SUMIN/HuggingFace_transformer
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model in evaluation mode by default after from_pretrained

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thomwolf
2019-07-16 15:41:57 +02:00
parent f289e6cfe4
commit 4acaa65068
2 changed files with 7 additions and 10 deletions
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9
README.md
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@@ -102,10 +102,6 @@ Let's see how we can use `BertModel` to encode our inputs in hidden-states:
# Load pre-trained model (weights) # Load pre-trained model (weights)
model = BertModel.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased')
# Set the model in evaluation mode to desactivate the DropOut modules
# This is IMPORTANT to have reproductible results during evaluation!
model.eval()
# If you have a GPU, put everything on cuda # If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda') tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda') segments_tensors = segments_tensors.to('cuda')
@@ -129,7 +125,6 @@ And how to use `BertForMaskedLM` to predict a masked token:
```python ```python
# Load pre-trained model (weights) # Load pre-trained model (weights)
model = BertForMaskedLM.from_pretrained('bert-base-uncased') model = BertForMaskedLM.from_pretrained('bert-base-uncased')
model.eval()
# If you have a GPU, put everything on cuda # If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda') tokens_tensor = tokens_tensor.to('cuda')
@@ -178,10 +173,6 @@ Let's see how to use `GPT2LMHeadModel` to generate the next token following our
# Load pre-trained model (weights) # Load pre-trained model (weights)
model = GPT2LMHeadModel.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2')
# Set the model in evaluation mode to desactivate the DropOut modules
# This is IMPORTANT to have reproductible results during evaluation!
model.eval()
# If you have a GPU, put everything on cuda # If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda') tokens_tensor = tokens_tensor.to('cuda')
model.to('cuda') model.to('cuda')

8
pytorch_transformers/modeling_utils.py
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@@ -306,7 +306,10 @@ class PreTrainedModel(nn.Module):
@classmethod @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
r""" Instantiate a PretrainedConfig from a pre-trained model configuration. r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are desactivated)
To train the model, you should first set it back in training mode with `model.train()`
Params: Params:
**pretrained_model_name_or_path**: either: **pretrained_model_name_or_path**: either:
@@ -460,6 +463,9 @@ class PreTrainedModel(nn.Module):
if hasattr(model, 'tie_weights'): if hasattr(model, 'tie_weights'):
model.tie_weights() # make sure word embedding weights are still tied model.tie_weights() # make sure word embedding weights are still tied
# Set model in evaluation mode to desactivate DropOut modules by default
model.eval()
if output_loading_info: if output_loading_info:
loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "error_msgs": error_msgs} loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys, "error_msgs": error_msgs}
return model, loading_info return model, loading_info