Added comments, changes to default rnvp config

wild_visual_navigation/cfg/experiment_params.py

@@ -124,7 +124,7 @@ class LinearRnvpCfgParams:
             mask_type: str = "odds"
             conditioning_size: int = 0
             use_permutation: bool = True
-            single_function: bool = True
+            single_function: bool = False
 
         linear_rnvp_cfg: LinearRnvpCfgParams = LinearRnvpCfgParams()
 

wild_visual_navigation/learning/model/linear_rnvp.py

@@ -114,7 +114,7 @@ def backward(self, x, y=None):
             _mx = mx
 
         s, t = self.st(_mx)
-        s = torch.tanh(s)
+        s = torch.tanh(s)   # Adding an activation function here with non-linearities
 
         u = mx + (1 - self.mask) * (x - t) * torch.exp(-s)
 
@@ -221,8 +221,8 @@ def __init__(
     ):
         super().__init__()
 
-        self.register_buffer("prior_mean", torch.zeros(input_dim))
-        self.register_buffer("prior_var", torch.ones(input_dim))
+        self.register_buffer("prior_mean", torch.zeros(input_dim))  # Normal Gaussian with zero mean
+        self.register_buffer("prior_var", torch.ones(input_dim))    # Normal Gaussian with unit variance
 
         if mask_type == "odds":
             mask = torch.arange(0, input_dim).float() % 2
@@ -258,16 +258,17 @@ def __init__(
         self.flows = SequentialFlow(*blocks)
 
     def logprob(self, x):
-        return self.prior.log_prob(x)
+        return self.prior.log_prob(x)   # Compute log probability of the input at the Gaussian distribution
 
     @property
     def prior(self):
-        return distributions.Normal(self.prior_mean, self.prior_var)
+        return distributions.Normal(self.prior_mean, self.prior_var)    # Normal Gaussian with zero mean and unit variance
 
     def forward(self, data: Data):
         x = data.x
-        z, log_det = self.flows.forward(x, None)
-        return {"z": z, "log_det": log_det, "logprob": self.logprob(z)}
+        z, log_det = self.flows.forward(x, y=None)
+        log_prob = self.logprob(z)
+        return {"z": z, "log_det": log_det, "logprob": log_prob}
 
     def backward(self, u, y=None, return_step=False):
         if return_step:

wild_visual_navigation/learning/utils/loss.py

@@ -25,7 +25,7 @@ def forward(
         loss_aux["loss_reco"] = torch.tensor([0.0])
         loss_aux["confidence"] = torch.tensor([0.0])
 
-        losses = -(res["logprob"].sum(1) + res["log_det"])
+        losses = -(res["logprob"].sum(1) + res["log_det"]) # Sum over all channels, resulting in h*w output dimensions
 
         # print(torch.mean(losses))
         l_clip = losses

wild_visual_navigation/traversability_estimator/traversability_estimator.py

@@ -631,10 +631,7 @@ def train(self):
                 with self._learning_lock:
                     # Forward pass
 
-                    if self._anomaly_detection:
-                        res = self._model(graph)
-                    else:
-                        res = self._model(graph)
+                    res = self._model(graph)
 
                     log_step = (self._step % 20) == 0
                     self._loss, loss_aux, trav = self._traversability_loss(graph, res, step=self._step, log_step=log_step, loss_mean=self._loss_mean, loss_std=self._loss_std, train=True)

wild_visual_navigation_ros/config/wild_visual_navigation/default.yaml

@@ -30,11 +30,11 @@ confidence_std_factor: 4.0
 scale_traversability: False  # This parameter needs to be false when using the anomaly detection model
 scale_traversability_max_fpr: 0.25
 min_samples_for_training: 5
-prediction_per_pixel: False
+prediction_per_pixel: false
 traversability_threshold: 0.55
-clip_to_binary: False
-anomaly_detection: True
-vis_training_samples: True
+clip_to_binary: false
+anomaly_detection: true
+vis_training_samples: true
 
 # Supervision Generator
 untraversable_thr: 0.01

wild_visual_navigation_ros/config/wild_visual_navigation/inputs/wide_angle_front.yaml

@@ -7,5 +7,5 @@ camera_topics:
     publish_input_image: true
 
 # Provides 1080 (height) x 1920 (width) images
-network_input_image_height: 224 # 448
-network_input_image_width: 224 # 448
+network_input_image_height: 448 # 448
+network_input_image_width: 448 # 448

wild_visual_navigation_ros/config/wild_visual_navigation/inputs/wide_angle_front_compressed.yaml

@@ -7,5 +7,5 @@ camera_topics:
     publish_input_image: true
 
 # Provides 1080 (height) x 1920 (width) images
-network_input_image_height: 224 # 448
-network_input_image_width: 224 # 448
+network_input_image_height: 448 # 448
+network_input_image_width: 448 # 448

wild_visual_navigation_ros/scripts/wvn_feature_extractor_node.py

@@ -256,7 +256,7 @@ def image_callback(self, image_msg: Image, cam: str):  #  info_msg: CameraInfo
             # Evaluate traversability
             data = Data(x=dense_feat[0].permute(1, 2, 0).reshape(-1, dense_feat.shape[1]))
         else:
-            input_feat = dense_feat[0].permute(1, 2, 0).reshape(-1, dense_feat.shape[1])
+            # input_feat = dense_feat[0].permute(1, 2, 0).reshape(-1, dense_feat.shape[1])
             input_feat = feat[seg.reshape(-1)]
             data = Data(x=input_feat)
 
@@ -350,7 +350,7 @@ def load_model(self):
                 res = torch.load(f"{WVN_ROOT_DIR}/tmp_state_dict2.pt")
                 k = list(self.model.state_dict().keys())[-1]
 
-                if (self.model.state_dict()[k] != res[k]).any():  # TODO: model params are changing
+                if (self.model.state_dict()[k] != res[k]).any():  # TODO: model params are changing?
                     if self.verbose:
                         self.log_data[f"time_last_model"] = rospy.get_time()
                         self.log_data[f"nr_model_updates"] += 1