lgan-com -> swei-dev -> main: Add CoM experiments' code.

cfg/solo-c2.yaml

@@ -0,0 +1,37 @@
+group_label: C2
+
+# QJ: Joint Space symmetries____________________________________
+#        _____RL____|___FL____|____RR____|____FR___|
+# q    = [ 0, 1, 2,  3, 4, 5,  6,  7,  8, 9, 10, 11]
+# Configure qj (joint-space) group actions
+#                  _______gs (Sagittal Symmetry)__________|_______gt (Transversal symmetry)______
+permutation_Q_js: [[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5], [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8]]
+# Reflections are determined by joint frame predefined orientation.
+reflection_Q_js: [[-1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1], [1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1]]
+
+# B: Body Space symmetries____________________________________
+#                    RL,         FL,         RR,         FR
+# q_lin (acc) = [x0, y0, z0, x1, y1, z1, x2, y2, z2, x3, y3, z3]
+# q_ang (vel) = [x0, y0, z0, x1, y1, z1, x2, y2, z2, x3, y3, z3]
+#                  _______gs (Sagittal Symmetry)__________|_______gt (Transversal symmetry)______
+permutation_Q_bs: [[3, 4, 5, 0, 1, 2], [0, 1, 2, 3, 4, 5]]
+# Reflections are determined by body frame predefined orientation.
+reflection_Q_bs_lin: [[1, -1, 1, 1, -1, 1], [-1, 1, 1, -1, 1, 1]]
+reflection_Q_bs_ang: [[-1, 1, -1, -1, 1, -1], [1, -1, -1, 1, -1, -1]]
+
+# F: Foot Space symmetries____________________________________
+#      RL,          FL,          RR,          FR
+# q = [x0, y0, z0, x1, y1, z1, x2, y2, z2, x3, y3, z3]
+#                  _______gs (Sagittal Symmetry)__________|_______gt (Transversal symmetry)______
+permutation_Q_fs: [[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5], [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8]]
+# Reflections are determined by body frame predefined orientation.
+# gs: [1, -1, 1], gt: [-1, 1, 1]
+reflection_Q_fs: [[1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1], [-1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1]]
+
+# L: Label Space symmetries____________________________________
+#      RL,FL,RR,FR
+# q = [0, 1, 2, 3]
+#                  _____gs______|______gt_____
+permutation_Q_ls: [[2, 3, 0, 1], [1, 0, 3, 2]]
+# Reflections are determined by body frame predefined orientation.
+reflection_Q_ls: [[1, 1, 1, 1], [1, 1, 1, 1]]

cfg/solo-k4.yaml

@@ -0,0 +1,38 @@
+group_label: K4
+
+# QJ: Joint Space symmetries____________________________________
+#        _____RL____|___FL____|____RR____|____FR___|
+# q    = [ 0, 1, 2,  3, 4, 5,  6,  7,  8, 9, 10, 11]
+# Configure qj (joint-space) group actions
+#                  _______gs (Sagittal Symmetry)__________|_______gt (Transversal symmetry)______
+permutation_Q_js: [[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5], [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8]]
+# Reflections are determined by joint frame predefined orientation.
+reflection_Q_js: [[-1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1], [1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1]]
+
+# B: Body Space symmetries____________________________________
+#                    RL,         FL,         RR,         FR
+# q_lin (acc) = [x0, y0, z0, x1, y1, z1, x2, y2, z2, x3, y3, z3]
+# q_ang (vel) = [x0, y0, z0, x1, y1, z1, x2, y2, z2, x3, y3, z3]
+#                  _______gs (Sagittal Symmetry)__________|_______gt (Transversal symmetry)______
+permutation_Q_bs: [[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5], [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8]]
+# Reflections are determined by body frame predefined orientation.
+reflection_Q_bs_lin: [[1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1], [-1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1]]
+reflection_Q_bs_ang: [[-1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1], [1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1]]
+
+# # F: Foot Space symmetries____________________________________
+# #      RL,          FL,          RR,          FR
+# # q = [x0, y0, z0, x1, y1, z1, x2, y2, z2, x3, y3, z3]
+# #                  _______gs (Sagittal Symmetry)__________|_______gt (Transversal symmetry)______
+# permutation_Q_fs: [[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5], [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8]]
+# # Reflections are determined by body frame predefined orientation.
+# # gs: [1, -1, 1], gt: [-1, 1, 1]
+# reflection_Q_fs: [[1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1], [-1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1]]
+
+# L: Label Space symmetries____________________________________
+#      RL,FL,RR,FR
+# q = [0, 1, 2, 3]
+#                  _____gs______|______gt_____
+permutation_Q_ls: [[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5], [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8]]
+# Reflections are determined by body frame predefined orientation.
+reflection_Q_ls_lin: [[1, -1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1], [-1, 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, 1]]
+reflection_Q_ls_ang: [[-1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1], [1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1]]

