今天在用openvino将pb文件转成ir文件的时候，遇到了一个小问题，记录下来分享给需要的人。
我用tensorflow自己存了个pb文件，具体的方法是在session中加入如下的语句
for n in tf.get_default_graph().as_graph_def().node: print(n.name) print(n.attr["value"].tensor.dtype) output_node_names = [n.name for n in tf.get_default_graph().as_graph_def().node] #print(output_node_names) frozen = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, output_node_names) graph_io.write_graph(frozen, './', 'inference_graph.pb', as_text=False)
然后把这个生成的inference_graph.pb文件用mo.py转成openvino识别的.xml和.bin，但是遇到了下面的错误
[ ERROR ] -------------------------------------------------[ ERROR ] ----------------- INTERNAL ERROR ----------------[ ERROR ] Unexpected exception happened.[ ERROR ] Please contact Model Optimizer developers and forward the following information:[ ERROR ] local variable 'new_attrs' referenced before assignment[ ERROR ] Traceback (most recent call last): File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\front\extractor.py", line 604, in extract_node_attrs supported, new_attrs = extractor(Node(graph, node)) File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\pipeline\tf.py", line 124, inextract_node_attrs(graph, lambda node: tf_op_extractor(node, check_for_duplicates(tf_op_extractors))) File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\front\tf\extractor.py", line 140, in tf_op_extractor attrs = tf_op_extractors[op](node) File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\front\tf\extractor.py", line 74, in return lambda node: pb_extractor(node.pb) File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\front\tf\extractors\const.py", line 32, in tf_const_ext result['value'] = tf_tensor_content(pb_tensor.dtype, result['shape'], pb_tensor) File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\front\tf\extractors\utils.py", line 70, in tf_tensor_content refer_to_faq_msg(50), tf_dtype)mo.utils.error.Error: Data type is unsupported: 19. For more information please refer to Model Optimizer FAQ ( /deployment_tools/documentation/docs/MO_FAQ.html), question #50.During handling of the above exception, another exception occurred:Traceback (most recent call last): File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\main.py", line 312, in main return driver(argv) File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\main.py", line 263, in driver is_binary=not argv.input_model_is_text) File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\pipeline\tf.py", line 124, in tf2nx extract_node_attrs(graph, lambda node: tf_op_extractor(node, check_for_duplicates(tf_op_extractors))) File "C:\Program Files (x86)\IntelSWTools\openvino_2019.1.148\deployment_tools\model_optimizer\mo\front\extractor.py", line 610, in extract_node_attrs new_attrs['name'] if 'name' in new_attrs else ' ',UnboundLocalError: local variable 'new_attrs' referenced before assignment[ ERROR ] ---------------- END OF BUG REPORT --------------[ ERROR ] -------------------------------------------------
主要的错误就是标红的这一条
mo.utils.error.Error: Data type is unsupported: 19. 根据 这里的定义，
| DT_INVALID = 0 | |
| // Data types that all computation devices are expected to be | |
| // capable to support. | |
| DT_FLOAT = 1; | |
| DT_DOUBLE = 2; | |
| DT_INT32 = 3; | |
| DT_UINT8 = 4; | |
| DT_INT16 = 5; | |
| DT_INT8 = 6; | |
| DT_STRING = 7; | |
| DT_COMPLEX64 = 8; // Single-precision complex | |
| DT_INT64 = 9; | |
| DT_BOOL = 10; | |
| DT_QINT8 = 11; // Quantized int8 | |
| DT_QUINT8 = 12; // Quantized uint8 | |
| DT_QINT32 = 13; // Quantized int32 | |
| DT_BFLOAT16 = 14; // Float32 truncated to 16 bits. Only for cast ops. | |
| DT_QINT16 = 15; // Quantized int16 | |
| DT_QUINT16 = 16; // Quantized uint16 | |
| DT_UINT16 = 17; | |
| DT_COMPLEX128 = 18; // Double-precision complex | |
| DT_HALF = 19; |
19指的就是fp16，所以这个错误的意思是说我的pb file里面有fp16类型的变量，我直接在mo.py后面指定 --data_type FP16，不过也不起作用，后面，还是在freeze model的时候，全部改用了fp32的类型，再保存，然后运行mo.py就没有错误了。 参考：
