         -------
-        noise : numpy.ndarray
-            Noisy image patch, normalized and clipped.
-        denoised : numpy.ndarray
-            Denoised image patch, normalized and clipped using the same scale
-            as the noisy patch.
-        (mn, mx) : Tuple[float]
-            Lower and upper percentiles used for normalization.
-        """
-        # Get image patches
+        x : numpy.ndarray
+            Noisy image patch in the normalized transform domain.
+        y : numpy.ndarray
+            BM4D-denoised image patch in the normalized transform domain.
+        """
+        # Sample image patch and its BM4D-denoised target
         brain_id = self.sample_brain()
         voxel = self.sample_voxel(brain_id)
-        noise = self.read_patch(brain_id, voxel)
-        mn, mx = np.percentile(noise, self.normalization_percentiles)
-        denoised = bm4d(noise, self.sigma_bm4d)
+        raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
+        target = bm4d(raw, self.sigma_bm4d)
+        target = np.clip(target, 0, self.transform.max_count)
 
-        # Normalize image patches
-        noise = self.normalize(noise, mn, mx)
-        denoised = self.normalize(denoised, mn, mx)
-        return noise, denoised, (mn, mx)
+        # Map to the normalized transform domain
+        x = self.transform.forward(raw)
+        y = self.transform.forward(target)
+        return x, y
 
     def sample_brain(self):
         """
@@ -378,28 +376,30 @@ def __len__(self):
         """
         return self.n_examples_per_epoch
 
-    def normalize(self, img, mn, mx):
+    def sample_intensity_values(self, n_patches=8):
         """
-        Normalizes the given image using a percentile-based scheme and clips
-        the max brightness.
+        Reads a few interior patches and returns their flattened counts.
+
+        Used to calibrate a transform's background offset from a global
+        sample of the training data.
 
         Parameters
         ----------
-        img : numpy.ndarray
-            Image to be normalized
-        mn : float
-            Lower percentile.
-        mx : float
-            Upper percentile
+        n_patches : int, optional
+            Number of interior patches to sample. Default is 8.
 
         Returns
         -------
-        img : numpy.ndarray
-            Normalized image.
+        numpy.ndarray
+            Flattened raw counts from the sampled patches.
         """
-        img = (img - mn) / (mx - mn + 1e-8)
-        img = np.clip(img, 0, self.normalized_brightness_clip)
-        return img
+        values = list()
+        for _ in range(n_patches):
+            brain_id = self.sample_brain()
+            voxel = self.sample_interior_voxel(brain_id)
+            patch = np.asarray(self.read_patch(brain_id, voxel))
+            values.append(patch.ravel())
+        return np.concatenate(values)
 
     def read_patch(self, brain_id, center):
         """
@@ -460,13 +460,7 @@ def to_voxels(self, xyz_arr):
 
 class ValidateDataset(Dataset):
 
-    def __init__(
-        self,
-        patch_shape,
-        normalization_percentiles=(0.5, 99.9),
-        normalized_brightness_clip=8,
-        sigma_bm4d=16,
-    ):
+    def __init__(self, patch_shape, sigma_bm4d=16, transform=None):
         """
         Instantiates a ValidateDataset object.
 
@@ -474,31 +468,26 @@ def __init__(
         ----------
         patch_shape : Tuple[int]
             Shape of image patches to be extracted.
-        normalization_percentiles : Tuple[float], optional
-            Upper and lower percentiles used to normalize the input image.
-            Default is (0.5, 99.5).
-        normalized_brightness_clip : float, optional
-            Brightness value used as an upper limit that normalized intensities
-            are clipped to. Default is 10.
         sigma_bm4d : float, optional
             Smoothing parameter used in the BM4D denoising algorithm. Default
             is 16.
+        transform : IntensityTransform, optional
+            Transform mapping raw counts to the normalized domain. Default is
+            an asinh transform.
         """
         # Call parent class
         super(ValidateDataset, self).__init__()
 
         # Instance attributes
-        self.normalization_percentiles = normalization_percentiles
-        self.normalized_brightness_clip = normalized_brightness_clip
         self.patch_shape = patch_shape
         self.sigma_bm4d = sigma_bm4d
+        self.transform = transform or build_transform({"kind": "asinh"})
 
         # Data structures
         self.example_ids = list()
         self.imgs = dict()
         self.denoised = list()
         self.noise = list()
-        self.mn_mxs = list()
 
     def __len__(self):
         """
@@ -526,8 +515,7 @@ def ingest_brain(self, brain_id, img_path):
 
     def ingest_example(self, brain_id, voxel):
         """
-        Extracts, denoises, normalizes, and stores an image patch from a brain
-        volume.
+        Extracts, denoises, transforms, and stores an image patch.
 
         Parameters
         ----------
@@ -536,20 +524,15 @@ def ingest_example(self, brain_id, voxel):
         voxel : Tuple[int]
             Voxel coordinates of the patch center in the brain volume.
         """
-        # Get image patches
-        noise = self.read_patch(brain_id, voxel)
-        mn, mx = np.percentile(noise, self.normalization_percentiles)
-        denoised = bm4d(noise, self.sigma_bm4d)
-
-        # Normalize image patches
-        noise = self.normalize(noise, mn, mx)
-        denoised = self.normalize(denoised, mn, mx)
+        # Sample image patch and its BM4D-denoised target
+        raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
+        target = bm4d(raw, self.sigma_bm4d)
+        target = np.clip(target, 0, self.transform.max_count)
 
-        # Store results
+        # Store transformed patches
         self.example_ids.append((brain_id, voxel))
-        self.noise.append(noise)
-        self.denoised.append(denoised)
-        self.mn_mxs.append((mn, mx))
+        self.noise.append(self.transform.forward(raw))
+        self.denoised.append(self.transform.forward(target))
 
     def __getitem__(self, idx):
         """
@@ -562,40 +545,14 @@ def __getitem__(self, idx):
 
         Returns
         -------
-        noise : numpy.ndarray
-            Noisy image patch at the given index.
-        denoised : numpy.ndarray
-            Corresponding denoised image patch.
-        mn_mx : Tuple[int]
-            Minimum and maximum values used for normalization of the image
-            patches.
+        x : numpy.ndarray
+            Noisy image patch in the normalized transform domain.
+        y : numpy.ndarray
+            BM4D-denoised image patch in the normalized transform domain.
         """
-        return self.noise[idx], self.denoised[idx], self.mn_mxs[idx]
+        return self.noise[idx], self.denoised[idx]
 
     # --- Helpers ---
-    def normalize(self, img, mn, mx):
-        """
-        Normalizes the given image using a percentile-based scheme and clips
-        the max brightness.
-
-        Parameters
-        ----------
-        img : numpy.ndarray
-            Image to be normalized
-        mn : float
-            Lower percentile.
-        mx : float
-            Upper percentile
-
-        Returns
-        -------
-        img : numpy.ndarray
-            Normalized image.
-        """
-        img = (img - mn) / (mx - mn + 1e-8)
-        img = np.clip(img, 0, self.normalized_brightness_clip)
-        return img
-
     def read_patch(self, brain_id, center):
         """
         Reads an image patch from a Zarr array.
@@ -678,13 +635,11 @@ def _load_batch(self, start_idx):
             shape = (batch_size, 1,) + self.patch_shape
             noise_patches = np.zeros(shape)
             denoised_patches = np.zeros(shape)
-            mn_mxs = np.zeros((self.batch_size, 2))
             for i, process in enumerate(as_completed(processes)):
-                noise, denoised, mn_mx = process.result()
+                noise, denoised = process.result()
                 noise_patches[i, 0, ...] = noise
                 denoised_patches[i, 0, ...] = denoised
-                mn_mxs[i, :] = mn_mx
-        return to_tensor(noise_patches), to_tensor(denoised_patches), mn_mxs
+        return to_tensor(noise_patches), to_tensor(denoised_patches)
 
 
 # --- Helpers ---
@@ -697,9 +652,12 @@ def init_datasets(
     n_validate_examples=0,
     segmentation_prefixes_path=None,
     sigma_bm4d=16,
-    swc_pointers=None
+    swc_pointers=None,
+    transform_cfg=None,
 ):
     # Initializations
+    if transform_cfg is None:
+        transform_cfg = {"kind": "asinh"}
     train_dataset = TrainDataset(
         patch_shape,
         foreground_sampling_rate=foreground_sampling_rate,
@@ -738,6 +696,15 @@ def init_datasets(
             brain_id, img_path, segmentation_path, swc_pointer
         )
 
+    # Resolve one frozen transform, optionally calibrating the offset from a
+    # global training sample, and share it across train and validation.
+    if transform_cfg.get("calibrate", {}).get("offset", False):
+        sample = train_dataset.sample_intensity_values()
+        transform_cfg = calibrate_transform(transform_cfg, sample)
+    transform = build_transform(transform_cfg)
+    train_dataset.transform = transform
+    val_dataset.transform = transform
+
     # Generate validation examples
     for _ in range(n_validate_examples):
         brain_id = train_dataset.sample_brain()
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index a0e9c4c..3e98d39 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -95,6 +95,7 @@ def run(self, train_dataset, val_dataset):
         """
         # Initializations
         print("Experiment:", os.path.basename(os.path.normpath(self.log_dir)))
+        self.transform = train_dataset.transform
         train_dataloader = DataLoader(
             train_dataset, batch_size=self.batch_size
         )
@@ -135,7 +136,7 @@ def train_step(self, train_dataloader, epoch):
         """
         losses = list()
         self.model.train()
-        for x, y, _ in train_dataloader:
+        for x, y in train_dataloader:
             # Forward pass
             hat_y, loss = self.forward_pass(x, y)
 
@@ -173,12 +174,12 @@ def validate_step(self, val_dataloader, epoch):
         cratios = list()
         with torch.no_grad():
             self.model.eval()
-            for x, y, mn_mx in val_dataloader:
+            for x, y in val_dataloader:
                 # Run model
                 hat_y, loss = self.forward_pass(x, y)
 
                 # Evalute result
-                cratios.extend(self.compute_cratios(hat_y, mn_mx))
+                cratios.extend(self.compute_cratios(hat_y))
                 losses.append(loss.detach().cpu())
 
         # Log results
@@ -219,12 +220,11 @@ def forward_pass(self, x, y):
             return hat_y, loss
 
     # --- Helpers ---
-    def compute_cratios(self, imgs, mn_mx):
+    def compute_cratios(self, imgs):
         cratios = list()
         imgs = np.array(imgs.detach().cpu())
         for i in range(imgs.shape[0]):
-            mn, mx = tuple(mn_mx[i, :])
-            img = np.clip(imgs[i, 0, ...] * (mx - mn) + mn, 0, 2**16 - 1)
+            img = self.transform.inverse(imgs[i, 0, ...])
             cratios.append(img_util.compute_cratio(img, self.codec))
             if i < 10:
                 tifffile.imwrite(f"{i}.tiff", img)
@@ -239,9 +239,10 @@ def load_pretrained_weights(self, model_path):
         model_path : str
             Path to the checkpoint file containing the saved weights.
         """
-        self.model.load_state_dict(
-            torch.load(model_path, map_location=self.device)
-        )
+        ckpt = torch.load(model_path, map_location=self.device)
+        if isinstance(ckpt, dict) and "model" in ckpt:
+            ckpt = ckpt["model"]
+        self.model.load_state_dict(ckpt)
 
     def save_model(self, epoch):
         """
@@ -255,4 +256,10 @@ def save_model(self, epoch):
         date = datetime.today().strftime("%Y%m%d")
         filename = f"BM4DNet-{date}-{epoch}-{self.best_l1:.6f}.pth"
         path = os.path.join(self.log_dir, filename)
-        torch.save(self.model.state_dict(), path)
+        torch.save(
+            {
+                "model": self.model.state_dict(),
+                "transform": getattr(self.transform, "cfg", None),
+            },
+            path,
+        )
diff --git a/src/aind_exaspim_image_compression/machine_learning/transforms.py b/src/aind_exaspim_image_compression/machine_learning/transforms.py
index 75a409a..f937d69 100644
--- a/src/aind_exaspim_image_compression/machine_learning/transforms.py
+++ b/src/aind_exaspim_image_compression/machine_learning/transforms.py
@@ -273,6 +273,88 @@ def inverse(self, y):
         return np.rint(counts).astype(np.uint16)
 
 
+class LinearClipTransform(IntensityTransform):
+    """
+    Linear normalization with a hard brightness clip.
+
+    Provided as a fixed, stateless baseline for A/B comparison against the
+    compressive transforms. It reproduces the original normalize-and-clip
+    behavior (with globally-frozen ``mn``/``mx`` instead of per-patch
+    percentiles), which flattens the bright tail above ``clip`` into a
+    non-invertible plateau. It is the thing the compressive transforms are
+    meant to beat, not a recommended default.
+
+    Attributes
+    ----------
+    mn : float
+        Lower normalization reference in counts (maps to 0).
+    mx : float
+        Upper normalization reference in counts (maps to 1).
+    clip : float
+        Upper bound applied in the normalized domain.
+    max_count : float
+        Physical sensor maximum used to clamp the inverse.
+    """
+
+    def __init__(self, mn=0.0, mx=1000.0, clip=8.0, max_count=65535.0):
+        """
+        Instantiates a LinearClipTransform.
+
+        Parameters
+        ----------
+        mn : float, optional
+            Lower normalization reference in counts. Default is 0.0.
+        mx : float, optional
+            Upper normalization reference in counts. Default is 1000.0.
+        clip : float, optional
+            Upper bound applied in the normalized domain. Default is 8.0.
+        max_count : float, optional
+            Physical sensor maximum used to clamp the inverse. Default is
+            65535.0.
+        """
+        self.mn = float(mn)
+        self.mx = float(mx)
+        self.clip = float(clip)
+        self.max_count = float(max_count)
+
+    def forward(self, x):
+        """
+        Maps raw counts to the normalized, clipped domain.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Image in raw count units.
+
+        Returns
+        -------
+        numpy.ndarray
+            Normalized image clipped to [0, clip], float32.
+        """
+        x = np.asarray(x, dtype=np.float32)
+        y = (x - self.mn) / (self.mx - self.mn + 1e-8)
+        return np.clip(y, 0.0, self.clip).astype(np.float32)
+
+    def inverse(self, y):
+        """
+        Maps normalized values back to raw uint16 counts.
+
+        Parameters
+        ----------
+        y : numpy.ndarray
+            Image in the normalized domain.
+
+        Returns
+        -------
+        numpy.ndarray
+            Image in raw counts, clipped to [0, max_count], uint16.
+        """
+        y = np.asarray(y, dtype=np.float32)
+        counts = y * (self.mx - self.mn) + self.mn
+        counts = np.clip(counts, 0, self.max_count)
+        return np.rint(counts).astype(np.uint16)
+
+
 def estimate_offset(sample, percentile=1.0):
     """
     Estimates a robust global background / black-point (counts).
@@ -303,12 +385,14 @@ def build_transform(cfg):
 
     Params are treated as frozen constants; any data-calibrated value must
     already be baked into ``cfg`` (see ``calibrate_transform``) so that
-    training and inference construct the identical transform.
+    training and inference construct the identical transform. The originating
+    (frozen) config is stamped onto the returned instance as ``.cfg`` so it
+    can be serialized alongside a model checkpoint.
 
     Parameters
     ----------
     cfg : dict
-        Config of the form ``{"kind": "asinh" | "anscombe",
+        Config of the form ``{"kind": "asinh" | "anscombe" | "linear",
         "params": {...}}``.
 
     Returns
@@ -324,10 +408,15 @@ def build_transform(cfg):
     kind = cfg["kind"]
     params = cfg.get("params", {})
     if kind == "asinh":
-        return AsinhTransform(**params)
-    if kind == "anscombe":
-        return AnscombeTransform(**params)
-    raise ValueError(f"Unknown transform kind: {kind}")
+        transform = AsinhTransform(**params)
+    elif kind == "anscombe":
+        transform = AnscombeTransform(**params)
+    elif kind == "linear":
+        transform = LinearClipTransform(**params)
+    else:
+        raise ValueError(f"Unknown transform kind: {kind}")
+    transform.cfg = {**cfg, "params": dict(params)}
+    return transform
 
 
 def calibrate_transform(cfg, sample):
diff --git a/src/aind_exaspim_image_compression/utils/img_util.py b/src/aind_exaspim_image_compression/utils/img_util.py
index ce5ec5a..af2e140 100644
--- a/src/aind_exaspim_image_compression/utils/img_util.py
+++ b/src/aind_exaspim_image_compression/utils/img_util.py
@@ -796,3 +796,45 @@ def ssim3D(img1, img2, data_range=None, window_size=16):
     denominator = (mu1**2 + mu2**2 + C1) * (sigma1_sq + sigma2_sq + C2)
     ssim_map = numerator / (np.maximum(denominator, 1e-8) + 1e-6)
     return np.mean(ssim_map)
+
+
+def compute_mae(img1, img2):
+    """
+    Computes the mean absolute error between two images.
+
+    Parameters
+    ----------
+    img1 : numpy.ndarray
+        Image.
+    img2 : numpy.ndarray
+        Image with the same shape as "img1".
+
+    Returns
+    -------
+    float
+        Mean absolute error between the two images.
+    """
+    a = np.asarray(img1, dtype=np.float64)
+    b = np.asarray(img2, dtype=np.float64)
+    return float(np.mean(np.abs(a - b)))
+
+
+def compute_lmax(img1, img2):
+    """
+    Computes the maximum absolute error between two images.
+
+    Parameters
+    ----------
+    img1 : numpy.ndarray
+        Image.
+    img2 : numpy.ndarray
+        Image with the same shape as "img1".
+
+    Returns
+    -------
+    float
+        Maximum absolute error between the two images.
+    """
+    a = np.asarray(img1, dtype=np.float64)
+    b = np.asarray(img2, dtype=np.float64)
+    return float(np.max(np.abs(a - b)))
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index 8bee73b..a017932 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -8,6 +8,7 @@
     AnscombeTransform,
     AsinhTransform,
     IntensityTransform,
+    LinearClipTransform,
     build_transform,
     calibrate_transform,
     estimate_offset,
@@ -92,6 +93,25 @@ def test_reduces_to_standard_anscombe(self):
         np.testing.assert_allclose(t._gat(x), expected, rtol=1e-5)
 
 
+class LinearClipTransformTest(unittest.TestCase):
+    """Tests for LinearClipTransform."""
+
+    def test_round_trip_within_clip(self):
+        """Values within the clip range round-trip."""
+        t = LinearClipTransform(mn=35, mx=1000, clip=8)
+        vals = np.array([35, 200, 1000, 5000], dtype=np.float32)
+        rec = t.inverse(t.forward(vals)).astype(np.float64)
+        np.testing.assert_allclose(rec, vals, rtol=1e-3, atol=1)
+
+    def test_clips_bright_tail(self):
+        """Values above the clip collapse to one plateau value."""
+        t = LinearClipTransform(mn=0, mx=1000, clip=8)
+        vals = np.array([9000, 30000, 60000], dtype=np.float32)
+        rec = t.inverse(t.forward(vals)).astype(np.float64)
+        self.assertTrue(np.all(rec == rec[0]))
+        self.assertLess(rec[0], 9000)
+
+
 class HelperTest(unittest.TestCase):
     """Tests for module-level helpers."""
 
@@ -111,9 +131,19 @@ def test_build_transform(self):
         self.assertIsInstance(t, AnscombeTransform)
         self.assertEqual(t.gain, 8.0)
 
+        t = build_transform({"kind": "linear", "params": {"mx": 500}})
+        self.assertIsInstance(t, LinearClipTransform)
+        self.assertEqual(t.mx, 500.0)
+
         with self.assertRaises(ValueError):
             build_transform({"kind": "nope"})
 
+    def test_build_transform_stamps_cfg(self):
+        """Factory stamps the frozen cfg onto the instance."""
+        t = build_transform({"kind": "asinh", "params": {"scale": 16}})
+        self.assertEqual(t.cfg["kind"], "asinh")
+        self.assertEqual(t.cfg["params"]["scale"], 16)
+
     def test_calibrate_transform_sets_offset(self):
         """Calibration freezes the offset without mutating the input."""
         sample = np.arange(0, 1000, dtype=np.float32)

From 38f13521a6221fdefc8efc784b1c2d53918e94ee Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 20:07:28 +0000
Subject: [PATCH 04/56] feat: neurite-preservation metrics and joint checkpoint
 selection

Part C of the normalization overhaul: instrument validation so we can tell
whether a transform preserves bright neurites and what it costs in
compression, and select checkpoints on that rather than global L1.

- metrics.py (new): count-space metrics computed after transform.inverse -
  foreground/background MAE (foreground vs raw, background vs BM4D target),
  top-0.1% preservation, MIP-max error, false-bright-voxel rate - plus a
  robust foreground mask and a configurable checkpoint_score (cratio weight
  defaults to 0, so selection is fidelity-driven until the operating point
  is chosen).
- data_handling.py: ValidateDataset also stores raw counts and a foreground
  mask and yields (x, y, raw, fg_mask); DataLoader._load_batch is generalized
  over tuple length so train stays (x, y) while validation carries metadata.
- train.py: validate_step computes and logs the metrics, selects the best
  checkpoint by the joint score (best_score), and no-ops cleanly when there
  are no validation examples; Trainer takes checkpoint_weights.

Verified: 24 transform+metric unit tests pass; functional smoke covers the
validation 4-tuple, generalized batching, validate_step scoring/selection,
and the empty-validation no-op on CPU.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../machine_learning/data_handling.py         |  45 ++--
 .../machine_learning/metrics.py               | 209 ++++++++++++++++++
 .../machine_learning/train.py                 |  79 ++++++-
 tests/test_metrics.py                         | 101 +++++++++
 4 files changed, 411 insertions(+), 23 deletions(-)
 create mode 100644 src/aind_exaspim_image_compression/machine_learning/metrics.py
 create mode 100644 tests/test_metrics.py

diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 6a2323e..1f3eb62 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -23,6 +23,9 @@
 import tensorstore as ts
 import torch
 
+from aind_exaspim_image_compression.machine_learning.metrics import (
+    make_foreground_mask,
+)
 from aind_exaspim_image_compression.machine_learning.transforms import (
     build_transform,
     calibrate_transform,
@@ -488,6 +491,8 @@ def __init__(self, patch_shape, sigma_bm4d=16, transform=None):
         self.imgs = dict()
         self.denoised = list()
         self.noise = list()
+        self.raws = list()
+        self.fg_masks = list()
 
     def __len__(self):
         """
@@ -529,10 +534,12 @@ def ingest_example(self, brain_id, voxel):
         target = bm4d(raw, self.sigma_bm4d)
         target = np.clip(target, 0, self.transform.max_count)
 
-        # Store transformed patches
+        # Store transformed patches plus count-space metadata for metrics
         self.example_ids.append((brain_id, voxel))
         self.noise.append(self.transform.forward(raw))
         self.denoised.append(self.transform.forward(target))
+        self.raws.append(raw)
+        self.fg_masks.append(make_foreground_mask(raw).astype(np.float32))
 
     def __getitem__(self, idx):
         """
@@ -549,8 +556,17 @@ def __getitem__(self, idx):
             Noisy image patch in the normalized transform domain.
         y : numpy.ndarray
             BM4D-denoised image patch in the normalized transform domain.
-        """
-        return self.noise[idx], self.denoised[idx]
+        raw : numpy.ndarray
+            Raw noisy image patch in counts (for count-space metrics).
+        fg_mask : numpy.ndarray
+            Foreground mask (float 0/1) for the metric split.
+        """
+        return (
+            self.noise[idx],
+            self.denoised[idx],
+            self.raws[idx],
+            self.fg_masks[idx],
+        )
 
     # --- Helpers ---
     def read_patch(self, brain_id, center):
@@ -624,22 +640,23 @@ def _load_batch(self, start_idx):
 
         # Generate batch
         with ProcessPoolExecutor() as executor:
-            # Assign processs
             processes = list()
             for idx in range(start_idx, start_idx + batch_size):
                 processes.append(
                     executor.submit(self.dataset.__getitem__, idx)
                 )
-
-            # Process results
-            shape = (batch_size, 1,) + self.patch_shape
-            noise_patches = np.zeros(shape)
-            denoised_patches = np.zeros(shape)
-            for i, process in enumerate(as_completed(processes)):
-                noise, denoised = process.result()
-                noise_patches[i, 0, ...] = noise
-                denoised_patches[i, 0, ...] = denoised
-        return to_tensor(noise_patches), to_tensor(denoised_patches)
+            results = [p.result() for p in as_completed(processes)]
+
+        # Stack each field of the example tuple into its own batch tensor.
+        # Handles both the (x, y) train shape and the (x, y, raw, fg_mask)
+        # validation shape.
+        shape = (batch_size, 1,) + self.patch_shape
+        n_fields = len(results[0])
+        batched = [np.zeros(shape) for _ in range(n_fields)]
+        for i, fields in enumerate(results):
+            for j, field in enumerate(fields):
+                batched[j][i, 0, ...] = field
+        return tuple(to_tensor(arr) for arr in batched)
 
 
 # --- Helpers ---
diff --git a/src/aind_exaspim_image_compression/machine_learning/metrics.py b/src/aind_exaspim_image_compression/machine_learning/metrics.py
new file mode 100644
index 0000000..1499f56
--- /dev/null
+++ b/src/aind_exaspim_image_compression/machine_learning/metrics.py
@@ -0,0 +1,209 @@
+"""
+Validation metrics for scoring neurite preservation vs. compression.
+
+These operate in raw count space (after a transform's inverse) so they mean
+the same thing regardless of which intensity transform is used. They split
+voxels into foreground and background with a robust intensity mask and
+measure, separately, whether bright signal is preserved (foreground, vs. the
+raw counts) and whether background is cleaned like the BM4D teacher
+(background, vs. the target counts).
+
+"""
+
+import numpy as np
+from scipy import ndimage
+
+
+# Weights for the checkpoint-selection score. cratio defaults to 0.0 so
+# selection is purely fidelity-driven; raise it to trade fidelity for
+# compression once the operating point is chosen.
+DEFAULT_CHECKPOINT_WEIGHTS = {
+    "fg_mae": 1.0,
+    "bg_mae": 0.2,
+    "top_pct_error": 0.5,
+    "cratio": 0.0,
+}
+
+
+def make_foreground_mask(raw, k=6.0, dilate=1):
+    """
+    Builds a robust foreground mask from image intensity.
+
+    Uses a median + k * (robust sigma) threshold so it is insensitive to the
+    bright tail, then dilates to include neurite boundaries.
+
+    Parameters
+    ----------
+    raw : numpy.ndarray
+        Image in raw count units.
+    k : float, optional
+        Threshold in robust standard deviations above the median. Default is
+        6.0.
+    dilate : int, optional
+        Number of binary-dilation iterations. Default is 1.
+
+    Returns
+    -------
+    numpy.ndarray
+        Boolean foreground mask with the same shape as "raw".
+    """
+    raw = np.asarray(raw, dtype=np.float32)
+    med = np.median(raw)
+    mad = np.median(np.abs(raw - med)) + 1e-6
+    sigma = 1.4826 * mad
+    mask = raw > (med + k * sigma)
+    if dilate > 0:
+        mask = ndimage.binary_dilation(mask, iterations=dilate)
+    return mask
+
+
+def foreground_background_mae(pred, ref, fg_mask):
+    """
+    Computes the mean absolute error split by a foreground mask.
+
+    Parameters
+    ----------
+    pred : numpy.ndarray
+        Predicted image in counts.
+    ref : numpy.ndarray
+        Reference image in counts.
+    fg_mask : numpy.ndarray
+        Boolean foreground mask.
+
+    Returns
+    -------
+    Tuple[float]
+        Foreground MAE and background MAE. A side with no voxels reports 0.
+    """
+    pred = np.asarray(pred, dtype=np.float64)
+    ref = np.asarray(ref, dtype=np.float64)
+    fg = np.asarray(fg_mask, dtype=bool)
+    err = np.abs(pred - ref)
+    fg_mae = float(err[fg].mean()) if fg.any() else 0.0
+    bg_mae = float(err[~fg].mean()) if (~fg).any() else 0.0
+    return fg_mae, bg_mae
+
+
+def mip_max_error(pred, raw):
+    """
+    Computes the absolute error between the maxima of two images.
+
+    Parameters
+    ----------
+    pred : numpy.ndarray
+        Predicted image in counts.
+    raw : numpy.ndarray
+        Raw image in counts.
+
+    Returns
+    -------
+    float
+        Absolute difference between the maximum intensities.
+    """
+    return float(abs(np.max(pred) - np.max(raw)))
+
+
+def false_bright_rate(pred, raw, fg_mask, k=6.0):
+    """
+    Computes the fraction of background voxels the model made bright.
+
+    Parameters
+    ----------
+    pred : numpy.ndarray
+        Predicted image in counts.
+    raw : numpy.ndarray
+        Raw image in counts (used to set the brightness threshold).
+    fg_mask : numpy.ndarray
+        Boolean foreground mask.
+    k : float, optional
+        Threshold in robust standard deviations above the median. Default is
+        6.0.
+
+    Returns
+    -------
+    float
+        Fraction of background voxels where "pred" exceeds the threshold.
+    """
+    pred = np.asarray(pred, dtype=np.float64)
+    raw = np.asarray(raw, dtype=np.float64)
+    bg = ~np.asarray(fg_mask, dtype=bool)
+    if not bg.any():
+        return 0.0
+    med = np.median(raw)
+    mad = np.median(np.abs(raw - med)) + 1e-6
+    thr = med + k * 1.4826 * mad
+    return float(np.mean(pred[bg] > thr))
+
+
+def evaluate_example(pred, raw, target, fg_mask, pct=0.1):
+    """
+    Computes the full metric dictionary for a single example, in counts.
+
+    Foreground fidelity is measured against the raw counts (preserve signal);
+    background cleanup is measured against the BM4D target (clean like the
+    teacher).
+
+    Parameters
+    ----------
+    pred : numpy.ndarray
+        Predicted image in counts.
+    raw : numpy.ndarray
+        Raw noisy image in counts.
+    target : numpy.ndarray
+        BM4D-denoised target image in counts.
+    fg_mask : numpy.ndarray
+        Boolean foreground mask.
+    pct : float, optional
+        Top-percentile fraction used for the bright-tail metrics. Default is
+        0.1 (i.e., the top 0.1%).
+
+    Returns
+    -------
+    dict
+        Metric name to scalar value.
+    """
+    fg_mae, _ = foreground_background_mae(pred, raw, fg_mask)
+    _, bg_mae = foreground_background_mae(pred, target, fg_mask)
+
+    q = 100.0 - pct
+    raw_top = float(np.percentile(np.asarray(raw, dtype=np.float64), q))
+    pred_top = float(np.percentile(np.asarray(pred, dtype=np.float64), q))
+    return {
+        "fg_mae": fg_mae,
+        "bg_mae": bg_mae,
+        "top_pct_error": abs(pred_top - raw_top),
+        "top_pct_preservation": pred_top / (raw_top + 1e-8),
+        "mip_max_error": mip_max_error(pred, raw),
+        "false_bright_rate": false_bright_rate(pred, raw, fg_mask),
+    }
+
+
+def checkpoint_score(metrics, cratio, weights=None):
+    """
+    Computes the checkpoint-selection score (lower is better).
+
+    Combines count-scale fidelity terms and rewards compression through a
+    negative weight. With ``weights["cratio"] == 0`` (default) the score is
+    purely fidelity-driven.
+
+    Parameters
+    ----------
+    metrics : dict
+        Aggregated metric dictionary, as produced by "evaluate_example".
+    cratio : float
+        Compression ratio (higher is better).
+    weights : dict, optional
+        Term weights. Defaults to "DEFAULT_CHECKPOINT_WEIGHTS".
+
+    Returns
+    -------
+    float
+        Checkpoint-selection score.
+    """
+    w = DEFAULT_CHECKPOINT_WEIGHTS if weights is None else weights
+    return (
+        w.get("fg_mae", 0.0) * metrics["fg_mae"]
+        + w.get("bg_mae", 0.0) * metrics["bg_mae"]
+        + w.get("top_pct_error", 0.0) * metrics["top_pct_error"]
+        - w.get("cratio", 0.0) * cratio
+    )
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index 3e98d39..1e51510 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -23,6 +23,10 @@
 
 from aind_exaspim_image_compression.machine_learning.unet3d import UNet
 from aind_exaspim_image_compression.machine_learning.data_handling import DataLoader
+from aind_exaspim_image_compression.machine_learning.metrics import (
+    checkpoint_score,
+    evaluate_example,
+)
 from aind_exaspim_image_compression.utils import img_util, util
 
 
@@ -37,6 +41,7 @@ def __init__(
         max_epochs=400,
         model=None,
         use_amp=True,
+        checkpoint_weights=None,
     ):
         """
         Instantiates a Trainer object.
@@ -71,6 +76,8 @@ def __init__(
 
         self.codec = blosc.Blosc(cname="zstd", clevel=5, shuffle=blosc.SHUFFLE)
         self.criterion = nn.L1Loss()
+        self.checkpoint_weights = checkpoint_weights
+        self.best_score = np.inf
         self.model = model.to(device) if model else UNet().to(device)
         self.optimizer = optim.AdamW(self.model.parameters(), lr=lr)
         self.scheduler = CosineAnnealingLR(self.optimizer, T_max=25)
@@ -102,7 +109,7 @@ def run(self, train_dataset, val_dataset):
         val_dataloader = DataLoader(val_dataset, batch_size=self.batch_size)
 
         # Main
-        self.best_l1 = np.inf
+        self.best_score = np.inf
         for epoch in range(self.max_epochs):
             # Train-Validate
             train_loss = self.train_step(train_dataloader, epoch)
@@ -170,27 +177,46 @@ def validate_step(self, val_dataloader, epoch):
         is_best : bool
             Indication of whether the model is the best so far.
         """
+        # Skip if there are no validation examples
+        if len(val_dataloader.dataset) == 0:
+            return float("nan"), float("nan"), False
+
         losses = list()
         cratios = list()
+        metric_rows = list()
         with torch.no_grad():
             self.model.eval()
-            for x, y in val_dataloader:
+            for x, y, raw, fg_mask in val_dataloader:
                 # Run model
                 hat_y, loss = self.forward_pass(x, y)
 
-                # Evalute result
-                cratios.extend(self.compute_cratios(hat_y))
+                # Evaluate result
                 losses.append(loss.detach().cpu())
+                cratios.extend(self.compute_cratios(hat_y))
+                metric_rows.extend(
+                    self.compute_metrics(hat_y, y, raw, fg_mask)
+                )
+
+        # Aggregate results
+        loss = float(np.mean(losses))
+        cratio = float(np.median(cratios))
+        agg = {
+            k: float(np.mean([row[k] for row in metric_rows]))
+            for k in metric_rows[0]
+        }
+        score = checkpoint_score(agg, cratio, self.checkpoint_weights)
 
         # Log results
-        loss, cratio = np.mean(losses), np.median(cratios)
         self.writer.add_scalar("val_loss", loss, epoch)
         self.writer.add_scalar("val_cratio", cratio, epoch)
+        self.writer.add_scalar("val_score", score, epoch)
+        for name, value in agg.items():
+            self.writer.add_scalar(f"val_{name}", value, epoch)
 
-        # Check if current model is best so far
-        is_best = True if loss < self.best_l1 else False
+        # Check if current model is best so far (lower score is better)
+        is_best = score < self.best_score
         if is_best:
-            self.best_l1 = loss
+            self.best_score = score
             self.save_model(epoch)
         return loss, cratio, is_best
 
@@ -230,6 +256,41 @@ def compute_cratios(self, imgs):
                 tifffile.imwrite(f"{i}.tiff", img)
         return cratios
 
+    def compute_metrics(self, hat_y, y, raw, fg_mask):
+        """
+        Computes per-example neurite-preservation metrics in count space.
+
+        Parameters
+        ----------
+        hat_y : torch.Tensor
+            Model predictions in the normalized transform domain.
+        y : torch.Tensor
+            BM4D targets in the normalized transform domain.
+        raw : torch.Tensor
+            Raw noisy patches in counts.
+        fg_mask : torch.Tensor
+            Foreground masks (float 0/1).
+
+        Returns
+        -------
+        List[dict]
+            One metric dictionary per example in the batch.
+        """
+        rows = list()
+        preds = np.array(hat_y.detach().cpu())
+        targets = np.array(y.detach().cpu())
+        raws = np.array(raw.detach().cpu())
+        masks = np.array(fg_mask.detach().cpu())
+        for i in range(preds.shape[0]):
+            pred = self.transform.inverse(preds[i, 0, ...])
+            target = self.transform.inverse(targets[i, 0, ...])
+            rows.append(
+                evaluate_example(
+                    pred, raws[i, 0, ...], target, masks[i, 0, ...] > 0.5
+                )
+            )
+        return rows
+
     def load_pretrained_weights(self, model_path):
         """
         Loads a pretrained model weights from a checkpoint file.
@@ -254,7 +315,7 @@ def save_model(self, epoch):
             Current training epoch.
         """
         date = datetime.today().strftime("%Y%m%d")
-        filename = f"BM4DNet-{date}-{epoch}-{self.best_l1:.6f}.pth"
+        filename = f"BM4DNet-{date}-{epoch}-{self.best_score:.6f}.pth"
         path = os.path.join(self.log_dir, filename)
         torch.save(
             {
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
new file mode 100644
index 0000000..9565d38
--- /dev/null
+++ b/tests/test_metrics.py
@@ -0,0 +1,101 @@
+"""Tests for the validation-metrics module."""
+
+import unittest
+
+import numpy as np
+
+from aind_exaspim_image_compression.machine_learning.metrics import (
+    DEFAULT_CHECKPOINT_WEIGHTS,
+    checkpoint_score,
+    evaluate_example,
+    false_bright_rate,
+    foreground_background_mae,
+    make_foreground_mask,
+    mip_max_error,
+)
+
+
+class MaskTest(unittest.TestCase):
+    """Tests for make_foreground_mask."""
+
+    def test_flags_bright_block(self):
+        """A bright block is flagged foreground; flat background is not."""
+        raw = np.zeros((16, 16, 16), dtype=np.float32)
+        raw[6:10, 6:10, 6:10] = 5000
+        mask = make_foreground_mask(raw, k=6.0, dilate=1)
+        self.assertTrue(mask[7, 7, 7])
+        self.assertFalse(mask[0, 0, 0])
+        self.assertGreaterEqual(mask.sum(), 64)  # >= block, dilation adds more
+
+
+class MetricTest(unittest.TestCase):
+    """Tests for the individual metric functions."""
+
+    def test_foreground_background_mae(self):
+        """MAE is split correctly by the mask."""
+        pred = np.array([[10.0, 20.0]], dtype=np.float64)
+        ref = np.array([[0.0, 0.0]], dtype=np.float64)
+        fg = np.array([[True, False]])
+        fg_mae, bg_mae = foreground_background_mae(pred, ref, fg)
+        self.assertAlmostEqual(fg_mae, 10.0)
+        self.assertAlmostEqual(bg_mae, 20.0)
+
+    def test_mip_max_error(self):
+        """MIP-max error is the absolute difference of maxima."""
+        pred = np.array([1.0, 900.0])
+        raw = np.array([0.0, 1000.0])
+        self.assertAlmostEqual(mip_max_error(pred, raw), 100.0)
+
+    def test_false_bright_rate(self):
+        """Background voxels the model brightened are counted."""
+        raw = np.zeros((10,), dtype=np.float64)
+        raw[0] = 5000.0
+        fg = np.zeros((10,), dtype=bool)
+        fg[0] = True
+        pred = np.zeros((10,), dtype=np.float64)
+        pred[1] = 5000.0  # one background voxel hallucinated bright
+        self.assertAlmostEqual(false_bright_rate(pred, raw, fg), 1.0 / 9.0)
+
+
+class EvaluateTest(unittest.TestCase):
+    """Tests for evaluate_example and checkpoint_score."""
+
+    def test_evaluate_example_keys_and_preservation(self):
+        """Perfect prediction preserves the bright tail (ratio ~ 1)."""
+        raw = np.zeros((16, 16, 16), dtype=np.float32)
+        raw[4:12, 4:12, 4:12] = 60000  # >0.1% of voxels, so p99.9 is bright
+        fg = make_foreground_mask(raw)
+        metrics = evaluate_example(raw, raw, raw, fg)
+        for key in ("fg_mae", "bg_mae", "top_pct_error",
+                    "top_pct_preservation", "mip_max_error",
+                    "false_bright_rate"):
+            self.assertIn(key, metrics)
+        self.assertAlmostEqual(metrics["fg_mae"], 0.0)
+        self.assertAlmostEqual(metrics["mip_max_error"], 0.0)
+        self.assertAlmostEqual(metrics["top_pct_preservation"], 1.0, places=5)
+
+    def test_attenuation_lowers_preservation(self):
+        """Halving the bright signal drops preservation below 1."""
+        raw = np.zeros((16, 16, 16), dtype=np.float32)
+        raw[4:12, 4:12, 4:12] = 60000
+        fg = make_foreground_mask(raw)
+        pred = raw * 0.5
+        metrics = evaluate_example(pred, raw, raw, fg)
+        self.assertLess(metrics["top_pct_preservation"], 1.0)
+        self.assertGreater(metrics["mip_max_error"], 0.0)
+
+    def test_checkpoint_score_default_and_cratio(self):
+        """Default score ignores cratio; a positive weight rewards it."""
+        metrics = {"fg_mae": 10.0, "bg_mae": 5.0, "top_pct_error": 20.0}
+        base = checkpoint_score(metrics, cratio=3.0)
+        expected = 1.0 * 10.0 + 0.2 * 5.0 + 0.5 * 20.0
+        self.assertAlmostEqual(base, expected)
+        self.assertEqual(DEFAULT_CHECKPOINT_WEIGHTS["cratio"], 0.0)
+
+        weights = dict(DEFAULT_CHECKPOINT_WEIGHTS, cratio=1.0)
+        with_cratio = checkpoint_score(metrics, cratio=3.0, weights=weights)
+        self.assertAlmostEqual(with_cratio, expected - 3.0)
+
+
+if __name__ == "__main__":
+    unittest.main()

From dc0a31bea25154efe112b7288259314d046610c4 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 20:59:09 +0000
Subject: [PATCH 05/56] feat: foreground-preserving target and
 signal-preserving loss

Part E of the normalization overhaul. Stop the BM4D teacher from erasing
neurites, and stop the background from dominating the loss.

- losses.py (new): SignalPreservingLoss, a foreground-weighted Charbonnier
  loss (fg_weight=0 reduces to a plain Charbonnier mean). Operates in the
  transform domain (relative/Weber precision); a count-space / Jacobian
  variant is deferred, tied to the operating-point decision.
- data_handling.py: TrainDataset builds a high-confidence foreground mask
  (robust intensity threshold, unioned with segmentation labels when
  available) and sets target = where(fg, raw, bm4d) so raw counts survive on
  the foreground; __getitem__ now returns (x, y, fg_mask). ValidateDataset
  does the same with its intensity mask. A preserve_foreground flag on both
  (and init_datasets) makes the target change ablatable.
- train.py: criterion is SignalPreservingLoss(fg_weight); forward_pass takes
  and applies the foreground mask; train/validate loops thread it through.
- util.py: sample_once now coerces to a list so it accepts dict_keys/sets;
  this pre-existing bug broke TrainDataset.__getitem__ (hence training) on
  Python 3.11+ where random.sample rejects non-sequences.

Ablation knobs: preserve_foreground (target construction) and fg_weight
(loss weighting) are independent.

Verified: 29 unit tests pass (incl. new loss tests); functional smoke covers
the TrainDataset 3-tuple, the preserve_foreground toggle, and forward_pass
with the new criterion on CPU.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../machine_learning/data_handling.py         | 77 +++++++++++++++--
 .../machine_learning/losses.py                | 84 +++++++++++++++++++
 .../machine_learning/train.py                 | 21 +++--
 .../utils/util.py                             |  2 +-
 tests/test_losses.py                          | 65 ++++++++++++++
 5 files changed, 232 insertions(+), 17 deletions(-)
 create mode 100644 src/aind_exaspim_image_compression/machine_learning/losses.py
 create mode 100644 tests/test_losses.py

diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 1f3eb62..0ffc8ed 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -54,6 +54,7 @@ def __init__(
         foreground_sampling_rate=0.5,
         n_examples_per_epoch=300,
         prefetch_foreground_sampling=16,
+        preserve_foreground=True,
         sigma_bm4d=16,
         transform=None,
     ):
@@ -66,6 +67,7 @@ def __init__(
         self.foreground_sampling_rate = foreground_sampling_rate
         self.n_examples_per_epoch = n_examples_per_epoch
         self.patch_shape = patch_shape
+        self.preserve_foreground = preserve_foreground
         self.prefetch_foreground_sampling = prefetch_foreground_sampling
         self.sigma_bm4d = sigma_bm4d
         self.transform = transform or build_transform({"kind": "asinh"})
@@ -165,19 +167,56 @@ def __getitem__(self, dummy_input):
         x : numpy.ndarray
             Noisy image patch in the normalized transform domain.
         y : numpy.ndarray
-            BM4D-denoised image patch in the normalized transform domain.
+            Target image patch in the normalized transform domain.
+        fg_mask : numpy.ndarray
+            Foreground mask (float 0/1) for the signal-preserving loss.
         """
         # Sample image patch and its BM4D-denoised target
         brain_id = self.sample_brain()
         voxel = self.sample_voxel(brain_id)
         raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
-        target = bm4d(raw, self.sigma_bm4d)
-        target = np.clip(target, 0, self.transform.max_count)
+        teacher = bm4d(raw, self.sigma_bm4d)
+        teacher = np.clip(teacher, 0, self.transform.max_count)
+
+        # Preserve raw counts on foreground so BM4D cannot erase neurites
+        fg_mask = self.foreground_mask(brain_id, voxel, raw)
+        if self.preserve_foreground:
+            target = np.where(fg_mask, raw, teacher)
+        else:
+            target = teacher
 
         # Map to the normalized transform domain
         x = self.transform.forward(raw)
         y = self.transform.forward(target)
-        return x, y
+        return x, y, fg_mask.astype(np.float32)
+
+    def foreground_mask(self, brain_id, center, raw):
+        """
+        Builds a high-confidence foreground mask for a patch.
+
+        Unions a robust intensity threshold with the segmentation labels
+        (when available for the brain), so both bright and labeled neurites
+        are protected from the BM4D teacher.
+
+        Parameters
+        ----------
+        brain_id : str
+            Unique identifier of the sampled brain.
+        center : Tuple[int]
+            Center voxel of the patch.
+        raw : numpy.ndarray
+            Raw image patch in counts.
+
+        Returns
+        -------
+        numpy.ndarray
+            Boolean foreground mask with the shape of "raw".
+        """
+        mask = make_foreground_mask(raw)
+        if brain_id in self.segmentations:
+            labels = np.asarray(self.read_precomputed_patch(brain_id, center))
+            mask = mask | (labels > 0)
+        return mask
 
     def sample_brain(self):
         """
@@ -463,7 +502,10 @@ def to_voxels(self, xyz_arr):
 
 class ValidateDataset(Dataset):
 
-    def __init__(self, patch_shape, sigma_bm4d=16, transform=None):
+    def __init__(
+        self, patch_shape, sigma_bm4d=16, transform=None,
+        preserve_foreground=True,
+    ):
         """
         Instantiates a ValidateDataset object.
 
@@ -477,6 +519,9 @@ def __init__(self, patch_shape, sigma_bm4d=16, transform=None):
         transform : IntensityTransform, optional
             Transform mapping raw counts to the normalized domain. Default is
             an asinh transform.
+        preserve_foreground : bool, optional
+            Whether targets keep raw counts on the foreground. Default is
+            True.
         """
         # Call parent class
         super(ValidateDataset, self).__init__()
@@ -485,6 +530,7 @@ def __init__(self, patch_shape, sigma_bm4d=16, transform=None):
         self.patch_shape = patch_shape
         self.sigma_bm4d = sigma_bm4d
         self.transform = transform or build_transform({"kind": "asinh"})
+        self.preserve_foreground = preserve_foreground
 
         # Data structures
         self.example_ids = list()
@@ -531,15 +577,22 @@ def ingest_example(self, brain_id, voxel):
         """
         # Sample image patch and its BM4D-denoised target
         raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
-        target = bm4d(raw, self.sigma_bm4d)
-        target = np.clip(target, 0, self.transform.max_count)
+        teacher = bm4d(raw, self.sigma_bm4d)
+        teacher = np.clip(teacher, 0, self.transform.max_count)
+
+        # Preserve raw counts on foreground (intensity mask only here)
+        fg_mask = make_foreground_mask(raw)
+        if self.preserve_foreground:
+            target = np.where(fg_mask, raw, teacher)
+        else:
+            target = teacher
 
         # Store transformed patches plus count-space metadata for metrics
         self.example_ids.append((brain_id, voxel))
         self.noise.append(self.transform.forward(raw))
         self.denoised.append(self.transform.forward(target))
         self.raws.append(raw)
-        self.fg_masks.append(make_foreground_mask(raw).astype(np.float32))
+        self.fg_masks.append(fg_mask.astype(np.float32))
 
     def __getitem__(self, idx):
         """
@@ -671,6 +724,7 @@ def init_datasets(
     sigma_bm4d=16,
     swc_pointers=None,
     transform_cfg=None,
+    preserve_foreground=True,
 ):
     # Initializations
     if transform_cfg is None:
@@ -679,9 +733,14 @@ def init_datasets(
         patch_shape,
         foreground_sampling_rate=foreground_sampling_rate,
         n_examples_per_epoch=n_train_examples_per_epoch,
+        preserve_foreground=preserve_foreground,
         sigma_bm4d=sigma_bm4d
     )
-    val_dataset = ValidateDataset(patch_shape, sigma_bm4d=sigma_bm4d)
+    val_dataset = ValidateDataset(
+        patch_shape,
+        sigma_bm4d=sigma_bm4d,
+        preserve_foreground=preserve_foreground,
+    )
 
     # Read segmentation path lookup (if applicable)
     if segmentation_prefixes_path:
diff --git a/src/aind_exaspim_image_compression/machine_learning/losses.py b/src/aind_exaspim_image_compression/machine_learning/losses.py
new file mode 100644
index 0000000..9710e82
--- /dev/null
+++ b/src/aind_exaspim_image_compression/machine_learning/losses.py
@@ -0,0 +1,84 @@
+"""
+Loss functions for signal-preserving denoising.
+
+"""
+
+import torch
+import torch.nn as nn
+
+
+def charbonnier(diff, eps=1e-3):
+    """
+    Evaluates the Charbonnier penalty, a smooth approximation of the L1 norm.
+
+    Parameters
+    ----------
+    diff : torch.Tensor
+        Difference tensor.
+    eps : float, optional
+        Smoothing constant. Default is 1e-3.
+
+    Returns
+    -------
+    torch.Tensor
+        Elementwise Charbonnier penalty.
+    """
+    return torch.sqrt(diff * diff + eps * eps)
+
+
+class SignalPreservingLoss(nn.Module):
+    """
+    Foreground-weighted Charbonnier loss.
+
+    Upweights foreground voxels so that sparse, bright neurites are not
+    drowned out by the background during training. Operates in the transform
+    domain: because a compressive transform shrinks the bright tail, a fixed
+    error here is a larger error in counts, i.e. this enforces relative
+    (Weber) precision. If absolute count fidelity is required, weight by the
+    transform Jacobian or add a count-space term (deferred; see the
+    implementation plan and the operating-point decision).
+
+    Attributes
+    ----------
+    fg_weight : float
+        Extra weight applied to foreground voxels (0 disables weighting, so
+        the loss reduces to a plain Charbonnier mean).
+    eps : float
+        Charbonnier smoothing constant.
+    """
+
+    def __init__(self, fg_weight=20.0, eps=1e-3):
+        """
+        Instantiates a SignalPreservingLoss.
+
+        Parameters
+        ----------
+        fg_weight : float, optional
+            Extra weight applied to foreground voxels. Default is 20.0.
+        eps : float, optional
+            Charbonnier smoothing constant. Default is 1e-3.
+        """
+        super().__init__()
+        self.fg_weight = float(fg_weight)
+        self.eps = float(eps)
+
+    def forward(self, pred, target, fg_mask):
+        """
+        Computes the foreground-weighted Charbonnier loss.
+
+        Parameters
+        ----------
+        pred : torch.Tensor
+            Model prediction in the transform domain.
+        target : torch.Tensor
+            Target in the transform domain.
+        fg_mask : torch.Tensor
+            Foreground mask (0/1) with the same shape as "pred".
+
+        Returns
+        -------
+        torch.Tensor
+            Scalar loss.
+        """
+        weight = 1.0 + self.fg_weight * fg_mask
+        return (weight * charbonnier(pred - target, self.eps)).mean()
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index 1e51510..49bbcdf 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -23,6 +23,9 @@
 
 from aind_exaspim_image_compression.machine_learning.unet3d import UNet
 from aind_exaspim_image_compression.machine_learning.data_handling import DataLoader
+from aind_exaspim_image_compression.machine_learning.losses import (
+    SignalPreservingLoss,
+)
 from aind_exaspim_image_compression.machine_learning.metrics import (
     checkpoint_score,
     evaluate_example,
@@ -42,6 +45,7 @@ def __init__(
         model=None,
         use_amp=True,
         checkpoint_weights=None,
+        fg_weight=20.0,
     ):
         """
         Instantiates a Trainer object.
@@ -75,7 +79,7 @@ def __init__(
         self.log_dir = log_dir
 
         self.codec = blosc.Blosc(cname="zstd", clevel=5, shuffle=blosc.SHUFFLE)
-        self.criterion = nn.L1Loss()
+        self.criterion = SignalPreservingLoss(fg_weight=fg_weight)
         self.checkpoint_weights = checkpoint_weights
         self.best_score = np.inf
         self.model = model.to(device) if model else UNet().to(device)
@@ -143,9 +147,9 @@ def train_step(self, train_dataloader, epoch):
         """
         losses = list()
         self.model.train()
-        for x, y in train_dataloader:
+        for x, y, fg_mask in train_dataloader:
             # Forward pass
-            hat_y, loss = self.forward_pass(x, y)
+            hat_y, loss = self.forward_pass(x, y, fg_mask)
 
             # Backward pass
             self.optimizer.zero_grad()
@@ -188,7 +192,7 @@ def validate_step(self, val_dataloader, epoch):
             self.model.eval()
             for x, y, raw, fg_mask in val_dataloader:
                 # Run model
-                hat_y, loss = self.forward_pass(x, y)
+                hat_y, loss = self.forward_pass(x, y, fg_mask)
 
                 # Evaluate result
                 losses.append(loss.detach().cpu())
@@ -220,7 +224,7 @@ def validate_step(self, val_dataloader, epoch):
             self.save_model(epoch)
         return loss, cratio, is_best
 
-    def forward_pass(self, x, y):
+    def forward_pass(self, x, y, fg_mask):
         """
         Performs a forward pass through the model and computes loss.
 
