diff --git a/pysegcnn/core/split.py b/pysegcnn/core/split.py
index 78f653a3b41c2d0f552c2485e026d8f604487060..588c2b488e8239e485f7c3f0f1512aed01921996 100644
--- a/pysegcnn/core/split.py
+++ b/pysegcnn/core/split.py
@@ -14,6 +14,9 @@ from torch.utils.data.dataset import Subset
# the names of the subsets
SUBSET_NAMES = ['train', 'valid', 'test']
+# valid split modes
+VALID_SPLIT_MODES = ['random', 'scene', 'date']
+
# function calculating number of samples in a dataset given a ratio
def _ds_len(ds, ratio):
@@ -129,7 +132,6 @@ def date_scene_split(ds, date, dateformat='%Y%m%d'):
# store the indices and corresponding tiles of the current subset to
# dictionary
subsets[SUBSET_NAMES[name]] = scenes
- # sbst.ids = np.unique([s['id'] for s in scenes.values()])
# check if the splits are disjoint
assert pairwise_disjoint([s.keys() for s in subsets.values()])
@@ -143,101 +145,130 @@ def pairwise_disjoint(sets):
return n == len(union)
-class Split(object):
+class SceneSubset(Subset):
- # the valid modes
- valid_modes = ['random', 'scene', 'date']
+ def __init__(self, ds, indices, name, scenes, scene_ids):
+ super().__init__(dataset=ds, indices=indices)
- def __init__(self, ds, mode, **kwargs):
+ # the name of the subset
+ self.name = name
- # check which mode is provided
- if mode not in self.valid_modes:
- raise ValueError('{} is not supported. Valid modes are {}, see '
- 'pysegcnn.main.config.py for a description of '
- 'each mode.'.format(mode, self.valid_modes))
- self.mode = mode
+ # the scene in the subset
+ self.scenes = scenes
- # the dataset to split
- self.ds = ds
+ # the names of the scenes
+ self.ids = scene_ids
- # the keyword arguments
- self.kwargs = kwargs
- # initialize split
- self._init_split()
+class RandomSubset(Subset):
- def _init_split(self):
+ def __init__(self, ds, indices, name, scenes, scene_ids):
+ super().__init__(dataset=ds, indices=indices)
- if self.mode == 'random':
- self.subset = RandomSubset
- self.split_function = random_tile_split
- self.allowed_kwargs = ['tvratio', 'ttratio', 'seed']
+ # the name of the subset
+ self.name = name
- if self.mode == 'scene':
- self.subset = SceneSubset
- self.split_function = random_scene_split
- self.allowed_kwargs = ['tvratio', 'ttratio', 'seed']
+ # the scene in the subset
+ self.scenes = scenes
- if self.mode == 'date':
- self.subset = SceneSubset
- self.split_function = date_scene_split
- self.allowed_kwargs = ['date', 'dateformat']
- self._check_kwargs()
+class Split(object):
- def _check_kwargs(self):
+ def __init__(self, ds):
- # check if the correct keyword arguments are provided
- if not set(self.allowed_kwargs).issubset(self.kwargs.keys()):
- raise TypeError('__init__() expecting keyword arguments: {}.'
- .format(', '.join(kwa for kwa in
- self.allowed_kwargs)))
- # select the correct keyword arguments
- self.kwargs = {k: self.kwargs[k] for k in self.allowed_kwargs}
+ # the dataset to split
+ self.ds = ds
- # function apply the split
def split(self):
- # create the subsets
- subsets = self.split_function(self.ds, **self.kwargs)
-
# build the subsets
ds_split = []
- for name, sub in subsets.items():
+ for name, sub in self.subsets().items():
# the scene identifiers of the current subset
ids = np.unique([s['id'] for s in sub.values()])
# build the subset
- subset = self.subset(self.ds, list(sub.keys()), name,
- list(sub.values()), ids)
- ds_split.append(subset)
+ sbst = self.subset_type()(self.ds, list(sub.keys()), name,
+ list(sub.values()), ids)
+ ds_split.append(sbst)
return ds_split
+ @property
+ def subsets(self):
+ raise NotImplementedError
-class SceneSubset(Subset):
+ def subset_type(self):
+ raise NotImplementedError
- def __init__(self, ds, indices, name, scenes, scene_ids):
- super().__init__(dataset=ds, indices=indices)
+ def __repr__(self):
- # the name of the subset
- self.name = name
+ # representation string to print
+ fs = self.__class__.__name__ + '(\n '
- # the scene in the subset
- self.scenes = scenes
+ # dataset split
+ fs += '\n '.join(
+ '- {}: {:d} batches ({:.2f}%)'
+ .format(k, len(v), len(v) * 100 / len(self.ds))
+ for k, v in self.subsets().items())
+ fs += '\n)'
+ return fs
- # the names of the scenes
- self.ids = scene_ids
+class DateSplit(Split):
+ def __init__(self, ds, date, dateformat):
+ super().__init__(ds)
-class RandomSubset(Subset):
+ # the date to split the dataset
+ # before: training set
+ # after : validation set
+ self.date = date
- def __init__(self, ds, indices, name, scenes):
- super().__init__(dataset=ds, indices=indices)
+ # the format of the date
+ self.dateformat = dateformat
- # the name of the subset
- self.name = name
+ def subsets(self):
+ return date_scene_split(self.ds, self.date, self.dateformat)
- # the scene in the subset
- self.scenes = scenes
+ def subset_type(self):
+ return SceneSubset
+
+
+class RandomSplit(Split):
+
+ def __init__(self, ds, ttratio, tvratio, seed):
+ super().__init__(ds)
+
+ # the training, validation and test set ratios
+ self.ttratio = ttratio
+ self.tvratio = tvratio
+
+ # the random seed: useful for reproducibility
+ self.seed = seed
+
+
+class RandomTileSplit(RandomSplit):
+
+ def __init__(self, ds, ttratio, tvratio, seed):
+ super().__init__(ds, ttratio, tvratio, seed)
+
+ def subsets(self):
+ return random_tile_split(self.ds, self.tvratio, self.ttratio,
+ self.seed)
+
+ def subset_type(self):
+ return RandomSubset
+
+
+class RandomSceneSplit(RandomSplit):
+
+ def __init__(self, ds, ttratio, tvratio, seed):
+ super().__init__(ds, ttratio, tvratio, seed)
+
+ def subsets(self):
+ return random_scene_split(self.ds, self.tvratio, self.ttratio,
+ self.seed)
+
+ def subset_type(self):
+ return SceneSubset