mirror of https://github.com/alibaba/EasyCV.git
470 lines
17 KiB
Python
470 lines
17 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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from __future__ import division
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import math
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import os
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import random
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import numpy as np
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import torch
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from mmcv.runner import get_dist_info
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from torch.utils.data import DistributedSampler as _DistributedSampler
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from torch.utils.data import Sampler
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class DistributedMPSampler(_DistributedSampler):
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def __init__(self,
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dataset,
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num_replicas=None,
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rank=None,
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shuffle=True,
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split_huge_listfile_byrank=False):
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""" A Distribute sampler which support sample m instance from one class once for classification dataset
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dataset: pytorch dataset object
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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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shuffle (optional): If true (default), sampler will shuffle the indices
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split_huge_listfile_byrank: if split, return all indice for each rank, because list for each rank has been
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split before build dataset in dist training
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"""
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super().__init__(dataset, num_replicas=num_replicas, rank=rank)
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current_env = os.environ.copy()
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self.local_rank = int(current_env['LOCAL_RANK'])
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self.shuffle = shuffle
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self.unif_sampling_flag = False
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self.split_huge_listfile_byrank = split_huge_listfile_byrank
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self.get_label_dict()
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def __iter__(self):
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# deterministically shuffle based on epoch
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indice_list = self.generate_indice()
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return iter(indice_list)
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def generate_indice(self):
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if self.shuffle:
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random.shuffle(self.label_list)
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for k in self.label_dict.keys():
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random.shuffle(self.label_dict[k])
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this_label_list, this_label_list_size = self.calculate_this_label_list(
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)
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if self.rank == 0:
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print('Each epoch has %d buckets of M imgs for per class' %
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(self.buckets_num))
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m_per_class = self.dataset.m_per_class
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indice_list = [] # [this_label_list_size x (m * buckets_num)]
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for label in this_label_list:
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idx_list = self.label_dict[label]
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if len(idx_list) < self.buckets_num * m_per_class:
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# this place need(could) add more random .
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idx_list = idx_list * int(self.buckets_num * m_per_class /
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len(idx_list) + 1)
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idx_list = idx_list[0:self.buckets_num * m_per_class]
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indice_list.append(idx_list)
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indice_list = np.array(indice_list).reshape(
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(this_label_list_size * self.buckets_num), m_per_class)
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if self.shuffle:
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np.random.shuffle(indice_list)
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indice_list = list(indice_list.astype(int).flatten())
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return indice_list
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def get_label_dict(self):
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self.label_dict = {}
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self.label_list = []
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if not self.dataset.data_source.has_labels:
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raise 'MPSampler need initial with classification datasets which has label!'
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for idx, label in enumerate(self.dataset.data_source.labels):
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if label in self.label_dict.keys():
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self.label_dict[label].append(idx)
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else:
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self.label_dict[label] = [idx]
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self.label_list.append(label)
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if self.rank == 0:
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print(
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self.rank, ' : Total %d Label in %s' %
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(len(self.label_list), type(self.dataset)))
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# calculate the After mpsampler, dataset length change and buckets_num
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self.calculate_this_label_list()
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if self.rank == 0:
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print('Before original dataset length is %d' %
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self.dataset.data_source.get_length())
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print('After MPRefine dataset length is %d' % (self.length))
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print('Total %d Label in %s' %
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(len(self.label_list), type(self.dataset)))
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return
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def calculate_this_label_list(self):
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label_size = len(self.label_list)
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if not self.split_huge_listfile_byrank:
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refine_label_size = int(1 + label_size /
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self.num_replicas) * self.num_replicas
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refine_label_list = self.label_list + self.label_list[0:(
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refine_label_size - label_size)]
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this_label_list_size = int(
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len(refine_label_list) / self.num_replicas)
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this_label_list = refine_label_list[self.rank *
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this_label_list_size:
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(self.rank + 1) *
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this_label_list_size]
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m_per_class = self.dataset.m_per_class
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self.buckets_num = int(
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int(self.dataset.data_source.get_length() / self.num_replicas)
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/ (m_per_class * this_label_list_size)) + 1
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self.length = self.buckets_num * m_per_class * int(
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1 +
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len(self.label_list) / self.num_replicas) # self.num_replicas
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else:
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this_label_list = self.label_list
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this_label_list_size = label_size
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m_per_class = self.dataset.m_per_class
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# this is a huge bug for split situation
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buckets_num = torch.Tensor([
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int(self.dataset.data_source.get_length() /
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(m_per_class * this_label_list_size))
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]).to(self.local_rank)
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torch.distributed.all_reduce(buckets_num,
