mirror of https://github.com/alibaba/MNN.git
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
# Copyright @ 2019 Alibaba. All rights reserved.
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""" python demo usage about MNN API """
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from __future__ import print_function
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import numpy as np
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import MNN
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import sys
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import cv2
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def inference():
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""" inference mobilenet_v1 using a specific picture """
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config = {}
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config['precision'] = 'low'
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config['backend'] = 3
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config['numThread'] = 4
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rt = MNN.nn.create_runtime_manager((config,))
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rt.set_cache(".cachefile")
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# set_mode(type) //type 9 for "auto_backend"
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rt.set_mode(9)
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# set_hint(type, value) //type 0 for "tune_num"
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rt.set_hint(0, 20)
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net = MNN.nn.load_module_from_file(sys.argv[1], ["input"], ["MobilenetV1/Predictions/Reshape_1"], runtime_manager=rt)
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image = cv2.imread(sys.argv[2])
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#cv2 read as bgr format
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image = image[..., ::-1]
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#change to rgb format
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image = cv2.resize(image, (224, 224))
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#resize to mobile_net tensor size
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image = image - (103.94, 116.78, 123.68)
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image = image * (0.017, 0.017, 0.017)
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#change numpy data type as np.float32 to match tensor's format
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image = image.astype(np.float32)
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input_var = MNN.expr.placeholder([1, 224, 224, 3], MNN.expr.NHWC)
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input_var.write(image)
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input_var = MNN.expr.convert(input_var, MNN.expr.NC4HW4)
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#inference
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output_var = net.forward([input_var])
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output_var = output_var[0]
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output_var = MNN.expr.convert(output_var, MNN.expr.NHWC)
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print("expect 983")
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print("output belong to class: {}".format(np.argmax(output_var.read())))
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rt.update_cache()
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if __name__ == "__main__":
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inference()
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