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#!/usr/bin/env python
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# NOTE: This requires PyTorch! We do not provide installation scripts to install PyTorch.
# It is up to you to install this dependency if you want to execute this example.
# PyTorch's website should give you clear instructions on this: http://pytorch.org/
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from torch.nn.modules.sparse import EmbeddingBag
import numpy as np
import torch
import random
import string
import time
from fasttext import load_model
from torch.autograd import Variable
class FastTextEmbeddingBag(EmbeddingBag):
def __init__(self, model_path):
self.model = load_model(model_path)
input_matrix = self.model.get_input_matrix()
input_matrix_shape = input_matrix.shape
super().__init__(input_matrix_shape[0], input_matrix_shape[1])
self.weight.data.copy_(torch.FloatTensor(input_matrix))
def forward(self, words):
word_subinds = np.empty([0], dtype=np.int64)
word_offsets = [0]
for word in words:
_, subinds = self.model.get_subwords(word)
word_subinds = np.concatenate((word_subinds, subinds))
word_offsets.append(word_offsets[-1] + len(subinds))
word_offsets = word_offsets[:-1]
ind = Variable(torch.LongTensor(word_subinds))
offsets = Variable(torch.LongTensor(word_offsets))
return super().forward(ind, offsets)
def random_word(N):
return ''.join(
random.choices(
string.ascii_uppercase + string.ascii_lowercase + string.digits,
k=N
)
)
if __name__ == "__main__":
ft_emb = FastTextEmbeddingBag("fil9.bin")
model = load_model("fil9.bin")
num_lines = 200
total_seconds = 0.0
total_words = 0
for _ in range(num_lines):
words = [
random_word(random.randint(1, 10))
for _ in range(random.randint(15, 25))
]
total_words += len(words)
words_average_length = sum([len(word) for word in words]) / len(words)
start = time.clock()
words_emb = ft_emb(words)
total_seconds += (time.clock() - start)
for i in range(len(words)):
word = words[i]
ft_word_emb = model.get_word_vector(word)
py_emb = np.array(words_emb[i].data)
assert (np.isclose(ft_word_emb, py_emb).all())
print(
"Avg. {:2.5f} seconds to build embeddings for {} lines with a total of {} words.".
format(total_seconds, num_lines, total_words)
)
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