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54 lines
1.5 KiB
Python
Executable File
54 lines
1.5 KiB
Python
Executable File
# RUN: %PYTHON %s
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import numpy as np
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from amdshark.amdshark_importer import AMDSharkImporter
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import pytest
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model_path = "https://tfhub.dev/tensorflow/lite-model/albert_lite_base/squadv1/1?lite-format=tflite"
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# Inputs modified to be useful albert inputs.
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def generate_inputs(input_details):
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for input in input_details:
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print(str(input["shape"]), input["dtype"].__name__)
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args = []
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args.append(
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np.random.randint(
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low=0,
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high=256,
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size=input_details[0]["shape"],
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dtype=input_details[0]["dtype"],
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)
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)
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args.append(
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np.ones(
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shape=input_details[1]["shape"], dtype=input_details[1]["dtype"]
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)
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)
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args.append(
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np.zeros(
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shape=input_details[2]["shape"], dtype=input_details[2]["dtype"]
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)
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)
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return args
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if __name__ == "__main__":
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my_amdshark_importer = AMDSharkImporter(
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model_path=model_path,
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model_type="tflite",
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model_source_hub="tfhub",
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device="cpu",
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dynamic=False,
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jit_trace=True,
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)
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# Case1: Use default inputs
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my_amdshark_importer.compile()
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amdshark_results = my_amdshark_importer.forward()
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# Case2: Use manually set inputs
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input_details, output_details = my_amdshark_importer.get_model_details()
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inputs = generate_inputs(input_details) # device_inputs
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my_amdshark_importer.compile(inputs)
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amdshark_results = my_amdshark_importer.forward(inputs)
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# print(amdshark_results)
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