research/evaluator_regression-com.py

@@ -19,6 +19,9 @@ def main(MorphSym_version: str,
     elif MorphSym_version == 'S4':
         from ms_hgnn.datasets_py.soloDataset import Solo12Dataset
         model_type = 'heterogeneous_gnn_s4_com'
+    elif MorphSym_version == 'C2':
+        from mi_hgnn.datasets_py.soloDataset import Solo12Dataset
+        model_type = 'heterogeneous_gnn_c2_com'
     else:
         raise ValueError("Other MorphSym versions are not supported for this script yet!")
 
@@ -56,33 +59,31 @@ def main(MorphSym_version: str,
         print(f"Model Type: {model_type}")
         print(f"Path to Checkpoint: {path_to_checkpoint}")
         print("================================================")
-
-        test_dataset = prepare_test_dataset(Solo12Dataset, path_to_urdf, model_type, history_length, normalize=True, swap_legs=swap_legs, symmetry_operator=symmetry_operator, symmetry_mode=symmetry_mode, group_operator_path=group_operator_path)
-
+
         root = Path(Path('.').parent, 'datasets', 'Solo-12').absolute()
+        test_dataset = prepare_test_dataset(Solo12Dataset, root, path_to_urdf, model_type, history_length, normalize=True, 
+                                                symmetry_operator=symmetry_operator, symmetry_mode=symmetry_mode, group_operator_path=group_operator_path)
+
         # Evaluate with model
-        pred, labels, loss, cos_sim_lin, cos_sim_ang = evaluate_model(path_to_checkpoint, test_dataset, symmetry_mode=symmetry_mode, group_operator_path=group_operator_path, data_path = root)
+        pred, labels, mse_loss, cos_sim_lin, cos_sim_ang = evaluate_model(path_to_checkpoint, test_dataset, symmetry_mode=symmetry_mode, group_operator_path=group_operator_path, data_path = root)
         # Save to DataFrame
-        df = pandas.concat([df, pandas.DataFrame([[symmetry_operator, loss.item(), cos_sim_lin.item(), cos_sim_ang.item()]], columns=columns)], ignore_index=True)
+        df = pandas.concat([df, pandas.DataFrame([[symmetry_operator, mse_loss.item(), cos_sim_lin.item(), cos_sim_ang.item()]], columns=columns)], ignore_index=True)
 
         # Print the results
-        print_results(loss, cos_sim_lin, cos_sim_ang)
+        print_results(mse_loss, cos_sim_lin, cos_sim_ang)
 
     # Save to csv
     df.to_csv(path_to_save_csv, index=False)
     print("===> Evaluation Finished! Results saved to: ", path_to_save_csv)
 
-def prepare_test_dataset(Solo12Dataset, path_to_urdf, model_type, history_length, normalize=True, swap_legs=None, symmetry_operator=None, symmetry_mode=None, group_operator_path=None):
+def prepare_test_dataset(Solo12Dataset, root, path_to_urdf, model_type, history_length, normalize=True, symmetry_operator=None, symmetry_mode=None, group_operator_path=None):
     """Prepare the test dataset"""
     # Define train and val sets
-    test_dataset = Solo12Dataset(Path(Path('.').parent, 'datasets', 'Solo-12').absolute(), path_to_urdf, 
-                       'package://yobotics_description/', 'mini-cheetah-gazebo-urdf/yobo_model/yobotics_description', model_type, history_length, normalize)
+    solo12data_test = Solo12Dataset(root, path_to_urdf, 
+                       'package://yobotics_description/', 'mini-cheetah-gazebo-urdf/yobo_model/yobotics_description', model_type, history_length, normalize, stage='test', 
+                       symmetry_operator=symmetry_operator, symmetry_mode=symmetry_mode, group_operator_path=group_operator_path)
 
