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import numpy as np
import cv2
from rknn.api import RKNN
ONNX_MODEL = 'resnet50.onnx'
RKNN_MODEL = 'resnet50.rknn'
def show_outputs(outputs):
output = outputs[0][0]
index = sorted(range(len(output)), key=lambda k : output[k], reverse=True)
fp = open('./labels.txt', 'r')
labels = fp.readlines()
top5_str = 'resnet50_sparse_infer\n-----TOP 5-----\n'
for i in range(5):
value = output[index[i]]
if value > 0:
topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1])
else:
topi = '[ -1]: 0.0\n'
top5_str += topi
print(top5_str.strip())
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN(verbose=True)
# Pre-process config
print('--> Config model')
rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 57.12, 57.375], target_platform='rk3576', sparse_infer=True)
print('done')
# Load model
print('--> Loading model')
ret = rknn.load_onnx(model=ONNX_MODEL)
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./datasets.txt')
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export rknn model')
ret = rknn.export_rknn(RKNN_MODEL)
if ret != 0:
print('Export rknn model failed!')
exit(ret)
print('done')
# Set inputs
img = cv2.imread('./dog_224x224.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.expand_dims(img, 0)
# Init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime(target='rk3576')
if ret != 0:
print('Init runtime environment failed!')
exit(ret)
print('done')
# Inference
print('--> Running model')
outputs = rknn.inference(inputs=[img], data_format=['nhwc'])
x = outputs[0]
output = np.exp(x)/np.sum(np.exp(x))
outputs = [output]
show_outputs(outputs)
print('done')
rknn.release()
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