Added smarter import strategy

main/config.py

@@ -6,17 +6,14 @@ Created on Tue Jun 30 11:40:35 2020
 @author: Daniel
 """
 # builtins
+from __future__ import absolute_import
 import os
-import sys
 import inspect
 
 # externals
 import torch.nn as nn
 import torch.optim as optim
 
-# append path to local files to the python search path
-sys.path.append('..')
-
 from pytorch.models import UNet
 
 # ------------------------- Dataset configuration -----------------------------

main/eval.py

 # builtins
+from __future__ import absolute_import
 import os
-import sys
 
 # externals
 import numpy as np
@@ -8,9 +8,6 @@ import torch
 import torch.nn.functional as F
 import matplotlib.pyplot as plt
 
-# append path to local files to the python search path
-sys.path.append('..')
-
 # local modules
 from pytorch.trainer import NetworkTrainer
 from pytorch.graphics import plot_confusion_matrix, plot_loss, plot_sample

main/train.py

@@ -6,15 +6,12 @@ Created on Tue Jun 30 09:33:38 2020
 @author: Daniel
 """
 # builtins
+from __future__ import absolute_import
 import os
-import sys
 
 # externals
 import torch
 
-# append path to local files to the python search path
-sys.path.append('..')
-
 # local modules
 from pytorch.trainer import NetworkTrainer
 from main.config import config

main/transfer.py

-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jul 15 09:45:49 2020
-
-@author: Daniel
-"""
-
-# builtins
-import os
-import sys
-
-# externals
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-# append path to local files to the python search path
-sys.path.append('..')
-
-# local modules
-from pytorch.trainer import NetworkTrainer
-from pytorch.layers import Conv2dSame
-from main.config import config
-
-
-if __name__ == '__main__':
-
-    # instanciate the NetworkTrainer class
-    trainer = NetworkTrainer(config)
-    trainer.initialize()
-
-    # freeze the model state
-    trainer.model.freeze()
-
-    # get the number of input features to the model classifier
-    in_features = trainer.model.classifier.in_channels
-
-    # replace the classification layer
-    trainer.model.classifier = Conv2dSame(in_channels=in_features,
-                                          out_channels=len(trainer.dataset.labels),
-                                          kernel_size=1)
-
-    # train the model on the new dataset
-    trainer.train()

pytorch/constants.py

@@ -7,6 +7,7 @@ Created on Tue Jul 14 10:58:20 2020
 # builtins
 import enum
 
+
 # Landsat 8 bands
 class Landsat8(enum.Enum):
     violet = 1
@@ -38,6 +39,7 @@ class Sentinel2(enum.Enum):
     swir1 = 11
     swir2 = 12
 
+
 # labels of the Sparcs dataset
 class SparcsLabels(enum.Enum):
     Shadow = 0, 'black'

pytorch/models.py

@@ -6,17 +6,14 @@ Created on Fri Jun 26 16:31:36 2020
 @author: Daniel
 """
 # builtins
+from __future__ import absolute_import
 import os
-import sys
 
 # externals
 import numpy as np
 import torch
 import torch.nn as nn
 
-# append path to local files to the python search path
-sys.path.append('..')
-
 # locals
 from pytorch.layers import (Encoder, Decoder, Conv2dPool, Conv2dUnpool,
                             Conv2dUpsample, Conv2dSame)

pytorch/trainer.py

@@ -6,8 +6,8 @@ Created on Fri Jun 26 16:31:36 2020
 @author: Daniel
 """
 # builtins
+from __future__ import absolute_import
 import os
-import sys
 
 # externals
 import numpy as np
@@ -15,9 +15,6 @@ import torch
 import torch.nn.functional as F
 from torch.utils.data import random_split, DataLoader
 
-# append path to local files to the python search path
-sys.path.append('..')
-
 # local modules
 from pytorch.dataset import SparcsDataset, Cloud95Dataset
 from pytorch.layers import Conv2dSame