New Dataset Generator, How to differentiate the loss function?

lib/models/full.py

@@ -0,0 +1,49 @@
+from lib.models.blocks import RecurrentModule, ConvModule, DeConvModule, Generator, LightningBaseModule
+from lib.models.losses import BinaryHomotopicLoss
+from lib.objects.map import Map
+from lib.objects.trajectory import Trajectory
+
+import torch
+import torch.functional as F
+import torch.nn as nn
+
+nn.MSELoss
+
+class LinearRouteGeneratorModel(LightningBaseModule):
+
+    name = 'LinearRouteGenerator'
+
+    def configure_optimizers(self):
+        pass
+
+    def validation_step(self, *args, **kwargs):
+        pass
+
+    def validation_end(self, outputs):
+        pass
+
+    def training_step(self, batch, batch_nb, *args, **kwargs):
+        # Type Annotation
+        traj_x: Trajectory
+        traj_o: Trajectory
+        label_x: int
+        map_name: str
+        map_x: Map
+        # Batch unpacking
+        traj_x, traj_o, label_x, map_name = batch
+        map_x = self.map_storage[map_name]
+        pred_y = self(map_x, traj_x, label_x)
+
+        loss = self.loss(traj_x, pred_y)
+        return dict(loss=loss, log=dict(loss=loss))
+
+    def test_step(self, *args, **kwargs):
+        pass
+
+    def __init__(self, *params):
+        super(LinearRouteGeneratorModel, self).__init__(*params)
+
+        self.loss = BinaryHomotopicLoss(self.map_storage)
+
+    def forward(self, map_x, traj_x, label_x):
+        pass