add split dataset

app_split.py

@@ -0,0 +1,9 @@
+from PytorchBoot.application import PytorchBootApplication
+from runners.data_splitor import DataSplitor
+
+@PytorchBootApplication("split")
+class DataSplitApp:
+    @staticmethod
+    def start():
+        DataSplitor(r"configs\split_dataset_config.yaml").run()
+        

configs/split_dataset_config.yaml

@@ -0,0 +1,22 @@
+
+runner:
+  general:
+    seed: 0
+    device: cpu
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    
+  experiment:
+    name: debug
+    root_dir: "experiments"
+  
+  split:
+    root_dir: "C:\\Document\\Local Project\\nbv_rec\\data\\sample"
+    type: "unseen_instance" # "unseen_category"
+    datasets:
+      OmniObject3d_train: 
+        path: "C:\\Document\\Local Project\\nbv_rec\\data\\OmniObject3d_train.txt"
+        ratio: 0.5
+
+      OmniObject3d_test: 
+        path: "C:\\Document\\Local Project\\nbv_rec\\data\\OmniObject3d_test.txt"
+        ratio: 0.5

configs/train_config.yaml

@@ -11,10 +11,14 @@ runner:
   
   train:
     dataset_list:
-      - OmniObject3d
+      - OmniObject3d_train
   
 datasets:
-    OmniObject3d:
+  OmniObject3d_train:
-      root_dir: "/media/hofee/data/data/nbv_rec/sample"
+    root_dir: "C:\\Document\\Local Project\\nbv_rec\\data\\sample"
+    split_file: "C:\\Document\\Local Project\\nbv_rec\\data\\OmniObject3d_train.txt"
 
+  OmniObject3d_test:
+    root_dir: "C:\\Document\\Local Project\\nbv_rec\\data\\sample"
+    split_file: "C:\\Document\\Local Project\\nbv_rec\\data\\OmniObject3d_test.txt"
 

core/dataset.py

@@ -1,10 +1,9 @@
-import os
 import numpy as np
 from PytorchBoot.dataset import BaseDataset
 import PytorchBoot.stereotype as stereotype
 
 import sys
-sys.path.append(r"/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction")
+sys.path.append(r"C:\Document\Local Project\nbv_rec\nbv_reconstruction")
 
 from utils.data_load import DataLoadUtil
 from utils.pose import PoseUtil
@@ -16,13 +15,22 @@ class NBVReconstructionDataset(BaseDataset):
         super(NBVReconstructionDataset, self).__init__(config)
         self.config = config
         self.root_dir = config["root_dir"]
+        self.split_file_path = config["split_file"]
+        self.scene_name_list = self.load_scene_name_list()
         self.datalist = self.get_datalist()
         self.pts_num = 1024
 
+    def load_scene_name_list(self):
+        scene_name_list = []
+        with open(self.split_file_path, "r") as f:
+            for line in f:
+                scene_name = line.strip()
+                scene_name_list.append(scene_name)
+        return scene_name_list
+    
     def get_datalist(self):
         datalist = []
-        scene_name_list = os.listdir(self.root_dir)
+        for scene_name in self.scene_name_list:
-        for scene_name in scene_name_list:
             label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name)
             label_data = DataLoadUtil.load_label(label_path)
             for data_pair in label_data["data_pairs"]:
@@ -97,8 +105,12 @@ class NBVReconstructionDataset(BaseDataset):
 
 if __name__ == "__main__":
     import torch
+    seed = 0
+    torch.manual_seed(seed)
+    np.random.seed(seed)
     config = {
-        "root_dir": "/media/hofee/data/data/nbv_rec/sample",
+        "root_dir": "C:\\Document\\Local Project\\nbv_rec\\data\\sample",
+        "split_file": "C:\\Document\\Local Project\\nbv_rec\\data\\OmniObject3d_train.txt",
         "ratio": 0.05,
         "batch_size": 1,
         "num_workers": 0,

runners/data_splitor.py

@@ -0,0 +1,55 @@
+import os
+import random
+from PytorchBoot.runners.runner import Runner
+from PytorchBoot.config import ConfigManager
+from PytorchBoot.utils import Log
+import PytorchBoot.stereotype as stereotype
+
+
+@stereotype.runner("data_splitor", comment="unfinished")
+class DataSplitor(Runner):
+    def __init__(self, config):
+        super().__init__(config)
+        self.load_experiment("data_split")
+        self.root_dir = ConfigManager.get("runner", "split", "root_dir")
+        self.type = ConfigManager.get("runner", "split", "type")
+        self.datasets = ConfigManager.get("runner", "split", "datasets")
+        self.datapath_list = self.load_all_datapath()
+    
+    def run(self):
+        self.split_dataset()
+        
+    def split_dataset(self):
+        
+        random.shuffle(self.datapath_list)
+        start_idx = 0
+        for dataset in self.datasets:
+            ratio = self.datasets[dataset]["ratio"]
+            path = self.datasets[dataset]["path"]
+            split_size = int(len(self.datapath_list) * ratio)
+            split_files = self.datapath_list[start_idx:start_idx + split_size]
+            start_idx += split_size
+            self.save_split_files(path, split_files)
+            Log.success(f"save {dataset} split files to {path}")
+    
+    def save_split_files(self, path, split_files):
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        with open(path, "w") as f:
+            f.write("\n".join(split_files))
+        
+        
+    def load_all_datapath(self):
+        return os.listdir(self.root_dir)
+    
+    def create_experiment(self, backup_name=None):
+        super().create_experiment(backup_name)
+    
+    def load_experiment(self, backup_name=None):
+        super().load_experiment(backup_name)
+    
+    
+
+
+    
+        
+