[Feature] Add RepeatAugSampler

mmocr/datasets/__init__.py

@@ -4,6 +4,7 @@
 from .ocr_dataset import OCRDataset
 from .recog_lmdb_dataset import RecogLMDBDataset
 from .recog_text_dataset import RecogTextDataset
+from .samplers import *  # NOQA
 from .transforms import *  # NOQA
 from .wildreceipt_dataset import WildReceiptDataset
 

mmocr/datasets/samplers/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .repeat_aug import RepeatAugSampler
+
+__all__ = ['RepeatAugSampler']

mmocr/datasets/samplers/repeat_aug.py

@@ -0,0 +1,101 @@
+import math
+from typing import Iterator, Optional, Sized
+
+import torch
+from mmengine.dist import get_dist_info, is_main_process, sync_random_seed
+from torch.utils.data import Sampler
+
+from mmocr.registry import DATA_SAMPLERS
+
+
+@DATA_SAMPLERS.register_module()
+class RepeatAugSampler(Sampler):
+    """Sampler that restricts data loading to a subset of the dataset for
+    distributed, with repeated augmentation. It ensures that different each
+    augmented version of a sample will be visible to a different process (GPU).
+    Heavily based on torch.utils.data.DistributedSampler.
+
+    This sampler was taken from
+    https://github.com/facebookresearch/deit/blob/0c4b8f60/samplers.py
+    Used in
+    Copyright (c) 2015-present, Facebook, Inc.
+
+    Args:
+        dataset (Sized): The dataset.
+        shuffle (bool): Whether shuffle the dataset or not. Defaults to True.
+        num_repeats (int): The repeat times of every sample. Defaults to 3.
+        seed (int, optional): Random seed used to shuffle the sampler if
+            :attr:`shuffle=True`. This number should be identical across all
+            processes in the distributed group. Defaults to None.
+    """
+
+    def __init__(self,
+                 dataset: Sized,
+                 shuffle: bool = True,
+                 num_repeats: int = 3,
+                 seed: Optional[int] = None):
+        rank, world_size = get_dist_info()
+        self.rank = rank
+        self.world_size = world_size
+
+        self.dataset = dataset
+        self.shuffle = shuffle
+        if not self.shuffle and is_main_process():
+            from mmengine.logging import MMLogger
+            logger = MMLogger.get_current_instance()
+            logger.warning('The RepeatAugSampler always picks a '
+                           'fixed part of data if `shuffle=False`.')
+
+        if seed is None:
+            seed = sync_random_seed()
+        self.seed = seed
+        self.epoch = 0
+        self.num_repeats = num_repeats
+
+        # The number of repeated samples in the rank
+        self.num_samples = math.ceil(
+            len(self.dataset) * num_repeats / world_size)
+        # The total number of repeated samples in all ranks.
+        self.total_size = self.num_samples * world_size
+        # The number of selected samples in the rank
+        self.num_selected_samples = math.ceil(len(self.dataset) / world_size)
+
+    def __iter__(self) -> Iterator[int]:
+        """Iterate the indices."""
+        # deterministically shuffle based on epoch and seed
+        if self.shuffle:
+            g = torch.Generator()
+            g.manual_seed(self.seed + self.epoch)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = list(range(len(self.dataset)))
+
+        # produce repeats e.g. [0, 0, 0, 1, 1, 1, 2, 2, 2....]
+        indices = [x for x in indices for _ in range(self.num_repeats)]
+        # add extra samples to make it evenly divisible
+        indices = (indices *
+                   int(self.total_size / len(indices) + 1))[:self.total_size]
+        assert len(indices) == self.total_size
+
+        # subsample per rank
+        indices = indices[self.rank:self.total_size:self.world_size]
+        assert len(indices) == self.num_samples
+
+        # return up to num selected samples
+        return iter(indices[:self.num_selected_samples])
+
+    def __len__(self) -> int:
+        """The number of samples in this rank."""
+        return self.num_selected_samples
+
+    def set_epoch(self, epoch: int) -> None:
+        """Sets the epoch for this sampler.
+
+        When :attr:`shuffle=True`, this ensures all replicas use a different
+        random ordering for each epoch. Otherwise, the next iteration of this
+        sampler will yield the same ordering.
+
+        Args:
+            epoch (int): Epoch number.
+        """
+        self.epoch = epoch

