105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
from __future__ import division, absolute_import
|
|
import copy
|
|
import numpy as np
|
|
import random
|
|
from collections import defaultdict
|
|
from torch.utils.data.sampler import Sampler, RandomSampler, SequentialSampler
|
|
|
|
AVAI_SAMPLERS = ['RandomIdentitySampler', 'SequentialSampler', 'RandomSampler']
|
|
|
|
|
|
class RandomIdentitySampler(Sampler):
|
|
"""Randomly samples N identities each with K instances.
|
|
|
|
Args:
|
|
data_source (list): contains tuples of (img_path(s), pid, camid).
|
|
batch_size (int): batch size.
|
|
num_instances (int): number of instances per identity in a batch.
|
|
"""
|
|
|
|
def __init__(self, data_source, batch_size, num_instances):
|
|
if batch_size < num_instances:
|
|
raise ValueError(
|
|
'batch_size={} must be no less '
|
|
'than num_instances={}'.format(batch_size, num_instances)
|
|
)
|
|
|
|
self.data_source = data_source
|
|
self.batch_size = batch_size
|
|
self.num_instances = num_instances
|
|
self.num_pids_per_batch = self.batch_size // self.num_instances
|
|
self.index_dic = defaultdict(list)
|
|
for index, (_, pid, _) in enumerate(self.data_source):
|
|
self.index_dic[pid].append(index)
|
|
self.pids = list(self.index_dic.keys())
|
|
|
|
# estimate number of examples in an epoch
|
|
# TODO: improve precision
|
|
self.length = 0
|
|
for pid in self.pids:
|
|
idxs = self.index_dic[pid]
|
|
num = len(idxs)
|
|
if num < self.num_instances:
|
|
num = self.num_instances
|
|
self.length += num - num % self.num_instances
|
|
|
|
def __iter__(self):
|
|
batch_idxs_dict = defaultdict(list)
|
|
|
|
for pid in self.pids:
|
|
idxs = copy.deepcopy(self.index_dic[pid])
|
|
if len(idxs) < self.num_instances:
|
|
idxs = np.random.choice(
|
|
idxs, size=self.num_instances, replace=True
|
|
)
|
|
random.shuffle(idxs)
|
|
batch_idxs = []
|
|
for idx in idxs:
|
|
batch_idxs.append(idx)
|
|
if len(batch_idxs) == self.num_instances:
|
|
batch_idxs_dict[pid].append(batch_idxs)
|
|
batch_idxs = []
|
|
|
|
avai_pids = copy.deepcopy(self.pids)
|
|
final_idxs = []
|
|
|
|
while len(avai_pids) >= self.num_pids_per_batch:
|
|
selected_pids = random.sample(avai_pids, self.num_pids_per_batch)
|
|
for pid in selected_pids:
|
|
batch_idxs = batch_idxs_dict[pid].pop(0)
|
|
final_idxs.extend(batch_idxs)
|
|
if len(batch_idxs_dict[pid]) == 0:
|
|
avai_pids.remove(pid)
|
|
|
|
return iter(final_idxs)
|
|
|
|
def __len__(self):
|
|
return self.length
|
|
|
|
|
|
def build_train_sampler(
|
|
data_source, train_sampler, batch_size=32, num_instances=4, **kwargs
|
|
):
|
|
"""Builds a training sampler.
|
|
|
|
Args:
|
|
data_source (list): contains tuples of (img_path(s), pid, camid).
|
|
train_sampler (str): sampler name (default: ``RandomSampler``).
|
|
batch_size (int, optional): batch size. Default is 32.
|
|
num_instances (int, optional): number of instances per identity in a
|
|
batch (when using ``RandomIdentitySampler``). Default is 4.
|
|
"""
|
|
assert train_sampler in AVAI_SAMPLERS, \
|
|
'train_sampler must be one of {}, but got {}'.format(AVAI_SAMPLERS, train_sampler)
|
|
|
|
if train_sampler == 'RandomIdentitySampler':
|
|
sampler = RandomIdentitySampler(data_source, batch_size, num_instances)
|
|
|
|
elif train_sampler == 'SequentialSampler':
|
|
sampler = SequentialSampler(data_source)
|
|
|
|
elif train_sampler == 'RandomSampler':
|
|
sampler = RandomSampler(data_source)
|
|
|
|
return sampler
|