Fix model preparation step failure in ONNXRT examples (#1423)

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>

examples/onnxrt/image_recognition/resnet50_mlperf/quantization/ptq_static/README.md

@@ -19,6 +19,8 @@ pip install -r requirements.txt
 python prepare_model.py --input_model='resnet50_v1.pb' --output_model='resnet50_v1.onnx'
 ```
 
+> Note: For now, use "onnx==1.14.1" in this step in case you get an error `ValueError: Could not infer attribute explicit_paddings type from empty iterator`. Refer to this [link](https://github.com/onnx/tensorflow-onnx/issues/2262) for more details of this error.
+
 ## 3. Prepare Dataset
 
 Download dataset [ILSVR2012 validation Imagenet dataset](http://www.image-net.org/challenges/LSVRC/2012/downloads).

examples/onnxrt/image_recognition/resnet50_mlperf/quantization/ptq_static/prepare_model.py

@@ -8,6 +8,12 @@
 MAX_TIMES_RETRY_DOWNLOAD = 5
 
 
+def reinstall_onnx():
+    # For now, use "onnx==1.14.1" in this step in case you get an error 
+    # `ValueError: Could not infer attribute explicit_paddings type from empty iterator`. 
+    # Refer to https://github.com/onnx/tensorflow-onnx/issues/2262 for more details of this error.
+    subprocess.call(['pip', 'install', 'onnx==1.14.1'])
+
 def parse_arguments():
     parser = argparse.ArgumentParser()
     parser.add_argument("--input_model", type=str, required=False, default="resnet50_v1.pb")
@@ -91,4 +97,5 @@ def prepare_model(input_model, output_model):
 
 if __name__ == "__main__":
     args = parse_arguments()
+    reinstall_onnx()
     prepare_model(args.input_model, args.output_model)

examples/onnxrt/object_detection/ssd_mobilenet_v1/quantization/ptq_static/README.md

@@ -19,6 +19,8 @@ pip install -r requirements.txt
 python prepare_model.py --output_model='ssd_mobilenet_v1_coco_2018_01_28.onnx'
 ```
 
+> Note: For now, use "onnx==1.14.1" in this step in case you get an error `ValueError: Could not infer attribute explicit_paddings type from empty iterator`. Refer to this [link](https://github.com/onnx/tensorflow-onnx/issues/2262) for more details of this error.
+
 ## 3. Prepare Dataset
 
 Download [MS COCO 2017 dataset](https://cocodataset.org/#download).

examples/onnxrt/object_detection/ssd_mobilenet_v1/quantization/ptq_static/prepare_model.py

@@ -10,6 +10,12 @@
 MAX_TIMES_RETRY_DOWNLOAD = 5
 
 
+def reinstall_onnx():
+    # For now, use "onnx==1.14.1" in this step in case you get an error 
+    # `ValueError: Could not infer attribute explicit_paddings type from empty iterator`. 
+    # Refer to https://github.com/onnx/tensorflow-onnx/issues/2262 for more details of this error.
+    subprocess.call(['pip', 'install', 'onnx==1.14.1'])
+
 def parse_arguments():
     parser = argparse.ArgumentParser()
     parser.add_argument("--input_model",
@@ -105,4 +111,5 @@ def prepare_model(input_model, output_model):
 
 if __name__ == "__main__":
     args = parse_arguments()
+    reinstall_onnx()
     prepare_model(args.input_model, args.output_model)

examples/onnxrt/object_detection/ssd_mobilenet_v2/quantization/ptq_static/README.md

@@ -19,6 +19,8 @@ pip install -r requirements.txt
 python prepare_model.py --output_model='ssd_mobilenet_v2_coco_2018_03_29.onnx'
 ```
 
+> Note: For now, use "onnx==1.14.1" in this step in case you get an error `ValueError: Could not infer attribute explicit_paddings type from empty iterator`. Refer to this [link](https://github.com/onnx/tensorflow-onnx/issues/2262) for more details of this error.
+
 ## 3. Prepare Dataset
 
 Download [MS COCO 2017 dataset](https://cocodataset.org/#download).

examples/onnxrt/object_detection/ssd_mobilenet_v2/quantization/ptq_static/prepare_model.py

@@ -9,6 +9,12 @@
 MAX_TIMES_RETRY_DOWNLOAD = 5
 
 
+def reinstall_onnx():
+    # For now, use "onnx==1.14.1" in this step in case you get an error 
+    # `ValueError: Could not infer attribute explicit_paddings type from empty iterator`. 
+    # Refer to https://github.com/onnx/tensorflow-onnx/issues/2262 for more details of this error.
+    subprocess.call(['pip', 'install', 'onnx==1.14.1'])
+
 def parse_arguments():
     parser = argparse.ArgumentParser()
     parser.add_argument("--input_model",
@@ -105,4 +111,5 @@ def prepare_model(input_model, output_model):
 
 if __name__ == "__main__":
     args = parse_arguments()
+    reinstall_onnx()
     prepare_model(args.input_model, args.output_model)