Add thread args to ThreadBuffer

monai/data/thread_buffer.py

@@ -33,11 +33,11 @@ class ThreadBuffer:
         timeout: Time to wait for an item from the buffer, or to wait while the buffer is full when adding items
     """
 
-    def __init__(self, src, buffer_size=1, timeout=0.01):
+    def __init__(self, src, buffer_size: int = 1, timeout: float = 0.01):
         self.src = src
         self.buffer_size = buffer_size
         self.timeout = timeout
-        self.buffer = Queue(self.buffer_size)
+        self.buffer: Queue = Queue(self.buffer_size)
         self.gen_thread = None
         self.is_running = False
 
@@ -82,11 +82,27 @@ class ThreadDataLoader(DataLoader):
     Subclass of `DataLoader` using a `ThreadBuffer` object to implement `__iter__` method asynchronously. This will
     iterate over data from the loader as expected however the data is generated on a separate thread. Use this class
     where a `DataLoader` instance is required and not just an iterable object.
+
+    Args:
+        dataset: input dataset.
+        buffer_size: number of items to buffer from the data source.
+        buffer_timeout: time to wait for an item from the buffer, or to wait while the buffer is full when adding items.
+        num_workers: number of the multi-prcessing workers in PyTorch DataLoader.
+
     """
 
-    def __init__(self, dataset: Dataset, num_workers: int = 0, **kwargs):
+    def __init__(
+        self,
+        dataset: Dataset,
+        buffer_size: int = 1,
+        buffer_timeout: float = 0.01,
+        num_workers: int = 0,
+        **kwargs,
+    ):
         super().__init__(dataset, num_workers, **kwargs)
+        self.buffer_size = buffer_size
+        self.buffer_timeout = buffer_timeout
 
     def __iter__(self):
-        buffer = ThreadBuffer(super().__iter__())
+        buffer = ThreadBuffer(src=super().__iter__(), buffer_size=self.buffer_size, timeout=self.buffer_timeout)
         yield from buffer