folivo/PINA
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Correct codacy warnings

Browse Source
This commit is contained in:
FilippoOlivo
2024-10-22 14:54:22 +02:00
committed by Nicola Demo
parent 1bc1b3a580
commit 3e30450e9a
10 changed files with 60 additions and 37 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
pina/trainer.py
View File

@@ -9,7 +9,8 @@ from .solvers.solver import SolverInterface
class Trainer(pytorch_lightning.Trainer):
def __init__(self, solver, batch_size=None, train_size=.7, test_size=.2, eval_size=.1, **kwargs):
def __init__(self, solver, batch_size=None, train_size=.7, test_size=.2,
eval_size=.1, **kwargs):
"""
PINA Trainer class for costumizing every aspect of training via flags.
@@ -39,10 +40,9 @@ class Trainer(pytorch_lightning.Trainer):
self._create_loader()
self._move_to_device()
def _move_to_device(self):
device = self._accelerator_connector._parallel_devices[0]
# move parameters to device
pb = self.solver.problem
if hasattr(pb, "unknown_parameters"):
@@ -59,11 +59,13 @@ class Trainer(pytorch_lightning.Trainer):
"""
if not self.solver.problem.collector.full:
error_message = '\n'.join(
[f'{" " * 13} ---> Condition {key} {"sampled" if value else "not sampled"}'
for key, value in self.solver.problem.collector._is_conditions_ready.items()])
[
f'{" " * 13} ---> Condition {key} {"sampled" if value else "not sampled"}'
for key, value in
self.solver.problem.collector._is_conditions_ready.items()])
raise RuntimeError('Cannot create Trainer if not all conditions '
'are sampled. The Trainer got the following:\n'
f'{error_message}')
'are sampled. The Trainer got the following:\n'
f'{error_message}')
devices = self._accelerator_connector._parallel_devices
if len(devices) > 1:
@@ -72,7 +74,8 @@ class Trainer(pytorch_lightning.Trainer):
device = devices[0]
data_module = PinaDataModule(problem=self.solver.problem, device=device,
train_size=self.train_size, test_size=self.test_size,
train_size=self.train_size,
test_size=self.test_size,
eval_size=self.eval_size)
data_module.setup()
self._loader = data_module.train_dataloader()