sampler.py 文件源码

def __iter__(self):
        indices = torch.randperm(self.num_samples)
        ret = []
        for i in indices:
            pid = self.pids[i]
            t = self.index_dic[pid]
            if len(t) >= self.num_instances:
                t = np.random.choice(t, size=self.num_instances, replace=False)
            else:
                t = np.random.choice(t, size=self.num_instances, replace=True)
            ret.extend(t)
        return iter(ret)