train running dataset fixed

.gitignore

@@ -69,3 +69,4 @@ fabric.properties
 
 # Android studio 3.1+ serialized cache file
 .idea/caches/build_file_checksums.ser
+/.idea/inspectionProfiles/

.idea/deployment.xml

@@ -1,15 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="PublishConfigData" autoUpload="On explicit save action" serverName="traj_gen-AiMachine" showAutoUploadSettingsWarning="false">
+  <component name="PublishConfigData" autoUpload="On explicit save action" serverName="steffen@aimachine:22" showAutoUploadSettingsWarning="false">
     <serverData>
-      <paths name="ErLoWa-AiMachine">
-        <serverdata>
-          <mappings>
-            <mapping local="$PROJECT_DIR$" web="/" />
-          </mappings>
-        </serverdata>
-      </paths>
-      <paths name="traj_gen-AiMachine">
+      <paths name="steffen@aimachine:22">
         <serverdata>
           <mappings>
             <mapping deploy="/" local="$PROJECT_DIR$" web="/" />

.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="jdk" jdkName="traj_gen@AiMachine" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="hom_traj_gen@aimachine" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -1,5 +1,6 @@
 <component name="InspectionProjectProfileManager">
   <settings>
+    <option name="PROJECT_PROFILE" value="Default" />
     <option name="USE_PROJECT_PROFILE" value="false" />
     <version value="1.0" />
   </settings>

.idea/misc.xml

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

datasets/trajectory_dataset.py

@@ -87,7 +87,7 @@ class TrajData(object):
         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,
-                                          preserve_equal_samples=True)
+                                          preserve_equal_samples=False)
                               for map_file in map_files])
 
     @property

lib/models/homotopy_classification/cnn_based.py

@@ -11,6 +11,7 @@ from datasets.trajectory_dataset import TrajData
 from lib.evaluation.classification import ROCEvaluation
 from lib.modules.utils import LightningBaseModule, Flatten
 from lib.modules.blocks import ConvModule, ResidualModule
+import matplotlib.pyplot as plt
 
 
 class ConvHomDetector(LightningBaseModule):
@@ -36,10 +37,9 @@ class ConvHomDetector(LightningBaseModule):
         predictions = torch.stack([x['prediction'] for x in outputs])
         labels = torch.stack([x['label'] for x in outputs])
 
-        scores = evaluation(predictions.numpy(), labels.numpy())
-        self.logger.log_metrics()
-
-
+        scores = evaluation(predictions.numpy(), labels.numpy(), )
+        self.logger.log_metrics({key:value for key, value in zip(['roc_auc', 'tpr', 'fpr'], scores)})
+        self.logger.log_image(f'{self.name}', plt.gcf())
         pass
 
     def __init__(self, *params):
@@ -88,6 +88,19 @@ class ConvHomDetector(LightningBaseModule):
         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)
+        tensor = self.map_res_1(tensor)
+        tensor = self.map_conv_1(tensor)
+        tensor = self.map_res_2(tensor)
+        tensor = self.map_conv_2(tensor)
+        tensor = self.map_conv_3(tensor)
+        tensor = self.flatten(tensor)
+        tensor = self.linear(tensor)
+        tensor = self.classifier(tensor)
+        tensor = self.out_activation(tensor)
+        return tensor
+
     # Dataloaders
     # ================================================================================
     # Train Dataloader
@@ -107,16 +120,3 @@ class ConvHomDetector(LightningBaseModule):
         return DataLoader(dataset=self.dataset.val_dataset, shuffle=True,
                           batch_size=self.hparams.data_param.batchsize,
                           num_workers=self.hparams.data_param.worker)
-
-    def forward(self, x):
-        tensor = self.map_conv_0(x)
-        tensor = self.map_res_1(tensor)
-        tensor = self.map_conv_1(tensor)
-        tensor = self.map_res_2(tensor)
-        tensor = self.map_conv_2(tensor)
-        tensor = self.map_conv_3(tensor)
-        tensor = self.flatten(tensor)
-        tensor = self.linear(tensor)
-        tensor = self.classifier(tensor)
-        tensor = self.out_activation(tensor)
-        return tensor

lib/objects/map.py

@@ -45,7 +45,7 @@ class Map(object):
 
