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 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 quanlization_darknet_model(model_path,
weight_path,
dataset_txt,
is_quantization=True,
pre_compile=False):
# 创建RKNN执行对象
rknn = RKNN(verbose=True, verbose_file='verbose.log')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=4)
flag = rknn.load_darknet(model=model_path, weight=weight_path)
# 后面不用修改
if flag == 0:
print('load_darknet success')
else:
print('load_darknet failure')
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')
print(os.getcwd())
# flag = rknn.build(do_quantization=False)
if is_quantization == True:
flag = rknn.build(do_quantization=True,
dataset=dataset_txt,
pre_compile=pre_compile)
print('do quantization ')
# 导出保存rknn模型文件
save_rknn_path = model_dir + '/' + model_path.split('/')[-1].split(
'.')[0] + '_quan.rknn'
else:
flag = rknn.build(do_quantization=False)
print('not do quantization')
# 导出保存rknn模型文件
save_rknn_path = model_dir + '/' + model_path.split('/')[-1].split(
'.')[0] + '.rknn'
if flag == 0:
print('build success')
else:
print('build failure')
print('done')
flag = rknn.export_rknn(save_rknn_path)
if flag == 0:
print('export success')
else:
print('export failure')
print('done')
print(save_rknn_path)
# Release RKNN Context
rknn.release()
print('save_rknn_path:', save_rknn_path)
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 convert_to_rknn():
from rknn.api import RKNN
# Create RKNN object
rknn = RKNN(verbose=True)
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='127.5 127.5 127.5 128',
reorder_channel='0 1 2')
print('done')
# Load onnx model
print('--> Loading model')
ret = rknn.load_onnx(model='lprnet.onnx')
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=False, pre_compile=True, dataset='./data/dataset.txt')
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn('./lprnet.rknn')
if ret != 0:
print('Export model failed!')
exit(ret)
print('done')
def rknn_convert(input_model, output_model, dataset_file, target_platform):
# Create RKNN object
rknn = RKNN()
print('--> config model')
rknn.config(channel_mean_value='127.5 127.5 127.5 127.5', reorder_channel='0 1 2', batch_size=1, target_platform=target_platform)
# Load onnx model
print('--> Loading model')
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 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 keras_to_rknn(cfg_path, h5_path, darknet_path, rknn_path, dataset_path,
flag):
# ------------------------------------------------------#
# h5 -> darknet, 保存成.weights
# ------------------------------------------------------#
keras_loader = KerasParser(cfg_path, h5_path, darknet_path)
for block in keras_loader.block_gen:
if 'convolutional' in block['type']:
keras_loader.conv(block)
keras_loader.close()
# ------------------------------------------------------#
# darknet -> rknn， 保存成.rknn
# ------------------------------------------------------#
# Create RKNN object
rknn = RKNN()
# Load tensorflow model
print('--> Loading model')
rknn.load_darknet(model=cfg_path, weight=darknet_path)
print('done')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=1)
# Build model
print('--> Building model')
if flag:
rknn.build(do_quantization=True,
dataset=dataset_path,
pre_compile=True)
else:
rknn.build(do_quantization=False, pre_compile=True)
print('done')
# export model
print('--> Export model')
rknn.export_rknn(rknn_path)
print('done')
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 caffe2rknn(caffe_proto,caffe_weight,rknn_model):
print("start export")
rknn=RKNN(verbose=True)
ret=rknn.load_caffe(model=caffe_proto,
proto="caffe",
blobs=caffe_weight)
rknn.config(channel_mean_value='127.5 127.5 127.5 128.0',
reorder_channel='2 1 0',
#reorder_channel='0 1 2',
#need_horizontal_merge=True
)
ret = rknn.build(do_quantization=False)
#ret = rknn.build(do_quantization=True)
ret=rknn.export_rknn(export_path=rknn_model)
print("export finished")
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()
# pre-process 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')
print('done')
# Load keras model
print('--> Loading model')
ret = rknn.load_keras(model=KERAS_MODEL_PATH)
if ret != 0:
print('Load keras model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
print('Build pytorch failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn('./xception.rknn')
if ret != 0:
print('Export xception.rknn failed!')
exit(ret)
print('done')
# ret = rknn.load_rknn('./xception.rknn')
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='103.94 116.78 123.68 58.82',
reorder_channel='0 1 2')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_darknet(model=yolov3_model_cfg, weight=yolov3_weights)
if ret != 0:
raise Exception('Load darknet yolov3 failed!')
print('done')
# Build model
print('--> Building model')
build_timer = timer()
# ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
# do_quantization:是否对模型进行量化,值为 True 或 False。
ret = rknn.build(do_quantization=False, pre_compile=pre_compile)
if ret != 0:
raise Exception('Build yolov3 failed!')
print('done, time: %.2fs' % (timer() - build_timer))
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn(rknn_model)
if ret != 0:
raise Exception('Export rknn model: %s failed!' % rknn_model)
print('done: %s, time: %.2fs' % (rknn_model, timer() - total_timer))
def main():
with open(yaml_file, 'r') as F:
config = yaml.load(F)
# print('config is:')
# print(config)
model_type = config['running']['model_type']
print('model_type is {}'.format(model_type))
rknn = RKNN(verbose=True)
print('--> config model')
rknn.config(**config['config'])
print('done')
print('--> Loading model')
load_function = getattr(rknn, _model_load_dict[model_type])
ret = load_function(**config['parameters'][model_type])
if ret != 0:
print('Load mobilenet_v2 failed! Ret = {}'.format(ret))
exit(ret)
print('done')
####
# print('hybrid_quantization')
# ret = rknn.hybrid_quantization_step1(dataset=config['build']['dataset'])
if model_type != 'rknn':
print('--> Building model')
ret = rknn.build(**config['build'])
if ret != 0:
print('Build mobilenet_v2 failed!')
