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Allow one to use the pretrained model in evaluation when do_train is not selected

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This commit is contained in:
Jade Abbott 2019-01-03 09:02:33 +02:00
parent 8da280ebbe
commit be3b9bcf4d
1 changed files with 4 additions and 3 deletions
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7
examples/run_classifier.py
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@ -430,8 +430,8 @@ def main():
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
@ -554,7 +554,8 @@ def main():
# Save a trained model
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
# Load a trained model that you have fine-tuned
model_state_dict = torch.load(output_model_file)