def transfer(pb_path, rknn_name):
# Create RKNN object
rknn = RKNN()
# init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Config for Model Input PreProcess
rknn.config()
# Load TensorFlow Model
print('--> Loading model')
rknn.load_tensorflow(tf_pb=pb_path,
inputs=['Reshape'],
outputs=['probability'],
input_size_list=[[INPUT_WIDTH, INPUT_HEIGHT, 1]])
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# Export RKNN Model
rknn.export_rknn(rknn_name)
# Release RKNN Context
rknn.release()
def to_rknn(pb_path, rknn_path):
rknn = RKNN(verbose=True)
rknn.config(channel_mean_value='0 0 0 1', reorder_channel='0 1 2')
# rknn.config(channel_mean_value='128 128 128 128', reorder_channel='0 1 2')
print('--> Loading model')
rknn.load_tensorflow(tf_pb=pb_path,
inputs=['Placeholder'],
outputs=['ConvPred/ConvPred'],
input_size_list=[[INPUT_HEIGHT, INPUT_WIDTH, 3]])
print('done')
print('--> Building model')
rknn.build(do_quantization=False, pre_compile=True)
print('done')
rknn.export_rknn(rknn_path)
def quantify_transfer(pb_name, dataset_name, export_name):
ret = 0
print(pb_name, dataset_name, export_name)
rknn = RKNN()
rknn.config(channel_mean_value='',
reorder_channel='',
quantized_dtype='dynamic_fixed_point-8')
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb=pb_name,
inputs=['test/x'],
outputs=['test/hypothesis'],
input_size_list=[[1, 4]])
if ret != 0:
print('load_tensorflow error')
rknn.release()
return ret
print('done')
print('--> Building model')
rknn.build(do_quantization=True, dataset=dataset_name)
print('done')
# rknn 모델 파일 저장 내보내기
rknn.export_rknn(export_name)
# Release RKNN Context
rknn.release()
return ret
def common_transfer(pb_name,export_name):
# 특정 로그를 참조하기위함.
ret = 0
rknn = RKNN()
# 이 단계는 grayscale 이미지를 다룰 때 필요하지 않음.
# rknn.config(channel_mean_value='', reorder_channel='')
print('--> Loading model')
ret = rknn.load_tensorflow(
tf_pb='./mnist_frozen_graph.pb',
inputs=['x'],
outputs=['y_conv'],
input_size_list=[[28,28,1]])
if ret != 0:
print('load_tensorflow error')
rknn.release()
return ret
print('done')
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# rknn 모델 파일 저장 내보내기
rknn.export_rknn('./mnist.rknn')
# Release RKNN Context
rknn.release()
return ret
def to_rknn(pb_path, rknn_path):
rknn = RKNN(verbose=True)
rknn.config(channel_mean_value='127.5 127.5 127.5 127.5',
reorder_channel='2 1 0')
rknn.load_tensorflow(
tf_pb=pb_path,
inputs=['input_tensor'],
outputs=[
#'lanenet_model/vgg_backend/binary_seg/ArgMax',
'lanenet_model/vgg_frontend/vgg16_decode_module/binary_seg_decode/binary_final_logits/binary_final_logits', # Workaround RKNN 1.1.0 bug
#'lanenet_model/vgg_backend/binary_seg/Softmax',
'lanenet_model/vgg_backend/instance_seg/pix_embedding_conv/pix_embedding_conv'
],
input_size_list=[[256, 512, 3]])
rknn.build(do_quantization=False, dataset='./dataset.txt')
rknn.export_rknn(rknn_path)
def transfer(pb_path, rknn_name):
# 创建RKNN执行对象
#rknn = RKNN(verbose=True, verbose_file='./mini_XCEPTION_build.log')
rknn = RKNN()
# 配置模型输入，用于NPU对数据输入的预处理
# channel_mean_value='0 0 0 255'，那么模型推理时，将会对RGB数据做如下转换
# (R - 0)/255, (G - 0)/255, (B - 0)/255。推理时，RKNN模型会自动做均值和归一化处理
# reorder_channel=’0 1 2’用于指定是否调整图像通道顺序，设置成0 1 2即按输入的图像通道顺序不做调整
# reorder_channel=’2 1 0’表示交换0和2通道，如果输入是RGB，将会被调整为BGR。如果是BGR将会被调整为RGB
#图像通道顺序不做调整
#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')
rknn.config(quantized_dtype='dynamic_fixed_point-8')
# 加载TensorFlow模型
# tf_pb='digital_gesture.pb'指定待转换的TensorFlow模型
# inputs指定模型中的输入节点
# outputs指定模型中输出节点
# input_size_list指定模型输入的大小
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb=pb_path,
inputs=['input_1'],
outputs=['predictions/Softmax'],
input_size_list=[[INPUT_WIDTH, INPUT_HEIGHT, 1]])
if ret != 0:
print('Load Model failed!')
