cSAXS/cdtools
0
0 Fork 0
mirror of https://github.com/cdtools-developers/cdtools.git synced 2026-11-12 17:23:54 +01:00
Code Issues Packages Projects Releases Wiki Activity

Switch to a simpler pattern for the CDIModel.<algorithm>_optimize functions that preserves the old behavior when the old pattern is used

Browse Source
This commit is contained in:
levita_a committed 2025-10-13 14:26:07 +02:00
1 parent 766a7d1adf
commit a089dc3b72
1 file changed
+48 -51
Download Patch File Download Diff File Expand all files Collapse all files
src/cdtools/models/base.py
+48 -51
View File
@@ -375,25 +375,24 @@ class CDIModel(t.nn.Module):
only the calculation speed.
"""
# We want to have model.Adam_optimize call AND store
# cdtools.reconstructors.AdamReconstructor to perform reconstructions
# without creating a new reconstructor each time we update the
# hyperparameters.
if not hasattr(self, 'reconstructor'):
self.reconstructor = AdamReconstructor(model=self,
dataset=dataset,
subset=subset)
reconstructor = AdamReconstructor(
model=self,
dataset=dataset,
subset=subset,
)
# Run some reconstructions
return self.reconstructor.optimize(iterations=iterations,
batch_size=batch_size,
lr=lr,
betas=betas,
schedule=schedule,
amsgrad=amsgrad,
regularization_factor=regularization_factor, # noqa
thread=thread,
calculation_width=calculation_width)
return reconstructor.optimize(
iterations=iterations,
batch_size=batch_size,
lr=lr,
betas=betas,
schedule=schedule,
amsgrad=amsgrad,
regularization_factor=regularization_factor, # noqa
thread=thread,
calculation_width=calculation_width,
)
def LBFGS_optimize(self,
iterations: int,
@@ -440,24 +439,23 @@ class CDIModel(t.nn.Module):
round of gradient accumulation. Does not affect the result, only
the calculation speed.
"""
# We want to have model.LBFGS_optimize store
# cdtools.reconstructors.LBFGSReconstructor as an attribute to run
# reconstructions without generating new reconstructors each time
# CDIModel.LBFGS_optimize is called.
if not hasattr(self, 'reconstructor'):
self.reconstructor = LBFGSReconstructor(model=self,
dataset=dataset,
subset=subset)
reconstructor = LBFGSReconstructor(
model=self,
dataset=dataset,
subset=subset,
)
# Run some reconstructions
return self.reconstructor.optimize(iterations=iterations,
lr=lr,
history_size=history_size,
regularization_factor=regularization_factor, # noqa
thread=thread,
calculation_width=calculation_width,
line_search_fn=line_search_fn)
return reconstructor.optimize(
iterations=iterations,
lr=lr,
history_size=history_size,
regularization_factor=regularization_factor, # noqa
thread=thread,
calculation_width=calculation_width,
line_search_fn=line_search_fn,
)
def SGD_optimize(self,
iterations: int,
dataset: CDataset,
@@ -510,26 +508,25 @@ class CDIModel(t.nn.Module):
round of gradient accumulation.
"""
# We want to have model.SGD_optimize store
# cdtools.reconstructors.SGDReconstructor as an attribute to run
# reconstructions without generating new reconstructors each time
# CDIModel.SGD_optimize is called.
if not hasattr(self, 'reconstructor'):
self.reconstructor = SGDReconstructor(model=self,
dataset=dataset,
subset=subset)
reconstructor = SGDReconstructor(
model=self,
dataset=dataset,
subset=subset,
)
# Run some reconstructions
return self.reconstructor.optimize(iterations=iterations,
batch_size=batch_size,
lr=lr,
momentum=momentum,
dampening=dampening,
weight_decay=weight_decay,
nesterov=nesterov,
regularization_factor=regularization_factor, # noqa
thread=thread,
calculation_width=calculation_width)
return reconstructor.optimize(
iterations=iterations,
batch_size=batch_size,
lr=lr,
momentum=momentum,
dampening=dampening,
weight_decay=weight_decay,
nesterov=nesterov,
regularization_factor=regularization_factor, # noqa
thread=thread,
calculation_width=calculation_width,
)
def report(self):