rng_seed, saving sets final episode

Author: acl21

pycls/core/config.py

@@ -32,7 +32,7 @@
 _C.CFG_DEST = 'config.yaml'
 # Note that non-determinism may still be present due to non-deterministic
 # operator implementations in GPU operator libraries
-_C.RNG_SEED = 1
+_C.RNG_SEED = None
 # Folder name where best model logs etc are saved. "auto" creates a timestamp based folder 
 _C.EXP_NAME = 'auto' 
 # Which GPU to run on

tools/ensemble_al.py

@@ -102,6 +102,10 @@ def main(cfg):
     device = torch.device("cuda" if use_cuda else "cpu")
     kwargs = {'num_workers': cfg.DATA_LOADER.NUM_WORKERS, 'pin_memory': cfg.DATA_LOADER.PIN_MEMORY} if use_cuda else {}
 
+    # Auto assign a RNG_SEED when not supplied a value
+    if cfg.RNG_SEED is None:
+        cfg.RNG_SEED = np.random.randint(100)
+
     # Using specific GPU
     # os.environ['NVIDIA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
     # os.environ['CUDA_VISIBLE_DEVICES'] = '0'
@@ -253,6 +257,9 @@ def main(cfg):
 
         # No need to perform active sampling in the last episode iteration
         if cur_episode == cfg.ACTIVE_LEARNING.MAX_ITER:
+            # Save lSet, uSet in the final episode directory
+            data_obj.saveSet(lSet, 'lSet', cfg.EPISODE_DIR)
+            data_obj.saveSet(uSet, 'uSet', cfg.EPISODE_DIR)
             break
 
         # Active Sample 

tools/ensemble_train.py

@@ -102,6 +102,10 @@ def main(cfg):
     device = torch.device("cuda" if use_cuda else "cpu")
     kwargs = {'num_workers': cfg.DATA_LOADER.NUM_WORKERS, 'pin_memory': cfg.DATA_LOADER.PIN_MEMORY} if use_cuda else {}
 
+    # Auto assign a RNG_SEED when not supplied a value
+    if cfg.RNG_SEED is None:
+        cfg.RNG_SEED = np.random.randint(100)
+
     # Using specific GPU
     # os.environ['NVIDIA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
     # os.environ['CUDA_VISIBLE_DEVICES'] = '0'

tools/train.py

@@ -40,7 +40,7 @@ def add_path(path):
 def argparser():
     parser = argparse.ArgumentParser(description='Passive Learning - Image Classification')
     parser.add_argument('--cfg', dest='cfg_file', help='Config file', required=True, type=str)
-
+    parser.add_argument('--exp-name', dest='exp_name', help='Experiment Name', required=True, type=str)
     return parser
 
 def plot_arrays(x_vals, y_vals, x_name, y_name, dataset_name, out_dir, isDebug=False):
@@ -97,6 +97,10 @@ def main(cfg):
     device = torch.device("cuda" if use_cuda else "cpu")
     kwargs = {'num_workers': cfg.DATA_LOADER.NUM_WORKERS, 'pin_memory': cfg.DATA_LOADER.PIN_MEMORY} if use_cuda else {}
 
+    # Auto assign a RNG_SEED when not supplied a value
+    if cfg.RNG_SEED is None:
+        cfg.RNG_SEED = np.random.randint(100)
+
     # Using specific GPU
     # os.environ['CUDA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
     # print("Using GPU : {}.\n".format(cfg.GPU_ID))
@@ -452,4 +456,5 @@ def test_epoch(test_loader, model, test_meter, cur_epoch):
 
 if __name__ == "__main__":
     cfg.merge_from_file(argparser().parse_args().cfg_file)
+    cfg.EXP_NAME = argparser().parse_args().exp_name
     main(cfg)

tools/train_al.py

@@ -103,6 +103,10 @@ def main(cfg):
     device = torch.device("cuda" if use_cuda else "cpu")
     kwargs = {'num_workers': cfg.DATA_LOADER.NUM_WORKERS, 'pin_memory': cfg.DATA_LOADER.PIN_MEMORY} if use_cuda else {}
 
+    # Auto assign a RNG_SEED when not supplied a value
+    if cfg.RNG_SEED is None:
+        cfg.RNG_SEED = np.random.randint(100)
+
     # Using specific GPU
     # os.environ['NVIDIA_VISIBLE_DEVICES'] = str(cfg.GPU_ID)
     # os.environ['CUDA_VISIBLE_DEVICES'] = '0'
@@ -209,6 +213,9 @@ def main(cfg):
 
         # No need to perform active sampling in the last episode iteration
         if cur_episode == cfg.ACTIVE_LEARNING.MAX_ITER:
+            # Save current lSet, uSet in the final episode directory
+            data_obj.saveSet(lSet, 'lSet', cfg.EPISODE_DIR)
+            data_obj.saveSet(uSet, 'uSet', cfg.EPISODE_DIR)
             break
 
         # Active Sample