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发表于 2021-1-7 18:18:39
谢谢各位路过的大神，
附一下test.py代码
import numpy as np
import cv2
from rknn.api import RKNN
def show_outputs(outputs):
output = outputs[0][0]
output_sorted = sorted(output, reverse=True)
top5_str = 'mobilenet_v1\n-----TOP 5-----\n'
for i in range(5):
value = output_sorted
index = np.where(output == value)
for j in range(len(index)):
if (i + j) >= 5:
break
if value > 0:
topi = '{}: {}\n'.format(index[j], value)
else:
topi = '-1: 0.0\n'
top5_str += topi
print(top5_str)
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='127.5 127.5 127.5 127.5', reorder_channel='0 1 2')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_tflite(model='./mobilenet_v1.tflite')
if ret != 0:
print('Load mobilenet_v1 failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt', pre_compile=False)
if ret != 0:
print('Build mobilenet_v1 failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn('./mobilenet_v1.rknn')
if ret != 0:
print('Export mobilenet_v1.rknn failed!')
exit(ret)
print('done')
# Set inputs
img = cv2.imread('./dog_224x224.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime(device_id='0123456789ABCDEF')
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Inference
print('--> Running model')
outputs = rknn.inference(inputs=[img])
show_outputs(outputs)
print('done')
# perf
print('--> Begin evaluate model performance')
perf_results = rknn.eval_perf(inputs=[img])
print('done')
rknn.release() |
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