-    # Take first 85% for training, and last 15% for validation
-    # Also remove the last entries, as dynamics models can't use last entry due to derivative calculation
-    data_len_minus_1 = test_dataset.__len__() - 1
-    split_index_test = int(np.round(data_len_minus_1 * 0.85)) # When value has .5, round to nearest-even
-    test_dataset = torch.utils.data.Subset(test_dataset, np.arange(0, split_index_test))
+    test_dataset = torch.utils.data.Subset(solo12data_test, np.arange(0, solo12data_test.__len__()))
 
     return test_dataset
 
@@ -94,9 +95,9 @@ def print_results(loss, cos_sim_lin, cos_sim_ang):
 
 if __name__ == "__main__":
     # K4
-    MorphSym_version = 'S4'
-    path_to_checkpoint = "models/rich-sky-40/epoch=22-val_MSE_loss=0.36576-val_avg_cos_sim=0.70491.ckpt"
-    group_operator_path = 'cfg/mini_cheetah-k4.yaml'
+    MorphSym_version = 'C2'
+    path_to_checkpoint = "models/com_exp/autumn-cloud-2/"
+    group_operator_path = "cfg/a1-c2.yaml"
     symmetry_operator_list = [None]  # Can be 'gs' or 'gt' or 'gr' or None
     symmetry_mode = 'MorphSym' # Can be 'Euclidean' or 'MorphSym' or None
 

research/train_regression-com_msgn.py

@@ -11,7 +11,10 @@ def main(seed,
          lr=0.0024,
          epochs=30,
          logger_project_name='com_debug',
-         model_type='heterogeneous_gnn_s4_com',
+         model_type='heterogeneous_gnn_k4_com',
+         symmetry_operator=None,
+         symmetry_mode='MorphSym',
+         group_operator_path='cfg/solo-k4.yaml',
          wandb_api_key = "eed5fa86674230b63649180cc343f14e1f1ace78"):
     # ================================= CHANGE THESE ===================================
 #     wandb_api_key = "eed5fa86674230b63649180cc343f14e1f1ace78"
@@ -61,6 +64,8 @@ def main(seed,
                 disable_test=True, 
                 data_path = root, 
                 subfoler_name=logger_project_name,
+                symmetry_mode=symmetry_mode,
+                group_operator_path=group_operator_path,
                 wandb_api_key=wandb_api_key)
 
 if __name__ == '__main__':
@@ -77,6 +82,7 @@ def main(seed,
     parser.add_argument('--logger_project_name', type=str, default='com_debug', help='Logger project name')
     # Model parameters
     parser.add_argument('--model_type', type=str, default='heterogeneous_gnn_s4_com', help='Model type, options: heterogeneous_gnn_s4_com, heterogeneous_gnn_k4_com')
+    parser.add_argument('--group_operator_path', type=str, default='cfg/solo-k4.yaml', help='cfg/solo-k4.yaml or cfg/solo-c2.yaml')
     parser.add_argument('--wandb_api_key', type=str, default='eed5fa86674230b63649180cc343f14e1f1ace78', help="Check your key at https://wandb.ai/authorize",)
     args = parser.parse_args()
 
@@ -90,4 +96,5 @@ def main(seed,
          epochs=args.epochs,
          logger_project_name=args.logger_project_name,
          model_type=args.model_type,
+         group_operator_path=args.group_operator_path,
          wandb_api_key=args.wandb_api_key)

src/ms_hgnn/datasets_py/flexibleDataset.py

@@ -64,9 +64,9 @@ def __init__(self,
         # Check for valid data format
         self.data_format = data_format
         if self.data_format != 'dynamics' and self.data_format != 'mlp' and self.data_format != 'heterogeneous_gnn' and self.data_format != 'heterogeneous_gnn_k4' and self.data_format != 'heterogeneous_gnn_c2' \
-            and self.data_format != 'heterogeneous_gnn_k4_com' and self.data_format != 'heterogeneous_gnn_s4_com' and self.data_format != 'mlp_com':
+            and self.data_format != 'heterogeneous_gnn_k4_com' and self.data_format != 'heterogeneous_gnn_c2_com' and self.data_format != 'heterogeneous_gnn_s4_com' and self.data_format != 'mlp_com':
             raise ValueError(
-                "Parameter 'data_format' must be 'dynamics', 'mlp', 'heterogeneous_gnn', or 'heterogeneous_gnn_k4' or 'heterogeneous_gnn_c2' or 'heterogeneous_gnn_k4_com' or 'heterogeneous_gnn_s4_com' or 'mlp_com'."
+                "Parameter 'data_format' must be 'dynamics', 'mlp', 'heterogeneous_gnn', or 'heterogeneous_gnn_k4' or 'heterogeneous_gnn_c2' or 'heterogeneous_gnn_k4_com' or 'heterogeneous_gnn_c2_com' or 'heterogeneous_gnn_s4_com' or 'mlp_com'."
             )
 
         # Setup the directories for raw and processed data, download it, 
@@ -163,7 +163,7 @@ def __init__(self,
             raise ValueError("Dataset must provide at least one input.")
 
         # Premake the tensors for edge attributes and connections for HGNN
-        if self.data_format == 'heterogeneous_gnn' or self.data_format == 'heterogeneous_gnn_k4' or self.data_format == 'heterogeneous_gnn_c2' or self.data_format == 'heterogeneous_gnn_k4_com' or self.data_format == 'heterogeneous_gnn_s4_com':
+        if self.data_format == 'heterogeneous_gnn' or self.data_format == 'heterogeneous_gnn_k4' or self.data_format == 'heterogeneous_gnn_c2' or self.data_format == 'heterogeneous_gnn_k4_com' or self.data_format == 'heterogeneous_gnn_c2_com' or self.data_format == 'heterogeneous_gnn_s4_com':
             bj, jb, jj, fj, jf = self.robotGraph.get_edge_index_matrices()
             self.bj = torch.tensor(bj, dtype=torch.long)
             self.jb = torch.tensor(jb, dtype=torch.long)
@@ -180,7 +180,7 @@ def __init__(self,
 
         # Precompute feature matrix sizes for HGNN
         # Calculate the size of the feature matrices
-        if self.data_format == 'heterogeneous_gnn' or self.data_format == 'heterogeneous_gnn_k4' or self.data_format == 'heterogeneous_gnn_c2' or self.data_format == 'heterogeneous_gnn_k4_com' or self.data_format == 'heterogeneous_gnn_s4_com':
+        if self.data_format == 'heterogeneous_gnn' or self.data_format == 'heterogeneous_gnn_k4' or self.data_format == 'heterogeneous_gnn_c2' or self.data_format == 'heterogeneous_gnn_k4_com' or self.data_format == 'heterogeneous_gnn_c2_com' or self.data_format == 'heterogeneous_gnn_s4_com':
             self.hgnn_number_nodes = self.robotGraph.get_num_of_each_node_type()
             self.base_width = len(self.variables_to_use_base) * 3 * self.history_length
             self.joint_width = len(self.variables_to_use_joint) * self.history_length
@@ -455,9 +455,7 @@ def get(self, idx):
             return self.get_helper_heterogeneous_gnn(idx)
         elif self.data_format == 'heterogeneous_gnn_c2':
             return self.get_helper_heterogeneous_gnn_c2(idx)
-        elif self.data_format == 'heterogeneous_gnn_k4_com':
-            return self.get_helper_heterogeneous_gnn(idx)
-        elif self.data_format == 'heterogeneous_gnn_s4_com':
+        elif self.data_format == 'heterogeneous_gnn_k4_com' or self.data_format == 'heterogeneous_gnn_s4_com' or self.data_format == 'heterogeneous_gnn_c2_com':
             return self.get_helper_heterogeneous_gnn(idx)
         else:
             raise ValueError("Invalid data format.")