Merge 28c7c9cb9ebe97df232e71c192662ad2fed3c393 into 7a1af7154511e0ce4e4be8d62faa8c5e5a3532d2

finetune/datasets/bucket_sampler.py

@@ -17,7 +17,8 @@ class BucketSampler(Sampler):
         batch_size (`int`, defaults to `8`):
             The batch size to use for training.
         shuffle (`bool`, defaults to `True`):
-            Whether or not to shuffle the data in each batch before dispatching to dataloader.
+            Whether or not to shuffle the dataset order and each batch before
+            dispatching to the dataloader.
         drop_last (`bool`, defaults to `False`):
             Whether or not to drop incomplete buckets of data after completely iterating over all data
             in the dataset. If set to True, only batches that have `batch_size` number of entries will
@@ -50,7 +51,22 @@ class BucketSampler(Sampler):
         return (len(self.data_source) + self.batch_size - 1) // self.batch_size
 
     def __iter__(self):
-        for index, data in enumerate(self.data_source):
+        # Buckets hold samples between yields.  They must be recreated for
+        # every epoch: with ``drop_last=True`` an incomplete bucket is
+        # intentionally discarded and must not be mixed with samples from the
+        # next call to ``__iter__``.
+        self.buckets = {resolution: [] for resolution in self.data_source.video_resolution_buckets}
+
+        # Shuffling individual buckets is not sufficient because the dataset
+        # is otherwise read in index order.  A global permutation changes the
+        # order in which buckets are filled while retaining homogeneous
+        # batches, which gives each epoch a genuinely different sample order.
+        indices = list(range(len(self.data_source)))
+        if self.shuffle:
+            random.shuffle(indices)
+
+        for index in indices:
+            data = self.data_source[index]
             video_metadata = data["video_metadata"]
             f, h, w = (
                 video_metadata["num_frames"],

tests/finetune/test_bucket_sampler.py

@@ -0,0 +1,86 @@
+import importlib.util
+from pathlib import Path
+
+
+def _load_bucket_sampler_module():
+    repo_root = Path(__file__).resolve().parents[2]
+    module_path = repo_root / "finetune" / "datasets" / "bucket_sampler.py"
+    spec = importlib.util.spec_from_file_location("bucket_sampler_under_test", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+class _Dataset:
+    def __init__(self, rows):
+        self.rows = rows
+        self.video_resolution_buckets = [
+            (
+                row["video_metadata"]["num_frames"],
+                row["video_metadata"]["height"],
+                row["video_metadata"]["width"],
+            )
+            for row in rows
+        ]
+        self.video_resolution_buckets = list(dict.fromkeys(self.video_resolution_buckets))
+
+    def __len__(self):
+        return len(self.rows)
+
+    def __getitem__(self, index):
+        return self.rows[index]
+
+
+def _row(index, bucket=(1, 32, 32)):
+    return {
+        "id": index,
+        "video_metadata": {
+            "num_frames": bucket[0],
+            "height": bucket[1],
+            "width": bucket[2],
+        },
+    }
+
+
+def _flatten_ids(batches):
+    return [row["id"] for batch in batches for row in batch]
+
+
+def test_shuffle_permutates_dataset_before_bucketing(monkeypatch):
+    module = _load_bucket_sampler_module()
+    dataset = _Dataset([_row(i, (1, 32 + i % 2, 32)) for i in range(8)])
+    sampler = module.BucketSampler(dataset, batch_size=2, shuffle=True)
+    shuffle_calls = []
+
+    def reverse(values):
+        shuffle_calls.append(len(values))
+        values.reverse()
+
+    monkeypatch.setattr(module.random, "shuffle", reverse)
+    batches = list(sampler)
+
+    assert shuffle_calls[0] == len(dataset)
+    assert sorted(_flatten_ids(batches)) == list(range(len(dataset)))
+    assert _flatten_ids(batches) != list(range(len(dataset)))
+    for batch in batches:
+        metadata = {
+            (
+                row["video_metadata"]["num_frames"],
+                row["video_metadata"]["height"],
+                row["video_metadata"]["width"],
+            )
+            for row in batch
+        }
+        assert len(metadata) == 1
+
+
+def test_drop_last_does_not_carry_partial_bucket_between_epochs():
+    module = _load_bucket_sampler_module()
+    dataset = _Dataset([_row(i) for i in range(3)])
+    sampler = module.BucketSampler(dataset, batch_size=2, shuffle=False, drop_last=True)
+
+    first_epoch = list(sampler)
+    second_epoch = list(sampler)
+
+    assert _flatten_ids(first_epoch) == [0, 1]
+    assert _flatten_ids(second_epoch) == [0, 1]