if __name__ == '__main__':
# Create RKNN object
rknn = RKNN(verbose=False)
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='103.94 116.78 123.68 58.82',
reorder_channel='0 1 2',
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')
print(top5_str)
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# pre-process config
print('--> config model')
rknn.config(channel_mean_value='127.5 127.5 127.5 127.5', reorder_channel='0 1 2')
print('done')
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_tflite(model='./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', pre_compile=False)
if ret != 0:
print('Build mobilenet_v1 failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export RKNN model')
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
path = 'images/IV_images/'
infrared = path + 'IR' + str(1) + '.png'
visible = path + 'VIS' + str(1) + '.png'
fusion_type = 'addition'
# Create RKNN object
rknn = RKNN(verbose=False)
# Config for Model Input PreProcess
rknn.config(channel_mean_value='128 128 128 128#128 128 128 128',
reorder_channel='0 1 2#0 1 2')
# Load TensorFlow Model
print('--> Loading model')
ret = rknn.load_tflite(model='./deepfusion_addition.tflite')
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=True, dataset='./dataset.txt')
print('done')
# Export RKNN Model
ret = rknn.export_rknn('./deepfusion.rknn')
# Direct Load RKNN Model
rknn.load_rknn('./deepfusion.rknn')
# Set inputs