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ssd/data/samplers/__init__.py

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+from .iteration_based_batch_sampler import IterationBasedBatchSampler
+from .distributed import DistributedSampler
+
+__all__ = ['IterationBasedBatchSampler', 'DistributedSampler']

ssd/data/samplers/distributed.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# Code is copy-pasted exactly as in torch.utils.data.distributed.
+# FIXME remove this once c10d fixes the bug it has
+import math
+import torch
+import torch.distributed as dist
+from torch.utils.data.sampler import Sampler
+
+
+class DistributedSampler(Sampler):
+    """Sampler that restricts data loading to a subset of the dataset.
+    It is especially useful in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
+    process can pass a DistributedSampler instance as a DataLoader sampler,
+    and load a subset of the original dataset that is exclusive to it.
+    .. note::
+        Dataset is assumed to be of constant size.
+    Arguments:
+        dataset: Dataset used for sampling.
+        num_replicas (optional): Number of processes participating in
+            distributed training.
+        rank (optional): Rank of the current process within num_replicas.
+    """
+
+    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) * 1.0 / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+        self.shuffle = shuffle
+
+    def __iter__(self):
+        if self.shuffle:
+            # deterministically shuffle based on epoch
+            g = torch.Generator()
+            g.manual_seed(self.epoch)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = torch.arange(len(self.dataset)).tolist()
+
+        # add extra samples to make it evenly divisible
+        indices += indices[: (self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        offset = self.num_samples * self.rank
+        indices = indices[offset: offset + self.num_samples]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch

ssd/data/samplers/iteration_based_batch_sampler.py

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+from torch.utils.data.sampler import BatchSampler
+
+
+class IterationBasedBatchSampler(BatchSampler):
+    """
+    Wraps a BatchSampler, re-sampling from it until
+    a specified number of iterations have been sampled
+    """
+
+    def __init__(self, batch_sampler, num_iterations, start_iter=0):
+        self.batch_sampler = batch_sampler
+        self.num_iterations = num_iterations
+        self.start_iter = start_iter
+
+    def __iter__(self):
+        iteration = self.start_iter
+        while iteration <= self.num_iterations:
+            # if the underlying sampler has a set_epoch method, like
+            # DistributedSampler, used for making each process see
+            # a different split of the dataset, then set it
+            if hasattr(self.batch_sampler.sampler, "set_epoch"):
+                self.batch_sampler.sampler.set_epoch(iteration)
+            for batch in self.batch_sampler:
+                iteration += 1
+                if iteration > self.num_iterations:
+                    break
+                yield batch
+
+    def __len__(self):
+        return self.num_iterations