Model blocks, Model files, rearrange project structure

.idea/dictionaries/steffen.xml

@@ -0,0 +1,8 @@
+<component name="ProjectDictionaryState">
+  <dictionary name="steffen">
+    <words>
+      <w>conv</w>
+      <w>numlayers</w>
+    </words>
+  </dictionary>
+</component>

.idea/hom_traj_gen.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.7 (traj_gen)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (traj_gen)" project-jdk-type="Python SDK" />
 </project>

build_data.py

@@ -1,11 +1,11 @@
-from pathlib import Path
-
-from lib.objects.map import Map
-from preprocessing.generator import Generator
-
-if __name__ == '__main__':
-    data_root = Path() / 'data'
-    maps_root = Path() / 'res' / 'maps'
-    map_object = Map('Tate').from_image(maps_root / 'tate_sw.bmp')
-    generator = Generator(data_root, map_object)
-    generator.generate_n_trajectories_m_alternatives(100, 10, 'test')
+from pathlib import Path
+
+from lib.objects.map import Map
+from lib.preprocessing.generator import Generator
+
+if __name__ == '__main__':
+    data_root = Path() / 'data'
+    maps_root = Path() / 'res' / 'maps'
+    map_object = Map('Tate').from_image(maps_root / 'tate_sw.bmp')
+    generator = Generator(data_root, map_object)
+    generator.generate_n_trajectories_m_alternatives(100, 10, 'test')

dataset/dataset.py

@@ -1,96 +1,96 @@
-import shelve
-from pathlib import Path
-
-import torch
-from torch.utils.data import ConcatDataset, Dataset
-
-from lib.objects.map import Map
-from preprocessing.generator import Generator
-
-
-class TrajDataset(Dataset):
-    def __init__(self, data):
-        super(TrajDataset, self).__init__()
-        self.alternatives = data['alternatives']
-        self.trajectory = data['trajectory']
-        self.labels = data['labels']
-
-    def __len__(self):
-        return len(self.alternatives)
-
-    def __getitem__(self, item):
-        return self.trajectory.vertices, self.alternatives[item].vertices, self.labels[item]
-
-
-class DataSetMapping(Dataset):
-    def __init__(self, dataset, mapping):
-        self._dataset = dataset
-        self._mapping = mapping
-
-    def __len__(self):
-        return self._mapping.shape[0]
-
-    def __getitem__(self, item):
-        return self._dataset[self._mapping[item]]
-
-
-class TrajData(object):
-    @property
-    def name(self):
-        return self.__class__.__name__
-
-    def __init__(self, data_root, mapname='tate_sw', trajectories=1000, alternatives=10,
-                 train_val_test_split=(0.6, 0.2, 0.2), rebuild=False, equal_samples=True, **_):
-
-        self.rebuild = rebuild
-        self.equal_samples = equal_samples
-        self._alternatives = alternatives
-        self._trajectories = trajectories
-        self.mapname = mapname
-        self.train_split, self.val_split, self.test_split = train_val_test_split
-        self.data_root = Path(data_root)
-        self._dataset = None
-        self._dataset, self._train_map, self._val_map, self._test_map = self._load_dataset()
-
-    def _build_data_on_demand(self):
-        maps_root = Path() / 'res' / 'maps'
-        map_object = Map(self.mapname).from_image(maps_root / f'{self.mapname}.bmp')
-        assert maps_root.exists()
-        dataset_file = Path(self.data_root) / f'{self.mapname}.pik'
-        if dataset_file.exists() and self.rebuild:
-            dataset_file.unlink()
-        if not dataset_file.exists():
-            generator = Generator(self.data_root, map_object)
-            generator.generate_n_trajectories_m_alternatives(self._trajectories, self._alternatives,
-                                                             self.mapname, equal_samples=self.equal_samples)
-        return True
-
-    def _load_dataset(self):
-        assert self._build_data_on_demand()
-        with shelve.open(str(self.data_root / f'{self.mapname}.pik')) as d:
-            dataset = ConcatDataset([TrajDataset(d[key]) for key in d.keys() if key != 'map'])
-            indices = torch.randperm(len(dataset))
-
-            train_size = int(len(dataset) * self.train_split)
-            val_size = int(len(dataset) * self.val_split)
-            test_size = int(len(dataset) * self.test_split)
-
-            train_map = indices[:train_size]
-            val_map = indices[train_size:val_size]
-            test_map = indices[test_size:]
-        return dataset, train_map, val_map, test_map
-
-    @property
-    def train_dataset(self):
-        return DataSetMapping(self._dataset, self._train_map)
-
-    @property
-    def val_dataset(self):
-        return DataSetMapping(self._dataset, self._val_map)
-
-    @property
-    def test_dataset(self):
-        return DataSetMapping(self._dataset, self._test_map)
-
-    def get_datasets(self):
-        return self.train_dataset, self.val_dataset, self.test_dataset
+import shelve
+from pathlib import Path
+
+import torch
+from torch.utils.data import ConcatDataset, Dataset
+
+from lib.objects.map import Map
+from lib.preprocessing.generator import Generator
+
+
+class TrajDataset(Dataset):
+    def __init__(self, data):
+        super(TrajDataset, self).__init__()
+        self.alternatives = data['alternatives']
+        self.trajectory = data['trajectory']
+        self.labels = data['labels']
+
+    def __len__(self):
+        return len(self.alternatives)
+
+    def __getitem__(self, item):
+        return self.trajectory.vertices, self.alternatives[item].vertices, self.labels[item]
+
+
+class DataSetMapping(Dataset):
+    def __init__(self, dataset, mapping):
+        self._dataset = dataset
+        self._mapping = mapping
+
+    def __len__(self):
+        return self._mapping.shape[0]
+
+    def __getitem__(self, item):
+        return self._dataset[self._mapping[item]]
+
+
+class TrajData(object):
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def __init__(self, data_root, mapname='tate_sw', trajectories=1000, alternatives=10,
+                 train_val_test_split=(0.6, 0.2, 0.2), rebuild=False, equal_samples=True, **_):
+
+        self.rebuild = rebuild
+        self.equal_samples = equal_samples
+        self._alternatives = alternatives
+        self._trajectories = trajectories
+        self.mapname = mapname
+        self.train_split, self.val_split, self.test_split = train_val_test_split
+        self.data_root = Path(data_root)
+        self._dataset = None
+        self._dataset, self._train_map, self._val_map, self._test_map = self._load_dataset()
+
+    def _build_data_on_demand(self):
+        maps_root = Path() / 'res' / 'maps'
+        map_object = Map(self.mapname).from_image(maps_root / f'{self.mapname}.bmp')
+        assert maps_root.exists()
+        dataset_file = Path(self.data_root) / f'{self.mapname}.pik'
+        if dataset_file.exists() and self.rebuild:
+            dataset_file.unlink()
+        if not dataset_file.exists():
+            generator = Generator(self.data_root, map_object)
+            generator.generate_n_trajectories_m_alternatives(self._trajectories, self._alternatives,
+                                                             self.mapname, equal_samples=self.equal_samples)
+        return True
+
+    def _load_dataset(self):
+        assert self._build_data_on_demand()
+        with shelve.open(str(self.data_root / f'{self.mapname}.pik')) as d:
+            dataset = ConcatDataset([TrajDataset(d[key]) for key in d.keys() if key != 'map'])
+            indices = torch.randperm(len(dataset))
+
+            train_size = int(len(dataset) * self.train_split)
+            val_size = int(len(dataset) * self.val_split)
+            test_size = int(len(dataset) * self.test_split)
+
+            train_map = indices[:train_size]
+            val_map = indices[train_size:val_size]
+            test_map = indices[test_size:]
+        return dataset, train_map, val_map, test_map
+
+    @property
+    def train_dataset(self):
+        return DataSetMapping(self._dataset, self._train_map)
+
+    @property
+    def val_dataset(self):
+        return DataSetMapping(self._dataset, self._val_map)
+
+    @property
+    def test_dataset(self):
+        return DataSetMapping(self._dataset, self._test_map)
+
+    def get_datasets(self):
+        return self.train_dataset, self.val_dataset, self.test_dataset

