all debug and train running

datasets/trajectory_dataset.py

@@ -3,7 +3,6 @@ from pathlib import Path
 from typing import Union, List
 
 import torch
-from random import choice
 from torch.utils.data import ConcatDataset, Dataset
 
 from lib.objects.map import Map
@@ -17,12 +16,14 @@ class TrajDataset(Dataset):
         return self.map.as_array.shape
 
     def __init__(self, *args, maps_root: Union[Path, str] = '', mapname='tate_sw',
-                 length=100000, all_in_map=True, embedding_size=None, **kwargs):
+                 length=100000, all_in_map=True, embedding_size=None, preserve_equal_samples=False, **kwargs):
         super(TrajDataset, self).__init__()
+        self.preserve_equal_samples = preserve_equal_samples
         self.all_in_map = all_in_map
         self.mapname = mapname if mapname.endswith('.bmp') else f'{mapname}.bmp'
         self.maps_root = maps_root
         self._len = length
+        self.last_label = -1
 
         self.map = Map(self.mapname).from_image(self.maps_root / self.mapname, embedding_size=embedding_size)
 
@@ -31,8 +32,16 @@ class TrajDataset(Dataset):
 
     def __getitem__(self, item):
         trajectory = self.map.get_random_trajectory()
-        alternative = self.map.generate_alternative(trajectory)
+        while True:
-        label = choice([0, 1])
+            # TODO: Sanity Check this while true loop...
+            alternative = self.map.generate_alternative(trajectory)
+            label = self.map.are_homotopic(trajectory, alternative)
+            if self.preserve_equal_samples and label == self.last_label:
+                continue
+            else:
+                break
+
+        self.last_label = label
         if self.all_in_map:
             blank_trajectory_space = torch.zeros(self.map.shape)
             blank_alternative_space = torch.zeros(self.map.shape)
@@ -41,7 +50,6 @@ class TrajDataset(Dataset):
                 blank_alternative_space[index] = 1
 
             map_array = torch.as_tensor(self.map.as_array).float()
-            label = self.map.are_homotopic(trajectory, alternative)
             return torch.cat((map_array, blank_trajectory_space, blank_alternative_space)), int(label)
         else:
             return trajectory.vertices, alternative.vertices, label, self.mapname
@@ -78,7 +86,8 @@ class TrajData(object):
         # find max image size among available maps:
         max_map_size = (1, ) + tuple(reversed(tuple(map(max, *[Image.open(map_file).size for map_file in map_files]))))
         return ConcatDataset([TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
-                                          all_in_map=self.all_in_map, embedding_size=max_map_size)
+                                          all_in_map=self.all_in_map, embedding_size=max_map_size,
+                                          preserve_equal_samples=True)
                               for map_file in map_files])
 
     @property

multi_run.py

@@ -0,0 +1,35 @@
+import warnings
+
+from lib.utils.config import Config
+
+warnings.filterwarnings('ignore', category=FutureWarning)
+warnings.filterwarnings('ignore', category=UserWarning)
+
+# Imports
+# =============================================================================
+from pathlib import Path
+import os
+
+
+if __name__ == '__main__':
+
+    # Model Settings
+    warnings.filterwarnings('ignore', category=FutureWarning)
+    # use_bias, activation, model, use_norm, max_epochs, filters
+    cnn_classifier = [True, 'leaky_relu', 'classifier_cnn', False, 2, [16, 32, 64]]
+    # use_bias, activation, model, use_norm, max_epochs, sr, lat_dim, filters
+
+    # Data Settings
+    data_shortcodes = ['mid', 'mid_5']
+
+    # Iteration over
+    for data_shortcode in data_shortcodes:
+        for use_bias, activation, model, use_norm, max_epochs, filters in [cnn_classifier]:
+            for seed in range(5):
+                arg_dict = dict(main_seed=seed, train_max_epochs=max_epochs,
+                                model_use_bias=use_bias, model_use_norm=use_norm,
+                                model_activation=activation, model_type=model,
+                                model_filters=filters,
+                                data_batch_size=512)
+
+                os.system(f'/home/steffen/envs/traj_gen/bin/python main.py {arg_dict}')