upload pipeline.py

README.md

@@ -1,3 +1,9 @@
+---
+tags:
+- image-classification
+library_name: generic
+---
+
 ## Example
 
 The model is by no means a state-of-the-art model, but nevertheless

pipeline.py

@@ -0,0 +1,48 @@
+import os
+from typing import Dict, List, Any
+from PIL import Image
+import jax
+from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel
+
+
+class PreTrainedPipeline():
+
+    def __init__(self, path=""):
+
+        model_dir = path
+
+        self.model = FlaxVisionEncoderDecoderModel.from_pretrained(model_dir)
+        self.feature_extractor = ViTFeatureExtractor.from_pretrained(model_dir)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
+
+        max_length = 16
+        num_beams = 4
+        self.gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
+
+        @jax.jit
+        def _generate(pixel_values):
+
+            output_ids = self.model.generate(pixel_values, **self.gen_kwargs).sequences
+            return output_ids
+
+        self.generate = _generate
+
+        # compile the model
+        image_path = os.path.join(path, 'val_000000039769.jpg')
+        image = Image.open(image_path)
+        self(image)
+        image.close()
+
+    def __call__(self, inputs: "Image.Image") -> List[str]:
+        """
+        Args:
+        Return:
+        """
+
+        pixel_values = self.feature_extractor(images=inputs, return_tensors="np").pixel_values
+
+        output_ids = self.generate(pixel_values)
+        preds = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+        preds = [pred.strip() for pred in preds]
+
+        return preds