lib/models/blocks.py

@@ -156,6 +156,26 @@ class LightningBaseModule(pl.LightningModule, ABC):
         self.apply(_weight_init)
 
 
+class FilterLayer(nn.Module):
+
+    def __init__(self):
+        super(FilterLayer, self).__init__()
+
+    def forward(self, x):
+        tensor = x[:, -1]
+        return tensor
+
+
+class MergingLayer(nn.Module):
+
+    def __init__(self):
+        super(MergingLayer, self).__init__()
+
+    def forward(self, x):
+        # ToDo: Which ones to combine?
+        return
+
+
 #
 # Sub - Modules
 ###################
@@ -241,6 +261,32 @@ class DeConvModule(nn.Module):
         return self.shape
 
 
+class RecurrentModule(nn.Module):
+
+    @property
+    def shape(self):
+        x = torch.randn(self.in_shape).unsqueeze(0)
+        output = self(x)
+        return output.shape[1:]
+
+    def __init__(self, in_shape, hidden_size, num_layers=1, cell_type=nn.GRU, use_bias=True, dropout=0):
+        super(RecurrentModule, self).__init__()
+        self.use_bias = use_bias
+        self.num_layers = num_layers
+        self.in_shape = in_shape
+        self.hidden_size = hidden_size
+        self.dropout = dropout
+        self.rnn = cell_type(self.in_shape[-1] * self.in_shape[-2], hidden_size,
+                             num_layers=num_layers,
+                             bias=self.use_bias,
+                             batch_first=True,
+                             dropout=self.dropout)
+
+    def forward(self, x):
+        tensor = self.rnn(x)
+        return tensor
+
+
 #
 # Full Model Parts
 ###################

lib/models/cnn.py

@@ -0,0 +1,29 @@
+from lib.models.blocks import RecurrentModule, ConvModule, DeConvModule, Generator, LightningBaseModule
+
+
+class CNNRouteGeneratorModel(LightningBaseModule):
+
+    @classmethod
+    def name(cls):
+        pass
+
+    def configure_optimizers(self):
+        pass
+
+    def validation_step(self, *args, **kwargs):
+        pass
+
+    def validation_end(self, outputs):
+        pass
+
+    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
+        pass
+
+    def test_step(self, *args, **kwargs):
+        pass
+
+    def __init__(self, *params):
+        super(CNNRouteGeneratorModel, self).__init__(*params)
+
+    def forward(self, x):
+        pass