| |
| |
| |
| |
| import os |
| import pickle |
| import numpy as np |
|
|
|
|
| class ShardedTensor(object): |
| def __init__(self, data, starts): |
| self.data = data |
| self.starts = starts |
| assert self.starts[0] == 0 |
| assert self.starts[-1] == len(self.data) |
| assert (self.starts[1:] >= self.starts[:-1]).all() |
| assert (self.starts > -1).all() |
|
|
| @staticmethod |
| def from_list(xs): |
| starts = np.full((len(xs) + 1,), -1, dtype=np.long) |
| data = np.concatenate(xs, axis=0) |
| starts[0] = 0 |
| for i, x in enumerate(xs): |
| starts[i + 1] = starts[i] + x.shape[0] |
| assert (starts > -1).all() |
| return ShardedTensor(data, starts) |
|
|
| def __getitem__(self, i): |
| return self.data[self.starts[i] : self.starts[i + 1]] |
|
|
| def __len__(self): |
| return len(self.starts) - 1 |
|
|
| def lengths(self): |
| return self.starts[1:] - self.starts[:-1] |
|
|
| def save(self, path): |
| np.save(path + "_starts", self.starts) |
| np.save(path + "_data", self.data) |
|
|
| @staticmethod |
| def load(path, mmap_mode=None): |
| starts = np.load(path + "_starts.npy", mmap_mode) |
| data = np.load(path + "_data.npy", mmap_mode) |
| return ShardedTensor(data, starts) |
|
|
