intent success

maas_lib/models/nlp/space/dialog_intent_model.py

@@ -65,5 +65,7 @@ class DialogIntentModel(Model):
         """
         from numpy import array, float32
         import torch
+        print('--forward--')
+        result = self.trainer.forward(input)
 
-        return {}
+        return result

maas_lib/pipelines/nlp/space/dialog_intent_pipeline.py

@@ -3,14 +3,14 @@ from typing import Any, Dict, Optional
 from maas_lib.models.nlp import DialogIntentModel
 from maas_lib.preprocessors import DialogIntentPreprocessor
 from maas_lib.utils.constant import Tasks
-from ...base import Model, Tensor
+from ...base import Input, Pipeline
 from ...builder import PIPELINES
 
 __all__ = ['DialogIntentPipeline']
 
 
 @PIPELINES.register_module(Tasks.dialog_intent, module_name=r'space-intent')
-class DialogIntentPipeline(Model):
+class DialogIntentPipeline(Pipeline):
 
     def __init__(self, model: DialogIntentModel,
                  preprocessor: DialogIntentPreprocessor, **kwargs):
@@ -23,9 +23,9 @@ class DialogIntentPipeline(Model):
 
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
         self.model = model
-        self.tokenizer = preprocessor.tokenizer
+        # self.tokenizer = preprocessor.tokenizer
 
-    def postprocess(self, inputs: Dict[str, Tensor]) -> Dict[str, str]:
+    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, str]:
         """process the prediction results
 
         Args:
@@ -35,16 +35,4 @@ class DialogIntentPipeline(Model):
             Dict[str, str]: the prediction results
         """
 
-        vocab_size = len(self.tokenizer.vocab)
-        pred_list = inputs['predictions']
-        pred_ids = pred_list[0][0].cpu().numpy().tolist()
-        for j in range(len(pred_ids)):
-            if pred_ids[j] >= vocab_size:
-                pred_ids[j] = 100
-        pred = self.tokenizer.convert_ids_to_tokens(pred_ids)
-        pred_string = ''.join(pred).replace(
-            '##',
-            '').split('[SEP]')[0].replace('[CLS]',
-                                          '').replace('[SEP]',
-                                                      '').replace('[UNK]', '')
-        return {'pred_string': pred_string}
+        return inputs

maas_lib/preprocessors/space/dialog_intent_preprocessor.py

@@ -43,5 +43,7 @@ class DialogIntentPreprocessor(Preprocessor):
         Returns:
             Dict[str, Any]: the preprocessed data
         """
+        samples = self.text_field.preprocessor([data])
+        samples, _ = self.text_field.collate_fn_multi_turn(samples)
 
-        return self.text_field.preprocessor(data)
+        return samples

maas_lib/trainers/nlp/space/trainers/intent_trainer.py

@@ -506,6 +506,28 @@ class IntentTrainer(Trainer):
         self.save_and_log_message(
             report_for_unlabeled_data, cur_valid_metric=-accuracy)
 
+    def forward(self, batch):
+        outputs, labels = [], []
+        pred, true = [], []
+
+        with torch.no_grad():
+            batch = type(batch)(
+                map(lambda kv: (kv[0], self.to_tensor(kv[1])), batch.items()))
+            result = self.model.infer(inputs=batch)
+            result = {
+                name: result[name].cpu().detach().numpy()
+                for name in result
+            }
+            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}
+
     def infer(self, data_iter, num_batches=None, ex_data_iter=None):
         """
         Inference interface.

tests/pipelines/nlp/test_dialog_intent.py

@@ -4,11 +4,12 @@ import os.path as osp
 import tempfile
 import unittest
 
-from tests.case.nlp.dialog_generation_case import test_case
+from tests.case.nlp.dialog_intent_case import test_case
 
 from maas_lib.models.nlp import DialogIntentModel
 from maas_lib.pipelines import DialogIntentPipeline, pipeline
 from maas_lib.preprocessors import DialogIntentPreprocessor
+from maas_lib.utils.constant import Tasks
 
 
 class DialogGenerationTest(unittest.TestCase):
@@ -22,19 +23,12 @@ class DialogGenerationTest(unittest.TestCase):
             model_dir=modeldir,
             text_field=preprocessor.text_field,
             config=preprocessor.config)
-        print(model.forward(None))
-        # pipeline = DialogGenerationPipeline(model=model, preprocessor=preprocessor)
-        #
-        # history_dialog_info = {}
-        # for step, item in enumerate(test_case['sng0073']['log']):
-        #     user_question = item['user']
-        #     print('user: {}'.format(user_question))
-        #
-        #     # history_dialog_info = merge(history_dialog_info,
-        #     #                             result) if step > 0 else {}
-        #     result = pipeline(user_question, history=history_dialog_info)
-        #     #
-        #     # print('sys : {}'.format(result['pred_answer']))
+        pipeline1 = DialogIntentPipeline(
+            model=model, preprocessor=preprocessor)
+        # pipeline1 = pipeline(task=Tasks.dialog_intent, model=model, preprocessor=preprocessor)
+
+        for item in test_case:
+            pipeline1(item)
 
 
 if __name__ == '__main__':