From 6faa33d1596589e6bb1cbc87719d124351f99931 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Thu, 30 Jul 2020 16:47:11 +0200
Subject: [PATCH] Included option to validate model on training set
---
pysegcnn/main/config.py | 18 ++++++++++--------
pysegcnn/main/eval.py | 10 +++++++---
2 files changed, 17 insertions(+), 11 deletions(-)
diff --git a/pysegcnn/main/config.py b/pysegcnn/main/config.py
index 61bdaaa..c03abc1 100644
--- a/pysegcnn/main/config.py
+++ b/pysegcnn/main/config.py
@@ -98,7 +98,7 @@ config = {
# (ttratio * tvratio) * 100 % will be used as for training
# (1 - ttratio * tvratio) * 100 % will be used for validation
# used if split_mode='random' and split_mode='scene'
- 'tvratio': 0.95,
+ 'tvratio': 0.8,
# the date to split the scenes
# format: 'yyyymmdd'
@@ -106,6 +106,7 @@ config = {
# the validation set, the test set is empty
# used if split_mode='date'
'date': 'yyyymmdd',
+ 'dateformat': '%Y%m%d',
# define the batch size
# determines how many samples of the dataset are processed until the
@@ -216,14 +217,14 @@ config = {
# whether to early stop training if the accuracy on the validation set
# does not increase more than delta over patience epochs
- 'early_stop': False,
+ 'early_stop': True,
'mode': 'max',
'delta': 0,
'patience': 10,
# define the number of epochs: the number of maximum iterations over
# the whole training dataset
- 'epochs': 10,
+ 'epochs': 200,
# define the number of threads
'nthreads': os.cpu_count(),
@@ -244,9 +245,10 @@ config = {
# these options are only used for evaluating a trained model using
# main.eval.py
- # whether to evaluate the model on the validation set or test set
+ # the dataset to evaluate the model on
# test=False means evaluating on the validation set
- # test=True means evaluationg on the test set
+ # test=True means evaluating on the test set
+ # test=None means evaluating on the training set
'test': False,
# whether to compute and plot the confusion matrix
@@ -261,13 +263,13 @@ config = {
# split_mode="date"
'predict_scene': True,
- # whether to save plots of (input, ground truth, prediction) of the
- # samples from the validation/test dataset to disk
+ # whether to save plots of (input, ground truth, prediction) of the samples
+ # from the validation/test dataset to disk, applies if predict_scene=False
# output path is: pysegcnn/main/_samples/
'plot_samples': False,
# whether to save plots of (input, ground truth, prediction) for each scene
- # to disk
+ # in the validation/test dataset to disk, applies if predict_scene=True
# output path is: pysegcnn/main/_samples/
'plot_scenes': True,
diff --git a/pysegcnn/main/eval.py b/pysegcnn/main/eval.py
index 85a5351..73faa10 100644
--- a/pysegcnn/main/eval.py
+++ b/pysegcnn/main/eval.py
@@ -10,7 +10,7 @@ import os
# locals
from pysegcnn.core.trainer import NetworkTrainer
from pysegcnn.core.predict import predict_samples, predict_scenes
-from pysegcnn.core.config import config, HERE
+from pysegcnn.main.config import config, HERE
from pysegcnn.core.graphics import plot_confusion_matrix, plot_loss
@@ -23,8 +23,12 @@ if __name__ == '__main__':
# plot loss and accuracy
plot_loss(trainer.loss_state, outpath=os.path.join(HERE, '_graphics/'))
- # check whether to evaluate the model on the validation set or the test set
- ds = trainer.test_ds if trainer.test else trainer.valid_ds
+ # check whether to evaluate the model on the training set, validation set
+ # or the test set
+ if trainer.test is None:
+ ds = trainer.train_ds
+ else:
+ ds = trainer.test_ds if trainer.test else trainer.valid_ds
# whether to predict each sample or each scene individually
if trainer.predict_scene:
--
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