add test cases

modelscope/models/nlp/space/dialog_intent_prediction_model.py

@@ -63,15 +63,16 @@ class SpaceForDialogIntent(Model):
         """return the result by the model
 
         Args:
-            input (Dict[str, Any]): the preprocessed data
+            input (Dict[str, Tensor]): the preprocessed data
 
         Returns:
-            Dict[str, np.ndarray]: results
+            Dict[str, Tensor]: results
                 Example:
                     {
-                        'predictions': array([1]), # lable 0-negative 1-positive
-                        'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
-                        'logits': array([[-0.53860897,  1.5029076 ]], dtype=float32) # true value
+                        'pred': array([2.62349960e-03 4.12110658e-03 4.12748595e-05 3.77560973e-05
+ 1.08599677e-04 1.72710388e-05 2.95618793e-05 1.93638436e-04
+ 6.45841064e-05 1.15997791e-04 5.11605394e-05 9.87020373e-01
+ 2.66957268e-05 4.72324500e-05 9.74208378e-05], dtype=float32)
                     }
         """
         import numpy as np

modelscope/models/nlp/space/dialog_modeling_model.py

@@ -62,15 +62,17 @@ class SpaceForDialogModeling(Model):
         """return the result by the model
 
         Args:
-            input (Dict[str, Any]): the preprocessed data
+            input (Dict[str, Tensor]): the preprocessed data
 
         Returns:
-            Dict[str, np.ndarray]: results
+            Dict[str, Tensor]: results
                 Example:
                     {
-                        'predictions': array([1]), # lable 0-negative 1-positive
-                        'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
-                        'logits': array([[-0.53860897,  1.5029076 ]], dtype=float32) # true value
+                        'labels': array([1,192,321,12]), # lable
+                        'resp': array([293,1023,123,1123]), #vocab label for response
+                        'bspn': array([123,321,2,24,1 ]),
+                        'aspn': array([47,8345,32,29,1983]),
+                        'db': array([19, 24, 20]),
                     }
         """
 

modelscope/models/nlp/space/dialog_state_tracking_model.py

@@ -2,6 +2,7 @@ import os
 from typing import Any, Dict
 
 from modelscope.utils.constant import Tasks
+from ....metainfo import Models
 from ....utils.nlp.space.utils_dst import batch_to_device
 from ...base import Model, Tensor
 from ...builder import MODELS
@@ -9,7 +10,7 @@ from ...builder import MODELS
 __all__ = ['SpaceForDialogStateTracking']
 
 
-@MODELS.register_module(Tasks.dialog_state_tracking, module_name=r'space')
+@MODELS.register_module(Tasks.dialog_state_tracking, module_name=Models.space)
 class SpaceForDialogStateTracking(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):
@@ -17,8 +18,6 @@ class SpaceForDialogStateTracking(Model):
 
         Args:
             model_dir (str): the model path.
-            model_cls (Optional[Any], optional): model loader, if None, use the
-                default loader to load model weights, by default None.
         """
 
         super().__init__(model_dir, *args, **kwargs)
@@ -27,7 +26,6 @@ class SpaceForDialogStateTracking(Model):
         self.model_dir = model_dir
 
         self.config = SpaceConfig.from_pretrained(self.model_dir)
-        # self.model = SpaceForDST(self.config)
         self.model = SpaceForDST.from_pretrained(self.model_dir)
         self.model.to(self.config.device)
 
@@ -35,15 +33,20 @@ class SpaceForDialogStateTracking(Model):
         """return the result by the model
 
         Args:
-            input (Dict[str, Any]): the preprocessed data
+            input (Dict[str, Tensor]): the preprocessed data
 
         Returns:
-            Dict[str, np.ndarray]: results
+            Dict[str, Tensor]: results
                 Example:
                     {
-                        'predictions': array([1]), # lable 0-negative 1-positive
-                        'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
-                        'logits': array([[-0.53860897,  1.5029076 ]], dtype=float32) # true value
+                        'inputs': dict(input_ids, input_masks,start_pos), # tracking states
+                        'outputs': dict(slots_logits),
+                        'unique_ids': str(test-example.json-0), # default value
+                        'input_ids_unmasked': array([101, 7632, 1010,0,0,0])
+                        'values': array([{'taxi-leaveAt': 'none', 'taxi-destination': 'none'}]),
+                        'inform':  array([{'taxi-leaveAt': 'none', 'taxi-destination': 'none'}]),
+                        'prefix': str('final'), #default value
+                        'ds':  array([{'taxi-leaveAt': 'none', 'taxi-destination': 'none'}])
                     }
         """
         import numpy as np
@@ -88,8 +91,6 @@ class SpaceForDialogStateTracking(Model):
                     if u != 0:
                         diag_state[slot][i] = u
 
