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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
import numpy as np
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
class ConcatDataset(_ConcatDataset):
"""
Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra
method for querying the sizes of the image
"""
def get_idxs(self, idx):
dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
if dataset_idx == 0:
sample_idx = idx
else:
sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
return dataset_idx, sample_idx
def get_img_info(self, idx):
dataset_idx, sample_idx = self.get_idxs(idx)
return self.datasets[dataset_idx].get_img_info(sample_idx)
class MixDataset(object):
def __init__(self, datasets, ratios):
self.datasets = datasets
self.ratios = ratios
self.lengths = []
for dataset in self.datasets:
self.lengths.append(len(dataset))
self.lengths = np.array(self.lengths)
self.seperate_inds = []
s = 0
for i in self.ratios[:-1]:
s += i
self.seperate_inds.append(s)
def __len__(self):
return self.lengths.sum()
def __getitem__(self, item):
i = np.random.rand()
ind = bisect.bisect_right(self.seperate_inds, i)
b_ind = np.random.randint(self.lengths[ind])
return self.datasets[ind][b_ind]
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