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torch.distributed.ReduceOp.MIN)
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torch.distributed.barrier()
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self.buckets_num = int(max(buckets_num, 1))
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self.length = self.buckets_num * m_per_class * int(
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len(self.label_list))
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return this_label_list, this_label_list_size
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def __len__(self):
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return self.length
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class DistributedSampler(_DistributedSampler):
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def __init__(
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self,
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dataset,
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num_replicas=None,
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rank=None,
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shuffle=True,
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replace=False,
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split_huge_listfile_byrank=False,
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):
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""" A Distribute sampler which support sample m instance from one class once for classification dataset
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Args:
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dataset: pytorch dataset object
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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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shuffle (optional): If true (default), sampler will shuffle the indices
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split_huge_listfile_byrank: if split, return all indice for each rank, because list for each rank has been
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split before build dataset in dist training
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"""
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super().__init__(dataset, num_replicas=num_replicas, rank=rank)
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self.shuffle = shuffle
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self.replace = replace
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self.unif_sampling_flag = False
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self.split_huge_listfile_byrank = split_huge_listfile_byrank
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def __iter__(self):
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# deterministically shuffle based on epoch
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if not self.unif_sampling_flag:
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self.generate_new_list()
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else:
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self.unif_sampling_flag = False
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if not self.split_huge_listfile_byrank:
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return iter(
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self.indices[self.rank * self.num_samples:(self.rank + 1) *
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self.num_samples])
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else:
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return iter(self.indices)
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def generate_new_list(self):
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if self.shuffle:
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g = torch.Generator()
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g.manual_seed(self.epoch)
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if self.replace:
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indices = torch.randint(
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low=0,
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high=len(self.dataset),
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size=(len(self.dataset), ),
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generator=g).tolist()
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else:
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indices = torch.randperm(
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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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self.indices = indices
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def set_uniform_indices(self, labels, num_classes):
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self.unif_sampling_flag = True
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assert self.shuffle, 'Using uniform sampling, the indices must be shuffled.'
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np.random.seed(self.epoch)
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assert (len(labels) == len(self.dataset))
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N = len(labels)
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size_per_label = int(N / num_classes) + 1
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indices = []
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images_lists = [[] for i in range(num_classes)]
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for i, l in enumerate(labels):
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images_lists[l].append(i)
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for i, l in enumerate(images_lists):
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if len(l) == 0:
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continue
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indices.extend(
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np.random.choice(
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l, size_per_label, replace=(len(l) <= size_per_label)))
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indices = np.array(indices)
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np.random.shuffle(indices)
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indices = indices[:N].astype(np.int).tolist()
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# add extra samples to make it evenly divisible
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assert len(indices) <= self.total_size, \
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'{} vs {}'.format(len(indices), self.total_size)
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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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'{} vs {}'.format(len(indices), self.total_size)
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self.indices = indices
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def __len__(self):
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return self.num_samples if not self.split_huge_listfile_byrank else self.num_samples * self.num_replicas
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class GroupSampler(Sampler):
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def __init__(self, dataset, samples_per_gpu=1):
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assert hasattr(dataset, 'flag')
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self.dataset = dataset
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self.samples_per_gpu = samples_per_gpu
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self.flag = dataset.flag.astype(np.int64)
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self.group_sizes = np.bincount(self.flag)
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self.num_samples = 0
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for i, size in enumerate(self.group_sizes):
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self.num_samples += int(np.ceil(
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size / self.samples_per_gpu)) * self.samples_per_gpu
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def __iter__(self):
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indices = []
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for i, size in enumerate(self.group_sizes):
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if size == 0:
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continue
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indice = np.where(self.flag == i)[0]
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assert len(indice) == size
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np.random.shuffle(indice)
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num_extra = int(np.ceil(size / self.samples_per_gpu)
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) * self.samples_per_gpu - len(indice)
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indice = np.concatenate(
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[indice, np.random.choice(indice, num_extra)])
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indices.append(indice)
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indices = np.concatenate(indices)
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indices = [
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indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu]
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for i in np.random.permutation(
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range(len(indices) // self.samples_per_gpu))
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]
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indices = np.concatenate(indices)
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indices = indices.astype(np.int64).tolist()
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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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class DistributedGroupSampler(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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Args:
