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)