wehub-resource-sync
145 lines
4.1 KiB
Python
145 lines
4.1 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 logging
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import os
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import contextlib
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import numpy as np
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import torch
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from fairseq.data import FairseqDataset, data_utils
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logger = logging.getLogger(__name__)
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class ExtractedFeaturesDataset(FairseqDataset):
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def __init__(
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self,
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path,
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split,
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min_length=3,
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max_length=None,
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labels=None,
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label_dict=None,
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shuffle=True,
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sort_by_length=True,
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):
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super().__init__()
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self.min_length = min_length
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self.max_length = max_length
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self.shuffle = shuffle
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self.sort_by_length = sort_by_length
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self.label_dict = label_dict
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if labels is not None:
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assert label_dict is not None
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self.sizes = []
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self.offsets = []
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self.labels = []
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path = os.path.join(path, split)
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data_path = path
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self.data = np.load(data_path + ".npy", mmap_mode="r")
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offset = 0
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skipped = 0
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if not os.path.exists(path + f".{labels}"):
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labels = None
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with open(data_path + ".lengths", "r") as len_f, open(
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path + f".{labels}", "r"
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) if labels is not None else contextlib.ExitStack() as lbl_f:
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for line in len_f:
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length = int(line.rstrip())
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lbl = None if labels is None else next(lbl_f).rstrip().split()
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if length >= min_length and (
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max_length is None or length <= max_length
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):
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self.sizes.append(length)
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self.offsets.append(offset)
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if lbl is not None:
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self.labels.append(lbl)
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offset += length
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self.sizes = np.asarray(self.sizes)
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self.offsets = np.asarray(self.offsets)
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logger.info(f"loaded {len(self.offsets)}, skipped {skipped} samples")
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def __getitem__(self, index):
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offset = self.offsets[index]
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end = self.sizes[index] + offset
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feats = torch.from_numpy(self.data[offset:end].copy()).float()
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res = {"id": index, "features": feats}
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if len(self.labels) > 0:
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res["target"] = self.label_dict.encode_line(
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self.labels[index],
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line_tokenizer=lambda x: x,
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append_eos=False,
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)
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return res
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def __len__(self):
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return len(self.sizes)
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def collater(self, samples):
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if len(samples) == 0:
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return {}
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features = [s["features"] for s in samples]
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sizes = [len(s) for s in features]
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target_size = max(sizes)
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collated_features = features[0].new_zeros(
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len(features), target_size, features[0].size(-1)
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)
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padding_mask = torch.BoolTensor(collated_features.shape[:-1]).fill_(False)
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for i, (f, size) in enumerate(zip(features, sizes)):
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collated_features[i, :size] = f
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padding_mask[i, size:] = True
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res = {
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"id": torch.LongTensor([s["id"] for s in samples]),
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"net_input": {"features": collated_features, "padding_mask": padding_mask},
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}
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if len(self.labels) > 0:
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target = data_utils.collate_tokens(
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[s["target"] for s in samples],
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pad_idx=self.label_dict.pad(),
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left_pad=False,
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)
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res["target"] = target
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return res
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def num_tokens(self, index):
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return self.size(index)
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def size(self, index):
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return self.sizes[index]
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def ordered_indices(self):
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"""Return an ordered list of indices. Batches will be constructed based
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on this order."""
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if self.shuffle:
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order = [np.random.permutation(len(self))]
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else:
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order = [np.arange(len(self))]
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if self.sort_by_length:
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order.append(self.sizes)
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return np.lexsort(order)[::-1]
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else:
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return order[0]
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