BVh,dZddlZddlZddlZddlmZddlmZddlm Z ddl m Z ddl m Z ddl mZdd lmZdZd Zd Zd Zed k(r0e\ZZej.eej0dgezyy)aRemoves parts of a graph that are only needed for training. There are several common transformations that can be applied to GraphDefs created to train a model, that help reduce the amount of computation needed when the network is used only for inference. These include: - Convert given PlaceholderWithDefault or Placeholder nodes to Constant - Removing training-only operations like checkpoint saving. - Stripping out parts of the graph that are never reached. - Removing debug operations like CheckNumerics. - Fusing a group of primitive ops for batch normalization to FusedBatchNorm op. - Folding batch normalization ops into the pre-calculated weights. - Fusing common operations into unified versions. This script takes either a frozen binary GraphDef file (where the weight variables have been converted into constants by the freeze_graph script), or a text GraphDef proto file (the weight variables are stored in a separate checkpoint file), and outputs a new GraphDef with the optimizations applied. If the input graph is a text graph file, make sure to include the node that restores the variable weights in output_names. That node is usually named "restore_all". An example of command-line usage is: bazel build tensorflow/python/tools:optimize_for_inference && \ bazel-bin/tensorflow/python/tools/optimize_for_inference \ --input=frozen_inception_graph.pb \ --output=optimized_inception_graph.pb \ --frozen_graph=True \ --input_names=Mul \ --output_names=softmax N)app) text_format) graph_pb2)dtypes)graph_io)gfile)optimize_for_inference_libc tjtjs t dtjzdzyt j }tjtjd5}|j}tjr|j|n%tj|jd|dddtj|tj j#dtj$j#dt'tj(tj*tj,j#d}tjrDtj.tj0d}|j3|j5yt7j8|t:j<j?tj0t:j<jAtj0y#1swYbxYw) NzInput graph file 'z' does not exist!rbzutf-8,wr)!rExistsFLAGSinputprintrGraphDefOpenread frozen_graphParseFromStringrMergedecoder optimize_for_inference input_namessplit output_names_parse_placeholder_typesplaceholder_type_enumtoco_compatibleplaceholder_to_const_namesGFileoutputwriteSerializeToStringr write_graphospathdirnamebasename) unused_argsinput_graph_deffdataoutput_graph_defs ^/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/tools/optimize_for_inference.pymainr1Js ekk "  ,/B BC &&(/ zz%++t$? 668D %%d+ G,o> ?0FF c" s#u::;  &&,,S1   ELL#&AGG  . . 01  )6))%,,79 /??s c|jdDcgc] }t|}}t|dkDr|S|dScc}w)z6Extracts placeholder types from a comma separate list.r r)rintlen)valuesvalues r0rrjs@$*LL$5 65CJ 6& 6v;?1q 1 7s=c@tj}|jddd|jdtdd|jdtdd |jd tdd |jd tdd |jdddddd|jdtt t j jd|jdtdd|jdtdd|jS)zParses command line arguments.typeboolc(|jdk(S)Ntrue)lower)vs r0zparse_args..ssAGGI,?z--inputz#TensorFlow 'GraphDef' file to load.)r9defaulthelpz--outputz!File to save the output graph to.z --input_namesz"Input node names, comma separated.z--output_namesz#Output node names, comma separated.z--frozen_graph?Tzx If true, the input graph is a binary frozen GraphDef file; if false, it is a text GraphDef proto file. )nargsconstr9rBrCz--placeholder_type_enumzx The AttrValue enum to use for placeholders. Or a comma separated list, one value for each placeholder. z--toco_compatibleFz] If true, only use ops compatible with Tensorflow Lite Optimizing Converter. z--placeholder_to_const_namesz List of PlaceholderWithDefault or Placeholder node names and their new value to be converted to Constant node, comma separated. eg: --placeholder_to_const_names=phase_train=False ) argparseArgumentParserregister add_argumentstrrfloat32as_datatype_enumr:parse_known_args)parsers r0 parse_argsrPpsU  " " $&//&&"?@  2 4    . 0    / 1    0 2          &..112       $     ""r@__main__)r1argv)__doc__rGr'sysabslrgoogle.protobufrtensorflow.core.frameworkrtensorflow.python.frameworkrrtensorflow.python.platformrtensorflow.python.toolsr rr1rrP__name__unparsedrunrRr@r0r_sz (T '/.0,>  @2 <#~ zL/% #''t388A;-(23r@