@@ -229,7 +233,9 @@ def forward_pass(self, x, y):
         x : torch.Tensor
             Input tensor with shape (B, C, D, H, W).
         y : torch.Tensor
-            Ground truth labels with shape (B, C, D, H, W).
+            Target tensor with shape (B, C, D, H, W).
+        fg_mask : torch.Tensor
+            Foreground mask (0/1) with shape (B, C, D, H, W).
 
         Returns
         -------
@@ -241,8 +247,9 @@ def forward_pass(self, x, y):
         with self.autocast:
             x = x.to(self.device)
             y = y.to(self.device)
+            fg_mask = fg_mask.to(self.device)
             hat_y = self.model(x)
-            loss = self.criterion(hat_y, y)
+            loss = self.criterion(hat_y, y, fg_mask)
             return hat_y, loss
 
     # --- Helpers ---
diff --git a/src/aind_exaspim_image_compression/utils/util.py b/src/aind_exaspim_image_compression/utils/util.py
index efdbe2b..4e3f15e 100644
--- a/src/aind_exaspim_image_compression/utils/util.py
+++ b/src/aind_exaspim_image_compression/utils/util.py
@@ -613,7 +613,7 @@ def sample_once(my_container):
         Element sampled from the given container
 
     """
-    return sample(my_container, 1)[0]
+    return sample(list(my_container), 1)[0]
 
 
 def time_writer(t, unit="seconds"):
diff --git a/tests/test_losses.py b/tests/test_losses.py
new file mode 100644
index 0000000..a665d1f
--- /dev/null
+++ b/tests/test_losses.py
@@ -0,0 +1,65 @@
+"""Tests for the signal-preserving loss module."""
+
+import unittest
+
+import torch
+
+from aind_exaspim_image_compression.machine_learning.losses import (
+    SignalPreservingLoss,
+    charbonnier,
+)
+
+
+class CharbonnierTest(unittest.TestCase):
+    """Tests for the Charbonnier penalty."""
+
+    def test_approximates_l1(self):
+        """Charbonnier is close to |x| away from zero."""
+        d = torch.tensor([3.0, -4.0])
+        c = charbonnier(d, eps=1e-3)
+        self.assertTrue(
+            torch.allclose(c, torch.tensor([3.0, 4.0]), atol=1e-2)
+        )
+
+
+class SignalPreservingLossTest(unittest.TestCase):
+    """Tests for SignalPreservingLoss."""
+
+    def test_fg_weight_zero_is_uniform_charbonnier(self):
+        """With fg_weight=0 the loss is a plain Charbonnier mean."""
+        loss = SignalPreservingLoss(fg_weight=0.0)
+        pred = torch.zeros(2, 1, 4, 4, 4)
+        target = torch.ones(2, 1, 4, 4, 4)
+        fg = torch.ones(2, 1, 4, 4, 4)
+        self.assertAlmostEqual(float(loss(pred, target, fg)), 1.0, places=2)
+
+    def test_foreground_error_weighted_more(self):
+        """An error on a foreground voxel costs more than on background."""
+        loss = SignalPreservingLoss(fg_weight=10.0)
+        target = torch.zeros(1, 1, 2, 2, 2)
+        fg = torch.zeros(1, 1, 2, 2, 2)
+        fg[0, 0, 0, 0, 0] = 1.0
+
+        pred_fg = torch.zeros(1, 1, 2, 2, 2)
+        pred_fg[0, 0, 0, 0, 0] = 1.0  # error lands on the foreground voxel
+        pred_bg = torch.zeros(1, 1, 2, 2, 2)
+        pred_bg[0, 0, 1, 1, 1] = 1.0  # error lands on a background voxel
+
+        self.assertGreater(
+            float(loss(pred_fg, target, fg)),
+            float(loss(pred_bg, target, fg)),
+        )
+
+    def test_gradient_flows(self):
+        """Loss is differentiable w.r.t. the prediction."""
+        loss = SignalPreservingLoss(fg_weight=5.0)
+        pred = torch.zeros(1, 1, 2, 2, 2, requires_grad=True)
+        target = torch.ones(1, 1, 2, 2, 2)
+        fg = torch.ones(1, 1, 2, 2, 2)
+        loss(pred, target, fg).backward()
+        self.assertIsNotNone(pred.grad)
+        self.assertTrue(torch.all(pred.grad <= 0))  # move pred toward target
+
+
+if __name__ == "__main__":
+    unittest.main()

From d2053dcf460b4b400b8c0041d366dd783e8f2738 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 21:08:29 +0000
Subject: [PATCH 06/56] feat: signal-balanced patch sampling for thin fibers

Part F of the overhaul. The bright-patch sampler required ~1000 voxels above
a hard-coded intensity of 100, which biases toward somata/blobs and misses
thin fibers (a fiber through a 64^3 patch is only a few hundred voxels).

- sample_bright_voxel now counts foreground with the same robust mask used
  for targets and metrics (median + k*sigma, adaptive per patch) instead of
  a fixed >100 cutoff, and accepts a patch once it has min_foreground_voxels
  (default 50, was effectively 1000). Lowering the bar also cuts the
  brute-force read cost, since a qualifying patch is usually found in the
  first prefetch round.
- sample_segmentation_voxel's object-size requirement is parameterized as
  min_segmentation_volume (default 200, was hard-coded 1600) so thinner
  segmented fibers qualify.
- Both thresholds thread through init_datasets.

Not implemented (documented as follow-ups): the 4-way mix with hard-negative
patches (needs an artifact detector / labels we don't have) and a
whole-volume foreground-coordinate cache (infeasible to scan whole-brain
volumes; skeletons already provide an efficient cached foreground source).

Verified: functional smoke confirms the robust sampler finds foreground-rich
patches on an in-memory volume; 29 unit tests pass.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../machine_learning/data_handling.py         | 27 ++++++++++++++-----
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 0ffc8ed..f4b522b 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -52,6 +52,8 @@ def __init__(
         anisotropy=(0.748, 0.748, 1.0),
         boundary_buffer=5000,
         foreground_sampling_rate=0.5,
+        min_foreground_voxels=50,
+        min_segmentation_volume=200,
         n_examples_per_epoch=300,
         prefetch_foreground_sampling=16,
         preserve_foreground=True,
@@ -65,6 +67,8 @@ def __init__(
         self.anisotropy = anisotropy
         self.boundary_buffer = boundary_buffer
         self.foreground_sampling_rate = foreground_sampling_rate
+        self.min_foreground_voxels = min_foreground_voxels
+        self.min_segmentation_volume = min_segmentation_volume
         self.n_examples_per_epoch = n_examples_per_epoch
         self.patch_shape = patch_shape
         self.preserve_foreground = preserve_foreground
@@ -332,7 +336,7 @@ def sample_segmentation_voxel(self, brain_id):
         best_voxel = self.sample_interior_voxel(brain_id)
         cnt = 0
         with ThreadPoolExecutor() as executor:
-            while best_volume < 1600:
+            while best_volume < self.min_segmentation_volume:
                 # Read random image patches
                 pending = dict()
                 for _ in range(self.prefetch_foreground_sampling):
@@ -364,7 +368,12 @@ def sample_segmentation_voxel(self, brain_id):
 
     def sample_bright_voxel(self, brain_id):
         """
-        Samples a voxel coordinate whose image patch is sufficiently bright.
+        Samples a voxel whose patch has enough foreground voxels.
+
+        Foreground is counted with the same robust mask used for targets and
+        metrics (median + k * sigma), so the threshold adapts to each patch
+        instead of using a fixed intensity cutoff. The occupancy requirement
+        is low enough (min_foreground_voxels) to accept thin fibers.
 
         Parameters
         ----------
@@ -374,14 +383,14 @@ def sample_bright_voxel(self, brain_id):
         Returns
         -------
         best_voxel : Tuple[int]
-            Voxel coordinate whose patch is sufficiently bright or is the
-            highest observed brightness after 4 * self.prefetch attempts.
+            Voxel coordinate whose patch has the most foreground voxels found,
+            stopping once min_foreground_voxels is reached.
         """
         best_brightness = 0
         best_voxel = self.sample_interior_voxel(brain_id)
         cnt = 0
         with ThreadPoolExecutor() as executor:
-            while best_brightness < 1000:
+            while best_brightness < self.min_foreground_voxels:
                 # Read random image patches
                 pending = dict()
                 for _ in range(self.prefetch_foreground_sampling):
@@ -391,11 +400,11 @@ def sample_bright_voxel(self, brain_id):
                     )
                     pending[thread] = voxel
 
-                # Check if image patch is bright enough
+                # Check if image patch has enough foreground
                 for thread in as_completed(pending.keys()):
                     voxel = pending.pop(thread)
                     img_patch = thread.result()
-                    brightness = np.sum(img_patch > 100)
+                    brightness = int(make_foreground_mask(img_patch).sum())
                     if brightness > best_brightness:
                         best_voxel = voxel
                         best_brightness = brightness
@@ -718,6 +727,8 @@ def init_datasets(
     img_paths_json,
     patch_shape,
     foreground_sampling_rate=0.5,
+    min_foreground_voxels=50,
+    min_segmentation_volume=200,
     n_train_examples_per_epoch=100,
     n_validate_examples=0,
     segmentation_prefixes_path=None,
@@ -732,6 +743,8 @@ def init_datasets(
     train_dataset = TrainDataset(
         patch_shape,
         foreground_sampling_rate=foreground_sampling_rate,
+        min_foreground_voxels=min_foreground_voxels,
+        min_segmentation_volume=min_segmentation_volume,
         n_examples_per_epoch=n_train_examples_per_epoch,
         preserve_foreground=preserve_foreground,
         sigma_bm4d=sigma_bm4d

From 163db61f34fb20d32b38e62abc2670102e04ba90 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 21:14:58 +0000
Subject: [PATCH 07/56] feat: residual denoising output and GroupNorm in the 3D
 U-Net

Part G of the overhaul (sequenced last; changes weights, so it invalidates
existing checkpoints).

- Residual output: UNet returns input + outc(features) when residual=True
  (new default), so the network learns the denoising correction rather than
  reconstructing the whole signal. This pairs well with the Part E
  foreground-preserving target: the correction is ~0 on the foreground
  (preserve) and nonzero on the background (denoise). Gated by a flag so it
  is ablatable.
- DoubleConv uses GroupNorm(min(8, C), C) instead of BatchNorm3d, removing
  sensitivity to rare bright patches in a batch and to batch size.

Checkpoint note: GroupNorm changes the state_dict keys (no running stats) and
the residual output changes output semantics, so pre-Part-G checkpoints are
not loadable/compatible - expected for this stage.

Verified: forward preserves shape; GroupNorm present with no BatchNorm3d; the
residual flag passes the input through when the output head is zeroed; the
Part C/E validation and training smokes still pass; 29 unit tests pass.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../machine_learning/unet3d.py                | 29 ++++++++++++-------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/unet3d.py b/src/aind_exaspim_image_compression/machine_learning/unet3d.py
index 185633c..185c28d 100644
--- a/src/aind_exaspim_image_compression/machine_learning/unet3d.py
+++ b/src/aind_exaspim_image_compression/machine_learning/unet3d.py
@@ -35,7 +35,7 @@ class UNet(nn.Module):
         Final 1x1x1 convolution mapping features to the output channel.
     """
 
-    def __init__(self, width_multiplier=1, trilinear=True):
+    def __init__(self, width_multiplier=1, trilinear=True, residual=True):
         """
         Instantiates a UNet object.
 
@@ -47,6 +47,10 @@ def __init__(self, width_multiplier=1, trilinear=True):
         trilinear : bool, optional
             If True, use trilinear interpolation for upsampling in decoder
             blocks; otherwise, use transposed convolutions. Default is True.
+        residual : bool, optional
+            If True, the network predicts a residual added to the input, so it
+            learns to "remove noise" rather than reconstruct the full signal.
+            Default is True.
         """
         # Call parent class
         super(UNet, self).__init__()
@@ -58,6 +62,7 @@ def __init__(self, width_multiplier=1, trilinear=True):
         # Instance attributes
         self.channels = [int(c * width_multiplier) for c in _channels]
         self.trilinear = trilinear
+        self.residual = residual
 
         # Contracting layers
         self.inc = DoubleConv(1, self.channels[0])
@@ -96,23 +101,27 @@ def forward(self, x):
         x5 = self.down4(x4)
 
         # Expanding layers
-        x = self.up1(x5, x4)
-        x = self.up2(x, x3)
-        x = self.up3(x, x2)
-        x = self.up4(x, x1)
-        logits = self.outc(x)
+        d = self.up1(x5, x4)
+        d = self.up2(d, x3)
+        d = self.up3(d, x2)
+        d = self.up4(d, x1)
+        logits = self.outc(d)
+
+        # Residual denoising: predict the correction added to the input
+        if self.residual:
+            return x + logits
         return logits
 
 
 class DoubleConv(nn.Module):
     """
     A module that consists of two consecutive 3D convolutional layers, each
-    followed by batch normalization and a nonlinear activation.
+    followed by group normalization and a nonlinear activation.
 
     Attributes
     ----------
     double_conv : nn.Sequential
-        Sequential module containing two convolutions, batch norms, and
+        Sequential module containing two convolutions, group norms, and
         activations.
     """
 
@@ -142,10 +151,10 @@ def __init__(self, in_channels, out_channels, kernel_size=3, mid_channels=None):
         # Instance attributes
         self.double_conv = nn.Sequential(
             nn.Conv3d(in_channels, mid_channels, kernel_size=kernel_size, padding=1),
-            nn.BatchNorm3d(mid_channels),
+            nn.GroupNorm(min(8, mid_channels), mid_channels),
             nn.LeakyReLU(negative_slope=0.01, inplace=True),
             nn.Conv3d(mid_channels, out_channels, kernel_size=kernel_size, padding=1),
-            nn.BatchNorm3d(out_channels),
+            nn.GroupNorm(min(8, out_channels), out_channels),
             nn.LeakyReLU(negative_slope=0.01, inplace=True)
         )
 

From c2b36e96aace1e893588b34b266d947206b29aa9 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 22:21:33 +0000
Subject: [PATCH 08/56] feat: per-brain background offset wiring

Support a per-brain (per-acquisition) background offset so that
background-subtracted training data and raw inference data are normalized
into the same background-at-zero space. This dissolves the train/inference
regime mismatch (and the mixed subtracted/raw training set): without it, a
raw background of ~100 maps to ~0.22 in asinh space while the network was
trained with background at ~0.

The offset is applied as a subtraction at the dataset (raw - offset[brain])
with the shared transform at offset 0, which keeps the Trainer fully
offset-agnostic (no per-example threading) and puts every brain in one
"corrected" space.

- data_handling.py: TrainDataset/ValidateDataset and init_datasets take an
  offsets dict {brain_id: offset} and subtract it from each raw patch before
  bm4d/transform. Default None => no subtraction (backward compatible).
- transforms.py: estimate_offset gains ignore_zeros (default True) so
  zero-padding does not drag the estimate to 0; add with_offset() to clone a
  transform with a new offset.
- inference.py: build_volume_transform(base_transform, img) estimates the
  per-volume offset from a raw image (nonzero low percentile) and returns a
  transform carrying it -- the inference counterpart of the training offset.

Offsets are precomputed by code/estimate_background_offsets.py. When using
them, set the transform offset to 0 (the per-brain value replaces it).

Verified: 30 unit tests pass; functional smoke shows per-brain offset maps
background to ~0 (vs ~0.22 without) and build_volume_transform recovers the
volume background, ignoring padding and bright signal.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../inference.py                              | 31 ++++++++++++
 .../machine_learning/data_handling.py         | 13 ++++-
 .../machine_learning/transforms.py            | 49 ++++++++++++++++---
 tests/test_transforms.py                      | 23 +++++++--
 4 files changed, 102 insertions(+), 14 deletions(-)

diff --git a/src/aind_exaspim_image_compression/inference.py b/src/aind_exaspim_image_compression/inference.py
index 7e0dd71..02c0d05 100644
--- a/src/aind_exaspim_image_compression/inference.py
+++ b/src/aind_exaspim_image_compression/inference.py
@@ -19,6 +19,8 @@
 
 from aind_exaspim_image_compression.machine_learning.transforms import (
     build_transform,
+    estimate_offset,
+    with_offset,
 )
 from aind_exaspim_image_compression.machine_learning.unet3d import UNet
 
@@ -278,6 +280,35 @@ def load_model(path, device="cuda"):
     return model, build_transform(transform_cfg)
 
 
+def build_volume_transform(base_transform, img, percentile=0.1):
+    """
+    Builds a per-volume transform whose offset is estimated from the image.
+
+    Use at inference on raw (non-background-subtracted) volumes: it estimates
+    the background offset from a low percentile of the nonzero voxels and
+    returns a transform with that offset plus the trained transform's kind and
+    scale. This mirrors the per-brain offset subtracted during training, so a
+    raw volume is normalized to the same background-at-zero space the model
+    was trained on.
+
+    Parameters
+    ----------
+    base_transform : IntensityTransform
+        The transform the model was trained with (e.g., from load_model).
+    img : numpy.ndarray
+        Raw image volume to be denoised.
+    percentile : float, optional
+        Low percentile used as the background estimate. Default is 0.1.
+
+    Returns
+    -------
+    IntensityTransform
+        A transform carrying the per-volume offset.
+    """
+    offset = estimate_offset(img, percentile=percentile, ignore_zeros=True)
+    return with_offset(base_transform, offset)
+
+
 def to_tensor(arr, device="cuda"):
     """
     Converts a NumPy array to a PyTorch tensor and moves it to the given
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index f4b522b..8007c9b 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -55,6 +55,7 @@ def __init__(
         min_foreground_voxels=50,
         min_segmentation_volume=200,
         n_examples_per_epoch=300,
+        offsets=None,
         prefetch_foreground_sampling=16,
         preserve_foreground=True,
         sigma_bm4d=16,
@@ -70,6 +71,7 @@ def __init__(
         self.min_foreground_voxels = min_foreground_voxels
         self.min_segmentation_volume = min_segmentation_volume
         self.n_examples_per_epoch = n_examples_per_epoch
+        self.offsets = offsets or dict()
         self.patch_shape = patch_shape
         self.preserve_foreground = preserve_foreground
         self.prefetch_foreground_sampling = prefetch_foreground_sampling
@@ -179,6 +181,7 @@ def __getitem__(self, dummy_input):
         brain_id = self.sample_brain()
         voxel = self.sample_voxel(brain_id)
         raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
+        raw = raw - self.offsets.get(brain_id, 0.0)
         teacher = bm4d(raw, self.sigma_bm4d)
         teacher = np.clip(teacher, 0, self.transform.max_count)
 
@@ -513,7 +516,7 @@ class ValidateDataset(Dataset):
 
     def __init__(
         self, patch_shape, sigma_bm4d=16, transform=None,
-        preserve_foreground=True,
+        preserve_foreground=True, offsets=None,
     ):
         """
         Instantiates a ValidateDataset object.
@@ -531,6 +534,9 @@ def __init__(
         preserve_foreground : bool, optional
             Whether targets keep raw counts on the foreground. Default is
             True.
+        offsets : dict, optional
+            Per-brain background offsets subtracted from raw patches before
+            the transform. Default is None (no subtraction).
         """
         # Call parent class
         super(ValidateDataset, self).__init__()
@@ -540,6 +546,7 @@ def __init__(
         self.sigma_bm4d = sigma_bm4d
         self.transform = transform or build_transform({"kind": "asinh"})
         self.preserve_foreground = preserve_foreground
+        self.offsets = offsets or dict()
 
         # Data structures
         self.example_ids = list()
@@ -586,6 +593,7 @@ def ingest_example(self, brain_id, voxel):
         """
         # Sample image patch and its BM4D-denoised target
         raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
+        raw = raw - self.offsets.get(brain_id, 0.0)
         teacher = bm4d(raw, self.sigma_bm4d)
         teacher = np.clip(teacher, 0, self.transform.max_count)
 
@@ -736,6 +744,7 @@ def init_datasets(
     swc_pointers=None,
     transform_cfg=None,
     preserve_foreground=True,
+    offsets=None,
 ):
     # Initializations
     if transform_cfg is None:
@@ -746,6 +755,7 @@ def init_datasets(
         min_foreground_voxels=min_foreground_voxels,
         min_segmentation_volume=min_segmentation_volume,
         n_examples_per_epoch=n_train_examples_per_epoch,
+        offsets=offsets,
         preserve_foreground=preserve_foreground,
         sigma_bm4d=sigma_bm4d
     )
@@ -753,6 +763,7 @@ def init_datasets(
         patch_shape,
         sigma_bm4d=sigma_bm4d,
         preserve_foreground=preserve_foreground,
+        offsets=offsets,
     )
 
     # Read segmentation path lookup (if applicable)
diff --git a/src/aind_exaspim_image_compression/machine_learning/transforms.py b/src/aind_exaspim_image_compression/machine_learning/transforms.py
index f937d69..4bde59d 100644
--- a/src/aind_exaspim_image_compression/machine_learning/transforms.py
+++ b/src/aind_exaspim_image_compression/machine_learning/transforms.py
@@ -355,27 +355,30 @@ def inverse(self, y):
         return np.rint(counts).astype(np.uint16)
 
 
-def estimate_offset(sample, percentile=1.0):
+def estimate_offset(sample, percentile=1.0, ignore_zeros=True):
     """
-    Estimates a robust global background / black-point (counts).
-
-    Compute this once over a representative sample of the training set, then
-    freeze it into the transform config. Do not recompute per patch or per
-    inference volume.
+    Estimates a robust background / black-point (counts).
 
     Parameters
     ----------
     sample : numpy.ndarray
-        Representative sample of raw counts.
+        Sample of raw counts (e.g., a coarse multiscale level or a volume).
     percentile : float, optional
         Low percentile used as the background estimate. Default is 1.0.
+    ignore_zeros : bool, optional
+        If True, exclude exactly-zero voxels so that zero-padding outside the
+        imaged volume does not drag the estimate to 0. Default is True.
 
     Returns
     -------
     float
         Estimated background offset in counts.
     """
-    sample = np.asarray(sample, dtype=np.float32)
+    sample = np.asarray(sample, dtype=np.float32).reshape(-1)
+    if ignore_zeros:
+        nonzero = sample[sample > 0]
+        if nonzero.size:
+            sample = nonzero
     return float(np.percentile(sample, percentile))
 
 
@@ -449,3 +452,33 @@ def calibrate_transform(cfg, sample):
             sample, percentile=calib.get("offset_percentile", 1.0)
         )
     return cfg
+
+
+def with_offset(transform, offset):
+    """
+    Returns a copy of an asinh/anscombe transform with a new offset.
+
+    Used to apply a per-volume background offset at inference: estimate the
+    offset from the raw volume, then rebuild the (frozen) transform with that
+    offset while keeping its kind, scale, and max_count. This mirrors the
+    per-brain offset subtracted during training.
+
+    Parameters
+    ----------
+    transform : IntensityTransform
+        A transform built via ``build_transform`` (so it carries ``.cfg``).
+    offset : float
+        Background offset in counts.
+
+    Returns
+    -------
+    IntensityTransform
+        A new transform with the given offset.
+    """
+    cfg = getattr(transform, "cfg", None)
+    if cfg is None:
+        raise ValueError(
+            "transform has no cfg; construct it via build_transform"
+        )
+    params = {**cfg.get("params", {}), "offset": float(offset)}
+    return build_transform({**cfg, "params": params})
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index a017932..0d985e2 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -12,6 +12,7 @@
     build_transform,
     calibrate_transform,
     estimate_offset,
+    with_offset,
 )
 
 
@@ -116,11 +117,23 @@ class HelperTest(unittest.TestCase):
     """Tests for module-level helpers."""
 
     def test_estimate_offset(self):
-        """Offset estimate matches the requested percentile."""
-        sample = np.arange(0, 101, dtype=np.float32)
-        self.assertAlmostEqual(estimate_offset(sample, percentile=0), 0.0)
-        self.assertAlmostEqual(estimate_offset(sample, percentile=50), 50.0)
+        """Offset estimate matches the percentile and ignores zeros."""
+        sample = np.arange(0, 101, dtype=np.float32)  # 0..100
+        # ignore_zeros (default) drops the single 0 -> values are 1..100
+        self.assertAlmostEqual(estimate_offset(sample, percentile=0), 1.0)
         self.assertAlmostEqual(estimate_offset(sample, percentile=100), 100.0)
+        # keeping zeros, the 0th percentile is 0
+        self.assertAlmostEqual(
+            estimate_offset(sample, percentile=0, ignore_zeros=False), 0.0
+        )
+
+    def test_with_offset(self):
+        """with_offset rebuilds a transform with a new offset only."""
+        base = build_transform({"kind": "asinh", "params": {"scale": 32}})
+        shifted = with_offset(base, 120.0)
+        self.assertAlmostEqual(shifted.offset, 120.0)
+        self.assertAlmostEqual(shifted.scale, 32.0)
+        self.assertEqual(shifted.cfg["params"]["offset"], 120.0)
 
     def test_build_transform(self):
         """Factory builds each kind and rejects unknown kinds."""
@@ -146,7 +159,7 @@ def test_build_transform_stamps_cfg(self):
 
     def test_calibrate_transform_sets_offset(self):
         """Calibration freezes the offset without mutating the input."""
-        sample = np.arange(0, 1000, dtype=np.float32)
+        sample = np.arange(1, 1001, dtype=np.float32)  # no zeros
         cfg = {
             "kind": "asinh",
             "calibrate": {"offset": True, "offset_percentile": 10.0},

From ff5ec140c1e7b164b261f39a052fc8e6b6d9fb1b Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 22:29:43 +0000
Subject: [PATCH 09/56] feat: per-volume/per-brain offset in the offline
 evaluators

Wire the per-brain offset handling into evaluate.py so raw (non-background-
subtracted) volumes are normalized to the same background-at-zero space the
model was trained on.

- SupervisedEvaluator: estimates each block's background offset via
  build_volume_transform (block-level, since blocks are standalone volumes).
- UnsupervisedEvaluator: applies a per-brain offset via with_offset using an
  offsets dict (the same per-brain offsets used in training), estimated once
  per brain rather than per patch to avoid content-dependent offsets.
- Both gated by a raw_input flag (default True); set raw_input=False when
  evaluating already-background-subtracted data.

For raw data, pass UnsupervisedEvaluator the training offsets dict; without
it the per-brain offset defaults to 0 (no correction).

Verified: evaluate imports and compiles; the new raw_input/offsets params are
present; 30 unit tests pass. (End-to-end eval needs cloud data + GPU, so the
wiring is verified structurally on top of the already-smoke-tested
build_volume_transform / with_offset.)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../evaluate.py                               | 36 +++++++++++++++----
 1 file changed, 30 insertions(+), 6 deletions(-)

diff --git a/src/aind_exaspim_image_compression/evaluate.py b/src/aind_exaspim_image_compression/evaluate.py
index acec1e7..ec0d194 100644
--- a/src/aind_exaspim_image_compression/evaluate.py
+++ b/src/aind_exaspim_image_compression/evaluate.py
@@ -18,10 +18,12 @@
 import pandas as pd
 import torch
 
-from aind_exaspim_image_compression.inference import predict, predict_patch
+from aind_exaspim_image_compression.inference import (
+    build_volume_transform, predict, predict_patch,
+)
 from aind_exaspim_image_compression.machine_learning import data_handling
 from aind_exaspim_image_compression.machine_learning.transforms import (
-    build_transform,
+    build_transform, with_offset,
 )
 from aind_exaspim_image_compression.utils import img_util, util
 from aind_exaspim_image_compression.utils.img_util import (
@@ -31,7 +33,8 @@
 
 class SupervisedEvaluator:
     def __init__(
-        self, img_paths, model, output_dir, transform=None, device="cuda"
+        self, img_paths, model, output_dir, transform=None, device="cuda",
+        raw_input=True,
     ):
         # Instance attributes
         self.codec = blosc.Blosc(cname="zstd", clevel=6, shuffle=blosc.SHUFFLE)
@@ -40,6 +43,7 @@ def __init__(
         self.model = model
         self.model.eval().to(device)
         self.transform = transform or build_transform({"kind": "asinh"})
+        self.raw_input = raw_input
 
         # Initialize output directory
         self.output_dir = output_dir
@@ -84,9 +88,16 @@ def run(self, model_path):
         df = pd.DataFrame(index=rows, columns=["cratio", "ssim"])
         desc = "Denoise Blocks"
         for block_id, noise in tqdm(self.noise_imgs.items(), desc=desc):
+            # For raw input, estimate this block's background offset so it is
+            # normalized to the same space the model was trained on.
+            if self.raw_input:
+                transform = build_volume_transform(self.transform, noise)
+            else:
+                transform = self.transform
+
             # Run model
             denoised = predict(
-                noise, self.model, self.transform, verbose=False
+                noise, self.model, transform, verbose=False
             )
 
             # Compute metrics
@@ -114,7 +125,8 @@ def find_img_name(self, img_path):
 
 class UnsupervisedEvaluator:
     def __init__(
-        self, root_dir, model, img_paths_json, patch_shape, transform=None
+        self, root_dir, model, img_paths_json, patch_shape, transform=None,
+        offsets=None, raw_input=True,
     ):
         # Class attributes
         self.codec = blosc.Blosc(cname="zstd", clevel=6, shuffle=blosc.SHUFFLE)
@@ -125,6 +137,8 @@ def __init__(
         self.data_dir = os.path.join(root_dir, "data")
         self.result_dir = os.path.join(root_dir, "models")
         self.transform = transform or build_transform({"kind": "asinh"})
+        self.offsets = offsets or dict()
+        self.raw_input = raw_input
 
         # Initialize directories
         util.mkdir(self.result_dir)
@@ -170,6 +184,16 @@ def compute_metrics(
             "lmax_gt": list()
         }
 
+        # For raw input, apply this brain's background offset (from the same
+        # per-brain offsets used in training). Estimated once per brain, not
+        # per patch, to avoid content-dependent offsets.
+        if self.raw_input:
+            transform = with_offset(
+                self.transform, self.offsets.get(brain_id, 0.0)
+            )
+        else:
+            transform = self.transform
+
         # Run evaluation
         for voxel in tqdm(voxels, desc=brain_id):
             # Get images
@@ -177,7 +201,7 @@ def compute_metrics(
             noise = input_noise[5:-5, 5:-5, 5:-5]
             denoised_gt = np.maximum(bm4d(noise, 10), 0).astype(int)
             denoised = predict_patch(
-                input_noise, self.model, self.transform
+                input_noise, self.model, transform
             )[5:-5, 5:-5, 5:-5]
 
             # Compute metrics

From 2767c7bf2843471c017f3f173d0fd3aa9fee04a8 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 8 Jul 2026 22:57:18 +0000
Subject: [PATCH 10/56] fix: strip gs:// prefix from segmentation kvstore path

_load_segmentation passed the segmentation prefix straight into the
tensorstore gcs kvstore "path" while hard-coding bucket="allen-nd-goog".
When the prefix is a full gs://allen-nd-goog/... URL (as in the segmentation
prefixes JSON), tensorstore concatenates bucket + path and looks for
gs://allen-nd-goog/gs://allen-nd-goog/.../info, which does not exist:

    ValueError: NOT_FOUND: Error opening "neuroglancer_precomputed" driver

Add _parse_gcs_path to split a path into (bucket, key), tolerating either a
full gs://bucket/key URL (prefix stripped, bucket taken from the URL) or a
bucket-relative key (default bucket). Pre-existing bug, unrelated to the
normalization overhaul.

Verified: the exact failing path resolves to bucket=allen-nd-goog and a
prefix-free key; relative paths and other buckets handled; 30 tests pass.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../machine_learning/data_handling.py         | 36 +++++++++++++++++--
 1 file changed, 34 insertions(+), 2 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 8007c9b..f5d539c 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -117,14 +117,19 @@ def _load_segmentation(self, brain_id, segmentation_path):
             Path to segmentation.
         """
         if segmentation_path:
+            # Parse into (bucket, key), tolerating a full gs://bucket/key URL
+            # or a bucket-relative key. The kvstore path must not include the
+            # gs://bucket/ prefix, or tensorstore doubles it.
+            bucket, key = self._parse_gcs_path(segmentation_path)
+
             # Load image
             label_mask = ts.open(
                 {
                     "driver": "neuroglancer_precomputed",
                     "kvstore": {
                         "driver": "gcs",
-                        "bucket": "allen-nd-goog",
-                        "path": segmentation_path,
+                        "bucket": bucket,
+                        "path": key,
                     },
                     "context": {
                         "cache_pool": {"total_bytes_limit": 1000000000},
@@ -141,6 +146,33 @@ def _load_segmentation(self, brain_id, segmentation_path):
             label_mask = label_mask[ts.d[0].transpose[1]]
             self.segmentations[brain_id] = label_mask
 
+    @staticmethod
+    def _parse_gcs_path(path, default_bucket="allen-nd-goog"):
+        """
+        Splits a GCS path into a (bucket, key) pair.
+
+        Accepts either a full ``gs://bucket/key`` URL or a bucket-relative
+        key. The kvstore "path" must be relative to the bucket, so the
+        ``gs://bucket/`` prefix is stripped when present.
+
+        Parameters
+        ----------
+        path : str
+            Full gs:// URL or bucket-relative key.
+        default_bucket : str, optional
+            Bucket used when "path" has no gs:// scheme. Default is
+            "allen-nd-goog".
+
+        Returns
+        -------
+        Tuple[str]
+            The (bucket, key) pair.
+        """
+        if path.startswith("gs://"):
+            bucket, _, key = path[len("gs://"):].partition("/")
+            return bucket, key
+        return default_bucket, path
+
     def _load_swcs(self, brain_id, swc_pointer):
         if swc_pointer:
             # Initializations

From 58c690fbaba1179134115540c4a189fad41e7b70 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 00:26:06 +0000
Subject: [PATCH 11/56] perf: patch cache + prefetching persistent-pool
 DataLoader

Training was CPU/IO-bound (per-patch BM4D + cloud reads), leaving the GPU
idle. Two changes decouple that work from the training step.

Patch cache:
- Factor TrainDataset.__getitem__ into _sample_counts() (the expensive cloud
  read + BM4D + foreground mask, in offset-subtracted counts) and a shared
  build_training_example() (cheap transform + target construction).
- Add CachedPatchDataset, which reads precomputed (raw, teacher, fg) patches
  from memory-mapped .npy pools and applies only the cheap step, so training
  becomes GPU-bound. Built by code/precompute_patches.py.

DataLoader:
- Rewrite as a prefetching loader: a background thread fills a bounded queue
  so batch prep overlaps GPU compute. The process pool is created once per
  epoch (not per batch), and the dataset is pickled to workers once at pool
  startup via an initializer instead of per task. num_workers=0 runs
  in-thread (ideal for the cheap cached dataset); None/>0 uses the pool (for
  the cloud dataset where BM4D dominates).
- Trainer gains num_workers / prefetch and passes them through.

code/ drivers (untracked): precompute_patches.py builds the cache;
train_bm4dnet.py gains a cache_dir switch (num_workers=0 when cached).

Verified: 30 unit tests pass; smoke covers build_training_example,
CachedPatchDataset, and the prefetch DataLoader in both in-thread and
persistent-pool modes; Part C validate_step and Part E getitem smokes still
pass.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 .../machine_learning/data_handling.py         | 260 +++++++++++++++---
 .../machine_learning/train.py                 |  16 +-
 2 files changed, 237 insertions(+), 39 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index f5d539c..6b9353b 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -19,8 +19,11 @@
 
 import fastremap
 import numpy as np
+import os
+import queue
 import random
 import tensorstore as ts
+import threading
 import torch
 
 from aind_exaspim_image_compression.machine_learning.metrics import (
@@ -31,6 +34,42 @@
     calibrate_transform,
 )
 from aind_exaspim_image_compression.utils import img_util, util
+
+
+def build_training_example(
+    transform, preserve_foreground, raw, teacher, fg_mask
+):
+    """
+    Assembles a training example from count-space arrays.
+
+    Applies the foreground-preserving target construction and the transform,
+    shared by the live TrainDataset and the CachedPatchDataset.
+
+    Parameters
+    ----------
+    transform : IntensityTransform
+        Transform mapping counts to the normalized domain.
+    preserve_foreground : bool
+        Whether the target keeps raw counts on the foreground.
+    raw : numpy.ndarray
+        Offset-subtracted raw counts.
+    teacher : numpy.ndarray
+        Clipped BM4D denoising in counts.
+    fg_mask : numpy.ndarray
+        Foreground mask.
+
+    Returns
+    -------
+    Tuple[numpy.ndarray]
+        (x, y, fg_mask) — model input, target, and mask (float 0/1).
+    """
+    fg = np.asarray(fg_mask).astype(bool)
+    target = np.where(fg, raw, teacher) if preserve_foreground else teacher
+    return (
+        transform.forward(raw),
+        transform.forward(target),
+        fg.astype(np.float32),
+    )
 from aind_exaspim_image_compression.utils.swc_util import Reader
 
 
@@ -209,25 +248,33 @@ def __getitem__(self, dummy_input):
         fg_mask : numpy.ndarray
             Foreground mask (float 0/1) for the signal-preserving loss.
         """
-        # Sample image patch and its BM4D-denoised target
+        raw, teacher, fg_mask = self._sample_counts()
+        return build_training_example(
+            self.transform, self.preserve_foreground, raw, teacher, fg_mask
+        )
+
+    def _sample_counts(self):
+        """
+        Samples one patch and its BM4D target in offset-subtracted counts.
+
+        This is the expensive step (cloud read + BM4D + foreground mask) and
+        is exactly what the patch cache stores; the cheap transform + target
+        construction is applied by build_training_example.
+
+        Returns
+        -------
+        Tuple[numpy.ndarray]
+            (raw, teacher, fg_mask) in count space. raw has the per-brain
+            offset subtracted; teacher is the clipped BM4D denoising.
+        """
         brain_id = self.sample_brain()
         voxel = self.sample_voxel(brain_id)
         raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
         raw = raw - self.offsets.get(brain_id, 0.0)
         teacher = bm4d(raw, self.sigma_bm4d)
         teacher = np.clip(teacher, 0, self.transform.max_count)
-
-        # Preserve raw counts on foreground so BM4D cannot erase neurites
         fg_mask = self.foreground_mask(brain_id, voxel, raw)
-        if self.preserve_foreground:
-            target = np.where(fg_mask, raw, teacher)
-        else:
-            target = teacher
-
-        # Map to the normalized transform domain
-        x = self.transform.forward(raw)
-        y = self.transform.forward(target)
-        return x, y, fg_mask.astype(np.float32)
+        return raw, teacher, fg_mask
 
     def foreground_mask(self, brain_id, center, raw):
         """
@@ -691,67 +738,206 @@ def read_patch(self, brain_id, center):
         return self.imgs[brain_id][(0, 0, *slices)]
 
 
+class CachedPatchDataset(Dataset):
+    """
+    Dataset that samples precomputed count-space patches from disk.
+
+    The expensive cloud reads + BM4D + foreground masks are precomputed once
+    (see code/precompute_patches.py) into memory-mapped arrays; this dataset
+    reads a random cached patch and applies only the cheap transform + target
+    construction, so training becomes GPU-bound instead of BM4D-bound.
+
+    Attributes
+    ----------
+    patch_shape : Tuple[int]
+        Shape of the cached patches.
+    transform : IntensityTransform
+        Transform mapping counts to the normalized domain.
+    """
+
+    def __init__(
+        self, cache_dir, transform=None, preserve_foreground=True,
+        n_examples_per_epoch=None,
+    ):
+        """
+        Instantiates a CachedPatchDataset.
+
+        Parameters
+        ----------
+        cache_dir : str
+            Directory holding raw.npy, teacher.npy, and fg.npy.
+        transform : IntensityTransform, optional
+            Transform mapping counts to the normalized domain. Default is an
+            asinh transform.
+        preserve_foreground : bool, optional
+            Whether the target keeps raw counts on the foreground. Default is
+            True.
+        n_examples_per_epoch : int, optional
+            Number of examples drawn per epoch. Default is the pool size.
+        """
+        super(CachedPatchDataset, self).__init__()
+        self.raw = np.load(os.path.join(cache_dir, "raw.npy"), mmap_mode="r")
+        self.teacher = np.load(
+            os.path.join(cache_dir, "teacher.npy"), mmap_mode="r"
+        )
+        self.fg = np.load(os.path.join(cache_dir, "fg.npy"), mmap_mode="r")
+        self.transform = transform or build_transform({"kind": "asinh"})
+        self.preserve_foreground = preserve_foreground
+        self.patch_shape = tuple(self.raw.shape[1:])
+        self.n_examples_per_epoch = (
+            n_examples_per_epoch if n_examples_per_epoch else len(self.raw)
+        )
+
+    def __len__(self):
+        """Number of examples drawn per epoch."""
+        return self.n_examples_per_epoch
+
+    def __getitem__(self, dummy_input):
+        """
+        Returns a random cached example as (x, y, fg_mask).
+
+        Parameters
+        ----------
+        dummy_input : Any
+            Unused index; patches are sampled at random from the pool.
+
+        Returns
+        -------
+        Tuple[numpy.ndarray]
+            (x, y, fg_mask) for the model.
+        """
+        idx = random.randint(0, len(self.raw) - 1)
+        raw = np.asarray(self.raw[idx], dtype=np.float32)
+        teacher = np.asarray(self.teacher[idx], dtype=np.float32)
+        fg_mask = np.asarray(self.fg[idx])
+        return build_training_example(
+            self.transform, self.preserve_foreground, raw, teacher, fg_mask
+        )
+
+
 # --- Custom Dataloader ---
+_WORKER_DATASET = None
+
+
+def _worker_init(dataset):
+    """Stores the dataset in a per-worker global (avoids per-task pickling)."""
+    global _WORKER_DATASET
+    _WORKER_DATASET = dataset
+
+
+def _worker_getitem(idx):
+    """Fetches one example from the per-worker dataset global."""
+    return _WORKER_DATASET[idx]
+
+
 class DataLoader:
     """
-    DataLoader that uses multithreading to fetch image patches from the cloud
-    to form batches.
+    Prefetching DataLoader that overlaps batch preparation with training.
+
+    A background thread fills a bounded queue of prepared batches while the
+    training loop consumes them, so the GPU is not starved. Per-example work
+    runs either in-thread (num_workers=0, best for the cheap cached dataset)
+    or in a persistent process pool (num_workers>0 or None, for the cloud
+    dataset where BM4D dominates). The pool is created once per epoch, and the
+    dataset is pickled to workers once at pool startup rather than per task.
 
     Attributes
     ----------
     dataset : torch.utils.data.Dataset
-        Dataset to iterated over.
+        Dataset to iterate over.
     batch_size : int
         Number of examples in each batch.
     patch_shape : Tuple[int]
         Shape of image patch expected by the model.
     """
 
-    def __init__(self, dataset, batch_size=16):
+    def __init__(self, dataset, batch_size=16, num_workers=None, prefetch=2):
         """
         Instantiates a DataLoader object.
 
         Parameters
         ----------
         dataset : torch.utils.data.Dataset
-            Dataset to iterated over.
+            Dataset to iterate over.
         batch_size : int, optional
             Number of examples in each batch. Default is 16.
+        num_workers : int, optional
+            Process-pool workers for per-example work. None uses the CPU count;
+            0 runs in-thread (best for the cheap cached dataset). Default is
+            None.
+        prefetch : int, optional
+            Number of batches prepared ahead of the consumer. Default is 2.
         """
-        # Instance attributes
         self.dataset = dataset
         self.batch_size = batch_size
         self.patch_shape = dataset.patch_shape
+        self.num_workers = num_workers
+        self.prefetch = prefetch
 
     def __iter__(self):
         """
-        Iterates over the dataset and yields batches of examples.
+        Yields batches, preparing them ahead of the consumer in a thread.
 
         Returns
         -------
         iterator
-            Yields batches of examples.
+            Yields batches of tensors.
         """
-        for idx in range(0, len(self.dataset), self.batch_size):
-            yield self._load_batch(idx)
-
-    def _load_batch(self, start_idx):
+        starts = list(range(0, len(self.dataset), self.batch_size))
+        if not starts:
+            return
+
+        executor = None
+        if self.num_workers != 0:
+            executor = ProcessPoolExecutor(
+                max_workers=self.num_workers,
+                initializer=_worker_init,
+                initargs=(self.dataset,),
+            )
+
+        result_queue = queue.Queue(maxsize=max(1, self.prefetch))
+        done = object()
+
+        def produce():
+            try:
+                for start in starts:
+                    result_queue.put((None, self._load_batch(executor, start)))
+            except Exception as exc:  # surface loader errors to the consumer
+                result_queue.put((exc, None))
+            else:
+                result_queue.put((done, None))
+
+        thread = threading.Thread(target=produce, daemon=True)
+        thread.start()
+        try:
+            while True:
+                flag, batch = result_queue.get()
+                if flag is done:
+                    break
+                if flag is not None:
+                    raise flag
+                yield batch
+        finally:
+            if executor is not None:
+                executor.shutdown(wait=False, cancel_futures=True)
+            thread.join(timeout=1)
+
+    def _load_batch(self, executor, start_idx):
         # Compute batch size
-        n_remaining_examples = len(self.dataset) - start_idx
-        batch_size = min(self.batch_size, n_remaining_examples)
-
-        # Generate batch
-        with ProcessPoolExecutor() as executor:
-            processes = list()
-            for idx in range(start_idx, start_idx + batch_size):
-                processes.append(
-                    executor.submit(self.dataset.__getitem__, idx)
-                )
-            results = [p.result() for p in as_completed(processes)]
+        n_remaining = len(self.dataset) - start_idx
+        batch_size = min(self.batch_size, n_remaining)
+        indices = range(start_idx, start_idx + batch_size)
+
+        # Per-example work: in-thread when there is no pool, else in parallel.
+        if executor is None:
+            results = [self.dataset[idx] for idx in indices]
+        else:
+            futures = [executor.submit(_worker_getitem, idx) for idx in indices]
+            results = [f.result() for f in as_completed(futures)]
 
         # Stack each field of the example tuple into its own batch tensor.
-        # Handles both the (x, y) train shape and the (x, y, raw, fg_mask)
-        # validation shape.
+        # Handles the (x, y, fg_mask) train shape and the
+        # (x, y, raw, fg_mask) validation shape alike.
         shape = (batch_size, 1,) + self.patch_shape
         n_fields = len(results[0])
         batched = [np.zeros(shape) for _ in range(n_fields)]
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index 49bbcdf..7f62617 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -46,6 +46,8 @@ def __init__(
         use_amp=True,
         checkpoint_weights=None,
         fg_weight=20.0,
+        num_workers=None,
+        prefetch=2,
     ):
         """
         Instantiates a Trainer object.
@@ -77,6 +79,8 @@ def __init__(
         self.device = device
         self.max_epochs = max_epochs
         self.log_dir = log_dir
+        self.num_workers = num_workers
+        self.prefetch = prefetch
 
         self.codec = blosc.Blosc(cname="zstd", clevel=5, shuffle=blosc.SHUFFLE)
         self.criterion = SignalPreservingLoss(fg_weight=fg_weight)
@@ -108,9 +112,17 @@ def run(self, train_dataset, val_dataset):
         print("Experiment:", os.path.basename(os.path.normpath(self.log_dir)))
         self.transform = train_dataset.transform
         train_dataloader = DataLoader(
-            train_dataset, batch_size=self.batch_size
+            train_dataset,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            prefetch=self.prefetch,
+        )
+        val_dataloader = DataLoader(
+            val_dataset,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            prefetch=self.prefetch,
         )
-        val_dataloader = DataLoader(val_dataset, batch_size=self.batch_size)
 
         # Main
         self.best_score = np.inf

From fd5e89017235fab88f3292b7e1a6dfafa03cee39 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 00:42:14 +0000
Subject: [PATCH 12/56] Adds training scripts

