forked from mrq/DL-Art-School
10 lines
296 B
Python
10 lines
296 B
Python
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import onnxruntime
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import numpy as np
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import time
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session = onnxruntime.InferenceSession("../results/gen.onnx")
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v = np.random.randn(1,3,1700,1500)
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st = time.time()
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prediction = session.run(None, {"lr_input": v.astype(np.float32)})
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print("Took %f" % (time.time() - st))
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print(prediction[0].shape)
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