Fix tf.data options setting for newer TF versions

timm/data/parsers/parser_tfds.py

@@ -25,8 +25,8 @@ from .parser import Parser
 
 
 MAX_TP_SIZE = 8  # maximum TF threadpool size, only doing jpeg decodes and queuing activities
-SHUFFLE_SIZE = 16834  # samples to shuffle in DS queue
+SHUFFLE_SIZE = 20480  # samples to shuffle in DS queue
-PREFETCH_SIZE = 4096  # samples to prefetch
+PREFETCH_SIZE = 2048  # samples to prefetch
 
 
 def even_split_indices(split, n, num_samples):
@@ -144,14 +144,16 @@ class ParserTfds(Parser):
         ds = self.builder.as_dataset(
             split=self.subsplit or self.split, shuffle_files=self.shuffle, read_config=read_config)
         # avoid overloading threading w/ combo fo TF ds threads + PyTorch workers
-        ds.options().experimental_threading.private_threadpool_size = max(1, MAX_TP_SIZE // num_workers)
+        options = tf.data.Options()
-        ds.options().experimental_threading.max_intra_op_parallelism = 1
+        options.experimental_threading.private_threadpool_size = max(1, MAX_TP_SIZE // num_workers)
+        options.experimental_threading.max_intra_op_parallelism = 1
+        ds = ds.with_options(options)
         if self.is_training or self.repeats > 1:
             # to prevent excessive drop_last batch behaviour w/ IterableDatasets
             # see warnings at https://pytorch.org/docs/stable/data.html#multi-process-data-loading
             ds = ds.repeat()  # allow wrap around and break iteration manually
         if self.shuffle:
-            ds = ds.shuffle(min(self.num_samples // self._num_pipelines, SHUFFLE_SIZE), seed=0)
+            ds = ds.shuffle(min(self.num_samples, SHUFFLE_SIZE) // self._num_pipelines, seed=0)
         ds = ds.prefetch(min(self.num_samples // self._num_pipelines, PREFETCH_SIZE))
         self.ds = tfds.as_numpy(ds)