From 420561b7f7f33f69bcdd297ae1b2c930e6d3409d Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Fri, 26 Feb 2021 09:08:33 +0100
Subject: [PATCH] Do not store scene inputs and use more descriptive figure
title.
---
pysegcnn/core/trainer.py | 16 ++++++++++------
1 file changed, 10 insertions(+), 6 deletions(-)
diff --git a/pysegcnn/core/trainer.py b/pysegcnn/core/trainer.py
index e663a33..2640ca9 100644
--- a/pysegcnn/core/trainer.py
+++ b/pysegcnn/core/trainer.py
@@ -2504,8 +2504,6 @@ class NetworkInference(BaseConfig):
the samples (``self.predict_scene=False``) or the name of the
scenes of the target dataset (``self.predict_scene=True``). The
values are dictionaries with keys:
- ``'x'``
- Model input data of the sample (:py:class:`numpy.ndarray`).
``'y_true'
Ground truth class labels (:py:class:`numpy.ndarray`).
``'y_pred'``
@@ -2586,19 +2584,25 @@ class NetworkInference(BaseConfig):
# save current scene to output dictionary
output[batch] = {k: v for k, v in zip(
- INFERENCE_NAMES, [inputs, labels, prdctn])}
+ INFERENCE_NAMES[1:], [labels, prdctn])}
# re-initialize scene dictionary
scenes = {k: [] for k in INFERENCE_NAMES}
# plot current scene
if self.plot:
+
+ # title for prediction
+ title = ''.join([(v[0] + str(k)) for k, v in
+ self.src_ds.sensor.band_dict().items()
+ if v in self.bands])
+
# plot inputs, ground truth and model predictions
fig = plot_sample(inputs.clip(0, 1),
self.bands,
self.use_labels,
y=labels,
- y_pred={'Prediction': prdctn},
+ y_pred={title: prdctn},
accuracy=True,
**self.plot_kwargs)
@@ -2618,8 +2622,8 @@ class NetworkInference(BaseConfig):
else:
# save current batch to output dictionary
- output[batch] = {k: v for k, v in zip(INFERENCE_NAMES,
- [inputs, labels, prdctn])
+ output[batch] = {k: v for k, v in zip(INFERENCE_NAMES[1:],
+ [labels, prdctn])
}
# calculate the accuracy of the prediction on the current batch
--
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