Initial release of SwiftFormer

util/samplers.py

@@ -0,0 +1,60 @@
+import torch
+import torch.distributed as dist
+import math
+
+
+class RASampler(torch.utils.data.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
+    """
+
+    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError(
+                    "Requires distributed package to be available")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError(
+                    "Requires distributed package to be available")
+            rank = dist.get_rank()
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.num_samples = int(
+            math.ceil(len(self.dataset) * 3.0 / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+        self.num_selected_samples = int(math.floor(
+            len(self.dataset) // 256 * 256 / self.num_replicas))
+        self.shuffle = shuffle
+
+    def __iter__(self):
+        # Deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        if self.shuffle:
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = list(range(len(self.dataset)))
+
+        # Add extra samples to make it evenly divisible
+        indices = [ele for ele in indices for i in range(3)]
+        indices += indices[:(self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # Subsample
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices[:self.num_selected_samples])
+
+    def __len__(self):
+        return self.num_selected_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch