downgrade onnx and tf2onnx, that don't work with newest tensorflow

Author: pythonlessons

CHANGELOG.md

@@ -1,3 +1,7 @@
+## [1.0.11] - 2022-06-07
+### Changed
+- Downgrade `tf2onnx` and `onnx` versions, they don't work with newest TensorFlow version
+
 ## [1.0.10] - 2022-06-06
 ### Changed
 - Updated `BaseModelConfigs` in `mltu.configs.py` to include instance_attributes

mltu/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.0.10"
+__version__ = "1.0.11"
 
 from .annotations.images import Image
 from .annotations.images import CVImage

mltu/tensorflow/callbacks.py

@@ -1,6 +1,4 @@
 import os
-import tf2onnx
-import onnx
 from keras.callbacks import Callback
 
 import logging
@@ -22,22 +20,29 @@ def __init__(
         self.metadata = metadata
 
     def on_train_end(self, logs=None):
-        self.model.load_weights(self.saved_model_path)
-        self.onnx_model_path = self.saved_model_path.replace(".h5", ".onnx")
-        tf2onnx.convert.from_keras(self.model, output_path=self.onnx_model_path)
-
-        if self.metadata and isinstance(self.metadata, dict):
-            # Load the ONNX model
-            onnx_model = onnx.load(self.onnx_model_path)
-
-            # Add the metadata dictionary to the model's metadata_props attribute
-            for key, value in self.metadata.items():
-                meta = onnx_model.metadata_props.add()
-                meta.key = key
-                meta.value = value
-
-            # Save the modified ONNX model
-            onnx.save(onnx_model, self.onnx_model_path)
+        """ Converts the model to onnx format after training is finished. """
+        try:
+            import onnx
+            import tf2onnx
+            self.model.load_weights(self.saved_model_path)
+            self.onnx_model_path = self.saved_model_path.replace(".h5", ".onnx")
+            tf2onnx.convert.from_keras(self.model, output_path=self.onnx_model_path)
+
+            if self.metadata and isinstance(self.metadata, dict):
+                # Load the ONNX model
+                onnx_model = onnx.load(self.onnx_model_path)
+
+                # Add the metadata dictionary to the model's metadata_props attribute
+                for key, value in self.metadata.items():
+                    meta = onnx_model.metadata_props.add()
+                    meta.key = key
+                    meta.value = value
+
+                # Save the modified ONNX model
+                onnx.save(onnx_model, self.onnx_model_path)
+
+        except Exception as e:
+            print(e)
 
 
 class TrainLogger(Callback):

requirements.txt

@@ -7,5 +7,5 @@ Pillow>=9.4.0
 onnxruntime>=1.15.0  # onnxruntime-gpu for GPU support
 librosa>=0.9.2
 matplotlib
-onnx>=1.14.0
-tf2onnx>=1.14.0
+onnx==1.12.0
+tf2onnx==1.12.0