diff --git a/pysegcnn/core/trainer.py b/pysegcnn/core/trainer.py
index 77b0b25daeeda80e76d706825bc9221b84b8730a..457671b72dd2ad313985f395ba113adf6849b00d 100644
--- a/pysegcnn/core/trainer.py
+++ b/pysegcnn/core/trainer.py
@@ -2173,30 +2173,14 @@ class NetworkInference(BaseConfig):
Path to store plots of model predictions for entire scenes.
perfmc_path : :py:class:`pathlib.Path`
Path to store plots of model performance, e.g. confusion matrix.
- animtn_path : :py:class:`pathlib.Path`
- Path to store animations.
models_path : :py:class:`pathlib.Path`
Path to search for model state files ``state_files``.
plot_kwargs : `dict`
Keyword arguments for :py:func:`pysegcnn.core.graphics.plot_sample`
- basename : `str`
- Base filename for each plot.
- model : :py:class:`pysegcnn.core.models.Network`
- The model to use for inference.
- model_state : `dict`
- A dictionary containing the model and optimizer state, as
- constructed by :py:meth:`~pysegcnn.core.Network.save`.
trg_ds : :py:class:`pysegcnn.core.split.CustomSubset`
The dataset to evaluate ``model`` on.
src_ds : :py:class:`pysegcnn.core.split.CustomSubset`
The model source domain training dataset.
- fig : :py:class:`matplotlib.figure.Figure`
- A :py:class:`matplotlib.figure.Figure` instance to iteratively plot to.
- anim : :py:class:`pysegcnn.core.graphics.Animate`
- An instance :py:class:`pysegcnn.core.graphics.Animate` Used to create
- animations if ``animate=True``.
- conf_mat : :py:class:`numpy.ndarray`
- The model confusion matrix.
"""
@@ -2214,7 +2198,6 @@ class NetworkInference(BaseConfig):
cm: bool = True
figsize: tuple = (10, 10)
alpha: int = 5
- animate: bool = False
def __post_init__(self):
"""Check the type of each argument.
@@ -2250,7 +2233,6 @@ class NetworkInference(BaseConfig):
self.sample_path = self.base_path.joinpath('_samples')
self.scenes_path = self.base_path.joinpath('_scenes')
self.perfmc_path = self.base_path.joinpath('_graphics')
- self.animtn_path = self.base_path.joinpath('_animations')
# input path for model state files
self.models_path = self.base_path.joinpath('_models')
@@ -2397,6 +2379,40 @@ class NetworkInference(BaseConfig):
"""
return self.trg_ds.labels
+ @property
+ def source_label_map(self):
+ """Mapping of the original source labels to the model source labels.
+
+ See
+ :py:meth:`pysegcnn.core.trainer.NetworkInference._original_source_labels`.
+
+ Returns
+ -------
+ source_labels : :py:class:`numpy.ndarray`
+ The mapping from the original source class identifiers to the
+ identifiers used during training.
+
+ """
+ return np.array([list(self.source_labels.keys()),
+ list(self._original_source_labels.keys())]).T
+
+ @property
+ def target_label_map(self):
+ """Mapping of the original target labels to the model target labels.
+
+ See
+ :py:meth:`pysegcnn.core.trainer.NetworkInference._original_source_labels`.
+
+ Returns
+ -------
+ target_labels : :py:class:`numpy.ndarray`
+ The mapping from the original target class identifiers to the
+ identifiers used for evaluation.
+
+ """
+ return np.array([list(self._original_target_labels_labels.keys()),
+ list(self.target_labels.keys())]).T
+
@property
def label_map(self):
"""Label mapping dictionary from the source to the target domain.
@@ -2508,6 +2524,34 @@ class NetworkInference(BaseConfig):
"""
return self.trg_ds.dataset.tiles if self.predict_scene else 1
+ @property
+ def _original_source_labels(self):
+ """Original source domain labels.
+
+ Since PyTorch requires class labels to be an ascending sequence
+ starting from 0, the actual class labels in the ground truth may differ
+ from the class labels fed to the model.
