test with model hub

modelscope/models/nlp/space/dialog_intent_model.py

@@ -1,6 +1,10 @@
-from typing import Any, Dict, Optional
+import os
+from typing import Any, Dict
 
+from modelscope.preprocessors.space.fields.intent_field import \
+    IntentBPETextField
 from modelscope.trainers.nlp.space.trainers.intent_trainer import IntentTrainer
+from modelscope.utils.config import Config
 from modelscope.utils.constant import Tasks
 from ...base import Model, Tensor
 from ...builder import MODELS
@@ -10,7 +14,7 @@ from .model.model_base import ModelBase
 __all__ = ['DialogIntentModel']
 
 
-@MODELS.register_module(Tasks.dialog_intent, module_name=r'space-intent')
+@MODELS.register_module(Tasks.dialog_intent, module_name=r'space')
 class DialogIntentModel(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):
@@ -24,8 +28,14 @@ class DialogIntentModel(Model):
 
         super().__init__(model_dir, *args, **kwargs)
         self.model_dir = model_dir
-        self.text_field = kwargs.pop('text_field')
-        self.config = kwargs.pop('config')
+        self.config = kwargs.pop(
+            'config',
+            Config.from_file(
+                os.path.join(self.model_dir, 'configuration.json')))
+        self.text_field = kwargs.pop(
+            'text_field',
+            IntentBPETextField(self.model_dir, config=self.config))
+
         self.generator = Generator.create(self.config, reader=self.text_field)
         self.model = ModelBase.create(
             model_dir=model_dir,
@@ -63,9 +73,8 @@ class DialogIntentModel(Model):
                         'logits': array([[-0.53860897,  1.5029076 ]], dtype=float32) # true value
                     }
         """
-        from numpy import array, float32
-        import torch
-        print('--forward--')
-        result = self.trainer.forward(input)
+        import numpy as np
+        pred = self.trainer.forward(input)
+        pred = np.squeeze(pred[0], 0)
 
-        return result
+        return {'pred': pred}

modelscope/pipelines/nlp/space/dialog_intent_pipeline.py

@@ -9,7 +9,7 @@ from ...builder import PIPELINES
 __all__ = ['DialogIntentPipeline']
 
 
-@PIPELINES.register_module(Tasks.dialog_intent, module_name=r'space-intent')
+@PIPELINES.register_module(Tasks.dialog_intent, module_name=r'space')
 class DialogIntentPipeline(Pipeline):
 
     def __init__(self, model: DialogIntentModel,
@@ -34,5 +34,10 @@ class DialogIntentPipeline(Pipeline):
         Returns:
             Dict[str, str]: the prediction results
         """
+        import numpy as np
+        pred = inputs['pred']
+        pos = np.where(pred == np.max(pred))
 
-        return inputs
+        result = {'pred': pred, 'label': pos[0]}
+
+        return result

modelscope/preprocessors/space/dialog_intent_preprocessor.py

@@ -1,13 +1,12 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 import os
-import uuid
-from typing import Any, Dict, Union
+from typing import Any, Dict
 
 from modelscope.preprocessors.space.fields.intent_field import \
     IntentBPETextField
 from modelscope.utils.config import Config
-from modelscope.utils.constant import Fields, InputFields
+from modelscope.utils.constant import Fields
 from modelscope.utils.type_assert import type_assert
 from ..base import Preprocessor
 from ..builder import PREPROCESSORS
@@ -15,7 +14,7 @@ from ..builder import PREPROCESSORS
 __all__ = ['DialogIntentPreprocessor']
 
 
-@PREPROCESSORS.register_module(Fields.nlp, module_name=r'space-intent')
+@PREPROCESSORS.register_module(Fields.nlp, module_name=r'space')
 class DialogIntentPreprocessor(Preprocessor):
 
     def __init__(self, model_dir: str, *args, **kwargs):

modelscope/trainers/nlp/space/trainers/intent_trainer.py

@@ -508,7 +508,6 @@ class IntentTrainer(Trainer):
             report_for_unlabeled_data, cur_valid_metric=-accuracy)
 
     def forward(self, batch):
-        outputs, labels = [], []
         pred, true = [], []
 
         with torch.no_grad():
@@ -522,12 +521,10 @@ class IntentTrainer(Trainer):
             intent_probs = result['intent_probs']
             if self.can_norm:
                 pred += [intent_probs]
-                true += batch['intent_label'].cpu().detach().tolist()
             else:
                 pred += np.argmax(intent_probs, axis=1).tolist()
-                true += batch['intent_label'].cpu().detach().tolist()
 