-            # print(outputs)
-
         return {
             'inputs': inputs,
             'outputs': outputs,

modelscope/pipelines/builder.py

@@ -41,6 +41,8 @@ DEFAULT_MODEL_FOR_PIPELINE = {
      'damo/nlp_space_dialog-intent-prediction'),
     Tasks.dialog_modeling: (Pipelines.dialog_modeling,
                             'damo/nlp_space_dialog-modeling'),
+    Tasks.dialog_state_tracking: (Pipelines.dialog_state_tracking,
+                                  'damo/nlp_space_dialog-state-tracking'),
     Tasks.image_captioning: (Pipelines.image_caption,
                              'damo/ofa_image-caption_coco_large_en'),
     Tasks.image_generation:

modelscope/pipelines/nlp/dialog_intent_prediction_pipeline.py

@@ -1,8 +1,9 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-from typing import Any, Dict
+from typing import Any, Dict, Union
 
 from ...metainfo import Pipelines
+from ...models import Model
 from ...models.nlp import SpaceForDialogIntent
 from ...preprocessors import DialogIntentPredictionPreprocessor
 from ...utils.constant import Tasks
@@ -18,17 +19,22 @@ __all__ = ['DialogIntentPredictionPipeline']
     module_name=Pipelines.dialog_intent_prediction)
 class DialogIntentPredictionPipeline(Pipeline):
 
-    def __init__(self, model: SpaceForDialogIntent,
-                 preprocessor: DialogIntentPredictionPreprocessor, **kwargs):
-        """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
+    def __init__(self,
+                 model: Union[SpaceForDialogIntent, str],
+                 preprocessor: DialogIntentPredictionPreprocessor = None,
+                 **kwargs):
+        """use `model` and `preprocessor` to create a dialog intent prediction pipeline
 
         Args:
-            model (SequenceClassificationModel): a model instance
-            preprocessor (SequenceClassificationPreprocessor): a preprocessor instance
+            model (SpaceForDialogIntent): a model instance
+            preprocessor (DialogIntentPredictionPreprocessor): a preprocessor instance
         """
-
-        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+        model = model if isinstance(
+            model, SpaceForDialogIntent) else Model.from_pretrained(model)
+        if preprocessor is None:
+            preprocessor = DialogIntentPredictionPreprocessor(model.model_dir)
         self.model = model
+        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
         self.categories = preprocessor.categories
 
     def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, str]:

modelscope/pipelines/nlp/dialog_modeling_pipeline.py

@@ -1,8 +1,9 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Union
 
 from ...metainfo import Pipelines
+from ...models import Model
 from ...models.nlp import SpaceForDialogModeling
 from ...preprocessors import DialogModelingPreprocessor
 from ...utils.constant import Tasks
@@ -17,17 +18,22 @@ __all__ = ['DialogModelingPipeline']
     Tasks.dialog_modeling, module_name=Pipelines.dialog_modeling)
 class DialogModelingPipeline(Pipeline):
 
-    def __init__(self, model: SpaceForDialogModeling,
-                 preprocessor: DialogModelingPreprocessor, **kwargs):
-        """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
+    def __init__(self,
+                 model: Union[SpaceForDialogModeling, str],
+                 preprocessor: DialogModelingPreprocessor = None,
+                 **kwargs):
+        """use `model` and `preprocessor` to create a dialog modleing pipeline for dialog response generation
 
         Args:
-            model (SequenceClassificationModel): a model instance
-            preprocessor (SequenceClassificationPreprocessor): a preprocessor instance
+            model (SpaceForDialogModeling): a model instance
+            preprocessor (DialogModelingPreprocessor): a preprocessor instance
         """
-
-        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+        model = model if isinstance(
+            model, SpaceForDialogModeling) else Model.from_pretrained(model)
         self.model = model
+        if preprocessor is None:
+            preprocessor = DialogModelingPreprocessor(model.model_dir)
+        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
         self.preprocessor = preprocessor
 
     def postprocess(self, inputs: Dict[str, Tensor]) -> Dict[str, str]:

modelscope/pipelines/nlp/dialog_state_tracking_pipeline.py

@@ -1,7 +1,7 @@
-from typing import Any, Dict
+from typing import Any, Dict, Union
 
 from ...metainfo import Pipelines
-from ...models import SpaceForDialogStateTracking
+from ...models import Model, SpaceForDialogStateTracking
 from ...preprocessors import DialogStateTrackingPreprocessor
 from ...utils.constant import Tasks
 from ..base import Pipeline
@@ -15,17 +15,26 @@ __all__ = ['DialogStateTrackingPipeline']
     Tasks.dialog_state_tracking, module_name=Pipelines.dialog_state_tracking)
 class DialogStateTrackingPipeline(Pipeline):
 