+
+ Returns
+ -------
+ original_source_labels : `dict` [`int`, `dict`]
+ The original class labels of the source domain.
+
+ """
+ return self.src_ds._labels
+
+ @property
+ def _original_target_labels(self):
+ """Original target domain labels.
+
+ Returns
+ -------
+ original_target_labels : `dict` [`int`, `dict`]
+ The original class labels of the target domain.
+
+ """
+ return self.trg_ds.dataset._labels
+
def map_to_target(self, prd):
"""Map source domain labels to target domain labels.
@@ -2523,16 +2567,13 @@ class NetworkInference(BaseConfig):
"""
# map actual source labels to original source labels
- # prd = array_replace(prd, np.array([self.src_ds.labels.keys(),
- # self.src_ds._labels.keys()]))
+ prd = array_replace(prd, self.source_label_map)
# apply the label mapping
- # prd = array_replace(prd, self.label_map.to_numpy())
+ prd = array_replace(prd, self.label_map.to_numpy())
# map original target labels to actual target labels
- # for oid, aid in zip(self.trg_ds._labels.keys(),
- # self.target_labels.keys()):
- # prd[torch.where(prd == oid)] = aid
+ prd = array_replace(prd, self.target_label_map)
return prd
@@ -2626,26 +2667,34 @@ class NetworkInference(BaseConfig):
**self.kwargs)
return output
+ def eval_file(self, state_file):
+ return pathlib.Path(str(state_file).replace('.pt', '_eval.pt'))
+
def evaluate(self):
"""Evaluate the models on a defined dataset.
Returns
-------
- output : `dict` [`str`, `dict`]
- The inference output dictionary. The keys are either the number of
- the samples (``self.predict_scene=False``) or the name of the
+ inference : `dict` [`str`, `dict`]
+ The inference output dictionary. The keys are the names of the
+ models in ``self.state_file`` and the values are dictionaries
+ where the keys are either the number of the batches
+ (``self.predict_scene=False``) or the name of the
scenes of the target dataset (``self.predict_scene=True``). The
- values are dictionaries with keys:
+ values of the nested dictionaries are again dictionaries with keys:
``'x'``
Model input data of the sample (:py:class:`numpy.ndarray`).
``'y'
Ground truth class labels (:py:class:`numpy.ndarray`).
``'y_pred'``
Model prediction class labels (:py:class:`numpy.ndarray`).
+ ``'cm'``
+ The confusion matrix of the model, which is only present if
+ ``self.cm=True`` (:py:class:`numpy.ndarray`).
"""
# iterate over the models to evaluate
- inference = []
+ inference = {}
for state in self.state_files:
# initialize logging
@@ -2653,6 +2702,15 @@ class NetworkInference(BaseConfig):
dictConfig(log_conf(log.log_file))
log.init_log('{}: ' + 'Evaluating model: {}.'.format(state.name))
+ # check whether model was already evaluated
+ if self.eval_file(state).exists():
+
+ # load existing model evaluation
+ LOGGER.info('Found existing model evaluation: {}.'
+ .format(self.eval_file(state)))
+ inference[state.stem] = torch.load(self.eval_file(state))
+ continue
+
# plot loss and accuracy
plot_loss(check_filename_length(state), outpath=self.perfmc_path)
@@ -2692,19 +2750,9 @@ class NetworkInference(BaseConfig):
outpath=self.perfmc_path)
# save model predictions to file
- torch.save(output, str(state).replace('.pt', '_eval.pt'))
+ torch.save(output, self.eval_file(state))
# save model predictions to list
- inference.append(output)
-
- # check whether to compute an aggregated confusion matrix
- if self.aggregate and self.cm:
-
- # initialize aggregated confusion matrix
- cm = np.zeros(shape=2 * (len(self.src_ds.labels), ))
-
- # iterate over the different model runs
- for out in inference:
- cm += out['cm']
+ inference[state.stem] = output
- # plot aggregated cm
+ return inference