---
 scripts/estimate_background_offsets.py | 125 +++++++++++++++++++++
 scripts/precompute_patches.py          | 138 +++++++++++++++++++++++
 scripts/train_bm4dnet.py               | 146 +++++++++++++++++++++++++
 3 files changed, 409 insertions(+)
 create mode 100644 scripts/estimate_background_offsets.py
 create mode 100644 scripts/precompute_patches.py
 create mode 100644 scripts/train_bm4dnet.py

diff --git a/scripts/estimate_background_offsets.py b/scripts/estimate_background_offsets.py
new file mode 100644
index 0000000..e497e2b
--- /dev/null
+++ b/scripts/estimate_background_offsets.py
@@ -0,0 +1,125 @@
+"""
+Estimate per-brain background offsets from the level-5 multiscale zarr.
+
+For each brain in the training list, this reads the coarse (level-5) image,
+computes a low percentile as the background / black-point, and writes a
+{brain_id: offset} JSON. It also prints the distribution of offsets across
+brains so the fixed-vs-per-brain decision falls out of the numbers:
+
+    * spread << scale  -> a single fixed offset (the median) is fine
+    * spread >= scale  -> prefer a per-brain offset
+
+The percentile is computed over NONZERO voxels so that zero-padding outside
+the imaged volume does not drag the estimate down to 0. The per-brain zero
+fraction is reported so padding is visible. Level 5 is ~32x downsampled, so
+its voxels are local averages: the estimate is a smoothed black point, not
+the raw-resolution noise floor.
+
+Note: this reads each whole level-5 volume into memory (tens to a few hundred
+MB per brain) and processes brains sequentially.
+
+"""
+
+import numpy as np
+from tqdm import tqdm
+
+from aind_exaspim_dataset_utils.s3_util import get_img_prefix
+
+from aind_exaspim_image_compression.utils import img_util, util
+
+
+def estimate_offset(brain_id, img_prefixes_path, level, percentile):
+    """
+    Estimates the background offset for a single brain.
+
+    Parameters
+    ----------
+    brain_id : str
+        Unique identifier of the brain.
+    img_prefixes_path : str
+        Path to the JSON mapping brain IDs to image prefixes.
+    level : int
+        Multiscale level to read (e.g., 5).
+    percentile : float
+        Low percentile used as the background estimate (e.g., 0.1).
+
+    Returns
+    -------
+    dict
+        Offset (nonzero), offset over all voxels, nonzero median, and the
+        fraction of zero voxels.
+    """
+    prefix = get_img_prefix(brain_id, img_prefixes_path)
+    arr = img_util.read(prefix + str(level))
+    vol = np.asarray(arr[0, 0]).reshape(-1)  # channel 0, timepoint 0
+    nonzero = vol[vol > 0]
+    zero_fraction = 1.0 - nonzero.size / vol.size
+    return {
+        "offset": (
+            float(np.percentile(nonzero, percentile))
+            if nonzero.size else float("nan")
+        ),
+        "offset_all_voxels": float(np.percentile(vol, percentile)),
+        "median": (
+            float(np.median(nonzero)) if nonzero.size else float("nan")
+        ),
+        "zero_fraction": zero_fraction,
+    }
+
+
+def main():
+    # Estimate an offset per brain
+    brain_ids = util.read_txt(brain_ids_path)
+    offsets = dict()
+    for brain_id in tqdm(brain_ids, desc="Estimate offsets"):
+        try:
+            result = estimate_offset(
+                brain_id, img_prefixes_path, level, percentile
+            )
+            offsets[brain_id] = result["offset"]
+            print(
+                f"{brain_id}: offset={result['offset']:.1f}  "
+                f"(all={result['offset_all_voxels']:.1f}, "
+                f"median={result['median']:.1f}, "
+                f"zeros={100 * result['zero_fraction']:.1f}%)"
+            )
+        except Exception as e:
+            print(f"{brain_id}: FAILED ({e})")
+
+    # Write per-brain offsets
+    util.write_json(output_path, offsets)
+    print(f"\nWrote {len(offsets)} offsets to {output_path}")
+
+    # Summarize the spread to inform fixed-vs-per-brain
+    values = np.array([v for v in offsets.values() if np.isfinite(v)])
+    if values.size:
+        lo, med, hi = float(values.min()), float(np.median(values)), \
+            float(values.max())
+        spread = hi - lo
+        print("\n--- Background offset distribution ---")
+        print(f"  brains:  {values.size}")
+        print(f"  min:     {lo:.1f}")
+        print(f"  median:  {med:.1f}")
+        print(f"  max:     {hi:.1f}")
+        print(
+            f"  spread:  {spread:.1f} counts "
+            f"({spread / scale_hint:.2f} x scale={scale_hint:g})"
+        )
+        if spread < scale_hint:
+            print(f"  => spread < scale: a FIXED offset ~{med:.0f} is fine.")
+        else:
+            print("  => spread >= scale: prefer a PER-BRAIN offset.")
+
+
+if __name__ == "__main__":
+    # Paths
+    brain_ids_path = "/data/train_brain_ids.txt"
+    img_prefixes_path = "/data/exaspim_image_prefixes.json"
+    output_path = "/data/exaspim_background_offsets.json"
+
+    # Parameters
+    level = 5
+    percentile = 0.1
+    scale_hint = 32.0  # asinh knee; only used to judge whether spread matters
+
+    main()
diff --git a/scripts/precompute_patches.py b/scripts/precompute_patches.py
new file mode 100644
index 0000000..f517a9f
--- /dev/null
+++ b/scripts/precompute_patches.py
@@ -0,0 +1,138 @@
+"""
+Precompute a pool of training patches to disk so training is GPU-bound.
+
+The training bottleneck is per-patch BM4D + cloud reads on the CPU, which
+leaves the GPU idle. This script samples a fixed pool of patches once and
+writes the expensive count-space intermediates -- (raw with the per-brain
+offset subtracted, clipped BM4D teacher, foreground mask) -- to memory-mapped
+arrays. Training then reads a random cached patch and applies only the cheap
+transform + target construction (see CachedPatchDataset).
+
+Outputs, under cache_dir:
+    raw.npy      float16  (N, *patch_shape)   offset-subtracted counts
+    teacher.npy  float16  (N, *patch_shape)   clipped BM4D denoising
+    fg.npy       uint8    (N, *patch_shape)   foreground mask (0/1)
+
+Each worker builds its own dataset once (via init_datasets) so the large
+skeleton arrays and cloud handles are not re-pickled per patch.
+
+"""
+
+import numpy as np
+from concurrent.futures import ProcessPoolExecutor
+from numpy.lib.format import open_memmap
+from tqdm import tqdm
+
+from aind_exaspim_image_compression.machine_learning import data_handling
+from aind_exaspim_image_compression.utils import util
+
+_WORKER_DATASET = None
+
+
+def _init_worker(init_kwargs):
+    """Builds one TrainDataset per worker process and caches it globally."""
+    global _WORKER_DATASET
+    _WORKER_DATASET, _ = data_handling.init_datasets(**init_kwargs)
+
+
+def _sample_counts(_):
+    """Samples one count-space example from the per-worker dataset."""
+    return _WORKER_DATASET._sample_counts()
+
+
+def _to_float16(arr):
+    """Clips to the float16 range before casting (avoids inf at saturation)."""
+    return np.clip(arr, -65504, 65504).astype(np.float16)
+
+
+def precompute():
+    # Build the config each worker uses to construct its dataset. n_validate
+    # is 0 (we only need training sampling) and the transform offset stays 0
+    # because per-brain offsets are subtracted in _sample_counts.
+    brain_ids = util.read_txt(brain_ids_path)
+    offsets = util.read_json(offsets_path) if offsets_path else None
+    init_kwargs = dict(
+        brain_ids=brain_ids,
+        img_paths_json=img_prefixes_path,
+        patch_shape=patch_shape,
+        foreground_sampling_rate=foreground_sampling_rate,
+        min_foreground_voxels=min_foreground_voxels,
+        min_segmentation_volume=min_segmentation_volume,
+        n_validate_examples=0,
+        offsets=offsets,
+        preserve_foreground=preserve_foreground,
+        segmentation_prefixes_path=segmentation_prefixes_path,
+        sigma_bm4d=sigma_bm4d,
+        swc_pointers=swc_pointers,
+        transform_cfg=transform_cfg,
+    )
+
+    # Pre-allocate memory-mapped outputs and stream results into them.
+    util.mkdir(cache_dir)
+    shape = (n_patches,) + tuple(patch_shape)
+    raw_mm = open_memmap(
+        f"{cache_dir}/raw.npy", mode="w+", dtype=np.float16, shape=shape
+    )
+    teacher_mm = open_memmap(
+        f"{cache_dir}/teacher.npy", mode="w+", dtype=np.float16, shape=shape
+    )
+    fg_mm = open_memmap(
+        f"{cache_dir}/fg.npy", mode="w+", dtype=np.uint8, shape=shape
+    )
+
+    with ProcessPoolExecutor(
+        max_workers=num_workers,
+        initializer=_init_worker,
+        initargs=(init_kwargs,),
+    ) as executor:
+        results = executor.map(
+            _sample_counts, range(n_patches), chunksize=1
+        )
+        for i, (raw, teacher, fg) in enumerate(
+            tqdm(results, total=n_patches, desc="Precompute")
+        ):
+            raw_mm[i] = _to_float16(raw)
+            teacher_mm[i] = _to_float16(teacher)
+            fg_mm[i] = np.asarray(fg, dtype=np.uint8)
+
+    raw_mm.flush()
+    teacher_mm.flush()
+    fg_mm.flush()
+    print(f"Wrote {n_patches} patches to {cache_dir}")
+
+
+if __name__ == "__main__":
+    # Paths (match train_bm4dnet.py)
+    brain_ids_path = "/data/train_brain_ids.txt"
+    img_prefixes_path = "/data/exaspim_image_prefixes.json"
+    segmentation_prefixes_path = "/data/exaspim_segmentation_prefixes.json"
+    offsets_path = "/data/exaspim_background_offsets.json"
+    cache_dir = "/results/patch_cache"
+
+    # SWC pointer
+    swc_pointers = {
+        "bucket_name": "allen-nd-goog",
+        "path": "ground_truth_tracings",
+    }
+
+    # Transform cfg (offset 0; per-brain offsets are subtracted per patch).
+    # Only max_count is used here, to clip the BM4D teacher.
+    transform_cfg = {
+        "kind": "asinh",
+        "params": {"offset": 0.0, "scale": 32.0},
+    }
+
+    # Sampling / patch parameters (match training)
+    foreground_sampling_rate = 0.5
+    min_foreground_voxels = 50
+    min_segmentation_volume = 200
+    patch_shape = (64, 64, 64)
+    preserve_foreground = True
+    sigma_bm4d = 24
+
+    # Pool size and parallelism. ~1.3 MB/patch (fp16 raw+teacher + uint8 fg),
+    # so 8000 patches ~= 10 GB. num_workers=None uses all CPUs.
+    n_patches = 30000
+    num_workers = None
+
+    precompute()
diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
new file mode 100644
index 0000000..eed325e
--- /dev/null
+++ b/scripts/train_bm4dnet.py
@@ -0,0 +1,146 @@
+import multiprocessing as mp
+import os
+
+from aind_exaspim_image_compression.machine_learning import data_handling
+from aind_exaspim_image_compression.machine_learning.train import Trainer
+from aind_exaspim_image_compression.machine_learning.unet3d import UNet
+from aind_exaspim_image_compression.utils import util
+
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+os.environ["GRPC_TRACE"] = ""
+
+
+def train():
+    # Load Brain IDs and per-brain background offsets
+    brain_ids = util.read_txt(brain_ids_path)
+    offsets = util.read_json(offsets_path) if offsets_path else None
+
+    # Datasets. The per-brain offset is subtracted from each patch, then one
+    # shared transform (offset 0) maps every brain to a background-at-zero
+    # space; the transform cfg is serialized with each checkpoint.
+    train_dataset, val_dataset = data_handling.init_datasets(
+        brain_ids,
+        img_prefixes_path,
+        patch_shape,
+        foreground_sampling_rate=foreground_sampling_rate,
+        min_foreground_voxels=min_foreground_voxels,
+        min_segmentation_volume=min_segmentation_volume,
+        n_train_examples_per_epoch=n_train_examples_per_epoch,
+        n_validate_examples=n_validate_examples,
+        offsets=offsets,
+        preserve_foreground=preserve_foreground,
+        segmentation_prefixes_path=segmentation_prefixes_path,
+        sigma_bm4d=sigma_bm4d,
+        swc_pointers=swc_pointers,
+        transform_cfg=transform_cfg,
+    )
+    print("Transform:", train_dataset.transform.cfg)
+    print("# Brains with Skeletons:", len(train_dataset.skeletons))
+    print("# Brains with Segmentations:", len(train_dataset.segmentations))
+
+    # Train from the precomputed patch cache when available (GPU-bound).
+    # Reuse the transform init_datasets built so the cache and validation
+    # share the identical mapping; validation stays on the cloud dataset.
+    if cache_dir:
+        train_dataset = data_handling.CachedPatchDataset(
+            cache_dir,
+            transform=train_dataset.transform,
+            preserve_foreground=preserve_foreground,
+            n_examples_per_epoch=n_train_examples_per_epoch,
+        )
+        print(
+            "Training from cache:", cache_dir,
+            "| pool size:", len(train_dataset.raw),
+        )
+
+    # Run. Cached patches are cheap, so load them in-thread (num_workers=0);
+    # the cloud dataset needs the process pool for parallel BM4D.
+    trainer = Trainer(
+        output_dir,
+        batch_size=batch_size,
+        lr=lr,
+        max_epochs=max_epochs,
+        model=model,
+        fg_weight=fg_weight,
+        checkpoint_weights=checkpoint_weights,
+        num_workers=0 if cache_dir else None,
+    )
+    if resume_path is not None:
+        trainer.load_pretrained_weights(resume_path)
+    trainer.run(train_dataset, val_dataset)
+
+
+if __name__ == "__main__":
+    # Paths
+    brain_ids_path = "/data/train_brain_ids.txt"
+    img_prefixes_path = "/data/exaspim_image_prefixes.json"
+    output_dir = "/results/training-sessions"
+    segmentation_prefixes_path = (
+        "/data/exaspim_segmentation_prefixes.json"
+    )
+    # Per-brain background offsets from estimate_background_offsets.py. Set to
+    # None to disable per-brain offset subtraction.
+    offsets_path = "/data/exaspim_background_offsets.json"
+    # Precomputed patch cache from precompute_patches.py. Leave None to sample
+    # + BM4D live from the cloud (slow, GPU-starved); after precomputing, set
+    # this to the cache dir (e.g. "/results/patch_cache") to train GPU-bound.
+    cache_dir = None
+    util.mkdir(output_dir)
+
+    # Resume path. Checkpoints from before the normalization overhaul are NOT
+    # compatible: GroupNorm changed the state_dict keys, the residual output
+    # changed the semantics, and the old model was trained under percentile
+    # normalization. Train from scratch (None), or point this at a NEW-format
+    # checkpoint (a dict of {"model", "transform"}) to resume.
+    resume_path = None
+
+    # SWC Pointer
+    swc_pointers = {
+        "bucket_name": "allen-nd-goog",
+        "path": "ground_truth_tracings",
+    }
+
+    # Intensity transform, shared by train and inference. Options:
+    #   {"kind": "asinh",    "params": {"offset": 35.0, "scale": 32.0}}
+    #   {"kind": "anscombe", "params": {"gain": 8.0, "read_noise": 5.0,
+    #                                   "offset": 35.0}}
+    #   {"kind": "linear",   "params": {"mn": 0.0, "mx": 1000.0, "clip": 8.0}}
+    # Per-brain offsets (offsets_path) are subtracted at the dataset, so the
+    # transform offset stays 0 to avoid double-subtracting. scale is the
+    # linear->log knee (tune from the noise floor).
+    transform_cfg = {
+        "kind": "asinh",
+        "params": {"offset": 0.0, "scale": 32.0},
+    }
+
+    # Model (new defaults: residual output + GroupNorm)
+    model = UNet()
+
+    # Training parameters
+    batch_size = 8
+    foreground_sampling_rate = 0.5
+    lr = 1e-4
+    max_epochs = 400
+    n_train_examples_per_epoch = 300
+    n_validate_examples = 60
+    patch_shape = (64, 64, 64)
+    sigma_bm4d = 24
+
+    # Signal-preserving loss + target/sampling (Parts E/F). fg_weight is
+    # aggressive; sweep it against foreground fraction. preserve_foreground
+    # keeps raw counts on the foreground so BM4D cannot erase neurites.
+    fg_weight = 20.0
+    preserve_foreground = True
+    min_foreground_voxels = 50
+    min_segmentation_volume = 200
+
+    # Checkpoint selection (Part C). None => fidelity-only (cratio weight 0).
+    # Once you pick the compression-vs-fidelity operating point, set e.g.
+    #   checkpoint_weights = dict(
+    #       fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=200.0
+    #   )
+    checkpoint_weights = None
+
+    # Main
+    mp.set_start_method("spawn", force=True)
+    train()

From 25c44c1d19d6d7d7a94f050910f811592808af50 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 01:56:35 +0000
Subject: [PATCH 13/56] perf: disable tensorstore caching to bound worker
 memory

Each ts.open handle got its own 1GB data + 1GB remote cache pool, so with
one dataset per worker (num_workers=CPU count) the un-shared per-brain
caches accumulated toward an aggregate ceiling far above RAM. Random 64^3
patch sampling has low cache hit rate anyway, so set total_bytes_limit to 0
(no caching) at both ts.open sites and drop the now-moot recheck_cached_data.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 .../machine_learning/data_handling.py                        | 5 ++---
 src/aind_exaspim_image_compression/utils/img_util.py         | 5 ++---
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 6b9353b..0d3e508 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -171,11 +171,10 @@ def _load_segmentation(self, brain_id, segmentation_path):
                         "path": key,
                     },
                     "context": {
-                        "cache_pool": {"total_bytes_limit": 1000000000},
-                        "cache_pool#remote": {"total_bytes_limit": 1000000000},
+                        "cache_pool": {"total_bytes_limit": 0},
+                        "cache_pool#remote": {"total_bytes_limit": 0},
                         "data_copy_concurrency": {"limit": 8},
                     },
-                    "recheck_cached_data": "open",
                 }
             ).result()
 
diff --git a/src/aind_exaspim_image_compression/utils/img_util.py b/src/aind_exaspim_image_compression/utils/img_util.py
index af2e140..fcd6c2e 100644
--- a/src/aind_exaspim_image_compression/utils/img_util.py
+++ b/src/aind_exaspim_image_compression/utils/img_util.py
@@ -108,11 +108,10 @@ def _read_neuroglancer_precompted(img_path):
                 "path": path,
                 },
             "context": {
-                "cache_pool": {"total_bytes_limit": 1000000000},
-                "cache_pool#remote": {"total_bytes_limit": 1000000000},
+                "cache_pool": {"total_bytes_limit": 0},
+                "cache_pool#remote": {"total_bytes_limit": 0},
                 "data_copy_concurrency": {"limit": 8},
             },
-            "recheck_cached_data": "open",
             }
     ).result()
 

From 6ca829231441979e52b76598796315e0432e72d6 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 11:35:44 +0000
Subject: [PATCH 14/56] perf: precomputed validation cache to remove live BM4D
 at startup

Cache-backed runs still paid a full cloud+BM4D startup because the
validation set was built live: init_datasets sampled voxels and ran a
serial BM4D per example while the GPU sat idle (~16 min for 60 examples
plus SWC downloads).

Add a validation cache mirroring the training cache:
- Extract ValidateDataset.sample_counts (the cloud read + BM4D +
  intensity-only foreground mask) and reuse it from ingest_example.
- Add CachedValidateDataset: fixed set iterated in order, __getitem__
  returns (x, y, raw, fg_mask) matching ValidateDataset for the
  count-space metrics.
- Add scripts/precompute_val_patches.py: draws validation voxels as
  init_datasets does and stamps transform.json into the cache.
- train_bm4dnet.py: when both cache_dir and val_cache_dir are set, run
  fully offline (no cloud reads or BM4D at startup); otherwise unchanged.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/precompute_val_patches.py             | 172 ++++++++++++++++++
 scripts/train_bm4dnet.py                      | 111 +++++++----
 .../machine_learning/data_handling.py         | 110 ++++++++++-
 3 files changed, 355 insertions(+), 38 deletions(-)
 create mode 100644 scripts/precompute_val_patches.py

diff --git a/scripts/precompute_val_patches.py b/scripts/precompute_val_patches.py
new file mode 100644
index 0000000..35c72a9
--- /dev/null
+++ b/scripts/precompute_val_patches.py
@@ -0,0 +1,172 @@
+"""
+Precompute a fixed pool of validation patches to disk.
+
+The training run's other startup cost (besides the training pool) is the live
+validation set: init_datasets samples a handful of voxels and, for each, does
+a cloud read + a serial BM4D denoising on the CPU while the GPU sits idle.
+This script does that work once, offline, and writes the expensive count-space
+intermediates -- (raw with the per-brain offset subtracted, clipped BM4D
+teacher, foreground mask) -- to memory-mapped arrays. A cache-backed training
+run then reads them via CachedValidateDataset and applies only the cheap
+transform + target construction, so no cloud access or BM4D happens at startup.
+
+Voxels are drawn exactly as init_datasets draws them for validation: the
+foreground-biased sampler on the TrainDataset (which needs the skeletons /
+segmentations), while the count-space example -- crucially the intensity-only
+foreground mask used by the validation metric split -- is computed by the
+ValidateDataset. Both are built once per worker via init_datasets.
+
+Outputs, under cache_dir (same layout as the training cache, so it loads with
+CachedValidateDataset):
+    raw.npy        float16  (N, *patch_shape)   offset-subtracted counts
+    teacher.npy    float16  (N, *patch_shape)   clipped BM4D denoising
+    fg.npy         uint8    (N, *patch_shape)   foreground mask (0/1)
+    transform.json                              resolved transform cfg
+
+The transform cfg is stamped alongside the patches so the training run can
+construct the identical transform without touching the cloud.
+
+"""
+
+import numpy as np
+from concurrent.futures import ProcessPoolExecutor
+from numpy.lib.format import open_memmap
+from tqdm import tqdm
+
+from aind_exaspim_image_compression.machine_learning import data_handling
+from aind_exaspim_image_compression.machine_learning.transforms import (
+    build_transform,
+)
+from aind_exaspim_image_compression.utils import util
+
+_WORKER_TRAIN = None
+_WORKER_VAL = None
+
+
+def _init_worker(init_kwargs):
+    """Builds one (train, val) dataset pair per worker and caches it."""
+    global _WORKER_TRAIN, _WORKER_VAL
+    _WORKER_TRAIN, _WORKER_VAL = data_handling.init_datasets(**init_kwargs)
+
+
+def _sample_val_counts(_):
+    """Samples one validation count-space example from the per-worker pair.
+
+    The voxel is drawn by the TrainDataset's foreground-biased sampler (as in
+    init_datasets); the count-space example, including the intensity-only mask,
+    is produced by the ValidateDataset so it matches the metric split.
+    """
+    brain_id = _WORKER_TRAIN.sample_brain()
+    voxel = _WORKER_TRAIN.sample_voxel(brain_id)
+    return _WORKER_VAL.sample_counts(brain_id, voxel)
+
+
+def _to_float16(arr):
+    """Clips to the float16 range before casting (avoids inf at saturation)."""
+    return np.clip(arr, -65504, 65504).astype(np.float16)
+
+
+def precompute():
+    # Offset calibration would need a cloud sample that this cache is meant to
+    # avoid; the training config subtracts per-brain offsets instead, so the
+    # transform offset stays fixed. Refuse the ambiguous case loudly.
+    if transform_cfg.get("calibrate", {}).get("offset", False):
+        raise ValueError(
+            "offset calibration is not supported by the cached path; bake the "
+            "offset into transform_cfg or use per-brain offsets"
+        )
+
+    # Build the config each worker uses to construct its datasets. n_validate
+    # is 0 (we draw validation voxels ourselves) and the transform offset stays
+    # 0 because per-brain offsets are subtracted in sample_counts.
+    brain_ids = util.read_txt(brain_ids_path)
+    offsets = util.read_json(offsets_path) if offsets_path else None
+    init_kwargs = dict(
+        brain_ids=brain_ids,
+        img_paths_json=img_prefixes_path,
+        patch_shape=patch_shape,
+        foreground_sampling_rate=foreground_sampling_rate,
+        min_foreground_voxels=min_foreground_voxels,
+        min_segmentation_volume=min_segmentation_volume,
+        n_validate_examples=0,
+        offsets=offsets,
+        preserve_foreground=preserve_foreground,
+        segmentation_prefixes_path=segmentation_prefixes_path,
+        sigma_bm4d=sigma_bm4d,
+        swc_pointers=swc_pointers,
+        transform_cfg=transform_cfg,
+    )
+
+    # Pre-allocate memory-mapped outputs and stream results into them.
+    util.mkdir(cache_dir)
+    shape = (n_patches,) + tuple(patch_shape)
+    raw_mm = open_memmap(
+        f"{cache_dir}/raw.npy", mode="w+", dtype=np.float16, shape=shape
+    )
+    teacher_mm = open_memmap(
+        f"{cache_dir}/teacher.npy", mode="w+", dtype=np.float16, shape=shape
+    )
+    fg_mm = open_memmap(
+        f"{cache_dir}/fg.npy", mode="w+", dtype=np.uint8, shape=shape
+    )
+
+    with ProcessPoolExecutor(
+        max_workers=num_workers,
+        initializer=_init_worker,
+        initargs=(init_kwargs,),
+    ) as executor:
+        results = executor.map(
+            _sample_val_counts, range(n_patches), chunksize=1
+        )
+        for i, (raw, teacher, fg) in enumerate(
+            tqdm(results, total=n_patches, desc="Precompute (val)")
+        ):
+            raw_mm[i] = _to_float16(raw)
+            teacher_mm[i] = _to_float16(teacher)
+            fg_mm[i] = np.asarray(fg, dtype=np.uint8)
+
+    raw_mm.flush()
+    teacher_mm.flush()
+    fg_mm.flush()
+
+    # Stamp the resolved transform cfg so training rebuilds it exactly without
+    # touching the cloud.
+    util.write_json(f"{cache_dir}/transform.json", build_transform(transform_cfg).cfg)
+    print(f"Wrote {n_patches} validation patches to {cache_dir}")
+
+
+if __name__ == "__main__":
+    # Paths (match train_bm4dnet.py)
+    brain_ids_path = "/data/train_brain_ids.txt"
+    img_prefixes_path = "/data/exaspim_image_prefixes.json"
+    segmentation_prefixes_path = "/data/exaspim_segmentation_prefixes.json"
+    offsets_path = "/data/exaspim_background_offsets.json"
+    cache_dir = "/results/val_patch_cache"
+
+    # SWC pointer
+    swc_pointers = {
+        "bucket_name": "allen-nd-goog",
+        "path": "ground_truth_tracings",
+    }
+
+    # Transform cfg (offset 0; per-brain offsets are subtracted per patch).
+    # Only max_count is used here, to clip the BM4D teacher.
+    transform_cfg = {
+        "kind": "asinh",
+        "params": {"offset": 0.0, "scale": 32.0},
+    }
+
+    # Sampling / patch parameters (match training)
+    foreground_sampling_rate = 0.5
+    min_foreground_voxels = 50
+    min_segmentation_volume = 200
+    patch_shape = (64, 64, 64)
+    preserve_foreground = True
+    sigma_bm4d = 24
+
+    # Pool size and parallelism. The validation set is small and fixed; match
+    # n_validate_examples from train_bm4dnet.py. num_workers=None uses all CPUs.
+    n_patches = 60
+    num_workers = None
+
+    precompute()
diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index eed325e..72bdb26 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -3,6 +3,9 @@
 
 from aind_exaspim_image_compression.machine_learning import data_handling
 from aind_exaspim_image_compression.machine_learning.train import Trainer
+from aind_exaspim_image_compression.machine_learning.transforms import (
+    build_transform,
+)
 from aind_exaspim_image_compression.machine_learning.unet3d import UNet
 from aind_exaspim_image_compression.utils import util
 
@@ -11,47 +14,82 @@
 
 
 def train():
-    # Load Brain IDs and per-brain background offsets
-    brain_ids = util.read_txt(brain_ids_path)
-    offsets = util.read_json(offsets_path) if offsets_path else None
-
-    # Datasets. The per-brain offset is subtracted from each patch, then one
-    # shared transform (offset 0) maps every brain to a background-at-zero
-    # space; the transform cfg is serialized with each checkpoint.
-    train_dataset, val_dataset = data_handling.init_datasets(
-        brain_ids,
-        img_prefixes_path,
-        patch_shape,
-        foreground_sampling_rate=foreground_sampling_rate,
-        min_foreground_voxels=min_foreground_voxels,
-        min_segmentation_volume=min_segmentation_volume,
-        n_train_examples_per_epoch=n_train_examples_per_epoch,
-        n_validate_examples=n_validate_examples,
-        offsets=offsets,
-        preserve_foreground=preserve_foreground,
-        segmentation_prefixes_path=segmentation_prefixes_path,
-        sigma_bm4d=sigma_bm4d,
-        swc_pointers=swc_pointers,
-        transform_cfg=transform_cfg,
-    )
-    print("Transform:", train_dataset.transform.cfg)
-    print("# Brains with Skeletons:", len(train_dataset.skeletons))
-    print("# Brains with Segmentations:", len(train_dataset.segmentations))
-
-    # Train from the precomputed patch cache when available (GPU-bound).
-    # Reuse the transform init_datasets built so the cache and validation
-    # share the identical mapping; validation stays on the cloud dataset.
-    if cache_dir:
+    # Fully-cached path: with both a training and a validation cache, no cloud
+    # reads or BM4D happen at startup. Rebuild the exact transform the caches
+    # were built with (stamped in the val cache) so the cached patches and the
+    # model share the identical mapping; per-brain offsets are already baked
+    # into the cached counts.
+    if cache_dir and val_cache_dir:
+        transform_path = os.path.join(val_cache_dir, "transform.json")
+        cached_cfg = (
+            util.read_json(transform_path)
+            if os.path.exists(transform_path)
+            else transform_cfg
+        )
+        transform = build_transform(cached_cfg)
         train_dataset = data_handling.CachedPatchDataset(
             cache_dir,
-            transform=train_dataset.transform,
+            transform=transform,
             preserve_foreground=preserve_foreground,
             n_examples_per_epoch=n_train_examples_per_epoch,
         )
+        val_dataset = data_handling.CachedValidateDataset(
+            val_cache_dir,
+            transform=transform,
+            preserve_foreground=preserve_foreground,
+        )
+        print("Transform:", transform.cfg)
         print(
             "Training from cache:", cache_dir,
             "| pool size:", len(train_dataset.raw),
         )
+        print(
+            "Validating from cache:", val_cache_dir,
+            "| examples:", len(val_dataset),
+        )
+    else:
+        # Load Brain IDs and per-brain background offsets
+        brain_ids = util.read_txt(brain_ids_path)
+        offsets = util.read_json(offsets_path) if offsets_path else None
+
+        # Datasets. The per-brain offset is subtracted from each patch, then
+        # one shared transform (offset 0) maps every brain to a
+        # background-at-zero space; the transform cfg is serialized with each
+        # checkpoint.
+        train_dataset, val_dataset = data_handling.init_datasets(
+            brain_ids,
+            img_prefixes_path,
+            patch_shape,
+            foreground_sampling_rate=foreground_sampling_rate,
+            min_foreground_voxels=min_foreground_voxels,
+            min_segmentation_volume=min_segmentation_volume,
+            n_train_examples_per_epoch=n_train_examples_per_epoch,
+            n_validate_examples=n_validate_examples,
+            offsets=offsets,
+            preserve_foreground=preserve_foreground,
+            segmentation_prefixes_path=segmentation_prefixes_path,
+            sigma_bm4d=sigma_bm4d,
+            swc_pointers=swc_pointers,
+            transform_cfg=transform_cfg,
+        )
+        print("Transform:", train_dataset.transform.cfg)
+        print("# Brains with Skeletons:", len(train_dataset.skeletons))
+        print("# Brains with Segmentations:", len(train_dataset.segmentations))
+
+        # Train from the precomputed patch cache when available (GPU-bound).
+        # Reuse the transform init_datasets built so the cache and validation
+        # share the identical mapping; validation stays on the cloud dataset.
+        if cache_dir:
+            train_dataset = data_handling.CachedPatchDataset(
+                cache_dir,
+                transform=train_dataset.transform,
+                preserve_foreground=preserve_foreground,
+                n_examples_per_epoch=n_train_examples_per_epoch,
+            )
+            print(
+                "Training from cache:", cache_dir,
+                "| pool size:", len(train_dataset.raw),
+            )
 
     # Run. Cached patches are cheap, so load them in-thread (num_workers=0);
     # the cloud dataset needs the process pool for parallel BM4D.
@@ -84,7 +122,12 @@ def train():
     # Precomputed patch cache from precompute_patches.py. Leave None to sample
     # + BM4D live from the cloud (slow, GPU-starved); after precomputing, set
     # this to the cache dir (e.g. "/results/patch_cache") to train GPU-bound.
-    cache_dir = None
+    cache_dir = "/root/capsule/data/denoise_net_patch_cache_10K_2026_07_09"
+    # Precomputed validation cache from precompute_val_patches.py. When set
+    # alongside cache_dir, training runs fully offline (no cloud reads or BM4D
+    # at startup) and the GPU is busy almost immediately. Leave None to build
+    # the validation set live from the cloud.
+    val_cache_dir = None
     util.mkdir(output_dir)
 
     # Resume path. Checkpoints from before the normalization overhaul are NOT
@@ -117,7 +160,7 @@ def train():
     model = UNet()
 
     # Training parameters
-    batch_size = 8
+    batch_size = 32
     foreground_sampling_rate = 0.5
     lr = 1e-4
     max_epochs = 400
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 0d3e508..ed8682c 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -658,9 +658,15 @@ def ingest_brain(self, brain_id, img_path):
         """
         self.imgs[brain_id] = img_util.read(img_path)
 
-    def ingest_example(self, brain_id, voxel):
+    def sample_counts(self, brain_id, voxel):
         """
-        Extracts, denoises, transforms, and stores an image patch.
+        Samples one validation patch and its BM4D target in count space.
+
+        This is the expensive step (cloud read + BM4D + foreground mask) and
+        is exactly what the validation cache stores; the cheap transform +
+        target construction is applied by build_training_example. The mask is
+        intensity-only here (no segmentation union), matching the validation
+        metric split.
 
         Parameters
         ----------
@@ -668,15 +674,35 @@ def ingest_example(self, brain_id, voxel):
             Unique identifier of the brain from which to extract the patch.
         voxel : Tuple[int]
             Voxel coordinates of the patch center in the brain volume.
+
+        Returns
+        -------
+        Tuple[numpy.ndarray]
+            (raw, teacher, fg_mask) in count space. raw has the per-brain
+            offset subtracted; teacher is the clipped BM4D denoising.
         """
-        # Sample image patch and its BM4D-denoised target
         raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
         raw = raw - self.offsets.get(brain_id, 0.0)
         teacher = bm4d(raw, self.sigma_bm4d)
         teacher = np.clip(teacher, 0, self.transform.max_count)
+        fg_mask = make_foreground_mask(raw)
+        return raw, teacher, fg_mask
+
+    def ingest_example(self, brain_id, voxel):
+        """
+        Extracts, denoises, transforms, and stores an image patch.
+
+        Parameters
+        ----------
+        brain_id : hashable
+            Unique identifier of the brain from which to extract the patch.
+        voxel : Tuple[int]
+            Voxel coordinates of the patch center in the brain volume.
+        """
+        # Sample image patch and its BM4D-denoised target
+        raw, teacher, fg_mask = self.sample_counts(brain_id, voxel)
 
         # Preserve raw counts on foreground (intensity mask only here)
-        fg_mask = make_foreground_mask(raw)
         if self.preserve_foreground:
             target = np.where(fg_mask, raw, teacher)
         else:
@@ -814,6 +840,82 @@ def __getitem__(self, dummy_input):
         )
 
 
+class CachedValidateDataset(Dataset):
+    """
+    Validation dataset backed by a precomputed count-space patch cache.
+
+    Mirrors CachedPatchDataset but reproduces the ValidateDataset interface:
+    a fixed set of examples iterated in order (not sampled at random), whose
+    __getitem__ also returns the raw counts needed for the count-space
+    metrics. The expensive cloud reads + BM4D + foreground masks are
+    precomputed once (see scripts/precompute_val_patches.py); this dataset
+    applies only the cheap transform + target construction, so a cache-backed
+    training run needs no cloud access or BM4D at startup.
+
+    Attributes
+    ----------
+    patch_shape : Tuple[int]
+        Shape of the cached patches.
+    transform : IntensityTransform
+        Transform mapping counts to the normalized domain.
+    """
+
+    def __init__(
+        self, cache_dir, transform=None, preserve_foreground=True,
+    ):
+        """
+        Instantiates a CachedValidateDataset.
+
+        Parameters
+        ----------
+        cache_dir : str
+            Directory holding raw.npy, teacher.npy, and fg.npy.
+        transform : IntensityTransform, optional
+            Transform mapping counts to the normalized domain. Default is an
+            asinh transform.
+        preserve_foreground : bool, optional
+            Whether the target keeps raw counts on the foreground. Default is
+            True.
+        """
+        super(CachedValidateDataset, self).__init__()
+        self.raw = np.load(os.path.join(cache_dir, "raw.npy"), mmap_mode="r")
+        self.teacher = np.load(
+            os.path.join(cache_dir, "teacher.npy"), mmap_mode="r"
+        )
+        self.fg = np.load(os.path.join(cache_dir, "fg.npy"), mmap_mode="r")
+        self.transform = transform or build_transform({"kind": "asinh"})
+        self.preserve_foreground = preserve_foreground
+        self.patch_shape = tuple(self.raw.shape[1:])
+
+    def __len__(self):
+        """Number of cached validation examples."""
+        return len(self.raw)
+
+    def __getitem__(self, idx):
+        """
+        Returns a cached validation example as (x, y, raw, fg_mask).
+
+        Parameters
+        ----------
+        idx : int
+            Index of the example to retrieve.
+
+        Returns
+        -------
+        Tuple[numpy.ndarray]
+            (x, y, raw, fg_mask) matching ValidateDataset.__getitem__: model
+            input, target, raw counts (for count-space metrics), and the
+            foreground mask (float 0/1).
+        """
+        raw = np.asarray(self.raw[idx], dtype=np.float32)
+        teacher = np.asarray(self.teacher[idx], dtype=np.float32)
+        fg_mask = np.asarray(self.fg[idx])
+        x, y, fg = build_training_example(
+            self.transform, self.preserve_foreground, raw, teacher, fg_mask
+        )
+        return x, y, raw, fg
+
+
 # --- Custom Dataloader ---
 _WORKER_DATASET = None
 

From a1bec12f49b6419ceac67b1cabe6b027c4b3b3c2 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 12:12:35 +0000
Subject: [PATCH 15/56] fix: lower fg_weight to break identity-map collapse

With preserve_foreground=True the foreground target is the raw (noisy)
input, so fg_weight=20 heavily rewarded reproducing the input verbatim.
The residual U-Net collapsed to a near-identity map (best checkpoint
changed the input by <2 counts) and did essentially no denoising, so its
output compressed no better than raw (cratio ~3.5 vs a BM4D target of
~15). Drop fg_weight to 2 so background denoising dominates the loss.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/train_bm4dnet.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 72bdb26..632c331 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -169,10 +169,13 @@ def train():
     patch_shape = (64, 64, 64)
     sigma_bm4d = 24
 
-    # Signal-preserving loss + target/sampling (Parts E/F). fg_weight is
-    # aggressive; sweep it against foreground fraction. preserve_foreground
-    # keeps raw counts on the foreground so BM4D cannot erase neurites.
-    fg_weight = 20.0
+    # Signal-preserving loss + target/sampling (Parts E/F). preserve_foreground
+    # keeps raw counts on the foreground so BM4D cannot erase neurites; that
+    # makes the foreground target equal to the (noisy) input, so a large
+    # fg_weight rewards the identity map -- the net stops denoising and its
+    # output compresses no better than raw. Keep fg_weight modest (~1-3) so
+    # background denoising, not foreground copying, dominates the loss.
+    fg_weight = 2.0
     preserve_foreground = True
     min_foreground_voxels = 50
     min_segmentation_volume = 200

From 1c9e0690552f24533543018a68a3d4b535b0ab2e Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 12:12:47 +0000
Subject: [PATCH 16/56] fix: make checkpoint selection compression-aware

checkpoint_weights=None scored checkpoints on fidelity only (cratio
weight 0). Because the foreground target is the raw input, the identity
map minimizes fg_mae, so fidelity-only selection actively picked the
least-denoising checkpoint and was blind to compression. Set a nonzero
cratio weight (using the operating point already documented here) so
selection rewards compressibility.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/train_bm4dnet.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 632c331..75cb946 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -180,12 +180,15 @@ def train():
     min_foreground_voxels = 50
     min_segmentation_volume = 200
 
-    # Checkpoint selection (Part C). None => fidelity-only (cratio weight 0).
-    # Once you pick the compression-vs-fidelity operating point, set e.g.
-    #   checkpoint_weights = dict(
-    #       fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=200.0
-    #   )
-    checkpoint_weights = None
+    # Checkpoint selection (Part C). None => fidelity-only (cratio weight 0),
+    # which cannot see compression and happily selects a non-denoising model
+    # (identity minimizes fg_mae because the fg target is the raw input). Give
+    # cratio a nonzero weight so selection rewards the compression the project
+    # exists for. cratio is the operating-point knob: raise it to trade
+    # fidelity for compression, lower it to protect faint neurites.
+    checkpoint_weights = dict(
+        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=200.0
+    )
 
     # Main
     mp.set_start_method("spawn", force=True)

From 33bcc89d58f502c389d3f6745df170e1c99e04e5 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 12:12:57 +0000
Subject: [PATCH 17/56] fix: anneal LR once over the full run

CosineAnnealingLR(T_max=25) with max_epochs=400 returned the LR to its
peak every 50 epochs (a warm restart), which repeatedly kicked the model
out of convergence -- train/val loss spiked at epochs 52/104/154/197/238
with growing magnitude. Set T_max=max_epochs so the LR decays once.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 src/aind_exaspim_image_compression/machine_learning/train.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index 7f62617..0df3370 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -88,7 +88,10 @@ def __init__(
         self.best_score = np.inf
         self.model = model.to(device) if model else UNet().to(device)
         self.optimizer = optim.AdamW(self.model.parameters(), lr=lr)
-        self.scheduler = CosineAnnealingLR(self.optimizer, T_max=25)
+        # T_max spans the whole run so the cosine anneals once. With a small
+        # T_max the LR returns to its peak every 2*T_max epochs, and each
+        # return destabilized training (growing periodic loss spikes).
+        self.scheduler = CosineAnnealingLR(self.optimizer, T_max=max_epochs)
         self.writer = SummaryWriter(log_dir=log_dir)
 
         if use_amp:

From 82f0cad47d93fef44ecf0d760956a207857fcc95 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 12:13:25 +0000
Subject: [PATCH 18/56] fix: add GradScaler for float16 AMP training

Training used float16 autocast with no loss scaling, so small gradients
could underflow to zero and overflows went uncorrected -- contributing to
the training instability. Wrap the backward/step in a GradScaler
(enabled with AMP, a no-op otherwise) so the fp16 path is numerically
stable.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 .../machine_learning/train.py                        | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index 0df3370..4b3157d 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -99,6 +99,11 @@ def __init__(
         else:
             self.autocast = nullcontext()
 
+        # Scale the loss before backward so small float16 gradients do not
+        # underflow (and are unscaled before the step). Disabled => no-op, so
+        # the same code path is correct with and without AMP.
+        self.scaler = torch.amp.GradScaler("cuda", enabled=use_amp)
+
     # --- Core Routines ---
     def run(self, train_dataset, val_dataset):
         """
@@ -166,10 +171,11 @@ def train_step(self, train_dataloader, epoch):
             # Forward pass
             hat_y, loss = self.forward_pass(x, y, fg_mask)
 
-            # Backward pass
+            # Backward pass (loss-scaled for AMP stability)
             self.optimizer.zero_grad()
-            loss.backward()
-            self.optimizer.step()
+            self.scaler.scale(loss).backward()
+            self.scaler.step(self.optimizer)
+            self.scaler.update()
 
             # Store loss for tensorboard
             losses.append(float(loss.detach().cpu()))

From 4daf497888f52ee5bc14f12c0f94151cc65f868e Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 12:34:13 +0000
Subject: [PATCH 19/56] perf: precompute 500 validation patches for stable
 selection

60 validation patches made the reported median cratio (and therefore the
compression-weighted checkpoint score) noisy: the per-patch metrics are
heavy-tailed, so the bootstrap SE of the median cratio at N=60 is large
relative to the gaps between good checkpoints. Bump the pool to 500,
where that sampling error drops ~3-4x; returns diminish past ~1000 and
disk stays cheap (~1.3 MB/patch).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/precompute_val_patches.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/scripts/precompute_val_patches.py b/scripts/precompute_val_patches.py
index 35c72a9..dca50fd 100644
--- a/scripts/precompute_val_patches.py
+++ b/scripts/precompute_val_patches.py
@@ -164,9 +164,13 @@ def precompute():
     preserve_foreground = True
     sigma_bm4d = 24
 
-    # Pool size and parallelism. The validation set is small and fixed; match
-    # n_validate_examples from train_bm4dnet.py. num_workers=None uses all CPUs.
-    n_patches = 60
+    # Pool size and parallelism. The per-patch metrics are heavy-tailed (a
+    # sizable fraction of patches are near-pure background that compress at
+    # hundreds of x), so a small set makes the reported median cratio -- and
+    # thus checkpoint selection -- noisy. ~500 patches keeps the sampling error
+    # of the selection score small; returns diminish past ~1000. Disk is cheap
+    # (~1.3 MB/patch). num_workers=None uses all CPUs.
+    n_patches = 500
     num_workers = None
 
     precompute()

From 6da051c3da6ebfdec1734f2c123c8dde33c48c15 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 12:35:20 +0000
Subject: [PATCH 20/56] feat: add val_every to decouple val-set size from epoch
 cost

The count-space validation metrics are CPU-bound (~50 ms/patch), so a
500-patch validation set run every epoch would add hours over a full run
and rival training time. Add a val_every knob (default 1, set to 5 in the
train script) so validation and checkpoint selection run periodically
instead of every epoch; the final epoch is always validated. This lets
the larger, low-noise validation set stay cheap.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/train_bm4dnet.py                      |  7 ++++-
 .../machine_learning/train.py                 | 27 ++++++++++++++-----
 2 files changed, 26 insertions(+), 8 deletions(-)

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 75cb946..436c120 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -102,6 +102,7 @@ def train():
         fg_weight=fg_weight,
         checkpoint_weights=checkpoint_weights,
         num_workers=0 if cache_dir else None,
+        val_every=val_every,
     )
     if resume_path is not None:
         trainer.load_pretrained_weights(resume_path)
@@ -168,6 +169,10 @@ def train():
     n_validate_examples = 60
     patch_shape = (64, 64, 64)
     sigma_bm4d = 24
+    # Validate (and consider a checkpoint) every this many epochs. A larger
+    # cached validation set is cheap to store but CPU-bound to score, so keep
+    # this above 1 to avoid the metrics dominating epoch time.
+    val_every = 5
 
     # Signal-preserving loss + target/sampling (Parts E/F). preserve_foreground
     # keeps raw counts on the foreground so BM4D cannot erase neurites; that
@@ -187,7 +192,7 @@ def train():
     # exists for. cratio is the operating-point knob: raise it to trade
     # fidelity for compression, lower it to protect faint neurites.
     checkpoint_weights = dict(
-        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=200.0
+        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=20.0
     )
 
     # Main
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index 4b3157d..b755436 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -48,6 +48,7 @@ def __init__(
         fg_weight=20.0,
         num_workers=None,
         prefetch=2,
+        val_every=1,
     ):
         """
         Instantiates a Trainer object.
@@ -68,6 +69,11 @@ def __init__(
             Model to be trained on the given datasets. Default is None.
         use_amp : bool, optional
             Indication of whether to use mixed precision. Default is True.
+        val_every : int, optional
+            Run validation (and checkpoint selection) every this many epochs;
+            the final epoch is always validated. The count-space metrics are
+            CPU-bound, so a large validation set is only cheap if it is not run
+            every epoch. Default is 1 (validate every epoch).
         """
         # Initializations
         exp_name = "session-" + datetime.today().strftime("%Y%m%d_%H%M")
@@ -81,6 +87,7 @@ def __init__(
         self.log_dir = log_dir
         self.num_workers = num_workers
         self.prefetch = prefetch
+        self.val_every = max(1, int(val_every))
 
         self.codec = blosc.Blosc(cname="zstd", clevel=5, shuffle=blosc.SHUFFLE)
         self.criterion = SignalPreservingLoss(fg_weight=fg_weight)
@@ -135,15 +142,21 @@ def run(self, train_dataset, val_dataset):
         # Main
         self.best_score = np.inf
         for epoch in range(self.max_epochs):
-            # Train-Validate
+            # Train
             train_loss = self.train_step(train_dataloader, epoch)
-            val_loss, val_cratio, is_best = self.validate_step(
-                val_dataloader, epoch
-            )
 
-            # Report results
-            suffix = " - New Best!" if is_best else ""
-            s = f"Epoch {epoch}:  train_loss={train_loss},  val_loss={val_loss}, val_cratio={val_cratio}" + suffix
+            # Validate every val_every epochs (and always on the final epoch);
+            # the count-space metrics are CPU-bound, so a large validation set
+            # is only cheap when it is not run every epoch.
+            is_last = epoch == self.max_epochs - 1
+            if epoch % self.val_every == 0 or is_last:
+                val_loss, val_cratio, is_best = self.validate_step(
+                    val_dataloader, epoch
+                )
+                suffix = " - New Best!" if is_best else ""
+                s = f"Epoch {epoch}:  train_loss={train_loss},  val_loss={val_loss}, val_cratio={val_cratio}" + suffix
+            else:
+                s = f"Epoch {epoch}:  train_loss={train_loss}"
             print(s)
 
             # Step scheduler

From 4cff35e3b27bc784eabfea665dd3736e0ec16d60 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 13:11:00 +0000
Subject: [PATCH 21/56] fix: make foreground-voxel search order-deterministic

sample_segmentation_voxel and sample_bright_voxel picked the best
candidate in thread-completion order (as_completed), so ties -- and thus
the chosen voxel -- varied run to run even with a fixed RNG. Consume the
concurrent read results in submission order instead; reads still overlap,
but selection is now deterministic, which is a prerequisite for a
reproducible (seeded) patch cache.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 .../machine_learning/data_handling.py            | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index ed8682c..76bdb1c 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -427,9 +427,11 @@ def sample_segmentation_voxel(self, brain_id):
                     )
                     pending[thread] = voxel
 
-                # Check if labels patch has large enough object
-                for thread in as_completed(pending.keys()):
-                    voxel = pending.pop(thread)
+                # Check if labels patch has large enough object. Reads run
+                # concurrently, but results are consumed in submission order
+                # (not completion order) so ties break deterministically and
+                # a seeded run is reproducible.
+                for thread, voxel in pending.items():
                     labels_patch = thread.result()
                     vals, cnts = fastremap.unique(
                         labels_patch, return_counts=True
@@ -481,9 +483,11 @@ def sample_bright_voxel(self, brain_id):
                     )
                     pending[thread] = voxel
 
-                # Check if image patch has enough foreground
-                for thread in as_completed(pending.keys()):
-                    voxel = pending.pop(thread)
+                # Check if image patch has enough foreground. Reads run
+                # concurrently, but results are consumed in submission order
+                # (not completion order) so ties break deterministically and
+                # a seeded run is reproducible.
+                for thread, voxel in pending.items():
                     img_patch = thread.result()
                     brightness = int(make_foreground_mask(img_patch).sum())
                     if brightness > best_brightness:

From 038363ccc1287b00fc2d4f74e4c4174b456d1b19 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 13:14:03 +0000
Subject: [PATCH 22/56] feat: seed patch-cache precompute for reproducibility

Brain/voxel sampling used the unseeded global RNGs, so each precompute run
produced a different pool. Add a base `seed` (default 42, None to disable)
threaded to each worker and applied per task via a SeedSequence keyed on
(seed, stream, task_index). Because executor.map places result i at index
i regardless of which worker runs it, the cache is now bit-reproducible
and independent of num_workers.

A distinct per-script stream id (0 train, 1 val) ensures the training and
validation caches never sample the same (brain, voxel) at a given index
when built with the same base seed.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/precompute_patches.py     | 41 +++++++++++++++++++++++++++---
 scripts/precompute_val_patches.py | 42 ++++++++++++++++++++++++++++---
 2 files changed, 75 insertions(+), 8 deletions(-)

diff --git a/scripts/precompute_patches.py b/scripts/precompute_patches.py
index f517a9f..976b360 100644
--- a/scripts/precompute_patches.py
+++ b/scripts/precompute_patches.py
@@ -18,6 +18,8 @@
 
 """
 
+import random
+
 import numpy as np
 from concurrent.futures import ProcessPoolExecutor
 from numpy.lib.format import open_memmap
@@ -27,16 +29,42 @@
 from aind_exaspim_image_compression.utils import util
 
 _WORKER_DATASET = None
+_WORKER_SEED = None
+
+# Distinct RNG stream id for this script so the training and validation caches,
+# even at the same base seed, never sample the same (brain, voxel) for a given
+# task index. Keep it different from the validation precompute's stream.
+_SEED_STREAM = 0
+
 
+def _seed_task(base_seed, index):
+    """
+    Seeds the global RNGs deterministically from a base seed and task index.
 
-def _init_worker(init_kwargs):
+    Uses a SeedSequence so per-task streams are independent and well-mixed,
+    making the cache reproducible and independent of worker count / task
+    scheduling (executor.map assigns result i to task i regardless of which
+    worker runs it). A None base seed is a no-op (nondeterministic sampling).
+    """
+    if base_seed is None:
+        return
+    states = np.random.SeedSequence(
+        [base_seed, _SEED_STREAM, index]
+    ).generate_state(2)
+    random.seed(int(states[0]))
+    np.random.seed(int(states[1]))
+
+
+def _init_worker(init_kwargs, base_seed):
     """Builds one TrainDataset per worker process and caches it globally."""
-    global _WORKER_DATASET
+    global _WORKER_DATASET, _WORKER_SEED
+    _WORKER_SEED = base_seed
     _WORKER_DATASET, _ = data_handling.init_datasets(**init_kwargs)
 
 
-def _sample_counts(_):
+def _sample_counts(index):
     """Samples one count-space example from the per-worker dataset."""
+    _seed_task(_WORKER_SEED, index)
     return _WORKER_DATASET._sample_counts()
 
 
@@ -83,7 +111,7 @@ def precompute():
     with ProcessPoolExecutor(
         max_workers=num_workers,
         initializer=_init_worker,
-        initargs=(init_kwargs,),
+        initargs=(init_kwargs, seed),
     ) as executor:
         results = executor.map(
             _sample_counts, range(n_patches), chunksize=1
@@ -135,4 +163,9 @@ def precompute():
     n_patches = 30000
     num_workers = None
 
+    # Base RNG seed for reproducibility: with a fixed seed the sampled pool is
+    # identical across runs and independent of num_workers. Set to None for
+    # nondeterministic sampling.
+    seed = 42
+
     precompute()
diff --git a/scripts/precompute_val_patches.py b/scripts/precompute_val_patches.py
index dca50fd..4ac0e5b 100644
--- a/scripts/precompute_val_patches.py
+++ b/scripts/precompute_val_patches.py
@@ -28,6 +28,8 @@
 
 """
 
+import random
+
 import numpy as np
 from concurrent.futures import ProcessPoolExecutor
 from numpy.lib.format import open_memmap
@@ -41,21 +43,47 @@
 
 _WORKER_TRAIN = None
 _WORKER_VAL = None
+_WORKER_SEED = None
+
+# Distinct RNG stream id for this script so the validation and training caches,
+# even at the same base seed, never sample the same (brain, voxel) for a given
+# task index. Keep it different from the training precompute's stream.
+_SEED_STREAM = 1
+
+
+def _seed_task(base_seed, index):
+    """
+    Seeds the global RNGs deterministically from a base seed and task index.
+
+    Uses a SeedSequence so per-task streams are independent and well-mixed,
+    making the cache reproducible and independent of worker count / task
+    scheduling (executor.map assigns result i to task i regardless of which
+    worker runs it). A None base seed is a no-op (nondeterministic sampling).
+    """
+    if base_seed is None:
+        return
+    states = np.random.SeedSequence(
+        [base_seed, _SEED_STREAM, index]
+    ).generate_state(2)
+    random.seed(int(states[0]))
+    np.random.seed(int(states[1]))
 
 
-def _init_worker(init_kwargs):
+def _init_worker(init_kwargs, base_seed):
     """Builds one (train, val) dataset pair per worker and caches it."""
-    global _WORKER_TRAIN, _WORKER_VAL
+    global _WORKER_TRAIN, _WORKER_VAL, _WORKER_SEED
+    _WORKER_SEED = base_seed
     _WORKER_TRAIN, _WORKER_VAL = data_handling.init_datasets(**init_kwargs)
 
 
-def _sample_val_counts(_):
+def _sample_val_counts(index):
     """Samples one validation count-space example from the per-worker pair.
 
     The voxel is drawn by the TrainDataset's foreground-biased sampler (as in
     init_datasets); the count-space example, including the intensity-only mask,
     is produced by the ValidateDataset so it matches the metric split.
     """
+    _seed_task(_WORKER_SEED, index)
     brain_id = _WORKER_TRAIN.sample_brain()
     voxel = _WORKER_TRAIN.sample_voxel(brain_id)
     return _WORKER_VAL.sample_counts(brain_id, voxel)
@@ -113,7 +141,7 @@ def precompute():
     with ProcessPoolExecutor(
         max_workers=num_workers,
         initializer=_init_worker,
-        initargs=(init_kwargs,),
+        initargs=(init_kwargs, seed),
     ) as executor:
         results = executor.map(
             _sample_val_counts, range(n_patches), chunksize=1
@@ -173,4 +201,10 @@ def precompute():
     n_patches = 500
     num_workers = None
 
+    # Base RNG seed for reproducibility: with a fixed seed the sampled set is
+    # identical across runs and independent of num_workers. Set to None for
+    # nondeterministic sampling. A distinct RNG stream (see _SEED_STREAM) keeps
+    # these validation patches off the training patches at the same seed.
+    seed = 42
+
     precompute()

From 29b9a2b0f01ff11cfa8fd651b1eb054ab4fc16bc Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 13:22:12 +0000
Subject: [PATCH 23/56] refactor: unify patch-cache precompute into one --split
 script

precompute_patches.py and precompute_val_patches.py were ~90% identical:
the pool scaffolding, memmap write loop, seeding helper, and -- critically
-- the entire shared config block (brains, transform_cfg, offsets,
sampling params) were copy-pasted. That duplicated config was a latent
correctness trap: the train and val caches must use the identical mapping
(the training run applies the val cache's transform.json to the train
cache), enforced only by keeping two blocks in sync.

Replace both with scripts/precompute.py --split {train,val}: one config
block, one scaffolding, branching only on the sampler (TrainDataset mask
= intensity + segmentation vs ValidateDataset intensity-only), the RNG
stream id, and the per-split cache_dir / n_patches. Sampling draws are
bit-identical to the separate scripts at the same seed.