exit(ret)
else:
print('--> skip Building model step, cause the model is already rknn')
if config['running']['export'] is True:
print('--> Export RKNN model')
ret = rknn.export_rknn(**config['export_rknn'])
if ret != 0:
print('Init runtime environment failed')
exit(ret)
else:
print('--> skip Export model')
if (config['running']['inference'] is True) or (config['running']['eval_perf'] is True):
print('--> Init runtime environment')
ret = rknn.init_runtime(**config['init_runtime'])
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('--> load img')
img = cv2.imread(config['img']['path'])
print('img shape is {}'.format(img.shape))
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
inputs = [img]
if config['running']['inference'] is True:
print('--> Running model')
config['inference']['inputs'] = inputs
#print(config['inference'])
outputs = rknn.inference(inputs)
#outputs = rknn.inference(config['inference'])
print('len of output {}'.format(len(outputs)))
print('outputs[0] shape is {}'.format(outputs[0].shape))
print(outputs[0][0][0:2])
else:
print('--> skip inference')
if config['running']['eval_perf'] is True:
print('--> Begin evaluate model performance')
config['inference']['inputs'] = inputs
perf_results = rknn.eval_perf(inputs=[img])
else:
print('--> skip eval_perf')
else:
print('--> skip inference')
print('--> skip eval_perf')
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'
encoding = 'euc-kr'
names = ['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']
# Read and Delete Axis
raw_dataframe = pd.read_csv(stock_file_name, names=names, encoding=encoding)
today = raw_dataframe.values[-1, 0]
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# Load tensorflow model
print('--> Loading model')
rknn.load_darknet(model='./yolov3.cfg', weight="./yolov3.weights")
#rknn.load_darknet(model='./yolov3-tiny.cfg', weight="./yolov3-tiny.weights")
print('done')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=1)
# Build model
print('--> Building model')
#rknn.build(do_quantization=True, dataset='./dataset.txt')
rknn.build(do_quantization=True, dataset='./dataset_608x608.txt')
print('done')
#rknn.export_rknn('./yolov3_tiny.rknn')
rknn.export_rknn('./yolov3.rknn')
#rknn.load_rknn('./yolov3.rknn')
#image = Image.open('./dog.jpg').resize((416, 416))
#rknn.eval_perf(inputs=[image], is_print=True)
exit(0)
quantized_dtype='asymmetric_quantized-u8')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_tflite(
model='../../tflite/mobilenet_v1/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',
rknn_batch_size=4)
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
quantized_dtype='dynamic_fixed_point-8')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_caffe(model='./PNet.prototxt',
proto='caffe',
blobs='./PNet.caffemodel') ##csq
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True,
dataset='./dataset_pnet_270_207.txt') ####csq csq
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn('./PNet_270_207.rknn') ##csq
if ret != 0:
print('Export model failed!')
exit(ret)
print('done')
# init runtime environment
#print('--> Init runtime environment')
]],
std_values=[[
255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255.0,
255.0, 255.0, 255.0, 255.0
]],
batch_size=opt.batch_size,
target_platform="rk3399pro") # reorder_channel='0 1 2',
else:
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='2 1 0',
batch_size=opt.batch_size,
target_platform="rk3399pro")
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_onnx(model=ONNX_MODEL)
assert ret == 0, "Load onnx failed!"
# Build model
print('--> Building model')
if opt.precompile:
ret = rknn.build(do_quantization=True,
dataset='./dataset.txt',
pre_compile=True) # pre_compile=True
else:
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
assert ret == 0, "Build onnx failed!"
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn(RKNN_MODEL)
assert ret == 0, "Export %s.rknn failed!" % opt.rknn
print('done')
import math
import random
from rknn.api import RKNN
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# Load tensorflow model
print('--> Loading model')
rknn.load_darknet(model='./yolov3_608x608.cfg', weight="./yolov3.weights")
print('done')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=1)
# Build model
print('--> Building model')
rknn.build(do_quantization=True,
dataset='./dataset_608x608.txt',
pre_compile=True)
print('done')
rknn.export_rknn('./yolov3_608x608.rknn')
exit(0)
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
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()
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()
print('--> Loading model')
rknn.load_rknn('./RNN_RKNN.rknn')
#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='/home/tkdidls/RNN/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, dataset='./AAPL_5Y_sq.csv')
print('done')
# Export RKNN Model
rknn.export_rknn('./LSTM_RKNN.rknn')
# Direct Load RKNN Model
rknn.load_rknn('./LSTM_RKNN.rknn')
stock_file_name = 'AAPL_5Y.csv'
storage = np.genfromtxt(stock_file_name, delimiter=',', dtype=np.float32)
storage = np.delete(storage, (0), axis=0)
storage = np.delete(storage, (0), axis=1)
print(storage)
