Train Active

datasets/trajectory_dataset.py

@@ -7,7 +7,7 @@ from random import choice
 from torch.utils.data import ConcatDataset, Dataset
 
 from lib.objects.map import Map
-from lib.preprocessing.generator import Generator
+from PIL import Image
 
 
 class TrajDataset(Dataset):
@@ -17,14 +17,14 @@ 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, **kwargs):
+                 length=100000, all_in_map=True, embedding_size=None, **kwargs):
         super(TrajDataset, self).__init__()
         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.map = Map(self.mapname).from_image(self.maps_root / self.mapname)
+        self.map = Map(self.mapname).from_image(self.maps_root / self.mapname, embedding_size=embedding_size)
 
     def __len__(self):
         return self._len
@@ -35,15 +35,14 @@ class TrajDataset(Dataset):
         label = choice([0, 1])
         if self.all_in_map:
             blank_trajectory_space = torch.zeros(self.map.shape)
-            blank_trajectory_space[trajectory.vertices] = 1
-
             blank_alternative_space = torch.zeros(self.map.shape)
-            blank_alternative_space[trajectory.np_vertices] = 1
+            for index in trajectory.vertices:
+                blank_trajectory_space[index] = 1
+                blank_alternative_space[index] = 1
 
-            map_array = torch.as_tensor(self.map.as_array)
+            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)), label
+            return torch.cat((map_array, blank_trajectory_space, blank_alternative_space)), int(label)
         else:
             return trajectory.vertices, alternative.vertices, label, self.mapname
 
@@ -56,7 +55,10 @@ class TrajData(object):
     @property
     def map_shapes_max(self):
         shapes = self.map_shapes
-        return list(map(max, zip(*shapes)))
+        shape_list = list(map(max, zip(*shapes)))
+        if self.all_in_map:
+            shape_list[0] += 2
+        return shape_list
 
     @property
     def name(self):
@@ -72,8 +74,12 @@ class TrajData(object):
     def _load_datasets(self):
         map_files = list(self.maps_root.glob('*.bmp'))
         equal_split = int(self.length // len(map_files))
-        return ConcatDataset([TrajDataset(maps_root=self.maps_root, mapname=map_image.name, length=equal_split,
-                                          all_in_map=self.all_in_map) for map_image in map_files])
+
+        # 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)
+                              for map_file in map_files])
 
     @property
     def train_dataset(self):

lib/models/homotopy_classification/cnn_based.py

@@ -18,21 +18,12 @@ class ConvHomDetector(LightningBaseModule):
     def configure_optimizers(self):
         return Adam(self.parameters(), lr=self.hparams.lr)
 
-    def validation_step(self, *args, **kwargs):
-        pass
-
-    def validation_end(self, outputs):
-        pass
-
     def training_step(self, batch_xy, batch_nb, *args, **kwargs):
         batch_x, batch_y = batch_xy
         pred_y = self(batch_x)
-        loss = F.binary_cross_entropy(pred_y, batch_y)
+        loss = F.binary_cross_entropy(pred_y, batch_y.float())
         return {'loss': loss, 'log': dict(loss=loss)}
 
-    def test_step(self, *args, **kwargs):
-        pass
-
     def __init__(self, *params):
         super(ConvHomDetector, self).__init__(*params)
 
@@ -75,8 +66,9 @@ class ConvHomDetector(LightningBaseModule):
         #
 
         self.linear = nn.Linear(reduce(mul, self.flatten.shape), self.hparams.model_param.classes * 10)
-        self.classifier = nn.Linear(self.hparams.model_param.classes * 10, self.hparams.model_param.classes)
-        self.softmax = nn.Softmax()
+        # Comments on Multi Class labels
+        self.classifier = nn.Linear(self.hparams.model_param.classes * 10, 1)  # self.hparams.model_param.classes)
+        self.out_activation = nn.Sigmoid()  # nn.Softmax
 
     def forward(self, x):
         tensor = self.map_conv_0(x)
@@ -88,5 +80,5 @@ class ConvHomDetector(LightningBaseModule):
         tensor = self.flatten(tensor)
         tensor = self.linear(tensor)
         tensor = self.classifier(tensor)
-        tensor = self.softmax(tensor)
+        tensor = self.out_activation(tensor)
         return tensor

lib/modules/blocks.py

@@ -106,7 +106,7 @@ class ResidualModule(nn.Module):
         self.in_shape = in_shape
         module_paramters.update(in_shape=in_shape)
         self.activation = activation() if activation else lambda x: x
-        self.residual_block = [module_class(**module_paramters) for _ in range(n)]
+        self.residual_block = nn.ModuleList([module_class(**module_paramters) for _ in range(n)])
         assert self.in_shape == self.shape, f'The in_shape: {self.in_shape} - must match the out_shape: {self.shape}.'
 