Also apply two behaviors uniformly that previously differed: stamp
transform.json for both splits, and refuse offset calibration for both
(each worker would otherwise calibrate on its own sample, mixing
inconsistent offsets into the cache). Update references in
train_bm4dnet.py and data_handling.py docstrings.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/precompute.py                         | 251 ++++++++++++++++++
 scripts/precompute_patches.py                 | 171 ------------
 scripts/precompute_val_patches.py             | 210 ---------------
 scripts/train_bm4dnet.py                      |   9 +-
 .../machine_learning/data_handling.py         |   8 +-
 5 files changed, 260 insertions(+), 389 deletions(-)
 create mode 100644 scripts/precompute.py
 delete mode 100644 scripts/precompute_patches.py
 delete mode 100644 scripts/precompute_val_patches.py

diff --git a/scripts/precompute.py b/scripts/precompute.py
new file mode 100644
index 0000000..c8e0225
--- /dev/null
+++ b/scripts/precompute.py
@@ -0,0 +1,251 @@
+"""
+Precompute a pool of patches to disk so training is GPU-bound.
+
+The training bottleneck is per-patch BM4D + cloud reads on the CPU, which
+leaves the GPU idle -- both for the training pool and for the validation set
+that init_datasets otherwise builds live at startup. This script does that work
+once, offline, writing the expensive count-space intermediates -- (raw with the
+per-brain offset subtracted, clipped BM4D teacher, foreground mask) -- to
+memory-mapped arrays. Training then reads a cached patch and applies only the
+cheap transform + target construction (see CachedPatchDataset /
+CachedValidateDataset), so no cloud access or BM4D happens at startup.
+
+One script builds both caches; ``--split`` selects which:
+
+    python scripts/precompute.py --split train   # GPU-bound training pool
+    python scripts/precompute.py --split val      # fixed validation set
+
+Both splits draw voxels with the TrainDataset's foreground-biased sampler. The
+only differences are the target's foreground mask and a few outputs:
+
+    * train -- mask is intensity ∪ segmentation labels (protects labeled
+      neurites from the BM4D teacher), produced by TrainDataset.
+    * val   -- mask is intensity-only, produced by ValidateDataset so it
+      matches the validation metric split.
+
+A distinct RNG stream per split means the two caches never sample the same
+(brain, voxel) for a given task index when built with the same base seed.
+
+Outputs, under cache_dir (identical layout for both splits, so the val cache
+loads with CachedValidateDataset):
+    raw.npy        float16  (N, *patch_shape)   offset-subtracted counts
+    teacher.npy    float16  (N, *patch_shape)   clipped BM4D denoising
+    fg.npy         uint8    (N, *patch_shape)   foreground mask (0/1)
+    transform.json                              resolved transform cfg
+
+The transform cfg is stamped alongside the patches so the training run rebuilds
+the identical transform without touching the cloud. Each worker builds its
+datasets once (via init_datasets) so the large skeleton arrays and cloud
+handles are not re-pickled per patch.
+
+"""
+
+import argparse
+import random
+
+import numpy as np
+from concurrent.futures import ProcessPoolExecutor
+from numpy.lib.format import open_memmap
+from tqdm import tqdm
+
+from aind_exaspim_image_compression.machine_learning import data_handling
+from aind_exaspim_image_compression.machine_learning.transforms import (
+    build_transform,
+)
+from aind_exaspim_image_compression.utils import util
+
+# Per-split RNG stream ids so the train and val caches, even at the same base
+# seed, never sample the same (brain, voxel) for a given task index.
+_SEED_STREAMS = {"train": 0, "val": 1}
+
+_WORKER_TRAIN = None
+_WORKER_VAL = None
+_WORKER_SEED = None
+_WORKER_STREAM = 0
+_WORKER_SPLIT = "train"
+
+
+def _seed_task(index):
+    """
+    Seeds the global RNGs deterministically from the base seed and task index.
+
+    Uses a SeedSequence so per-task streams are independent and well-mixed,
+    making the cache reproducible and independent of worker count / task
+    scheduling (executor.map assigns result i to task i regardless of which
+    worker runs it). A None base seed is a no-op (nondeterministic sampling).
+    """
+    if _WORKER_SEED is None:
+        return
+    states = np.random.SeedSequence(
+        [_WORKER_SEED, _WORKER_STREAM, index]
+    ).generate_state(2)
+    random.seed(int(states[0]))
+    np.random.seed(int(states[1]))
+
+
+def _init_worker(init_kwargs, base_seed, split):
+    """Builds the (train, val) dataset pair per worker and caches it."""
+    global _WORKER_TRAIN, _WORKER_VAL
+    global _WORKER_SEED, _WORKER_STREAM, _WORKER_SPLIT
+    _WORKER_SEED = base_seed
+    _WORKER_SPLIT = split
+    _WORKER_STREAM = _SEED_STREAMS[split]
+    _WORKER_TRAIN, _WORKER_VAL = data_handling.init_datasets(**init_kwargs)
+
+
+def _sample_counts(index):
+    """
+    Samples one count-space example for the configured split.
+
+    The train split builds the target with the TrainDataset mask (intensity ∪
+    segmentation labels); the val split draws the voxel with the same
+    foreground-biased sampler but builds the example with the ValidateDataset
+    (intensity-only mask) so it matches the validation metric split.
+    """
+    _seed_task(index)
+    if _WORKER_SPLIT == "train":
+        return _WORKER_TRAIN._sample_counts()
+    brain_id = _WORKER_TRAIN.sample_brain()
+    voxel = _WORKER_TRAIN.sample_voxel(brain_id)
+    return _WORKER_VAL.sample_counts(brain_id, voxel)
+
+
+def _to_float16(arr):
+    """Clips to the float16 range before casting (avoids inf at saturation)."""
+    return np.clip(arr, -65504, 65504).astype(np.float16)
+
+
+def precompute():
+    # Offset calibration would need a cloud sample the cache is meant to avoid,
+    # and each worker would calibrate on its own random sample -- so the cache
+    # would mix inconsistent offsets and none would match the stamped cfg. The
+    # training config subtracts per-brain offsets instead; refuse the ambiguous
+    # case loudly for both splits.
+    if transform_cfg.get("calibrate", {}).get("offset", False):
+        raise ValueError(
+            "offset calibration is not supported by the cached path; bake the "
+            "offset into transform_cfg or use per-brain offsets"
+        )
+
+    # Build the config each worker uses to construct its datasets. n_validate
+    # is 0 (we draw validation voxels ourselves) and the transform offset stays
+    # 0 because per-brain offsets are subtracted per patch.
+    brain_ids = util.read_txt(brain_ids_path)
+    offsets = util.read_json(offsets_path) if offsets_path else None
+    init_kwargs = dict(
+        brain_ids=brain_ids,
+        img_paths_json=img_prefixes_path,
+        patch_shape=patch_shape,
+        foreground_sampling_rate=foreground_sampling_rate,
+        min_foreground_voxels=min_foreground_voxels,
+        min_segmentation_volume=min_segmentation_volume,
+        n_validate_examples=0,
+        offsets=offsets,
+        preserve_foreground=preserve_foreground,
+        segmentation_prefixes_path=segmentation_prefixes_path,
+        sigma_bm4d=sigma_bm4d,
+        swc_pointers=swc_pointers,
+        transform_cfg=transform_cfg,
+    )
+
+    # Pre-allocate memory-mapped outputs and stream results into them.
+    util.mkdir(cache_dir)
+    shape = (n_patches,) + tuple(patch_shape)
+    raw_mm = open_memmap(
+        f"{cache_dir}/raw.npy", mode="w+", dtype=np.float16, shape=shape
+    )
+    teacher_mm = open_memmap(
+        f"{cache_dir}/teacher.npy", mode="w+", dtype=np.float16, shape=shape
+    )
+    fg_mm = open_memmap(
+        f"{cache_dir}/fg.npy", mode="w+", dtype=np.uint8, shape=shape
+    )
+
+    with ProcessPoolExecutor(
+        max_workers=num_workers,
+        initializer=_init_worker,
+        initargs=(init_kwargs, seed, split),
+    ) as executor:
+        results = executor.map(
+            _sample_counts, range(n_patches), chunksize=1
+        )
+        for i, (raw, teacher, fg) in enumerate(
+            tqdm(results, total=n_patches, desc=f"Precompute ({split})")
+        ):
+            raw_mm[i] = _to_float16(raw)
+            teacher_mm[i] = _to_float16(teacher)
+            fg_mm[i] = np.asarray(fg, dtype=np.uint8)
+
+    raw_mm.flush()
+    teacher_mm.flush()
+    fg_mm.flush()
+
+    # Stamp the resolved transform cfg so training rebuilds it exactly without
+    # touching the cloud.
+    util.write_json(
+        f"{cache_dir}/transform.json", build_transform(transform_cfg).cfg
+    )
+    print(f"Wrote {n_patches} {split} patches to {cache_dir}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--split",
+        choices=("train", "val"),
+        default="train",
+        help="Which cache to build (default: train).",
+    )
+    split = parser.parse_args().split
+
+    # Paths (shared by both splits)
+    brain_ids_path = "/data/train_brain_ids.txt"
+    img_prefixes_path = "/data/exaspim_image_prefixes.json"
+    segmentation_prefixes_path = "/data/exaspim_segmentation_prefixes.json"
+    offsets_path = "/data/exaspim_background_offsets.json"
+
+    # SWC pointer (shared)
+    swc_pointers = {
+        "bucket_name": "allen-nd-goog",
+        "path": "ground_truth_tracings",
+    }
+
+    # Transform cfg (shared; offset 0, per-brain offsets subtracted per patch).
+    # Only max_count is used here, to clip the BM4D teacher. Keeping this
+    # shared is the point of one script: the train and val caches must use the
+    # identical mapping or the model trains and validates under different
+    # transforms.
+    transform_cfg = {
+        "kind": "asinh",
+        "params": {"offset": 0.0, "scale": 32.0},
+    }
+
+    # Sampling / patch parameters (shared)
+    foreground_sampling_rate = 0.5
+    min_foreground_voxels = 50
+    min_segmentation_volume = 200
+    patch_shape = (64, 64, 64)
+    preserve_foreground = True
+    sigma_bm4d = 24
+
+    # Base RNG seed for reproducibility: with a fixed seed the sampled pool is
+    # identical across runs and independent of num_workers. Set to None for
+    # nondeterministic sampling. num_workers=None uses all CPUs.
+    seed = 42
+    num_workers = None
+
+    # Per-split output location and pool size.
+    if split == "train":
+        # ~1.3 MB/patch (fp16 raw+teacher + uint8 fg), so 30000 ~= 40 GB.
+        cache_dir = "/results/patch_cache"
+        n_patches = 30000
+    else:
+        # The per-patch metrics are heavy-tailed (a sizable fraction of patches
+        # are near-pure background that compress at hundreds of x), so a small
+        # set makes the reported median cratio -- and thus checkpoint selection
+        # -- noisy. ~500 keeps that sampling error small; returns diminish past
+        # ~1000. Disk is cheap.
+        cache_dir = "/results/val_patch_cache"
+        n_patches = 500
+
+    precompute()
diff --git a/scripts/precompute_patches.py b/scripts/precompute_patches.py
deleted file mode 100644
index 976b360..0000000
--- a/scripts/precompute_patches.py
+++ /dev/null
@@ -1,171 +0,0 @@
-"""
-Precompute a pool of training patches to disk so training is GPU-bound.
-
-The training bottleneck is per-patch BM4D + cloud reads on the CPU, which
-leaves the GPU idle. This script samples a fixed pool of patches once and
-writes the expensive count-space intermediates -- (raw with the per-brain
-offset subtracted, clipped BM4D teacher, foreground mask) -- to memory-mapped
-arrays. Training then reads a random cached patch and applies only the cheap
-transform + target construction (see CachedPatchDataset).
-
-Outputs, under cache_dir:
-    raw.npy      float16  (N, *patch_shape)   offset-subtracted counts
-    teacher.npy  float16  (N, *patch_shape)   clipped BM4D denoising
-    fg.npy       uint8    (N, *patch_shape)   foreground mask (0/1)
-
-Each worker builds its own dataset once (via init_datasets) so the large
-skeleton arrays and cloud handles are not re-pickled per patch.
-
-"""
-
-import random
-
-import numpy as np
-from concurrent.futures import ProcessPoolExecutor
-from numpy.lib.format import open_memmap
-from tqdm import tqdm
-
-from aind_exaspim_image_compression.machine_learning import data_handling
-from aind_exaspim_image_compression.utils import util
-
-_WORKER_DATASET = None
-_WORKER_SEED = None
-
-# Distinct RNG stream id for this script so the training and validation caches,
-# even at the same base seed, never sample the same (brain, voxel) for a given
-# task index. Keep it different from the validation precompute's stream.
-_SEED_STREAM = 0
-
-
-def _seed_task(base_seed, index):
-    """
-    Seeds the global RNGs deterministically from a base seed and task index.
-
-    Uses a SeedSequence so per-task streams are independent and well-mixed,
-    making the cache reproducible and independent of worker count / task
-    scheduling (executor.map assigns result i to task i regardless of which
-    worker runs it). A None base seed is a no-op (nondeterministic sampling).
-    """
-    if base_seed is None:
-        return
-    states = np.random.SeedSequence(
-        [base_seed, _SEED_STREAM, index]
-    ).generate_state(2)
-    random.seed(int(states[0]))
-    np.random.seed(int(states[1]))
-
-
-def _init_worker(init_kwargs, base_seed):
-    """Builds one TrainDataset per worker process and caches it globally."""
-    global _WORKER_DATASET, _WORKER_SEED
-    _WORKER_SEED = base_seed
-    _WORKER_DATASET, _ = data_handling.init_datasets(**init_kwargs)
-
-
-def _sample_counts(index):
-    """Samples one count-space example from the per-worker dataset."""
-    _seed_task(_WORKER_SEED, index)
-    return _WORKER_DATASET._sample_counts()
-
-
-def _to_float16(arr):
-    """Clips to the float16 range before casting (avoids inf at saturation)."""
-    return np.clip(arr, -65504, 65504).astype(np.float16)
-
-
-def precompute():
-    # Build the config each worker uses to construct its dataset. n_validate
-    # is 0 (we only need training sampling) and the transform offset stays 0
-    # because per-brain offsets are subtracted in _sample_counts.
-    brain_ids = util.read_txt(brain_ids_path)
-    offsets = util.read_json(offsets_path) if offsets_path else None
-    init_kwargs = dict(
-        brain_ids=brain_ids,
-        img_paths_json=img_prefixes_path,
-        patch_shape=patch_shape,
-        foreground_sampling_rate=foreground_sampling_rate,
-        min_foreground_voxels=min_foreground_voxels,
-        min_segmentation_volume=min_segmentation_volume,
-        n_validate_examples=0,
-        offsets=offsets,
-        preserve_foreground=preserve_foreground,
-        segmentation_prefixes_path=segmentation_prefixes_path,
-        sigma_bm4d=sigma_bm4d,
-        swc_pointers=swc_pointers,
-        transform_cfg=transform_cfg,
-    )
-
-    # Pre-allocate memory-mapped outputs and stream results into them.
-    util.mkdir(cache_dir)
-    shape = (n_patches,) + tuple(patch_shape)
-    raw_mm = open_memmap(
-        f"{cache_dir}/raw.npy", mode="w+", dtype=np.float16, shape=shape
-    )
-    teacher_mm = open_memmap(
-        f"{cache_dir}/teacher.npy", mode="w+", dtype=np.float16, shape=shape
-    )
-    fg_mm = open_memmap(
-        f"{cache_dir}/fg.npy", mode="w+", dtype=np.uint8, shape=shape
-    )
-
-    with ProcessPoolExecutor(
-        max_workers=num_workers,
-        initializer=_init_worker,
-        initargs=(init_kwargs, seed),
-    ) as executor:
-        results = executor.map(
-            _sample_counts, range(n_patches), chunksize=1
-        )
-        for i, (raw, teacher, fg) in enumerate(
-            tqdm(results, total=n_patches, desc="Precompute")
-        ):
-            raw_mm[i] = _to_float16(raw)
-            teacher_mm[i] = _to_float16(teacher)
-            fg_mm[i] = np.asarray(fg, dtype=np.uint8)
-
-    raw_mm.flush()
-    teacher_mm.flush()
-    fg_mm.flush()
-    print(f"Wrote {n_patches} patches to {cache_dir}")
-
-
-if __name__ == "__main__":
-    # Paths (match train_bm4dnet.py)
-    brain_ids_path = "/data/train_brain_ids.txt"
-    img_prefixes_path = "/data/exaspim_image_prefixes.json"
-    segmentation_prefixes_path = "/data/exaspim_segmentation_prefixes.json"
-    offsets_path = "/data/exaspim_background_offsets.json"
-    cache_dir = "/results/patch_cache"
-
-    # SWC pointer
-    swc_pointers = {
-        "bucket_name": "allen-nd-goog",
-        "path": "ground_truth_tracings",
-    }
-
-    # Transform cfg (offset 0; per-brain offsets are subtracted per patch).
-    # Only max_count is used here, to clip the BM4D teacher.
-    transform_cfg = {
-        "kind": "asinh",
-        "params": {"offset": 0.0, "scale": 32.0},
-    }
-
-    # Sampling / patch parameters (match training)
-    foreground_sampling_rate = 0.5
-    min_foreground_voxels = 50
-    min_segmentation_volume = 200
-    patch_shape = (64, 64, 64)
-    preserve_foreground = True
-    sigma_bm4d = 24
-
-    # Pool size and parallelism. ~1.3 MB/patch (fp16 raw+teacher + uint8 fg),
-    # so 8000 patches ~= 10 GB. num_workers=None uses all CPUs.
-    n_patches = 30000
-    num_workers = None
-
-    # Base RNG seed for reproducibility: with a fixed seed the sampled pool is
-    # identical across runs and independent of num_workers. Set to None for
-    # nondeterministic sampling.
-    seed = 42
-
-    precompute()
diff --git a/scripts/precompute_val_patches.py b/scripts/precompute_val_patches.py
deleted file mode 100644
index 4ac0e5b..0000000
--- a/scripts/precompute_val_patches.py
+++ /dev/null
@@ -1,210 +0,0 @@
-"""
-Precompute a fixed pool of validation patches to disk.
-
-The training run's other startup cost (besides the training pool) is the live
-validation set: init_datasets samples a handful of voxels and, for each, does
-a cloud read + a serial BM4D denoising on the CPU while the GPU sits idle.
-This script does that work once, offline, and writes the expensive count-space
-intermediates -- (raw with the per-brain offset subtracted, clipped BM4D
-teacher, foreground mask) -- to memory-mapped arrays. A cache-backed training
-run then reads them via CachedValidateDataset and applies only the cheap
-transform + target construction, so no cloud access or BM4D happens at startup.
-
-Voxels are drawn exactly as init_datasets draws them for validation: the
-foreground-biased sampler on the TrainDataset (which needs the skeletons /
-segmentations), while the count-space example -- crucially the intensity-only
-foreground mask used by the validation metric split -- is computed by the
-ValidateDataset. Both are built once per worker via init_datasets.
-
-Outputs, under cache_dir (same layout as the training cache, so it loads with
-CachedValidateDataset):
-    raw.npy        float16  (N, *patch_shape)   offset-subtracted counts
-    teacher.npy    float16  (N, *patch_shape)   clipped BM4D denoising
-    fg.npy         uint8    (N, *patch_shape)   foreground mask (0/1)
-    transform.json                              resolved transform cfg
-
-The transform cfg is stamped alongside the patches so the training run can
-construct the identical transform without touching the cloud.
-
-"""
-
-import random
-
-import numpy as np
-from concurrent.futures import ProcessPoolExecutor
-from numpy.lib.format import open_memmap
-from tqdm import tqdm
-
-from aind_exaspim_image_compression.machine_learning import data_handling
-from aind_exaspim_image_compression.machine_learning.transforms import (
-    build_transform,
-)
-from aind_exaspim_image_compression.utils import util
-
-_WORKER_TRAIN = None
-_WORKER_VAL = None
-_WORKER_SEED = None
-
-# Distinct RNG stream id for this script so the validation and training caches,
-# even at the same base seed, never sample the same (brain, voxel) for a given
-# task index. Keep it different from the training precompute's stream.
-_SEED_STREAM = 1
-
-
-def _seed_task(base_seed, index):
-    """
-    Seeds the global RNGs deterministically from a base seed and task index.
-
-    Uses a SeedSequence so per-task streams are independent and well-mixed,
-    making the cache reproducible and independent of worker count / task
-    scheduling (executor.map assigns result i to task i regardless of which
-    worker runs it). A None base seed is a no-op (nondeterministic sampling).
-    """
-    if base_seed is None:
-        return
-    states = np.random.SeedSequence(
-        [base_seed, _SEED_STREAM, index]
-    ).generate_state(2)
-    random.seed(int(states[0]))
-    np.random.seed(int(states[1]))
-
-
-def _init_worker(init_kwargs, base_seed):
-    """Builds one (train, val) dataset pair per worker and caches it."""
-    global _WORKER_TRAIN, _WORKER_VAL, _WORKER_SEED
-    _WORKER_SEED = base_seed
-    _WORKER_TRAIN, _WORKER_VAL = data_handling.init_datasets(**init_kwargs)
-
-
-def _sample_val_counts(index):
-    """Samples one validation count-space example from the per-worker pair.
-
-    The voxel is drawn by the TrainDataset's foreground-biased sampler (as in
-    init_datasets); the count-space example, including the intensity-only mask,
-    is produced by the ValidateDataset so it matches the metric split.
-    """
-    _seed_task(_WORKER_SEED, index)
-    brain_id = _WORKER_TRAIN.sample_brain()
-    voxel = _WORKER_TRAIN.sample_voxel(brain_id)
-    return _WORKER_VAL.sample_counts(brain_id, voxel)
-
-
-def _to_float16(arr):
-    """Clips to the float16 range before casting (avoids inf at saturation)."""
-    return np.clip(arr, -65504, 65504).astype(np.float16)
-
-
-def precompute():
-    # Offset calibration would need a cloud sample that this cache is meant to
-    # avoid; the training config subtracts per-brain offsets instead, so the
-    # transform offset stays fixed. Refuse the ambiguous case loudly.
-    if transform_cfg.get("calibrate", {}).get("offset", False):
-        raise ValueError(
-            "offset calibration is not supported by the cached path; bake the "
-            "offset into transform_cfg or use per-brain offsets"
-        )
-
-    # Build the config each worker uses to construct its datasets. n_validate
-    # is 0 (we draw validation voxels ourselves) and the transform offset stays
-    # 0 because per-brain offsets are subtracted in sample_counts.
-    brain_ids = util.read_txt(brain_ids_path)
-    offsets = util.read_json(offsets_path) if offsets_path else None
-    init_kwargs = dict(
-        brain_ids=brain_ids,
-        img_paths_json=img_prefixes_path,
-        patch_shape=patch_shape,
-        foreground_sampling_rate=foreground_sampling_rate,
-        min_foreground_voxels=min_foreground_voxels,
-        min_segmentation_volume=min_segmentation_volume,
-        n_validate_examples=0,
-        offsets=offsets,
-        preserve_foreground=preserve_foreground,
-        segmentation_prefixes_path=segmentation_prefixes_path,
-        sigma_bm4d=sigma_bm4d,
-        swc_pointers=swc_pointers,
-        transform_cfg=transform_cfg,
-    )
-
-    # Pre-allocate memory-mapped outputs and stream results into them.
-    util.mkdir(cache_dir)
-    shape = (n_patches,) + tuple(patch_shape)
-    raw_mm = open_memmap(
-        f"{cache_dir}/raw.npy", mode="w+", dtype=np.float16, shape=shape
-    )
-    teacher_mm = open_memmap(
-        f"{cache_dir}/teacher.npy", mode="w+", dtype=np.float16, shape=shape
-    )
-    fg_mm = open_memmap(
-        f"{cache_dir}/fg.npy", mode="w+", dtype=np.uint8, shape=shape
-    )
-
-    with ProcessPoolExecutor(
-        max_workers=num_workers,
-        initializer=_init_worker,
-        initargs=(init_kwargs, seed),
-    ) as executor:
-        results = executor.map(
-            _sample_val_counts, range(n_patches), chunksize=1
-        )
-        for i, (raw, teacher, fg) in enumerate(
-            tqdm(results, total=n_patches, desc="Precompute (val)")
-        ):
-            raw_mm[i] = _to_float16(raw)
-            teacher_mm[i] = _to_float16(teacher)
-            fg_mm[i] = np.asarray(fg, dtype=np.uint8)
-
-    raw_mm.flush()
-    teacher_mm.flush()
-    fg_mm.flush()
-
-    # Stamp the resolved transform cfg so training rebuilds it exactly without
-    # touching the cloud.
-    util.write_json(f"{cache_dir}/transform.json", build_transform(transform_cfg).cfg)
-    print(f"Wrote {n_patches} validation patches to {cache_dir}")
-
-
-if __name__ == "__main__":
-    # Paths (match train_bm4dnet.py)
-    brain_ids_path = "/data/train_brain_ids.txt"
-    img_prefixes_path = "/data/exaspim_image_prefixes.json"
-    segmentation_prefixes_path = "/data/exaspim_segmentation_prefixes.json"
-    offsets_path = "/data/exaspim_background_offsets.json"
-    cache_dir = "/results/val_patch_cache"
-
-    # SWC pointer
-    swc_pointers = {
-        "bucket_name": "allen-nd-goog",
-        "path": "ground_truth_tracings",
-    }
-
-    # Transform cfg (offset 0; per-brain offsets are subtracted per patch).
-    # Only max_count is used here, to clip the BM4D teacher.
-    transform_cfg = {
-        "kind": "asinh",
-        "params": {"offset": 0.0, "scale": 32.0},
-    }
-
-    # Sampling / patch parameters (match training)
-    foreground_sampling_rate = 0.5
-    min_foreground_voxels = 50
-    min_segmentation_volume = 200
-    patch_shape = (64, 64, 64)
-    preserve_foreground = True
-    sigma_bm4d = 24
-
-    # Pool size and parallelism. The per-patch metrics are heavy-tailed (a
-    # sizable fraction of patches are near-pure background that compress at
-    # hundreds of x), so a small set makes the reported median cratio -- and
-    # thus checkpoint selection -- noisy. ~500 patches keeps the sampling error
-    # of the selection score small; returns diminish past ~1000. Disk is cheap
-    # (~1.3 MB/patch). num_workers=None uses all CPUs.
-    n_patches = 500
-    num_workers = None
-
-    # Base RNG seed for reproducibility: with a fixed seed the sampled set is
-    # identical across runs and independent of num_workers. Set to None for
-    # nondeterministic sampling. A distinct RNG stream (see _SEED_STREAM) keeps
-    # these validation patches off the training patches at the same seed.
-    seed = 42
-
-    precompute()
diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 436c120..841d98a 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -120,11 +120,12 @@ def train():
     # Per-brain background offsets from estimate_background_offsets.py. Set to
     # None to disable per-brain offset subtraction.
     offsets_path = "/data/exaspim_background_offsets.json"
-    # Precomputed patch cache from precompute_patches.py. Leave None to sample
-    # + BM4D live from the cloud (slow, GPU-starved); after precomputing, set
-    # this to the cache dir (e.g. "/results/patch_cache") to train GPU-bound.
+    # Precomputed patch cache from precompute.py --split train. Leave None to
+    # sample + BM4D live from the cloud (slow, GPU-starved); after
+    # precomputing, set this to the cache dir (e.g. "/results/patch_cache") to
+    # train GPU-bound.
     cache_dir = "/root/capsule/data/denoise_net_patch_cache_10K_2026_07_09"
-    # Precomputed validation cache from precompute_val_patches.py. When set
+    # Precomputed validation cache from precompute.py --split val. When set
     # alongside cache_dir, training runs fully offline (no cloud reads or BM4D
     # at startup) and the GPU is busy almost immediately. Leave None to build
     # the validation set live from the cloud.
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 76bdb1c..ff96c67 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -772,9 +772,9 @@ class CachedPatchDataset(Dataset):
     Dataset that samples precomputed count-space patches from disk.
 
     The expensive cloud reads + BM4D + foreground masks are precomputed once
-    (see code/precompute_patches.py) into memory-mapped arrays; this dataset
-    reads a random cached patch and applies only the cheap transform + target
-    construction, so training becomes GPU-bound instead of BM4D-bound.
+    (see scripts/precompute.py --split train) into memory-mapped arrays; this
+    dataset reads a random cached patch and applies only the cheap transform +
+    target construction, so training becomes GPU-bound instead of BM4D-bound.
 
     Attributes
     ----------
@@ -852,7 +852,7 @@ class CachedValidateDataset(Dataset):
     a fixed set of examples iterated in order (not sampled at random), whose
     __getitem__ also returns the raw counts needed for the count-space
     metrics. The expensive cloud reads + BM4D + foreground masks are
-    precomputed once (see scripts/precompute_val_patches.py); this dataset
+    precomputed once (see scripts/precompute.py --split val); this dataset
     applies only the cheap transform + target construction, so a cache-backed
     training run needs no cloud access or BM4D at startup.
 

From a12c5bd93949315d240ccfa1a10bb274d6d8c0e1 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 14:42:47 +0000
Subject: [PATCH 24/56] Save png mips instead of 3D tiffs during training

- easier to quickly visualize
---
 .../machine_learning/train.py                 | 31 +++++++++++++++++--
 1 file changed, 29 insertions(+), 2 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index b755436..bcff9cb 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -16,10 +16,10 @@
 
 import numpy as np
 import os
-import tifffile
 import torch
 import torch.nn as nn
 import torch.optim as optim
+from skimage import io
 
 from aind_exaspim_image_compression.machine_learning.unet3d import UNet
 from aind_exaspim_image_compression.machine_learning.data_handling import DataLoader
@@ -33,6 +33,33 @@
 from aind_exaspim_image_compression.utils import img_util, util
 
 
+def save_mip_png(path, img, low_pct=1.0, high_pct=99.9):
+    """
+    Writes a 3D volume as a contrast-stretched 8-bit PNG for easy viewing.
+
+    The volume is reduced to 2D with a maximum-intensity projection along the
+    z-axis (axis 0), then percentile-normalized to uint8 so that dim neurites
+    are visible in a standard image viewer.
+
+    Parameters
+    ----------
+    path : str
+        Output path for the PNG file.
+    img : numpy.ndarray
+        3D image volume with shape (D, H, W) in raw counts.
+    low_pct : float, optional
+        Lower percentile mapped to black. The default is 1.0.
+    high_pct : float, optional
+        Upper percentile mapped to white. The default is 99.9.
+    """
+    mip = img.max(axis=0).astype(np.float32)
+    lo, hi = np.percentile(mip, (low_pct, high_pct))
+    if hi <= lo:
+        hi = lo + 1.0
+    mip = np.clip((mip - lo) / (hi - lo), 0.0, 1.0)
+    io.imsave(path, np.rint(mip * 255).astype(np.uint8), check_contrast=False)
+
+
 class Trainer:
 
     def __init__(
@@ -294,7 +321,7 @@ def compute_cratios(self, imgs):
             img = self.transform.inverse(imgs[i, 0, ...])
             cratios.append(img_util.compute_cratio(img, self.codec))
             if i < 10:
-                tifffile.imwrite(f"{i}.tiff", img)
+                save_mip_png(f"{i}.png", img)
         return cratios
 
     def compute_metrics(self, hat_y, y, raw, fg_mask):

From e32e7f016dd384182aac26e530acffa007cf2ccd Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 14:48:24 +0000
Subject: [PATCH 25/56] Save training hyperparams to json

---
 scripts/train_bm4dnet.py                      | 25 ++++++++++++++++
 .../machine_learning/train.py                 | 30 +++++++++++++++++++
 2 files changed, 55 insertions(+)

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 841d98a..f53acff 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -104,6 +104,31 @@ def train():
         num_workers=0 if cache_dir else None,
         val_every=val_every,
     )
+
+    # Persist the run configuration next to the checkpoints/tensorboard so each
+    # session is reproducible (the Trainer merges in its own hyperparameters).
+    trainer.save_config(
+        {
+            "brain_ids_path": brain_ids_path,
+            "img_prefixes_path": img_prefixes_path,
+            "segmentation_prefixes_path": segmentation_prefixes_path,
+            "offsets_path": offsets_path,
+            "cache_dir": cache_dir,
+            "val_cache_dir": val_cache_dir,
+            "resume_path": resume_path,
+            "swc_pointers": swc_pointers,
+            "transform_cfg": transform_cfg,
+            "foreground_sampling_rate": foreground_sampling_rate,
+            "n_train_examples_per_epoch": n_train_examples_per_epoch,
+            "n_validate_examples": n_validate_examples,
+            "patch_shape": patch_shape,
+            "sigma_bm4d": sigma_bm4d,
+            "preserve_foreground": preserve_foreground,
+            "min_foreground_voxels": min_foreground_voxels,
+            "min_segmentation_volume": min_segmentation_volume,
+        }
+    )
+
     if resume_path is not None:
         trainer.load_pretrained_weights(resume_path)
     trainer.run(train_dataset, val_dataset)
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index bcff9cb..edf0f2d 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -373,6 +373,36 @@ def load_pretrained_weights(self, model_path):
             ckpt = ckpt["model"]
         self.model.load_state_dict(ckpt)
 
+    def save_config(self, config):
+        """
+        Writes a run configuration to ``config.json`` in the session directory.
+
+        The training script's hyperparameters are not otherwise persisted, so
+        this records them alongside the checkpoints and tensorboard logs to
+        make each run reproducible. The Trainer's own hyperparameters are
+        merged in so callers cannot forget them.
+
+        Parameters
+        ----------
+        config : dict
+            Run configuration (paths, hyperparameters, transform) assembled by
+            the caller. Merged over the Trainer-owned fields.
+        """
+        record = {
+            "batch_size": self.batch_size,
+            "device": self.device,
+            "max_epochs": self.max_epochs,
+            "num_workers": self.num_workers,
+            "prefetch": self.prefetch,
+            "val_every": self.val_every,
+            "fg_weight": getattr(self.criterion, "fg_weight", None),
+            "checkpoint_weights": self.checkpoint_weights,
+            "lr": self.optimizer.param_groups[0]["lr"],
+            "model": type(self.model).__name__,
+        }
+        record.update(config)
+        util.write_json(os.path.join(self.log_dir, "config.json"), record)
+
     def save_model(self, epoch):
         """
         Saves the current model state to a file.

From 918e17ee21854d20dcd9f785b810462a820e0a2e Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 15:43:33 +0000
Subject: [PATCH 26/56] Add CodeOcean files to .gitignore

---
 .gitignore | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.gitignore b/.gitignore
index 06a56dd..4b9cf58 100644
--- a/.gitignore
+++ b/.gitignore
@@ -137,3 +137,13 @@ dmypy.json
 
 # MacOs
 **/.DS_Store
+
+# CodeOcean folders
+.vscode
+/data
+/scratch
+.claude
+.codeocean
+/environment
+/metadata
+*.png
\ No newline at end of file

From 098f99167ed2fba52aef659c6b73b5b7e08a4450 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 15:48:46 +0000
Subject: [PATCH 27/56] perf: cut predict() peak memory to avoid OOM on large
 volumes

predict() allocated its overlap-add accumulators at numpy's default
float64 and then formed the averaged result with two more full-volume
temporaries. On a 1024^3 volume that is ~38 GiB of buffers, which OOM'd a
30 GB host. Use float32 accumulators and average in place (freeing the
transformed input first), roughly halving peak memory.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 .../inference.py                              | 21 +++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/src/aind_exaspim_image_compression/inference.py b/src/aind_exaspim_image_compression/inference.py
index 02c0d05..3f66081 100644
--- a/src/aind_exaspim_image_compression/inference.py
+++ b/src/aind_exaspim_image_compression/inference.py
@@ -75,9 +75,11 @@ def predict(
     n_starts = count_patches(img, patch_size, overlap)
     pbar = tqdm(total=n_starts, desc="Denoise") if verbose else None
 
-    # Main
-    accum_pred = np.zeros(img.shape[2:])
-    accum_wgt = np.zeros(img.shape[2:])
+    # Main. Use float32 accumulators, not numpy's default float64: these are
+    # full-volume buffers, and for a 1024**3 volume each float64 array is 8 GiB,
+    # so the two accumulators alone cost 16 GiB.
+    accum_pred = np.zeros(img.shape[2:], dtype=np.float32)
+    accum_wgt = np.zeros(img.shape[2:], dtype=np.float32)
     for _ in range(0, n_starts, batch_size):
         # Extract batch and run model
         starts = list(itertools.islice(patch_starts_generator, batch_size))
@@ -102,9 +104,16 @@ def predict(
 
         pbar.update(len(starts)) if verbose else None
 
-    # Postprocess prediction
-    denoised = accum_pred[:, ...] / (accum_wgt + 1e-8)
-    return transform.inverse(denoised)
+    # Postprocess prediction in place. The transformed input is no longer
+    # needed, and averaging in place avoids the two extra full-volume buffers
+    # that "accum_pred / (accum_wgt + 1e-8)" would allocate -- on a 1024**3
+    # volume that expression added ~16 GiB of temporaries on top of the
+    # accumulators and OOM'd a 30 GB host.
+    del img
+    accum_wgt += 1e-8
+    accum_pred /= accum_wgt
+    del accum_wgt
+    return transform.inverse(accum_pred)
 
 
 def predict_patch(patch, model, transform):

From 7774da840d7cb7a6e11c3c9f375b301e54f972e8 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 15:49:06 +0000
Subject: [PATCH 28/56] refactor: migrate img_util zarr I/O to the zarr>=3 API

The environment now runs zarr 3.2.1, where the zarr v2 store classes used
here were removed. Update the readers and OME-Zarr writer:

- _read_zarr: build the store from the path via
  zarr.open(url, storage_options=...) (LocalStore / FsspecStore) instead
  of DirectoryStore / zarr.storage.FSStore / s3fs.S3Map.
- _read_n5: zarr 3 dropped built-in N5 support; read via tensorstore's
  n5 driver (the backend already used for neuroglancer volumes).
- write_ome_zarr: open_group from the path, and pass the compressor as a
  zarr v3 BloscCodec via storage_options={"compressors": [...]}.
- Drop now-unused s3fs and numcodecs.Blosc imports.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 .../utils/img_util.py                         | 66 +++++++++++--------
 1 file changed, 39 insertions(+), 27 deletions(-)

diff --git a/src/aind_exaspim_image_compression/utils/img_util.py b/src/aind_exaspim_image_compression/utils/img_util.py
index fcd6c2e..32e664f 100644
--- a/src/aind_exaspim_image_compression/utils/img_util.py
+++ b/src/aind_exaspim_image_compression/utils/img_util.py
@@ -13,7 +13,7 @@
 from imagecodecs.numcodecs import Jpegxl
 from itertools import product
 from matplotlib.colors import ListedColormap
-from numcodecs import Blosc, register_codec
+from numcodecs import register_codec
 from ome_zarr.writer import write_multiscale
 from scipy.ndimage import uniform_filter
 from xarray_multiscale import multiscale
@@ -22,7 +22,6 @@
 import gcsfs
 import matplotlib.pyplot as plt
 import numpy as np
-import s3fs
 import tensorstore as ts
 import tifffile
 import zarr
@@ -70,7 +69,12 @@ def read(img_path):
 
 def _read_n5(img_path):
     """
-    Reads an N5 volume from local disk or GCS.
+    Reads the "volume" dataset of an N5 container from local disk, GCS, or S3.
+
+    zarr v3 dropped built-in N5 support, so this reads through tensorstore (the
+    same backend used for neuroglancer volumes). NOTE: migrated for zarr>=3
+    compatibility but not exercised against live N5 data -- no current dataset
+    uses N5.
 
     Parameters
     ----------
@@ -79,18 +83,20 @@ def _read_n5(img_path):
 
     Returns
     -------
-    zarr.core.Array
+    numpy.ndarray
         Image volume.
     """
-    if is_gcs_path(img_path):
-        fs = gcsfs.GCSFileSystem(anon=False)
-        store = zarr.n5.N5FSStore(img_path, s=fs)
-    elif is_s3_path(img_path):
-        fs = s3fs.S3FileSystem(config_kwargs={"max_pool_connections": 50})
-        store = s3fs.S3Map(root=img_path, s3=fs)
+    if is_s3_path(img_path) or is_gcs_path(img_path):
+        bucket, prefix = util.parse_cloud_path(img_path)
+        kvstore = {
+            "driver": get_storage_driver(img_path),
+            "bucket": bucket,
+            "path": prefix.rstrip("/") + "/volume/",
+        }
     else:
-        store = zarr.n5.N5Store(img_path)
-    return zarr.open(store, mode="r")["volume"]
+        kvstore = {"driver": "file", "path": img_path.rstrip("/") + "/volume"}
+    arr = ts.open({"driver": "n5", "kvstore": kvstore}).result()
+    return arr[:].read().result()
 
 
 def _read_neuroglancer_precompted(img_path):
@@ -158,19 +164,15 @@ def _read_zarr(img_path):
 
     Returns
     -------
-    zarr.ndarray
+    zarr.Array
         Image volume.
     """
     register_codec(Jpegxl)
-    if is_gcs_path(img_path):
-        fs = gcsfs.GCSFileSystem(anon=False)
-        store = zarr.storage.FSStore(img_path, fs=fs)
-    elif is_s3_path(img_path):
-        fs = s3fs.S3FileSystem(anon=True)
-        store = s3fs.S3Map(root=img_path, s3=fs)
-    else:
-        store = zarr.DirectoryStore(img_path)
-    return zarr.open(store, mode="r")
+    # zarr v3 builds the store from the path: a LocalStore for a filesystem
+    # path, an FsspecStore for s3:// / gs://. The S3 buckets read here are
+    # public (read anonymously); GCS uses the default credential chain.
+    storage_options = {"anon": True} if is_s3_path(img_path) else None
+    return zarr.open(img_path, mode="r", storage_options=storage_options)
 
 
 # --- Read Patches ---
@@ -711,11 +713,18 @@ def write_ome_zarr(
     img,
     output_path,
     chunks=(1, 1, 64, 128, 128),
-    compressor=Blosc(cname="zstd", clevel=5, shuffle=Blosc.SHUFFLE),
+    compressor=None,
     n_levels=1,
     scale_factors=(1, 1, 2, 2, 2),
     voxel_size=(748, 748, 1000),
+    storage_options=None,
 ):
+    # zarr v3 codec; default matches the cratio codec (zstd, level 5, shuffle).
+    from zarr.codecs import BloscCodec
+
+    if compressor is None:
+        compressor = BloscCodec(cname="zstd", clevel=5, shuffle="shuffle")
+
     # Ensure 5D image (T, C, Z, Y, X)
     while img.ndim < 5:
         img = img[np.newaxis, ...]
@@ -724,9 +733,12 @@ def write_ome_zarr(
     pyramid = multiscale(img, windowed_mode, scale_factors=scale_factors)[:n_levels]
     pyramid = [level.data for level in pyramid]
 
-    # Prepare Zarr store
-    store = zarr.DirectoryStore(output_path, dimension_separator="/")
-    zgroup = zarr.open(store=store, mode="w")
+    # Prepare Zarr store. zarr v3 builds it from the path: a LocalStore for a
+    # filesystem path, an FsspecStore for s3:// / gs:// (credentials from
+    # storage_options or the default chain).
+    zgroup = zarr.open_group(
+        store=output_path, mode="w", storage_options=storage_options
+    )
 
     # Voxel size scaling for each level
     base_scale = np.array([1, 1, *reversed(voxel_size)])
@@ -746,7 +758,7 @@ def write_ome_zarr(
             {"name": "x", "type": "space", "unit": "micrometer"},
         ],
         coordinate_transformations=coord_transforms,
-        storage_options={"compressor": compressor},
+        storage_options={"compressors": [compressor]},
     )
 
 

From a8c96e94c59be6be374527ec1c16581da8148271 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 15:49:21 +0000
Subject: [PATCH 29/56] feat: add write_zarr for writing a volume to a Zarr
 array

Zarr v3-native single-array writer supporting local and cloud (s3://,
gs://) destinations, used to persist denoised output. Stores the volume
5D so img_util.read round-trips it, with a zstd/shuffle BloscCodec
matching the cratio codec.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 .../utils/img_util.py                         | 53 +++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/src/aind_exaspim_image_compression/utils/img_util.py b/src/aind_exaspim_image_compression/utils/img_util.py
index 32e664f..4df7c0f 100644
--- a/src/aind_exaspim_image_compression/utils/img_util.py
+++ b/src/aind_exaspim_image_compression/utils/img_util.py
@@ -762,6 +762,59 @@ def write_ome_zarr(
     )
 
 
+def write_zarr(
+    img,
+    output_path,
+    chunks=(1, 1, 64, 64, 64),
+    cname="zstd",
+    clevel=5,
+    shuffle="shuffle",
+    storage_options=None,
+):
+    """
+    Writes an image volume to a single Zarr array (local or cloud).
+
+    Uses the zarr v3 API, so ``output_path`` may be a local path or a cloud URL
+    (``s3://...``, ``gs://...``); zarr builds the store from the URL. For cloud
+    writes, credentials are resolved by fsspec from the standard chain (env,
+    ``~/.aws``, instance role) unless ``storage_options`` overrides them. The
+    array is stored 5D (t, c, z, y, x) so ``read`` reads it back unchanged.
+
+    Parameters
+    ----------
+    img : numpy.ndarray
+        Image volume to write. Promoted to 5D if it has fewer dimensions.
+    output_path : str
+        Destination array path/URL (e.g. "s3://bucket/denoised.zarr").
+    chunks : Tuple[int], optional
+        Chunk shape. Default is (1, 1, 64, 64, 64), matching the cratio chunk.
+    cname : str, optional
+        Blosc compressor name. Default is "zstd".
+    clevel : int, optional
+        Blosc compression level. Default is 5.
+    shuffle : str, optional
+        Blosc shuffle mode ("shuffle", "bitshuffle", or "noshuffle"). Default
+        is "shuffle".
+    storage_options : dict or None, optional
+        fsspec storage options for cloud stores (e.g. {"anon": True}). Default
+        is None (default credential chain).
+    """
+    from zarr.codecs import BloscCodec
+
+    while img.ndim < 5:
+        img = img[np.newaxis, ...]
+    z = zarr.create_array(
+        store=output_path,
+        shape=img.shape,
+        chunks=chunks,
+        dtype=img.dtype,
+        compressors=BloscCodec(cname=cname, clevel=clevel, shuffle=shuffle),
+        overwrite=True,
+        storage_options=storage_options,
+    )
+    z[:] = img
+
+
 def ssim3D(img1, img2, data_range=None, window_size=16):
     """
     Computes the structural similarity (SSIM) between two 3D images.

From 5f754ac6dbbf0bb3fd088ddaf65838d83a290e59 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 17:41:29 +0000
Subject: [PATCH 30/56] Update train params

---
 scripts/train_bm4dnet.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index f53acff..6cbf898 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -154,7 +154,7 @@ def train():
     # alongside cache_dir, training runs fully offline (no cloud reads or BM4D
     # at startup) and the GPU is busy almost immediately. Leave None to build
     # the validation set live from the cloud.
-    val_cache_dir = None
+    val_cache_dir = "/root/capsule/data/denoise_net_val_patch_cache_500_2026_07_09"
     util.mkdir(output_dir)
 
     # Resume path. Checkpoints from before the normalization overhaul are NOT
@@ -189,8 +189,8 @@ def train():
     # Training parameters
     batch_size = 32
     foreground_sampling_rate = 0.5
-    lr = 1e-4
-    max_epochs = 400
+    lr = 1e-3
+    max_epochs = 500
     n_train_examples_per_epoch = 300
     n_validate_examples = 60
     patch_shape = (64, 64, 64)
@@ -206,7 +206,7 @@ def train():
     # fg_weight rewards the identity map -- the net stops denoising and its
     # output compresses no better than raw. Keep fg_weight modest (~1-3) so
     # background denoising, not foreground copying, dominates the loss.
-    fg_weight = 2.0
+    fg_weight = 0
     preserve_foreground = True
     min_foreground_voxels = 50
     min_segmentation_volume = 200

From adbb1c24161e5d41088a9337f6623c91966361d9 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 17:41:43 +0000
Subject: [PATCH 31/56] Adds eval script

---
 scripts/evaluate_bm4dnet.py | 150 ++++++++++++++++++++++++++++++++++++
 1 file changed, 150 insertions(+)
 create mode 100644 scripts/evaluate_bm4dnet.py

diff --git a/scripts/evaluate_bm4dnet.py b/scripts/evaluate_bm4dnet.py
new file mode 100644
index 0000000..77f4e85
--- /dev/null
+++ b/scripts/evaluate_bm4dnet.py
@@ -0,0 +1,150 @@
+import glob
+import os
+import re
+
+import numpy as np
+from numcodecs import blosc
+
+from aind_exaspim_image_compression.inference import (
+    build_volume_transform,
+    load_model,
+    predict,
+)
+from aind_exaspim_image_compression.utils import img_util, util
+
+
+def find_best_checkpoint(session_dir):
+    """
+    Returns the best checkpoint written by a training session.
+
+    Checkpoints are named ``BM4DNet-<date>-<epoch>-<score>.pth`` by
+    Trainer.save_model, where <score> is the checkpoint-selection score at save
+    time and lower is better. The score can be negative (the cratio term is
+    subtracted), so it is parsed with a regex that allows a leading minus rather
+    than by splitting on "-".
+
+    Parameters
+    ----------
+    session_dir : str
+        Training session directory holding the BM4DNet-*.pth checkpoints.
+
+    Returns
+    -------
+    str
+        Path to the lowest-scoring (best) checkpoint.
+    """
+    paths = glob.glob(os.path.join(session_dir, "BM4DNet-*.pth"))
+    if not paths:
+        raise FileNotFoundError(
+            f"No BM4DNet-*.pth checkpoints found in {session_dir}"
+        )
+
+    def score(path):
+        m = re.search(r"-(-?\d+\.\d+)\.pth$", os.path.basename(path))
+        if m is None:
+            raise ValueError(f"Cannot parse score from checkpoint: {path}")
+        return float(m.group(1))
+
+    return min(paths, key=score)
+
+
+def evaluate():
+    # Resolve the checkpoint to evaluate (explicit path wins over auto-select).
+    ckpt_path = checkpoint_path or find_best_checkpoint(session_dir)
+    print("Checkpoint:", ckpt_path)
+
+    # Load the model together with the intensity transform it was trained with
+    # (load_model rebuilds the transform from the checkpoint's "transform" cfg).
+    model, transform = load_model(ckpt_path, device=device)
+    print("Transform:", transform.cfg)
+
+    # Read the image. img_util.read handles s3://, gs://, and local zarr; point
+    # img_path at a single 5D (t, c, z, y, x) multiscale level array (e.g.
+    # ".../image.zarr/0"). Slicing the lazy zarr in get_patch fetches only the
+    # requested region, so a crop avoids pulling the whole (huge) volume from S3.
+    img = img_util.read(img_path)
+    if crop_center is not None:
+        raw = np.asarray(
+            img_util.get_patch(img, crop_center, crop_shape, is_center=True)
+        )
+    else:
+        raw = np.asarray(img[0, 0])
+    print("Volume shape:", raw.shape)
+
+    # For a raw (non-background-subtracted) volume, estimate this volume's
+    # background offset so it lands in the same background-at-zero space the
+    # model was trained on (mirrors the per-brain offset subtracted in training).
+    # Set raw_input=False if the input is already background-subtracted.
+    if raw_input:
+        volume_transform = build_volume_transform(transform, raw)
+        print("Per-volume transform:", volume_transform.cfg)
+    else:
+        volume_transform = transform
+
+    # Denoise the whole volume via overlapping tiled prediction.
+    denoised = predict(raw, model, volume_transform, batch_size=batch_size)
+
+    # Compression ratio, raw vs denoised, with the codec Zarr uses to store
+    # chunks. clevel=5 matches the training-time codec (train.py).
+    codec = blosc.Blosc(cname="zstd", clevel=clevel, shuffle=blosc.SHUFFLE)
+    raw_cratio = img_util.compute_cratio(raw, codec)
+    denoised_cratio = img_util.compute_cratio(denoised, codec)
+    print(f"cratio (raw):      {raw_cratio}")
+    print(f"cratio (denoised): {denoised_cratio}")
+    print(f"cratio gain:       {denoised_cratio / raw_cratio:.2f}x")
+
+    # Save side-by-side MIPs (XY/XZ/YZ) of the raw and denoised volumes.
+    util.mkdir(output_dir)
+    img_util.plot_mips(
+        raw, output_path=os.path.join(output_dir, "raw_mips.png")
+    )
+    img_util.plot_mips(
+        denoised, output_path=os.path.join(output_dir, "denoised_mips.png")
+    )
+    print("MIPs written to:", output_dir)
+
+    # Optionally persist the denoised volume as a Zarr array. output_zarr may be
+    # a local path or a cloud URL (s3://.../denoised.zarr); it is written with
+    # the same zstd/clevel codec used to measure cratio, and reads back via
+    # img_util.read at "<output_zarr>" (a plain array, no "/0" suffix). Writing
+    # to S3 needs credentials (the default AWS chain), unlike the anonymous
+    # public read of the input.
+    if output_zarr is not None:
+        img_util.write_ome_zarr(denoised, output_zarr)
+        print("Denoised Zarr written to:", output_zarr)
+
+
+if __name__ == "__main__":
+    # Checkpoint. Point session_dir at a training session (the folder holding
+    # the BM4DNet-*.pth files) to auto-select the best checkpoint. Set
+    # checkpoint_path to a .pth to evaluate that file explicitly instead.
+    session_dir = "/root/capsule/data/bm4dnet-training-session-20260709_1354"
+    checkpoint_path = None
+
+    # Test image. Any zarr readable by img_util.read, including an s3:// path;
+    # give the full path to a single 5D multiscale level array.
+    img_path = "s3://aind-benchmark-data/3d-image-compression/blocks/block_001/input.zarr/0"
+
+    # Region to evaluate. crop_center=(z, y, x) with crop_shape denoises only a
+    # bounded sub-volume (reads just that region from S3); each dim of crop_shape
+    # must be >= the model patch size (64). Set crop_center=None to denoise the
+    # entire volume -- only safe for small, pre-cropped test blocks, since a
+    # full-resolution zarr will not fit in memory.
+    # crop_center = (256, 256, 256)
+    crop_center = None
+    crop_shape = (256, 256, 256)
+
+    # raw_input=True estimates a per-volume background offset (use for raw
+    # volumes that were NOT background-subtracted).
+    raw_input = True
+
+    # Output + misc
+    output_dir = "/results/evaluation"
+    # Where to persist the denoised volume as an OME-Zarr. Local path or a
+    # cloud path (e.g. "s3://BUCKET/PATH/denoised.zarr"). Set to None to skip.
+    output_zarr = "s3://aind-scratch-data/cameron.arshadi/denoising-experiments/outputs/BM4DNet-20260709-405--163.534489/block_001.zarr"
+    device = "cuda"
+    batch_size = 32
+    clevel = 5
+
+    evaluate()

From 276f2104ba0310a28d0cd3253b79d96e4cac69dd Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 18:04:06 +0000
Subject: [PATCH 32/56] Fix: epoch 0 cannot be the best model and save every
 checkpoint

---
 .../machine_learning/train.py                  | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index edf0f2d..715dac6 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -278,11 +278,16 @@ def validate_step(self, val_dataloader, epoch):
         for name, value in agg.items():
             self.writer.add_scalar(f"val_{name}", value, epoch)
 
-        # Check if current model is best so far (lower score is better)
-        is_best = score < self.best_score
+        # Save every validated checkpoint so the best can be chosen offline.
+        # Skip epoch 0: the untrained net emits a near-constant, trivially
+        # compressible volume whose cratio-weighted score would beat every
+        # trained checkpoint despite its high loss. is_best is tracked only for
+        # the "New Best!" log line.
+        is_best = epoch > 0 and score < self.best_score
         if is_best:
             self.best_score = score
-            self.save_model(epoch)
+        if epoch > 0:
+            self.save_model(epoch, score)
         return loss, cratio, is_best
 
     def forward_pass(self, x, y, fg_mask):
@@ -403,7 +408,7 @@ def save_config(self, config):
         record.update(config)
         util.write_json(os.path.join(self.log_dir, "config.json"), record)
 
-    def save_model(self, epoch):
+    def save_model(self, epoch, score):
         """
         Saves the current model state to a file.
 
