mirror of
https://github.com/wassname/multifit.git
synced 2026-09-07 17:00:27 +08:00
Aayush
69ed7b4169
Other minor changes: - Function renaming: `test_pretrain_lm` -> `test_ulmfit_default_end_to_end` - Delete test models after completing each test.
104 lines
3.0 KiB
Python
104 lines
3.0 KiB
Python
import os
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import glob
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import fire
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import pytest
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import ulmfit.pretrain_lm
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import ulmfit.train_clas
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from fastai import *
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from fastai.text import *
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from fastai_contrib.utils import *
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"""
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It is a mixture of a pytest unit test and woven together to compose an end to end functional test.
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"""
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def delete_test_models():
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wt2 = data / 'wiki' / 'wikitext-2'
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imdb = data / 'imdb'
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# delete test models from the pretraining step
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for test_file in glob.iglob(f'{str(wt2)}/models/end-to-end-test*'):
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if os.path.isfile(test_file): os.remove(test_file)
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# delete test vocab and model of sentencepiece training
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for test_file in [wt2 / 'models' / 'spm.model',
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wt2 / 'models' / 'spm.vocab']:
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if os.path.isfile(test_file): os.remove(test_file)
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# delete test models from the finetuning/classifier training step
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for test_file in glob.iglob(f'{str(imdb)}/models/end-to-end-test*'):
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if os.path.isfile(test_file): os.remove(test_file)
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def check_data_exists():
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data = get_data_folder()
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wt2 = data / 'wiki' / 'wikitext-2'
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imdb = data / 'imdb'
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ensure_paths_exists(wt2 / 'en.wiki.train.tokens',
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imdb / 'train.csv',
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message="We don't run data preparation"
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" scripts automatically as it takes ages,"
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" run prepare_wiki-en.sh & prepare_imdb.sh")
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return imdb, wt2
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def test_ulmfit_default_end_to_end():
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""" Test ulmfit with (default) Moses tokenizer on small wikipedia dataset.
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"""
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imdb, wt2 = check_data_exists()
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lm_name = 'end-to-end-test-default'
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cuda_id = 0
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results = ulmfit.pretrain_lm.pretrain_lm(
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dir_path=wt2,
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lang='en',
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cuda_id=cuda_id,
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qrnn=True,
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subword=False,
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max_vocab=1000,
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bs=80,
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num_epochs=1,
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name=lm_name,
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ds_pct=0.03
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)
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assert results['accuracy'] > 0.30
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results = ulmfit.train_clas.new_train_clas(
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data_dir=get_data_folder(),
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lang='en', pretrain_name=lm_name, model_dir=wt2/'models',
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qrnn=True,
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cuda_id=cuda_id,
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fine_tune=True,
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max_vocab=1000,
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bs=20, bptt=70, name=lm_name+'-imdb-clas',
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dataset='imdb',
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ds_pct=0.03)
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delete_test_models()
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def test_ulmfit_sentencepiece_end_to_end():
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""" Test ulmfit with sentencepiece tokenizer on small wikipedia dataset.
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"""
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imdb, wt2 = check_data_exists()
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lm_name = 'end-to-end-test-spm'
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cuda_id = 0
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results = ulmfit.pretrain_lm.pretrain_lm(
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dir_path=wt2,
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lang='en',
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cuda_id=cuda_id,
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qrnn=True,
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subword=True,
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max_vocab=1000,
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bs=80,
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num_epochs=1,
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name=lm_name,
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)
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assert results['accuracy'] > 0.30
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# NOTE: ds_pct is not available for sentencepiece -- tests are on the complete dataset
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# sentencepiece for finetuning/classification is currently not implemented
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delete_test_models()
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