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# coding=utf-8 | |
# Copyright 2020 Huggingface | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import io | |
import json | |
import unittest | |
from parameterized import parameterized | |
from transformers import FSMTForConditionalGeneration, FSMTTokenizer | |
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device | |
from utils import calculate_bleu | |
filename = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json" | |
with io.open(filename, "r", encoding="utf-8") as f: | |
bleu_data = json.load(f) | |
class ModelEvalTester(unittest.TestCase): | |
def get_tokenizer(self, mname): | |
return FSMTTokenizer.from_pretrained(mname) | |
def get_model(self, mname): | |
model = FSMTForConditionalGeneration.from_pretrained(mname).to(torch_device) | |
if torch_device == "cuda": | |
model.half() | |
return model | |
def test_bleu_scores(self, pair, min_bleu_score): | |
# note: this test is not testing the best performance since it only evals a small batch | |
# but it should be enough to detect a regression in the output quality | |
mname = f"facebook/wmt19-{pair}" | |
tokenizer = self.get_tokenizer(mname) | |
model = self.get_model(mname) | |
src_sentences = bleu_data[pair]["src"] | |
tgt_sentences = bleu_data[pair]["tgt"] | |
batch = tokenizer(src_sentences, return_tensors="pt", truncation=True, padding="longest").to(torch_device) | |
outputs = model.generate( | |
input_ids=batch.input_ids, | |
num_beams=8, | |
) | |
decoded_sentences = tokenizer.batch_decode( | |
outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False | |
) | |
scores = calculate_bleu(decoded_sentences, tgt_sentences) | |
print(scores) | |
self.assertGreaterEqual(scores["bleu"], min_bleu_score) | |