datasets/ClassPrioritySampler.py [328:338]:
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        self.cls_cnts = []
        self.labels = labels = np.array(self.dataset.labels)
        for l in np.unique(labels):
            self.cls_cnts.append(np.sum(labels==l))
        self.num_classes = len(self.cls_cnts)
        self.cnts = np.array(self.cls_cnts).astype(float)
        
        # Get per-class image indexes
        self.cls_idxs = [[] for _ in range(self.num_classes)]
        for i, label in enumerate(self.dataset.labels):
            self.cls_idxs[label].append(i)
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datasets/MixedPrioritizedSampler.py [247:257]:
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        self.cls_cnts = []
        self.labels = labels = np.array(self.dataset.labels)
        for l in np.unique(labels):
            self.cls_cnts.append(np.sum(labels==l))
        self.num_classes = len(self.cls_cnts)
        self.cnts = np.array(self.cls_cnts).astype(float)
        
        # Get per-class image indexes
        self.cls_idxs = [[] for _ in range(self.num_classes)]
        for i, label in enumerate(self.dataset.labels):
            self.cls_idxs[label].append(i)
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