9aa1aa24c0eaafb95f3a0a580de9df38e803e039,torchreid/samplers.py,RandomIdentitySampler,__iter__,#RandomIdentitySampler#,40

Before Change

            self.length += num - num % self.num_instances

    def __iter__(self):
        list_container = []

        for pid in self.pids:
            idxs = copy.deepcopy(self.index_dic[pid])
            if len(idxs) < self.num_instances:
                idxs = np.random.choice(idxs, size=self.num_instances, replace=True)
            random.shuffle(idxs)
            batch_idxs = []
            for idx in idxs:
                batch_idxs.append(idx)
                if len(batch_idxs) == self.num_instances:
                    list_container.append(batch_idxs)
                    batch_idxs = []

        random.shuffle(list_container)

        ret = []
        for batch_idxs in list_container:
            ret.extend(batch_idxs)

        return iter(ret)

    def __len__(self):
        return self.length

After Change

        final_idxs = []

        while len(avai_pids) >= self.num_pids_per_batch:
            selected_pids = random.sample(avai_pids, self.num_pids_per_batch)
            for pid in selected_pids:
                batch_idxs = batch_idxs_dict[pid].pop(0)
                final_idxs.extend(batch_idxs)
                if len(batch_idxs_dict[pid]) == 0:
                    avai_pids.remove(pid)

        return iter(final_idxs)

    def __len__(self):
        return self.length

Instances