exit(ret)
print('done')
# 创建解析pb模型
# do_quantization=False指定不进行量化
# 量化会减小模型的体积和提升运算速度，但是会有精度的丢失
print('--> Building model')
ret = rknn.build(do_quantization=False)
if ret != 0:
print('Build Model failed!')
exit(ret)
print('done')
# 导出保存rknn模型文件
print('--> Export RKNN model')
ret = rknn.export_rknn(rknn_name)
if ret != 0:
print('Export Model failed!')
exit(ret)
print('done')
# Release RKNN Context
rknn.release()
def save_rknn(self,
rknnpath,
verbose=True,
verbose_file=None,
input_mean_value='0 0 0 1',
input_channels='0 1 2',
do_quantization=True,
pre_compile=True):
TMP_PB_PATH = './tmp.pb'
from rknn.api import RKNN
self.save_pb(TMP_PB_PATH)
rknn = RKNN(verbose=verbose, verbose_file=verbose_file)
print('--> config model')
rknn.config(channel_mean_value=input_mean_value,
reorder_channel=input_channels)
print('done')
print('--> Loading pb, input shape = ' + str([self.__input_shape]))
ret = rknn.load_tensorflow(tf_pb=TMP_PB_PATH,
inputs=[self.input.op.name],
outputs=[self.output.op.name],
input_size_list=[list(self.__input_shape)])
if ret != 0:
print('Load pb failed! Ret = {}'.format(ret))
exit(ret)
print('done')
print('--> Building model')
ret = rknn.build(do_quantization=do_quantization,
dataset='./rknn_quantization.txt',
pre_compile=pre_compile)
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
print('--> Export RKNN model')
ret = rknn.export_rknn(rknnpath)
if ret != 0:
print('Export rknn failed!')
exit(ret)
print('done')
rknn.release()
def rknn_convert(input_model, output_model, model_input_shape, output_tensor_num, dataset_file, target_platform):
# Create RKNN object
rknn = RKNN()
print('--> config model')
rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2', batch_size=1, target_platform=target_platform)
# Load tensorflow model
print('--> Loading model')
if output_tensor_num == 1:
output_tensor_names = ['predict_conv/BiasAdd']
elif output_tensor_num == 2:
output_tensor_names = ['predict_conv_1/BiasAdd', 'predict_conv_2/BiasAdd']
elif output_tensor_num == 3:
output_tensor_names = ['predict_conv_1/BiasAdd', 'predict_conv_2/BiasAdd', 'predict_conv_3/BiasAdd']
else:
raise ValueError('invalid output tensor number ', output_tensor_num)
ret = rknn.load_tensorflow(tf_pb=input_model,
inputs=['image_input'],
outputs=output_tensor_names,
input_size_list=[model_input_shape+(3,)],
predef_file=None)
#ret = rknn.load_onnx(model=input_model)
if ret != 0:
print('Load failed!')
exit(ret)
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset=dataset_file, pre_compile=True)
if ret != 0:
print('Build failed!')
exit(ret)
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn(output_model)
if ret != 0:
print('Export .rknn failed!')
exit(ret)
# Release RKNN object
rknn.release()
def rknn_convert(input_model, output_model, model_input_shape, dataset_file,
target_platform):
# Create RKNN object
rknn = RKNN()
print('--> config model')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=1,
target_platform=target_platform)
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb=input_model,
inputs=['image_input'],
outputs=['dense/Softmax'],
input_size_list=[model_input_shape + (3, )],
predef_file=None)
#ret = rknn.load_onnx(model=input_model)
if ret != 0:
print('Load failed!')
exit(ret)
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True,
dataset=dataset_file,
pre_compile=True)
if ret != 0:
print('Build failed!')
exit(ret)
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn(output_model)
if ret != 0:
print('Export .rknn failed!')
