update strong pointnet++

app_sim.py

@@ -0,0 +1,9 @@
+from PytorchBoot.application import PytorchBootApplication
+from runners.simulator import Simulator
+
+@PytorchBootApplication("sim")
+class SimulateApp:
+    @staticmethod
+    def start():
+        Simulator("configs/server/server_split_dataset_config.yaml").run()
+        

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_dense
+    name: train_ab_global_only_p++_wp
     root_dir: "experiments"
-    epoch: 441 # -1 stands for last epoch
+    epoch: 922 # -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++_dense"
+  output_dir: "/media/hofee/data/data/p++_wp"
   pipeline: nbv_reconstruction_pipeline
   voxel_size: 0.003
   min_new_area: 1.0
@@ -62,7 +62,7 @@ pipeline:
 module:
   pointnet++_encoder:
     in_dim: 3
-    params_name: dense
+    params_name: light
 
   pointnet_encoder:
     in_dim: 3

configs/local/simulation_config.yaml

@@ -0,0 +1,14 @@
+
+runner:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: simulation_debug
+    root_dir: "experiments"
+
+simulation:
+  robot:
+  displaytable:

modules/pointnet++_encoder.py

@@ -33,6 +33,30 @@ ClsMSG_CFG_Light = {
     'DP_RATIO': 0.5,
 }
 
+ClsMSG_CFG_Light_2048 = {
+    'NPOINTS': [512, 256, 128, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
+    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [None, None]],
+    'MLPS': [[[16, 16, 32], [32, 32, 64]],
+         [[64, 64, 128], [64, 96, 128]],
+         [[128, 196, 256], [128, 196, 256]],
+         [[256, 256, 1024], [256, 512, 1024]]],
+    'DP_RATIO': 0.5,
+}
+
+ClsMSG_CFG_Strong = {
+    'NPOINTS': [512, 256, 128, 64, None],
+    'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16],[0.16, 0.32], [None, None]],
+    'NSAMPLE': [[16, 32], [16, 32], [16, 32], [16, 32], [None, None]],
+    'MLPS': [[[16, 16, 32], [32, 32, 64]],
+         [[64, 64, 128], [64, 96, 128]],
+         [[128, 196, 256], [128, 196, 256]],
+         [[256, 256, 512], [256, 512, 512]],
+         [[512, 512, 2048], [512, 1024, 2048]]
+         ],
+    'DP_RATIO': 0.5,
+}
+
 ClsMSG_CFG_Lighter = {
     'NPOINTS': [512, 256, 128, 64, None],
     'RADIUS': [[0.01], [0.02], [0.04], [0.08], [None]],
@@ -53,6 +77,10 @@ def select_params(name):
         return ClsMSG_CFG_Lighter
     elif name == 'dense':
         return ClsMSG_CFG_Dense
+    elif name == 'light_2048':
+        return ClsMSG_CFG_Light_2048
+    elif name == 'strong':
+        return ClsMSG_CFG_Strong
     else:
         raise NotImplementedError
 
@@ -114,8 +142,8 @@ if __name__ == '__main__':
     seed = 100
     torch.manual_seed(seed)
     torch.cuda.manual_seed(seed)
-    net = PointNet2Encoder(config={"in_dim": 3, "params_name": "light"}).cuda()
-    pts = torch.randn(2, 1024, 3).cuda()
+    net = PointNet2Encoder(config={"in_dim": 3, "params_name": "strong"}).cuda()
+    pts = torch.randn(2, 2444, 3).cuda()
     print(torch.mean(pts, dim=1))
     pre = net.encode_points(pts)
     print(pre.shape)

runners/inferencer.py

@@ -137,7 +137,7 @@ class Inferencer(Runner):
         pred_cr_seq = [last_pred_cr]
         success = 0
         last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], voxel_threshold).shape[0]
-        import time
+        #import time
         while len(pred_cr_seq) < max_iter and retry < max_retry and success < max_success:
             Log.green(f"iter: {len(pred_cr_seq)}, retry: {retry}/{max_retry}, success: {success}/{max_success}")
             combined_scanned_pts = np.vstack(scanned_view_pts)
@@ -229,7 +229,6 @@ class Inferencer(Runner):
                     Log.success(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
 
                 last_pts_num = pts_num
-                break
             
 
         input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()

runners/simulator.py

@@ -0,0 +1,23 @@
+from PytorchBoot.runners.runner import Runner
+import PytorchBoot.stereotype as stereotype
+
+@stereotype.runner("simulator")
+class Simulator(Runner):
+    def __init__(self, config_path):
+        super().__init__(config_path)
+        self.config_path = config_path
+        
+    def run(self):
+        print()
+
+    def prepare_env(self):
+        pass
+
+    def create_env(self):
+        pass
+
+    def create_experiment(self, backup_name=None):
+        return super().create_experiment(backup_name)
+    
+    def load_experiment(self, backup_name=None):
+        super().load_experiment(backup_name)