diff --git a/pysegcnn/main/eval.py b/pysegcnn/main/eval.py
index 2ecfe6055aa079b876a95158e55d1aae420648a5..d97deec7e44fb5a80527901c9640b4c09761af13 100644
--- a/pysegcnn/main/eval.py
+++ b/pysegcnn/main/eval.py
@@ -1,9 +1,9 @@
"""Main script to evaluate a model.
+This is a command line script, which can be customized on the terminal.
+
Steps to run a model evaluation:
- 1. Configure the model evaluation in :py:mod:`pysegcnn.main.eval_config.py`
- 2. Save :py:mod:`pysegcnn.main.eval_config.py`
3. In a terminal, navigate to the repository's root directory
4. Run
@@ -11,6 +11,8 @@ Steps to run a model evaluation:
python pysegcnn/main/eval.py
+ This will print a list of options for the evaluation.
+
License
-------
@@ -26,15 +28,59 @@ License
# !/usr/bin/env python
# -*- coding: utf-8 -*-
+# builtins
+import sys
+
# locals
+from pysegcnn.core.cli import evaluation_parser
from pysegcnn.core.trainer import NetworkInference
-from pysegcnn.main.eval_config import eval_config
+from pysegcnn.core.utils import search_files
+from pysegcnn.main.eval_config import trg_ds, trg_ds_split
if __name__ == '__main__':
- # instanciate the network inference class
- inference = NetworkInference(**eval_config)
-
- # evaluate model
- output = inference.evaluate()
+ # define command line argument parser
+ parser = evaluation_parser()
+
+ # parse command line arguments
+ args = sys.argv[1:]
+ if not args:
+ parser.print_help()
+ sys.exit()
+ else:
+ args = parser.parse_args(args)
+
+ # check whether the input path exists
+ if args.source.exists():
+
+ # get the model state files
+ state_files = search_files(args.source, args.pattern)
+
+ # check whether to evaluate on datasets defined at training time or
+ # on explicitly defined datasets
+ ds = ds_split = {}
+ if not args.implicit:
+ ds = trg_ds
+ ds_split = trg_ds_split
+
+ # instanciate the network inference class
+ inference = NetworkInference(
+ state_files=state_files,
+ implicit=args.implicit,
+ domain=args.domain,
+ test=args.subset,
+ aggregate=args.aggregate,
+ ds=ds,
+ ds_split=ds_split,
+ map_labels=args.map_labels,
+ predict_scene=args.predict_scene,
+ plot_scenes=args.plot_scenes,
+ cm=args.confusion_matrix)
+
+ # evaluate models
+ output = inference.evaluate()
+
+ else:
+ print('{} does not exist.'.format(str(args.source)))
+ sys.exit()
diff --git a/pysegcnn/main/eval_config.py b/pysegcnn/main/eval_config.py
index ad6a5ada5de3d5b68d5b03ac332159e99a8288d2..da215db0120dda742a5fa721b2a8a07fd835f8c6 100644
--- a/pysegcnn/main/eval_config.py
+++ b/pysegcnn/main/eval_config.py
@@ -1,8 +1,6 @@
-"""The configuration file to train and evaluate a model.
+"""The configuration file to evaluate a model on an explicitly defined dataset.
-The configuration is handled by the configuration dictionaries.
-
-Modify the values to your needs, but DO NOT modify the keys.
+See pysegcnn/main/eval.py for more details.
