From 43be16a9650cea61d975a122147f238c6fc1527c Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Tue, 11 Aug 2020 16:40:23 +0200
Subject: [PATCH] Divided the configuration dictionary into sub-dictionaries
---
pysegcnn/main/config.py | 160 +++++++++++++++++++++++-----------------
1 file changed, 92 insertions(+), 68 deletions(-)
diff --git a/pysegcnn/main/config.py b/pysegcnn/main/config.py
index eea5173..64dbb94 100644
--- a/pysegcnn/main/config.py
+++ b/pysegcnn/main/config.py
@@ -24,26 +24,23 @@ from pysegcnn.core.transforms import Augment, FlipLr, FlipUd, Noise
HERE = os.path.abspath(os.path.dirname(__file__))
# path to the datasets
-wd = 'C:/Eurac/2020/_Datasets/'
-# wd = '//projectdata.eurac.edu/projects/cci_snow/dfrisinghelli/_Datasets/'
-# wd = '/mnt/CEPH_PROJECTS/cci_snow/dfrisinghelli/_Datasets/'
+DATASET_PATH = 'C:/Eurac/2020/_Datasets/'
+# DATASET_PATH = '/mnt/CEPH_PROJECTS/cci_snow/dfrisinghelli/_Datasets/'
-# the configuration dictionary
-config = {
+# name of the datasets
+DATASET_NAME = 'Sparcs'
+# DATASET_NAME = 'Cloud95/Training/'
+# DATASET_NAME =' ProSnow/Garmisch/
+
+# the dataset configuration dictionary
+dataset_config = {
# ------------------------------- Dataset ---------------------------------
# -------------------------------------------------------------------------
- # define which dataset to train on
- 'dataset_name': 'Sparcs',
- # 'dataset_name': 'Cloud95'
- # 'dataset_name': 'Garmisch'
-
# path to the dataset
- 'dataset_path': os.path.join(wd, 'Sparcs/'),
- # 'dataset_path': os.path.join(wd, 'ProSnow/Garmisch/),
- # 'dataset_path': os.path.join(wd, 'Cloud95/Training/'),
+ 'root_dir': os.path.join(DATASET_PATH, DATASET_NAME),
# a pattern to match the ground truth file naming convention
'gt_pattern': '*mask.png',
@@ -70,59 +67,6 @@ config = {
# series data
'sort': False,
- # the mode to split the dataset:
- #
- # - 'random': randomly split the scenes
- # for each scene, the tiles can be distributed among the
- # training, validation and test set
- #
- # - 'scene': randomly split the scenes
- # for each scene, all the tiles of the scene are included in
- # either the training set, the validation set or the test
- # set, respectively
- #
- # - 'date': split the scenes of a dataset based on a date, useful for
- # time series data
- # scenes before date build the training set, scenes after
- # the date build the validation set, the test set is empty
- 'split_mode': 'scene',
-
- # set random seed for reproducibility of the training, validation
- # and test data split
- # used if split_mode='random' and split_mode='scene'
- 'seed': 0,
-
- # (ttratio * 100) % of the dataset will be used for training and
- # validation
- # used if split_mode='random' and split_mode='scene'
- 'ttratio': 1,
-
- # (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': 0.8,
-
- # the date to split the scenes
- # format: 'yyyymmdd'
- # scenes before date build the training set, scenes after the date build
- # 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
- # weights of the network are updated (via mini-batch gradient descent)
- 'batch_size': 64,
-
- # whether to drop samples (during training only) with a fraction of
- # pixels equal to the constant padding value cval >= drop
- # drop=1 means, do not use a sample if all pixels = cval
- # drop=0.8 means, do not use a sample if 80% or more of the pixels are
- # equal to cval
- # drop=0.2 means, ...
- 'drop': 1,
-
# whether to artificially increase the training data size using data
# augmentation methods
@@ -167,13 +111,62 @@ config = {
# FlipUd(p=0.5)
# ]),
# ],
+}
+
+# the dataset split configuration dictionary
+split_config = {
+
+ # the mode to split the dataset:
+ #
+ # - 'random': randomly split the scenes
+ # for each scene, the tiles can be distributed among the
+ # training, validation and test set
+ #
+ # - 'scene': randomly split the scenes
+ # for each scene, all the tiles of the scene are included in
+ # either the training set, the validation set or the test
+ # set, respectively
+ #
+ # - 'date': split the scenes of a dataset based on a date, useful for
+ # time series data
+ # scenes before date build the training set, scenes after
+ # the date build the validation set, the test set is empty
+ 'split_mode': 'scene',
+
+ # set random seed for reproducibility of the training, validation
+ # and test data split
+ # used if split_mode='random' and split_mode='scene'
+ 'seed': 0,
+
+ # (ttratio * 100) % of the dataset will be used for training and
+ # validation
+ # used if split_mode='random' and split_mode='scene'
+ 'ttratio': 1,
+
+ # (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': 0.8,
+
+ # the date to split the scenes
+ # format: 'yyyymmdd'
+ # scenes before date build the training set, scenes after the date build
+ # the validation set, the test set is empty
+ # used if split_mode='date'
+ 'date': 'yyyymmdd',
+ 'dateformat': '%Y%m%d',
+
+ }
+
+# the model configuration dictionary
+model_config = {
# ------------------------------ Network ----------------------------------
# -------------------------------------------------------------------------
# define the model
- 'net': UNet,
+ 'model': UNet,
# define the number of filters for each convolutional layer
# the number of filters should increase with depth
@@ -189,6 +182,12 @@ config = {
'dilation': 1 # the field of view of the kernel
},
+}
+
+
+# the training configuration dictionary
+training_config = {
+
# ----------------------------- Training ---------------------------------
# -------------------------------------------------------------------------
@@ -215,7 +214,7 @@ config = {
# Training ----------------------------------------------------------------
# whether to resume training from an existing model checkpoint
- 'checkpoint': False,
+ 'checkpoint': True,
# whether to early stop training if the accuracy on the validation set
# does not increase more than delta over patience epochs
@@ -224,6 +223,19 @@ config = {
'delta': 0,
'patience': 10,
+ # whether to drop samples (during training only) with a fraction of
+ # pixels equal to the constant padding value cval >= drop
+ # drop=1 means, do not use a sample if all pixels = cval
+ # drop=0.8 means, do not use a sample if 80% or more of the pixels are
+ # equal to cval
+ # drop=0.2 means, ...
+ 'drop': 1,
+
+ # define the batch size
+ # determines how many samples of the dataset are processed until the
+ # weights of the network are updated (via mini-batch gradient descent)
+ 'batch_size': 64,
+
# define the number of epochs: the number of maximum iterations over
# the whole training dataset
'epochs': 200,
@@ -240,6 +252,11 @@ config = {
# define the learning rate
'lr': 0.001,
+}
+
+# the evaluation configuration file
+evaluation_config = {
+
# ----------------------------- Evaluation --------------------------------
# -------------------------------------------------------------------------
@@ -280,3 +297,10 @@ config = {
'plot_bands': ['nir', 'red', 'green'],
}
+
+# the complete configuration
+config = {**dataset_config,
+ **split_config,
+ **model_config,
+ **training_config,
+ **evaluation_config}
--
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