merge feat nlp

modelscope/pipelines/nlp/__init__.py

@@ -8,3 +8,4 @@ from .sentiment_classification_pipeline import *  # noqa F403
 from .sequence_classification_pipeline import *  # noqa F403
 from .text_generation_pipeline import *  # noqa F403
 from .word_segmentation_pipeline import *  # noqa F403
+from .zero_shot_classification_pipeline import *  # noqa F403

modelscope/pipelines/nlp/zero_shot_classification_pipeline.py

@@ -0,0 +1,88 @@
+import os
+import uuid
+from typing import Any, Dict, Union
+
+import json
+import numpy as np
+import torch
+from scipy.special import softmax
+
+from ...metainfo import Pipelines
+from ...models import Model
+from ...models.nlp import SbertForZeroShotClassification
+from ...preprocessors import ZeroShotClassificationPreprocessor
+from ...utils.constant import Tasks
+from ..base import Input, Pipeline
+from ..builder import PIPELINES
+
+__all__ = ['ZeroShotClassificationPipeline']
+
+
+@PIPELINES.register_module(
+    Tasks.zero_shot_classification,
+    module_name=Pipelines.zero_shot_classification)
+class ZeroShotClassificationPipeline(Pipeline):
+
+    def __init__(self,
+                 model: Union[SbertForZeroShotClassification, str],
+                 preprocessor: ZeroShotClassificationPreprocessor = None,
+                 **kwargs):
+        """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
+        Args:
+            model (SbertForSentimentClassification): a model instance
+            preprocessor (SentimentClassificationPreprocessor): a preprocessor instance
+        """
+        assert isinstance(model, str) or isinstance(model, SbertForZeroShotClassification), \
+            'model must be a single str or SbertForZeroShotClassification'
+        model = model if isinstance(
+            model,
+            SbertForZeroShotClassification) else Model.from_pretrained(model)
+        self.entailment_id = 0
+        self.contradiction_id = 2
+        if preprocessor is None:
+            preprocessor = ZeroShotClassificationPreprocessor(model.model_dir)
+        model.eval()
+        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_params = {}
+        postprocess_params = {}
+        if 'candidate_labels' in kwargs:
+            candidate_labels = kwargs.pop('candidate_labels')
+            preprocess_params['candidate_labels'] = candidate_labels
+            postprocess_params['candidate_labels'] = candidate_labels
+        else:
+            raise ValueError('You must include at least one label.')
+        preprocess_params['hypothesis_template'] = kwargs.pop(
+            'hypothesis_template', '{}')
+        postprocess_params['multi_label'] = kwargs.pop('multi_label', False)
+        return preprocess_params, {}, postprocess_params
+
+    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],
+                    candidate_labels,
+                    multi_label=False) -> Dict[str, Any]:
+        """process the prediction results
+        Args:
+            inputs (Dict[str, Any]): _description_
+        Returns:
+            Dict[str, Any]: the prediction results
+        """
+        logits = inputs['logits']
+        if multi_label or len(candidate_labels) == 1:
+            logits = logits[..., [self.contradiction_id, self.entailment_id]]
+            scores = softmax(logits, axis=-1)[..., 1]
+        else:
+            logits = logits[..., self.entailment_id]
+            scores = softmax(logits, axis=-1)
+        reversed_index = list(reversed(scores.argsort()))
+        result = {
+            'labels': [candidate_labels[i] for i in reversed_index],
+            'scores': [scores[i].item() for i in reversed_index]
+        }
+        return result

modelscope/preprocessors/__init__.py

@@ -1,8 +1,8 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-# from .audio import LinearAECAndFbank
+from .audio import LinearAECAndFbank
 from .base import Preprocessor
-# from .builder import PREPROCESSORS, build_preprocessor
+from .builder import PREPROCESSORS, build_preprocessor
 from .common import Compose
 from .image import LoadImage, load_image
 from .kws import WavToLists
@@ -11,5 +11,4 @@ from .nlp import *  # noqa F403
 from .space.dialog_intent_prediction_preprocessor import *  # noqa F403
 from .space.dialog_modeling_preprocessor import *  # noqa F403
 from .space.dialog_state_tracking_preprocessor import *  # noqa F403
-
-# from .text_to_speech import *  # noqa F403
+from .text_to_speech import *  # noqa F403