-        return {'pred': pred}
+        return pred
 
     def infer(self, data_iter, num_batches=None, ex_data_iter=None):
         """

tests/case/nlp/dialog_generation_case.py

@@ -1,76 +0,0 @@
-test_case = {
-    'sng0073': {
-        'goal': {
-            'taxi': {
-                'info': {
-                    'leaveat': '17:15',
-                    'destination': 'pizza hut fen ditton',
-                    'departure': "saint john's college"
-                },
-                'reqt': ['car', 'phone'],
-                'fail_info': {}
-            }
-        },
-        'log': [{
-            'user':
-            "i would like a taxi from saint john 's college to pizza hut fen ditton .",
-            'user_delex':
-            'i would like a taxi from [value_departure] to [value_destination] .',
-            'resp':
-            'what time do you want to leave and what time do you want to arrive by ?',
-            'sys':
-            'what time do you want to leave and what time do you want to arrive by ?',
-            'pointer': '0,0,0,0,0,0',
-            'match': '',
-            'constraint':
-            "[taxi] destination pizza hut fen ditton departure saint john 's college",
-            'cons_delex': '[taxi] destination departure',
-            'sys_act': '[taxi] [request] leave arrive',
-            'turn_num': 0,
-            'turn_domain': '[taxi]'
-        }, {
-            'user': 'i want to leave after 17:15 .',
-            'user_delex': 'i want to leave after [value_leave] .',
-            'resp':
-            'booking completed ! your taxi will be [value_car] contact number is [value_phone]',
-            'sys':
-            'booking completed ! your taxi will be blue honda contact number is 07218068540',
-            'pointer': '0,0,0,0,0,0',
-            'match': '',
-            'constraint':
-            "[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
-            'cons_delex': '[taxi] destination departure leave',
-            'sys_act': '[taxi] [inform] car phone',
-            'turn_num': 1,
-            'turn_domain': '[taxi]'
-        }, {
-            'user': 'thank you for all the help ! i appreciate it .',
-            'user_delex': 'thank you for all the help ! i appreciate it .',
-            'resp':
-            'you are welcome . is there anything else i can help you with today ?',
-            'sys':
-            'you are welcome . is there anything else i can help you with today ?',
-            'pointer': '0,0,0,0,0,0',
-            'match': '',
-            'constraint':
-            "[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
-            'cons_delex': '[taxi] destination departure leave',
-            'sys_act': '[general] [reqmore]',
-            'turn_num': 2,
-            'turn_domain': '[general]'
-        }, {
-            'user': 'no , i am all set . have a nice day . bye .',
-            'user_delex': 'no , i am all set . have a nice day . bye .',
-            'resp': 'you too ! thank you',
-            'sys': 'you too ! thank you',
-            'pointer': '0,0,0,0,0,0',
-            'match': '',
-            'constraint':
-            "[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
-            'cons_delex': '[taxi] destination departure leave',
-            'sys_act': '[general] [bye]',
-            'turn_num': 3,
-            'turn_domain': '[general]'
-        }]
-    }
-}

tests/case/nlp/dialog_intent_case.py

@@ -1,4 +0,0 @@
-test_case = [
-    'How do I locate my card?',
-    'I still have not received my new card, I ordered over a week ago.'
-]

tests/pipelines/nlp/test_dialog_generation.py

@@ -4,8 +4,6 @@ import os.path as osp
 import tempfile
 import unittest
 
-from tests.case.nlp.dialog_generation_case import test_case
-
 from modelscope.models.nlp import DialogGenerationModel
 from modelscope.pipelines import DialogGenerationPipeline, pipeline
 from modelscope.preprocessors import DialogGenerationPreprocessor
@@ -16,6 +14,82 @@ def merge(info, result):
 
