File size: 3,127 Bytes
0a95064 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import random
import numpy as np
from torch.utils.data.dataset import Dataset
from config import cfg
class MultipleDatasets(Dataset):
def __init__(self, dbs, make_same_len=True, total_len=None, verbose=False):
self.dbs = dbs
self.db_num = len(self.dbs)
self.max_db_data_num = max([len(db) for db in dbs])
self.db_len_cumsum = np.cumsum([len(db) for db in dbs])
self.make_same_len = make_same_len
if total_len == 'auto':
self.total_len = self.db_len_cumsum[-1]
self.auto_total_len = True
else:
self.total_len = total_len
self.auto_total_len = False
if total_len is not None:
self.per_db_len = self.total_len // self.db_num
if verbose:
print('datasets:', [len(self.dbs[i]) for i in range(self.db_num)])
print(f'Auto total length: {self.auto_total_len}, {self.total_len}')
def __len__(self):
# all dbs have the same length
if self.make_same_len:
if self.total_len is None:
# match the longest length
return self.max_db_data_num * self.db_num
else:
# each dataset has the same length and total len is fixed
return self.total_len
else:
# each db has different length, simply concat
return sum([len(db) for db in self.dbs])
def __getitem__(self, index):
if self.make_same_len:
if self.total_len is None:
# match the longest length
db_idx = index // self.max_db_data_num
data_idx = index % self.max_db_data_num
if data_idx >= len(self.dbs[db_idx]) * (self.max_db_data_num // len(self.dbs[db_idx])): # last batch: random sampling
data_idx = random.randint(0,len(self.dbs[db_idx])-1)
else: # before last batch: use modular
data_idx = data_idx % len(self.dbs[db_idx])
else:
db_idx = index // self.per_db_len
data_idx = index % self.per_db_len
if db_idx > (self.db_num - 1):
# last batch: randomly choose one dataset
db_idx = random.randint(0,self.db_num - 1)
if len(self.dbs[db_idx]) < self.per_db_len and \
data_idx >= len(self.dbs[db_idx]) * (self.per_db_len // len(self.dbs[db_idx])):
# last batch: random sampling in this dataset
data_idx = random.randint(0,len(self.dbs[db_idx]) - 1)
else:
# before last batch: use modular
data_idx = data_idx % len(self.dbs[db_idx])
else:
for i in range(self.db_num):
if index < self.db_len_cumsum[i]:
db_idx = i
break
if db_idx == 0:
data_idx = index
else:
data_idx = index - self.db_len_cumsum[db_idx-1]
return self.dbs[db_idx][data_idx]
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