wehub-resource-sync
107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import pickle
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import os
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import argparse
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import numpy as np
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from torch.utils.data import Dataset, DataLoader
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from mmpt.processors import PKLJSONStrTextProcessor
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from mmpt.utils import ShardedTensor, recursive_config
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class TokenizerDataset(Dataset):
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def __init__(self, config):
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self.text_processor = PKLJSONStrTextProcessor(config)
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self.video_ids = list(self.text_processor.data.keys())
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def __getitem__(self, idx):
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video_id = self.video_ids[idx]
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return video_id, self.text_processor(video_id)
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def __len__(self):
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return len(self.video_ids)
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def numpify(shard_idx, video_ids, captions, target_dir, split, prefix, max_cap_len=32):
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startends = []
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caps_ids = []
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for video_id in video_ids:
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caption = captions[video_id]
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startend = []
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cap_ids = []
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for start, end, cap in zip(
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caption["start"], caption["end"], caption["cap"]):
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startend.append(np.array([start, end]).astype("float32"))
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cap_id = np.full((max_cap_len,), -1, dtype=np.int32)
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cap = cap[:max_cap_len]
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cap_id[:len(cap)] = cap
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cap_ids.append(cap_id)
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startends.append(np.stack(startend))
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caps_ids.append(np.stack(cap_ids))
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startends = ShardedTensor.from_list(startends)
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target_path = os.path.join(
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target_dir,
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prefix + split + "_" + str(shard_idx)
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)
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print("save to", target_path)
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startends.save(target_path + ".startends")
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caps_ids = ShardedTensor.from_list(caps_ids)
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caps_ids.save(target_path + ".caps_ids")
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def sharding(config, out_file):
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with open(out_file, "rb") as fr:
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captions = pickle.load(fr)
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target_dir = config.target_dir
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prefix = os.path.basename(
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os.path.splitext(config.caption_pkl_path)[0]
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) + "." + config.bert_name + "."
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for split in ["train", "val"]:
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target_path = os.path.join(target_dir, split + "_meta")
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with open(target_path + ".pkl", "rb") as fr:
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meta = pickle.load(fr)
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print("load meta", target_path, len(meta))
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for shard_id in meta:
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numpify(
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shard_id, meta[shard_id], captions,
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target_dir, split, prefix
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)
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def tokenize(config, out_file):
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def collator(samples):
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return samples
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dataset = TokenizerDataset(config)
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data = {}
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for idx, batch in enumerate(
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DataLoader(dataset, collate_fn=collator, num_workers=16)):
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for video_id, caption in batch:
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data[video_id] = caption
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if idx % 5000 == 0:
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print(idx)
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with open(out_file, "wb") as fw:
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pickle.dump(data, fw, pickle.HIGHEST_PROTOCOL)
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def main(args):
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config = recursive_config(args.config).dataset
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out_file = os.path.splitext(config.caption_pkl_path)[0] \
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+ "." + config.bert_name + ".pkl"
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if not os.path.isfile(out_file):
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tokenize(config, out_file)
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sharding(config, out_file)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="pretokenize (raw_)caption.json into pkl.")
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parser.add_argument('config', type=str)
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args = parser.parse_args()
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main(args)
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