tests/test_datasets/test_samplers/test_repeat_aug.py

@@ -0,0 +1,98 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+import math
+from unittest import TestCase
+from unittest.mock import patch
+
+import torch
+from mmengine.logging import MMLogger
+
+from mmocr.datasets import RepeatAugSampler
+
+file = 'mmocr.datasets.samplers.repeat_aug.'
+
+
+class MockDist:
+
+    def __init__(self, dist_info=(0, 1), seed=7):
+        self.dist_info = dist_info
+        self.seed = seed
+
+    def get_dist_info(self):
+        return self.dist_info
+
+    def sync_random_seed(self):
+        return self.seed
+
+    def is_main_process(self):
+        return self.dist_info[0] == 0
+
+
+class TestRepeatAugSampler(TestCase):
+
+    def setUp(self):
+        self.data_length = 100
+        self.dataset = list(range(self.data_length))
+
+    @patch(file + 'get_dist_info', return_value=(0, 1))
+    def test_non_dist(self, mock):
+        sampler = RepeatAugSampler(self.dataset, num_repeats=3, shuffle=False)
+        self.assertEqual(sampler.world_size, 1)
+        self.assertEqual(sampler.rank, 0)
+        self.assertEqual(sampler.total_size, self.data_length * 3)
+        self.assertEqual(sampler.num_samples, self.data_length * 3)
+        self.assertEqual(sampler.num_selected_samples, self.data_length)
+        self.assertEqual(len(sampler), sampler.num_selected_samples)
+        indices = [x for x in range(self.data_length) for _ in range(3)]
+        self.assertEqual(list(sampler), indices[:self.data_length])
+
+        logger = MMLogger.get_current_instance()
+        with self.assertLogs(logger, 'WARN') as log:
+            sampler = RepeatAugSampler(self.dataset, shuffle=False)
+        self.assertIn('always picks a fixed part', log.output[0])
+
+    @patch(file + 'get_dist_info', return_value=(2, 3))
+    @patch(file + 'is_main_process', return_value=False)
+    def test_dist(self, mock1, mock2):
+        sampler = RepeatAugSampler(self.dataset, num_repeats=3, shuffle=False)
+        self.assertEqual(sampler.world_size, 3)
+        self.assertEqual(sampler.rank, 2)
+        self.assertEqual(sampler.num_samples, self.data_length)
+        self.assertEqual(sampler.total_size, self.data_length * 3)
+        self.assertEqual(sampler.num_selected_samples,
+                         math.ceil(self.data_length / 3))
+        self.assertEqual(len(sampler), sampler.num_selected_samples)
+        indices = [x for x in range(self.data_length) for _ in range(3)]
+        self.assertEqual(
+            list(sampler), indices[2::3][:sampler.num_selected_samples])
+
+        logger = MMLogger.get_current_instance()
+        with patch.object(logger, 'warning') as mock_log:
+            sampler = RepeatAugSampler(self.dataset, shuffle=False)
+            mock_log.assert_not_called()
+
+    @patch(file + 'get_dist_info', return_value=(0, 1))
+    @patch(file + 'sync_random_seed', return_value=7)
+    def test_shuffle(self, mock1, mock2):
+        # test seed=None
+        sampler = RepeatAugSampler(self.dataset, seed=None)
+        self.assertEqual(sampler.seed, 7)
+
+        # test random seed
+        sampler = RepeatAugSampler(self.dataset, shuffle=True, seed=0)
+        sampler.set_epoch(10)
+        g = torch.Generator()
+        g.manual_seed(10)
+        indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        indices = [x for x in indices
+                   for _ in range(3)][:sampler.num_selected_samples]
+        self.assertEqual(list(sampler), indices)
+
+        sampler = RepeatAugSampler(self.dataset, shuffle=True, seed=42)
+        sampler.set_epoch(10)
+        g = torch.Generator()
+        g.manual_seed(42 + 10)
+        indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        indices = [x for x in indices
+                   for _ in range(3)][:sampler.num_selected_samples]
+        self.assertEqual(list(sampler), indices)