 
 class DialogGenerationTest(unittest.TestCase):
+    test_case = {
+        'sng0073': {
+            'goal': {
+                'taxi': {
+                    'info': {
+                        'leaveat': '17:15',
+                        'destination': 'pizza hut fen ditton',
+                        'departure': "saint john's college"
+                    },
+                    'reqt': ['car', 'phone'],
+                    'fail_info': {}
+                }
+            },
+            'log': [{
+                'user':
+                "i would like a taxi from saint john 's college to pizza hut fen ditton .",
+                'user_delex':
+                'i would like a taxi from [value_departure] to [value_destination] .',
+                'resp':
+                'what time do you want to leave and what time do you want to arrive by ?',
+                'sys':
+                'what time do you want to leave and what time do you want to arrive by ?',
+                'pointer': '0,0,0,0,0,0',
+                'match': '',
+                'constraint':
+                "[taxi] destination pizza hut fen ditton departure saint john 's college",
+                'cons_delex': '[taxi] destination departure',
+                'sys_act': '[taxi] [request] leave arrive',
+                'turn_num': 0,
+                'turn_domain': '[taxi]'
+            }, {
+                'user': 'i want to leave after 17:15 .',
+                'user_delex': 'i want to leave after [value_leave] .',
+                'resp':
+                'booking completed ! your taxi will be [value_car] contact number is [value_phone]',
+                'sys':
+                'booking completed ! your taxi will be blue honda contact number is 07218068540',
+                'pointer': '0,0,0,0,0,0',
+                'match': '',
+                'constraint':
+                "[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
+                'cons_delex': '[taxi] destination departure leave',
+                'sys_act': '[taxi] [inform] car phone',
+                'turn_num': 1,
+                'turn_domain': '[taxi]'
+            }, {
+                'user': 'thank you for all the help ! i appreciate it .',
+                'user_delex': 'thank you for all the help ! i appreciate it .',
+                'resp':
+                'you are welcome . is there anything else i can help you with today ?',
+                'sys':
+                'you are welcome . is there anything else i can help you with today ?',
+                'pointer': '0,0,0,0,0,0',
+                'match': '',
+                'constraint':
+                "[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
+                'cons_delex': '[taxi] destination departure leave',
+                'sys_act': '[general] [reqmore]',
+                'turn_num': 2,
+                'turn_domain': '[general]'
+            }, {
+                'user': 'no , i am all set . have a nice day . bye .',
+                'user_delex': 'no , i am all set . have a nice day . bye .',
+                'resp': 'you too ! thank you',
+                'sys': 'you too ! thank you',
+                'pointer': '0,0,0,0,0,0',
+                'match': '',
+                'constraint':
+                "[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
+                'cons_delex': '[taxi] destination departure leave',
+                'sys_act': '[general] [bye]',
+                'turn_num': 3,
+                'turn_domain': '[general]'
+            }]
+        }
+    }
 
     def test_run(self):
 

tests/pipelines/nlp/test_dialog_intent.py

@@ -1,11 +1,9 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
-import os
-import os.path as osp
-import tempfile
 import unittest
 
-from tests.case.nlp.dialog_intent_case import test_case
+from maas_hub.snapshot_download import snapshot_download
 
+from modelscope.models import Model
 from modelscope.models.nlp import DialogIntentModel
 from modelscope.pipelines import DialogIntentPipeline, pipeline
 from modelscope.preprocessors import DialogIntentPreprocessor
@@ -13,22 +11,46 @@ from modelscope.utils.constant import Tasks
 
 
 class DialogGenerationTest(unittest.TestCase):
+    model_id = 'damo/nlp_space_dialog-intent'
+    test_case = [
+        'How do I locate my card?',
+        'I still have not received my new card, I ordered over a week ago.'
+    ]
 
+    @unittest.skip('test with snapshot_download')
     def test_run(self):
-
-        modeldir = '/Users/yangliu/Desktop/space-dialog-intent'
-
-        preprocessor = DialogIntentPreprocessor(model_dir=modeldir)
+        cache_path = snapshot_download(self.model_id)
+        preprocessor = DialogIntentPreprocessor(model_dir=cache_path)
         model = DialogIntentModel(
-            model_dir=modeldir,
+            model_dir=cache_path,
             text_field=preprocessor.text_field,
             config=preprocessor.config)
-        pipeline1 = DialogIntentPipeline(
-            model=model, preprocessor=preprocessor)
-        # pipeline1 = pipeline(task=Tasks.dialog_intent, model=model, preprocessor=preprocessor)
 
-        for item in test_case:
-            print(pipeline1(item))
+        pipelines = [
+            DialogIntentPipeline(model=model, preprocessor=preprocessor),
+            pipeline(
+                task=Tasks.dialog_intent,
+                model=model,
+                preprocessor=preprocessor)
+        ]
+
+        for my_pipeline, item in list(zip(pipelines, self.test_case)):
+            print(my_pipeline(item))
+
+    def test_run_with_model_from_modelhub(self):
+        model = Model.from_pretrained(self.model_id)
+        preprocessor = DialogIntentPreprocessor(model_dir=model.model_dir)
+
+        pipelines = [
+            DialogIntentPipeline(model=model, preprocessor=preprocessor),
+            pipeline(
+                task=Tasks.dialog_intent,
+                model=model,
+                preprocessor=preprocessor)
+        ]
+
+        for my_pipeline, item in list(zip(pipelines, self.test_case)):
+            print(my_pipeline(item))
 
 
 if __name__ == '__main__':