From 8ee32614a70d73edb48e0b501017f96374f0572b Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Fri, 19 Feb 2021 15:40:24 +0100
Subject: [PATCH] Added option to save or not save model evaluations.
---
pysegcnn/core/cli.py | 6 ++++++
pysegcnn/core/trainer.py | 13 +++++++++----
pysegcnn/main/eval.py | 1 +
3 files changed, 16 insertions(+), 4 deletions(-)
diff --git a/pysegcnn/core/cli.py b/pysegcnn/core/cli.py
index b870fcc..855694f 100644
--- a/pysegcnn/core/cli.py
+++ b/pysegcnn/core/cli.py
@@ -162,6 +162,12 @@ def evaluation_parser():
.format(default)),
default=False, nargs='?', const=True, metavar='')
+ # optional argument: whether to save model evaluations
+ parser.add_argument('-sv', '--save', type=bool,
+ help=('Save model evaluations {}.'
+ .format(default)),
+ default=False, nargs='?', const=True, metavar='')
+
# optional argument: whether to overwrite existing files
parser.add_argument('-o', '--overwrite', type=bool,
help=('Overwrite existing model evaluations {}.'
diff --git a/pysegcnn/core/trainer.py b/pysegcnn/core/trainer.py
index 7b13d67..6f34059 100644
--- a/pysegcnn/core/trainer.py
+++ b/pysegcnn/core/trainer.py
@@ -2013,6 +2013,9 @@ class NetworkInference(BaseConfig):
Whether to aggregate the statistics of the different models in
``state_files``. Useful to aggregate the results of mutliple model
runs in cross validation. The default is `False`.
+ save : `bool`
+ Whether to save the model evaluations to a file. The default is
+ `False`.
ds : `dict`
The dataset configuration dictionary passed to
:py:class:`pysegcnn.core.trainer.DatasetConfig` when evaluating on
@@ -2084,6 +2087,7 @@ class NetworkInference(BaseConfig):
domain: str = 'src'
test: object = False
aggregate: bool = False
+ save: bool = False
ds: dict = dataclasses.field(default_factory={})
ds_split: dict = dataclasses.field(default_factory={})
drive_path: str = ''
@@ -2668,7 +2672,7 @@ class NetworkInference(BaseConfig):
log.init_log('Evaluating model: {}.'.format(state))
# check whether model was already evaluated
- if self.eval_file(state).exists():
+ if self.save and self.eval_file(state).exists():
LOGGER.info('Found existing model evaluation: {}.'
.format(self.eval_file(state)))
@@ -2740,9 +2744,10 @@ class NetworkInference(BaseConfig):
outpath=self.perfmc_path)
# save model predictions to file
- LOGGER.info('Saving model evaluation: {}'
- .format(self.eval_file(state)))
- torch.save(output, self.eval_file(state))
+ if self.save:
+ LOGGER.info('Saving model evaluation: {}'
+ .format(self.eval_file(state)))
+ torch.save(output, self.eval_file(state))
# save model predictions to list
inference[state.stem] = output
diff --git a/pysegcnn/main/eval.py b/pysegcnn/main/eval.py
index 1d09aba..c4ac3d3 100644
--- a/pysegcnn/main/eval.py
+++ b/pysegcnn/main/eval.py
@@ -71,6 +71,7 @@ if __name__ == '__main__':
domain=args.domain,
test=args.subset,
aggregate=args.aggregate,
+ save=args.save,
ds=ds,
ds_split=ds_split,
drive_path=args.dataset_path,
--
GitLab