Simple debug version

pipeline.py

@@ -21,7 +21,7 @@ class PreTrainedPipeline():
         # load the model
         self.model = tf.saved_model.load(os.path.join(path, "saved_model"))
 
-#     def __call__(self, inputs: "Image.Image")-> List[Dict[str, Any]]:
+    def __call__(self, inputs: "Image.Image")-> List[Dict[str, Any]]:
 
         # # convert img to numpy array, resize and normalize to make the prediction
         # img = np.array(inputs)
@@ -81,12 +81,12 @@ class PreTrainedPipeline():
         #         "score": 1.0,
         #     })
 		
-# labels = [{"score":0.9509243965148926,"label":"car","box":{"xmin":142,"ymin":106,"xmax":376,"ymax":229}},
-# 			{"score":0.9981777667999268,"label":"car","box":{"xmin":405,"ymin":146,"xmax":640,"ymax":297}},
-# 			{"score":0.9963648915290833,"label":"car","box":{"xmin":0,"ymin":115,"xmax":61,"ymax":167}},
-# 			{"score":0.974663257598877,"label":"car","box":{"xmin":155,"ymin":104,"xmax":290,"ymax":141}},
-# 			{"score":0.9986898303031921,"label":"car","box":{"xmin":39,"ymin":117,"xmax":169,"ymax":188}},
-# 			{"score":0.9998276233673096,"label":"person","box":{"xmin":172,"ymin":60,"xmax":482,"ymax":396}},
-# 			{"score":0.9996274709701538,"label":"skateboard","box":{"xmin":265,"ymin":348,"xmax":440,"ymax":413}}]
+		labels = [{"score":0.9509243965148926,"label":"car","box":{"xmin":142,"ymin":106,"xmax":376,"ymax":229}},
+					{"score":0.9981777667999268,"label":"car","box":{"xmin":405,"ymin":146,"xmax":640,"ymax":297}},
+					{"score":0.9963648915290833,"label":"car","box":{"xmin":0,"ymin":115,"xmax":61,"ymax":167}},
+					{"score":0.974663257598877,"label":"car","box":{"xmin":155,"ymin":104,"xmax":290,"ymax":141}},
+					{"score":0.9986898303031921,"label":"car","box":{"xmin":39,"ymin":117,"xmax":169,"ymax":188}},
+					{"score":0.9998276233673096,"label":"person","box":{"xmin":172,"ymin":60,"xmax":482,"ymax":396}},
+					{"score":0.9996274709701538,"label":"skateboard","box":{"xmin":265,"ymin":348,"xmax":440,"ymax":413}}]
 
 # return labels