Create pipeline.py

pipeline.py

@@ -0,0 +1,33 @@
+from torchvision import transforms
+from pair_classification import PairClassificationPipeline
+from typing import  Dict
+
+
+class PreTrainedPipeline():
+    def __init__(self, path):
+        """
+        Initialize model
+        """
+        model_flag = 'google/vit-base-patch16-224-in21k'
+        # self.processor = feature_extractor = ViTFeatureExtractor.from_pretrained(model_flag)
+        self.pipe = pipeline("pair-classification", model=model_flag , feature_extractor=model_flag , 
+                model_kwargs={'num_labels':len(label2id),
+                            'label2id':label2id,
+                            'id2label':id2label,
+                            'num_channels':6,
+                            'ignore_mismatched_sizes': True })
+        self.model = self.pipe.model.from_pretrained(path)
+        
+
+    def __call__(self, inputs):
+        """
+        Args:
+            inputs (:obj:`np.array`):
+                The raw waveform of audio received. By default at 16KHz.
+        Return:
+            A :obj:`dict`:. The object return should be liked {"text": "XXX"} containing
+            the detected text from the input audio.
+        """
+        # input_values = self.processor(inputs, return_tensors="pt", sampling_rate=self.sampling_rate).input_values  # Batch size 1
+        # logits = self.model(input_values).logits.cpu().detach().numpy()[0]
+        return self.pipe(inputs)