From b85ed1be95e5ba6c9c309681caed3bcad92a6e03 Mon Sep 17 00:00:00 2001
From: David Rubinstein <drubinstein@spotify.com>
Date: Sun, 1 Oct 2023 19:49:21 -0400
Subject: [PATCH] More informative error

---
 basic_pitch/inference.py | 44 ++++++++++++++++++++++++++++++++++------
 1 file changed, 38 insertions(+), 6 deletions(-)

diff --git a/basic_pitch/inference.py b/basic_pitch/inference.py
index 0fd7c76f..746f22d2 100644
--- a/basic_pitch/inference.py
+++ b/basic_pitch/inference.py
@@ -18,6 +18,7 @@
 import csv
 import enum
 import json
+import logging
 import os
 import pathlib
 from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union, cast
@@ -72,44 +73,75 @@ class MODEL_TYPES(enum.Enum):
         ONNX = enum.auto()
 
     def __init__(self, model_path: Union[pathlib.Path, str]):
+        present = []
         if TF_PRESENT:
+            present.append("TensorFlow")
             try:
                 self.model_type = Model.MODEL_TYPES.TENSORFLOW
                 self.model = tf.saved_model.load(model_path)
                 return
             except Exception:
-                pass
+                if os.path.isdir(model_path) and {"saved_model.pb", "variables"} & set(os.path.listdir(model_path)):
+                    logging.warning(
+                        "Could not load TensorFlowLite file %s even "
+                        "though it looks like a TFLite file with error %s. Are you sure it's a TFLite file",
+                        model_path,
+                        e.__repr__(),
+                    )
 
         if CT_PRESENT:
+            present.append("CoreML")
             try:
                 self.model_type = Model.MODEL_TYPES.COREML
                 self.model = ct.models.MLModel(str(model_path))
                 return
             except Exception:
-                pass
+                if str(model_path).endswith(".mlpackage"):
+                    logging.warning(
+                        "Could not load TensorFlowLite file %s even "
+                        "though it looks like a TFLite file with error %s. Are you sure it's a TFLite file",
+                        model_path,
+                        e.__repr__(),
+                    )
 
         if TFLITE_PRESENT:
+            present.append("TensorFlowLite")
             try:
                 self.model_type = Model.MODEL_TYPES.TFLITE
                 self.interpreter = tflite.Interpreter(model_path=model_path)
                 self.interpreter.allocate_tensors()
                 self.model = self.interpreter.get_signature_runner()
                 return
-            except Exception:
-                pass
+            except Exception as e:
+                if str(model_path).endswith(".tflite"):
+                    logging.warning(
+                        "Could not load TensorFlowLite file %s even "
+                        "though it looks like a TFLite file with error %s. Are you sure it's a TFLite file",
+                        model_path,
+                        e.__repr__(),
+                    )
 
         if ONNX_PRESENT:
+            present.append("ONNX")
             try:
                 self.model_type = Model.MODEL_TYPES.ONNX
                 self.model = ort.InferenceSession(model_path)
                 return
             except Exception:
-                pass
+                if str(model_path).endswith(".onnx"):
+                    logging.warning(
+                        "Could not load TensorFlowLite file %s even "
+                        "though it looks like a TFLite file with error %s. Are you sure it's a TFLite file",
+                        model_path,
+                        e.__repr__(),
+                    )
 
         raise ValueError(
             f"File {model_path} cannot be loaded into either "
             "TensorFlow, CoreML, TFLite or ONNX. "
-            "Please check if it is a supported and valid serialized model"
+            "Please check if it is a supported and valid serialized model "
+            "and that one of these packages are installed. On this system, "
+            f"{present} is installed."
         )
 
     def predict(self, x: npt.NDArray[np.float32]) -> Dict[str, npt.NDArray[np.float32]]: