update inference server

configs/local/inference_config.yaml

@@ -6,16 +6,16 @@ runner:
     cuda_visible_devices: "0,1,2,3,4,5,6,7"
     
   experiment:
-    name: train_ab_global_only_p++_wp
+    name: train_ab_global_only_dense
     root_dir: "experiments"
-    epoch: 922 # -1 stands for last epoch
+    epoch: 441 # -1 stands for last epoch
 
   test:
     dataset_list:
       - OmniObject3d_test
 
   blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
-  output_dir: "/media/hofee/data/data/p++_wp_temp_cluster"
+  output_dir: "/media/hofee/data/data/p++_dense"
   pipeline: nbv_reconstruction_pipeline
   voxel_size: 0.003
   min_new_area: 1.0
@@ -62,6 +62,7 @@ pipeline:
 module:
   pointnet++_encoder:
     in_dim: 3
+    params_name: dense
 
   pointnet_encoder:
     in_dim: 3

runners/inference_server.py

@@ -12,6 +12,7 @@ from PytorchBoot.runners.runner import Runner
 from PytorchBoot.utils import Log
 
 from utils.pts import PtsUtil
+from beans.predict_result import PredictResult
 
 @stereotype.runner("inferencer_server")
 class InferencerServer(Runner):
@@ -50,6 +51,7 @@ class InferencerServer(Runner):
     def get_result(self, output_data):
 
         pred_pose_9d = output_data["pred_pose_9d"]
+        pred_pose_9d = np.asarray(PredictResult(pred_pose_9d.cpu().numpy(), None, cluster_params=dict(eps=0.25, min_samples=3)).candidate_9d_poses, dtype=np.float32)
         result = {
             "pred_pose_9d": pred_pose_9d.tolist()
         }

runners/inferencer.py

@@ -156,7 +156,12 @@ class Inferencer(Runner):
             # np.save(pred_9d_path, pred_pose_9d.cpu().numpy())
             # np.savetxt(pts_path, np_combined_scanned_pts)
             # # ----- ----- -----
-            pred_pose_9d_candidates = PredictResult(pred_pose_9d.cpu().numpy(), input_pts=input_data["combined_scanned_pts"][0].cpu().numpy(), cluster_params=dict(eps=0.25, min_samples=3)).candidate_9d_poses
+            predict_result = PredictResult(pred_pose_9d.cpu().numpy(), input_pts=input_data["combined_scanned_pts"][0].cpu().numpy(), cluster_params=dict(eps=0.25, min_samples=3))
+            # -----------------------
+            # import ipdb; ipdb.set_trace()
+            # predict_result.visualize()
+            # -----------------------
+            pred_pose_9d_candidates = predict_result.candidate_9d_poses
             for pred_pose_9d in pred_pose_9d_candidates:
                 #import ipdb; ipdb.set_trace()
                 pred_pose_9d = torch.tensor(pred_pose_9d, dtype=torch.float32).to(self.device).unsqueeze(0)