259 lines
8.5 KiB
Python
259 lines
8.5 KiB
Python
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"""
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This file contains primitives for multi-gpu communication.
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This is useful when doing distributed training.
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"""
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import math
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import pickle
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import torch
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import torch.utils.data as data
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import torch.distributed as dist
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from torch.utils.data.sampler import Sampler, BatchSampler
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__all__ = ['get_world_size', 'get_rank', 'synchronize', 'is_main_process',
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'all_gather', 'make_data_sampler', 'make_batch_data_sampler',
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'reduce_dict', 'reduce_loss_dict']
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# reference: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/utils/comm.py
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def get_world_size():
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if not dist.is_available():
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return 1
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if not dist.is_initialized():
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return 1
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return dist.get_world_size()
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def get_rank():
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if not dist.is_available():
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return 0
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if not dist.is_initialized():
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return 0
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return dist.get_rank()
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def is_main_process():
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return get_rank() == 0
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def synchronize():
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"""
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Helper function to synchronize (barrier) among all processes when
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using distributed training
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"""
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if not dist.is_available():
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return
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if not dist.is_initialized():
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return
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world_size = dist.get_world_size()
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if world_size == 1:
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return
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dist.barrier()
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def all_gather(data):
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"""
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Run all_gather on arbitrary picklable data (not necessarily tensors)
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Args:
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data: any picklable object
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Returns:
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list[data]: list of data gathered from each rank
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"""
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world_size = get_world_size()
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if world_size == 1:
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return [data]
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# serialized to a Tensor
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buffer = pickle.dumps(data)
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storage = torch.ByteStorage.from_buffer(buffer)
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tensor = torch.ByteTensor(storage).to("cuda")
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# obtain Tensor size of each rank
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local_size = torch.IntTensor([tensor.numel()]).to("cuda")
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size_list = [torch.IntTensor([0]).to("cuda") for _ in range(world_size)]
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dist.all_gather(size_list, local_size)
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size_list = [int(size.item()) for size in size_list]
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max_size = max(size_list)
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# receiving Tensor from all ranks
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# we pad the tensor because torch all_gather does not support
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# gathering tensors of different shapes
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tensor_list = []
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for _ in size_list:
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tensor_list.append(torch.ByteTensor(size=(max_size,)).to("cuda"))
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if local_size != max_size:
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padding = torch.ByteTensor(size=(max_size - local_size,)).to("cuda")
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tensor = torch.cat((tensor, padding), dim=0)
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dist.all_gather(tensor_list, tensor)
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data_list = []
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for size, tensor in zip(size_list, tensor_list):
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buffer = tensor.cpu().numpy().tobytes()[:size]
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data_list.append(pickle.loads(buffer))
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return data_list
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def reduce_dict(input_dict, average=True):
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"""
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Args:
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input_dict (dict): all the values will be reduced
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average (bool): whether to do average or sum
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Reduce the values in the dictionary from all processes so that process with rank
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0 has the averaged results. Returns a dict with the same fields as
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input_dict, after reduction.
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"""
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world_size = get_world_size()
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if world_size < 2:
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return input_dict
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with torch.no_grad():
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names = []
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values = []
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# sort the keys so that they are consistent across processes
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for k in sorted(input_dict.keys()):
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names.append(k)
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values.append(input_dict[k])
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values = torch.stack(values, dim=0)
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dist.reduce(values, dst=0)
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if dist.get_rank() == 0 and average:
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# only main process gets accumulated, so only divide by
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# world_size in this case
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values /= world_size
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reduced_dict = {k: v for k, v in zip(names, values)}
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return reduced_dict
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def reduce_loss_dict(loss_dict):
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"""
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Reduce the loss dictionary from all processes so that process with rank
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0 has the averaged results. Returns a dict with the same fields as
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loss_dict, after reduction.
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"""
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world_size = get_world_size()
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if world_size < 2:
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return loss_dict
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with torch.no_grad():
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loss_names = []
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all_losses = []
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for k in sorted(loss_dict.keys()):
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loss_names.append(k)
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all_losses.append(loss_dict[k])
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all_losses = torch.stack(all_losses, dim=0)
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dist.reduce(all_losses, dst=0)
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if dist.get_rank() == 0:
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# only main process gets accumulated, so only divide by
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# world_size in this case
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all_losses /= world_size
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reduced_losses = {k: v for k, v in zip(loss_names, all_losses)}
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return reduced_losses
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def make_data_sampler(dataset, shuffle, distributed):
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if distributed:
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return DistributedSampler(dataset, shuffle=shuffle)
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if shuffle:
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sampler = data.sampler.RandomSampler(dataset)
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else:
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sampler = data.sampler.SequentialSampler(dataset)
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return sampler
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def make_batch_data_sampler(sampler, images_per_batch, num_iters=None, start_iter=0):
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batch_sampler = data.sampler.BatchSampler(sampler, images_per_batch, drop_last=True)
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if num_iters is not None:
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batch_sampler = IterationBasedBatchSampler(batch_sampler, num_iters, start_iter)
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return batch_sampler
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# Code is copy-pasted from https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/data/samplers/distributed.py
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class DistributedSampler(Sampler):
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"""Sampler that restricts data loading to a subset of the dataset.
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It is especially useful in conjunction with
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:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
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process can pass a DistributedSampler instance as a DataLoader sampler,
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and load a subset of the original dataset that is exclusive to it.
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.. note::
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Dataset is assumed to be of constant size.
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Arguments:
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dataset: Dataset used for sampling.
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num_replicas (optional): Number of processes participating in
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distributed training.
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rank (optional): Rank of the current process within num_replicas.
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"""
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def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
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if num_replicas is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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num_replicas = dist.get_world_size()
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if rank is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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rank = dist.get_rank()
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self.dataset = dataset
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self.num_replicas = num_replicas
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self.rank = rank
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self.epoch = 0
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self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
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self.total_size = self.num_samples * self.num_replicas
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self.shuffle = shuffle
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def __iter__(self):
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if self.shuffle:
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# deterministically shuffle based on epoch
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g = torch.Generator()
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g.manual_seed(self.epoch)
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indices = torch.randperm(len(self.dataset), generator=g).tolist()
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else:
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indices = torch.arange(len(self.dataset)).tolist()
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# add extra samples to make it evenly divisible
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indices += indices[: (self.total_size - len(indices))]
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assert len(indices) == self.total_size
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# subsample
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offset = self.num_samples * self.rank
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indices = indices[offset: offset + self.num_samples]
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assert len(indices) == self.num_samples
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return iter(indices)
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def __len__(self):
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return self.num_samples
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def set_epoch(self, epoch):
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self.epoch = epoch
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class IterationBasedBatchSampler(BatchSampler):
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"""
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Wraps a BatchSampler, resampling from it until
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a specified number of iterations have been sampled
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"""
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def __init__(self, batch_sampler, num_iterations, start_iter=0):
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self.batch_sampler = batch_sampler
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self.num_iterations = num_iterations
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self.start_iter = start_iter
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def __iter__(self):
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iteration = self.start_iter
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while iteration <= self.num_iterations:
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# if the underlying sampler has a set_epoch method, like
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# DistributedSampler, used for making each process see
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# a different split of the dataset, then set it
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if hasattr(self.batch_sampler.sampler, "set_epoch"):
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self.batch_sampler.sampler.set_epoch(iteration)
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for batch in self.batch_sampler:
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iteration += 1
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if iteration > self.num_iterations:
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break
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yield batch
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def __len__(self):
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return self.num_iterations
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if __name__ == '__main__':
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pass
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