     @property
     def as_2d_array(self):
-        return self.map_array[1:]
+        return self.map_array.squeeze()
 
     def __init__(self, name='', array_like_map_representation=None):
         if array_like_map_representation is not None:
@@ -145,9 +145,9 @@ class Map(object):
 
         img = Image.new('L', (self.height, self.width), 0)
         draw = ImageDraw.Draw(img)
-        draw.polygon(polyline, outline=255, fill=255)
+        draw.polygon(polyline, outline=1, fill=1)
 
-        a = (np.asarray(img) * np.where(self.as_2d_array == self.white, 0, 1)).sum()
+        a = (np.where(np.asarray(img) == self.white, 1, 0) * np.where(self.as_2d_array == self.white, 1, 0)).sum()
 
         if a:
             return False  # Non-Homotoph
@@ -159,7 +159,7 @@ class Map(object):
         # The standard colormaps also all have reversed versions.
         # They have the same names with _r tacked on to the end.
         # https: // matplotlib.org / api / pyplot_summary.html?highlight = colormaps
-        img = ax.imshow(self.as_array, cmap='Greys_r')
+        img = ax.imshow(self.as_2d_array, cmap='Greys_r')
         return dict(img=img, fig=fig, ax=ax)
 
 

lib/objects/trajectory.py

@@ -14,7 +14,7 @@ class Trajectory(object):
 
     @property
     def xy_vertices(self):
-        return [(x,y) for _, x,y in self._vertices]
+        return [(x, y) for _, y, x in self._vertices]
 
     @property
     def endpoints(self):
@@ -30,11 +30,11 @@ class Trajectory(object):
 
     @property
     def xs(self):
-        return [x[1] for x in self._vertices]
+        return [x[2] for x in self._vertices]
 
     @property
     def ys(self):
-        return [x[0] for x in self._vertices]
+        return [x[1] for x in self._vertices]
 
     @property
     def as_paired_list(self):
@@ -59,7 +59,7 @@ class Trajectory(object):
             kwargs.update(color='red' if label == V.HOMOTOPIC else 'green',
                           label='Homotopic' if label == V.HOMOTOPIC else 'Alternative')
         if highlights:
-            kwargs.update(marker='bo')
+            kwargs.update(marker='o')
         fig, ax = plt.gcf(), plt.gca()
         img = plt.plot(self.xs, self.ys, **kwargs)
         return dict(img=img, fig=fig, ax=ax)

lib/utils/logging.py

@@ -76,7 +76,7 @@ class Logger(LightningLoggerBase):
         self.neptunelogger.close()
 
     def log_config_as_ini(self):
-        self.config.write(self.log_dir)
+        self.config.write(self.log_dir / 'config.ini')
 
     def log_metric(self, metric_name, metric_value, **kwargs):
         self.testtubelogger.log_metrics(dict(metric_name=metric_value))
@@ -91,8 +91,8 @@ class Logger(LightningLoggerBase):
         self.neptunelogger.save()
 
     def finalize(self, status):
-        self.testtubelogger.finalize()
-        self.neptunelogger.finalize()
+        self.testtubelogger.finalize(status)
+        self.neptunelogger.finalize(status)
         self.log_config_as_ini()
 
     def __enter__(self):

multi_run.py

@@ -8,7 +8,7 @@ warnings.filterwarnings('ignore', category=UserWarning)
 # Imports
 # =============================================================================
 
-from main import run_training, args
+from main import run_lightning_loop, args
 
 
 if __name__ == '__main__':
@@ -16,17 +16,14 @@ if __name__ == '__main__':
     # Model Settings
     config = Config().read_namespace(args)
     # use_bias, activation, model, use_norm, max_epochs, filters
-    cnn_classifier = [True, 'leaky_relu', 'classifier_cnn', False, 2, [16, 32, 64]]
+    cnn_classifier = dict(train_epochs=100, model_use_bias=True, model_use_norm=True, model_activation='leaky_relu',
+                          model_type='classifier_cnn', model_filters=[16, 32, 64], data_batchsize=512)
     # use_bias, activation, model, use_norm, max_epochs, sr, lat_dim, filters
 
-    for use_bias, activation, model, use_norm, max_epochs, filters in [cnn_classifier]:
+    for arg_dict 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)
+            arg_dict.update(main_seed=seed)
 
             config = config.update(arg_dict)
 
-            run_training(config)
+            run_lightning_loop(config)