exit(ret)
# Release RKNN object
rknn.release()
from rknn.api import RKNN
#create RKNN object
rknn = RKNN(verbose=True)
print('--> Loading model')
#freeze된 pb파일, input 노드, output노드, input 사이즈 리스트 입력
#노드 확인은 tensorboard, netron을 활용
ret = rknn.load_tensorflow(tf_pb='/home/yoona/Autopilot-TensorFlow/freeze.pb',
inputs=['Placeholder'],
outputs=['add_9'],
input_size_list=[[66,200,3]])
#오류 발생시 오류 메시지 출력
if ret !=0:
print('Load failed!')
exit(ret)
#Load 완료시 'done'출력
print('done')
print('--> Building model') #빌드하는 중임을 알려주는 출력
ret = rknn.build(do_quantization=False)
#오류 발생시 오류 메시지 출력
if ret !=0:
print('Build failed!')
exit(ret)
#Build 완료시 'done'출력
print('done')
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# Config for Model Input PreProcess
rknn.config(mean_values=[[127.5, 127.5, 127.5]],
std_values=[[127.5, 127.5, 127.5]],
reorder_channel='0 1 2')
# Load TensorFlow Model
print('--> Loading model')
rknn.load_tensorflow(
tf_pb='./ssd_mobilenet_v1_coco_2017_11_17.pb',
inputs=[
'FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1'
],
outputs=['concat', 'concat_1'],
input_size_list=[[INPUT_SIZE, INPUT_SIZE, 3]])
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=True, dataset='./dataset.txt')
print('done')
# Export RKNN Model
rknn.export_rknn('./ssd_mobilenet_v1_coco.rknn')
# Direct Load RKNN Model
# rknn.load_rknn('./ssd_mobilenet_v1_coco.rknn')
from rknn.api import RKNN
#create RKNN object
rknn = RKNN(verbose=True)
#Load model
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb='./convert/freeze.pb',
inputs=['Placeholder'],
outputs=['Softmax'],
input_size_list=[[2304]])
#오류 발생시 오류 메시지 출력
if ret != 0:
print('Load failed!')
exit(ret)
#Load 완료시 'done'출력
print('done')
#Build model
print('--> Building model') #빌드하는 중임을 알려주는 출력
ret = rknn.build(do_quantization=False)
#오류 발생시 오류 메시지 출력
if ret != 0:
print('Build failed!')
exit(ret)
#Build 완료시 'done'출력
print('done')
#Export rknn model
print('--> Export RKNN model')
from rknn.api import RKNN
rknn = RKNN(verbose=True)
#load
ret = rknn.load_tensorflow(tf_pb='./lstm_tanh.pb',
inputs=['Placeholder'],
outputs=['fully_connected/Identity'],
input_size_list=[[ 28, 8]])
if ret !=0:
print('Load failed!')
exit(ret)
print('Load success!')
#build
ret = rknn.build(do_quantization=False)
if ret !=0:
print('Build failed!')
exit(ret)
print('Build success!')
#export
ret = rknn.export_rknn('./lstm.rknn')
if ret != 0:
print('Export failed!')
exit(ret)
print('Saved model')
shutil.copyfile(pb_file, './inception_v3_quant_frozen.pb')
shutil.rmtree(target_dir)
os.remove(download_file)
print('done')
# Set model config
print('--> Config model')
rknn.config(mean_values=[[127.5, 127.5, 127.5]],
std_values=[[128, 128, 128]],
reorder_channel='0 1 2')
print('done')
# Load TensorFlow model
print('--> Loading TensorFlow model')
ret = rknn.load_tensorflow(tf_pb=PB_FILE,
inputs=INPUTS,
outputs=OUTPUTS,
input_size_list=[[INPUT_SIZE, INPUT_SIZE, 3]],
predef_file=None)
if ret != 0:
print('Load inception_v3_quant_frozen failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=False)
if ret != 0:
print('Build inception_v3_quant_frozen.rknn failed!')