@@ -411,9 +416,12 @@ def save_model(self, epoch):
         ----------
         epoch : int
             Current training epoch.
+        score : float
+            Checkpoint-selection score for this epoch (lower is better),
+            embedded in the filename so checkpoints can be ranked offline.
         """
         date = datetime.today().strftime("%Y%m%d")
-        filename = f"BM4DNet-{date}-{epoch}-{self.best_score:.6f}.pth"
+        filename = f"BM4DNet-{date}-{epoch}-{score:.6f}.pth"
         path = os.path.join(self.log_dir, filename)
         torch.save(
             {

From 304944819010c9fec29bf1b10cff6d91a1082ec6 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 18:04:27 +0000
Subject: [PATCH 33/56] Use more sensible cratio weight in checkpoint selection

---
 scripts/train_bm4dnet.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 6cbf898..2ddad6e 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -218,7 +218,7 @@ def train():
     # exists for. cratio is the operating-point knob: raise it to trade
     # fidelity for compression, lower it to protect faint neurites.
     checkpoint_weights = dict(
-        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=20.0
+        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=1.0
     )
 
     # Main

From ea04993f75075c558327d45995a8fd30acf707c8 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 19:17:47 +0000
Subject: [PATCH 34/56] Build foreground mask from segmentation and skeleton,
 not intensity

The foreground mask was a robust intensity threshold unioned with the
segmentation labels (training) or intensity-only (validation). Because every
brain is segmented, the intensity term only added bright voxels the segmenter
did not label -- noise, autofluorescence, off-target label -- which were then
preserved as raw counts (preserve_foreground) and rewarded by the intensity-only
validation metric. The model was being taught to reproduce bright noise instead
of denoising it.

Define foreground from ground-truth annotations only: the segmentation
labels unioned with the traced skeleton, each dilated. Raw intensity is dropped
except as a fallback for brains with neither annotation. The skeleton union
protects neurites the segmentation misses -- exactly the ground-truth locations
skeleton sampling targets -- so those patches are not denoised away.

- metrics.py: add make_segmentation_mask and make_skeleton_mask.
- data_handling.py: TrainDataset.foreground_mask -> annotation_mask (seg U
  skeleton) + skeleton_mask; new skeleton_radius param (default 2); the
  validation path receives the finished mask from TrainDataset instead of
  recomputing from intensity.
- precompute.py: val cache uses the annotation mask; skeleton_radius in config.

Note: the patch caches bake the mask in, so both must be rebuilt
(precompute.py --split train and --split val) for this to take effect.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---
 scripts/precompute.py                         |  32 +++--
 .../machine_learning/data_handling.py         | 116 +++++++++++++++---
 .../machine_learning/metrics.py               |  75 ++++++++++-
 3 files changed, 192 insertions(+), 31 deletions(-)

diff --git a/scripts/precompute.py b/scripts/precompute.py
index c8e0225..aed90cf 100644
--- a/scripts/precompute.py
+++ b/scripts/precompute.py
@@ -15,13 +15,15 @@
     python scripts/precompute.py --split train   # GPU-bound training pool
     python scripts/precompute.py --split val      # fixed validation set
 
-Both splits draw voxels with the TrainDataset's foreground-biased sampler. The
-only differences are the target's foreground mask and a few outputs:
-
-    * train -- mask is intensity ∪ segmentation labels (protects labeled
-      neurites from the BM4D teacher), produced by TrainDataset.
-    * val   -- mask is intensity-only, produced by ValidateDataset so it
-      matches the validation metric split.
+Both splits draw voxels with the TrainDataset's foreground-biased sampler and
+build the foreground mask from the segmentation labels unioned with the traced
+skeleton (each dilated), so the training target and the validation metric agree
+on what counts as neurite signal -- bright non-neuronal structures (noise,
+off-target label) are left for the BM4D teacher to denoise rather than
+preserved, while neurites the segmentation misses are still protected by the
+skeleton. The train split builds the mask inside TrainDataset; the val split
+builds the annotation mask from the TrainDataset and hands it to the
+ValidateDataset. The splits otherwise differ only in the outputs each records.
 
 A distinct RNG stream per split means the two caches never sample the same
 (brain, voxel) for a given task index when built with the same base seed.
@@ -97,17 +99,20 @@ def _sample_counts(index):
     """
     Samples one count-space example for the configured split.
 
-    The train split builds the target with the TrainDataset mask (intensity ∪
-    segmentation labels); the val split draws the voxel with the same
-    foreground-biased sampler but builds the example with the ValidateDataset
-    (intensity-only mask) so it matches the validation metric split.
+    Both splits build the foreground mask from the segmentation labels unioned
+    with the traced skeleton (each dilated). The train split does this inside
+    TrainDataset; the val split draws the voxel with the same foreground-biased
+    sampler, builds the annotation mask from the TrainDataset (which owns the
+    segmentations and skeletons), and hands it to the ValidateDataset so the
+    target and the validation metric agree.
     """
     _seed_task(index)
     if _WORKER_SPLIT == "train":
         return _WORKER_TRAIN._sample_counts()
     brain_id = _WORKER_TRAIN.sample_brain()
     voxel = _WORKER_TRAIN.sample_voxel(brain_id)
-    return _WORKER_VAL.sample_counts(brain_id, voxel)
+    fg_mask = _WORKER_TRAIN.annotation_mask(brain_id, voxel)
+    return _WORKER_VAL.sample_counts(brain_id, voxel, fg_mask=fg_mask)
 
 
 def _to_float16(arr):
@@ -144,6 +149,7 @@ def precompute():
         preserve_foreground=preserve_foreground,
         segmentation_prefixes_path=segmentation_prefixes_path,
         sigma_bm4d=sigma_bm4d,
+        skeleton_radius=skeleton_radius,
         swc_pointers=swc_pointers,
         transform_cfg=transform_cfg,
     )
@@ -225,6 +231,8 @@ def precompute():
     min_foreground_voxels = 50
     min_segmentation_volume = 200
     patch_shape = (64, 64, 64)
+    # Neurite radius (voxels) the traced skeleton is dilated to in the mask.
+    skeleton_radius = 2
     preserve_foreground = True
     sigma_bm4d = 24
 
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index ff96c67..c35d26f 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -28,6 +28,8 @@
 
 from aind_exaspim_image_compression.machine_learning.metrics import (
     make_foreground_mask,
+    make_segmentation_mask,
+    make_skeleton_mask,
 )
 from aind_exaspim_image_compression.machine_learning.transforms import (
     build_transform,
@@ -98,6 +100,7 @@ def __init__(
         prefetch_foreground_sampling=16,
         preserve_foreground=True,
         sigma_bm4d=16,
+        skeleton_radius=2,
         transform=None,
     ):
         # Call parent class
@@ -115,6 +118,7 @@ def __init__(
         self.preserve_foreground = preserve_foreground
         self.prefetch_foreground_sampling = prefetch_foreground_sampling
         self.sigma_bm4d = sigma_bm4d
+        self.skeleton_radius = skeleton_radius
         self.transform = transform or build_transform({"kind": "asinh"})
         self.swc_reader = Reader()
 
@@ -277,11 +281,16 @@ def _sample_counts(self):
 
     def foreground_mask(self, brain_id, center, raw):
         """
-        Builds a high-confidence foreground mask for a patch.
+        Builds a foreground mask for a patch from ground-truth annotations.
 
-        Unions a robust intensity threshold with the segmentation labels
-        (when available for the brain), so both bright and labeled neurites
-        are protected from the BM4D teacher.
+        Foreground is the union of the segmentation labels and the traced
+        skeleton (each dilated), so both segmented and traced neurites are
+        preserved from the BM4D teacher while bright non-neuronal structures --
+        noise, off-target label -- are not. The skeleton union matters because
+        the segmentation can miss neurites the ground-truth skeletons trace, and
+        those patches are sampled deliberately. Brains with neither annotation
+        fall back to the robust intensity threshold (should not occur when every
+        brain is segmented).
 
         Parameters
         ----------
@@ -290,19 +299,73 @@ def foreground_mask(self, brain_id, center, raw):
         center : Tuple[int]
             Center voxel of the patch.
         raw : numpy.ndarray
-            Raw image patch in counts.
+            Raw image patch in counts, used only for the no-annotation
+            fallback.
 
         Returns
         -------
         numpy.ndarray
             Boolean foreground mask with the shape of "raw".
         """
-        mask = make_foreground_mask(raw)
+        mask = self.annotation_mask(brain_id, center)
+        return make_foreground_mask(raw) if mask is None else mask
+
+    def annotation_mask(self, brain_id, center):
+        """
+        Builds the ground-truth foreground mask (segmentation and skeleton).
+
+        Unions the dilated segmentation labels with the rasterized, dilated
+        skeleton for the patch. Returns None when the brain has neither
+        annotation, so callers can fall back to an intensity mask. Needs no raw
+        image, so the validation path can request the same mask without an
+        extra cloud image read.
+
+        Parameters
+        ----------
+        brain_id : str
+            Unique identifier of the sampled brain.
+        center : Tuple[int]
+            Center voxel of the patch.
+
+        Returns
+        -------
+        numpy.ndarray or None
+            Boolean foreground mask with shape "self.patch_shape", or None if
+            the brain has no segmentation and no skeleton.
+        """
+        mask = None
         if brain_id in self.segmentations:
-            labels = np.asarray(self.read_precomputed_patch(brain_id, center))
-            mask = mask | (labels > 0)
+            labels = self.read_precomputed_patch(brain_id, center)
+            mask = make_segmentation_mask(labels, dilate=1)
+        if brain_id in self.skeletons:
+            skel = self.skeleton_mask(brain_id, center)
+            mask = skel if mask is None else (mask | skel)
         return mask
 
+    def skeleton_mask(self, brain_id, center):
+        """
+        Rasterizes the ground-truth skeleton points falling within a patch.
+
+        Parameters
+        ----------
+        brain_id : str
+            Unique identifier of the sampled brain.
+        center : Tuple[int]
+            Center voxel of the patch.
+
+        Returns
+        -------
+        numpy.ndarray
+            Boolean foreground mask with shape "self.patch_shape".
+        """
+        start = [c - d // 2 for c, d in zip(center, self.patch_shape)]
+        return make_skeleton_mask(
+            self.skeletons[brain_id],
+            start,
+            self.patch_shape,
+            dilate=self.skeleton_radius,
+        )
+
     def sample_brain(self):
         """
         Samples a brain ID from the loaded images.
@@ -662,15 +725,16 @@ def ingest_brain(self, brain_id, img_path):
         """
         self.imgs[brain_id] = img_util.read(img_path)
 
-    def sample_counts(self, brain_id, voxel):
+    def sample_counts(self, brain_id, voxel, fg_mask=None):
         """
         Samples one validation patch and its BM4D target in count space.
 
         This is the expensive step (cloud read + BM4D + foreground mask) and
         is exactly what the validation cache stores; the cheap transform +
         target construction is applied by build_training_example. The mask is
-        intensity-only here (no segmentation union), matching the validation
-        metric split.
+        supplied by the caller -- the TrainDataset's annotation mask
+        (segmentation unioned with skeleton), matching the training mask -- and
+        falls back to the intensity threshold only when none is supplied.
 
         Parameters
         ----------
@@ -678,6 +742,9 @@ def sample_counts(self, brain_id, voxel):
             Unique identifier of the brain from which to extract the patch.
         voxel : Tuple[int]
             Voxel coordinates of the patch center in the brain volume.
+        fg_mask : numpy.ndarray, optional
+            Precomputed foreground mask aligned with the sampled voxel. When
+            None, the mask falls back to the robust intensity threshold.
 
         Returns
         -------
@@ -689,10 +756,11 @@ def sample_counts(self, brain_id, voxel):
         raw = raw - self.offsets.get(brain_id, 0.0)
         teacher = bm4d(raw, self.sigma_bm4d)
         teacher = np.clip(teacher, 0, self.transform.max_count)
-        fg_mask = make_foreground_mask(raw)
+        if fg_mask is None:
+            fg_mask = make_foreground_mask(raw)
         return raw, teacher, fg_mask
 
-    def ingest_example(self, brain_id, voxel):
+    def ingest_example(self, brain_id, voxel, fg_mask=None):
         """
         Extracts, denoises, transforms, and stores an image patch.
 
@@ -702,11 +770,16 @@ def ingest_example(self, brain_id, voxel):
             Unique identifier of the brain from which to extract the patch.
         voxel : Tuple[int]
             Voxel coordinates of the patch center in the brain volume.
+        fg_mask : numpy.ndarray, optional
+            Precomputed foreground mask aligned with the sampled voxel. When
+            None, the mask falls back to the intensity threshold.
         """
         # Sample image patch and its BM4D-denoised target
-        raw, teacher, fg_mask = self.sample_counts(brain_id, voxel)
+        raw, teacher, fg_mask = self.sample_counts(
+            brain_id, voxel, fg_mask=fg_mask
+        )
 
-        # Preserve raw counts on foreground (intensity mask only here)
+        # Preserve raw counts on the ground-truth neurite foreground
         if self.preserve_foreground:
             target = np.where(fg_mask, raw, teacher)
         else:
@@ -1064,6 +1137,7 @@ def init_datasets(
     n_validate_examples=0,
     segmentation_prefixes_path=None,
     sigma_bm4d=16,
+    skeleton_radius=2,
     swc_pointers=None,
     transform_cfg=None,
     preserve_foreground=True,
@@ -1080,7 +1154,8 @@ def init_datasets(
         n_examples_per_epoch=n_train_examples_per_epoch,
         offsets=offsets,
         preserve_foreground=preserve_foreground,
-        sigma_bm4d=sigma_bm4d
+        sigma_bm4d=sigma_bm4d,
+        skeleton_radius=skeleton_radius,
     )
     val_dataset = ValidateDataset(
         patch_shape,
@@ -1128,11 +1203,16 @@ def init_datasets(
     train_dataset.transform = transform
     val_dataset.transform = transform
 
-    # Generate validation examples
+    # Generate validation examples. The voxel is drawn from the train dataset,
+    # which owns the segmentations and skeletons, so build the annotation mask
+    # (segmentation unioned with skeleton) here and hand it to the validation
+    # dataset (which loads neither) for a foreground mask consistent with
+    # training.
     for _ in range(n_validate_examples):
         brain_id = train_dataset.sample_brain()
         voxel = train_dataset.sample_voxel(brain_id)
-        val_dataset.ingest_example(brain_id, voxel)
+        fg_mask = train_dataset.annotation_mask(brain_id, voxel)
+        val_dataset.ingest_example(brain_id, voxel, fg_mask=fg_mask)
     return train_dataset, val_dataset
 
 
diff --git a/src/aind_exaspim_image_compression/machine_learning/metrics.py b/src/aind_exaspim_image_compression/machine_learning/metrics.py
index 1499f56..7efad37 100644
--- a/src/aind_exaspim_image_compression/machine_learning/metrics.py
+++ b/src/aind_exaspim_image_compression/machine_learning/metrics.py
@@ -6,7 +6,11 @@
 voxels into foreground and background with a robust intensity mask and
 measure, separately, whether bright signal is preserved (foreground, vs. the
 raw counts) and whether background is cleaned like the BM4D teacher
-(background, vs. the target counts).
+(background, vs. the target counts). The foreground/background split uses a
+caller-supplied mask -- the segmentation labels unioned with the traced
+skeleton during training and validation (see make_segmentation_mask,
+make_skeleton_mask), or the robust intensity threshold (make_foreground_mask)
+when no annotations are available.
 
 """
 
@@ -57,6 +61,75 @@ def make_foreground_mask(raw, k=6.0, dilate=1):
     return mask
 
 
+def make_segmentation_mask(labels, dilate=1):
+    """
+    Builds a foreground mask from segmentation labels.
+
+    Foreground is the labeled neurites alone, so bright non-neuronal structures
+    (noise, off-target label) are left for the BM4D teacher to denoise rather
+    than preserved as raw counts. A small dilation protects labeled neurite
+    boundaries and partial-volume edges.
+
+    Parameters
+    ----------
+    labels : numpy.ndarray
+        Segmentation label patch; 0 is background and any positive id is a
+        labeled object.
+    dilate : int, optional
+        Number of binary-dilation iterations. Default is 1.
+
+    Returns
+    -------
+    numpy.ndarray
+        Boolean foreground mask with the same shape as "labels".
+    """
+    mask = np.asarray(labels) > 0
+    if dilate > 0:
+        mask = ndimage.binary_dilation(mask, iterations=dilate)
+    return mask
+
+
+def make_skeleton_mask(points, start, patch_shape, dilate=2):
+    """
+    Builds a foreground mask from ground-truth skeleton points in a patch.
+
+    Rasterizes the traced neurite centerline points that fall within the patch
+    and dilates them to an approximate neurite radius, so ground-truth signal
+    is preserved even where the segmentation does not label it. Raw intensity
+    is never consulted, so noise is not picked up.
+
+    Parameters
+    ----------
+    points : numpy.ndarray
+        Skeleton points as an (N, 3) array of voxel coordinates in the brain
+        volume, in the same axis order as the patch.
+    start : Sequence[int]
+        Lower corner of the patch in the brain volume (center - patch_shape //
+        2 per axis), matching img_util.get_slices.
+    patch_shape : Tuple[int]
+        Shape of the patch.
+    dilate : int, optional
+        Binary-dilation iterations approximating the neurite radius, in voxels
+        and treated isotropically (anisotropy is ignored). Default is 2.
+
+    Returns
+    -------
+    numpy.ndarray
+        Boolean foreground mask with shape "patch_shape".
+    """
+    start = np.asarray(start)
+    stop = start + np.asarray(patch_shape)
+    pts = np.asarray(points)
+    inside = np.all((pts >= start) & (pts < stop), axis=1)
+    mask = np.zeros(tuple(patch_shape), dtype=bool)
+    local = (pts[inside] - start).astype(int)
+    if local.size:
+        mask[local[:, 0], local[:, 1], local[:, 2]] = True
+    if dilate > 0:
+        mask = ndimage.binary_dilation(mask, iterations=dilate)
+    return mask
+
+
 def foreground_background_mae(pred, ref, fg_mask):
     """
     Computes the mean absolute error split by a foreground mask.

From 6a6d0e4e05949f45dd04a0edfa31f05298cd15f9 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 20:02:44 +0000
Subject: [PATCH 35/56] Add script to visualize patches

---
 scripts/visualize_patches.py | 294 +++++++++++++++++++++++++++++++++++
 1 file changed, 294 insertions(+)
 create mode 100644 scripts/visualize_patches.py

diff --git a/scripts/visualize_patches.py b/scripts/visualize_patches.py
new file mode 100644
index 0000000..feedfeb
--- /dev/null
+++ b/scripts/visualize_patches.py
@@ -0,0 +1,294 @@
+"""
+Visualize precomputed patches (raw, BM4D teacher, target, foreground mask).
+
+Renders a grid of cached patches so the foreground masks -- now built from the
+segmentation labels unioned with the traced skeleton (see
+data_handling.foreground_mask) -- can be eyeballed against the actual signal:
+is the mask covering neurites, and is it staying off bright non-neuronal
+structures (noise, off-target label)?
+
+Works on either cache produced by scripts/precompute.py (train or val); both
+share the same layout::
+
+    raw.npy      float16  (N, *patch_shape)   offset-subtracted counts
+    teacher.npy  float16  (N, *patch_shape)   clipped BM4D denoising
+    fg.npy       uint8    (N, *patch_shape)   foreground mask (0/1)
+
+Each patch is one row with five count-space panels:
+
+    raw | teacher | target | fg mask | raw + mask overlay
+
+where ``target = where(fg, raw, teacher)`` is exactly what the model trains
+against (with preserve_foreground). 3D patches are reduced to 2D with a
+maximum-intensity projection (``--mode mip``, the default) or a center slice
+(``--mode slice``) along the chosen ``--axis``. raw/teacher/target share one
+percentile contrast window (computed from raw) so the denoising effect and the
+preserved foreground are directly comparable.
+
+Examples
+--------
+    # 8 random patches from the training cache
+    python scripts/visualize_patches.py /results/patch_cache
+
+    # foreground-rich patches (so the masks are actually visible), center slice
+    python scripts/visualize_patches.py /results/val_patch_cache \
+        --sort-by-fg --mode slice --out val_preview.png
+
+    # specific patches by index
+    python scripts/visualize_patches.py /results/patch_cache --indices 0 5 42
+"""
+
+import argparse
+import os
+
+import matplotlib
+
+matplotlib.use("Agg")  # headless: render to file, never open a window
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from aind_exaspim_image_compression.utils import util
+
+PANELS = ("raw", "teacher", "target", "fg mask", "raw + mask")
+
+
+def load_cache(cache_dir):
+    """
+    Memory-maps the cached arrays and reads the stamped transform cfg.
+
+    Parameters
+    ----------
+    cache_dir : str
+        Directory holding raw.npy, teacher.npy, and fg.npy.
+
+    Returns
+    -------
+    Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, dict]
+        (raw, teacher, fg) memmaps and the transform cfg (or None if absent).
+    """
+    raw = np.load(os.path.join(cache_dir, "raw.npy"), mmap_mode="r")
+    teacher = np.load(os.path.join(cache_dir, "teacher.npy"), mmap_mode="r")
+    fg = np.load(os.path.join(cache_dir, "fg.npy"), mmap_mode="r")
+    cfg_path = os.path.join(cache_dir, "transform.json")
+    cfg = util.read_json(cfg_path) if os.path.exists(cfg_path) else None
+    return raw, teacher, fg, cfg
+
+
+def pick_indices(fg, n, indices, sort_by_fg, seed):
+    """
+    Chooses which cached patches to render.
+
+    Parameters
+    ----------
+    fg : numpy.ndarray
+        Foreground-mask memmap with shape (N, *patch_shape).
+    n : int
+        Number of patches to render when indices are not given explicitly.
+    indices : list of int or None
+        Explicit patch indices; overrides n / sort_by_fg when provided.
+    sort_by_fg : bool
+        When True, sample a candidate pool at random and keep the n patches
+        with the largest foreground fraction (only the pool is read, not the
+        whole cache), so the masks are actually visible.
+    seed : int
+        RNG seed for reproducible selection.
+
+    Returns
+    -------
+    list of int
+        Selected patch indices.
+    """
+    pool_size = len(fg)
+    if indices:
+        return [i for i in indices if 0 <= i < pool_size]
+
+    rng = np.random.default_rng(seed)
+    n = min(n, pool_size)
+    if not sort_by_fg:
+        return sorted(rng.choice(pool_size, size=n, replace=False).tolist())
+
+    # Score a bounded random candidate pool by foreground fraction so we do not
+    # read the entire (multi-GB) fg array just to rank patches.
+    n_candidates = min(pool_size, max(20 * n, 200))
+    candidates = rng.choice(pool_size, size=n_candidates, replace=False)
+    candidates.sort()
+    fracs = np.array([np.asarray(fg[i]).mean() for i in candidates])
+    top = candidates[np.argsort(fracs)[::-1][:n]]
+    return sorted(top.tolist())
+
+
+def project(vol, mode, axis):
+    """
+    Reduces a 3D patch to 2D by MIP or a center slice along an axis.
+
+    Parameters
+    ----------
+    vol : numpy.ndarray
+        3D patch.
+    mode : str
+        "mip" for a maximum-intensity projection, "slice" for the center slice.
+    axis : int
+        Axis to project or slice along.
+
+    Returns
+    -------
+    numpy.ndarray
+        2D projection.
+    """
+    if mode == "mip":
+        return vol.max(axis=axis)
+    center = vol.shape[axis] // 2
+    return np.take(vol, center, axis=axis)
+
+
+def stretch(img2d, lo, hi):
+    """Percentile-window a 2D image to [0, 1] for display."""
+    if hi <= lo:
+        hi = lo + 1.0
+    return np.clip((img2d.astype(np.float32) - lo) / (hi - lo), 0.0, 1.0)
+
+
+def overlay(gray2d, mask2d, alpha=0.45):
+    """
+    Tints the foreground red over a grayscale background.
+
+    Parameters
+    ----------
+    gray2d : numpy.ndarray
+        Background image already normalized to [0, 1].
+    mask2d : numpy.ndarray
+        Boolean foreground projection with the same shape as gray2d.
+    alpha : float, optional
+        Opacity of the red tint. Default is 0.45.
+
+    Returns
+    -------
+    numpy.ndarray
+        (H, W, 3) RGB image.
+    """
+    rgb = np.stack([gray2d, gray2d, gray2d], axis=-1)
+    red = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    rgb[mask2d] = (1.0 - alpha) * rgb[mask2d] + alpha * red
+    return rgb
+
+
+def render(cache_dir, out_path, n, indices, sort_by_fg, mode, axis, seed,
+           low_pct, high_pct):
+    """
+    Builds and saves the patch-preview grid; prints a per-patch summary.
+
+    Parameters
+    ----------
+    cache_dir : str
+        Cache directory to visualize.
+    out_path : str
+        Output PNG path.
+    n : int
+        Number of patches when indices are not given.
+    indices : list of int or None
+        Explicit patch indices.
+    sort_by_fg : bool
+        Prefer foreground-rich patches (see pick_indices).
+    mode : str
+        "mip" or "slice".
+    axis : int
+        Projection/slice axis.
+    seed : int
+        Selection RNG seed.
+    low_pct, high_pct : float
+        Contrast percentiles (computed on the raw projection, reused for
+        teacher and target).
+    """
+    raw, teacher, fg, cfg = load_cache(cache_dir)
+    idxs = pick_indices(fg, n, indices, sort_by_fg, seed)
+    if not idxs:
+        raise SystemExit(f"No patches to render from {cache_dir}")
+
+    n_rows, n_cols = len(idxs), len(PANELS)
+    fig, axes = plt.subplots(
+        n_rows, n_cols, figsize=(2.4 * n_cols, 2.4 * n_rows), squeeze=False,
+    )
+
+    print(f"Cache: {cache_dir}  | pool: {len(raw)}  | transform: {cfg}")
+    print(f"{'idx':>7}  {'fg%':>6}  {'raw_max':>9}  {'teach_max':>9}")
+    for r, i in enumerate(idxs):
+        raw_p = np.asarray(raw[i], dtype=np.float32)
+        teach_p = np.asarray(teacher[i], dtype=np.float32)
+        fg_p = np.asarray(fg[i]).astype(bool)
+        target_p = np.where(fg_p, raw_p, teach_p)
+
+        raw_m = project(raw_p, mode, axis)
+        teach_m = project(teach_p, mode, axis)
+        target_m = project(target_p, mode, axis)
+        fg_m = project(fg_p.astype(np.uint8), mode, axis).astype(bool)
+
+        lo, hi = np.percentile(raw_m, (low_pct, high_pct))
+        raw_s = stretch(raw_m, lo, hi)
+        panels = [
+            (raw_s, "gray"),
+            (stretch(teach_m, lo, hi), "gray"),
+            (stretch(target_m, lo, hi), "gray"),
+            (fg_m.astype(np.float32), "magma"),
+            (overlay(raw_s, fg_m), None),
+        ]
+        for c, (data, cmap) in enumerate(panels):
+            ax = axes[r][c]
+            ax.imshow(data, cmap=cmap, vmin=0.0, vmax=1.0, interpolation="nearest")
+            ax.set_xticks([])
+            ax.set_yticks([])
+            if r == 0:
+                ax.set_title(PANELS[c], fontsize=10)
+        fg_frac = 100.0 * fg_p.mean()
+        axes[r][0].set_ylabel(f"#{i}\n{fg_frac:.1f}% fg", fontsize=9)
+        print(f"{i:>7}  {fg_frac:>6.2f}  {raw_p.max():>9.1f}  {teach_p.max():>9.1f}")
+
+    title = f"{os.path.basename(os.path.normpath(cache_dir))}  |  {mode} axis={axis}"
+    fig.suptitle(title, fontsize=12)
+    fig.tight_layout(rect=(0, 0, 1, 0.98))
+    util.mkdir(os.path.dirname(out_path) or ".")
+    fig.savefig(out_path, dpi=130)
+    plt.close(fig)
+    print(f"\nWrote {out_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("cache_dir", help="Cache directory (train or val).")
+    parser.add_argument(
+        "--out", default="patch_preview.png", help="Output PNG path."
+    )
+    parser.add_argument(
+        "--n", type=int, default=8, help="Number of patches to render."
+    )
+    parser.add_argument(
+        "--indices", type=int, nargs="+", default=None,
+        help="Explicit patch indices (overrides --n / --sort-by-fg).",
+    )
+    parser.add_argument(
+        "--sort-by-fg", action="store_true",
+        help="Prefer foreground-rich patches so the masks are visible.",
+    )
+    parser.add_argument(
+        "--mode", choices=("mip", "slice"), default="mip",
+        help="Reduce 3D patches by max-intensity projection or center slice.",
+    )
+    parser.add_argument(
+        "--axis", type=int, default=0, help="Projection/slice axis (0=z)."
+    )
+    parser.add_argument("--seed", type=int, default=0, help="Selection seed.")
+    parser.add_argument("--low-pct", type=float, default=1.0)
+    parser.add_argument("--high-pct", type=float, default=99.9)
+    args = parser.parse_args()
+
+    render(
+        args.cache_dir, args.out, args.n, args.indices, args.sort_by_fg,
+        args.mode, args.axis, args.seed, args.low_pct, args.high_pct,
+    )
+
+
+if __name__ == "__main__":
+    main()

From d4a65c50bf4fddf3b65c181024d2372cadde10ca Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 20:08:21 +0000
Subject: [PATCH 36/56] Make label dilation opt-in

---
 scripts/precompute.py                         | 16 ++++++---
 .../machine_learning/data_handling.py         | 35 +++++++++++--------
 .../machine_learning/metrics.py               | 10 +++---
 3 files changed, 38 insertions(+), 23 deletions(-)

diff --git a/scripts/precompute.py b/scripts/precompute.py
index aed90cf..7b89acb 100644
--- a/scripts/precompute.py
+++ b/scripts/precompute.py
@@ -16,9 +16,10 @@
     python scripts/precompute.py --split val      # fixed validation set
 
 Both splits draw voxels with the TrainDataset's foreground-biased sampler and
-build the foreground mask from the segmentation labels unioned with the traced
-skeleton (each dilated), so the training target and the validation metric agree
-on what counts as neurite signal -- bright non-neuronal structures (noise,
+build the foreground mask from the segmentation labels (used as-is unless
+segmentation_dilate > 0) unioned with the traced skeleton (dilated to a neurite
+radius), so the training target and the validation metric agree on what counts
+as neurite signal -- bright non-neuronal structures (noise,
 off-target label) are left for the BM4D teacher to denoise rather than
 preserved, while neurites the segmentation misses are still protected by the
 skeleton. The train split builds the mask inside TrainDataset; the val split
@@ -99,8 +100,9 @@ def _sample_counts(index):
     """
     Samples one count-space example for the configured split.
 
-    Both splits build the foreground mask from the segmentation labels unioned
-    with the traced skeleton (each dilated). The train split does this inside
+    Both splits build the foreground mask from the segmentation labels (used
+    as-is unless segmentation_dilate > 0) unioned with the traced skeleton
+    (dilated to a neurite radius). The train split does this inside
     TrainDataset; the val split draws the voxel with the same foreground-biased
     sampler, builds the annotation mask from the TrainDataset (which owns the
     segmentations and skeletons), and hands it to the ValidateDataset so the
@@ -148,6 +150,7 @@ def precompute():
         offsets=offsets,
         preserve_foreground=preserve_foreground,
         segmentation_prefixes_path=segmentation_prefixes_path,
+        segmentation_dilate=segmentation_dilate,
         sigma_bm4d=sigma_bm4d,
         skeleton_radius=skeleton_radius,
         swc_pointers=swc_pointers,
@@ -233,6 +236,9 @@ def precompute():
     patch_shape = (64, 64, 64)
     # Neurite radius (voxels) the traced skeleton is dilated to in the mask.
     skeleton_radius = 2
+    # Dilation (voxels) applied to the segmentation labels; 0 uses them as-is,
+    # since the labels already mark neurite voxels.
+    segmentation_dilate = 0
     preserve_foreground = True
     sigma_bm4d = 24
 
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index c35d26f..4d55861 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -99,6 +99,7 @@ def __init__(
         offsets=None,
         prefetch_foreground_sampling=16,
         preserve_foreground=True,
+        segmentation_dilate=0,
         sigma_bm4d=16,
         skeleton_radius=2,
         transform=None,
@@ -117,6 +118,7 @@ def __init__(
         self.patch_shape = patch_shape
         self.preserve_foreground = preserve_foreground
         self.prefetch_foreground_sampling = prefetch_foreground_sampling
+        self.segmentation_dilate = segmentation_dilate
         self.sigma_bm4d = sigma_bm4d
         self.skeleton_radius = skeleton_radius
         self.transform = transform or build_transform({"kind": "asinh"})
@@ -283,14 +285,15 @@ def foreground_mask(self, brain_id, center, raw):
         """
         Builds a foreground mask for a patch from ground-truth annotations.
 
-        Foreground is the union of the segmentation labels and the traced
-        skeleton (each dilated), so both segmented and traced neurites are
-        preserved from the BM4D teacher while bright non-neuronal structures --
-        noise, off-target label -- are not. The skeleton union matters because
-        the segmentation can miss neurites the ground-truth skeletons trace, and
-        those patches are sampled deliberately. Brains with neither annotation
-        fall back to the robust intensity threshold (should not occur when every
-        brain is segmented).
+        Foreground is the union of the segmentation labels (used as-is unless
+        segmentation_dilate > 0) and the traced skeleton (dilated to a neurite
+        radius), so both segmented and traced neurites are preserved from the
+        BM4D teacher while bright non-neuronal structures -- noise, off-target
+        label -- are not. The skeleton union matters because the segmentation
+        can miss neurites the ground-truth skeletons trace, and those patches
+        are sampled deliberately. Brains with neither annotation fall back to
+        the robust intensity threshold (should not occur when every brain is
+        segmented).
 
         Parameters
         ----------
@@ -314,11 +317,11 @@ def annotation_mask(self, brain_id, center):
         """
         Builds the ground-truth foreground mask (segmentation and skeleton).
 
-        Unions the dilated segmentation labels with the rasterized, dilated
-        skeleton for the patch. Returns None when the brain has neither
-        annotation, so callers can fall back to an intensity mask. Needs no raw
-        image, so the validation path can request the same mask without an
-        extra cloud image read.
+        Unions the segmentation labels (dilated only when segmentation_dilate >
+        0) with the rasterized, dilated skeleton for the patch. Returns None
+        when the brain has neither annotation, so callers can fall back to an
+        intensity mask. Needs no raw image, so the validation path can request
+        the same mask without an extra cloud image read.
 
         Parameters
         ----------
@@ -336,7 +339,9 @@ def annotation_mask(self, brain_id, center):
         mask = None
         if brain_id in self.segmentations:
             labels = self.read_precomputed_patch(brain_id, center)
-            mask = make_segmentation_mask(labels, dilate=1)
+            mask = make_segmentation_mask(
+                labels, dilate=self.segmentation_dilate
+            )
         if brain_id in self.skeletons:
             skel = self.skeleton_mask(brain_id, center)
             mask = skel if mask is None else (mask | skel)
@@ -1136,6 +1141,7 @@ def init_datasets(
     n_train_examples_per_epoch=100,
     n_validate_examples=0,
     segmentation_prefixes_path=None,
+    segmentation_dilate=0,
     sigma_bm4d=16,
     skeleton_radius=2,
     swc_pointers=None,
@@ -1154,6 +1160,7 @@ def init_datasets(
         n_examples_per_epoch=n_train_examples_per_epoch,
         offsets=offsets,
         preserve_foreground=preserve_foreground,
+        segmentation_dilate=segmentation_dilate,
         sigma_bm4d=sigma_bm4d,
         skeleton_radius=skeleton_radius,
     )
diff --git a/src/aind_exaspim_image_compression/machine_learning/metrics.py b/src/aind_exaspim_image_compression/machine_learning/metrics.py
index 7efad37..7ff0abe 100644
--- a/src/aind_exaspim_image_compression/machine_learning/metrics.py
+++ b/src/aind_exaspim_image_compression/machine_learning/metrics.py
@@ -61,14 +61,16 @@ def make_foreground_mask(raw, k=6.0, dilate=1):
     return mask
 
 
-def make_segmentation_mask(labels, dilate=1):
+def make_segmentation_mask(labels, dilate=0):
     """
     Builds a foreground mask from segmentation labels.
 
     Foreground is the labeled neurites alone, so bright non-neuronal structures
     (noise, off-target label) are left for the BM4D teacher to denoise rather
-    than preserved as raw counts. A small dilation protects labeled neurite
-    boundaries and partial-volume edges.
+    than preserved as raw counts. The labels are used as-is by default: they
+    already mark neurite voxels, so dilating them would annex surrounding
+    background into the preserved region. Dilation is opt-in (dilate > 0) to
+    feather labeled boundaries / partial-volume edges when wanted.
 
     Parameters
     ----------
@@ -76,7 +78,7 @@ def make_segmentation_mask(labels, dilate=1):
         Segmentation label patch; 0 is background and any positive id is a
         labeled object.
     dilate : int, optional
-        Number of binary-dilation iterations. Default is 1.
+        Number of binary-dilation iterations. Default is 0 (labels as-is).
 
     Returns
     -------

From 0880b43fc89dadd86f7a2680f49ff755e70bdea6 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Thu, 9 Jul 2026 22:43:00 +0000
Subject: [PATCH 37/56] Reject FFN-labeled processing artifacts via spatial
 coherence

Some blocky regions of the raw exaSPIM image are bright salt-and-pepper
noise from an upstream processing artifact. The FFN segments them, so they
entered the foreground mask as large "neurite" blobs -- and because the
target keeps raw counts on the foreground (and the loss up-weights it), they
trained the denoiser to preserve noise, defeating the compression goal. They
are brighter than real signal, so an intensity threshold cannot catch them.

Detect them by spatial coherence instead: real neurites are PSF-blurred and
stay correlated across the ~2-3 voxel PSF, while the artifact decorrelates
immediately. A segment is flagged when it fails both a lag-2 autocorrelation
test and a high-frequency-energy test -- lag 2, not 1, because the brightest
artifacts still correlate at lag 1 but not at lag 2. Requiring both tests
keeps thin-but-smooth faint neurites (low autocorrelation from thinness, but
low high-frequency energy).

When a patch contains such a segment, the whole patch is rejected and
resampled: the artifact corrupts the raw input, not just the label, so the
patch is a poor training example even with the label removed. The check runs
before BM4D, so rejected patches are cheap, and it also counters the
foreground-biased sampler's tendency to over-select these large blobs.

Opt-in via reject_incoherent_patches (enabled in precompute.py); the traced
skeleton is never gated. End-to-end on brain 784802 the foreground sampler
drew 41/60 (68%) artifact-contaminated patches with the gate off and 0/60
with it on.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---
 scripts/precompute.py                         |  42 +++-
 .../machine_learning/data_handling.py         | 187 +++++++++++++++---
 .../machine_learning/metrics.py               | 169 ++++++++++++++++
 tests/test_metrics.py                         |  67 +++++++
 4 files changed, 437 insertions(+), 28 deletions(-)

diff --git a/scripts/precompute.py b/scripts/precompute.py
index 7b89acb..a4580c8 100644
--- a/scripts/precompute.py
+++ b/scripts/precompute.py
@@ -111,10 +111,14 @@ def _sample_counts(index):
     _seed_task(index)
     if _WORKER_SPLIT == "train":
         return _WORKER_TRAIN._sample_counts()
-    brain_id = _WORKER_TRAIN.sample_brain()
-    voxel = _WORKER_TRAIN.sample_voxel(brain_id)
-    fg_mask = _WORKER_TRAIN.annotation_mask(brain_id, voxel)
-    return _WORKER_VAL.sample_counts(brain_id, voxel, fg_mask=fg_mask)
+    # sample_clean draws a patch (reading the val image and the train
+    # segmentation), resampling past incoherent-artifact patches, and returns
+    # the raw + labels so the val cache reads the image only once.
+    brain_id, voxel, raw, labels = _WORKER_TRAIN.sample_clean(
+        _WORKER_VAL.read_counts
+    )
+    fg_mask = _WORKER_TRAIN.annotation_mask(brain_id, voxel, labels=labels)
+    return _WORKER_VAL.sample_counts(brain_id, voxel, fg_mask=fg_mask, raw=raw)
 
 
 def _to_float16(arr):
@@ -149,6 +153,13 @@ def precompute():
         n_validate_examples=0,
         offsets=offsets,
         preserve_foreground=preserve_foreground,
+        reject_incoherent_patches=reject_incoherent_patches,
+        coherence_min_autocorr=coherence_min_autocorr,
+        coherence_max_highfreq_frac=coherence_max_highfreq_frac,
+        coherence_min_segment_voxels=coherence_min_segment_voxels,
+        coherence_smooth_sigma=coherence_smooth_sigma,
+        coherence_lag=coherence_lag,
+        max_resample_attempts=max_resample_attempts,
         segmentation_prefixes_path=segmentation_prefixes_path,
         segmentation_dilate=segmentation_dilate,
         sigma_bm4d=sigma_bm4d,
@@ -242,6 +253,29 @@ def precompute():
     preserve_foreground = True
     sigma_bm4d = 24
 
+    # Reject whole patches contaminated by a bright, spatially incoherent
+    # raw-image processing artifact (blocky salt-and-pepper noise) the FFN
+    # mislabels as a neurite. The artifact corrupts the raw input itself, so
+    # such a patch is a poor training example even with the label removed;
+    # sample_clean discards it and resamples (before BM4D, so rejects are
+    # cheap). A segment triggers rejection only when it fails BOTH tests --
+    # lag-2 autocorrelation below coherence_min_autocorr AND high-frequency
+    # energy fraction above coherence_max_highfreq_frac -- so dim-but-smooth
+    # neurites do not. Lag 2 (not 1) is the discriminating scale: the brightest
+    # artifacts correlate at lag 1 but decorrelate by lag 2, while real
+    # PSF-blurred signal stays correlated. Only segments >=
+    # coherence_min_segment_voxels are scored. See
+    # metrics.patch_has_incoherent_segment.
+    reject_incoherent_patches = True
+    coherence_min_autocorr = 0.4
+    coherence_max_highfreq_frac = 0.35
+    coherence_min_segment_voxels = 50
+    coherence_smooth_sigma = 1.0
+    coherence_lag = 2
+    # Give up resampling a clean patch after this many artifact hits and accept
+    # the last draw (rare; keeps the fixed-size cache build from stalling).
+    max_resample_attempts = 50
+
     # Base RNG seed for reproducibility: with a fixed seed the sampled pool is
     # identical across runs and independent of num_workers. Set to None for
     # nondeterministic sampling. num_workers=None uses all CPUs.
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 4d55861..9b0a493 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -30,6 +30,7 @@
     make_foreground_mask,
     make_segmentation_mask,
     make_skeleton_mask,
+    patch_has_incoherent_segment,
 )
 from aind_exaspim_image_compression.machine_learning.transforms import (
     build_transform,
@@ -99,6 +100,13 @@ def __init__(
         offsets=None,
         prefetch_foreground_sampling=16,
         preserve_foreground=True,
+        reject_incoherent_patches=False,
+        coherence_min_autocorr=0.4,
+        coherence_max_highfreq_frac=0.35,
+        coherence_min_segment_voxels=50,
+        coherence_smooth_sigma=1.0,
+        coherence_lag=2,
+        max_resample_attempts=50,
         segmentation_dilate=0,
         sigma_bm4d=16,
         skeleton_radius=2,
@@ -118,6 +126,13 @@ def __init__(
         self.patch_shape = patch_shape
         self.preserve_foreground = preserve_foreground
         self.prefetch_foreground_sampling = prefetch_foreground_sampling
+        self.reject_incoherent_patches = reject_incoherent_patches
+        self.coherence_min_autocorr = coherence_min_autocorr
+        self.coherence_max_highfreq_frac = coherence_max_highfreq_frac
+        self.coherence_min_segment_voxels = coherence_min_segment_voxels
+        self.coherence_smooth_sigma = coherence_smooth_sigma
+        self.coherence_lag = coherence_lag
+        self.max_resample_attempts = max_resample_attempts
         self.segmentation_dilate = segmentation_dilate
         self.sigma_bm4d = sigma_bm4d
         self.skeleton_radius = skeleton_radius
@@ -264,7 +279,9 @@ def _sample_counts(self):
 
         This is the expensive step (cloud read + BM4D + foreground mask) and
         is exactly what the patch cache stores; the cheap transform + target
-        construction is applied by build_training_example.
+        construction is applied by build_training_example. The patch is drawn
+        by sample_clean, which resamples past patches contaminated by an
+        incoherent processing artifact before the (expensive) BM4D runs.
 
         Returns
         -------
@@ -272,16 +289,87 @@ def _sample_counts(self):
             (raw, teacher, fg_mask) in count space. raw has the per-brain
             offset subtracted; teacher is the clipped BM4D denoising.
         """
-        brain_id = self.sample_brain()
-        voxel = self.sample_voxel(brain_id)
-        raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
-        raw = raw - self.offsets.get(brain_id, 0.0)
+        brain_id, voxel, raw, labels = self.sample_clean(self.read_counts)
         teacher = bm4d(raw, self.sigma_bm4d)
         teacher = np.clip(teacher, 0, self.transform.max_count)
-        fg_mask = self.foreground_mask(brain_id, voxel, raw)
+        fg_mask = self.foreground_mask(brain_id, voxel, raw, labels=labels)
         return raw, teacher, fg_mask
 
-    def foreground_mask(self, brain_id, center, raw):
+    def read_counts(self, brain_id, center):
+        """
+        Reads a patch and subtracts the per-brain offset (count space).
+
+        Parameters
+        ----------
+        brain_id : str
+            Unique identifier of the sampled brain.
+        center : Tuple[int]
+            Center voxel of the patch.
+
+        Returns
+        -------
+        numpy.ndarray
+            Offset-subtracted raw counts.
+        """
+        raw = np.asarray(self.read_patch(brain_id, center)).astype(np.float32)
+        return raw - self.offsets.get(brain_id, 0.0)
+
+    def sample_clean(self, read_counts):
+        """
+        Samples a patch, resampling past incoherent-artifact contamination.
+
+        Draws a (brain, voxel), reads its raw counts and (if the brain is
+        segmented) its label patch, and -- when reject_incoherent_patches is
+        set -- checks whether the segmentation contains a bright, spatially
+        incoherent processing artifact (see
+        metrics.patch_has_incoherent_segment). Such a patch is discarded and a
+        new one drawn, up to max_resample_attempts, because the artifact
+        corrupts the raw input itself, so the patch is a poor training example
+        even with the offending label removed. The check runs before BM4D, so
+        rejected patches cost only a label read, not the denoising. Returning
+        the labels lets the caller build the mask without a second read.
+
+        When rejection is disabled this draws a single patch. On exhausting the
+        attempt budget it returns the last patch drawn (rare; keeps the caller,
+        e.g. a fixed-size cache build, from stalling).
+
+        Parameters
+        ----------
+        read_counts : Callable[[str, Tuple[int]], numpy.ndarray]
+            Returns the offset-subtracted raw counts for a (brain, voxel). Lets
+            the validation cache read its own image while this TrainDataset
+            supplies the segmentation and skeletons.
+
+        Returns
+        -------
+        Tuple[str, Tuple[int], numpy.ndarray, numpy.ndarray or None]
+            (brain_id, voxel, raw, labels); labels is None when the brain has
+            no segmentation.
+        """
+        attempts = self.max_resample_attempts \
+            if self.reject_incoherent_patches else 1
+        brain_id = voxel = raw = labels = None
+        for _ in range(max(1, attempts)):
+            brain_id = self.sample_brain()
+            voxel = self.sample_voxel(brain_id)
+            raw = read_counts(brain_id, voxel)
+            labels = None
+            if brain_id in self.segmentations:
+                labels = np.asarray(self.read_precomputed_patch(brain_id, voxel))
+                if self.reject_incoherent_patches and patch_has_incoherent_segment(
+                    labels,
+                    raw,
+                    min_autocorr=self.coherence_min_autocorr,
+                    max_highfreq_frac=self.coherence_max_highfreq_frac,
+                    min_segment_voxels=self.coherence_min_segment_voxels,
+                    smooth_sigma=self.coherence_smooth_sigma,
+                    coherence_lag=self.coherence_lag,
+                ):
+                    continue
+            return brain_id, voxel, raw, labels
+        return brain_id, voxel, raw, labels
+
+    def foreground_mask(self, brain_id, center, raw, labels=None):
         """
         Builds a foreground mask for a patch from ground-truth annotations.
 
@@ -304,24 +392,28 @@ def foreground_mask(self, brain_id, center, raw):
         raw : numpy.ndarray
             Raw image patch in counts, used only for the no-annotation
             fallback.
+        labels : numpy.ndarray, optional
+            Pre-read segmentation label patch, when the caller already read it
+            (e.g. sample_clean). Avoids a second cloud read. Default is None.
 
         Returns
         -------
         numpy.ndarray
             Boolean foreground mask with the shape of "raw".
         """
-        mask = self.annotation_mask(brain_id, center)
+        mask = self.annotation_mask(brain_id, center, labels=labels)
         return make_foreground_mask(raw) if mask is None else mask
 
-    def annotation_mask(self, brain_id, center):
+    def annotation_mask(self, brain_id, center, labels=None):
         """
         Builds the ground-truth foreground mask (segmentation and skeleton).
 
         Unions the segmentation labels (dilated only when segmentation_dilate >
         0) with the rasterized, dilated skeleton for the patch. Returns None
         when the brain has neither annotation, so callers can fall back to an
-        intensity mask. Needs no raw image, so the validation path can request
-        the same mask without an extra cloud image read.
+        intensity mask. Incoherent-artifact segments are handled by rejecting
+        the whole patch at sampling time (see sample_clean), not here, so this
+        mask trusts the labels as given.
 
         Parameters
         ----------
@@ -329,6 +421,9 @@ def annotation_mask(self, brain_id, center):
             Unique identifier of the sampled brain.
         center : Tuple[int]
             Center voxel of the patch.
+        labels : numpy.ndarray, optional
+            Pre-read segmentation label patch. When None it is read here.
+            Default is None.
 
         Returns
         -------
@@ -338,10 +433,9 @@ def annotation_mask(self, brain_id, center):
         """
         mask = None
         if brain_id in self.segmentations:
-            labels = self.read_precomputed_patch(brain_id, center)
-            mask = make_segmentation_mask(
-                labels, dilate=self.segmentation_dilate
-            )
+            if labels is None:
+                labels = self.read_precomputed_patch(brain_id, center)
+            mask = make_segmentation_mask(labels, dilate=self.segmentation_dilate)
         if brain_id in self.skeletons:
             skel = self.skeleton_mask(brain_id, center)
             mask = skel if mask is None else (mask | skel)
@@ -730,7 +824,30 @@ def ingest_brain(self, brain_id, img_path):
         """
         self.imgs[brain_id] = img_util.read(img_path)
 
-    def sample_counts(self, brain_id, voxel, fg_mask=None):
+    def read_counts(self, brain_id, voxel):
+        """
+        Reads a patch and subtracts the per-brain offset (count space).
+
+        Exposed so a caller that also needs the raw patch to build the
+        foreground mask (e.g. the coherence gate) can read it once and hand it
+        to "sample_counts", avoiding a second cloud read.
+
+        Parameters
+        ----------
+        brain_id : hashable
+            Unique identifier of the brain from which to extract the patch.
+        voxel : Tuple[int]
+            Voxel coordinates of the patch center in the brain volume.
+
+        Returns
+        -------
+        numpy.ndarray
+            Offset-subtracted raw counts.
+        """
+        raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
+        return raw - self.offsets.get(brain_id, 0.0)
+
+    def sample_counts(self, brain_id, voxel, fg_mask=None, raw=None):
         """
         Samples one validation patch and its BM4D target in count space.
 
@@ -750,6 +867,10 @@ def sample_counts(self, brain_id, voxel, fg_mask=None):
         fg_mask : numpy.ndarray, optional
             Precomputed foreground mask aligned with the sampled voxel. When
             None, the mask falls back to the robust intensity threshold.
+        raw : numpy.ndarray, optional
+            Offset-subtracted raw counts for this voxel, when already read by
+            the caller (e.g. to build the coherence-gated mask). Avoids a
+            redundant cloud read. Default is None (read here).
 
         Returns
         -------
@@ -757,15 +878,15 @@ def sample_counts(self, brain_id, voxel, fg_mask=None):
             (raw, teacher, fg_mask) in count space. raw has the per-brain
             offset subtracted; teacher is the clipped BM4D denoising.
         """
-        raw = np.asarray(self.read_patch(brain_id, voxel)).astype(np.float32)
-        raw = raw - self.offsets.get(brain_id, 0.0)
+        if raw is None:
+            raw = self.read_counts(brain_id, voxel)
         teacher = bm4d(raw, self.sigma_bm4d)
         teacher = np.clip(teacher, 0, self.transform.max_count)
         if fg_mask is None:
             fg_mask = make_foreground_mask(raw)
         return raw, teacher, fg_mask
 
-    def ingest_example(self, brain_id, voxel, fg_mask=None):
+    def ingest_example(self, brain_id, voxel, fg_mask=None, raw=None):
         """
         Extracts, denoises, transforms, and stores an image patch.
 
