Reordered the inputs to each configuration dictionary

pysegcnn/main/config.py

@@ -11,13 +11,7 @@ Modify the variable values to your needs, but DO NOT modify the variable names.
 # builtins
 import os
 
-# externals
-import torch.nn as nn
-import torch.optim as optim
-
-# locals
-from pysegcnn.core.models import UNet
-from pysegcnn.core.transforms import Augment, FlipLr, FlipUd, Noise
+# from pysegcnn.core.transforms import Augment, FlipLr, FlipUd, Noise
 
 # path to this file
 HERE = os.path.abspath(os.path.dirname(__file__))
@@ -59,6 +53,11 @@ dataset_config = {
     # tiles of size (tile_size, tile_size)
     'pad': True,
 
+    # set random seed for reproducibility of the training, validation
+    # and test data split
+    # used if split_mode='random' and split_mode='scene'
+    'seed': 0,
+
     # the constant value to pad around the ground truth mask if pad=True
     'cval': 99,
 
@@ -132,11 +131,6 @@ split_config = {
     #                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'
@@ -155,6 +149,15 @@ split_config = {
     'date': 'yyyymmdd',
     'dateformat': '%Y%m%d',
 
+    # 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=0 means, do not drop any samples
+    'drop': 0,
+
     }
 
 # the model configuration dictionary
@@ -165,7 +168,7 @@ model_config = {
     # -------------------------------------------------------------------------
 
     # define the model
-    'model': UNet,
+    'model_name': 'Unet',
 
     # define the number of filters for each convolutional layer
     # the number of filters should increase with depth
@@ -181,16 +184,6 @@ model_config = {
                'dilation': 1  # the field of view of the kernel
                },
 
-}
-
-
-# the training configuration dictionary
-training_config = {
-
-    # ----------------------------- Training  ---------------------------------
-
-    # -------------------------------------------------------------------------
-
     # path to save trained models
     'state_path': os.path.join(HERE, '_models/'),
 
@@ -213,7 +206,22 @@ training_config = {
     # Training ----------------------------------------------------------------
 
     # whether to resume training from an existing model checkpoint
-    'checkpoint': True,
+    'checkpoint': False,
+
+    # 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
+
+}
+
+
+# the training configuration dictionary
+train_config = {
+
+    # ----------------------------- Training  ---------------------------------
+
+    # -------------------------------------------------------------------------
 
     # whether to early stop training if the accuracy on the validation set
     # does not increase more than delta over patience epochs
@@ -222,31 +230,15 @@ training_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,
 
-    # define the number of threads
-    'nthreads': os.cpu_count(),
-
     # define a loss function to calculate the network error
-    'loss_function': nn.CrossEntropyLoss(),
+    'loss_name': 'CrossEntropy',
 
     # define an optimizer to update the network weights
-    'optimizer': optim.Adam,
+    'optim_name': 'Adam',
 
     # define the learning rate
     'lr': 0.001,
@@ -301,5 +293,5 @@ evaluation_config = {
 config = {**dataset_config,
           **split_config,
           **model_config,
-          **training_config,
+          **train_config,
           **evaluation_config}