mirror of
https://github.com/wassname/multifit.git
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106 lines
3.0 KiB
Python
106 lines
3.0 KiB
Python
import os
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import glob
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import fire
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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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import fastai.core
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fastai.core.turn_off_parallel_execution=True
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def copy_head(src_fn, dst_fn, n=1000):
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with src_fn.open("r") as s, dst_fn.open("w") as d:
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for i in range(n):
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d.write(s.readline())
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def get_test_data():
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data = get_data_folder()
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wt = data / "wiki" / "wikitext-2"
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imdb = data / "imdb"
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test_data = data / "test"
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shutil.rmtree(test_data)
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test_wt = test_data / 'wikitext-s'
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test_imdb = test_data / 'imdb'
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test_wt.mkdir(exist_ok=True, parents=True)
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test_imdb.mkdir(exist_ok=True, parents=True)
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sz=1
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# we use the same text to see if models can overfit
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copy_head(wt / 'en.wiki.train.tokens', test_wt / 'en.wiki.train.tokens', n=10*sz)
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copy_head(wt / 'en.wiki.train.tokens', test_wt / 'en.wiki.valid.tokens', n=6*sz)
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copy_head(wt / 'en.wiki.train.tokens', test_wt / 'en.wiki.test.tokens', n=6*sz)
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copy_head(imdb / 'train.csv', test_imdb / 'train.csv', n=10*sz)
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copy_head(imdb / 'train.csv', test_imdb / 'test.csv', n=6*sz)
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return test_data, test_wt
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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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test_data, wt2 = get_test_data()
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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=2,
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num_epochs=1,
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name=lm_name)
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assert results['accuracy'] > 0.02
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results = ulmfit.train_clas.new_train_clas(
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data_dir=test_data,
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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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num_lm_epochs=0,
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bs=4, # minimum size is 4 otherwise it somewhere becomes 1 and fit stops working
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bptt=70,
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name=lm_name + '-imdb-clas',
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dataset='imdb')
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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 = get_test_data()
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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=100,
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bs=2,
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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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if __name__ == "__main__":
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fire.Fire() # allows using all functions via CLI e.g. python utils.py prepare_imdb aclImdb.tgz
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