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import numpy as np
def load_deep1b(filename, start_idx=0, chunk_size=None):
""" Read *.fbin file that contains float32 vectors
Args:
:param filename (str): path to *.fbin file
:param start_idx (int): start reading vectors from this index
:param chunk_size (int): number of vectors to read.
If None, read all vectors
Returns:
Array of float32 vectors (numpy.ndarray)
"""
with open(filename, "rb") as f:
nvecs, dim = np.fromfile(f, count=2, dtype=np.int32)
nvecs = (nvecs - start_idx) if chunk_size is None else chunk_size
arr = np.fromfile(f, count=nvecs * dim, dtype=np.float32,
offset=start_idx * 4 * dim)
return arr.reshape(nvecs, dim)
def load_glove(filename):
from gensim.models import KeyedVectors
return KeyedVectors.load_word2vec_format(filename).vectors
def load_sift1m(fname):
data = np.fromfile(fname, dtype=np.float32)
dim = data[0].view(np.int32)
data = data.reshape(-1, dim + 1)
data = np.ascontiguousarray(data[:, 1:])
ndata, dim = data.shape
return data, ndata, dim |