Basic Framework

configs/config.py

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+import argparse
+import os.path
+import shutil
+import yaml
+
+
+class ConfigManager:
+    config = None
+    config_path = None
+
+    @staticmethod
+    def get(*args):
+        result = ConfigManager.config
+        for arg in args:
+            result = result[arg]
+        return result
+
+    @staticmethod
+    def load_config_with(config_file_path):
+        ConfigManager.config_path = config_file_path
+        if not os.path.exists(ConfigManager.config_path):
+            raise ValueError(f"Config file <{config_file_path}> does not exist")
+        with open(config_file_path, 'r') as file:
+            ConfigManager.config = yaml.safe_load(file)
+
+    @staticmethod
+    def backup_config_to(target_config_dir, file_name, prefix="config"):
+        file_name = f"{prefix}_{file_name}.yaml"
+        target_config_file_path = str(os.path.join(target_config_dir, file_name))
+        shutil.copy(ConfigManager.config_path, target_config_file_path)
+
+    @staticmethod
+    def load_config():
+        parser = argparse.ArgumentParser()
+        parser.add_argument('--config', type=str, default='', help='config file path')
+        args = parser.parse_args()
+        if args.config:
+            ConfigManager.load_config_with(args.config)
+
+    @staticmethod
+    def print_config(key: str = None, group: dict = None, level=0):
+        table_size = 80
+        if key and group:
+            value = group[key]
+            if type(value) is dict:
+                print("\t" * level + f"+-{key}:")
+                for k in value:
+                    ConfigManager.print_config(k, value, level=level + 1)
+            else:
+                print("\t" * level + f"| {key}: {value}")
+        elif key:
+            ConfigManager.print_config(key, ConfigManager.config, level=level)
+        else:
+            print("+" + "-" * table_size + "+")
+            print(f"| Configurations in <{ConfigManager.config_path}>:")
+            print("+" + "-" * table_size + "+")
+            for key in ConfigManager.config:
+                ConfigManager.print_config(key, level=level + 1)
+            print("+" + "-" * table_size + "+")
+
+
+''' ------------ Debug ------------ '''
+if __name__ == "__main__":
+    test_args = ['--config', r'configs\train_config.yaml']
+    test_parser = argparse.ArgumentParser()
+    test_parser.add_argument('--config', type=str, default='', help='config file path')
+    test_args = test_parser.parse_args(test_args)
+    if test_args.config:
+        ConfigManager.load_config_with(test_args.config)
+    ConfigManager.print_config()
+    print()
+    pipeline = ConfigManager.get('settings', 'train', "dataset", 'batch_size')
+    ConfigManager.print_config('settings')
+    print(pipeline)

configs/train_config.yaml

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+# Train config file
+
+settings:
+  general:
+    seed: 0
+    device: cuda
+    cuda_visible_devices: "0,1,2,3,4,5,6,7"
+    parallel: True
+    
+  experiment:
+    name: train_experiment
+    root_dir: "experiments"
+    use_checkpoint: True
+    epoch: -1 # -1 stands for last epoch
+    max_epochs: 5000
+    save_checkpoint_interval: 1
+    test_first: True       
+      
+  train:
+    optimizer:
+      type: adam
+      lr: 0.0001
+    losses: # loss type : weight
+      gf_loss: 1.0
+    dataset:
+      name: synthetic_train_train_dataset
+      source: nbv1
+      data_type: train
+      ratio: 0.1
+      batch_size: 128
+      num_workers: 96
+
+  test:
+    batch_size: 16
+    frequency: 3
+    dataset_list:
+      - name: synthetic_test_train_dataset
+        source: nbv1
+        data_type: train
+        eval_list:
+        ratio: 0.00001
+        batch_size: 16
+        num_workers: 16
+
+  pipeline:
+    pts_encoder: pointnet
+    view_finder: gradient_field
+
+datasets:
+  general:
+    data_dir: "../data"
+
+modules:
+  general:
+    pts_channels: 3
+    feature_dim: 1024
+    per_point_feature: False
+  pts_encoder:
+    pointnet:
+    pointnet++:
+      params_name: light
+    pointnet++rgb:
+      params_name: light
+      target_layer: 3
+      rgb_feat_dim: 384
+  view_finder:
+    gradient_field:
+      pose_mode: rot_matrix
+      regression_head: Rx_Ry
+      sample_mode: ode
+      sample_repeat: 50
+      sampling_steps: 500
+      sde_mode: ve