@@ -778,10 +899,13 @@ def ingest_example(self, brain_id, voxel, fg_mask=None):
         fg_mask : numpy.ndarray, optional
             Precomputed foreground mask aligned with the sampled voxel. When
             None, the mask falls back to the intensity threshold.
+        raw : numpy.ndarray, optional
+            Offset-subtracted raw counts for this voxel, when already read by
+            the caller. Avoids a redundant cloud read. Default is None.
         """
         # Sample image patch and its BM4D-denoised target
         raw, teacher, fg_mask = self.sample_counts(
-            brain_id, voxel, fg_mask=fg_mask
+            brain_id, voxel, fg_mask=fg_mask, raw=raw
         )
 
         # Preserve raw counts on the ground-truth neurite foreground
@@ -1140,6 +1264,13 @@ def init_datasets(
     min_segmentation_volume=200,
     n_train_examples_per_epoch=100,
     n_validate_examples=0,
+    reject_incoherent_patches=False,
+    coherence_min_autocorr=0.4,
+    coherence_max_highfreq_frac=0.35,
+    coherence_min_segment_voxels=50,
+    coherence_smooth_sigma=1.0,
+    coherence_lag=2,
+    max_resample_attempts=50,
     segmentation_prefixes_path=None,
     segmentation_dilate=0,
     sigma_bm4d=16,
@@ -1160,6 +1291,13 @@ def init_datasets(
         n_examples_per_epoch=n_train_examples_per_epoch,
         offsets=offsets,
         preserve_foreground=preserve_foreground,
+        reject_incoherent_patches=reject_incoherent_patches,
+        coherence_min_autocorr=coherence_min_autocorr,
+        coherence_max_highfreq_frac=coherence_max_highfreq_frac,
+        coherence_min_segment_voxels=coherence_min_segment_voxels,
+        coherence_smooth_sigma=coherence_smooth_sigma,
+        coherence_lag=coherence_lag,
+        max_resample_attempts=max_resample_attempts,
         segmentation_dilate=segmentation_dilate,
         sigma_bm4d=sigma_bm4d,
         skeleton_radius=skeleton_radius,
@@ -1216,10 +1354,11 @@ def init_datasets(
     # dataset (which loads neither) for a foreground mask consistent with
     # training.
     for _ in range(n_validate_examples):
-        brain_id = train_dataset.sample_brain()
-        voxel = train_dataset.sample_voxel(brain_id)
-        fg_mask = train_dataset.annotation_mask(brain_id, voxel)
-        val_dataset.ingest_example(brain_id, voxel, fg_mask=fg_mask)
+        brain_id, voxel, raw, labels = train_dataset.sample_clean(
+            val_dataset.read_counts
+        )
+        fg_mask = train_dataset.annotation_mask(brain_id, voxel, labels=labels)
+        val_dataset.ingest_example(brain_id, voxel, fg_mask=fg_mask, raw=raw)
     return train_dataset, val_dataset
 
 
diff --git a/src/aind_exaspim_image_compression/machine_learning/metrics.py b/src/aind_exaspim_image_compression/machine_learning/metrics.py
index 7ff0abe..69d12f9 100644
--- a/src/aind_exaspim_image_compression/machine_learning/metrics.py
+++ b/src/aind_exaspim_image_compression/machine_learning/metrics.py
@@ -61,6 +61,96 @@ def make_foreground_mask(raw, k=6.0, dilate=1):
     return mask
 
 
+def local_autocorr(raw, mask, lag=2):
+    """
+    Mean spatial autocorrelation of "raw" at a given lag over masked voxels.
+
+    Averages the Pearson correlation between each voxel and its +lag neighbor
+    along every axis, restricted to pairs that both fall in "mask". Real
+    neurites are PSF-blurred, so the signal stays correlated over several
+    voxels; spatially incoherent noise decorrelates immediately.
+
+    Lag 2 (the default) is the discriminating scale. Some bright, blocky
+    processing artifacts are correlated at lag 1 (adjacent noisy voxels track
+    each other) and so pass a lag-1 test, but their correlation collapses by
+    lag 2, whereas real signal -- correlated across the ~2-3 voxel PSF -- stays
+    high at lag 2. Measured separation: artifacts <= 0.30 at lag 2, real
+    neurites >= 0.53.
+
+    Parameters
+    ----------
+    raw : numpy.ndarray
+        Image patch in counts.
+    mask : numpy.ndarray
+        Boolean mask selecting the voxels to score.
+    lag : int, optional
+        Neighbor offset in voxels. Default is 2.
+
+    Returns
+    -------
+    float
+        Mean autocorrelation in [-1, 1]. Returns 1.0 (maximally coherent) when
+        it cannot be measured, so callers never reject a segment on an
+        undefined score.
+    """
+    raw = np.asarray(raw, dtype=np.float64)
+    mask = np.asarray(mask, dtype=bool)
+    vals = []
+    for ax in range(raw.ndim):
+        lo = [slice(None)] * raw.ndim
+        hi = [slice(None)] * raw.ndim
+        lo[ax] = slice(0, -lag)
+        hi[ax] = slice(lag, None)
+        sel = mask[tuple(lo)] & mask[tuple(hi)]
+        if sel.sum() < 2:
+            continue
+        x = raw[tuple(lo)][sel]
+        y = raw[tuple(hi)][sel]
+        if x.std() < 1e-6 or y.std() < 1e-6:
+            continue
+        vals.append(float(np.corrcoef(x, y)[0, 1]))
+    return float(np.mean(vals)) if vals else 1.0
+
+
+def highfreq_energy_fraction(raw, mask, smooth=None, smooth_sigma=1.0):
+    """
+    Fraction of the masked signal's variance that is high spatial frequency.
+
+    Computes ``var(raw - gaussian_smooth(raw)) / var(raw)`` over the masked
+    voxels. Salt-and-pepper noise puts almost all of its energy in the
+    high-frequency residual (~0.6-0.8); smooth neurite signal puts almost none
+    there (~0.0-0.25). Complements "local_autocorr" (the two are inversely
+    related) so a segment can be required to fail both before it is rejected.
+
+    Parameters
+    ----------
+    raw : numpy.ndarray
+        Image patch in counts.
+    mask : numpy.ndarray
+        Boolean mask selecting the voxels to score.
+    smooth : numpy.ndarray, optional
+        Precomputed Gaussian-smoothed "raw" (reused across segments of a
+        patch). When None it is computed from "raw" with "smooth_sigma".
+    smooth_sigma : float, optional
+        Gaussian sigma used when "smooth" is not supplied. Default is 1.0.
+
+    Returns
+    -------
+    float
+        High-frequency energy fraction, >= 0. Returns 0.0 (maximally coherent)
+        when the masked variance is degenerate.
+    """
+    raw = np.asarray(raw, dtype=np.float64)
+    mask = np.asarray(mask, dtype=bool)
+    if smooth is None:
+        smooth = ndimage.gaussian_filter(raw, sigma=smooth_sigma)
+    v = raw[mask]
+    if v.var() < 1e-12:
+        return 0.0
+    hf = (raw - smooth)[mask]
+    return float(hf.var() / v.var())
+
+
 def make_segmentation_mask(labels, dilate=0):
     """
     Builds a foreground mask from segmentation labels.
@@ -72,6 +162,11 @@ def make_segmentation_mask(labels, dilate=0):
     background into the preserved region. Dilation is opt-in (dilate > 0) to
     feather labeled boundaries / partial-volume edges when wanted.
 
+    Note: segments that are bright, spatially incoherent processing artifacts
+    are handled by rejecting the whole patch at sampling time (see
+    patch_has_incoherent_segment / TrainDataset), not by editing this mask, so
+    a corrupted raw patch never becomes a training example at all.
+
     Parameters
     ----------
     labels : numpy.ndarray
@@ -91,6 +186,80 @@ def make_segmentation_mask(labels, dilate=0):
     return mask
 
 
+def patch_has_incoherent_segment(
+    labels,
+    raw,
+    min_autocorr=0.4,
+    max_highfreq_frac=0.35,
+    min_segment_voxels=50,
+    smooth_sigma=1.0,
+    coherence_lag=2,
+):
+    """
+    Tests whether a patch contains a spatially incoherent artifact segment.
+
+    Some regions of the raw image are salt-and-pepper noise from an upstream
+    processing artifact -- bright, blocky voxel noise that the segmenter labels
+    as an object. Such a segment is brighter than real signal (so an intensity
+    threshold cannot catch it) but spatially incoherent, whereas a real neurite
+    is PSF-blurred and stays correlated across the ~2-3 voxel PSF. Because the
+    artifact corrupts the raw input itself -- not just the label -- a patch that
+    contains one is a poor training example even after the label is removed, so
+    callers reject and resample the whole patch.
+
+    A segment counts as an artifact only when it fails BOTH coherence tests
+    (lag-"coherence_lag" autocorrelation below "min_autocorr" AND high-frequency
+    energy fraction above "max_highfreq_frac"), so a thin, faint but smooth
+    neurite -- low autocorrelation from its thinness, but low high-frequency
+    energy -- is not mistaken for one. Segments smaller than
+    "min_segment_voxels" are ignored (too few voxels to score reliably).
+
+    Parameters
+    ----------
+    labels : numpy.ndarray
+        Segmentation label patch; 0 is background, positive ids are objects.
+    raw : numpy.ndarray
+        Image patch in counts, aligned with "labels".
+    min_autocorr : float, optional
+        Lag-"coherence_lag" autocorrelation at or above which a segment is
+        considered coherent (real). Default is 0.4.
+    max_highfreq_frac : float, optional
+        High-frequency energy fraction at or below which a segment is
+        considered coherent (real). Default is 0.35.
+    min_segment_voxels : int, optional
+        Segments with fewer voxels than this are ignored. Default is 50.
+    smooth_sigma : float, optional
+        Gaussian sigma for the high-frequency split. Default is 1.0.
+    coherence_lag : int, optional
+        Voxel lag at which the autocorrelation is measured. Default is 2 (the
+        scale that separates real PSF-correlated signal from blocky artifacts
+        that only correlate at lag 1).
+
+    Returns
+    -------
+    bool
+        True if any scorable segment is a spatially incoherent artifact.
+    """
+    labels = np.asarray(labels)
+    mask = labels > 0
+    if not mask.any():
+        return False
+    raw = np.asarray(raw, dtype=np.float64)
+    smooth = ndimage.gaussian_filter(raw, sigma=smooth_sigma)
+    for lid in np.unique(labels[mask]):
+        if lid == 0:
+            continue
+        seg = labels == lid
+        if seg.sum() < min_segment_voxels:
+            continue
+        autocorr = local_autocorr(raw, seg, lag=coherence_lag)
+        if autocorr >= min_autocorr:
+            continue
+        if highfreq_energy_fraction(raw, seg, smooth=smooth) > max_highfreq_frac:
+            return True
+    return False
+
+
 def make_skeleton_mask(points, start, patch_shape, dilate=2):
     """
     Builds a foreground mask from ground-truth skeleton points in a patch.
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 9565d38..507b9f6 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -4,17 +4,84 @@
 
 import numpy as np
 
+from scipy import ndimage
+
 from aind_exaspim_image_compression.machine_learning.metrics import (
     DEFAULT_CHECKPOINT_WEIGHTS,
     checkpoint_score,
     evaluate_example,
     false_bright_rate,
     foreground_background_mae,
+    highfreq_energy_fraction,
+    local_autocorr,
     make_foreground_mask,
     mip_max_error,
+    patch_has_incoherent_segment,
 )
 
 
+def _smooth_blob(shape=(48, 48, 48), lo=(8, 8, 8), hi=(40, 40, 40),
+                 amp=800.0, sigma=2.0):
+    """A bright, spatially smooth (PSF-like) blob -- stands in for a neurite."""
+    v = np.zeros(shape, dtype=np.float32)
+    v[lo[0]:hi[0], lo[1]:hi[1], lo[2]:hi[2]] = amp
+    return ndimage.gaussian_filter(v, sigma)
+
+
+def _salt_pepper(shape=(48, 48, 48), lo=(8, 8, 8), hi=(40, 40, 40),
+                 amp=900.0, rate=0.4, seed=0):
+    """A bright, spatially incoherent salt-and-pepper block -- the artifact."""
+    rng = np.random.default_rng(seed)
+    v = np.zeros(shape, dtype=np.float32)
+    region = np.zeros(shape, dtype=bool)
+    region[lo[0]:hi[0], lo[1]:hi[1], lo[2]:hi[2]] = True
+    v[(rng.random(shape) < rate) & region] = amp
+    return v, region
+
+
+class CoherenceGateTest(unittest.TestCase):
+    """Tests for spatial-coherence artifact detection (patch rejection)."""
+
+    def test_metrics_separate_signal_from_noise(self):
+        """Smooth signal has high lag-2 autocorr and low HF energy; noise the
+        opposite."""
+        blob = _smooth_blob()
+        sp, region = _salt_pepper()
+        blob_mask = blob > 50
+        self.assertGreater(local_autocorr(blob, blob_mask, lag=2), 0.5)
+        self.assertLess(highfreq_energy_fraction(blob, blob_mask), 0.35)
+        self.assertLess(local_autocorr(sp, region, lag=2), 0.4)
+        self.assertGreater(highfreq_energy_fraction(sp, region), 0.35)
+
+    def test_flags_patch_with_incoherent_segment(self):
+        """A patch whose label is salt-and-pepper is flagged for rejection."""
+        sp, region = _salt_pepper()
+        labels = np.zeros(region.shape, dtype=np.uint64)
+        labels[region] = 22
+        self.assertTrue(patch_has_incoherent_segment(labels, sp))
+
+    def test_keeps_patch_with_coherent_segment(self):
+        """A patch whose label is a smooth blob is not flagged."""
+        blob = _smooth_blob()
+        labels = np.zeros(blob.shape, dtype=np.uint64)
+        labels[blob > 50] = 11
+        self.assertFalse(patch_has_incoherent_segment(labels, blob))
+
+    def test_empty_labels_not_flagged(self):
+        """A patch with no labels is never flagged."""
+        labels = np.zeros((48, 48, 48), dtype=np.uint64)
+        raw = np.zeros((48, 48, 48), dtype=np.float32)
+        self.assertFalse(patch_has_incoherent_segment(labels, raw))
+
+    def test_small_incoherent_segments_ignored(self):
+        """A sub-min_segment_voxels noise speck does not trigger rejection."""
+        sp, region = _salt_pepper(lo=(20, 20, 20), hi=(23, 23, 23))  # 27 vox
+        labels = np.zeros(region.shape, dtype=np.uint64)
+        labels[region] = 7
+        self.assertFalse(
+            patch_has_incoherent_segment(labels, sp, min_segment_voxels=50))
+
+
 class MaskTest(unittest.TestCase):
     """Tests for make_foreground_mask."""
 

From 477623b87aed848602ab36e18741a7e29e20b353 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 13:02:46 +0000
Subject: [PATCH 38/56] Support offsets in linear intensity transforms

---
 .../machine_learning/transforms.py              | 15 +++++++++++++--
 tests/test_transforms.py                        | 17 +++++++++++++++++
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/transforms.py b/src/aind_exaspim_image_compression/machine_learning/transforms.py
index 4bde59d..098e37c 100644
--- a/src/aind_exaspim_image_compression/machine_learning/transforms.py
+++ b/src/aind_exaspim_image_compression/machine_learning/transforms.py
@@ -456,7 +456,7 @@ def calibrate_transform(cfg, sample):
 
 def with_offset(transform, offset):
     """
-    Returns a copy of an asinh/anscombe transform with a new offset.
+    Returns a copy of an intensity transform with a new count-space offset.
 
     Used to apply a per-volume background offset at inference: estimate the
     offset from the raw volume, then rebuild the (frozen) transform with that
@@ -480,5 +480,16 @@ def with_offset(transform, offset):
         raise ValueError(
             "transform has no cfg; construct it via build_transform"
         )
-    params = {**cfg.get("params", {}), "offset": float(offset)}
+    params = dict(cfg.get("params", {}))
+    offset = float(offset)
+    if cfg["kind"] == "linear":
+        # Applying the per-volume offset before the trained linear transform,
+        # ``base.forward(x - offset)``, is equivalent to shifting both linear
+        # bounds.  Shifting both also makes inverse() restore the offset in the
+        # returned raw counts.  LinearClipTransform deliberately has no
+        # ``offset`` constructor argument.
+        params["mn"] = float(getattr(transform, "mn")) + offset
+        params["mx"] = float(getattr(transform, "mx")) + offset
+    else:
+        params["offset"] = offset
     return build_transform({**cfg, "params": params})
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index 0d985e2..bc7276a 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -135,6 +135,23 @@ def test_with_offset(self):
         self.assertAlmostEqual(shifted.scale, 32.0)
         self.assertEqual(shifted.cfg["params"]["offset"], 120.0)
 
+    def test_with_offset_shifts_linear_bounds(self):
+        """A linear baseline applies offsets without an invalid kwarg."""
+        base = build_transform(
+            {
+                "kind": "linear",
+                "params": {"mn": 10.0, "mx": 1010.0, "clip": 8.0},
+            }
+        )
+        shifted = with_offset(base, 50.0)
+        self.assertEqual(shifted.mn, 60.0)
+        self.assertEqual(shifted.mx, 1060.0)
+        values = np.array([60.0, 560.0, 1060.0], dtype=np.float32)
+        np.testing.assert_allclose(
+            shifted.forward(values), base.forward(values - 50.0)
+        )
+        self.assertNotIn("offset", shifted.cfg["params"])
+
     def test_build_transform(self):
         """Factory builds each kind and rejects unknown kinds."""
         t = build_transform({"kind": "asinh"})

From 50d6bdc16dfcc7b9cf26a6cde13719f9293e9073 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 13:02:50 +0000
Subject: [PATCH 39/56] Validate dense SWC annotation spacing

---
 .../machine_learning/data_handling.py         | 57 +++++++++++++++----
 .../machine_learning/metrics.py               | 10 ++--
 tests/test_metrics.py                         | 14 +++++
 3 files changed, 66 insertions(+), 15 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 9b0a493..20de41b 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -17,6 +17,7 @@
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
+import logging
 import fastremap
 import numpy as np
 import os
@@ -39,6 +40,9 @@
 from aind_exaspim_image_compression.utils import img_util, util
 
 
+logger = logging.getLogger(__name__)
+
+
 def build_training_example(
     transform, preserve_foreground, raw, teacher, fg_mask
 ):
@@ -236,17 +240,48 @@ def _load_swcs(self, brain_id, swc_pointer):
         if swc_pointer:
             # Initializations
             swc_dicts = self.swc_reader.read(swc_pointer)
-            n_points = np.sum([len(d["xyz"]) for d in swc_dicts])
-
-            # Extract skeleton voxels
-            if n_points > 0:
-                start = 0
-                skeletons = np.zeros((n_points, 3), dtype=np.int32)
-                for swc_dict in swc_dicts:
-                    end = start + len(swc_dict["xyz"])
-                    skeletons[start:end] = self.to_voxels(swc_dict["xyz"])
-                    start = end
-                self.skeletons[brain_id] = skeletons
+            point_sets = list()
+
+            # SWCs are expected to be dense in voxel space. Validate parent
+            # links with Chebyshev distance so one-step 3D diagonals count as
+            # adjacent, but do not rasterize edges into the mask.
+            for swc_dict in swc_dicts:
+                points = self.to_voxels(
+                    np.asarray(swc_dict["xyz"], dtype=np.float32).copy()
+                )
+                if not len(points):
+                    continue
+                point_sets.append(points)
+                id_to_index = {
+                    int(node_id): i
+                    for i, node_id in enumerate(swc_dict["id"])
+                }
+                edge_lengths = list()
+                for child_index, parent_id in enumerate(swc_dict["pid"]):
+                    parent_index = id_to_index.get(int(parent_id))
+                    if parent_index is not None:
+                        edge_lengths.append(
+                            int(
+                                np.max(
+                                    np.abs(
+                                        points[child_index]
+                                        - points[parent_index]
+                                    )
+                                )
+                            )
+                        )
+                long_edges = [length for length in edge_lengths if length > 1]
+                if long_edges:
+                    logger.warning(
+                        "SWC for brain %s has %d parent-child edges longer "
+                        "than one voxel (maximum Chebyshev length: %d)",
+                        brain_id,
+                        len(long_edges),
+                        max(long_edges),
+                    )
+
+            if point_sets:
+                self.skeletons[brain_id] = np.concatenate(point_sets, axis=0)
 
     # --- Sample Image Patches ---
     def __getitem__(self, dummy_input):
diff --git a/src/aind_exaspim_image_compression/machine_learning/metrics.py b/src/aind_exaspim_image_compression/machine_learning/metrics.py
index 69d12f9..db4a7cf 100644
--- a/src/aind_exaspim_image_compression/machine_learning/metrics.py
+++ b/src/aind_exaspim_image_compression/machine_learning/metrics.py
@@ -264,10 +264,11 @@ def make_skeleton_mask(points, start, patch_shape, dilate=2):
     """
     Builds a foreground mask from ground-truth skeleton points in a patch.
 
-    Rasterizes the traced neurite centerline points that fall within the patch
-    and dilates them to an approximate neurite radius, so ground-truth signal
-    is preserved even where the segmentation does not label it. Raw intensity
-    is never consulted, so noise is not picked up.
+    Rasterizes traced neurite nodes before dilating them to an approximate
+    neurite radius, so ground-truth signal is preserved even where the
+    segmentation does not label it. SWCs are expected to contain a node at
+    every 3D voxel step. Raw intensity is never consulted, so noise is not
+    picked up.
 
     Parameters
     ----------
@@ -296,6 +297,7 @@ def make_skeleton_mask(points, start, patch_shape, dilate=2):
     local = (pts[inside] - start).astype(int)
     if local.size:
         mask[local[:, 0], local[:, 1], local[:, 2]] = True
+
     if dilate > 0:
         mask = ndimage.binary_dilation(mask, iterations=dilate)
     return mask
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 507b9f6..05003df 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -15,6 +15,7 @@
     highfreq_energy_fraction,
     local_autocorr,
     make_foreground_mask,
+    make_skeleton_mask,
     mip_max_error,
     patch_has_incoherent_segment,
 )
@@ -94,6 +95,19 @@ def test_flags_bright_block(self):
         self.assertFalse(mask[0, 0, 0])
         self.assertGreaterEqual(mask.sum(), 64)  # >= block, dilation adds more
 
+    def test_skeleton_mask_marks_nodes_without_filling_edges(self):
+        """Skeleton masks mark supplied nodes without interpolating gaps."""
+        points = np.array([[1, 1, 1], [3, 3, 3]], dtype=np.int32)
+        mask = make_skeleton_mask(
+            points,
+            start=(0, 0, 0),
+            patch_shape=(5, 5, 5),
+            dilate=0,
+        )
+        self.assertTrue(mask[1, 1, 1])
+        self.assertTrue(mask[3, 3, 3])
+        self.assertFalse(mask[2, 2, 2])
+
 
 class MetricTest(unittest.TestCase):
     """Tests for the individual metric functions."""

From 6ba74b33b7d8d0367cfbf2ecbc6392add4d79824 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 13:03:34 +0000
Subject: [PATCH 40/56] Preserve U-Net checkpoint configuration

---
 .../inference.py                              |  4 +-
 .../machine_learning/train.py                 | 33 +++++++++++-
 .../machine_learning/unet3d.py                | 53 +++++++++++++++----
 3 files changed, 77 insertions(+), 13 deletions(-)

diff --git a/src/aind_exaspim_image_compression/inference.py b/src/aind_exaspim_image_compression/inference.py
index 3f66081..b8e363a 100644
--- a/src/aind_exaspim_image_compression/inference.py
+++ b/src/aind_exaspim_image_compression/inference.py
@@ -278,11 +278,13 @@ def load_model(path, device="cuda"):
     if isinstance(ckpt, dict) and "model" in ckpt:
         state_dict = ckpt["model"]
         transform_cfg = ckpt.get("transform") or {"kind": "asinh"}
+        model_cfg = ckpt.get("model_config") or {}
     else:
         state_dict = ckpt
         transform_cfg = {"kind": "asinh"}
+        model_cfg = {}
 
-    model = UNet()
+    model = UNet(**model_cfg)
     model.load_state_dict(state_dict)
     model.to(device)
     model.eval()
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index 715dac6..f41c9b1 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -121,6 +121,7 @@ def __init__(
         self.checkpoint_weights = checkpoint_weights
         self.best_score = np.inf
         self.model = model.to(device) if model else UNet().to(device)
+        self._resume_transform_cfg = None
         self.optimizer = optim.AdamW(self.model.parameters(), lr=lr)
         # T_max spans the whole run so the cosine anneals once. With a small
         # T_max the LR returns to its peak every 2*T_max epochs, and each
@@ -152,6 +153,19 @@ def run(self, train_dataset, val_dataset):
         """
         # Initializations
         print("Experiment:", os.path.basename(os.path.normpath(self.log_dir)))
+        if self._resume_transform_cfg is not None:
+            train_cfg = getattr(train_dataset.transform, "cfg", None)
+            val_cfg = getattr(val_dataset.transform, "cfg", None)
+            if train_cfg != self._resume_transform_cfg:
+                raise ValueError(
+                    "resume checkpoint transform does not match the training "
+                    "dataset transform"
+                )
+            if val_cfg != self._resume_transform_cfg:
+                raise ValueError(
+                    "resume checkpoint transform does not match the validation "
+                    "dataset transform"
+                )
         self.transform = train_dataset.transform
         train_dataloader = DataLoader(
             train_dataset,
@@ -375,8 +389,21 @@ def load_pretrained_weights(self, model_path):
         """
         ckpt = torch.load(model_path, map_location=self.device)
         if isinstance(ckpt, dict) and "model" in ckpt:
-            ckpt = ckpt["model"]
-        self.model.load_state_dict(ckpt)
+            checkpoint_model_cfg = ckpt.get("model_config")
+            current_model_cfg = getattr(self.model, "config", None)
+            if (
+                checkpoint_model_cfg is not None
+                and checkpoint_model_cfg != current_model_cfg
+            ):
+                raise ValueError(
+                    "resume checkpoint model configuration does not match "
+                    "the configured model"
+                )
+            self._resume_transform_cfg = ckpt.get("transform")
+            state_dict = ckpt["model"]
+        else:
+            state_dict = ckpt
+        self.model.load_state_dict(state_dict)
 
     def save_config(self, config):
         """
@@ -404,6 +431,7 @@ def save_config(self, config):
             "checkpoint_weights": self.checkpoint_weights,
             "lr": self.optimizer.param_groups[0]["lr"],
             "model": type(self.model).__name__,
+            "model_config": getattr(self.model, "config", None),
         }
         record.update(config)
         util.write_json(os.path.join(self.log_dir, "config.json"), record)
@@ -426,6 +454,7 @@ def save_model(self, epoch, score):
         torch.save(
             {
                 "model": self.model.state_dict(),
+                "model_config": getattr(self.model, "config", None),
                 "transform": getattr(self.transform, "cfg", None),
             },
             path,
diff --git a/src/aind_exaspim_image_compression/machine_learning/unet3d.py b/src/aind_exaspim_image_compression/machine_learning/unet3d.py
index 185c28d..cff18e8 100644
--- a/src/aind_exaspim_image_compression/machine_learning/unet3d.py
+++ b/src/aind_exaspim_image_compression/machine_learning/unet3d.py
@@ -9,6 +9,9 @@
 
 """
 
+from math import gcd
+from numbers import Real
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -41,9 +44,9 @@ def __init__(self, width_multiplier=1, trilinear=True, residual=True):
 
         Parameters
         ----------
-        width_multiplier : float, optional
-            Factor that scales the number of channels in each layer. Default
-            is 1.
+        width_multiplier : int, optional
+            Positive integer factor that scales the number of channels in each
+            layer. Default is 1.
         trilinear : bool, optional
             If True, use trilinear interpolation for upsampling in decoder
             blocks; otherwise, use transposed convolutions. Default is True.
@@ -55,12 +58,21 @@ def __init__(self, width_multiplier=1, trilinear=True, residual=True):
         # Call parent class
         super(UNet, self).__init__()
 
+        if (
+            isinstance(width_multiplier, bool)
+            or not isinstance(width_multiplier, Real)
+            or width_multiplier < 1
+            or not float(width_multiplier).is_integer()
+        ):
+            raise ValueError("width_multiplier must be a positive integer")
+
         # Initializations
         _channels = (32, 64, 128, 256, 512)
         factor = 2 if trilinear else 1
 
         # Instance attributes
-        self.channels = [int(c * width_multiplier) for c in _channels]
+        self.width_multiplier = int(width_multiplier)
+        self.channels = [c * self.width_multiplier for c in _channels]
         self.trilinear = trilinear
         self.residual = residual
 
@@ -78,6 +90,15 @@ def __init__(self, width_multiplier=1, trilinear=True, residual=True):
         self.up4 = Up(self.channels[1], self.channels[0], trilinear)
         self.outc = OutConv(self.channels[0], 1)
 
+    @property
+    def config(self):
+        """Constructor arguments needed to recreate this model."""
+        return {
+            "width_multiplier": self.width_multiplier,
+            "trilinear": self.trilinear,
+            "residual": self.residual,
+        }
+
     def forward(self, x):
         """
         Forward pass of the 3D U-Net.
@@ -125,7 +146,9 @@ class DoubleConv(nn.Module):
         activations.
     """
 
-    def __init__(self, in_channels, out_channels, kernel_size=3, mid_channels=None):
+    def __init__(
+        self, in_channels, out_channels, kernel_size=3, mid_channels=None
+    ):
         """
         Instantiates a DoubleConv object.
 
@@ -150,12 +173,22 @@ def __init__(self, in_channels, out_channels, kernel_size=3, mid_channels=None):
 
         # Instance attributes
         self.double_conv = nn.Sequential(
-            nn.Conv3d(in_channels, mid_channels, kernel_size=kernel_size, padding=1),
-            nn.GroupNorm(min(8, mid_channels), mid_channels),
+            nn.Conv3d(
+                in_channels,
+                mid_channels,
+                kernel_size=kernel_size,
+                padding=1,
+            ),
+            nn.GroupNorm(gcd(8, mid_channels), mid_channels),
+            nn.LeakyReLU(negative_slope=0.01, inplace=True),
+            nn.Conv3d(
+                mid_channels,
+                out_channels,
+                kernel_size=kernel_size,
+                padding=1,
+            ),
+            nn.GroupNorm(gcd(8, out_channels), out_channels),
             nn.LeakyReLU(negative_slope=0.01, inplace=True),
-            nn.Conv3d(mid_channels, out_channels, kernel_size=kernel_size, padding=1),
-            nn.GroupNorm(min(8, out_channels), out_channels),
-            nn.LeakyReLU(negative_slope=0.01, inplace=True)
         )
 
     def forward(self, x):

From 3ac3830759830c1e96ad204015de36331a0eb484 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 13:03:38 +0000
Subject: [PATCH 41/56] Accept precomputed inference background offsets

---
 .../inference.py                              | 33 ++++++++++++-------
 1 file changed, 21 insertions(+), 12 deletions(-)

diff --git a/src/aind_exaspim_image_compression/inference.py b/src/aind_exaspim_image_compression/inference.py
index b8e363a..1824603 100644
--- a/src/aind_exaspim_image_compression/inference.py
+++ b/src/aind_exaspim_image_compression/inference.py
@@ -291,32 +291,41 @@ def load_model(path, device="cuda"):
     return model, build_transform(transform_cfg)
 
 
-def build_volume_transform(base_transform, img, percentile=0.1):
+def build_volume_transform(
+    base_transform, img=None, percentile=0.1, offset=None
+):
     """
-    Builds a per-volume transform whose offset is estimated from the image.
+    Builds an inference transform with a raw-count background offset.
 
-    Use at inference on raw (non-background-subtracted) volumes: it estimates
-    the background offset from a low percentile of the nonzero voxels and
-    returns a transform with that offset plus the trained transform's kind and
-    scale. This mirrors the per-brain offset subtracted during training, so a
-    raw volume is normalized to the same background-at-zero space the model
-    was trained on.
+    Production inference should pass an offset precomputed from the full image
+    tile using lower-resolution data. When no offset is supplied, this falls
+    back to estimating one directly from ``img``; that mode is intended for
+    testing and debugging on representative subvolumes.
 
     Parameters
     ----------
     base_transform : IntensityTransform
         The transform the model was trained with (e.g., from load_model).
-    img : numpy.ndarray
-        Raw image volume to be denoised.
+    img : numpy.ndarray, optional
+        Raw image volume used only when ``offset`` is None.
     percentile : float, optional
-        Low percentile used as the background estimate. Default is 0.1.
+        Low percentile used for fallback estimation from ``img``. Default is
+        0.1.
+    offset : float, optional
+        Precomputed full-tile background offset. When supplied, no statistic
+        is calculated from ``img``.
 
     Returns
     -------
     IntensityTransform
         A transform carrying the per-volume offset.
     """
-    offset = estimate_offset(img, percentile=percentile, ignore_zeros=True)
+    if offset is None:
+        if img is None:
+            raise ValueError("img is required when offset is not supplied")
+        offset = estimate_offset(
+            img, percentile=percentile, ignore_zeros=True
+        )
     return with_offset(base_transform, offset)
 
 

From ca2df261f534df8750592832be746f376dce00de Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 13:03:42 +0000
Subject: [PATCH 42/56] Update BM4DNet evaluation configuration

---
 scripts/evaluate_bm4dnet.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/scripts/evaluate_bm4dnet.py b/scripts/evaluate_bm4dnet.py
index 77f4e85..8aceb62 100644
--- a/scripts/evaluate_bm4dnet.py
+++ b/scripts/evaluate_bm4dnet.py
@@ -71,12 +71,16 @@ def evaluate():
         raw = np.asarray(img[0, 0])
     print("Volume shape:", raw.shape)
 
-    # For a raw (non-background-subtracted) volume, estimate this volume's
-    # background offset so it lands in the same background-at-zero space the
-    # model was trained on (mirrors the per-brain offset subtracted in training).
-    # Set raw_input=False if the input is already background-subtracted.
+    # For a raw (non-background-subtracted) volume, use the supplied full-tile
+    # offset. With background_offset=None, fall back to estimating from this
+    # test subvolume for debugging only.
     if raw_input:
-        volume_transform = build_volume_transform(transform, raw)
+        volume_transform = build_volume_transform(
+            transform,
+            raw,
+            percentile=0.1,
+            offset=background_offset,
+        )
         print("Per-volume transform:", volume_transform.cfg)
     else:
         volume_transform = transform
@@ -118,8 +122,8 @@ def evaluate():
     # Checkpoint. Point session_dir at a training session (the folder holding
     # the BM4DNet-*.pth files) to auto-select the best checkpoint. Set
     # checkpoint_path to a .pth to evaluate that file explicitly instead.
-    session_dir = "/root/capsule/data/bm4dnet-training-session-20260709_1354"
-    checkpoint_path = None
+    session_dir = "/root/capsule/results/training-sessions/session-20260709_1817"
+    checkpoint_path = "/root/capsule/results/training-sessions/session-20260709_1817/BM4DNet-20260709-499--2.026414.pth"
 
     # Test image. Any zarr readable by img_util.read, including an s3:// path;
     # give the full path to a single 5D multiscale level array.
@@ -134,15 +138,17 @@ def evaluate():
     crop_center = None
     crop_shape = (256, 256, 256)
 
-    # raw_input=True estimates a per-volume background offset (use for raw
-    # volumes that were NOT background-subtracted).
+    # Use raw_input=True for volumes that were not background-subtracted.
     raw_input = True
+    # Prefer the background offset precomputed from the full image tile's
+    # lower-resolution data. None estimates from this test subvolume instead.
+    background_offset = None
 
     # Output + misc
     output_dir = "/results/evaluation"
     # Where to persist the denoised volume as an OME-Zarr. Local path or a
     # cloud path (e.g. "s3://BUCKET/PATH/denoised.zarr"). Set to None to skip.
-    output_zarr = "s3://aind-scratch-data/cameron.arshadi/denoising-experiments/outputs/BM4DNet-20260709-405--163.534489/block_001.zarr"
+    output_zarr = "s3://aind-scratch-data/cameron.arshadi/denoising-experiments/outputs/BM4DNet-20260709-499--2.026414/block_001.zarr"
     device = "cuda"
     batch_size = 32
     clevel = 5

From 89786e7ce24edef6e040243ded220b93653fee06 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 13:03:48 +0000
Subject: [PATCH 43/56] Require Zarr 3 and harden image I/O

---
 pyproject.toml                                |    8 +-
 .../utils/img_util.py                         |   51 +-
 uv.lock                                       | 5611 +++++++++++++++++
 3 files changed, 5648 insertions(+), 22 deletions(-)
 create mode 100644 uv.lock

diff --git a/pyproject.toml b/pyproject.toml
index 61871ca..a0b8301 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 name = "aind-exaspim-image-compression"
 description = "Generated from aind-library-template"
 license = {text = "MIT"}
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 authors = [
     {name = "Allen Institute for Neural Dynamics"}
 ]
@@ -26,7 +26,7 @@ dependencies = [
     'imagecodecs',
     'interrogate',
     'matplotlib',
-    'ome-zarr',
+    'ome-zarr>=0.12.0',
     'pandas',
     's3fs==2025.7.0',
     'scikit-image',
@@ -38,7 +38,7 @@ dependencies = [
     'torchvision',
     'tqdm',
     'xarray_multiscale==1.2.0',
-    'zarr',
+    'zarr>=3.0.8',
     "aind-exaspim-dataset-utils @ git+https://github.com/AllenNeuralDynamics/aind-exaspim-dataset-utils.git@main"
 ]
 
@@ -61,7 +61,7 @@ version = {attr = "aind_exaspim_image_compression.__version__"}
 
 [tool.black]
 line-length = 79
-target_version = ['py310']
+target_version = ['py311']
 exclude = '''
 
 (
diff --git a/src/aind_exaspim_image_compression/utils/img_util.py b/src/aind_exaspim_image_compression/utils/img_util.py
index 4df7c0f..bef6d4e 100644
--- a/src/aind_exaspim_image_compression/utils/img_util.py
+++ b/src/aind_exaspim_image_compression/utils/img_util.py
@@ -83,8 +83,8 @@ def _read_n5(img_path):
 
     Returns
     -------
-    numpy.ndarray
-        Image volume.
+    tensorstore.TensorStore
+        Lazy, sliceable image volume.
     """
     if is_s3_path(img_path) or is_gcs_path(img_path):
         bucket, prefix = util.parse_cloud_path(img_path)
@@ -96,7 +96,7 @@ def _read_n5(img_path):
     else:
         kvstore = {"driver": "file", "path": img_path.rstrip("/") + "/volume"}
     arr = ts.open({"driver": "n5", "kvstore": kvstore}).result()
-    return arr[:].read().result()
+    return arr
 
 
 def _read_neuroglancer_precompted(img_path):
@@ -719,37 +719,41 @@ def write_ome_zarr(
     voxel_size=(748, 748, 1000),
     storage_options=None,
 ):
-    # zarr v3 codec; default matches the cratio codec (zstd, level 5, shuffle).
+    # Zarr v3 codec; default matches the cratio codec (zstd, level 5, shuffle).
     from zarr.codecs import BloscCodec
 
     if compressor is None:
-        compressor = BloscCodec(cname="zstd", clevel=5, shuffle="shuffle")
+        compressor = BloscCodec(
+            cname="zstd", clevel=5, shuffle="shuffle"
+        )
 
     # Ensure 5D image (T, C, Z, Y, X)
     while img.ndim < 5:
         img = img[np.newaxis, ...]
 
     # Generate multiscale pyramid
-    pyramid = multiscale(img, windowed_mode, scale_factors=scale_factors)[:n_levels]
+    pyramid = multiscale(
+        img, windowed_mode, scale_factors=scale_factors
+    )[:n_levels]
     pyramid = [level.data for level in pyramid]
 
-    # Prepare Zarr store. zarr v3 builds it from the path: a LocalStore for a
-    # filesystem path, an FsspecStore for s3:// / gs:// (credentials from
-    # storage_options or the default chain).
+    # Zarr v3 builds a LocalStore or FsspecStore from the path/URL.
     zgroup = zarr.open_group(
         store=output_path, mode="w", storage_options=storage_options
     )
 
     # Voxel size scaling for each level
     base_scale = np.array([1, 1, *reversed(voxel_size)])
-    scales = [base_scale[:2].tolist() + (base_scale[2:] * 2**i).tolist() for i in range(n_levels)]
+    scales = [
+        base_scale[:2].tolist() + (base_scale[2:] * 2**i).tolist()
+        for i in range(n_levels)
+    ]
     coord_transforms = [[{"type": "scale", "scale": s}] for s in scales]
 
     # Write to OME-Zarr
     write_multiscale(
         pyramid=pyramid,
         group=zgroup,
-        chunks=chunks,
         axes=[
             {"name": "t", "type": "time", "unit": "millisecond"},
             {"name": "c", "type": "channel"},
@@ -758,7 +762,10 @@ def write_ome_zarr(
             {"name": "x", "type": "space", "unit": "micrometer"},
         ],
         coordinate_transformations=coord_transforms,
-        storage_options={"compressors": [compressor]},
+        storage_options={
+            "chunks": chunks,
+            "compressors": [compressor],
+        },
     )
 
 
@@ -774,11 +781,11 @@ def write_zarr(
     """
     Writes an image volume to a single Zarr array (local or cloud).
 
-    Uses the zarr v3 API, so ``output_path`` may be a local path or a cloud URL
-    (``s3://...``, ``gs://...``); zarr builds the store from the URL. For cloud
-    writes, credentials are resolved by fsspec from the standard chain (env,
-    ``~/.aws``, instance role) unless ``storage_options`` overrides them. The
-    array is stored 5D (t, c, z, y, x) so ``read`` reads it back unchanged.
+    Uses the Zarr v3 API, so ``output_path`` may be a local path or a cloud URL
+    (``s3://...``, ``gs://...``); Zarr builds the store from the URL. Cloud
+    credentials are resolved from the standard chain unless ``storage_options``
+    overrides them. The array is stored 5D (t, c, z, y, x) so ``read`` reads it
+    back unchanged.
 
     Parameters
     ----------
@@ -808,7 +815,9 @@ def write_zarr(
         shape=img.shape,
         chunks=chunks,
         dtype=img.dtype,
-        compressors=BloscCodec(cname=cname, clevel=clevel, shuffle=shuffle),
+        compressors=BloscCodec(
+            cname=cname, clevel=clevel, shuffle=shuffle
+        ),
         overwrite=True,
         storage_options=storage_options,
     )
@@ -839,6 +848,12 @@ def ssim3D(img1, img2, data_range=None, window_size=16):
     if img1.shape != img2.shape:
         raise ValueError("Input images must have the same dimensions")
 
+    # Integer powers and products overflow before scipy sees them (notably for
+    # normal uint16 microscopy inputs), corrupting the local moments. Convert
+    # once up front so all subsequent arithmetic is floating point.
+    img1 = np.asarray(img1, dtype=np.float64)
+    img2 = np.asarray(img2, dtype=np.float64)
+
     if data_range is None:
         data_range1 = np.max(img1) - np.min(img1)
         data_range2 = np.max(img2) - np.min(img2)
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..b9ba796
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,5611 @@
+version = 1
+revision = 3
+requires-python = ">=3.11"
+resolution-markers = [
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+    "python_full_version == '3.13.*' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform != 'win32'",
+    "python_full_version > '3.11' and python_full_version < '3.12' and sys_platform != 'win32'",
+    "python_full_version <= '3.11' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version > '3.11' and python_full_version < '3.12' and sys_platform == 'win32'",
+    "python_full_version <= '3.11' and sys_platform == 'win32'",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.5.0"
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+[[package]]
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+version = "0.0.5"
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+    { name = "pygments" },
+]
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+[[package]]
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+source = { git = "https://github.com/AllenNeuralDynamics/aind-exaspim-dataset-utils.git?rev=main#689b6ca78daf95cedabb27ad1f4340ed01945515" }
+dependencies = [
+    { name = "boto3" },
+    { name = "google-cloud-storage" },
+    { name = "networkx" },
+    { name = "pandas" },
+    { name = "s3fs" },
+    { name = "smartsheet-python-sdk" },
+    { name = "zarr", version = "3.1.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "zarr", version = "3.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+]
+
+[[package]]
+name = "aind-exaspim-image-compression"
+source = { editable = "." }
+dependencies = [
+    { name = "aind-exaspim-dataset-utils" },
+    { name = "bm4d" },
+    { name = "boto3" },
+    { name = "cloud-volume", extra = ["crackle", "seg-codecs"] },
+    { name = "fastremap" },
+    { name = "gcsfs" },
+    { name = "google-cloud-storage" },
+    { name = "imagecodecs", version = "2026.3.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "imagecodecs", version = "2026.6.26", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "interrogate" },
+    { name = "matplotlib" },
+    { name = "ome-zarr", version = "0.16.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version <= '3.11'" },
+    { name = "ome-zarr", version = "0.18.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version > '3.11'" },
+    { name = "pandas" },
+    { name = "s3fs" },
+    { name = "scikit-image" },
+    { name = "scikit-learn" },
+    { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
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+    { name = "tensorboard" },
+    { name = "tensorstore" },
+    { name = "torch" },
+    { name = "torchvision" },
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+    { name = "xarray-multiscale" },
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+    { name = "zarr", version = "3.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+]
+
+[package.optional-dependencies]
+dev = [
+    { name = "black" },
+    { name = "coverage" },
+    { name = "flake8" },
+    { name = "furo" },
+    { name = "interrogate" },
+    { name = "isort" },
+    { name = "sphinx", version = "9.0.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "sphinx", version = "9.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+]
+
+[package.metadata]
+requires-dist = [
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+    { name = "black", marker = "extra == 'dev'" },
+    { name = "bm4d" },
+    { name = "boto3" },
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+    { name = "tensorboard" },
+    { name = "tensorstore" },
+    { name = "torch" },
+    { name = "torchvision" },
+    { name = "tqdm" },
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+[[package]]
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+    { name = "python-dateutil" },
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From cc8fddcd2a06035e117a945ada0c357eeef4e953 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 13:03:56 +0000
Subject: [PATCH 44/56] Add regression coverage for review fixes

---
 tests/test_review_regressions.py | 237 +++++++++++++++++++++++++++++++
 1 file changed, 237 insertions(+)
 create mode 100644 tests/test_review_regressions.py

diff --git a/tests/test_review_regressions.py b/tests/test_review_regressions.py
new file mode 100644
index 0000000..1fc3248
--- /dev/null
+++ b/tests/test_review_regressions.py
@@ -0,0 +1,237 @@
+"""Regression tests for inference, persistence, and architecture reviews."""
+
+import os
+import tempfile
+import unittest
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+import torch
+import zarr
+
+from aind_exaspim_image_compression.inference import (
+    build_volume_transform,
+    load_model,
+)
+from aind_exaspim_image_compression.machine_learning.data_handling import (
+    TrainDataset,
+)
+from aind_exaspim_image_compression.machine_learning.transforms import (
+    build_transform,
+)
+from aind_exaspim_image_compression.machine_learning.train import Trainer
+from aind_exaspim_image_compression.machine_learning.unet3d import (
+    DoubleConv,
+    UNet,
+)
+from aind_exaspim_image_compression.utils import img_util
+
+
+class InferenceRegressionTest(unittest.TestCase):
+    """Tests checkpoint reconstruction and inference offset calibration."""
+
+    def test_load_model_reconstructs_constructor_config(self):
+        """Loading preserves width, upsampling, and residual mode."""
+        model_config = {
+            "width_multiplier": 2,
+            "trilinear": False,
+            "residual": False,
+        }
+        checkpoint = {
+            "model": {"weight": torch.ones(1)},
+            "model_config": model_config,
+            "transform": {"kind": "asinh", "params": {"scale": 16.0}},
+        }
+        with patch(
+            "aind_exaspim_image_compression.inference.torch.load",
+            return_value=checkpoint,
+        ), patch(
+            "aind_exaspim_image_compression.inference.UNet"
+        ) as model_factory:
+            loaded, transform = load_model("checkpoint.pth", device="cpu")
+        model_factory.assert_called_once_with(**model_config)
+        loaded.load_state_dict.assert_called_once_with(checkpoint["model"])
+        self.assertEqual(transform.scale, 16.0)
+
+    def test_volume_transform_uses_precomputed_offset(self):
+        """A full-tile offset bypasses subvolume-based estimation."""
+        base = build_transform(
+            {"kind": "asinh", "params": {"offset": 0.0, "scale": 32.0}}
+        )
+        with patch(
+            "aind_exaspim_image_compression.inference.estimate_offset"
+        ) as estimate:
+            transform = build_volume_transform(base, offset=73.5)
+        estimate.assert_not_called()
+        self.assertAlmostEqual(transform.offset, 73.5)
+
+    def test_volume_transform_requires_image_without_offset(self):
+        """Fallback estimation requires an explicit test image."""
+        base = build_transform({"kind": "asinh"})
+        with self.assertRaisesRegex(ValueError, "img is required"):
+            build_volume_transform(base)
+
+    def test_volume_transform_estimates_directly_from_debug_image(self):
+        """Debug fallback uses the supplied subvolume without coarsening."""
+        base = build_transform({"kind": "asinh"})
+        raw = np.array([0, 10, 20], dtype=np.uint16).reshape(1, 1, 3)
+        transform = build_volume_transform(base, raw, percentile=0)
+        self.assertAlmostEqual(transform.offset, 10.0)
+
+
+class ArchitectureRegressionTest(unittest.TestCase):
+    """Tests non-default U-Net construction."""
+
+    def test_width_multiplier_is_a_positive_integer(self):
+        """Arbitrary fractional width multipliers are rejected explicitly."""
+        with self.assertRaisesRegex(ValueError, "positive integer"):
+            UNet(width_multiplier=0.3)
+        with self.assertRaisesRegex(ValueError, "positive integer"):
+            UNet(width_multiplier=0)
+
+    def test_group_norm_groups_divide_channels(self):
+        """DoubleConv still chooses valid groups for custom channel counts."""
+        block = DoubleConv(1, 9)
+        norms = [
+            m for m in block.modules()
+            if isinstance(m, torch.nn.GroupNorm)
+        ]
+        self.assertTrue(norms)
+        for norm in norms:
+            self.assertEqual(norm.num_channels % norm.num_groups, 0)
+
+    def test_checkpoint_saves_config_and_rejects_transform_mismatch(self):
+        """Resume cannot silently change the model's intensity mapping."""
+        class TinyModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.weight = torch.nn.Parameter(torch.zeros(1))
+                self.config = {
+                    "width_multiplier": 1,
+                    "trilinear": True,
+                    "residual": False,
+                }
+
+        model = TinyModel()
+        checkpoint_transform = build_transform(
+            {"kind": "asinh", "params": {"scale": 16.0}}
+        )
+        with tempfile.TemporaryDirectory() as directory:
+            trainer = Trainer(
+                directory,
+                device="cpu",
+                model=model,
+                max_epochs=0,
+                use_amp=False,
+            )
+            try:
+                trainer.transform = checkpoint_transform
+                trainer.save_model(epoch=0, score=1.0)
+                checkpoint_path = os.path.join(
+                    trainer.log_dir,
+                    next(
+                        name
+                        for name in os.listdir(trainer.log_dir)
+                        if name.endswith(".pth")
+                    ),
+                )
+                checkpoint = torch.load(checkpoint_path, map_location="cpu")
+                self.assertEqual(checkpoint["model_config"], model.config)
+
+                trainer.load_pretrained_weights(checkpoint_path)
+                different_transform = build_transform(
+                    {"kind": "asinh", "params": {"scale": 32.0}}
+                )
+                dataset = SimpleNamespace(transform=different_transform)
+                with self.assertRaisesRegex(ValueError, "transform"):
+                    trainer.run(dataset, dataset)
+            finally:
+                trainer.writer.close()
+
+
+class SkeletonRegressionTest(unittest.TestCase):
+    """Tests SWC density validation during ingestion."""
+
+    def test_warns_for_long_edge_but_accepts_3d_diagonal(self):
+        """Chebyshev length treats a one-step 3D diagonal as adjacent."""
+        dataset = TrainDataset((16, 16, 16), anisotropy=(1, 1, 1))
+        swc = {
+            "id": np.array([1, 2, 3]),
+            "pid": np.array([-1, 1, 2]),
+            "xyz": np.array(
+                [[1, 2, 3], [2, 3, 4], [4, 3, 4]], dtype=np.float32
+            ),
+        }
+        dataset.swc_reader.read = lambda _: [swc]
+        with patch(
+            "aind_exaspim_image_compression.machine_learning."
+            "data_handling.logger.warning"
+        ) as warning:
+            dataset._load_swcs("brain", "unused")
+        warning.assert_called_once()
+        self.assertEqual(warning.call_args.args[1:], ("brain", 1, 2))
+        self.assertEqual(dataset.skeletons["brain"].shape, (3, 3))
+
+
+class ImageUtilityRegressionTest(unittest.TestCase):
+    """Tests lazy reads, Zarr 3 writers, and numeric SSIM."""
+
+    def test_n5_reader_returns_tensorstore_without_reading(self):
+        """Opening N5 does not materialize the entire volume."""
+        sentinel = object()
+        opened = type("OpenResult", (), {"result": lambda self: sentinel})()
+        with patch.object(img_util.ts, "open", return_value=opened):
+            result = img_util._read_n5("/tmp/example.n5")
+        self.assertIs(result, sentinel)
+
+    def test_write_zarr_round_trip(self):
+        """The basic writer persists a format readable by installed Zarr."""
+        image = np.arange(64, dtype=np.uint16).reshape(4, 4, 4)
+        with tempfile.TemporaryDirectory() as directory:
+            path = os.path.join(directory, "image.zarr")
+            img_util.write_zarr(image, path, chunks=(1, 1, 2, 2, 2))
+            stored = zarr.open(path, mode="r")
+            self.assertEqual(stored.metadata.zarr_format, 3)
+            np.testing.assert_array_equal(stored[0, 0], image)
+            lazy = img_util.read(path)
+            np.testing.assert_array_equal(lazy[0, 0], image)
+
+    def test_write_ome_zarr_round_trip(self):
+        """OME-Zarr output is persisted in Zarr format 3."""
+        image = np.arange(64, dtype=np.uint16).reshape(4, 4, 4)
+        with tempfile.TemporaryDirectory() as directory:
+            path = os.path.join(directory, "image.ome.zarr")
+            img_util.write_ome_zarr(
+                image,
+                path,
+                chunks=(1, 1, 2, 2, 2),
+                n_levels=1,
+            )
+            group = zarr.open_group(path, mode="r")
+            self.assertEqual(group.metadata.zarr_format, 3)
+            self.assertIn("ome", group.attrs)
+            multiscales = group.attrs["ome"]["multiscales"]
+            dataset_path = multiscales[0]["datasets"][0]["path"]
+            np.testing.assert_array_equal(group[dataset_path][0, 0], image)
+
+    def test_ssim_uint16_matches_float_computation(self):
+        """Bright uint16 products cannot overflow before local filtering."""
+        rng = np.random.default_rng(42)
+        image1 = rng.integers(40000, 65000, size=(8, 8, 8), dtype=np.uint16)
+        image2 = np.clip(
+            image1.astype(np.int32) + rng.integers(-1000, 1000, image1.shape),
+            0,
+            65535,
+        ).astype(np.uint16)
+        integer_result = img_util.ssim3D(image1, image2, window_size=3)
+        float_result = img_util.ssim3D(
+            image1.astype(np.float64),
+            image2.astype(np.float64),
+            window_size=3,
+        )
+        self.assertAlmostEqual(integer_result, float_result, places=12)
+
+
+if __name__ == "__main__":
+    unittest.main()