License
-------
@@ -21,25 +19,19 @@ License
# builtins
import pathlib
-# locals
-from pysegcnn.core.utils import search_files
-
-# path to this file
-HERE = pathlib.Path(__file__).resolve().parent
-
# path to the datasets on the current machine
DRIVE_PATH = pathlib.Path('C:/Eurac/Projects/CCISNOW/Datasets/')
# DRIVE_PATH = pathlib.Path('/mnt/CEPH_PROJECTS/cci_snow/dfrisinghelli/Datasets/') # nopep8
# name and paths to the datasets
DATASETS = {'Sparcs': DRIVE_PATH.joinpath('Sparcs'),
- 'Alcd': DRIVE_PATH.joinpath('Alcd/60m')
+ 'Alcd': DRIVE_PATH.joinpath('Alcd')
}
# name of the target dataset
TRG_DS = 'Alcd'
-# spectral bands to use for training
+# spectral bands to use for evaluation
BANDS = ['red', 'green', 'blue', 'nir', 'swir1', 'swir2']
# tile size of a single sample
@@ -72,99 +64,3 @@ trg_ds_split = {
'tvratio': 0.8,
}
-
-# the evaluation configuration
-eval_config = {
-
- # -------------------------------------------------------------------------
- # ----------------------------- Evaluation --------------------------------
- # -------------------------------------------------------------------------
-
- # these options are only used for evaluating a trained model using
- # pysegcnn.main.eval.py
-
- # the model(s) to evaluate
- 'state_files': search_files(HERE, '*.pt'),
-
- # Evaluate on datasets defined at training time ---------------------------
-
- # implicit=True, models are evaluated on the training, validation
- # and test datasets defined at training time
- # implicit=False, models are evaluated on an explicitly defined dataset
- # 'ds'
- 'implicit': True,
- # 'implicit': False,
-
- # The options 'domain' and 'test' define on which domain (source, target)
- # and on which set (training, validation, test) to evaluate the model.
- # NOTE: If the specified set was not available at training time, an error
- # is raised.
-
- # whether to evaluate the model on the labelled source domain or the
- # (un)labelled target domain
- # if domain='trg', target domain
- # if domain='src', source domain
- # 'domain': 'src',
- 'domain': 'trg',
-
- # the subset to evaluate the model on
- # test=False, 0 means evaluating on the validation set
- # test=True, 1 means evaluating on the test set
- # test=None means evaluating on the training set
- # 'test': True,
- 'test': None,
- # 'test': False,
-
- # whether to map the model labels from the model source domain to the
- # defined 'domain'
- # For models trained via unsupervised domain adaptation, the classes of the
- # source domain, i.e. the classes the model is trained with, may differ
- # from the classes of the target domain. Setting 'map_labels'=True, means
- # mapping the source classes to the target classes. Obviously, this is only
- # possible if the target classes are a subset of the source classes.
- 'map_labels': False,
-
- # Evaluate on an explicitly defined dataset -------------------------------
-
- # OPTIONAL: If 'trg_ds' is specified and 'implicit'=False, the model is not
- # evaluated on the datasets defined at training time, but on the
- # dataset defined by 'trg_ds'.
-
- # the dataset to evaluate the model on (optional)
- 'ds': trg_ds,
-
- # the dataset split to use for 'ds'
- 'ds_split': trg_ds_split,
-
- # Evaluation options ------------------------------------------------------
-
- # whether to compute and plot the confusion matrix
- # output path is: pysegcnn/main/_graphics/
- # 'cm': True,
- 'cm': False,
-
- # whether to predict each sample or each scene individually
- # False: each sample is predicted individually and the scenes are not
- # reconstructed
- # True: each scene is first reconstructed and then the whole scene is
- # predicted at once
- # NOTE: this option works only for datasets split by split_mode="scene"
- 'predict_scene': True,
-
- # whether to save plots of (input, ground truth, prediction) for each scene
- # in the train/validation/test dataset to disk, applies if
- # predict_scene=True
- # output path is: pysegcnn/main/_scenes/
- 'plot_scenes': True,
-
- # plot_bands defines the bands used to plot a false color composite of
- # the input scene: red': bands[0], green': bands[1], blue': bands[2]
- 'plot_bands': ['nir', 'red', 'green'],
-
- # size of the figures
- 'figsize': (16, 9),
-
- # degree of constrast stretching for false color composite
- 'alpha': 5
-
-}