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Improve ulmfit eval to allow for zeroshot laser evaluation

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This commit is contained in:
Piotr Czapla
2019-02-13 15:29:55 +01:00
parent b14a393671
commit 1340f4235c
1 changed files with 23 additions and 19 deletions
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ulmfit/__main__.py
+23 -19
View File
@@ -1,5 +1,7 @@
import gc
import pprint
import shutil
from collections import OrderedDict
from functools import wraps
import fire
@@ -12,15 +14,16 @@ class FireView:
for k,v in kwargs.items():
setattr(self, k, v)
def get_dataset_path(p):
return [x for x in p.parents if x.name == "models"][0].parent
def get_lang_from_dataset_path(ds):
lang,*_ = ds.name.split("-")
if len(lang) == 2:
return lang
return "en"
def get_dataset_path(p, dataset_template):
ds = [x for x in p.parents if x.name == "models"][0].parent
return ds.parent.glob(dataset_template.format(ds.name))
class ULMFiT:
@wraps(LMHyperParams)
def lm(self, dataset_path, **changes):
@@ -34,23 +37,24 @@ class ULMFiT:
params = CLSHyperParams.from_lm(dataset_path, base_lm_path, **changes)
return FireView(train=params.train_cls, validate_cls=params.validate_cls)
def eval(self, glob="mldoc/*-1/models/sp30k/lstm_nl4.m", name="tmp-100", cuda_id=0, **trn_params):
results={}
for base_model in Path("data").glob(glob):
dataset_path = get_dataset_path(base_model)
lang = get_lang_from_dataset_path(dataset_path)
params = CLSHyperParams.from_lm(dataset_path, base_model, lang=lang, name=name, cuda_id=cuda_id)
key = str(params.model_dir.relative_to(Path.cwd()))
if params.model_dir.exists():
print("Evaluating previously trained model")
results[key] = params.validate_cls()[1]
else:
print("Training")
results[key] = params.train_cls(num_lm_epochs=0, **trn_params)[1]
params = None
gc.collect()
def eval(self, glob="mldoc/*-1/models/sp30k/lstm_nl4.m", dataset_template='{}', name="tmp-100", cuda_id=0, **trn_params):
results = OrderedDict()
for base_model in sorted(Path("data").glob(glob)):
for dataset_path in sorted(get_dataset_path(base_model, dataset_template)):
lang = get_lang_from_dataset_path(dataset_path)
params = CLSHyperParams.from_lm(dataset_path, base_model, lang=lang, name=name, cuda_id=cuda_id)
key = str(params.model_dir.relative_to(Path.cwd()))
if params.model_dir.exists():
print("Evaluating previously trained model")
results[key] = params.validate_cls()[1]
else:
print("Training")
results[key] = params.train_cls(num_lm_epochs=0, **trn_params)[1]
del params
gc.collect()
print(list(sorted(results.items())))
pprint.pprint(results)
# python -m ulmfit cls --dataset-path data/mldoc/de-1-laser --base-lm-path data/mldoc/de-1/models/sp30k/lstm_nl4.m --lang=de --name 'nl4' --cuda-id=1 - train 0 --bs 40 --num-cls-epochs=2
if __name__ == '__main__':
fire.Fire(ULMFiT())