Description
Description
I'm trying to run an inference on uint8 static quantized tflite model. The usb accelerator is detected properly, I can also allocate tensors and also set tensor with relevant input. However, when I run the cell with code interpreter.invoke() (I'm using jupyter notebooks in VS Code), it keeps on running indefinitely without either finishing the execution or giving any error.
Model
I'm using Resnet18 model modified for segmentation (decoder part written using standard modules). The model is originally a pytorch model.
Model Conversion
To convert the model to tflite, I'm following the below mentioned process:
PyTorch -> ONNX -> saved_model (onnx2tf) -> Quantization + tflite (tf.lite.TFLiteConverter) -> edgetpu_compiler
PyTorch to ONNX,
ONNX to saved_model,
$ onnx2tf -i torch_resnet18_128.onnx
saved_model to tflite (uint8 Quantized)
tflite to edgetpu
edgetpu_compiler resnet18_128_quantized_uint8.tflite
Inference
Also here's the netron graph of final edgetpu compiled model
Actual Behaviour
When I execute the cells to run the inference code, the execution is stuck at interpreter.invoke call and continues to execute until I kill that process manually.
WELP
PLEASE LET ME KNOW WHAT AM I DOING WRONG
Click to expand!
Issue Type
Bug
Operating System
Windows 10
Coral Device
USB Accelerator
Other Devices
No response
Programming Language
Python 3.8
Relevant Log Output
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