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mnist-runPyRuntime.py
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mnist-runPyRuntime.py
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#!/usr/bin/env python3
import numpy as np
from PyRuntime import OMExecutionSession
# Load the model mnist.so compiled with onnx-mlir.
session = OMExecutionSession('./mnist.so')
# Print the models input/output signature, for display.
# Signature functions for info only, commented out if they cause problems.
print("input signature in json", session.input_signature())
print("output signature in json",session.output_signature())
# Create an input arbitrarily filled of 1.0 values.
input = np.array([-0.4242129623889923, -0.4242129623889923,
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2.668747901916504, 2.808758497238159, 2.796030282974243,
2.5541937351226807, 1.5104787349700928, 1.5232069492340088,
2.5414655208587646, 2.808758497238159, 2.5414655208587646,
1.1413596868515015, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
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-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, 0.08491640537977219, 1.4977505207061768,
2.5287373065948486, 2.796030282974243, 2.783302068710327,
2.796030282974243, 2.783302068710327, 1.4977505207061768,
0.21219873428344727, -0.4242129623889923, -0.4242129623889923,
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-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
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-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
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-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
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-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923, -0.4242129623889923,
-0.4242129623889923, -0.4242129623889923], np.dtype(np.float32)).reshape(1,1,28,28)
# Run the model. It is best to always use the [] around the inputs as the inputs
# are an vector of numpy arrays.
outputs = session.run([input])
# Analyze the output (first array in the list, of signature 1x10xf32).
prediction = outputs[0]
digit = -1
prob = 0.0
for i in range(0, 10):
print("prediction ", i, "=", prediction[0, i])
if prediction[0, i] > prob:
digit = i
prob = prediction[0, i]
# Print the value with the highest prediction (8 here).
print("The digit is", digit)