-    def __init__(self, model: SpaceForDialogStateTracking,
-                 preprocessor: DialogStateTrackingPreprocessor, **kwargs):
-        """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
+    def __init__(self,
+                 model: Union[SpaceForDialogStateTracking, str],
+                 preprocessor: DialogStateTrackingPreprocessor = None,
+                 **kwargs):
+        """use `model` and `preprocessor` to create a dialog state tracking pipeline for
+        observation of dialog states tracking after many turns of open domain dialogue
 
         Args:
-            model (SequenceClassificationModel): a model instance
-            preprocessor (SequenceClassificationPreprocessor): a preprocessor instance
+            model (SpaceForDialogStateTracking): a model instance
+            preprocessor (DialogStateTrackingPreprocessor): a preprocessor instance
         """
 
-        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+        model = model if isinstance(
+            model,
+            SpaceForDialogStateTracking) else Model.from_pretrained(model)
         self.model = model
+        if preprocessor is None:
+            preprocessor = DialogStateTrackingPreprocessor(model.model_dir)
+        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+
         self.tokenizer = preprocessor.tokenizer
         self.config = preprocessor.config
 
@@ -46,9 +55,7 @@ class DialogStateTrackingPipeline(Pipeline):
         values = inputs['values']
         inform = inputs['inform']
         prefix = inputs['prefix']
-        # ds = {slot: 'none' for slot in self.config.dst_slot_list}
         ds = inputs['ds']
-
         ds = predict_and_format(self.config, self.tokenizer, _inputs,
                                 _outputs[2], _outputs[3], _outputs[4],
                                 _outputs[5], unique_ids, input_ids_unmasked,

modelscope/pipelines/outputs.py

@@ -138,13 +138,6 @@ TASK_OUTPUTS = {
     #   }
     Tasks.sentiment_classification: [OutputKeys.SCORES, OutputKeys.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'],
-
     # zero-shot classification result for single sample
     #   {
     #       "scores": [0.9, 0.1, 0.05, 0.05]

tests/pipelines/test_dialog_intent_prediction.py

@@ -18,7 +18,7 @@ class DialogIntentPredictionTest(unittest.TestCase):
     ]
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
-    def test_run(self):
+    def test_run_by_direct_model_download(self):
         cache_path = snapshot_download(self.model_id)
         preprocessor = DialogIntentPredictionPreprocessor(model_dir=cache_path)
         model = SpaceForDialogIntent(
@@ -56,6 +56,20 @@ class DialogIntentPredictionTest(unittest.TestCase):
         for my_pipeline, item in list(zip(pipelines, self.test_case)):
             print(my_pipeline(item))
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_with_model_name(self):
+        pipelines = [
+            pipeline(task=Tasks.dialog_intent_prediction, model=self.model_id)
+        ]
+        for my_pipeline, item in list(zip(pipelines, self.test_case)):
+            print(my_pipeline(item))
+
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_run_with_default_model(self):
+        pipelines = [pipeline(task=Tasks.dialog_intent_prediction)]
+        for my_pipeline, item in list(zip(pipelines, self.test_case)):
+            print(my_pipeline(item))
+
 
 if __name__ == '__main__':
     unittest.main()

tests/pipelines/test_dialog_modeling.py

@@ -1,5 +1,6 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import unittest
+from typing import List
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
@@ -89,8 +90,22 @@ class DialogModelingTest(unittest.TestCase):
         }
     }
 
+    def generate_and_print_dialog_response(
+            self, pipelines: List[DialogModelingPipeline]):
+
+        result = {}
+        for step, item in enumerate(self.test_case['sng0073']['log']):
+            user = item['user']
+            print('user: {}'.format(user))
+
+            result = pipelines[step % 2]({
+                'user_input': user,
+                'history': result
+            })
+            print('response : {}'.format(result['response']))
+
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
-    def test_run(self):
+    def test_run_by_direct_model_download(self):
 
         cache_path = snapshot_download(self.model_id)
 