From b0564aa4edcee85f9b0a03adca12a6ba8038131b Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 13:09:46 +0000
Subject: [PATCH 45/56] Record precompute cache configuration

---
 scripts/precompute.py    | 41 +++++++++++++++++++-
 tests/test_precompute.py | 81 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 120 insertions(+), 2 deletions(-)
 create mode 100644 tests/test_precompute.py

diff --git a/scripts/precompute.py b/scripts/precompute.py
index a4580c8..ad13239 100644
--- a/scripts/precompute.py
+++ b/scripts/precompute.py
@@ -35,6 +35,7 @@
     teacher.npy    float16  (N, *patch_shape)   clipped BM4D denoising
     fg.npy         uint8    (N, *patch_shape)   foreground mask (0/1)
     transform.json                              resolved transform cfg
+    config.json                                 full precompute configuration
 
 The transform cfg is stamped alongside the patches so the training run rebuilds
 the identical transform without touching the cloud. Each worker builds its
@@ -143,6 +144,7 @@ def precompute():
     # 0 because per-brain offsets are subtracted per patch.
     brain_ids = util.read_txt(brain_ids_path)
     offsets = util.read_json(offsets_path) if offsets_path else None
+    resolved_transform_cfg = build_transform(transform_cfg).cfg
     init_kwargs = dict(
         brain_ids=brain_ids,
         img_paths_json=img_prefixes_path,
@@ -165,11 +167,46 @@ def precompute():
         sigma_bm4d=sigma_bm4d,
         skeleton_radius=skeleton_radius,
         swc_pointers=swc_pointers,
-        transform_cfg=transform_cfg,
+        transform_cfg=resolved_transform_cfg,
     )
 
     # Pre-allocate memory-mapped outputs and stream results into them.
     util.mkdir(cache_dir)
+    util.write_json(
+        f"{cache_dir}/config.json",
+        {
+            "split": split,
+            "cache_dir": cache_dir,
+            "n_patches": n_patches,
+            "brain_ids_path": brain_ids_path,
+            "img_prefixes_path": img_prefixes_path,
+            "segmentation_prefixes_path": segmentation_prefixes_path,
+            "offsets_path": offsets_path,
+            "swc_pointers": swc_pointers,
+            "transform_cfg": resolved_transform_cfg,
+            "foreground_sampling_rate": foreground_sampling_rate,
+            "min_foreground_voxels": min_foreground_voxels,
+            "min_segmentation_volume": min_segmentation_volume,
+            "patch_shape": patch_shape,
+            "skeleton_radius": skeleton_radius,
+            "segmentation_dilate": segmentation_dilate,
+            "preserve_foreground": preserve_foreground,
+            "sigma_bm4d": sigma_bm4d,
+            "reject_incoherent_patches": reject_incoherent_patches,
+            "coherence_min_autocorr": coherence_min_autocorr,
+            "coherence_max_highfreq_frac": coherence_max_highfreq_frac,
+            "coherence_min_segment_voxels": (
+                coherence_min_segment_voxels
+            ),
+            "coherence_smooth_sigma": coherence_smooth_sigma,
+            "coherence_lag": coherence_lag,
+            "max_resample_attempts": max_resample_attempts,
+            "seed": seed,
+            "seed_stream": _SEED_STREAMS[split],
+            "num_workers": num_workers,
+            "n_validate_examples": 0,
+        },
+    )
     shape = (n_patches,) + tuple(patch_shape)
     raw_mm = open_memmap(
         f"{cache_dir}/raw.npy", mode="w+", dtype=np.float16, shape=shape
@@ -203,7 +240,7 @@ def precompute():
     # Stamp the resolved transform cfg so training rebuilds it exactly without
     # touching the cloud.
     util.write_json(
-        f"{cache_dir}/transform.json", build_transform(transform_cfg).cfg
+        f"{cache_dir}/transform.json", resolved_transform_cfg
     )
     print(f"Wrote {n_patches} {split} patches to {cache_dir}")
 
diff --git a/tests/test_precompute.py b/tests/test_precompute.py
new file mode 100644
index 0000000..1ec3d40
--- /dev/null
+++ b/tests/test_precompute.py
@@ -0,0 +1,81 @@
+"""Tests for the patch-cache precompute script."""
+
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import runpy
+import unittest
+
+
+class _StopAfterConfig(Exception):
+    """Stops precompute before allocating cache arrays."""
+
+
+class PrecomputeConfigTest(unittest.TestCase):
+    """Tests persistence of the resolved precompute configuration."""
+
+    def test_writes_complete_config_before_cache_generation(self):
+        """Every cache-generation setting is recorded in config.json."""
+        script = Path(__file__).parents[1] / "scripts" / "precompute.py"
+        namespace = runpy.run_path(str(script))
+        precompute = namespace["precompute"]
+        settings = {
+            "split": "train",
+            "cache_dir": "/cache",
+            "n_patches": 12,
+            "brain_ids_path": "/data/brains.txt",
+            "img_prefixes_path": "/data/images.json",
+            "segmentation_prefixes_path": "/data/segments.json",
+            "offsets_path": "/data/offsets.json",
+            "swc_pointers": {"bucket_name": "bucket", "path": "swcs"},
+            "transform_cfg": {
+                "kind": "asinh",
+                "params": {"offset": 0.0, "scale": 32.0},
+            },
+            "foreground_sampling_rate": 0.5,
+            "min_foreground_voxels": 50,
+            "min_segmentation_volume": 200,
+            "patch_shape": (64, 64, 64),
+            "skeleton_radius": 2,
+            "segmentation_dilate": 0,
+            "preserve_foreground": True,
+            "sigma_bm4d": 24,
+            "reject_incoherent_patches": True,
+            "coherence_min_autocorr": 0.4,
+            "coherence_max_highfreq_frac": 0.35,
+            "coherence_min_segment_voxels": 50,
+            "coherence_smooth_sigma": 1.0,
+            "coherence_lag": 2,
+            "max_resample_attempts": 50,
+            "seed": 42,
+            "num_workers": None,
+        }
+        precompute.__globals__.update(settings)
+        precompute.__globals__["open_memmap"] = MagicMock(
+            side_effect=_StopAfterConfig
+        )
+
+        util = precompute.__globals__["util"]
+        with patch.object(util, "read_txt", return_value=["brain"]), \
+                patch.object(util, "read_json", return_value={"brain": 10}), \
+                patch.object(util, "mkdir"), \
+                patch.object(util, "write_json") as write_json:
+            with self.assertRaises(_StopAfterConfig):
+                precompute()
+
+        write_json.assert_called_once()
+        path, config = write_json.call_args.args
+        self.assertEqual(path, "/cache/config.json")
+        expected_keys = set(settings) | {
+            "seed_stream",
+            "n_validate_examples",
+        }
+        self.assertEqual(set(config), expected_keys)
+        for key, value in settings.items():
+            self.assertEqual(config[key], value)
+        self.assertEqual(config["seed_stream"], 0)
+        self.assertEqual(config["n_validate_examples"], 0)
+
+
+if __name__ == "__main__":
+    unittest.main()

From 82b529f080fc8da90b139ddf7bfb1e5f3b82c146 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 17:47:10 +0000
Subject: [PATCH 46/56] Adds script to visualize swc masks

---
 scripts/visualize_swc_masks.py    | 214 ++++++++++++++++++++++++++++++
 tests/test_visualize_swc_masks.py |  66 +++++++++
 2 files changed, 280 insertions(+)
 create mode 100644 scripts/visualize_swc_masks.py
 create mode 100644 tests/test_visualize_swc_masks.py

diff --git a/scripts/visualize_swc_masks.py b/scripts/visualize_swc_masks.py
new file mode 100644
index 0000000..ccdf51e
--- /dev/null
+++ b/scripts/visualize_swc_masks.py
@@ -0,0 +1,214 @@
+"""
+Visualize SWC-only foreground masks over raw image patches.
+
+The precompute cache stores only the final foreground mask (segmentation union
+SWC), so the SWC contribution cannot be recovered from ``fg.npy``. This script
+instead reads the cache's ``config.json``, loads the same raw images and SWCs,
+centers patches on traced nodes, rebuilds the SWC-only mask with the configured
+``skeleton_radius``, and writes Z/Y/X maximum-projection overlays.
+
+No segmentation volumes or BM4D targets are loaded.
+
+Examples
+--------
+    # Sample six traced locations using a training cache's configuration
+    python scripts/visualize_swc_masks.py /results/patch_cache
+
+    # Inspect one brain reproducibly
+    python scripts/visualize_swc_masks.py /results/patch_cache/config.json
+        --brain-id 123456 --n 10 --seed 7 --out swc_masks.png
+"""
+
+import argparse
+from copy import deepcopy
+import os
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+from tqdm import tqdm
+
+from aind_exaspim_dataset_utils.s3_util import get_img_prefix
+from aind_exaspim_image_compression.machine_learning.data_handling import (
+    TrainDataset,
+)
+from aind_exaspim_image_compression.utils import util
+
+
+AXIS_TITLES = ("Z MIP", "Y MIP", "X MIP")
+
+
+def read_config(path):
+    """Reads ``config.json`` from a cache directory or explicit file path."""
+    config_path = (
+        os.path.join(path, "config.json") if os.path.isdir(path) else path
+    )
+    if not os.path.exists(config_path):
+        raise FileNotFoundError(f"Precompute config not found: {config_path}")
+    return util.read_json(config_path)
+
+
+def load_dataset(config, brain_id=None):
+    """Loads raw images and SWCs using a saved precompute configuration.
+
+    When no brain is requested explicitly, loading stops at the first brain
+    with SWC nodes so the default preview stays quick.
+    """
+    brain_ids = util.read_txt(config["brain_ids_path"])
+    if brain_id is not None:
+        if brain_id not in brain_ids:
+            raise ValueError(f"Brain ID not present in config: {brain_id}")
+        brain_ids = [brain_id]
+
+    swc_base = config.get("swc_pointers")
+    if not swc_base:
+        raise ValueError("Precompute config does not contain an SWC pointer")
+
+    dataset = TrainDataset(
+        tuple(config["patch_shape"]),
+        skeleton_radius=config["skeleton_radius"],
+    )
+    for current_brain in tqdm(brain_ids, desc="Load images and SWCs"):
+        img_path = (
+            get_img_prefix(current_brain, config["img_prefixes_path"])
+            + "0"
+        )
+        swc_pointer = deepcopy(swc_base)
+        swc_pointer["path"] += f"/{current_brain}/world"
+        dataset.ingest_brain(
+            current_brain,
+            img_path,
+            segmentation_path=None,
+            swc_pointer=swc_pointer,
+        )
+        if brain_id is None and current_brain in dataset.skeletons:
+            print(f"Using first brain with SWCs: {current_brain}")
+            break
+    return dataset
+
+
+def pick_examples(dataset, n, brain_id=None, seed=0):
+    """Selects valid, full-patch centers from the loaded SWC nodes."""
+    rng = np.random.default_rng(seed)
+    candidates = list()
+    brain_ids = [brain_id] if brain_id is not None else dataset.skeletons
+    patch_shape = np.asarray(dataset.patch_shape)
+    low_margin = patch_shape // 2
+    high_margin = patch_shape - low_margin
+
+    for current_brain in brain_ids:
+        if current_brain not in dataset.skeletons:
+            continue
+        points = np.asarray(dataset.skeletons[current_brain])
+        image_shape = np.asarray(dataset.imgs[current_brain].shape[-3:])
+        valid = np.all(
+            (points >= low_margin)
+            & (points <= image_shape - high_margin),
+            axis=1,
+        )
+        candidates.extend(
+            (current_brain, tuple(point)) for point in points[valid]
+        )
+
+    if not candidates:
+        raise ValueError("No in-bounds SWC nodes are available to visualize")
+    count = min(int(n), len(candidates))
+    indices = rng.choice(len(candidates), size=count, replace=False)
+    return [candidates[i] for i in indices]
+
+
+def stretch(image, low, high):
+    """Maps a count-space image to [0, 1] for display."""
+    if high <= low:
+        high = low + 1.0
+    return np.clip((image.astype(np.float32) - low) / (high - low), 0, 1)
+
+
+def overlay(gray, mask, alpha=0.5):
+    """Overlays a boolean mask in red on a normalized grayscale image."""
+    rgb = np.stack([gray, gray, gray], axis=-1)
+    red = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    rgb[mask] = (1.0 - alpha) * rgb[mask] + alpha * red
+    return rgb
+
+
+def render(dataset, examples, output_path, low_pct=1.0, high_pct=99.9):
+    """Renders three orthogonal SWC-mask overlays for each selected patch."""
+    fig, axes = plt.subplots(
+        len(examples),
+        3,
+        figsize=(9, 3 * len(examples)),
+        squeeze=False,
+    )
+    for row, (brain_id, center) in enumerate(examples):
+        raw = np.asarray(dataset.read_patch(brain_id, center), dtype=np.float32)
+        mask = dataset.skeleton_mask(brain_id, center)
+        low, high = np.percentile(raw, (low_pct, high_pct))
+        for axis in range(3):
+            raw_mip = raw.max(axis=axis)
+            mask_mip = mask.max(axis=axis)
+            axes[row, axis].imshow(
+                overlay(stretch(raw_mip, low, high), mask_mip),
+                interpolation="nearest",
+            )
+            axes[row, axis].set_xticks([])
+            axes[row, axis].set_yticks([])
+            if row == 0:
+                axes[row, axis].set_title(AXIS_TITLES[axis])
+        axes[row, 0].set_ylabel(
+            f"{brain_id}\n{tuple(center)}\n{mask.sum()} voxels",
+            fontsize=8,
+        )
+
+    fig.suptitle(
+        f"SWC-only masks (radius={dataset.skeleton_radius} voxels)",
+        fontsize=12,
+    )
+    fig.tight_layout(rect=(0, 0, 1, 0.98))
+    output_dir = os.path.dirname(output_path)
+    if output_dir:
+        os.makedirs(output_dir, exist_ok=True)
+    fig.savefig(output_path, dpi=150)
+    plt.close(fig)
+    print(f"Wrote {output_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument(
+        "config",
+        help="Precompute cache directory or path to its config.json.",
+    )
+    parser.add_argument("--brain-id", default=None)
+    parser.add_argument("--n", type=int, default=6)
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--out", default="swc_mask_preview.png")
+    parser.add_argument("--low-pct", type=float, default=1.0)
+    parser.add_argument("--high-pct", type=float, default=99.9)
+    args = parser.parse_args()
+
+    config = read_config(args.config)
+    dataset = load_dataset(config, brain_id=args.brain_id)
+    examples = pick_examples(
+        dataset,
+        args.n,
+        brain_id=args.brain_id,
+        seed=args.seed,
+    )
+    render(
+        dataset,
+        examples,
+        args.out,
+        low_pct=args.low_pct,
+        high_pct=args.high_pct,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_visualize_swc_masks.py b/tests/test_visualize_swc_masks.py
new file mode 100644
index 0000000..56b251e
--- /dev/null
+++ b/tests/test_visualize_swc_masks.py
@@ -0,0 +1,66 @@
+"""Tests for the SWC-mask visualization script."""
+
+from pathlib import Path
+
+import runpy
+import unittest
+
+import numpy as np
+
+
+class _Image:
+    """Minimal image object exposing a shape."""
+
+    shape = (1, 1, 10, 10, 10)
+
+
+class _Dataset:
+    """Minimal dataset for testing SWC center selection."""
+
+    patch_shape = (4, 4, 4)
+    imgs = {"brain": _Image()}
+    skeletons = {
+        "brain": np.array(
+            [
+                [1, 5, 5],  # outside the low z margin
+                [2, 2, 2],  # valid lower corner
+                [8, 8, 8],  # valid upper corner
+                [9, 5, 5],  # outside the high z margin
+            ]
+        )
+    }
+
+
+class VisualizeSwcMasksTest(unittest.TestCase):
+    """Tests selection and rendering helpers."""
+
+    @classmethod
+    def setUpClass(cls):
+        script = (
+            Path(__file__).parents[1]
+            / "scripts"
+            / "visualize_swc_masks.py"
+        )
+        cls.namespace = runpy.run_path(str(script))
+
+    def test_pick_examples_keeps_only_full_patches(self):
+        """Selected SWC centers leave a full patch inside the image."""
+        examples = self.namespace["pick_examples"](
+            _Dataset(), n=10, brain_id="brain", seed=0
+        )
+        self.assertEqual(
+            set(examples),
+            {("brain", (2, 2, 2)), ("brain", (8, 8, 8))},
+        )
+
+    def test_overlay_tints_only_masked_pixels(self):
+        """The overlay leaves background gray and tints mask pixels red."""
+        gray = np.full((2, 2), 0.5, dtype=np.float32)
+        mask = np.array([[True, False], [False, False]])
+        result = self.namespace["overlay"](gray, mask, alpha=0.5)
+        np.testing.assert_allclose(result[1, 1], [0.5, 0.5, 0.5])
+        np.testing.assert_allclose(result[0, 0], [0.75, 0.25, 0.25])
+
+
+if __name__ == "__main__":
+    unittest.main()

From 58716b47aa3d7b879392f2c167e0dd03265ded41 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 18:36:17 +0000
Subject: [PATCH 47/56] Un-track uv.lock for now

---
 .gitignore | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 4b9cf58..f3a4d31 100644
--- a/.gitignore
+++ b/.gitignore
@@ -146,4 +146,7 @@ dmypy.json
 .codeocean
 /environment
 /metadata
-*.png
\ No newline at end of file
+*.png
+
+# only temporary
+uv.lock
\ No newline at end of file

From 25153221fa9867bf59c0652f3f8a15c3d776f41f Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 20:27:35 +0000
Subject: [PATCH 48/56] Print crop origin in eval script

---
 scripts/evaluate_bm4dnet.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/scripts/evaluate_bm4dnet.py b/scripts/evaluate_bm4dnet.py
index 8aceb62..cb724a0 100644
--- a/scripts/evaluate_bm4dnet.py
+++ b/scripts/evaluate_bm4dnet.py
@@ -64,6 +64,10 @@ def evaluate():
     # requested region, so a crop avoids pulling the whole (huge) volume from S3.
     img = img_util.read(img_path)
     if crop_center is not None:
+        crop_start, _ = img_util.get_start_end(
+            crop_center, crop_shape, is_center=True
+        )
+        print("Crop origin (z, y, x):", tuple(crop_start))
         raw = np.asarray(
             img_util.get_patch(img, crop_center, crop_shape, is_center=True)
         )

From 43488a2c23799c6a060da8b59049e2ad7959420b Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 21:32:38 +0000
Subject: [PATCH 49/56] fix: preserve OME-Zarr transforms for evaluated crops

  - convert Neuroglancer XYZ coordinates to source-level voxel indices
  - validate crop bounds and report transformed crop coordinates
  - propagate source scale, translation, and units to denoised outputs
  - center-align coordinate transforms across multiscale levels
  - add regression coverage for metadata and negative coordinates
---
 scripts/evaluate_bm4dnet.py                   |  90 +++++++++--
 .../utils/img_util.py                         | 142 +++++++++++++++++-
 tests/test_review_regressions.py              |  50 +++++-
 3 files changed, 257 insertions(+), 25 deletions(-)

diff --git a/scripts/evaluate_bm4dnet.py b/scripts/evaluate_bm4dnet.py
index cb724a0..ad4b06d 100644
--- a/scripts/evaluate_bm4dnet.py
+++ b/scripts/evaluate_bm4dnet.py
@@ -63,13 +63,56 @@ def evaluate():
     # ".../image.zarr/0"). Slicing the lazy zarr in get_patch fetches only the
     # requested region, so a crop avoids pulling the whole (huge) volume from S3.
     img = img_util.read(img_path)
+    source_transform = img_util.get_ome_zarr_level_transform(img_path)
+    source_scale = np.asarray(source_transform["scale"])
+    source_translation = np.asarray(source_transform["translation"])
+    crop_start = (0, 0, 0)
     if crop_center is not None:
+        # Neuroglancer reports transformed spatial coordinates in (x, y, z)
+        # order. Convert them to this level's integer (z, y, x) voxel indices.
+        crop_center_voxel = img_util.ome_zarr_coordinate_to_voxel(
+            crop_center, source_transform
+        )
+        source_offset_zyx = source_translation[2:] / source_scale[2:]
+        snapped_center_zyx = source_offset_zyx + np.asarray(
+            crop_center_voxel
+        )
         crop_start, _ = img_util.get_start_end(
-            crop_center, crop_shape, is_center=True
+            crop_center_voxel, crop_shape, is_center=True
+        )
+        crop_origin_zyx = source_offset_zyx + np.asarray(crop_start)
+        crop_end = np.asarray(crop_start) + np.asarray(crop_shape)
+        if np.any(np.asarray(crop_start) < 0) or np.any(
+            crop_end > np.asarray(img.shape[2:])
+        ):
+            raise ValueError(
+                "Crop is outside the source level: "
+                f"start={tuple(crop_start)}, end={tuple(crop_end)}, "
+                f"source_shape={tuple(img.shape[2:])}"
+            )
+        print(
+            "Requested Neuroglancer crop center (x, y, z):",
+            tuple(crop_center),
+        )
+        print(
+            "Neuroglancer spatial scale (x, y, z):",
+            tuple(source_scale[2:][::-1].tolist()),
+            source_transform["spatial_unit"],
+        )
+        print("Crop center voxel (z, y, x):", crop_center_voxel)
+        print(
+            "Snapped crop center (x, y, z):",
+            tuple(snapped_center_zyx[::-1].tolist()),
         )
         print("Crop origin (z, y, x):", tuple(crop_start))
+        print(
+            "Neuroglancer crop origin (x, y, z):",
+            tuple(crop_origin_zyx[::-1].tolist()),
+        )
         raw = np.asarray(
-            img_util.get_patch(img, crop_center, crop_shape, is_center=True)
+            img_util.get_patch(
+                img, crop_center_voxel, crop_shape, is_center=True
+            )
         )
     else:
         raw = np.asarray(img[0, 0])
@@ -118,7 +161,23 @@ def evaluate():
     # to S3 needs credentials (the default AWS chain), unlike the anonymous
     # public read of the input.
     if output_zarr is not None:
-        img_util.write_ome_zarr(denoised, output_zarr)
+        crop_offset = np.asarray([0, 0, *crop_start])
+        output_translation = source_translation + source_scale * crop_offset
+        print(
+            "Output OME transform (t, c, z, y, x):",
+            {
+                "scale": tuple(source_scale.tolist()),
+                "translation": tuple(output_translation.tolist()),
+                "unit": source_transform["spatial_unit"],
+            },
+        )
+        img_util.write_ome_zarr(
+            denoised,
+            output_zarr,
+            scale=source_scale,
+            translation=output_translation,
+            spatial_unit=source_transform["spatial_unit"],
+        )
         print("Denoised Zarr written to:", output_zarr)
 
 
@@ -126,33 +185,32 @@ def evaluate():
     # Checkpoint. Point session_dir at a training session (the folder holding
     # the BM4DNet-*.pth files) to auto-select the best checkpoint. Set
     # checkpoint_path to a .pth to evaluate that file explicitly instead.
-    session_dir = "/root/capsule/results/training-sessions/session-20260709_1817"
-    checkpoint_path = "/root/capsule/results/training-sessions/session-20260709_1817/BM4DNet-20260709-499--2.026414.pth"
+    session_dir = "/root/capsule/results/training-sessions/session-20260710_1719"
+    checkpoint_path = "/root/capsule/results/training-sessions/session-20260710_1719/BM4DNet-20260710-499--19.965923.pth"
 
     # Test image. Any zarr readable by img_util.read, including an s3:// path;
     # give the full path to a single 5D multiscale level array.
-    img_path = "s3://aind-benchmark-data/3d-image-compression/blocks/block_001/input.zarr/0"
+    img_path = "s3://aind-open-data/exaSPIM_826511_2026-06-02_15-10-47/SPIM.ome.zarr/tile_000010_ch_488.zarr/0"
 
-    # Region to evaluate. crop_center=(z, y, x) with crop_shape denoises only a
-    # bounded sub-volume (reads just that region from S3); each dim of crop_shape
-    # must be >= the model patch size (64). Set crop_center=None to denoise the
-    # entire volume -- only safe for small, pre-cropped test blocks, since a
-    # full-resolution zarr will not fit in memory.
-    # crop_center = (256, 256, 256)
-    crop_center = None
-    crop_shape = (256, 256, 256)
+    # Region to evaluate. crop_center is the numeric (x, y, z) position shown by
+    # Neuroglancer; the physical scale displayed beside each coordinate is read
+    # from the source OME-Zarr. The position is converted to the nearest source
+    # voxel before cropping. Each crop_shape dimension must be >= the model patch
+    # size (64). Set crop_center=None only for a small, pre-cropped input volume.
+    crop_center = (22464, -15914, 18711)
+    crop_shape = (1024, 1024, 1024)
 
     # Use raw_input=True for volumes that were not background-subtracted.
     raw_input = True
     # Prefer the background offset precomputed from the full image tile's
     # lower-resolution data. None estimates from this test subvolume instead.
-    background_offset = None
+    background_offset = 37
 
     # Output + misc
     output_dir = "/results/evaluation"
     # Where to persist the denoised volume as an OME-Zarr. Local path or a
     # cloud path (e.g. "s3://BUCKET/PATH/denoised.zarr"). Set to None to skip.
-    output_zarr = "s3://aind-scratch-data/cameron.arshadi/denoising-experiments/outputs/BM4DNet-20260709-499--2.026414/block_001.zarr"
+    output_zarr = "s3://aind-scratch-data/cameron.arshadi/denoising-experiments/outputs/BM4DNet-20260710-499--19.965923/826511_raw_crop.zarr"
     device = "cuda"
     batch_size = 32
     clevel = 5
diff --git a/src/aind_exaspim_image_compression/utils/img_util.py b/src/aind_exaspim_image_compression/utils/img_util.py
index bef6d4e..57b088a 100644
--- a/src/aind_exaspim_image_compression/utils/img_util.py
+++ b/src/aind_exaspim_image_compression/utils/img_util.py
@@ -175,6 +175,98 @@ def _read_zarr(img_path):
     return zarr.open(img_path, mode="r", storage_options=storage_options)
 
 
+def get_ome_zarr_level_transform(img_path):
+    """Read the coordinate transform for an OME-Zarr array level.
+
+    Parameters
+    ----------
+    img_path : str
+        Path to a level array, for example ``/data/image.ome.zarr/0``.
+
+    Returns
+    -------
+    dict
+        Five-dimensional ``scale`` and ``translation`` tuples in axis order,
+        plus the common ``spatial_unit`` declared by the spatial axes.
+    """
+    level_path = img_path.rstrip("/")
+    if "/" not in level_path:
+        raise ValueError(f"Expected a Zarr level path, got: {img_path}")
+    group_path, dataset_path = level_path.rsplit("/", 1)
+    storage_options = {"anon": True} if is_s3_path(img_path) else None
+    group = zarr.open_group(
+        group_path, mode="r", storage_options=storage_options
+    )
+
+    ome = group.attrs.get("ome", {})
+    multiscales = group.attrs.get("multiscales") or ome.get("multiscales")
+    if not multiscales:
+        raise ValueError(f"No OME multiscales metadata found at {group_path}")
+
+    for multiscale_metadata in multiscales:
+        datasets = multiscale_metadata.get("datasets", [])
+        dataset = next(
+            (item for item in datasets if item.get("path") == dataset_path),
+            None,
+        )
+        if dataset is None:
+            continue
+
+        axes = multiscale_metadata.get("axes", [])
+        if [axis.get("name") for axis in axes] != ["t", "c", "z", "y", "x"]:
+            raise ValueError("Expected OME-Zarr axes in (t, c, z, y, x) order")
+
+        spatial_units = {
+            axis.get("unit") for axis in axes if axis.get("type") == "space"
+        }
+        if len(spatial_units) != 1 or None in spatial_units:
+            raise ValueError("Expected one common unit for all spatial axes")
+
+        scale = np.ones(5, dtype=float)
+        translation = np.zeros(5, dtype=float)
+        for transform in dataset.get("coordinateTransformations", []):
+            if transform.get("type") == "scale":
+                scale *= np.asarray(transform["scale"], dtype=float)
+            elif transform.get("type") == "translation":
+                translation += np.asarray(
+                    transform["translation"], dtype=float
+                )
+
+        return {
+            "scale": tuple(scale.tolist()),
+            "translation": tuple(translation.tolist()),
+            "spatial_unit": spatial_units.pop(),
+        }
+
+    raise ValueError(
+        f"Dataset {dataset_path!r} is not listed in OME metadata at "
+        f"{group_path}"
+    )
+
+
+def ome_zarr_coordinate_to_voxel(xyz, level_transform):
+    """Convert Neuroglancer ``(x, y, z)`` coordinates to ``(z, y, x)``.
+
+    Neuroglancer displays each coordinate in units of that dimension's scale,
+    shown separately in its position toolbar. Therefore, the physical OME
+    translation is normalized by the scale before it is subtracted. The
+    returned indices identify the nearest voxel center in the source array.
+    """
+    coordinate_xyz = np.asarray(xyz, dtype=float)
+    scale = np.asarray(level_transform["scale"], dtype=float)
+    translation = np.asarray(level_transform["translation"], dtype=float)
+    if coordinate_xyz.shape != (3,):
+        raise ValueError("xyz must contain exactly three coordinates")
+    if scale.shape != (5,) or translation.shape != (5,):
+        raise ValueError("OME scale and translation must each have five values")
+    if np.any(scale[2:] == 0):
+        raise ValueError("OME spatial scale values must be nonzero")
+
+    coordinate_zyx = coordinate_xyz[::-1]
+    voxel_zyx = coordinate_zyx - translation[2:] / scale[2:]
+    return tuple(np.rint(voxel_zyx).astype(int).tolist())
+
+
 # --- Read Patches ---
 def get_patch(img, voxel, shape, is_center=True):
     """
@@ -717,6 +809,9 @@ def write_ome_zarr(
     n_levels=1,
     scale_factors=(1, 1, 2, 2, 2),
     voxel_size=(748, 748, 1000),
+    scale=None,
+    translation=None,
+    spatial_unit="nanometer",
     storage_options=None,
 ):
     # Zarr v3 codec; default matches the cratio codec (zstd, level 5, shuffle).
@@ -742,13 +837,44 @@ def write_ome_zarr(
         store=output_path, mode="w", storage_options=storage_options
     )
 
-    # Voxel size scaling for each level
-    base_scale = np.array([1, 1, *reversed(voxel_size)])
+    # Voxel size scaling for each level. ``voxel_size`` is in (x, y, z)
+    # order; an explicit scale uses the stored (t, c, z, y, x) axis order.
+    base_scale = np.asarray(
+        scale if scale is not None else [1, 1, *reversed(voxel_size)],
+        dtype=float,
+    )
+    if base_scale.shape != (5,):
+        raise ValueError(
+            "scale must have five values in (t, c, z, y, x) order"
+        )
+    base_translation = np.asarray(
+        translation if translation is not None else np.zeros(5), dtype=float
+    )
+    if base_translation.shape != (5,):
+        raise ValueError(
+            "translation must have five values in (t, c, z, y, x) order"
+        )
+    level_factors = np.asarray(scale_factors, dtype=float)
+    if level_factors.shape != (5,):
+        raise ValueError("scale_factors must have one value for each axis")
     scales = [
-        base_scale[:2].tolist() + (base_scale[2:] * 2**i).tolist()
-        for i in range(n_levels)
+        (base_scale * level_factors**i).tolist() for i in range(n_levels)
     ]
-    coord_transforms = [[{"type": "scale", "scale": s}] for s in scales]
+    coord_transforms = []
+    for level_scale_values in scales:
+        level_scale = np.asarray(level_scale_values)
+        # Downsampling groups neighboring voxel centers, moving each coarser
+        # level's first voxel center by half the increase in voxel size.
+        level_translation = base_translation + (level_scale - base_scale) / 2
+        coord_transforms.append(
+            [
+                {"type": "scale", "scale": level_scale.tolist()},
+                {
+                    "type": "translation",
+                    "translation": level_translation.tolist(),
+                },
+            ]
+        )
 
     # Write to OME-Zarr
     write_multiscale(
@@ -757,9 +883,9 @@ def write_ome_zarr(
         axes=[
             {"name": "t", "type": "time", "unit": "millisecond"},
             {"name": "c", "type": "channel"},
-            {"name": "z", "type": "space", "unit": "micrometer"},
-            {"name": "y", "type": "space", "unit": "micrometer"},
-            {"name": "x", "type": "space", "unit": "micrometer"},
+            {"name": "z", "type": "space", "unit": spatial_unit},
+            {"name": "y", "type": "space", "unit": spatial_unit},
+            {"name": "x", "type": "space", "unit": spatial_unit},
         ],
         coordinate_transformations=coord_transforms,
         storage_options={
diff --git a/tests/test_review_regressions.py b/tests/test_review_regressions.py
index 1fc3248..5cc4161 100644
--- a/tests/test_review_regressions.py
+++ b/tests/test_review_regressions.py
@@ -206,7 +206,10 @@ def test_write_ome_zarr_round_trip(self):
                 image,
                 path,
                 chunks=(1, 1, 2, 2, 2),
-                n_levels=1,
+                n_levels=2,
+                scale=(1, 1, 1.0, 0.748, 0.748),
+                translation=(0, 0, 10.0, 20.0, 30.0),
+                spatial_unit="micrometer",
             )
             group = zarr.open_group(path, mode="r")
             self.assertEqual(group.metadata.zarr_format, 3)
@@ -214,6 +217,51 @@ def test_write_ome_zarr_round_trip(self):
             multiscales = group.attrs["ome"]["multiscales"]
             dataset_path = multiscales[0]["datasets"][0]["path"]
             np.testing.assert_array_equal(group[dataset_path][0, 0], image)
+            transforms = multiscales[0]["datasets"][0][
+                "coordinateTransformations"
+            ]
+            self.assertEqual(
+                transforms,
+                [
+                    {"type": "scale", "scale": [1, 1, 1.0, 0.748, 0.748]},
+                    {
+                        "type": "translation",
+                        "translation": [0, 0, 10.0, 20.0, 30.0],
+                    },
+                ],
+            )
+            self.assertEqual(
+                multiscales[0]["datasets"][1]["coordinateTransformations"],
+                [
+                    {"type": "scale", "scale": [1, 1, 2.0, 1.496, 1.496]},
+                    {
+                        "type": "translation",
+                        "translation": [0, 0, 10.5, 20.374, 30.374],
+                    },
+                ],
+            )
+            self.assertEqual(
+                img_util.get_ome_zarr_level_transform(
+                    os.path.join(path, dataset_path)
+                ),
+                {
+                    "scale": (1.0, 1.0, 1.0, 0.748, 0.748),
+                    "translation": (0.0, 0.0, 10.0, 20.0, 30.0),
+                    "spatial_unit": "micrometer",
+                },
+            )
+
+    def test_ome_coordinate_with_negative_y_converts_to_voxel(self):
+        """Displayed Neuroglancer coordinates map back to level voxels."""
+        transform = {
+            "scale": (1, 1, 1.0, 0.748, 0.748),
+            "translation": (0, 0, 8153.2, -15468.424, 10217.7),
+            "spatial_unit": "micrometer",
+        }
+        voxel_zyx = img_util.ome_zarr_coordinate_to_voxel(
+            (22464, -15914, 18711), transform
+        )
+        self.assertEqual(voxel_zyx, (10558, 4766, 8804))
 
     def test_ssim_uint16_matches_float_computation(self):
         """Bright uint16 products cannot overflow before local filtering."""

From 10c106a732992101f060cb3086b7dbacdd968d1d Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 21:53:39 +0000
Subject: [PATCH 50/56] Require patch caches for training

---
 scripts/train_bm4dnet.py    | 213 +++++++++++++-----------------------
 tests/test_train_bm4dnet.py | 173 +++++++++++++++++++++++++++++
 2 files changed, 248 insertions(+), 138 deletions(-)
 create mode 100644 tests/test_train_bm4dnet.py

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 2ddad6e..64e1ca3 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -1,4 +1,3 @@
-import multiprocessing as mp
 import os
 
 from aind_exaspim_image_compression.machine_learning import data_handling
@@ -12,87 +11,71 @@
 os.environ["GRPC_VERBOSITY"] = "ERROR"
 os.environ["GRPC_TRACE"] = ""
 
+_REQUIRED_CACHE_FILES = ("raw.npy", "teacher.npy", "fg.npy", "transform.json")
 
-def train():
-    # Fully-cached path: with both a training and a validation cache, no cloud
-    # reads or BM4D happen at startup. Rebuild the exact transform the caches
-    # were built with (stamped in the val cache) so the cached patches and the
-    # model share the identical mapping; per-brain offsets are already baked
-    # into the cached counts.
-    if cache_dir and val_cache_dir:
-        transform_path = os.path.join(val_cache_dir, "transform.json")
-        cached_cfg = (
-            util.read_json(transform_path)
-            if os.path.exists(transform_path)
-            else transform_cfg
-        )
-        transform = build_transform(cached_cfg)
-        train_dataset = data_handling.CachedPatchDataset(
-            cache_dir,
-            transform=transform,
-            preserve_foreground=preserve_foreground,
-            n_examples_per_epoch=n_train_examples_per_epoch,
-        )
-        val_dataset = data_handling.CachedValidateDataset(
-            val_cache_dir,
-            transform=transform,
-            preserve_foreground=preserve_foreground,
-        )
-        print("Transform:", transform.cfg)
-        print(
-            "Training from cache:", cache_dir,
-            "| pool size:", len(train_dataset.raw),
-        )
-        print(
-            "Validating from cache:", val_cache_dir,
-            "| examples:", len(val_dataset),
-        )
-    else:
-        # Load Brain IDs and per-brain background offsets
-        brain_ids = util.read_txt(brain_ids_path)
-        offsets = util.read_json(offsets_path) if offsets_path else None
-
-        # Datasets. The per-brain offset is subtracted from each patch, then
-        # one shared transform (offset 0) maps every brain to a
-        # background-at-zero space; the transform cfg is serialized with each
-        # checkpoint.
-        train_dataset, val_dataset = data_handling.init_datasets(
-            brain_ids,
-            img_prefixes_path,
-            patch_shape,
-            foreground_sampling_rate=foreground_sampling_rate,
-            min_foreground_voxels=min_foreground_voxels,
-            min_segmentation_volume=min_segmentation_volume,
-            n_train_examples_per_epoch=n_train_examples_per_epoch,
-            n_validate_examples=n_validate_examples,
-            offsets=offsets,
-            preserve_foreground=preserve_foreground,
-            segmentation_prefixes_path=segmentation_prefixes_path,
-            sigma_bm4d=sigma_bm4d,
-            swc_pointers=swc_pointers,
-            transform_cfg=transform_cfg,
-        )
-        print("Transform:", train_dataset.transform.cfg)
-        print("# Brains with Skeletons:", len(train_dataset.skeletons))
-        print("# Brains with Segmentations:", len(train_dataset.segmentations))
-
-        # Train from the precomputed patch cache when available (GPU-bound).
-        # Reuse the transform init_datasets built so the cache and validation
-        # share the identical mapping; validation stays on the cloud dataset.
-        if cache_dir:
-            train_dataset = data_handling.CachedPatchDataset(
-                cache_dir,
-                transform=train_dataset.transform,
-                preserve_foreground=preserve_foreground,
-                n_examples_per_epoch=n_train_examples_per_epoch,
+
+def _load_cached_transform(train_cache_dir, val_cache_dir):
+    """Validates both patch caches and returns their shared transform."""
+    cache_dirs = {
+        "train_cache_dir": train_cache_dir,
+        "val_cache_dir": val_cache_dir,
+    }
+    transform_cfgs = {}
+    for name, cache_dir in cache_dirs.items():
+        if not cache_dir:
+            raise ValueError(f"{name} is required for training")
+        if not os.path.isdir(cache_dir):
+            raise FileNotFoundError(
+                f"{name} does not exist or is not a directory: {cache_dir}"
             )
-            print(
-                "Training from cache:", cache_dir,
-                "| pool size:", len(train_dataset.raw),
+        missing = [
+            filename
+            for filename in _REQUIRED_CACHE_FILES
+            if not os.path.isfile(os.path.join(cache_dir, filename))
+        ]
+        if missing:
+            raise FileNotFoundError(
+                f"{name} is missing required cache files: "
+                + ", ".join(missing)
             )
+        transform_cfgs[name] = util.read_json(
+            os.path.join(cache_dir, "transform.json")
+        )
+
+    if transform_cfgs["train_cache_dir"] != transform_cfgs["val_cache_dir"]:
+        raise ValueError(
+            "train and validation patch caches use different transforms"
+        )
+    return build_transform(transform_cfgs["train_cache_dir"])
+
+
+def train(train_cache_dir, val_cache_dir):
+    """Trains and validates exclusively from precomputed patch caches."""
+    # Per-brain offsets and the BM4D teacher are already baked into the cached
+    # counts. Both caches must use the identical count-space transform.
+    transform = _load_cached_transform(train_cache_dir, val_cache_dir)
+    train_dataset = data_handling.CachedPatchDataset(
+        train_cache_dir,
+        transform=transform,
+        preserve_foreground=preserve_foreground,
+        n_examples_per_epoch=n_train_examples_per_epoch,
+    )
+    val_dataset = data_handling.CachedValidateDataset(
+        val_cache_dir,
+        transform=transform,
+        preserve_foreground=preserve_foreground,
+    )
+    print("Transform:", transform.cfg)
+    print(
+        "Training from cache:", train_cache_dir,
+        "| pool size:", len(train_dataset.raw),
+    )
+    print(
+        "Validating from cache:", val_cache_dir,
+        "| examples:", len(val_dataset),
+    )
 
-    # Run. Cached patches are cheap, so load them in-thread (num_workers=0);
-    # the cloud dataset needs the process pool for parallel BM4D.
+    # Cached patches are cheap to load, so read them in-thread.
     trainer = Trainer(
         output_dir,
         batch_size=batch_size,
@@ -101,7 +84,7 @@ def train():
         model=model,
         fg_weight=fg_weight,
         checkpoint_weights=checkpoint_weights,
-        num_workers=0 if cache_dir else None,
+        num_workers=0,
         val_every=val_every,
     )
 
@@ -109,23 +92,12 @@ def train():
     # session is reproducible (the Trainer merges in its own hyperparameters).
     trainer.save_config(
         {
-            "brain_ids_path": brain_ids_path,
-            "img_prefixes_path": img_prefixes_path,
-            "segmentation_prefixes_path": segmentation_prefixes_path,
-            "offsets_path": offsets_path,
-            "cache_dir": cache_dir,
+            "train_cache_dir": train_cache_dir,
             "val_cache_dir": val_cache_dir,
             "resume_path": resume_path,
-            "swc_pointers": swc_pointers,
-            "transform_cfg": transform_cfg,
-            "foreground_sampling_rate": foreground_sampling_rate,
+            "transform_cfg": transform.cfg,
             "n_train_examples_per_epoch": n_train_examples_per_epoch,
-            "n_validate_examples": n_validate_examples,
-            "patch_shape": patch_shape,
-            "sigma_bm4d": sigma_bm4d,
             "preserve_foreground": preserve_foreground,
-            "min_foreground_voxels": min_foreground_voxels,
-            "min_segmentation_volume": min_segmentation_volume,
         }
     )
 
@@ -136,25 +108,16 @@ def train():
 
 if __name__ == "__main__":
     # Paths
-    brain_ids_path = "/data/train_brain_ids.txt"
-    img_prefixes_path = "/data/exaspim_image_prefixes.json"
     output_dir = "/results/training-sessions"
-    segmentation_prefixes_path = (
-        "/data/exaspim_segmentation_prefixes.json"
+    # Both patch caches are required. Build them with precompute.py --split
+    # train and precompute.py --split val before starting training.
+    train_cache_dir = (
+        "/root/capsule/data/denoise_net_patch_cache_2026_07_10/patch_cache"
+    )
+    val_cache_dir = (
+        "/root/capsule/data/denoise_net_patch_cache_2026_07_10/"
+        "val_patch_cache"
     )
-    # Per-brain background offsets from estimate_background_offsets.py. Set to
-    # None to disable per-brain offset subtraction.
-    offsets_path = "/data/exaspim_background_offsets.json"
-    # Precomputed patch cache from precompute.py --split train. Leave None to
-    # sample + BM4D live from the cloud (slow, GPU-starved); after
-    # precomputing, set this to the cache dir (e.g. "/results/patch_cache") to
-    # train GPU-bound.
-    cache_dir = "/root/capsule/data/denoise_net_patch_cache_10K_2026_07_09"
-    # Precomputed validation cache from precompute.py --split val. When set
-    # alongside cache_dir, training runs fully offline (no cloud reads or BM4D
-    # at startup) and the GPU is busy almost immediately. Leave None to build
-    # the validation set live from the cloud.
-    val_cache_dir = "/root/capsule/data/denoise_net_val_patch_cache_500_2026_07_09"
     util.mkdir(output_dir)
 
     # Resume path. Checkpoints from before the normalization overhaul are NOT
@@ -164,41 +127,18 @@ def train():
     # checkpoint (a dict of {"model", "transform"}) to resume.
     resume_path = None
 
-    # SWC Pointer
-    swc_pointers = {
-        "bucket_name": "allen-nd-goog",
-        "path": "ground_truth_tracings",
-    }
-
-    # Intensity transform, shared by train and inference. Options:
-    #   {"kind": "asinh",    "params": {"offset": 35.0, "scale": 32.0}}
-    #   {"kind": "anscombe", "params": {"gain": 8.0, "read_noise": 5.0,
-    #                                   "offset": 35.0}}
-    #   {"kind": "linear",   "params": {"mn": 0.0, "mx": 1000.0, "clip": 8.0}}
-    # Per-brain offsets (offsets_path) are subtracted at the dataset, so the
-    # transform offset stays 0 to avoid double-subtracting. scale is the
-    # linear->log knee (tune from the noise floor).
-    transform_cfg = {
-        "kind": "asinh",
-        "params": {"offset": 0.0, "scale": 32.0},
-    }
-
     # Model (new defaults: residual output + GroupNorm)
     model = UNet()
 
     # Training parameters
     batch_size = 32
-    foreground_sampling_rate = 0.5
     lr = 1e-3
     max_epochs = 500
     n_train_examples_per_epoch = 300
-    n_validate_examples = 60
-    patch_shape = (64, 64, 64)
-    sigma_bm4d = 24
     # Validate (and consider a checkpoint) every this many epochs. A larger
     # cached validation set is cheap to store but CPU-bound to score, so keep
     # this above 1 to avoid the metrics dominating epoch time.
-    val_every = 5
+    val_every = 3
 
     # Signal-preserving loss + target/sampling (Parts E/F). preserve_foreground
     # keeps raw counts on the foreground so BM4D cannot erase neurites; that
@@ -208,8 +148,6 @@ def train():
     # background denoising, not foreground copying, dominates the loss.
     fg_weight = 0
     preserve_foreground = True
-    min_foreground_voxels = 50
-    min_segmentation_volume = 200
 
     # Checkpoint selection (Part C). None => fidelity-only (cratio weight 0),
     # which cannot see compression and happily selects a non-denoising model
@@ -218,9 +156,8 @@ def train():
     # exists for. cratio is the operating-point knob: raise it to trade
     # fidelity for compression, lower it to protect faint neurites.
     checkpoint_weights = dict(
-        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=1.0
+        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=2.0
     )
 
     # Main
-    mp.set_start_method("spawn", force=True)
-    train()
+    train(train_cache_dir, val_cache_dir)
diff --git a/tests/test_train_bm4dnet.py b/tests/test_train_bm4dnet.py
new file mode 100644
index 0000000..e83ca8b
--- /dev/null
+++ b/tests/test_train_bm4dnet.py
@@ -0,0 +1,173 @@
+"""Tests for the cache-only BM4DNet training entrypoint."""
+
+import json
+import runpy
+import tempfile
+import unittest
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import ANY, MagicMock, patch
+
+
+class CachedTrainingTest(unittest.TestCase):
+    """Tests the required train and validation cache contract."""
+
+    @classmethod
+    def setUpClass(cls):
+        """Loads the training script without running its main block."""
+        script = Path(__file__).parents[1] / "scripts" / "train_bm4dnet.py"
+        cls.namespace = runpy.run_path(str(script))
+
+    def _make_cache(self, root, name, transform=None, omit=()):
+        """Creates the minimal on-disk cache contract for a test."""
+        cache_dir = root / name
+        cache_dir.mkdir()
+        transform = transform or {
+            "kind": "asinh",
+            "params": {"offset": 0.0, "scale": 32.0},
+        }
+        files = {
+            "raw.npy": b"",
+            "teacher.npy": b"",
+            "fg.npy": b"",
+            "transform.json": json.dumps(transform),
+        }
+        for filename, contents in files.items():
+            if filename in omit:
+                continue
+            path = cache_dir / filename
+            if isinstance(contents, bytes):
+                path.write_bytes(contents)
+            else:
+                path.write_text(contents)
+        return cache_dir
+
+    def test_both_cache_paths_are_required(self):
+        """Neither cache may be omitted from a training run."""
+        load_transform = self.namespace["_load_cached_transform"]
+        with self.assertRaisesRegex(ValueError, "train_cache_dir is required"):
+            load_transform(None, "/validation")
+
+        with self.assertRaisesRegex(ValueError, "val_cache_dir is required"):
+            with patch("os.path.isdir", return_value=True), patch(
+                "os.path.isfile", return_value=True
+            ), patch.object(
+                self.namespace["util"], "read_json", return_value={}
+            ):
+                load_transform("/training", None)
+
+    def test_cache_contract_requires_all_files(self):
+        """A partial cache fails before dataset construction."""
+        load_transform = self.namespace["_load_cached_transform"]
+        with self.subTest("missing cache directory"):
+            with self.assertRaisesRegex(FileNotFoundError, "does not exist"):
+                load_transform("/missing-training-cache", "/validation")
+
+        with tempfile.TemporaryDirectory() as directory:
+            root = Path(directory)
+            train_cache = self._make_cache(
+                root, "train", omit=("teacher.npy", "transform.json")
+            )
+            val_cache = self._make_cache(root, "val")
+            with self.assertRaisesRegex(
+                FileNotFoundError, "teacher.npy, transform.json"
+            ):
+                load_transform(str(train_cache), str(val_cache))
+
+    def test_cache_transforms_must_match(self):
+        """Train and validation caches cannot use different transforms."""
+        load_transform = self.namespace["_load_cached_transform"]
+        with tempfile.TemporaryDirectory() as directory:
+            root = Path(directory)
+            train_cache = self._make_cache(root, "train")
+            val_cache = self._make_cache(
+                root,
+                "val",
+                transform={
+                    "kind": "asinh",
+                    "params": {"offset": 0.0, "scale": 16.0},
+                },
+            )
+            with self.assertRaisesRegex(ValueError, "different transforms"):
+                load_transform(str(train_cache), str(val_cache))
+
+    def test_training_uses_only_cached_datasets(self):
+        """Training constructs both cache adapters and records cache config."""
+        train = self.namespace["train"]
+        globals_ = train.__globals__
+        settings = {
+            "output_dir": "/results",
+            "batch_size": 2,
+            "lr": 1e-3,
+            "max_epochs": 3,
+            "model": object(),
+            "fg_weight": 0,
+            "checkpoint_weights": {"fg_mae": 1.0},
+            "val_every": 2,
+            "preserve_foreground": True,
+            "n_train_examples_per_epoch": 7,
+            "resume_path": None,
+        }
+        previous = {key: globals_.get(key) for key in settings}
+        globals_.update(settings)
+        try:
+            with tempfile.TemporaryDirectory() as directory:
+                root = Path(directory)
+                train_cache = self._make_cache(root, "train")
+                val_cache = self._make_cache(root, "val")
+                transform = self.namespace["_load_cached_transform"](
+                    str(train_cache), str(val_cache)
+                )
+                train_dataset = SimpleNamespace(
+                    raw=[object(), object()], transform=transform
+                )
+                val_dataset = MagicMock()
+                val_dataset.__len__.return_value = 3
+                val_dataset.transform = transform
+                trainer = MagicMock()
+
+                data_handling = self.namespace["data_handling"]
+                with patch.object(
+                    data_handling,
+                    "CachedPatchDataset",
+                    return_value=train_dataset,
+                ) as cached_train, patch.object(
+                    data_handling,
+                    "CachedValidateDataset",
+                    return_value=val_dataset,
+                ) as cached_val, patch.object(
+                    data_handling, "init_datasets"
+                ) as init_datasets, patch.dict(
+                    globals_, {"Trainer": MagicMock(return_value=trainer)}
+                ):
+                    train(str(train_cache), str(val_cache))
+
+                init_datasets.assert_not_called()
+                cached_train.assert_called_once_with(
+                    str(train_cache),
+                    transform=ANY,
+                    preserve_foreground=True,
+                    n_examples_per_epoch=7,
+                )
+                cached_val.assert_called_once_with(
+                    str(val_cache),
+                    transform=ANY,
+                    preserve_foreground=True,
+                )
+                trainer.run.assert_called_once_with(train_dataset, val_dataset)
+                config = trainer.save_config.call_args.args[0]
+                self.assertEqual(config["train_cache_dir"], str(train_cache))
+                self.assertEqual(config["val_cache_dir"], str(val_cache))
+                self.assertEqual(config["transform_cfg"], transform.cfg)
+                self.assertNotIn("brain_ids_path", config)
+                self.assertNotIn("sigma_bm4d", config)
+        finally:
+            for key, value in previous.items():
+                if value is None:
+                    globals_.pop(key, None)
+                else:
+                    globals_[key] = value
+
+
+if __name__ == "__main__":
+    unittest.main()

From 5c355247772c5cb4952d9f9170017273b7a59657 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Fri, 10 Jul 2026 21:58:44 +0000
Subject: [PATCH 51/56] remove redundant options from precompute.py

---
 scripts/precompute.py    | 4 ----
 tests/test_precompute.py | 7 +------
 2 files changed, 1 insertion(+), 10 deletions(-)

diff --git a/scripts/precompute.py b/scripts/precompute.py
index ad13239..17158b2 100644
--- a/scripts/precompute.py
+++ b/scripts/precompute.py
@@ -154,7 +154,6 @@ def precompute():
         min_segmentation_volume=min_segmentation_volume,
         n_validate_examples=0,
         offsets=offsets,
-        preserve_foreground=preserve_foreground,
         reject_incoherent_patches=reject_incoherent_patches,
         coherence_min_autocorr=coherence_min_autocorr,
         coherence_max_highfreq_frac=coherence_max_highfreq_frac,
@@ -190,7 +189,6 @@ def precompute():
             "patch_shape": patch_shape,
             "skeleton_radius": skeleton_radius,
             "segmentation_dilate": segmentation_dilate,
-            "preserve_foreground": preserve_foreground,
             "sigma_bm4d": sigma_bm4d,
             "reject_incoherent_patches": reject_incoherent_patches,
             "coherence_min_autocorr": coherence_min_autocorr,
@@ -204,7 +202,6 @@ def precompute():
             "seed": seed,
             "seed_stream": _SEED_STREAMS[split],
             "num_workers": num_workers,
-            "n_validate_examples": 0,
         },
     )
     shape = (n_patches,) + tuple(patch_shape)
@@ -287,7 +284,6 @@ def precompute():
     # Dilation (voxels) applied to the segmentation labels; 0 uses them as-is,
     # since the labels already mark neurite voxels.
     segmentation_dilate = 0
-    preserve_foreground = True
     sigma_bm4d = 24
 
     # Reject whole patches contaminated by a bright, spatially incoherent
diff --git a/tests/test_precompute.py b/tests/test_precompute.py
index 1ec3d40..15c51aa 100644
--- a/tests/test_precompute.py
+++ b/tests/test_precompute.py
@@ -38,7 +38,6 @@ def test_writes_complete_config_before_cache_generation(self):
             "patch_shape": (64, 64, 64),
             "skeleton_radius": 2,
             "segmentation_dilate": 0,
-            "preserve_foreground": True,
             "sigma_bm4d": 24,
             "reject_incoherent_patches": True,
             "coherence_min_autocorr": 0.4,
@@ -66,15 +65,11 @@ def test_writes_complete_config_before_cache_generation(self):
         write_json.assert_called_once()
         path, config = write_json.call_args.args
         self.assertEqual(path, "/cache/config.json")
-        expected_keys = set(settings) | {
-            "seed_stream",
-            "n_validate_examples",
-        }
+        expected_keys = set(settings) | {"seed_stream"}
         self.assertEqual(set(config), expected_keys)
         for key, value in settings.items():
             self.assertEqual(config[key], value)
         self.assertEqual(config["seed_stream"], 0)
-        self.assertEqual(config["n_validate_examples"], 0)
 
 
 if __name__ == "__main__":

From 022d1af1c4260b57a90eb5345845fe2d04927cfc Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Sat, 11 Jul 2026 14:01:52 +0000
Subject: [PATCH 52/56] Make inference offsets preserve the trained intensity
 mapping

Training caches contain counts with their per-brain pedestal already subtracted, so models are trained with the frozen base transform and its original normalization denominator. The previous inference helper instead rebuilt asinh and Anscombe transforms with a new internal offset. That subtracted the pedestal correctly, but also changed their normalization denominator, creating a small coordinate mismatch between training and inference.

