fix randomization

enhancer/data/dataset.py

@@ -1,6 +1,7 @@
 import math
 import multiprocessing
 import os
+import random
 from itertools import chain, cycle
 from typing import Optional
 
@@ -120,7 +121,7 @@ class TaskDataset(pl.LightningDataModule):
         valid_min_now = 0.0
         valid_indices = []
         random_indices = list(range(0, len(data)))
-        rng = create_unique_rng(random_state, 0)
+        rng = create_unique_rng(random_state)
         rng.shuffle(random_indices)
         i = 0
         while valid_min_now <= valid_minutes:
@@ -285,14 +286,15 @@ class EnhancerDataset(TaskDataset):
 
         super().setup(stage=stage)
 
-    def random_sample(self, index):
+    def random_sample(self, train_data):
-        rng = create_unique_rng(self.model.current_epoch, index)
+        return random.sample(train_data, len(train_data))
-        return rng.sample(self.train_data, len(self.train_data))
 
     def train__iter__(self):
+        rng = create_unique_rng(self.model.current_epoch)
+        train_data = rng.sample(self.train_data, len(self.train_data))
         return zip(
             *[
-                self.get_stream(self.random_sample(i))
+                self.get_stream(self.random_sample(train_data))
                 for i in range(self.batch_size)
             ]
         )
@@ -353,8 +355,10 @@ class EnhancerDataset(TaskDataset):
         }
 
     def train__len__(self):
-        worker_info = torch.utils.data.get_worker_info()
+        if self.num_workers > 1:
-        num_workers = worker_info.num_workers if worker_info else 1
+            num_workers = 2
+        else:
+            num_workers = 1
         return sum([len(item) for item in self.train_data]) // (
             self.batch_size * num_workers
         )

enhancer/utils/random.py

@@ -4,7 +4,7 @@ import random
 import torch
 
 
-def create_unique_rng(epoch: int, index: int):
+def create_unique_rng(epoch: int):
     """create unique random number generator for each (worker_id,epoch) combination"""
 
     rng = random.Random()
@@ -29,7 +29,6 @@ def create_unique_rng(epoch: int, index: int):
         + local_rank * num_workers
         + node_rank * num_workers * global_rank
         + epoch * num_workers * world_size
-        + index
     )
 
     rng.seed(seed)