@@ -106,17 +121,7 @@ class DialogModelingTest(unittest.TestCase):
                 model=model,
                 preprocessor=preprocessor)
         ]
-
-        result = {}
-        for step, item in enumerate(self.test_case['sng0073']['log']):
-            user = item['user']
-            print('user: {}'.format(user))
-
-            result = pipelines[step % 2]({
-                'user_input': user,
-                'history': result
-            })
-            print('response : {}'.format(result['response']))
+        self.generate_and_print_dialog_response(pipelines)
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
@@ -131,16 +136,23 @@ class DialogModelingTest(unittest.TestCase):
                 preprocessor=preprocessor)
         ]
 
-        result = {}
-        for step, item in enumerate(self.test_case['sng0073']['log']):
-            user = item['user']
-            print('user: {}'.format(user))
+        self.generate_and_print_dialog_response(pipelines)
 
-            result = pipelines[step % 2]({
-                'user_input': user,
-                'history': result
-            })
-            print('response : {}'.format(result['response']))
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_with_model_name(self):
+        pipelines = [
+            pipeline(task=Tasks.dialog_modeling, model=self.model_id),
+            pipeline(task=Tasks.dialog_modeling, model=self.model_id)
+        ]
+        self.generate_and_print_dialog_response(pipelines)
+
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_run_with_default_model(self):
+        pipelines = [
+            pipeline(task=Tasks.dialog_modeling),
+            pipeline(task=Tasks.dialog_modeling)
+        ]
+        self.generate_and_print_dialog_response(pipelines)
 
 
 if __name__ == '__main__':

tests/pipelines/test_dialog_state_tracking.py

@@ -1,5 +1,6 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import unittest
+from typing import List
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model, SpaceForDialogStateTracking
@@ -75,23 +76,10 @@ class DialogStateTrackingTest(unittest.TestCase):
         'User-8': 'Thank you, goodbye',
     }]
 
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
-    def test_run(self):
-        cache_path = snapshot_download(self.model_id)
-
-        model = SpaceForDialogStateTracking(cache_path)
-        preprocessor = DialogStateTrackingPreprocessor(model_dir=cache_path)
-        pipelines = [
-            DialogStateTrackingPipeline(
-                model=model, preprocessor=preprocessor),
-            pipeline(
-                task=Tasks.dialog_state_tracking,
-                model=model,
-                preprocessor=preprocessor)
-        ]
-
-        pipelines_len = len(pipelines)
+    def tracking_and_print_dialog_states(
+            self, pipelines: List[DialogStateTrackingPipeline]):
         import json
+        pipelines_len = len(pipelines)
         history_states = [{}]
         utter = {}
         for step, item in enumerate(self.test_case):
@@ -106,6 +94,22 @@ class DialogStateTrackingTest(unittest.TestCase):
 
             history_states.extend([result['dialog_states'], {}])
 
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_run_by_direct_model_download(self):
+        cache_path = snapshot_download(self.model_id)
+
+        model = SpaceForDialogStateTracking(cache_path)
+        preprocessor = DialogStateTrackingPreprocessor(model_dir=cache_path)
+        pipelines = [
+            DialogStateTrackingPipeline(
+                model=model, preprocessor=preprocessor),
+            pipeline(
+                task=Tasks.dialog_state_tracking,
+                model=model,
+                preprocessor=preprocessor)
+        ]
+        self.tracking_and_print_dialog_states(pipelines)
+
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained(self.model_id)
@@ -120,21 +124,19 @@ class DialogStateTrackingTest(unittest.TestCase):
                 preprocessor=preprocessor)
         ]
 
-        pipelines_len = len(pipelines)
-        import json
-        history_states = [{}]
-        utter = {}
-        for step, item in enumerate(self.test_case):
-            utter.update(item)
-            result = pipelines[step % pipelines_len]({
-                'utter':
-                utter,
-                'history_states':
-                history_states
-            })
-            print(json.dumps(result))
+        self.tracking_and_print_dialog_states(pipelines)
 
-            history_states.extend([result['dialog_states'], {}])
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_with_model_name(self):
+        pipelines = [
+            pipeline(task=Tasks.dialog_state_tracking, model=self.model_id)
+        ]
+        self.tracking_and_print_dialog_states(pipelines)
+
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    def test_run_with_default_model(self):
+        pipelines = [pipeline(task=Tasks.dialog_state_tracking)]
+        self.tracking_and_print_dialog_states(pipelines)
 
 
 if __name__ == '__main__':