From 643b49562e6afef1bac0bf65c3db27b21a8d72e2 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Thu, 4 Feb 2021 18:08:02 +0100
Subject: [PATCH] Consistent use of source and target datasets.
---
pysegcnn/core/trainer.py | 17 ++++++++++-------
1 file changed, 10 insertions(+), 7 deletions(-)
diff --git a/pysegcnn/core/trainer.py b/pysegcnn/core/trainer.py
index 59037cc..cc805cc 100644
--- a/pysegcnn/core/trainer.py
+++ b/pysegcnn/core/trainer.py
@@ -2244,7 +2244,7 @@ class NetworkInference(BaseConfig):
The class labels of the source domain.
"""
- return self.src_ds.labels
+ return self.src_ds.dataset.labels
@property
def target_labels(self):
@@ -2256,7 +2256,7 @@ class NetworkInference(BaseConfig):
The class labels of the target domain.
"""
- return self.trg_ds.labels
+ return self.trg_ds.dataset.labels
@property
def source_label_map(self):
@@ -2307,7 +2307,7 @@ class NetworkInference(BaseConfig):
"""
# check whether the source domain labels are the same as the target
# domain labels
- return map_labels(self.src_ds.get_labels(),
+ return map_labels(self.src_ds.dataset.get_labels(),
self.trg_ds.dataset.get_labels())
@property
@@ -2359,7 +2359,7 @@ class NetworkInference(BaseConfig):
A list of the named spectral bands used to train the model.
"""
- return self.src_ds.use_bands
+ return self.src_ds.dataset.use_bands
@property
def plot(self):
@@ -2417,7 +2417,7 @@ class NetworkInference(BaseConfig):
The original class labels of the source domain.
"""
- return self.src_ds._labels
+ return self.src_ds.dataset._labels
@property
def _original_target_labels(self):
@@ -2607,7 +2607,7 @@ class NetworkInference(BaseConfig):
domain=self.domain)
# load the source dataset the model was trained on
- self.src_ds = self.load_dataset(state, test=None).dataset
+ self.src_ds = self.load_dataset(state, test=None)
# load the pretrained model
model, _ = Network.load_pretrained_model(state)
@@ -2618,6 +2618,8 @@ class NetworkInference(BaseConfig):
# check whether to calculate confusion matrix
if self.cm:
+ # TODO: merge predictions for all scenes in output
+
# calculate confusion matrix
conf_mat = confusion_matrix(output['y'].numpy().flatten(),
output['y_pred'].numpy().flatten())
@@ -2643,7 +2645,8 @@ class NetworkInference(BaseConfig):
# chech whether to compute the aggregated confusion matrix
if self.cm:
# initialize the aggregated confusion matrix
- cm_agg = np.zeros(shape=2 * (len(self.src_ds.labels), ))
+ cm_agg = np.zeros(shape=2 * (len(self.src_ds.dataset.labels), )
+ )
# update aggregated confusion matrix
for _, output in inference.items():
--
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