     def forward(self, x):

lib/modules/utils.py

@@ -133,12 +133,6 @@ class LightningBaseModule(pl.LightningModule, ABC):
     def forward(self, *args, **kwargs):
         raise NotImplementedError
 
-    def validation_step(self, *args, **kwargs):
-        raise NotImplementedError
-
-    def validation_end(self, outputs):
-        raise NotImplementedError
-
     def training_step(self, batch_xy, batch_nb, *args, **kwargs):
         raise NotImplementedError
 
@@ -146,21 +140,7 @@ class LightningBaseModule(pl.LightningModule, ABC):
         raise NotImplementedError
 
     def test_end(self, outputs):
-        from sklearn.metrics import roc_auc_score
-
-        y_scores, y_true = [], []
-        for output in outputs:
-            y_scores.append(output['y_pred'])
-            y_true.append(output['y_true'])
-
-        y_true = torch.cat(y_true, dim=0)
-        # FIXME: What did this do do i need it?
-        # y_true = (y_true != V.HOMOTOPIC).long()
-        y_scores = torch.cat(y_scores, dim=0)
-
-        roc_auc_scores = roc_auc_score(y_true.cpu().numpy(), y_scores.cpu().numpy())
-        print(f'AUC Score: {roc_auc_scores}')
-        return {'roc_auc_scores': roc_auc_scores}
+        raise NotImplementedError
 
     def init_weights(self):
         def _weight_init(m):

lib/objects/map.py

@@ -29,11 +29,11 @@ class Map(object):
 
     @property
     def width(self):
-        return self.shape[0]
+        return self.shape[-2]
 
     @property
     def height(self):
-        return self.shape[1]
+        return self.shape[-1]
 
     @property
     def as_graph(self):
@@ -43,6 +43,10 @@ class Map(object):
     def as_array(self):
         return self.map_array
 
+    @property
+    def as_2d_array(self):
+        return self.map_array[1:]
+
     def __init__(self, name='', array_like_map_representation=None):
         if array_like_map_representation is not None:
             if array_like_map_representation.ndim == 2:
@@ -72,22 +76,26 @@ class Map(object):
                     # Differentiate between 8 and 4 neighbors
                     if not full_neighbors and n >= 2:
                         break
-
                     # ToDO: make this explicite and less ugly
                     query_node = idx[:1] + (idx[1] + ydif,) + (idx[2] + xdif,)
                     if graph.has_node(query_node):
                         graph.add_edge(idx, query_node, weight=weight)
-
         return graph
 
     @classmethod
-    def from_image(cls, imagepath: Path):
+    def from_image(cls, imagepath: Path, embedding_size=None):
         with Image.open(imagepath) as image:
             # Turn the image to single Channel Greyscale
             if image.mode != 'L':
                 image = image.convert('L')
             map_array = np.expand_dims(np.array(image), axis=0)
-            return cls(name=imagepath.name, array_like_map_representation=map_array)
+        if embedding_size:
+            assert isinstance(embedding_size, tuple), f'embedding_size was of type: {type(embedding_size)}'
+            embedding = np.zeros(embedding_size)
+            embedding[:map_array.shape[0], :map_array.shape[1], :map_array.shape[2]] = map_array
+            map_array = embedding
+
+        return cls(name=imagepath.name, array_like_map_representation=map_array)
 
     def simple_trajectory_between(self, start, dest):
         vertices = list(nx.shortest_path(self._G, start, dest))
@@ -105,36 +113,46 @@ class Map(object):
         return Trajectory(coords)
 
     def get_random_trajectory(self):
-        start = self.get_valid_position()
-        dest = self.get_valid_position()
-        return self.simple_trajectory_between(start, dest)
+        simple_trajectory = None
+        while simple_trajectory is None:
+            try:
+                start = self.get_valid_position()
+                dest = self.get_valid_position()
+                simple_trajectory = self.simple_trajectory_between(start, dest)
+            except nx.exception.NetworkXNoPath:
+                pass
+        return simple_trajectory
 
     def generate_alternative(self, trajectory, mode='one_patching'):
         start, dest = trajectory.endpoints
-        if mode == 'one_patching':
-            patch = self.get_valid_position()
-            alternative = self.get_trajectory_from_vertices(start, patch, dest)
-        else:
-            raise RuntimeError(f'mode checking went wrong...')
-
+        alternative = None
+        while alternative is None:
+            try:
+                if mode == 'one_patching':
+                    patch = self.get_valid_position()
+                    alternative = self.get_trajectory_from_vertices(start, patch, dest)
+                else:
+                    raise RuntimeError(f'mode checking went wrong...')
+            except nx.exception.NetworkXNoPath:
+                pass
         return alternative
 
     def are_homotopic(self, trajectory, other_trajectory):
         if not all(isinstance(x, Trajectory) for x in [trajectory, other_trajectory]):
             raise TypeError
-        polyline = trajectory.vertices.copy()
-        polyline.extend(reversed(other_trajectory.vertices))
+        polyline = trajectory.xy_vertices
+        polyline.extend(reversed(other_trajectory.xy_vertices))
 
         img = Image.new('L', (self.height, self.width), 0)
         draw = ImageDraw.Draw(img)
         draw.polygon(polyline, outline=255, fill=255)
 