exit(ret)
print('done')
def main(folder="test"):
folder = folder
files = os.listdir(folder)
for i in range(len(files)):
img = cv2.imread("{}/{}".format(folder, files[i]))
img = (img - 127.5) / 127.5
h, w = img.shape[:2]
print("w, h = ", w, h)
input = cv2.resize(img, (PRESET, PRESET),
interpolation=cv2.INTER_CUBIC)
input = input.reshape(PRESET, PRESET, 3)
input = np.array(input, dtype=np.float32)
rknn = RKNN()
print('--> Loading model')
#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')
# Load TensorFlow Model
print('--> Loading model')
rknn.load_tensorflow(tf_pb='pretrained/SR_freeze.pb',
inputs=['ESRGAN_g/Conv2D'],
outputs=['output_image'],
input_size_list=[[PRESET, PRESET, 3]])
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# Export RKNN Model
rknn.export_rknn('./sr_rknn.rknn')
# Direct Load RKNN Model
rknn.load_rknn('./sr_rknn.rknn')
# init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
# Inference
print('--> Running model')
output_image = rknn.inference(inputs=[input])
print('complete')
out = np.array(output_image, dtype=np.float64)
print("output_image = ", out.shape)
out = np.squeeze(out)
Y_ = out.reshape(PRESET * 4, PRESET * 4, 3)
Y_ = cv2.resize(Y_, (w * 4, h * 4), interpolation=cv2.INTER_CUBIC)
print("output shape is ", Y_.shape)
#후처리 과정
Y_ = (Y_ + 1) * 127.5
cv2.imwrite("{}/{}_yval.png".format(OUT_DIR, i), Y_)
# Evaluate Perf on Simulator
#rknn.eval_perf()
# Release RKNN Context
rknn.release()
predict_days = 30
if __name__ == '__main__':
rknn = RKNN()
print('--> Loading model')
#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')
# Load TensorFlow Model
print('--> Loading model')
rknn.load_tensorflow(tf_pb='./freeze.pb',
inputs=['Placeholder'],
outputs=['fully_connected/Identity'],
input_size_list=[[28, 6]])
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# Export RKNN Model
rknn.export_rknn('./MTM_LSTM_RKNN.rknn')
# Direct Load RKNN Model
rknn.load_rknn('./MTM_LSTM_RKNN.rknn')
stock_file_name = 'AAPL_1m.csv'
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)
print('--> Init runtime environment')
ret = rknn.init_runtime(host='rk3399pro')
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Config for Model Input PreProcess
rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')
#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='2 1 0')
# Load TensorFlow Model
print('--> Loading model')
rknn.load_tensorflow(
tf_pb='../digital_gesture_recognition/model_2500/digital_gesture.pb',
inputs=['input_x'],
outputs=['probability'],
input_size_list=[[INPUT_SIZE, INPUT_SIZE, 3]])
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=False, dataset='./dataset.txt')
print('done')
# Export RKNN Model
rknn.export_rknn('./digital_gesture.rknn')
# Release RKNN Context
rknn.release()
if os.path.exists(pb_file):
shutil.copyfile(pb_file, './inception_v3_quant_frozen.pb')
shutil.rmtree(target_dir)
os.remove(download_file)
print('done')
# Set model config
print('--> Config model')
rknn.config(reorder_channel='0 1 2')
print('done')
# Load TensorFlow model
print('--> Loading TensorFlow model')
ret = rknn.load_tensorflow(tf_pb=PB_FILE,
inputs=INPUTS,
outputs=OUTPUTS,
input_size_list=[[INPUT_SIZE, INPUT_SIZE, 3]],
predef_file=None,
mean_values='127.5',
std_values='128')
if ret != 0:
print('Load inception_v3_quant_frozen failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=False)
if ret != 0:
print('Build inception_v3_quant_frozen.rknn failed!')
exit(ret)
print('done')
def convert_model(model_path, out_path, pre_compile):
if os.path.isfile(model_path):
yaml_config_file = model_path
model_path = os.path.dirname(yaml_config_file)
else:
yaml_config_file = os.path.join(model_path, 'model_config.yml')
if not os.path.exists(yaml_config_file):
print('model config % not exist!' % yaml_config_file)
exit(-1)
model_configs = parse_model_config(yaml_config_file)
exported_rknn_model_path_list = []
for model_name in model_configs['models']:
model = model_configs['models'][model_name]
rknn = RKNN()
rknn.config(**model['configs'])
print('--> Loading model...')
if model['platform'] == 'tensorflow':
model_file_path = os.path.join(model_path,
model['model_file_path'])
input_size_list = []
for input_size_str in model['subgraphs']['input-size-list']:
input_size = list(map(int, input_size_str.split(',')))
input_size_list.append(input_size)
pass
rknn.load_tensorflow(tf_pb=model_file_path,
inputs=model['subgraphs']['inputs'],
outputs=model['subgraphs']['outputs'],
input_size_list=input_size_list)
elif model['platform'] == 'tflite':
model_file_path = os.path.join(model_path,
model['model_file_path'])
rknn.load_tflite(model=model_file_path)
elif model['platform'] == 'caffe':
prototxt_file_path = os.path.join(model_path,
model['prototxt_file_path'])
caffemodel_file_path = os.path.join(model_path,
model['caffemodel_file_path'])
rknn.load_caffe(model=prototxt_file_path,
proto='caffe',
blobs=caffemodel_file_path)
elif model['platform'] == 'onnx':
model_file_path = os.path.join(model_path,
model['model_file_path'])
rknn.load_onnx(model=model_file_path)
else:
print("platform %s not support!" % (model['platform']))
print('done')
if model['quantize']:
dataset_path = os.path.join(model_path, model['dataset'])
else:
dataset_path = './dataset'
print('--> Build RKNN model...')
