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[examples] fix summarization do_predict (#3866)

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Sam Shleifer 2020-04-20 10:49:56 -04:00 committed by GitHub
parent 52c85f847a
commit a504cb49ec
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3 changed files with 20 additions and 6 deletions
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6
examples/summarization/bart/finetune.py
View File

@ -166,8 +166,12 @@ def main(args):
# Optionally, predict on dev set and write to output_dir # Optionally, predict on dev set and write to output_dir
if args.do_predict: if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L169
checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True))) checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpointepoch=*.ckpt"), recursive=True)))
SummarizationTrainer.load_from_checkpoint(checkpoints[-1]) model = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model) trainer.test(model)

9
examples/summarization/bart/test_bart_examples.py
View File

@ -94,7 +94,15 @@ class TestBartExamples(unittest.TestCase):
) )
main(argparse.Namespace(**args_d)) main(argparse.Namespace(**args_d))
args_d.update({"do_train": False, "do_predict": True}) args_d.update({"do_train": False, "do_predict": True})
main(argparse.Namespace(**args_d)) main(argparse.Namespace(**args_d))
contents = os.listdir(output_dir)
expected_contents = {
"checkpointepoch=0.ckpt",
"test_results.txt",
}
created_files = {os.path.basename(p) for p in contents}
self.assertSetEqual(expected_contents, created_files)
def test_t5_run_sum_cli(self): def test_t5_run_sum_cli(self):
args_d: dict = DEFAULT_ARGS.copy() args_d: dict = DEFAULT_ARGS.copy()
@ -111,6 +119,7 @@ class TestBartExamples(unittest.TestCase):
do_predict=True, do_predict=True,
) )
main(argparse.Namespace(**args_d)) main(argparse.Namespace(**args_d))
# args_d.update({"do_train": False, "do_predict": True}) # args_d.update({"do_train": False, "do_predict": True})
# main(argparse.Namespace(**args_d)) # main(argparse.Namespace(**args_d))

11
examples/transformer_base.py
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@ -1,3 +1,4 @@
import argparse
import logging import logging
import os import os
import random import random
@ -38,7 +39,7 @@ MODEL_MODES = {
} }
def set_seed(args): def set_seed(args: argparse.Namespace):
random.seed(args.seed) random.seed(args.seed)
np.random.seed(args.seed) np.random.seed(args.seed)
torch.manual_seed(args.seed) torch.manual_seed(args.seed)
@ -47,7 +48,7 @@ def set_seed(args):
class BaseTransformer(pl.LightningModule): class BaseTransformer(pl.LightningModule):
def __init__(self, hparams, num_labels=None, mode="base", **config_kwargs): def __init__(self, hparams: argparse.Namespace, num_labels=None, mode="base", **config_kwargs):
"Initialize a model." "Initialize a model."
super(BaseTransformer, self).__init__() super(BaseTransformer, self).__init__()
@ -192,7 +193,7 @@ class BaseTransformer(pl.LightningModule):
class LoggingCallback(pl.Callback): class LoggingCallback(pl.Callback):
def on_validation_end(self, trainer, pl_module): def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
logger.info("***** Validation results *****") logger.info("***** Validation results *****")
if pl_module.is_logger(): if pl_module.is_logger():
metrics = trainer.callback_metrics metrics = trainer.callback_metrics
@ -201,7 +202,7 @@ class LoggingCallback(pl.Callback):
if key not in ["log", "progress_bar"]: if key not in ["log", "progress_bar"]:
logger.info("{} = {}\n".format(key, str(metrics[key]))) logger.info("{} = {}\n".format(key, str(metrics[key])))
def on_test_end(self, trainer, pl_module): def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
logger.info("***** Test results *****") logger.info("***** Test results *****")
if pl_module.is_logger(): if pl_module.is_logger():
@ -256,7 +257,7 @@ def add_generic_args(parser, root_dir):
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
def generic_train(model, args): def generic_train(model: BaseTransformer, args: argparse.Namespace):
# init model # init model
set_seed(args) set_seed(args)