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update sequence classfication outpus

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智丞
2022-06-23 20:34:53 +08:00
parent 08e03ca9b0
commit 6499b904ec
3 changed files with 48 additions and 66 deletions
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50
modelscope/pipelines/nlp/nli_pipeline.py
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@@ -32,24 +32,25 @@ class NLIPipeline(Pipeline):
"""
assert isinstance(model, str) or isinstance(model, SbertForNLI), \
'model must be a single str or SbertForNLI'
sc_model = model if isinstance(
model = model if isinstance(
model, SbertForNLI) else Model.from_pretrained(model)
if preprocessor is None:
preprocessor = NLIPreprocessor(
sc_model.model_dir,
model.model_dir,
first_sequence=first_sequence,
second_sequence=second_sequence)
sc_model.eval()
super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
assert len(sc_model.id2label) > 0
model.eval()
super().__init__(model=model, preprocessor=preprocessor, **kwargs)
assert len(model.id2label) > 0
def forward(self, inputs: Dict[str, Any],
**forward_params) -> Dict[str, Any]:
with torch.no_grad():
return super().forward(inputs, **forward_params)
def postprocess(self, inputs: Dict[str, Any],
**postprocess_params) -> Dict[str, str]:
def postprocess(self,
inputs: Dict[str, Any],
topk: int = 5) -> Dict[str, str]:
"""process the prediction results
Args:
@@ -59,30 +60,13 @@ class NLIPipeline(Pipeline):
Dict[str, str]: the prediction results
"""
probs = inputs['probabilities']
logits = inputs['logits']
predictions = np.argsort(-probs, axis=-1)
preds = predictions[0]
b = 0
new_result = list()
for pred in preds:
new_result.append({
'pred': self.model.id2label[pred],
'prob': float(probs[b][pred]),
'logit': float(logits[b][pred])
})
new_results = list()
new_results.append({
'id':
inputs['id'][b] if 'id' in inputs else str(uuid.uuid4()),
'output':
new_result,
'predictions':
new_result[0]['pred'],
'probabilities':
','.join([str(t) for t in inputs['probabilities'][b]]),
'logits':
','.join([str(t) for t in inputs['logits'][b]])
})
probs = inputs['probabilities'][0]
num_classes = probs.shape[0]
topk = min(topk, num_classes)
top_indices = np.argpartition(probs, -topk)[-topk:]
cls_ids = top_indices[np.argsort(probs[top_indices])]
probs = probs[cls_ids].tolist()
return new_results[0]
cls_names = [self.model.id2label[cid] for cid in cls_ids]
return {'scores': probs, 'labels': cls_names}

50
modelscope/pipelines/nlp/sentiment_classification_pipeline.py
View File

@@ -36,25 +36,26 @@ class SentimentClassificationPipeline(Pipeline):
"""
assert isinstance(model, str) or isinstance(model, SbertForSentimentClassification), \
'model must be a single str or SbertForSentimentClassification'
sc_model = model if isinstance(
model = model if isinstance(
model,
SbertForSentimentClassification) else Model.from_pretrained(model)
if preprocessor is None:
preprocessor = SentimentClassificationPreprocessor(
sc_model.model_dir,
model.model_dir,
first_sequence=first_sequence,
second_sequence=second_sequence)
sc_model.eval()
super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
assert len(sc_model.id2label) > 0
model.eval()
super().__init__(model=model, preprocessor=preprocessor, **kwargs)
assert len(model.id2label) > 0
def forward(self, inputs: Dict[str, Any],
**forward_params) -> Dict[str, Any]:
with torch.no_grad():
return super().forward(inputs, **forward_params)
def postprocess(self, inputs: Dict[str, Any],
**postprocess_params) -> Dict[str, str]:
def postprocess(self,
inputs: Dict[str, Any],
topk: int = 5) -> Dict[str, str]:
"""process the prediction results
Args:
@@ -64,30 +65,13 @@ class SentimentClassificationPipeline(Pipeline):
Dict[str, str]: the prediction results
"""
probs = inputs['probabilities']
logits = inputs['logits']
predictions = np.argsort(-probs, axis=-1)
preds = predictions[0]
b = 0
new_result = list()
for pred in preds:
new_result.append({
'pred': self.model.id2label[pred],
'prob': float(probs[b][pred]),
'logit': float(logits[b][pred])
})
new_results = list()
new_results.append({
'id':
inputs['id'][b] if 'id' in inputs else str(uuid.uuid4()),
'output':
new_result,
'predictions':
new_result[0]['pred'],
'probabilities':
','.join([str(t) for t in inputs['probabilities'][b]]),
'logits':
','.join([str(t) for t in inputs['logits'][b]])
})
probs = inputs['probabilities'][0]
num_classes = probs.shape[0]
topk = min(topk, num_classes)
top_indices = np.argpartition(probs, -topk)[-topk:]
cls_ids = top_indices[np.argsort(probs[top_indices])]
probs = probs[cls_ids].tolist()
return new_results[0]
cls_names = [self.model.id2label[cid] for cid in cls_ids]
return {'scores': probs, 'labels': cls_names}

14
modelscope/pipelines/outputs.py
View File

@@ -101,6 +101,20 @@ TASK_OUTPUTS = {
# }
Tasks.sentence_similarity: ['scores', 'labels'],
# sentiment classification result for single sample
# {
# "labels": ["happy", "sad", "calm", "angry"],
# "scores": [0.9, 0.1, 0.05, 0.05]
# }
Tasks.sentiment_classification: ['scores', 'labels'],
# nli result for single sample
# {
# "labels": ["happy", "sad", "calm", "angry"],
# "scores": [0.9, 0.1, 0.05, 0.05]
# }
Tasks.nli: ['scores', 'labels'],
# ============ audio tasks ===================
# audio processed for single file in PCM format