rknn.build(do_quantization=model['quantize'],
dataset=dataset_path,
pre_compile=pre_compile)
print('done')
export_rknn_model_path = "%s.rknn" % (os.path.join(
out_path, model_name))
print('--> Export RKNN model to: {}'.format(export_rknn_model_path))
rknn.export_rknn(export_path=export_rknn_model_path)
exported_rknn_model_path_list.append(export_rknn_model_path)
print('done')
return exported_rknn_model_path_list
from rknn.api import RKNN
#create RKNN object
rknn = RKNN(verbose=True)
#model config
rknn.config(channel_mean_value='103.94 116.78 123.68 58.82',
reorder_channel='0 1 2',
need_horizontal_merge=True)
# input data가 3 channel 일때만 이 config 사용
# input이 channel1이면 무시
#Load model
print('--> Loading model') #로딩하는 중임을 알려주는 출력
ret = rknn.load_tensorflow(tf_pb='./freeze.pb',
inputs=['Placeholder'],
outputs=['Softmax'],
input_size_list=[[2500]]) # w*h(w, h, 1)
#오류 발생시 오류 메시지 출력
if ret != 0:
print('Load failed!')
exit(ret)
#Load 완료시 'done'출력
print('Load done')
#Build model
print('--> Buliding model') # 빌드하려는 중
ret = rknn.build(do_quantization=False)
#Bulid 오류 발생시 오류 메시지 출력
# channel_mean_value "128 128 128 128" while normalize the image data to range [-1, 1]
# reorder_channel "0 1 2" will keep the color channel, "2 1 0" will swap the R and B channel,
# i.e. if the input is BGR loaded by cv2.imread, it will convert it to RGB for the model input.
# need_horizontal_merge is suggested for inception models (v1/v3/v4).
rknn.config(
channel_mean_value="128 128 128 128",
reorder_channel="0 1 2",
need_horizontal_merge=True,
quantized_dtype="asymmetric_quantized-u8",
)
# Load tensorflow model
print("--> Loading model")
ret = rknn.load_tensorflow(
tf_pb="./model/frozen_model.pb",
inputs=INPUT_NODE,
outputs=OUTPUT_NODE,
input_size_list=[[img_height, img_height, 3]],
)
if ret != 0:
print("Load inception_v3 failed!")
exit(ret)
# Build model
print("--> Building model")
# dataset: A input data set for rectifying quantization parameters.
ret = rknn.build(do_quantization=True, dataset="./dataset.txt")
if ret != 0:
print("Build inception_v3 failed!")
exit(ret)
# Export rknn model
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)
rknn = RKNN()
# Set model config
print('--> Config model')
rknn.config(mean_values=[[127.5, 127.5, 127.5]],
std_values=[[127.5, 127.5, 127.5]],
reorder_channel='0 1 2',
quantized_dtype='asymmetric_quantized-u8',
batch_size=16)
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_tensorflow(
tf_pb='./ssd_mobilenet_v2.pb',
inputs=['FeatureExtractor/MobilenetV2/MobilenetV2/input'],
outputs=['concat_1', 'concat'],
input_size_list=[[300, 300, 3]],
predef_file=None)
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Hybrid quantization step1
print('--> hybrid_quantization_step1')
ret = rknn.hybrid_quantization_step1(dataset='./dataset.txt')
if ret != 0:
print('hybrid_quantization_step1 failed!')
exit(ret)
print('done')
fingerprint_size = fingerprint_width * spectrogram_length
# 메인 함수가 시작됩니다
if __name__ == '__main__':
rknn = RKNN()
print('--> Loading model')
#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')
# Load TensorFlow Model
print('--> Loading model')
rknn.load_tensorflow(tf_pb='./freeze_conv.pb',
inputs=['fingerprint_input'],
outputs=['ArgMax'],
input_size_list=[[3920]])
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# Export RKNN Model
rknn.export_rknn('./CNN_RKNN.rknn')
# Direct Load RKNN Model
rknn.load_rknn('./CNN_RKNN.rknn')
# init runtime environment
