datasets/ClassPrioritySampler.py [311:325]:
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        if self.lam is None:
            self.freeze = False
            if cycle == 0:
                self.lams = np.linspace(0, 1, epochs)
            elif cycle == 1:
                self.lams = np.concatenate([np.linspace(0,1,epochs//3)] * 3)
            elif cycle == 2:
                self.lams = np.concatenate([np.linspace(0,1,epochs//3),
                                            np.linspace(0,1,epochs//3)[::-1],
                                            np.linspace(0,1,epochs//3)])
            else:
                raise NotImplementedError(
                    'cycle = {} not implemented'.format(cycle))
        else:
            self.lams = [self.lam]
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datasets/MixedPrioritizedSampler.py [229:243]:
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        if self.lam is None:
            self.freeze = False
            if cycle == 0:
                self.lams = np.linspace(0, 1, epochs)
            elif cycle == 1:
                self.lams = np.concatenate([np.linspace(0,1,epochs//3)] * 3)
            elif cycle == 2:
                self.lams = np.concatenate([np.linspace(0,1,epochs//3),
                                            np.linspace(0,1,epochs//3)[::-1],
                                            np.linspace(0,1,epochs//3)])
            else:
                raise NotImplementedError(
                    'cycle = {} not implemented'.format(cycle))
        else:
            self.lams = [self.lam]
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