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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,
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dataset,
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samples_per_gpu=1,
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num_replicas=None,
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rank=None):
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_rank, _num_replicas = get_dist_info()
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if num_replicas is None:
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num_replicas = _num_replicas
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if rank is None:
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rank = _rank
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self.dataset = dataset
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self.samples_per_gpu = samples_per_gpu
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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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assert hasattr(self.dataset, 'flag')
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self.flag = self.dataset.flag
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self.group_sizes = np.bincount(self.flag)
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self.num_samples = 0
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for i, j in enumerate(self.group_sizes):
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self.num_samples += int(
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math.ceil(self.group_sizes[i] * 1.0 / self.samples_per_gpu /
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self.num_replicas)) * self.samples_per_gpu
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self.total_size = self.num_samples * self.num_replicas
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def __iter__(self):
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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 = []
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for i, size in enumerate(self.group_sizes):
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if size > 0:
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indice = np.where(self.flag == i)[0]
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assert len(indice) == size
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indice = indice[list(torch.randperm(int(size),
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generator=g))].tolist()
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extra = int(
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math.ceil(
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size * 1.0 / self.samples_per_gpu / self.num_replicas)
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) * self.samples_per_gpu * self.num_replicas - len(indice)
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# pad indice
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tmp = indice.copy()
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for _ in range(extra // size):
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indice.extend(tmp)
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indice.extend(tmp[:extra % size])
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indices.extend(indice)
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assert len(indices) == self.total_size
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indices = [
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indices[j] for i in list(
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torch.randperm(
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len(indices) // self.samples_per_gpu, generator=g))
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for j in range(i * self.samples_per_gpu, (i + 1) *
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self.samples_per_gpu)
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]
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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 DistributedGivenIterationSampler(Sampler):
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def __init__(self,
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dataset,
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total_iter,
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batch_size,
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num_replicas=None,
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rank=None,
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last_iter=-1):
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rank, world_size = get_dist_info()
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assert rank < world_size
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self.dataset = dataset
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self.total_iter = total_iter
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self.batch_size = batch_size
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self.world_size = world_size
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self.rank = rank
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self.last_iter = last_iter
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self.total_size = self.total_iter * self.batch_size
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self.indices = self.gen_new_list()
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def __iter__(self):
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return iter(self.indices[(self.last_iter + 1) * self.batch_size:])
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def set_uniform_indices(self, labels, num_classes):
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np.random.seed(0)
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assert (len(labels) == len(self.dataset))
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N = len(labels)
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size_per_label = int(N / num_classes) + 1
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indices = []
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images_lists = [[] for i in range(num_classes)]
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for i, l in enumerate(labels):
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images_lists[l].append(i)
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for i, l in enumerate(images_lists):
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if len(l) == 0:
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continue
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indices.extend(
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np.random.choice(
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l, size_per_label, replace=(len(l) <= size_per_label)))
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indices = np.array(indices)
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np.random.shuffle(indices)
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indices = indices[:N].astype(np.int)
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# repeat
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all_size = self.total_size * self.world_size
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indices = indices[:all_size]
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num_repeat = (all_size - 1) // indices.shape[0] + 1
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indices = np.tile(indices, num_repeat)
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indices = indices[:all_size]
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np.random.shuffle(indices)
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# slice
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beg = self.total_size * self.rank
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indices = indices[beg:beg + self.total_size]
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assert len(indices) == self.total_size
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# set
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self.indices = indices
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def gen_new_list(self):
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# each process shuffle all list with same seed, and pick one piece according to rank
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np.random.seed(0)
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all_size = self.total_size * self.world_size
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indices = np.arange(len(self.dataset))
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indices = indices[:all_size]
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num_repeat = (all_size - 1) // indices.shape[0] + 1
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indices = np.tile(indices, num_repeat)
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indices = indices[:all_size]
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np.random.shuffle(indices)
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beg = self.total_size * self.rank
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indices = indices[beg:beg + self.total_size]
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assert len(indices) == self.total_size
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return indices
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def __len__(self):
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# note here we do not take last iter into consideration, since __len__
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# should only be used for displaying, the correct remaining size is
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# handled by dataloader
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# return self.total_size - (self.last_iter+1)*self.batch_size
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return self.total_size
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def set_epoch(self, epoch):
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pass
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