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| from .how2processor import ( |
| ShardedHow2MetaProcessor, |
| ShardedVideoProcessor, |
| ShardedTextProcessor, |
| VariedLenAligner, |
| OverlappedAligner |
| ) |
|
|
|
|
| class ShardedHow2VideoRetriMetaProcessor(ShardedHow2MetaProcessor): |
| def __init__(self, config): |
| super().__init__(config) |
| self.num_video_per_batch = config.num_video_per_batch |
| self.cands = [ |
| self.data[batch_offset:batch_offset + self.num_video_per_batch] |
| for batch_offset in |
| range(0, (len(self.data) // (8 * self.num_video_per_batch)) * 8 * self.num_video_per_batch, self.num_video_per_batch)] |
|
|
| def __len__(self): |
| return len(self.cands) |
|
|
| def set_candidates(self, cands): |
| |
| print(len(self.cands), "->", len(cands)) |
| |
| self.cands = cands |
|
|
| def __getitem__(self, idx): |
| video_ids = self.cands[idx] |
| assert isinstance(video_ids, list) |
| sharded_video_idxs = [] |
| for video_id in video_ids: |
| shard_id, video_idx = self.video_id_to_shard[video_id] |
| sharded_video_idxs.append((video_id, -1, shard_id, video_idx)) |
| return sharded_video_idxs, sharded_video_idxs |
|
|
|
|
| class ShardedVideoRetriVideoProcessor(ShardedVideoProcessor): |
| """In retrival case the video_id |
| is a list of tuples: `(shard_id, video_idx)` .""" |
|
|
| def __call__(self, sharded_video_idxs): |
| assert isinstance(sharded_video_idxs, list) |
| cand_feats = [] |
| for shared_video_idx in sharded_video_idxs: |
| feat = super().__call__(shared_video_idx) |
| cand_feats.append(feat) |
| return cand_feats |
|
|
|
|
| class ShardedVideoRetriTextProcessor(ShardedTextProcessor): |
| """In retrival case the video_id |
| is a list of tuples: `(shard_id, video_idx)` .""" |
|
|
| def __call__(self, sharded_video_idxs): |
| assert isinstance(sharded_video_idxs, list) |
| cand_caps = [] |
| for shared_video_idx in sharded_video_idxs: |
| caps = super().__call__(shared_video_idx) |
| cand_caps.append(caps) |
| return cand_caps |
|
|
|
|
| class VideoRetriAligner(VariedLenAligner): |
| |
| def __call__(self, sharded_video_idxs, video_features, text_features): |
| from transformers import default_data_collator |
| batch, video_ids = [], [] |
| for video_id, video_feature, text_feature in \ |
| zip(sharded_video_idxs, video_features, text_features): |
| sub_batch = super().__call__(video_id, video_feature, text_feature) |
| batch.append(sub_batch) |
| if isinstance(video_id, tuple): |
| video_id = video_id[0] |
| video_ids.append(video_id) |
| batch = default_data_collator(batch) |
| batch["video_id"] = video_ids |
| return batch |
|
|
|
|
| class VideoRetriOverlappedAligner(OverlappedAligner): |
| |
| def __call__(self, sharded_video_idxs, video_features, text_features): |
| from transformers import default_data_collator |
| batch, video_ids = [], [] |
| for video_id, video_feature, text_feature in \ |
| zip(sharded_video_idxs, video_features, text_features): |
| sub_batch = super().__call__(video_id, video_feature, text_feature) |
| batch.append(sub_batch) |
| if isinstance(video_id, tuple): |
| video_id = video_id[0] |
| video_ids.append(video_id) |
| batch = default_data_collator(batch) |
| batch["video_id"] = video_ids |
| return batch |
|
|
