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import numpy as np | |
from math import isclose | |
from .base import ISDataset | |
class ComposeDataset(ISDataset): | |
def __init__(self, datasets, **kwargs): | |
super(ComposeDataset, self).__init__(**kwargs) | |
self._datasets = datasets | |
self.dataset_samples = [] | |
for dataset_indx, dataset in enumerate(self._datasets): | |
self.dataset_samples.extend([(dataset_indx, i) for i in range(len(dataset))]) | |
def get_sample(self, index): | |
dataset_indx, sample_indx = self.dataset_samples[index] | |
return self._datasets[dataset_indx].get_sample(sample_indx) | |
class ProportionalComposeDataset(ISDataset): | |
def __init__(self, datasets, ratios, **kwargs): | |
super().__init__(**kwargs) | |
assert len(ratios) == len(datasets),\ | |
"The number of datasets must match the number of ratios" | |
assert isclose(sum(ratios), 1.0),\ | |
"The sum of ratios must be equal to 1" | |
self._ratios = ratios | |
self._datasets = datasets | |
self.dataset_samples = [] | |
for dataset_indx, dataset in enumerate(self._datasets): | |
self.dataset_samples.extend([(dataset_indx, i) for i in range(len(dataset))]) | |
def get_sample(self, index): | |
dataset_indx = np.random.choice(len(self._datasets), p=self._ratios) | |
sample_indx = np.random.choice(len(self._datasets[dataset_indx])) | |
return self._datasets[dataset_indx].get_sample(sample_indx) | |