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README.md

@@ -15,7 +15,7 @@ The training script is provided as `train.ipynb`.
 ## Dependencies
 The program was developed and tested in the following environment.
 - Python 3.10
-- `torch==0.13.1`
+- `torch==1.13.1`
 - `jax==0.4.23`
 - `jaxlib==0.4.23`
 - `diffusers==0.18.2`

tests/test_datasets.py

@@ -19,13 +19,12 @@ def setUp(self):
         self.obs_dim = self.config["controller"]["networks"]["obs_dim"]
         self.obstacle_encode_dim = self.config["controller"]["networks"]["obstacle_encode_dim"]
 
-
     def test_init(self):
         dataset = PlanarQuadrotorStateDataset(
             dataset_path=self.dataset_path,
             pred_horizon=self.pred_horizon,
             obs_horizon=self.obs_horizon,
-            action_horizon=self.action_horizon
+            action_horizon=self.action_horizon,
         )
         self.assertTrue(isinstance(dataset, PlanarQuadrotorStateDataset))
 
@@ -34,19 +33,21 @@ def test_iter(self):
             dataset_path=self.dataset_path,
             pred_horizon=self.pred_horizon,
             obs_horizon=self.obs_horizon,
-            action_horizon=self.action_horizon
+            action_horizon=self.action_horizon,
         )
         dataloader = torch.utils.data.DataLoader(
             dataset,
             batch_size=self.config["dataloader"]["batch_size"],
             shuffle=True,
             pin_memory=True,
         )
-        
+
         # batch context matches expectecd shapes
         batch = next(iter(dataloader))
-        self.assertEqual(batch['obs'].shape, (self.batch_size, self.obs_dim*self.obs_horizon+self.obstacle_encode_dim))
-        self.assertEqual(batch['action'].shape, (self.batch_size, self.pred_horizon, self.action_dim))
+        self.assertEqual(
+            batch["obs"].shape, (self.batch_size, self.obs_dim * self.obs_horizon + self.obstacle_encode_dim)
+        )
+        self.assertEqual(batch["action"].shape, (self.batch_size, self.pred_horizon, self.action_dim))
 
 
 if __name__ == "__main__":

tests/test_networks.py

@@ -7,7 +7,6 @@
 
 class TestConditionalUnet1D(unittest.TestCase):
     def setUp(self):
-
         with open("tests/config/test_config.yaml", "r") as file:
             self.config = yaml.safe_load(file)
 
@@ -20,20 +19,18 @@ def setUp(self):
 
     def test_init(self):
         noise_pred_net = ConditionalUnet1D(
-            input_dim=self.action_dim,
-            global_cond_dim=self.obs_dim*self.obs_horizon + self.obstacle_encode_dim
+            input_dim=self.action_dim, global_cond_dim=self.obs_dim * self.obs_horizon + self.obstacle_encode_dim
         )
         self.assertTrue(isinstance(noise_pred_net, ConditionalUnet1D))
 
     def test_inference(self):
         net = ConditionalUnet1D(
-            input_dim=self.action_dim,
-            global_cond_dim=self.obs_dim*self.obs_horizon + self.obstacle_encode_dim
+            input_dim=self.action_dim, global_cond_dim=self.obs_dim * self.obs_horizon + self.obstacle_encode_dim
         )
 
         # example inputs
         noised_action = torch.randn((1, self.pred_horizon, self.action_dim))
-        obs = torch.zeros((1, self.obs_horizon*self.obs_dim+self.obstacle_encode_dim))
+        obs = torch.zeros((1, self.obs_horizon * self.obs_dim + self.obstacle_encode_dim))
         diffusion_iter = torch.zeros((1,))
 
         # the noise prediction network