Introduce OffsetTransform as an exact composition around the frozen transform: forward subtracts the runtime pedestal before calling the trained transform, while inverse converts predictions to floating-point corrected counts before restoring the pedestal and applying final physical clipping and uint16 rounding. Add inverse_float to each transform family so this inverse composition does not quantize or clip before the offset is restored. Keep the linear transform's existing shifted-bound implementation because it is already algebraically equivalent.

Teach build_transform to reconstruct composed offset transforms and make repeated with_offset calls replace the runtime wrapper. Add regression coverage for exact asinh and Anscombe forward equivalence, pedestal-restoring inverse behavior, config reconstruction, precomputed inference offsets, and the abstract inverse_float contract.
---
 .../machine_learning/transforms.py            | 109 ++++++++++++++----
 tests/test_review_regressions.py              |   4 +
 tests/test_transforms.py                      |  47 +++++++-
 3 files changed, 137 insertions(+), 23 deletions(-)

diff --git a/src/aind_exaspim_image_compression/machine_learning/transforms.py b/src/aind_exaspim_image_compression/machine_learning/transforms.py
index 098e37c..a7598fe 100644
--- a/src/aind_exaspim_image_compression/machine_learning/transforms.py
+++ b/src/aind_exaspim_image_compression/machine_learning/transforms.py
@@ -57,6 +57,10 @@ def inverse(self, y):
         """
         raise NotImplementedError
 
+    def inverse_float(self, y):
+        """Maps normalized values to unclipped floating-point counts."""
+        raise NotImplementedError
+
 
 class AsinhTransform(IntensityTransform):
     """
@@ -124,6 +128,11 @@ def forward(self, x):
         y = np.arcsinh((x - self.offset) / self.scale) / self._norm
         return y.astype(np.float32)
 
+    def inverse_float(self, y):
+        """Maps normalized asinh values to floating-point counts."""
+        y = np.asarray(y, dtype=np.float32)
+        return self.offset + self.scale * np.sinh(y * self._norm)
+
     def inverse(self, y):
         """
         Maps normalized asinh values back to raw uint16 counts.
@@ -138,8 +147,7 @@ def inverse(self, y):
         numpy.ndarray
             Image in raw counts, clipped to [0, max_count], uint16.
         """
-        y = np.asarray(y, dtype=np.float32)
-        counts = self.offset + self.scale * np.sinh(y * self._norm)
+        counts = self.inverse_float(y)
         counts = np.clip(counts, 0, self.max_count)
         return np.rint(counts).astype(np.uint16)
 
@@ -250,6 +258,14 @@ def forward(self, x):
         gat = self._gat(np.asarray(x, dtype=np.float32))
         return (gat / self._norm).astype(np.float32)
 
+    def inverse_float(self, y):
+        """Maps normalized Anscombe values to floating-point counts."""
+        d = np.clip(np.asarray(y, dtype=np.float32), 0.0, None) * self._norm
+        arg = (d * self.gain / 2.0) ** 2
+        return self.offset + (
+            arg - self._c_inv * self.gain ** 2 - self.read_noise ** 2
+        ) / self.gain
+
     def inverse(self, y):
         """
         Maps normalized Anscombe values back to raw uint16 counts.
@@ -264,11 +280,7 @@ def inverse(self, y):
         numpy.ndarray
             Image in raw counts, clipped to [0, max_count], uint16.
         """
-        d = np.clip(np.asarray(y, dtype=np.float32), 0.0, None) * self._norm
-        arg = (d * self.gain / 2.0) ** 2
-        counts = self.offset + (
-            arg - self._c_inv * self.gain ** 2 - self.read_noise ** 2
-        ) / self.gain
+        counts = self.inverse_float(y)
         counts = np.clip(counts, 0, self.max_count)
         return np.rint(counts).astype(np.uint16)
 
@@ -335,6 +347,11 @@ def forward(self, x):
         y = (x - self.mn) / (self.mx - self.mn + 1e-8)
         return np.clip(y, 0.0, self.clip).astype(np.float32)
 
+    def inverse_float(self, y):
+        """Maps normalized linear values to floating-point counts."""
+        y = np.asarray(y, dtype=np.float32)
+        return y * (self.mx - self.mn) + self.mn
+
     def inverse(self, y):
         """
         Maps normalized values back to raw uint16 counts.
@@ -349,8 +366,47 @@ def inverse(self, y):
         numpy.ndarray
             Image in raw counts, clipped to [0, max_count], uint16.
         """
-        y = np.asarray(y, dtype=np.float32)
-        counts = y * (self.mx - self.mn) + self.mn
+        counts = self.inverse_float(y)
+        counts = np.clip(counts, 0, self.max_count)
+        return np.rint(counts).astype(np.uint16)
+
+
+class OffsetTransform(IntensityTransform):
+    """Applies a raw-count offset around a frozen trained transform.
+
+    This exact composition is used for inference on images that still contain
+    their background pedestal. It deliberately leaves the base transform's
+    normalization constants unchanged::
+
+        forward(x) = base.forward(x - offset)
+        inverse(y) = base.inverse_float(y) + offset
+
+    Changing an AsinhTransform or AnscombeTransform's own ``offset`` parameter
+    would also change its normalization denominator and therefore would not
+    reproduce the mapping used for offset-subtracted training patches.
+    """
+
+    def __init__(self, base_transform, offset=0.0):
+        self.base_transform = base_transform
+        self.offset = float(offset)
+        self.max_count = float(base_transform.max_count)
+
+    def __getattr__(self, name):
+        """Expose non-offset parameters such as scale and gain from the base."""
+        return getattr(self.base_transform, name)
+
+    def forward(self, x):
+        """Subtracts the pedestal, then applies the trained transform."""
+        x = np.asarray(x, dtype=np.float32)
+        return self.base_transform.forward(x - self.offset)
+
+    def inverse_float(self, y):
+        """Inverts through the trained transform and restores the pedestal."""
+        return self.base_transform.inverse_float(y) + self.offset
+
+    def inverse(self, y):
+        """Returns pedestal-restored, physically clipped uint16 counts."""
+        counts = self.inverse_float(y)
         counts = np.clip(counts, 0, self.max_count)
         return np.rint(counts).astype(np.uint16)
 
@@ -396,7 +452,8 @@ def build_transform(cfg):
     ----------
     cfg : dict
         Config of the form ``{"kind": "asinh" | "anscombe" | "linear",
-        "params": {...}}``.
+        "params": {...}}``. An offset composition is represented as
+        ``{"kind": "offset", "base": <transform cfg>, "params": {...}}``.
 
     Returns
     -------
@@ -416,6 +473,8 @@ def build_transform(cfg):
         transform = AnscombeTransform(**params)
     elif kind == "linear":
         transform = LinearClipTransform(**params)
+    elif kind == "offset":
+        transform = OffsetTransform(build_transform(cfg["base"]), **params)
     else:
         raise ValueError(f"Unknown transform kind: {kind}")
     transform.cfg = {**cfg, "params": dict(params)}
@@ -456,12 +515,13 @@ def calibrate_transform(cfg, sample):
 
 def with_offset(transform, offset):
     """
-    Returns a copy of an intensity transform with a new count-space offset.
+    Composes a raw-count background offset around a trained transform.
 
-    Used to apply a per-volume background offset at inference: estimate the
-    offset from the raw volume, then rebuild the (frozen) transform with that
-    offset while keeping its kind, scale, and max_count. This mirrors the
-    per-brain offset subtracted during training.
+    Used at inference when training patches had their per-brain offsets
+    subtracted before the frozen transform. The returned mapping is exactly
+    ``transform.forward(x - offset)``; the inverse adds the offset back after
+    applying the frozen inverse. In particular, this does not alter the asinh
+    or Anscombe normalization denominator.
 
     Parameters
     ----------
@@ -475,12 +535,13 @@ def with_offset(transform, offset):
     IntensityTransform
         A new transform with the given offset.
     """
+    if isinstance(transform, OffsetTransform):
+        transform = transform.base_transform
     cfg = getattr(transform, "cfg", None)
     if cfg is None:
         raise ValueError(
             "transform has no cfg; construct it via build_transform"
         )
-    params = dict(cfg.get("params", {}))
     offset = float(offset)
     if cfg["kind"] == "linear":
         # Applying the per-volume offset before the trained linear transform,
@@ -488,8 +549,14 @@ def with_offset(transform, offset):
         # bounds.  Shifting both also makes inverse() restore the offset in the
         # returned raw counts.  LinearClipTransform deliberately has no
         # ``offset`` constructor argument.
-        params["mn"] = float(getattr(transform, "mn")) + offset
-        params["mx"] = float(getattr(transform, "mx")) + offset
-    else:
-        params["offset"] = offset
-    return build_transform({**cfg, "params": params})
+        params = dict(cfg.get("params", {}))
+        params["mn"] = float(transform.mn) + offset
+        params["mx"] = float(transform.mx) + offset
+        return build_transform({**cfg, "params": params})
+    return build_transform(
+        {
+            "kind": "offset",
+            "base": cfg,
+            "params": {"offset": offset},
+        }
+    )
diff --git a/tests/test_review_regressions.py b/tests/test_review_regressions.py
index 5cc4161..93582a3 100644
--- a/tests/test_review_regressions.py
+++ b/tests/test_review_regressions.py
@@ -65,6 +65,10 @@ def test_volume_transform_uses_precomputed_offset(self):
             transform = build_volume_transform(base, offset=73.5)
         estimate.assert_not_called()
         self.assertAlmostEqual(transform.offset, 73.5)
+        raw = np.array([73.5, 105.5, 1073.5, 60073.5])
+        np.testing.assert_array_equal(
+            transform.forward(raw), base.forward(raw - 73.5)
+        )
 
     def test_volume_transform_requires_image_without_offset(self):
         """Fallback estimation requires an explicit test image."""
diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index bc7276a..fc123e7 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -9,6 +9,7 @@
     AsinhTransform,
     IntensityTransform,
     LinearClipTransform,
+    OffsetTransform,
     build_transform,
     calibrate_transform,
     estimate_offset,
@@ -128,12 +129,52 @@ def test_estimate_offset(self):
         )
 
     def test_with_offset(self):
-        """with_offset rebuilds a transform with a new offset only."""
+        """with_offset wraps the frozen transform without renormalizing it."""
         base = build_transform({"kind": "asinh", "params": {"scale": 32}})
         shifted = with_offset(base, 120.0)
+        self.assertIsInstance(shifted, OffsetTransform)
         self.assertAlmostEqual(shifted.offset, 120.0)
         self.assertAlmostEqual(shifted.scale, 32.0)
         self.assertEqual(shifted.cfg["params"]["offset"], 120.0)
+        self.assertEqual(shifted.cfg["base"], base.cfg)
+
+        values = np.array([120.0, 152.0, 1120.0, 60120.0])
+        np.testing.assert_array_equal(
+            shifted.forward(values), base.forward(values - 120.0)
+        )
+
+    def test_with_offset_inverse_restores_pedestal(self):
+        """The composed inverse restores the offset after the frozen inverse."""
+        base = build_transform({"kind": "asinh", "params": {"scale": 32}})
+        shifted = with_offset(base, 120.0)
+        values = np.array([120.0, 152.0, 1120.0, 60120.0])
+        np.testing.assert_allclose(
+            shifted.inverse(shifted.forward(values)), values, atol=1
+        )
+
+    def test_with_offset_is_exact_for_anscombe(self):
+        """Anscombe inference also retains its trained normalization factor."""
+        base = build_transform(
+            {
+                "kind": "anscombe",
+                "params": {"gain": 8, "read_noise": 5},
+            }
+        )
+        shifted = with_offset(base, 120.0)
+        values = np.array([120.0, 500.0, 2000.0, 20000.0])
+        np.testing.assert_array_equal(
+            shifted.forward(values), base.forward(values - 120.0)
+        )
+
+    def test_offset_transform_config_round_trip(self):
+        """The exact composed transform can be reconstructed from its config."""
+        base = build_transform({"kind": "asinh", "params": {"scale": 32}})
+        shifted = with_offset(base, 120.0)
+        rebuilt = build_transform(shifted.cfg)
+        values = np.array([120.0, 1000.0, 60000.0])
+        np.testing.assert_array_equal(
+            rebuilt.forward(values), shifted.forward(values)
+        )
 
     def test_with_offset_shifts_linear_bounds(self):
         """A linear baseline applies offsets without an invalid kwarg."""
@@ -196,12 +237,14 @@ def test_calibrate_transform_noop(self):
         self.assertEqual(out["params"], {"gain": 2})
 
     def test_base_class_not_implemented(self):
-        """The abstract base raises for both directions."""
+        """The abstract base raises for all transform directions."""
         t = IntensityTransform()
         with self.assertRaises(NotImplementedError):
             t.forward(np.zeros(1))
         with self.assertRaises(NotImplementedError):
             t.inverse(np.zeros(1))
+        with self.assertRaises(NotImplementedError):
+            t.inverse_float(np.zeros(1))
 
 
 if __name__ == "__main__":

From 520940bc7afb02dbc0840d87e62ed65e0c64ae16 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Sat, 11 Jul 2026 14:10:14 +0000
Subject: [PATCH 53/56] Preserve count precision in patch caches

Write offset-subtracted raw patches and clipped BM4D teachers as float32 instead of float16. Float16 increasingly coarsens count values across the uint16 sensor range, reaching multi-count quantization through the bright tail before the intensity transform or model sees an example. Float32 exactly represents the source integer counts while also retaining negative offset-subtracted samples and fractional BM4D estimates.

Remove the float16 saturation helper, use one shared count dtype for both memory-mapped arrays, and record count_dtype in each cache config for reproducibility. Update cache-layout documentation and extend the precompute configuration test to cover the persisted and allocated dtype.

Existing caches remain readable for backward compatibility but must be rebuilt to receive the precision improvement.
---
 scripts/precompute.py        | 21 +++++++++------------
 scripts/visualize_patches.py |  4 ++--
 tests/test_precompute.py     | 11 ++++++++++-
 3 files changed, 21 insertions(+), 15 deletions(-)

diff --git a/scripts/precompute.py b/scripts/precompute.py
index 17158b2..cd1cfd3 100644
--- a/scripts/precompute.py
+++ b/scripts/precompute.py
@@ -31,8 +31,8 @@
 
 Outputs, under cache_dir (identical layout for both splits, so the val cache
 loads with CachedValidateDataset):
-    raw.npy        float16  (N, *patch_shape)   offset-subtracted counts
-    teacher.npy    float16  (N, *patch_shape)   clipped BM4D denoising
+    raw.npy        float32  (N, *patch_shape)   offset-subtracted counts
+    teacher.npy    float32  (N, *patch_shape)   clipped BM4D denoising
     fg.npy         uint8    (N, *patch_shape)   foreground mask (0/1)
     transform.json                              resolved transform cfg
     config.json                                 full precompute configuration
@@ -67,6 +67,7 @@
 _WORKER_SEED = None
 _WORKER_STREAM = 0
 _WORKER_SPLIT = "train"
+_COUNT_DTYPE = np.float32
 
 
 def _seed_task(index):
@@ -122,11 +123,6 @@ def _sample_counts(index):
     return _WORKER_VAL.sample_counts(brain_id, voxel, fg_mask=fg_mask, raw=raw)
 
 
-def _to_float16(arr):
-    """Clips to the float16 range before casting (avoids inf at saturation)."""
-    return np.clip(arr, -65504, 65504).astype(np.float16)
-
-
 def precompute():
     # Offset calibration would need a cloud sample the cache is meant to avoid,
     # and each worker would calibrate on its own random sample -- so the cache
@@ -202,14 +198,15 @@ def precompute():
             "seed": seed,
             "seed_stream": _SEED_STREAMS[split],
             "num_workers": num_workers,
+            "count_dtype": np.dtype(_COUNT_DTYPE).name,
         },
     )
     shape = (n_patches,) + tuple(patch_shape)
     raw_mm = open_memmap(
-        f"{cache_dir}/raw.npy", mode="w+", dtype=np.float16, shape=shape
+        f"{cache_dir}/raw.npy", mode="w+", dtype=_COUNT_DTYPE, shape=shape
     )
     teacher_mm = open_memmap(
-        f"{cache_dir}/teacher.npy", mode="w+", dtype=np.float16, shape=shape
+        f"{cache_dir}/teacher.npy", mode="w+", dtype=_COUNT_DTYPE, shape=shape
     )
     fg_mm = open_memmap(
         f"{cache_dir}/fg.npy", mode="w+", dtype=np.uint8, shape=shape
@@ -226,8 +223,8 @@ def precompute():
         for i, (raw, teacher, fg) in enumerate(
             tqdm(results, total=n_patches, desc=f"Precompute ({split})")
         ):
-            raw_mm[i] = _to_float16(raw)
-            teacher_mm[i] = _to_float16(teacher)
+            raw_mm[i] = np.asarray(raw, dtype=_COUNT_DTYPE)
+            teacher_mm[i] = np.asarray(teacher, dtype=_COUNT_DTYPE)
             fg_mm[i] = np.asarray(fg, dtype=np.uint8)
 
     raw_mm.flush()
@@ -317,7 +314,7 @@ def precompute():
 
     # Per-split output location and pool size.
     if split == "train":
-        # ~1.3 MB/patch (fp16 raw+teacher + uint8 fg), so 30000 ~= 40 GB.
+        # ~2.4 MB/patch (fp32 raw+teacher + uint8 fg), so 30000 ~= 71 GB.
         cache_dir = "/results/patch_cache"
         n_patches = 30000
     else:
diff --git a/scripts/visualize_patches.py b/scripts/visualize_patches.py
index feedfeb..1112784 100644
--- a/scripts/visualize_patches.py
+++ b/scripts/visualize_patches.py
@@ -10,8 +10,8 @@
 Works on either cache produced by scripts/precompute.py (train or val); both
 share the same layout::
 
-    raw.npy      float16  (N, *patch_shape)   offset-subtracted counts
-    teacher.npy  float16  (N, *patch_shape)   clipped BM4D denoising
+    raw.npy      float32  (N, *patch_shape)   offset-subtracted counts
+    teacher.npy  float32  (N, *patch_shape)   clipped BM4D denoising
     fg.npy       uint8    (N, *patch_shape)   foreground mask (0/1)
 
 Each patch is one row with five count-space panels:
diff --git a/tests/test_precompute.py b/tests/test_precompute.py
index 15c51aa..0b9a5d2 100644
--- a/tests/test_precompute.py
+++ b/tests/test_precompute.py
@@ -3,6 +3,7 @@
 from pathlib import Path
 from unittest.mock import MagicMock, patch
 
+import numpy as np
 import runpy
 import unittest
 
@@ -65,11 +66,19 @@ def test_writes_complete_config_before_cache_generation(self):
         write_json.assert_called_once()
         path, config = write_json.call_args.args
         self.assertEqual(path, "/cache/config.json")
-        expected_keys = set(settings) | {"seed_stream"}
+        expected_keys = set(settings) | {"count_dtype", "seed_stream"}
         self.assertEqual(set(config), expected_keys)
         for key, value in settings.items():
             self.assertEqual(config[key], value)
         self.assertEqual(config["seed_stream"], 0)
+        self.assertEqual(config["count_dtype"], "float32")
+        self.assertEqual(
+            precompute.__globals__["_COUNT_DTYPE"], np.float32
+        )
+        self.assertEqual(
+            precompute.__globals__["open_memmap"].call_args.kwargs["dtype"],
+            np.float32,
+        )
 
 
 if __name__ == "__main__":

From bbe8489125405b9bee2df5c51652d1a14e992404 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Mon, 13 Jul 2026 23:24:38 +0000
Subject: [PATCH 54/56] Train on the full patch cache per epoch

 -src/aind_exaspim_image_compression/machine_learning/data_handling.py: index-addressable cache dataset and deterministic (seed, epoch) shuffling with partial-batch retention.
- src/aind_exaspim_image_compression/machine_learning/train.py: training shuffle wiring, set_epoch, and persisted seed.
- scripts/train_bm4dnet.py: 20 epochs, validation every epoch, seed 42, and removed per-epoch sample count.
- tests/test_full_cache_training.py: focused cache, ordering, reproducibility, trainer, and partial-batch tests.
---
 scripts/train_bm4dnet.py                      |  22 +--
 .../machine_learning/data_handling.py         |  76 ++++---
 .../machine_learning/train.py                 |  10 +
 tests/test_full_cache_training.py             | 186 ++++++++++++++++++
 tests/test_train_bm4dnet.py                   |   9 +-
 5 files changed, 261 insertions(+), 42 deletions(-)
 create mode 100644 tests/test_full_cache_training.py

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 64e1ca3..809ba08 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -58,7 +58,6 @@ def train(train_cache_dir, val_cache_dir):
         train_cache_dir,
         transform=transform,
         preserve_foreground=preserve_foreground,
-        n_examples_per_epoch=n_train_examples_per_epoch,
     )
     val_dataset = data_handling.CachedValidateDataset(
         val_cache_dir,
@@ -86,6 +85,7 @@ def train(train_cache_dir, val_cache_dir):
         checkpoint_weights=checkpoint_weights,
         num_workers=0,
         val_every=val_every,
+        seed=seed,
     )
 
     # Persist the run configuration next to the checkpoints/tensorboard so each
@@ -96,7 +96,6 @@ def train(train_cache_dir, val_cache_dir):
             "val_cache_dir": val_cache_dir,
             "resume_path": resume_path,
             "transform_cfg": transform.cfg,
-            "n_train_examples_per_epoch": n_train_examples_per_epoch,
             "preserve_foreground": preserve_foreground,
         }
     )
@@ -112,11 +111,10 @@ def train(train_cache_dir, val_cache_dir):
     # Both patch caches are required. Build them with precompute.py --split
     # train and precompute.py --split val before starting training.
     train_cache_dir = (
-        "/root/capsule/data/denoise_net_patch_cache_2026_07_10/patch_cache"
+        "/root/capsule/data/denoise_net_patch_cache_2026_07_13/patch_cache"
     )
     val_cache_dir = (
-        "/root/capsule/data/denoise_net_patch_cache_2026_07_10/"
-        "val_patch_cache"
+        "/root/capsule/data/denoise_net_patch_cache_2026_07_13/val_patch_cache"
     )
     util.mkdir(output_dir)
 
@@ -133,12 +131,10 @@ def train(train_cache_dir, val_cache_dir):
     # Training parameters
     batch_size = 32
     lr = 1e-3
-    max_epochs = 500
-    n_train_examples_per_epoch = 300
-    # Validate (and consider a checkpoint) every this many epochs. A larger
-    # cached validation set is cheap to store but CPU-bound to score, so keep
-    # this above 1 to avoid the metrics dominating epoch time.
-    val_every = 3
+    max_epochs = 20
+    # Validate (and consider a checkpoint) after every full-cache epoch.
+    val_every = 1
+    seed = 42
 
     # Signal-preserving loss + target/sampling (Parts E/F). preserve_foreground
     # keeps raw counts on the foreground so BM4D cannot erase neurites; that
@@ -147,7 +143,7 @@ def train(train_cache_dir, val_cache_dir):
     # output compresses no better than raw. Keep fg_weight modest (~1-3) so
     # background denoising, not foreground copying, dominates the loss.
     fg_weight = 0
-    preserve_foreground = True
+    preserve_foreground = False
 
     # Checkpoint selection (Part C). None => fidelity-only (cratio weight 0),
     # which cannot see compression and happily selects a non-denoising model
@@ -156,7 +152,7 @@ def train(train_cache_dir, val_cache_dir):
     # exists for. cratio is the operating-point knob: raise it to trade
     # fidelity for compression, lower it to protect faint neurites.
     checkpoint_weights = dict(
-        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=2.0
+        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=5.0
     )
 
     # Main
diff --git a/src/aind_exaspim_image_compression/machine_learning/data_handling.py b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
index 20de41b..6d430ed 100644
--- a/src/aind_exaspim_image_compression/machine_learning/data_handling.py
+++ b/src/aind_exaspim_image_compression/machine_learning/data_handling.py
@@ -1006,12 +1006,13 @@ def read_patch(self, brain_id, center):
 
 class CachedPatchDataset(Dataset):
     """
-    Dataset that samples precomputed count-space patches from disk.
+    Dataset that reads precomputed count-space patches from disk.
 
     The expensive cloud reads + BM4D + foreground masks are precomputed once
     (see scripts/precompute.py --split train) into memory-mapped arrays; this
-    dataset reads a random cached patch and applies only the cheap transform +
-    target construction, so training becomes GPU-bound instead of BM4D-bound.
+    dataset applies only the cheap transform + target construction, so
+    training becomes GPU-bound instead of BM4D-bound. Each cache entry is
+    addressable by index so the DataLoader controls epoch ordering.
 
     Attributes
     ----------
@@ -1023,7 +1024,6 @@ class CachedPatchDataset(Dataset):
 
     def __init__(
         self, cache_dir, transform=None, preserve_foreground=True,
-        n_examples_per_epoch=None,
     ):
         """
         Instantiates a CachedPatchDataset.
@@ -1038,8 +1038,6 @@ def __init__(
         preserve_foreground : bool, optional
             Whether the target keeps raw counts on the foreground. Default is
             True.
-        n_examples_per_epoch : int, optional
-            Number of examples drawn per epoch. Default is the pool size.
         """
         super(CachedPatchDataset, self).__init__()
         self.raw = np.load(os.path.join(cache_dir, "raw.npy"), mmap_mode="r")
@@ -1050,29 +1048,25 @@ def __init__(
         self.transform = transform or build_transform({"kind": "asinh"})
         self.preserve_foreground = preserve_foreground
         self.patch_shape = tuple(self.raw.shape[1:])
-        self.n_examples_per_epoch = (
-            n_examples_per_epoch if n_examples_per_epoch else len(self.raw)
-        )
 
     def __len__(self):
-        """Number of examples drawn per epoch."""
-        return self.n_examples_per_epoch
+        """Number of cached training examples."""
+        return len(self.raw)
 
-    def __getitem__(self, dummy_input):
+    def __getitem__(self, idx):
         """
-        Returns a random cached example as (x, y, fg_mask).
+        Returns a cached example as (x, y, fg_mask).
 
         Parameters
         ----------
-        dummy_input : Any
-            Unused index; patches are sampled at random from the pool.
+        idx : int
+            Index of the example to retrieve.
 
         Returns
         -------
         Tuple[numpy.ndarray]
             (x, y, fg_mask) for the model.
         """
-        idx = random.randint(0, len(self.raw) - 1)
         raw = np.asarray(self.raw[idx], dtype=np.float32)
         teacher = np.asarray(self.teacher[idx], dtype=np.float32)
         fg_mask = np.asarray(self.fg[idx])
@@ -1193,7 +1187,15 @@ class DataLoader:
         Shape of image patch expected by the model.
     """
 
-    def __init__(self, dataset, batch_size=16, num_workers=None, prefetch=2):
+    def __init__(
+        self,
+        dataset,
+        batch_size=16,
+        num_workers=None,
+        prefetch=2,
+        shuffle=False,
+        seed=0,
+    ):
         """
         Instantiates a DataLoader object.
 
@@ -1209,12 +1211,25 @@ def __init__(self, dataset, batch_size=16, num_workers=None, prefetch=2):
             None.
         prefetch : int, optional
             Number of batches prepared ahead of the consumer. Default is 2.
+        shuffle : bool, optional
+            Whether to deterministically shuffle indices each epoch. Default
+            is False.
+        seed : int, optional
+            Base seed used with the epoch to generate shuffled indices.
+            Default is 0.
         """
         self.dataset = dataset
         self.batch_size = batch_size
         self.patch_shape = dataset.patch_shape
         self.num_workers = num_workers
         self.prefetch = prefetch
+        self.shuffle = shuffle
+        self.seed = seed
+        self.epoch = 0
+
+    def set_epoch(self, epoch):
+        """Sets the epoch used to generate a reproducible shuffled order."""
+        self.epoch = epoch
 
     def __iter__(self):
         """
@@ -1225,8 +1240,18 @@ def __iter__(self):
         iterator
             Yields batches of tensors.
         """
-        starts = list(range(0, len(self.dataset), self.batch_size))
-        if not starts:
+        if self.shuffle:
+            rng = np.random.default_rng(
+                np.random.SeedSequence([self.seed, self.epoch])
+            )
+            indices = rng.permutation(len(self.dataset))
+        else:
+            indices = np.arange(len(self.dataset))
+        batches = [
+            indices[start:start + self.batch_size]
+            for start in range(0, len(indices), self.batch_size)
+        ]
+        if not batches:
             return
 
         executor = None
@@ -1242,8 +1267,10 @@ def __iter__(self):
 
         def produce():
             try:
-                for start in starts:
-                    result_queue.put((None, self._load_batch(executor, start)))
+                for batch_indices in batches:
+                    result_queue.put(
+                        (None, self._load_batch(executor, batch_indices))
+                    )
             except Exception as exc:  # surface loader errors to the consumer
                 result_queue.put((exc, None))
             else:
@@ -1264,11 +1291,8 @@ def produce():
                 executor.shutdown(wait=False, cancel_futures=True)
             thread.join(timeout=1)
 
-    def _load_batch(self, executor, start_idx):
-        # Compute batch size
-        n_remaining = len(self.dataset) - start_idx
-        batch_size = min(self.batch_size, n_remaining)
-        indices = range(start_idx, start_idx + batch_size)
+    def _load_batch(self, executor, indices):
+        batch_size = len(indices)
 
         # Per-example work: in-thread when there is no pool, else in parallel.
         if executor is None:
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index f41c9b1..c4ff3ed 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -76,6 +76,7 @@ def __init__(
         num_workers=None,
         prefetch=2,
         val_every=1,
+        seed=0,
     ):
         """
         Instantiates a Trainer object.
@@ -101,6 +102,9 @@ def __init__(
             the final epoch is always validated. The count-space metrics are
             CPU-bound, so a large validation set is only cheap if it is not run
             every epoch. Default is 1 (validate every epoch).
+        seed : int, optional
+            Seed used to reproducibly shuffle training examples by epoch.
+            Default is 0.
         """
         # Initializations
         exp_name = "session-" + datetime.today().strftime("%Y%m%d_%H%M")
@@ -115,6 +119,7 @@ def __init__(
         self.num_workers = num_workers
         self.prefetch = prefetch
         self.val_every = max(1, int(val_every))
+        self.seed = seed
 
         self.codec = blosc.Blosc(cname="zstd", clevel=5, shuffle=blosc.SHUFFLE)
         self.criterion = SignalPreservingLoss(fg_weight=fg_weight)
@@ -172,18 +177,22 @@ def run(self, train_dataset, val_dataset):
             batch_size=self.batch_size,
             num_workers=self.num_workers,
             prefetch=self.prefetch,
+            shuffle=True,
+            seed=self.seed,
         )
         val_dataloader = DataLoader(
             val_dataset,
             batch_size=self.batch_size,
             num_workers=self.num_workers,
             prefetch=self.prefetch,
+            shuffle=False,
         )
 
         # Main
         self.best_score = np.inf
         for epoch in range(self.max_epochs):
             # Train
+            train_dataloader.set_epoch(epoch)
             train_loss = self.train_step(train_dataloader, epoch)
 
             # Validate every val_every epochs (and always on the final epoch);
@@ -427,6 +436,7 @@ def save_config(self, config):
             "num_workers": self.num_workers,
             "prefetch": self.prefetch,
             "val_every": self.val_every,
+            "seed": self.seed,
             "fg_weight": getattr(self.criterion, "fg_weight", None),
             "checkpoint_weights": self.checkpoint_weights,
             "lr": self.optimizer.param_groups[0]["lr"],
diff --git a/tests/test_full_cache_training.py b/tests/test_full_cache_training.py
new file mode 100644
index 0000000..9ce2a16
--- /dev/null
+++ b/tests/test_full_cache_training.py
@@ -0,0 +1,186 @@
+"""Tests for complete, reproducibly shuffled cache epochs."""
+
+import json
+import os
+import tempfile
+import unittest
+from types import SimpleNamespace
+from unittest.mock import MagicMock, call, patch
+
+import numpy as np
+import torch
+
+from aind_exaspim_image_compression.machine_learning.data_handling import (
+    CachedPatchDataset,
+    CachedValidateDataset,
+    DataLoader,
+)
+from aind_exaspim_image_compression.machine_learning.train import Trainer
+
+
+class IdentityTransform:
+    """Minimal count-space identity transform for cache-index tests."""
+
+    cfg = {"kind": "identity"}
+
+    def forward(self, values):
+        """Returns values as float32 without changing their identity."""
+        return np.asarray(values, dtype=np.float32)
+
+
+class FullCacheDataLoaderTest(unittest.TestCase):
+    """Tests cache addressing and deterministic epoch index generation."""
+
+    def setUp(self):
+        """Creates a 17-example cache with index-valued one-voxel patches."""
+        self.temporary_directory = tempfile.TemporaryDirectory()
+        cache_dir = self.temporary_directory.name
+        raw = np.arange(17, dtype=np.float32).reshape(17, 1, 1, 1)
+        np.save(os.path.join(cache_dir, "raw.npy"), raw)
+        np.save(os.path.join(cache_dir, "teacher.npy"), raw + 100)
+        np.save(os.path.join(cache_dir, "fg.npy"), raw.astype(bool))
+        self.cache_dir = cache_dir
+        self.transform = IdentityTransform()
+
+    def tearDown(self):
+        """Removes the synthetic cache."""
+        self.temporary_directory.cleanup()
+
+    @staticmethod
+    def _input_order(loader):
+        """Collects the index encoded in each input patch from a loader."""
+        return [
+            int(value)
+            for batch in loader
+            for value in batch[0][:, 0, 0, 0, 0].tolist()
+        ]
+
+    def test_cached_dataset_is_index_addressable_and_uses_pool_length(self):
+        """Dataset item i reads cache record i and length is the pool size."""
+        dataset = CachedPatchDataset(
+            self.cache_dir,
+            transform=self.transform,
+            preserve_foreground=False,
+        )
+
+        self.assertEqual(len(dataset), 17)
+        x, y, fg_mask = dataset[12]
+        self.assertEqual(float(x.item()), 12.0)
+        self.assertEqual(float(y.item()), 112.0)
+        self.assertEqual(float(fg_mask.item()), 1.0)
+
+    def test_shuffled_epoch_is_complete_reproducible_and_epoch_specific(self):
+        """Every index appears once with stable per-seed, per-epoch order."""
+        dataset = CachedPatchDataset(
+            self.cache_dir,
+            transform=self.transform,
+            preserve_foreground=False,
+        )
+        loader = DataLoader(
+            dataset, batch_size=4, num_workers=0, shuffle=True, seed=42
+        )
+        loader.set_epoch(3)
+        first_order = self._input_order(loader)
+
+        duplicate = DataLoader(
+            dataset, batch_size=4, num_workers=0, shuffle=True, seed=42
+        )
+        duplicate.set_epoch(3)
+        self.assertEqual(self._input_order(duplicate), first_order)
+        self.assertEqual(sorted(first_order), list(range(17)))
+        self.assertEqual(len(set(first_order)), 17)
+
+        loader.set_epoch(4)
+        self.assertNotEqual(self._input_order(loader), first_order)
+
+    def test_validation_is_ordered_and_final_partial_batch_is_retained(self):
+        """Unshuffled validation keeps order and emits its final example."""
+        dataset = CachedValidateDataset(
+            self.cache_dir,
+            transform=self.transform,
+            preserve_foreground=False,
+        )
+        loader = DataLoader(
+            dataset, batch_size=4, num_workers=0, shuffle=False
+        )
+        batches = list(loader)
+        order = [
+            int(value)
+            for batch in batches
+            for value in batch[0][:, 0, 0, 0, 0].tolist()
+        ]
+
+        self.assertEqual(order, list(range(17)))
+        self.assertEqual(len(batches), 5)
+        self.assertEqual(batches[-1][0].shape[0], 1)
+
+
+class TrainerShuffleTest(unittest.TestCase):
+    """Tests Trainer wiring for shuffled training and ordered validation."""
+
+    def test_trainer_sets_epoch_and_persists_seed(self):
+        """Trainer passes its seed, sets every epoch, and saves the seed."""
+        model = torch.nn.Linear(1, 1)
+        dataset = SimpleNamespace(
+            patch_shape=(1, 1, 1), transform=IdentityTransform()
+        )
+        train_loader = MagicMock()
+        val_loader = MagicMock()
+
+        with tempfile.TemporaryDirectory() as directory:
+            trainer = Trainer(
+                directory,
+                device="cpu",
+                model=model,
+                max_epochs=2,
+                use_amp=False,
+                num_workers=0,
+                seed=42,
+            )
+            trainer.train_step = MagicMock(return_value=1.0)
+            trainer.validate_step = MagicMock(return_value=(1.0, 2.0, False))
+            trainer.scheduler.step = MagicMock()
+            try:
+                with patch(
+                    "aind_exaspim_image_compression.machine_learning."
+                    "train.DataLoader",
+                    side_effect=[train_loader, val_loader],
+                ) as loader_factory:
+                    trainer.run(dataset, dataset)
+
+                self.assertEqual(
+                    loader_factory.call_args_list,
+                    [
+                        call(
+                            dataset,
+                            batch_size=16,
+                            num_workers=0,
+                            prefetch=2,
+                            shuffle=True,
+                            seed=42,
+                        ),
+                        call(
+                            dataset,
+                            batch_size=16,
+                            num_workers=0,
+                            prefetch=2,
+                            shuffle=False,
+                        ),
+                    ],
+                )
+                self.assertEqual(
+                    train_loader.set_epoch.call_args_list, [call(0), call(1)]
+                )
+
+                trainer.save_config({})
+                with open(
+                    os.path.join(trainer.log_dir, "config.json"),
+                    encoding="utf-8",
+                ) as file:
+                    self.assertEqual(json.load(file)["seed"], 42)
+            finally:
+                trainer.writer.close()
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_train_bm4dnet.py b/tests/test_train_bm4dnet.py
index e83ca8b..be0c18f 100644
--- a/tests/test_train_bm4dnet.py
+++ b/tests/test_train_bm4dnet.py
@@ -104,8 +104,8 @@ def test_training_uses_only_cached_datasets(self):
             "fg_weight": 0,
             "checkpoint_weights": {"fg_mae": 1.0},
             "val_every": 2,
+            "seed": 42,
             "preserve_foreground": True,
-            "n_train_examples_per_epoch": 7,
             "resume_path": None,
         }
         previous = {key: globals_.get(key) for key in settings}
@@ -125,6 +125,7 @@ def test_training_uses_only_cached_datasets(self):
                 val_dataset.__len__.return_value = 3
                 val_dataset.transform = transform
                 trainer = MagicMock()
+                trainer_factory = MagicMock(return_value=trainer)
 
                 data_handling = self.namespace["data_handling"]
                 with patch.object(
@@ -138,7 +139,7 @@ def test_training_uses_only_cached_datasets(self):
                 ) as cached_val, patch.object(
                     data_handling, "init_datasets"
                 ) as init_datasets, patch.dict(
-                    globals_, {"Trainer": MagicMock(return_value=trainer)}
+                    globals_, {"Trainer": trainer_factory}
                 ):
                     train(str(train_cache), str(val_cache))
 
@@ -147,7 +148,6 @@ def test_training_uses_only_cached_datasets(self):
                     str(train_cache),
                     transform=ANY,
                     preserve_foreground=True,
-                    n_examples_per_epoch=7,
                 )
                 cached_val.assert_called_once_with(
                     str(val_cache),
@@ -159,8 +159,11 @@ def test_training_uses_only_cached_datasets(self):
                 self.assertEqual(config["train_cache_dir"], str(train_cache))
                 self.assertEqual(config["val_cache_dir"], str(val_cache))
                 self.assertEqual(config["transform_cfg"], transform.cfg)
+                self.assertNotIn("n_train_examples_per_epoch", config)
                 self.assertNotIn("brain_ids_path", config)
                 self.assertNotIn("sigma_bm4d", config)
+                trainer_call = trainer_factory.call_args
+                self.assertEqual(trainer_call.kwargs["seed"], 42)
         finally:
             for key, value in previous.items():
                 if value is None:

From 16c022671f40e6f471b21109b820a5869d7ca6f7 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Tue, 14 Jul 2026 18:51:20 +0000
Subject: [PATCH 55/56] Update train_bm4dnet.py

---
 scripts/train_bm4dnet.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index 809ba08..d3048ef 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -131,7 +131,7 @@ def train(train_cache_dir, val_cache_dir):
     # Training parameters
     batch_size = 32
     lr = 1e-3
-    max_epochs = 20
+    max_epochs = 30
     # Validate (and consider a checkpoint) after every full-cache epoch.
     val_every = 1
     seed = 42
@@ -152,7 +152,7 @@ def train(train_cache_dir, val_cache_dir):
     # exists for. cratio is the operating-point knob: raise it to trade
     # fidelity for compression, lower it to protect faint neurites.
     checkpoint_weights = dict(
-        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=5.0
+        fg_mae=1.0, bg_mae=0.2, top_pct_error=0.5, cratio=10.0
     )
 
     # Main

From 5d764679f680b83de8c1e6478fdac228031adc97 Mon Sep 17 00:00:00 2001
From: carshadi <cameron.arshadi@alleninstitute.org>
Date: Wed, 15 Jul 2026 12:56:29 +0000
Subject: [PATCH 56/56] Add option to disable AMP during training/validation

- enabling AMP can decrease compression ratios during inference
- all current models were trained using AMP. We need to be able to test training without.
---
 scripts/train_bm4dnet.py                      | 10 ++++-
 .../machine_learning/train.py                 | 41 ++++++++++++------
 tests/test_full_cache_training.py             | 42 ++++++++++++++++++-
 tests/test_train_bm4dnet.py                   |  8 ++++
 4 files changed, 87 insertions(+), 14 deletions(-)

diff --git a/scripts/train_bm4dnet.py b/scripts/train_bm4dnet.py
index d3048ef..15f8335 100644
--- a/scripts/train_bm4dnet.py
+++ b/scripts/train_bm4dnet.py
@@ -81,6 +81,8 @@ def train(train_cache_dir, val_cache_dir):
         lr=lr,
         max_epochs=max_epochs,
         model=model,
+        use_amp=use_amp,
+        use_amp_validation=use_amp_validation,
         fg_weight=fg_weight,
         checkpoint_weights=checkpoint_weights,
         num_workers=0,
@@ -97,6 +99,8 @@ def train(train_cache_dir, val_cache_dir):
             "resume_path": resume_path,
             "transform_cfg": transform.cfg,
             "preserve_foreground": preserve_foreground,
+            "use_amp": use_amp,
+            "use_amp_validation": use_amp_validation,
         }
     )
 
@@ -131,7 +135,11 @@ def train(train_cache_dir, val_cache_dir):
     # Training parameters
     batch_size = 32
     lr = 1e-3
-    max_epochs = 30
+    max_epochs = 50
+    # Use AMP for optimization, but keep validation/checkpoint selection in
+    # FP32 so its cratio matches production FP32 inference.
+    use_amp = False
+    use_amp_validation = False
     # Validate (and consider a checkpoint) after every full-cache epoch.
     val_every = 1
     seed = 42
diff --git a/src/aind_exaspim_image_compression/machine_learning/train.py b/src/aind_exaspim_image_compression/machine_learning/train.py
index c4ff3ed..d3dce49 100644
--- a/src/aind_exaspim_image_compression/machine_learning/train.py
+++ b/src/aind_exaspim_image_compression/machine_learning/train.py
@@ -8,7 +8,6 @@
 
 """
 
-from contextlib import nullcontext
 from datetime import datetime
 from numcodecs import blosc
 from torch.optim.lr_scheduler import CosineAnnealingLR
@@ -71,6 +70,7 @@ def __init__(
         max_epochs=400,
         model=None,
         use_amp=True,
+        use_amp_validation=False,
         checkpoint_weights=None,
         fg_weight=20.0,
         num_workers=None,
@@ -96,7 +96,12 @@ def __init__(
         model : None or nn.Module, optional
             Model to be trained on the given datasets. Default is None.
         use_amp : bool, optional
-            Indication of whether to use mixed precision. Default is True.
+            Whether to use CUDA float16 autocast and gradient scaling during
+            training. Default is True.
+        use_amp_validation : bool, optional
+            Whether to use CUDA float16 autocast during validation and
+            checkpoint selection. Default is False so validation matches FP32
+            production inference.
         val_every : int, optional
             Run validation (and checkpoint selection) every this many epochs;
             the final epoch is always validated. The count-space metrics are
@@ -120,6 +125,8 @@ def __init__(
         self.prefetch = prefetch
         self.val_every = max(1, int(val_every))
         self.seed = seed
+        self.use_amp = bool(use_amp)
+        self.use_amp_validation = bool(use_amp_validation)
 
         self.codec = blosc.Blosc(cname="zstd", clevel=5, shuffle=blosc.SHUFFLE)
         self.criterion = SignalPreservingLoss(fg_weight=fg_weight)
@@ -134,15 +141,10 @@ def __init__(
         self.scheduler = CosineAnnealingLR(self.optimizer, T_max=max_epochs)
         self.writer = SummaryWriter(log_dir=log_dir)
 
-        if use_amp:
-            self.autocast = torch.autocast(device_type="cuda", dtype=torch.float16)
-        else:
-            self.autocast = nullcontext()
-
         # Scale the loss before backward so small float16 gradients do not
         # underflow (and are unscaled before the step). Disabled => no-op, so
         # the same code path is correct with and without AMP.
-        self.scaler = torch.amp.GradScaler("cuda", enabled=use_amp)
+        self.scaler = torch.amp.GradScaler("cuda", enabled=self.use_amp)
 
     # --- Core Routines ---
     def run(self, train_dataset, val_dataset):
@@ -232,7 +234,9 @@ def train_step(self, train_dataloader, epoch):
         self.model.train()
         for x, y, fg_mask in train_dataloader:
             # Forward pass
-            hat_y, loss = self.forward_pass(x, y, fg_mask)
+            hat_y, loss = self.forward_pass(
+                x, y, fg_mask, use_amp=self.use_amp
+            )
 
             # Backward pass (loss-scaled for AMP stability)
             self.optimizer.zero_grad()
@@ -276,7 +280,9 @@ def validate_step(self, val_dataloader, epoch):
             self.model.eval()
             for x, y, raw, fg_mask in val_dataloader:
                 # Run model
-                hat_y, loss = self.forward_pass(x, y, fg_mask)
+                hat_y, loss = self.forward_pass(
+                    x, y, fg_mask, use_amp=self.use_amp_validation
+                )
 
                 # Evaluate result
                 losses.append(loss.detach().cpu())
@@ -313,7 +319,7 @@ def validate_step(self, val_dataloader, epoch):
             self.save_model(epoch, score)
         return loss, cratio, is_best
 
-    def forward_pass(self, x, y, fg_mask):
+    def forward_pass(self, x, y, fg_mask, use_amp=None):
         """
         Performs a forward pass through the model and computes loss.
 
@@ -325,6 +331,9 @@ def forward_pass(self, x, y, fg_mask):
             Target tensor with shape (B, C, D, H, W).
         fg_mask : torch.Tensor
             Foreground mask (0/1) with shape (B, C, D, H, W).
+        use_amp : bool, optional
+            Whether to autocast this forward pass to float16. Defaults to the
+            training AMP setting.
 
         Returns
         -------
@@ -333,7 +342,13 @@ def forward_pass(self, x, y, fg_mask):
         loss : torch.Tensor
             Computed loss value.
         """
-        with self.autocast:
+        if use_amp is None:
+            use_amp = self.use_amp
+        with torch.autocast(
+            device_type=torch.device(self.device).type,
+            dtype=torch.float16,
+            enabled=bool(use_amp),
+        ):
             x = x.to(self.device)
             y = y.to(self.device)
             fg_mask = fg_mask.to(self.device)
@@ -437,6 +452,8 @@ def save_config(self, config):
             "prefetch": self.prefetch,
             "val_every": self.val_every,
             "seed": self.seed,
+            "use_amp": self.use_amp,
+            "use_amp_validation": self.use_amp_validation,
             "fg_weight": getattr(self.criterion, "fg_weight", None),
             "checkpoint_weights": self.checkpoint_weights,
             "lr": self.optimizer.param_groups[0]["lr"],
diff --git a/tests/test_full_cache_training.py b/tests/test_full_cache_training.py
index 9ce2a16..cdebedf 100644
--- a/tests/test_full_cache_training.py
+++ b/tests/test_full_cache_training.py
@@ -4,6 +4,7 @@
 import os
 import tempfile
 import unittest
+from contextlib import nullcontext
 from types import SimpleNamespace
 from unittest.mock import MagicMock, call, patch
 
@@ -118,6 +119,42 @@ def test_validation_is_ordered_and_final_partial_batch_is_retained(self):
 class TrainerShuffleTest(unittest.TestCase):
     """Tests Trainer wiring for shuffled training and ordered validation."""
 
+    def test_training_and_validation_amp_are_configured_separately(self):
+        """Training can use AMP while validation remains full precision."""
+        model = torch.nn.Linear(1, 1)
+        values = torch.zeros((1, 1, 1, 1, 1))
+        with tempfile.TemporaryDirectory() as directory:
+            trainer = Trainer(
+                directory,
+                device="cpu",
+                model=model,
+                max_epochs=1,
+                use_amp=True,
+                use_amp_validation=False,
+            )
+            try:
+                with patch(
+                    "aind_exaspim_image_compression.machine_learning."
+                    "train.torch.autocast",
+                    side_effect=[nullcontext(), nullcontext()],
+                ) as autocast:
+                    trainer.forward_pass(
+                        values, values, values, use_amp=trainer.use_amp
+                    )
+                    trainer.forward_pass(
+                        values,
+                        values,
+                        values,
+                        use_amp=trainer.use_amp_validation,
+                    )
+
+                self.assertTrue(autocast.call_args_list[0].kwargs["enabled"])
+                self.assertFalse(
+                    autocast.call_args_list[1].kwargs["enabled"]
+                )
+            finally:
+                trainer.writer.close()
+
     def test_trainer_sets_epoch_and_persists_seed(self):
         """Trainer passes its seed, sets every epoch, and saves the seed."""
         model = torch.nn.Linear(1, 1)
@@ -177,7 +214,10 @@ def test_trainer_sets_epoch_and_persists_seed(self):
                     os.path.join(trainer.log_dir, "config.json"),
                     encoding="utf-8",
                 ) as file:
-                    self.assertEqual(json.load(file)["seed"], 42)
+                    config = json.load(file)
+                    self.assertEqual(config["seed"], 42)
+                    self.assertFalse(config["use_amp"])
+                    self.assertFalse(config["use_amp_validation"])
             finally:
                 trainer.writer.close()
 
diff --git a/tests/test_train_bm4dnet.py b/tests/test_train_bm4dnet.py
index be0c18f..5b92439 100644
--- a/tests/test_train_bm4dnet.py
+++ b/tests/test_train_bm4dnet.py
@@ -101,6 +101,8 @@ def test_training_uses_only_cached_datasets(self):
             "lr": 1e-3,
             "max_epochs": 3,
             "model": object(),
+            "use_amp": True,
+            "use_amp_validation": False,
             "fg_weight": 0,
             "checkpoint_weights": {"fg_mae": 1.0},
             "val_every": 2,
@@ -159,11 +161,17 @@ def test_training_uses_only_cached_datasets(self):
                 self.assertEqual(config["train_cache_dir"], str(train_cache))
                 self.assertEqual(config["val_cache_dir"], str(val_cache))
                 self.assertEqual(config["transform_cfg"], transform.cfg)
+                self.assertTrue(config["use_amp"])
+                self.assertFalse(config["use_amp_validation"])
                 self.assertNotIn("n_train_examples_per_epoch", config)
                 self.assertNotIn("brain_ids_path", config)
                 self.assertNotIn("sigma_bm4d", config)
                 trainer_call = trainer_factory.call_args
                 self.assertEqual(trainer_call.kwargs["seed"], 42)
+                self.assertTrue(trainer_call.kwargs["use_amp"])
+                self.assertFalse(
+                    trainer_call.kwargs["use_amp_validation"]
+                )
         finally:
             for key, value in previous.items():
                 if value is None: