SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Model output test (#6155)

Browse Source 
* Use return_dict=True in all tests

* Formatting
This commit is contained in:
Sylvain Gugger
2020-07-31 09:44:37 -04:00
committed by GitHub
parent 86caab1e0b
commit d951c14ae4
26 changed files with 320 additions and 765 deletions
Download Patch File Download Diff File Expand all files Collapse all files

44
tests/test_modeling_distilbert.py
View File

@@ -110,6 +110,7 @@ if is_torch_available():
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
@@ -123,14 +124,10 @@ if is_torch_available():
model = DistilBertModel(config=config)
model.to(torch_device)
model.eval()
(sequence_output,) = model(input_ids, input_mask)
(sequence_output,) = model(input_ids)
result = {
"sequence_output": sequence_output,
}
result = model(input_ids, input_mask)
result = model(input_ids)
self.parent.assertListEqual(
list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
list(result["last_hidden_state"].size()), [self.batch_size, self.seq_length, self.hidden_size]
)
def create_and_check_distilbert_for_masked_lm(
@@ -139,13 +136,9 @@ if is_torch_available():
model = DistilBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
loss, prediction_scores = model(input_ids, attention_mask=input_mask, labels=token_labels)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size]
)
self.check_loss_output(result)
@@ -155,14 +148,9 @@ if is_torch_available():
model = DistilBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
loss, start_logits, end_logits = model(
result = model(
input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels
)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
self.check_loss_output(result)
@@ -174,11 +162,7 @@ if is_torch_available():
model = DistilBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
self.check_loss_output(result)
@@ -190,11 +174,7 @@ if is_torch_available():
model.to(torch_device)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertListEqual(
list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels]
)
@@ -209,13 +189,9 @@ if is_torch_available():
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
loss, logits = model(
result = model(
multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, labels=choice_labels,
)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices])
self.check_loss_output(result)