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本帖最后由 iamher0 于 2019-8-26 09:40 编辑
http://download.tensorflow.org/models/object_detection/facessd_mobilenet_v2_quantized_320x320_open_image_v4.tar.gz
转换源码:
from rknn.api import RKNN
INPUT_SIZE = 64
if __name__ == '__main__':
# 创建RKNN执行对象
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')
# 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')
rknn.load_tensorflow(tf_pb='frozen_inference_graph.pb',
inputs=['image_tensor'],
outputs=['detection_classes','num_detections','detection_boxes','detection_scores'],
input_size_list=[[320, 320, 3]])
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')
rknn.build(do_quantization=False)
print('done')
# 导出保存rknn模型文件
rknn.export_rknn('./facemobilenetv2ssd.rknn')
# Release RKNN Context
rknn.release()
错误输出:
/usr/lib64/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
--> Loading model
E Catch exception when loading tensorflow model: frozen_inference_graph.pb!
T Traceback (most recent call last):
T File "/home/toybrick/.local/lib/python3.6/site-packages/tensorflow/python/framework/importer.py", line 418, in import_graph_def
T graph._c_graph, serialized, options) # pylint: disable=protected-access
T tensorflow.python.framework.errors_impl.InvalidArgumentError: NodeDef mentions attr 'Truncate' not in Op<name=Cast; signature=x:SrcT -> y stT; attr=SrcT:type; attr=DstT:type>; NodeDef: Cast = Cast[DstT=DT_FLOAT, SrcT=DT_UINT8, Truncate=false](image_tensor). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
T During handling of the above exception, another exception occurred:
T Traceback (most recent call last):
T File "rknn/api/rknn_base.py", line 137, in rknn.api.rknn_base.RKNNBase.load_tensorflow
T File "rknn/base/RKNNlib/converter/convert_tf.py", line 482, in rknn.base.RKNNlib.converter.convert_tf.convert_tf.pre_process
T File "rknn/base/RKNNlib/converter/tensorflowloader.py", line 102, in rknn.base.RKNNlib.converter.tensorflowloader.TF_Graph_Preprocess.pre_proces
T File "rknn/base/RKNNlib/converter/tensorflowloader.py", line 627, in rknn.base.RKNNlib.converter.tensorflowloader.TF_Graph_Preprocess.calc_2_const
T File "rknn/base/RKNNlib/converter/tf_util.py", line 371, in rknn.base.RKNNlib.converter.tf_util.TFProto_Util.query_tensor
T File "rknn/base/RKNNlib/converter/tf_util.py", line 372, in rknn.base.RKNNlib.converter.tf_util.TFProto_Util.query_tensor
T File "/home/toybrick/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 454, in new_func
T return func(*args, **kwargs)
T File "/home/toybrick/.local/lib/python3.6/site-packages/tensorflow/python/framework/importer.py", line 422, in import_graph_def
T raise ValueError(str(e))
T ValueError: NodeDef mentions attr 'Truncate' not in Op<name=Cast; signature=x:SrcT -> ystT; attr=SrcT:type; attr=DstT:type>; NodeDef: Cast = Cast[DstT=DT_FLOAT, SrcT=DT_UINT8, Truncate=false](image_tensor). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
done
--> Building model
E Model or data is None, please load model first.
done
E RKNN model data is None, please load model first!
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