diff --git a/examples/mlperf/dataloader.py b/examples/mlperf/dataloader.py
index 9aec24f4a2..56a90d542c 100644
--- a/examples/mlperf/dataloader.py
+++ b/examples/mlperf/dataloader.py
@@ -351,8 +351,8 @@ def batch_load_unet3d(preprocessed_dataset_dir:Path, batch_size:int=6, val:bool=
 ### RetinaNet
 
 def load_retinanet_data(base_dir:Path, val:bool, queue_in:Queue, queue_out:Queue,
-                        imgs:Tensor, boxes:Tensor, labels:Tensor, matches:Tensor,
-                        anchors:Tensor, img_ids:Tensor, seed:Optional[int] = None):
+                        imgs:Tensor, boxes:Tensor, labels:Tensor, matches:Optional[Tensor] = None,
+                        anchors:Optional[Tensor] = None, seed:Optional[int] = None):
   from extra.datasets.openimages import image_load, prepare_target, random_horizontal_flip, resize
   from examples.mlperf.helpers import box_iou, find_matches, generate_anchors
   import torch
@@ -391,8 +391,7 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
   def _enqueue_batch(bc):
     for idx in range(bc * batch_size, (bc+1) * batch_size):
       img = dataset.loadImgs(next(dataset_iter))[0]
-      ann = dataset.loadAnns(dataset.getAnnIds((img_id:=img["id"])))
-      img_ids[idx] = img_id
+      ann = dataset.loadAnns(dataset.getAnnIds(img["id"]))
       queue_in.put((idx, img, ann))
 
   def _setup_shared_mem(shm_name:str, size:Tuple[int, ...], dtype:dtypes) -> Tuple[shared_memory.SharedMemory, Tensor]:
@@ -408,12 +407,15 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
   procs, data_out_count = [], [0] * batch_count
 
   shm_imgs, imgs = _setup_shared_mem("retinanet_imgs", (batch_size * batch_count, 800, 800, 3), dtypes.float32)
-  shm_boxes, boxes = _setup_shared_mem("retinanet_boxes", (batch_size * batch_count, 120087, 4), dtypes.float32)
-  shm_labels, labels = _setup_shared_mem("retinanet_labels", (batch_size * batch_count, 120087), dtypes.int64)
-  shm_matches, matches = _setup_shared_mem("retinanet_matches", (batch_size * batch_count, 120087), dtypes.int64)
-  shm_anchors, anchors = _setup_shared_mem("retinanet_anchors", (batch_size * batch_count, 120087, 4), dtypes.float64)
 
-  img_ids = [None] * (batch_size * batch_count)
+  if val:
+    matches, anchors = None, None
+    boxes, labels = [None] * (batch_size * batch_count), [None] * (batch_size * batch_count)
+  else:
+    shm_boxes, boxes = _setup_shared_mem("retinanet_boxes", (batch_size * batch_count, 120087, 4), dtypes.float32)
+    shm_labels, labels = _setup_shared_mem("retinanet_labels", (batch_size * batch_count, 120087), dtypes.int64)
+    shm_matches, matches = _setup_shared_mem("retinanet_matches", (batch_size * batch_count, 120087), dtypes.int64)
+    shm_anchors, anchors = _setup_shared_mem("retinanet_anchors", (batch_size * batch_count, 120087, 4), dtypes.float64)
 
   shutdown = False
   class Cookie:
@@ -435,8 +437,8 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
     for _ in range(cpu_count()):
       proc = Process(
         target=load_retinanet_data,
-        args=(base_dir, val, queue_in, queue_out, imgs, boxes, labels, matches, anchors, img_ids),
-        kwargs={"seed": seed}
+        args=(base_dir, val, queue_in, queue_out, imgs, boxes, labels),
+        kwargs={"matches": matches, "anchors": anchors, "seed": seed}
       )
       proc.daemon = True
       proc.start()
@@ -455,7 +457,7 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
 
       if val:
         yield (imgs[bc * batch_size:(bc + 1) * batch_size],
-               img_ids[bc * batch_size:(bc + 1) * batch_size],
+               image_ids[bc * batch_size:(bc + 1) * batch_size],
                Cookie(bc))
       else:
         yield (imgs[bc * batch_size:(bc + 1) * batch_size],
@@ -478,17 +480,21 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
     for proc in procs: proc.join()
 
     shm_imgs.close()
-    shm_boxes.close()
-    shm_labels.close()
-    shm_matches.close()
-    shm_anchors.close()
+
+    if not val:
+      shm_boxes.close()
+      shm_labels.close()
+      shm_matches.close()
+      shm_anchors.close()
 
     try:
       shm_imgs.unlink()
-      shm_boxes.unlink()
-      shm_labels.unlink()
-      shm_matches.unlink()
-      shm_anchors.unlink()
+
+      if not val:
+        shm_boxes.unlink()
+        shm_labels.unlink()
+        shm_matches.unlink()
+        shm_anchors.unlink()
     except FileNotFoundError:
       # happens with BENCHMARK set
       pass