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Andrei-Claudiu Roibu
BrainMapper
Commits
4d76c5ff
Commit
4d76c5ff
authored
Apr 16, 2020
by
Andrei-Claudiu Roibu
🖥
Browse files
creating save_model_directory if it does not exist
parent
16550d2e
Changes
1
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run.py
View file @
4d76c5ff
...
...
@@ -39,7 +39,7 @@ import torch.utils.data as data
from
solver
import
Solver
from
BrainMapperUNet
import
BrainMapperUNet3D
from
utils.data_utils
import
get_datasets
,
data_test_train_validation_split
,
update_shuffling_flag
from
utils.data_utils
import
get_datasets
,
data_test_train_validation_split
,
update_shuffling_flag
,
create_folder
import
utils.data_evaluation_utils
as
evaluations
from
utils.data_logging_utils
import
LogWriter
...
...
@@ -137,33 +137,36 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
else
:
BrainMapperModel
=
BrainMapperUNet3D
(
network_parameters
)
# solver = Solver(model=BrainMapperModel,
# device=misc_parameters['device'],
# number_of_classes=network_parameters['number_of_classes'],
# experiment_name=training_parameters['experiment_name'],
# optimizer_arguments={'lr': training_parameters['learning_rate'],
# 'betas': training_parameters['optimizer_beta'],
# 'eps': training_parameters['optimizer_epsilon'],
# 'weight_decay': training_parameters['optimizer_weigth_decay']
# },
# model_name=misc_parameters['model_name'],
# number_epochs=training_parameters['number_of_epochs'],
# loss_log_period=training_parameters['loss_log_period'],
# learning_rate_scheduler_step_size=training_parameters[
# 'learning_rate_scheduler_step_size'],
# learning_rate_scheduler_gamma=training_parameters['learning_rate_scheduler_gamma'],
# use_last_checkpoint=training_parameters['use_last_checkpoint'],
# experiment_directory=misc_parameters['experiments_directory'],
# logs_directory=misc_parameters['logs_directory']
# )
# solver.train(train_loader, validation_loader)
# model_output_path = os.path.join(
# misc_parameters['save_model_directory'], training_parameters['final_model_output_file'])
# BrainMapperModel.save(model_output_path)
# print("Final Model Saved in: {}".format(model_output_path))
solver
=
Solver
(
model
=
BrainMapperModel
,
device
=
misc_parameters
[
'device'
],
number_of_classes
=
network_parameters
[
'number_of_classes'
],
experiment_name
=
training_parameters
[
'experiment_name'
],
optimizer_arguments
=
{
'lr'
:
training_parameters
[
'learning_rate'
],
'betas'
:
training_parameters
[
'optimizer_beta'
],
'eps'
:
training_parameters
[
'optimizer_epsilon'
],
'weight_decay'
:
training_parameters
[
'optimizer_weigth_decay'
]
},
model_name
=
misc_parameters
[
'model_name'
],
number_epochs
=
training_parameters
[
'number_of_epochs'
],
loss_log_period
=
training_parameters
[
'loss_log_period'
],
learning_rate_scheduler_step_size
=
training_parameters
[
'learning_rate_scheduler_step_size'
],
learning_rate_scheduler_gamma
=
training_parameters
[
'learning_rate_scheduler_gamma'
],
use_last_checkpoint
=
training_parameters
[
'use_last_checkpoint'
],
experiment_directory
=
misc_parameters
[
'experiments_directory'
],
logs_directory
=
misc_parameters
[
'logs_directory'
]
)
solver
.
train
(
train_loader
,
validation_loader
)
model_output_path
=
os
.
path
.
join
(
misc_parameters
[
'save_model_directory'
],
training_parameters
[
'final_model_output_file'
])
create_folder
(
misc_parameters
[
'save_model_directory'
])
BrainMapperModel
.
save
(
model_output_path
)
print
(
"Final Model Saved in: {}"
.
format
(
model_output_path
))
def
evaluate_score
(
training_parameters
,
network_parameters
,
misc_parameters
,
evaluation_parameters
):
...
...
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