import cv2
import numpy as np
from rknn.api import RKNN
if __name__ == '__main__':
rknn = RKNN(verbose=False)
rknn.register_op('./truncatediv/TruncateDiv.rknnop')
rknn.register_op('./exp/Exp.rknnop')
rknn.load_tensorflow(tf_pb='./custom_op_math.pb',
inputs=['input'],
outputs=['exp_0'],
input_size_list=[[1, 512]])
rknn.build(do_quantization=False)
# rknn.export_rknn('./rknn_test.rknn')
# rknn.load_rknn('./rknn_test.rknn')
rknn.init_runtime()
print("init runtime done")
in_data = np.full((1, 512), 50.0)
in_data = in_data.astype(dtype='float32')
output = rknn.inference(inputs=[in_data])
print(output)
import cv2
import numpy as np
from rknn.api import RKNN
if __name__ == '__main__':
rknn = RKNN(verbose=False)
rknn.register_op('./resize_area/ResizeArea.rknnop')
rknn.load_tensorflow(tf_pb='./resize_area_test.pb',
inputs=['input'],
outputs=['resize_area_0'],
input_size_list=[[32, 32, 3]])
rknn.build(do_quantization=False)
# rknn.export_rknn('./resize_area.rknn')
# rknn.load_rknn('./resize_area.rknn')
rknn.init_runtime()
img = cv2.imread('./dog_32x32.jpg')
outs = rknn.inference(inputs=[img])
out_img = outs[0].astype('uint8')
out_img = np.reshape(out_img, (64, 64, 3))
cv2.imwrite('./out.jpg', out_img)
