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Fixed full chain pre-calibration (was completely wrong) #65

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Feb 28, 2025
Merged

Fixed full chain pre-calibration (was completely wrong) #65

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71 changes: 35 additions & 36 deletions spine/model/full_chain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -232,7 +232,7 @@ def __init__(self, chain, uresnet_deghost=None, uresnet=None,
assert 'stage' in calibration, (
"If the calibration is to be applied, must provide the "
"`stage` to specify where to apply it.")
self.calibration_stage = calibration.pop(stage)
self.calibration_stage = calibration.pop('stage')
self.calibrator = CalibrationManager(**calibration)
calibration['stage'] = self.calibration_stage

Expand All @@ -249,7 +249,7 @@ def modes(self):
return dict(self._modes)

def forward(self, data, sources=None, seg_label=None, clust_label=None,
coord_label=None, energy_label=None, run_info=None):
coord_label=None, energy_label=None, meta=None, run_info=None):
"""Run a batch of data through the full chain.

Parameters
Expand All @@ -274,6 +274,8 @@ def forward(self, data, sources=None, seg_label=None, clust_label=None,
energy_label : TensorBatch, optional
(N, 1 + D + 1) Tensor of true energy deposition values
- 1 is the energy deposition value in each voxel
meta : Meta, optional
Image metadata information
run_info : List[RunInfo], optional
Object containing information about the run, subrun and event

Expand All @@ -289,27 +291,27 @@ def forward(self, data, sources=None, seg_label=None, clust_label=None,

# Run the semantic segmentation (and point proposal) stage
if self.calibration_stage == 'segmentation':
data = self.run_calibration(data, sources, energy_label, run_info)
data = self.run_calibration(data, sources, energy_label, meta, run_info)
clust_label = self.run_segmentation_ppn(data, seg_label, clust_label)

# Run the fragmentation stage
if self.calibration_stage == 'fragmentation':
data = self.run_calibration(data, sources, energy_label, run_info)
data = self.run_calibration(data, sources, energy_label, meta, run_info)
self.run_fragmentation(data, clust_label)

# Run the particle aggregation
if self.calibration_stage == 'particle_aggregation':
data = self.run_calibration(data, sources, energy_label, run_info)
data = self.run_calibration(data, sources, energy_label, meta, run_info)
self.run_part_aggregation(data, clust_label, coord_label)

# Run the interaction aggregation
if self.calibration_stage == 'inter_aggregation':
data = self.run_calibration(data, sources, energy_label, run_info)
data = self.run_calibration(data, sources, energy_label, meta, run_info)
self.run_inter_aggregation(data, clust_label, coord_label)

# Run an independant particle classification stage
if self.calibration_stage == 'particle_classification':
data = self.run_calibration(data, sources, energy_label, run_info)
data = self.run_calibration(data, sources, energy_label, meta, run_info)
# TODO

# Run the interaction classification
Expand Down Expand Up @@ -720,7 +722,7 @@ def run_inter_aggregation(self, data, clust_label=None, coord_label=None):
if self.inter_aggregation is not None:
self.result['interaction_clusts'] = interactions

def run_calibration(self, data, sources=None, energy_label=None,
def run_calibration(self, data, sources=None, energy_label=None, meta=None,
run_info=None):
"""Run the calibration algorithm.

Expand All @@ -737,6 +739,8 @@ def run_calibration(self, data, sources=None, energy_label=None,
energy_label : TensorBatch, optional
(N, 1 + D + 1) Tensor of true energy deposition values
- 1 is the energy deposition value in each voxel
meta : Meta, optional
Image metadata information
run_info : List[RunInfo], optional
Object containing information about the run, subrun and event

Expand All @@ -746,39 +750,34 @@ def run_calibration(self, data, sources=None, energy_label=None,
(N, 1 + D + N_f) tensor of calibrated voxel/value pairs
"""
if self.calibration == 'apply':
# Apply calibration routines
# Check that the metadata is provided
assert meta is not None, (
"Must provide the metadata to convert pixel coordinates "
"to detector coordinates and apply calibrations.")

# Loop over entries in the batch (might have different meta/run IDs)
data_np = data.to_numpy().tensor
sources = sources.to_numpy().tensor if sources is not None else None
if run_info is None:
# Fetch points for the whole batch
voxels = data_np[:, COORD_COLS]
values = data_np[:, VALUE_COL]
rep = data.batch_size//len(meta)
for b in range(data.batch_size):
# Fetch necessary information for this batch entry
lower, upper = data.edges[b], data.edges[b+1]
data_b = data_np[lower:upper]
voxels_b = data_b[:, COORD_COLS]
values_b = data_b[:, VALUE_COL]
sources_b = sources[lower:upper] if sources is not None else None

# Fetch meta/run information for this batch entry
meta_b = meta[b//rep]
run_id = run_info[b//rep].run if run_info is not None else None

# Calibrate voxel values
values = self.calibrator(voxels, values, sources)
data.tensor[:, VALUE_COL] = torch.tensor(
values, dtype=data.dtype, device=data.device)
values_b = self.calibrator(
voxels_b, values_b, sources_b, run_id,
meta=meta_b, module_id=b%rep)

else:
# Loop over entries in the batch (might have different run IDs)
rep = data.batch_size//len(run_info)
for b in range(data.batch_size):
# Fetch points for this batch entry
lower, upper = data.edges[b], data.edges[b+1]
data_b = data_np[lower:upper]
voxels_b = data_b[:, COORD_COLS]
values_b = data_b[:, VALUE_COL]

# Fetch run ID for this batch entry
run_id = run_info[b//rep].run

# Calibrate voxel values
sources_b = sources[lower:upper] if sources is not None else None
values_b = self.calibrator(
voxels_b, values_b, sources_b, run_id)

data.tensor[lower:upper, VALUE_COL] = torch.tensor(
values_b, dtype=data.dtype, device=data.device)
data.tensor[lower:upper, VALUE_COL] = torch.tensor(
values_b, dtype=data.dtype, device=data.device)

self.result['data_adapt'] = data

Expand Down
15 changes: 14 additions & 1 deletion spine/utils/calib/manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,8 @@ def __init__(self, geometry, gain_applied=False, **cfg):
# Append
self.modules[key] = calibrator_factory(key, value)

def __call__(self, points, values, sources=None, run_id=None, track=None):
def __call__(self, points, values, sources=None, run_id=None, track=None,
meta=None, module_id=None):
"""Main calibration driver.

Parameters
Expand All @@ -67,12 +68,24 @@ def __call__(self, points, values, sources=None, run_id=None, track=None):
track : bool, defaut `False`
Whether the object is a track or not. If it is, the track gets
segmented to evaluate local dE/dx and track angle.
meta : Meta, optional
If provided, use to convert the coordinates from image pixel
coordinates to detector coordinates
module_id : int, optional
If provided, shift points to the requested module assuming that the
points currently live in module ID 0

Returns
-------
np.ndarray
(N) array of calibrated depositions in ADC, e- or MeV
"""
# If necessary, convert all points to detector coordinates
if meta is not None:
points = meta.to_cm(points, center=True)
if module_id is not None:
points = self.geo.translate(points, 0, module_id)

# Create a mask for each of the TPC volume in the detector
if sources is not None:
tpc_indexes = []
Expand Down
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