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sampler.py
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import numpy as np
import random
import torch
class BalancedDatasetSampler(torch.utils.data.sampler.Sampler):
def __init__(self, dataset, indices=None, num_samples=None):
# if indices is not provided,
# all elements in the dataset will be considered
self.indices = list(range(len(dataset))) \
if indices is None else indices
# if num_samples is not provided,
# draw `len(indices)` samples in each iteration
self.num_samples = len(self.indices) \
if num_samples is None else num_samples
# distribution of classes in the dataset
label_to_count = [0] * len(np.unique(dataset.targets))
for idx in self.indices:
label = self._get_label(dataset, idx)
label_to_count[label] += 1
per_cls_weights = 1 / np.array(label_to_count)
# weight for each sample
weights = [per_cls_weights[self._get_label(dataset, idx)]
for idx in self.indices]
self.per_cls_weights = per_cls_weights
self.weights = torch.DoubleTensor(weights)
def _get_label(self, dataset, idx):
return dataset.targets[idx]
def __iter__(self):
return iter(torch.multinomial(self.weights, self.num_samples, replacement=True).tolist())
def __len__(self):
return self.num_samples
class EffectNumSampler(torch.utils.data.sampler.Sampler):
def __init__(self, dataset, indices=None, num_samples=None):
# if indices is not provided,
# all elements in the dataset will be considered
self.indices = list(range(len(dataset))) \
if indices is None else indices
# if num_samples is not provided,
# draw `len(indices)` samples in each iteration
self.num_samples = len(self.indices) \
if num_samples is None else num_samples
# distribution of classes in the dataset
label_to_count = [0] * len(np.unique(dataset.targets))
for idx in self.indices:
label = self._get_label(dataset, idx)
label_to_count[label] += 1
beta = 0.9999
effective_num = 1.0 - np.power(beta, label_to_count)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
# weight for each sample
weights = [per_cls_weights[self._get_label(dataset, idx)]
for idx in self.indices]
self.per_cls_weights = per_cls_weights
self.weights = torch.DoubleTensor(weights)
def _get_label(self, dataset, idx):
return dataset.targets[idx]
def __iter__(self):
return iter(torch.multinomial(self.weights, self.num_samples, replacement=True).tolist())
def __len__(self):
return self.num_samples
class CBEffectNumSampler(torch.utils.data.sampler.Sampler):
def __init__(self, dataset, indices=None, num_samples=None):
# if indices is not provided,
# all elements in the dataset will be considered
self.indices = list(range(len(dataset))) \
if indices is None else indices
# if num_samples is not provided,
# draw `len(indices)` samples in each iteration
self.num_samples = len(self.indices) \
if num_samples is None else num_samples
# distribution of classes in the dataset
label_to_count = [0] * len(np.unique(dataset.targets))
for idx in self.indices:
label = self._get_label(dataset, idx)
label_to_count[label] += 1
beta_a = 0.9999
beta_b = 1e-5
delta = np.log(np.array(label_to_count).astype(np.float32))
delta = delta.max()-delta
delta = (delta - delta.min())/(delta.max() - delta.min())
beta = beta_a + beta_b*delta
effective_num = 1.0 - np.power(beta, label_to_count)
per_cls_weights = (1.0 - beta) / np.array(effective_num)
# weight for each sample
weights = [per_cls_weights[self._get_label(dataset, idx)]
for idx in self.indices]
self.per_cls_weights = per_cls_weights
self.weights = torch.DoubleTensor(weights)
def _get_label(self, dataset, idx):
return dataset.targets[idx]
def __iter__(self):
return iter(torch.multinomial(self.weights, self.num_samples, replacement=True).tolist())
def __len__(self):
return self.num_samples
class RandomCycleIter:
def __init__ (self, data, test_mode=False):
self.data_list = list(data)
self.length = len(self.data_list)
self.i = self.length - 1
self.test_mode = test_mode
def __iter__ (self):
return self
def __next__ (self):
self.i += 1
if self.i == self.length:
self.i = 0
if not self.test_mode:
random.shuffle(self.data_list)
return self.data_list[self.i]
def class_aware_sample_generator(cls_iter, data_iter_list, n, num_samples_cls=1):
i = 0
j = 0
while i < n:
# yield next(data_iter_list[next(cls_iter)])
if j >= num_samples_cls:
j = 0
if j == 0:
temp_tuple = next(zip(*[data_iter_list[next(cls_iter)]]*num_samples_cls))
yield temp_tuple[j]
else:
yield temp_tuple[j]
i += 1
j += 1
class ClassAwareSampler(torch.utils.data.sampler.Sampler):
def __init__(self, data_source, num_samples_cls=4,):
# pdb.set_trace()
num_classes = len(np.unique(data_source.targets))
self.class_iter = RandomCycleIter(range(num_classes))
cls_data_list = [list() for _ in range(num_classes)]
for i, label in enumerate(data_source.targets):
cls_data_list[label].append(i)
self.data_iter_list = [RandomCycleIter(x) for x in cls_data_list]
self.num_samples = max([len(x) for x in cls_data_list]) * len(cls_data_list)
self.num_samples_cls = num_samples_cls
def __iter__ (self):
return class_aware_sample_generator(self.class_iter, self.data_iter_list,
self.num_samples, self.num_samples_cls)
def __len__ (self):
return self.num_samples
def get_sampler():
return ClassAwareSampler