Add generator and worker seed (#8602)

utils/dataloaders.py

@@ -91,6 +91,13 @@ def exif_transpose(image):
     return image
 
 
+def seed_worker(worker_id):
+    # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader
+    worker_seed = torch.initial_seed() % 2 ** 32
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
+
+
 def create_dataloader(path,
                       imgsz,
                       batch_size,
@@ -130,13 +137,17 @@ def create_dataloader(path,
     nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
+    generator = torch.Generator()
+    generator.manual_seed(0)
     return loader(dataset,
                   batch_size=batch_size,
                   shuffle=shuffle and sampler is None,
                   num_workers=nw,
                   sampler=sampler,
                   pin_memory=True,
-                  collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn), dataset
+                  collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
+                  worker_init_fn=seed_worker,
+                  generator=generator), dataset
 
 
 class InfiniteDataLoader(dataloader.DataLoader):