-        a = (np.array(img) * np.where(self.map_array == self.white, 0, 1)).sum()
+        a = (np.asarray(img) * np.where(self.as_2d_array == self.white, 0, 1)).sum()
 
-        if a >= 1:
-            return False
+        if a:
+            return False  # Non-Homotoph
         else:
-            return True
+            return True  # Homotoph
 
     def draw(self):
         fig, ax = plt.gcf(), plt.gca()

lib/objects/trajectory.py

@@ -8,43 +8,51 @@ import numpy as np
 
 class Trajectory(object):
 
+    @property
+    def vertices(self):
+        return self._vertices
+
+    @property
+    def xy_vertices(self):
+        return [(x,y) for _, x,y in self._vertices]
+
     @property
     def endpoints(self):
         return self.start, self.dest
 
     @property
     def start(self):
-        return self.vertices[0]
+        return self._vertices[0]
 
     @property
     def dest(self):
-        return self.vertices[-1]
+        return self._vertices[-1]
 
     @property
     def xs(self):
-        return [x[1] for x in self.vertices]
+        return [x[1] for x in self._vertices]
 
     @property
     def ys(self):
-        return [x[0] for x in self.vertices]
+        return [x[0] for x in self._vertices]
 
     @property
     def as_paired_list(self):
-        return list(zip(self.vertices[:-1], self.vertices[1:]))
+        return list(zip(self._vertices[:-1], self._vertices[1:]))
 
     @property
     def np_vertices(self):
-        return [np.array(vertice) for vertice in self.vertices]
+        return [np.array(vertice) for vertice in self._vertices]
 
     def __init__(self, vertices: Union[List[Tuple[int]], None] = None):
         assert any((isinstance(vertices, list), vertices is None))
         if vertices is not None:
-            self.vertices = vertices
+            self._vertices = vertices
         pass
 
     def is_equal_to(self, other_trajectory):
         # ToDo: do further equality Checks here
-        return self.vertices == other_trajectory.vertices
+        return self._vertices == other_trajectory.vertices
 
     def draw(self, highlights=True, label=None, **kwargs):
         if label is not None:

main.py

@@ -31,7 +31,7 @@ main_arg_parser.add_argument("--main_eval", type=strtobool, default=False, help=
 main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
 
 # Data Parameters
-main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
+main_arg_parser.add_argument("--data_worker", type=int, default=0, help="")
 main_arg_parser.add_argument("--data_batchsize", type=int, default=100, help="")
 main_arg_parser.add_argument("--data_root", type=str, default='/data/', help="")
 main_arg_parser.add_argument("--data_map_root", type=str, default='/res/maps', help="")
@@ -76,29 +76,29 @@ checkpoint_callback = ModelCheckpoint(
             period=1
 )
 
-# Model
-# =============================================================================
-# Init
-model: LightningBaseModule = config.model_class(config.model_paramters)
-model.init_weights()
-
-# Trainer
-# =============================================================================
-trainer = Trainer(max_nb_epochs=config.train.epochs,
-                  show_progress_bar=True,
-                  weights_save_path=logger.log_dir,
-                  gpus=[0] if torch.cuda.is_available() else None,
-                  row_log_interval=model.data_len // 40,            # TODO: Better Value / Setting
-                  log_save_interval=model.data_len // 10,           # TODO: Better Value / Setting
-                  checkpoint_callback=checkpoint_callback,
-                  logger=logger,
-                  fast_dev_run=config.get('main', 'debug'),
-                  early_stop_callback=None
-                  )
 
 if __name__ == "__main__":
-    # Check Cuda availability
-    print(f'Cuda is {"" if torch.cuda.is_available() else "not"} available!!!')
+
+    # Model
+    # =============================================================================
+    # Init
+    model: LightningBaseModule = config.model_class(config.model_paramters)
+    model.init_weights()
+
+    # Trainer
+    # =============================================================================
+    trainer = Trainer(max_nb_epochs=config.train.epochs,
+                      show_progress_bar=True,
+                      weights_save_path=logger.log_dir,
+                      gpus=[0] if torch.cuda.is_available() else None,
+                      row_log_interval=model.data_len // 40,  # TODO: Better Value / Setting
+                      log_save_interval=model.data_len // 10,  # TODO: Better Value / Setting
+                      checkpoint_callback=checkpoint_callback,
+                      logger=logger,
+                      fast_dev_run=config.get('main', 'debug'),
+                      early_stop_callback=None
+                      )
+
     # Train it
     trainer.fit(model)