o Qe@slddlmZddlZddlZddlZddlZddlZddlZddlZ ddl Z ddl m Z m Z mZmZmZmZmZmZddlmZmZddlmZmZddlmZmZddlmZgZee ej!dd Z"d,d d Z#d d Z$ddZ%d-ddZ&ddZ'ddZ(ddZ)eddZ*ddZ+eddZ,ddZ-d d!Z.ed"d#Z/ed$d%Z0ed&d'Z1d(d)Z2ed*d+Z3dS).)print_functionN)coreVariableCompiledProgramdefault_main_programProgramlayers unique_name program_guard)prepend_feed_opsappend_fetch_ops) static_only Parameter)Executor global_scope) get_loggerz&%(asctime)s-%(levelname)s: %(message)s)fmtcCsb|durgn|}|durgn|}|D]}||vr!td|||||vr.td|||qdS)NzDargument '{}' in function '{}' is deprecated, only {} are supported.zDfunction '{}' doesn't support argument '{}', only {} are supported.) ValueErrorformat)callerargssupported_argsdeprecated_argsargr@D:\Projects\ConvertPro\env\Lib\site-packages\paddle/static/io.py _check_args0s"rcCs8t|ts|g}|rtdd|Dstd|dS)NcSsg|]}t|tqSr isinstancer.0varrrr Esz_check_vars..z0'{}' should be a Variable or a list of Variable.)rlistallrr)nameZvar_listrrr _check_varsBs r&cCsBt|tjs td|drtdtj|}tj|}|S)z/ convert path_prefix to absolute path. z!'path_prefix' should be a string./z''path_prefix' should not be a directory) rsix string_typesrendswithospathnormpathabspath) path_prefixrrr_normalize_path_prefixKs    r0cCsX|durt}nt|tr|j}|durtdtdt|ts*tdt||S)z9 return default main program if program is None. NzQThe type of input program is invalid, expected tyep is Program, but received Nonez8The input is a CompiledProgram, this is not recommended.zUThe type of input program is invalid, expected type is fluid.Program, but received %s) rrrZ_program TypeErrorwarningswarnrtypeprogramrrr_get_valid_programXs$  r7cCs^t|tsJ|jtjjjkr!|j|j |j |j |j|j ddS|j|j |j |j |jddS)NT)r%shapedtyper4 lod_level persistable)r%r8r9r4r;) rrdescr4rVarDescVarTypeZ LOD_TENSOR create_varr%r8r9r:)blockr!rrr_clone_var_in_blockos" rAcCst|ts tdt|t|ts|g}tdd|Ds"tdt|ts*|g}tdd|Ds7td|jD]}tj }| |d|jdkrUt d nqz$normalize_program..z8feed_vars type must be a Variable or a list of Variable.csrCrDrrErrrrGrHz9fetch_vars type must be a Variable or a list of Variable.ZauczHBe sure that you have set auc states to 0 before saving inference model.g?zsave_infer_model/scale_{}r%NFfeedfetchcSg|]}|jqSrrJrrrrr"z%normalize_program..)Zfeeded_var_namestargetsT)Z prune_read_opcSrNrrJrrrrr"rO)!rrr1r4r#r$ global_blockopsrZop_proto_and_checker_makerZkOpDeviceAttrNameZ _set_attrr2r3r enumerater9paddleboolrscalerappendcloner<Z set_is_targetZ _remove_opflushZ_prune_with_inputZ_inference_optimizer r Z _set_version)r6 feed_vars fetch_varsopZdevice_attr_nameZuniq_fetch_varsir!Z copy_programrQZ remove_op_idxidxZfeed_var_namesZfetch_var_namesrrrnormalize_programst &                  r_cCsF|jtjjjks|jtjjjks|jtjjjkr dS|jS)a Check whether the given variable is persistable. Args: var(Variable): The variable to be checked. Returns: bool: True if the given `var` is persistable False if not. Examples: .. code-block:: python import paddle import paddle.fluid as fluid paddle.enable_static() param = fluid.default_main_program().global_block().var('fc.b') res = fluid.io.is_persistable(param) F) r<r4rr=r>ZFEED_MINIBATCHZ FETCH_LISTZREADERr;)r!rrris_persistables r`cKs8td|td|t|dd}t|||}t|S)a :api_attr: Static Graph Serialize default main program according to feed_vars and fetch_vars. Args: feed_vars(Variable | list[Variable]): Variables needed by inference. fetch_vars(Variable | list[Variable]): Variables returned by inference. kwargs: Supported keys including 'program'.Attention please, kwargs is used for backward compatibility mainly. - program(Program): specify a program if you don't want to use default main program. Returns: bytes: serialized program. Examples: .. code-block:: python import paddle paddle.enable_static() path_prefix = "./infer_model" # User defined network, here a softmax regession example image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') predict = paddle.static.nn.fc(image, 10, activation='softmax') loss = paddle.nn.functional.cross_entropy(predict, label) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) # serialize the default main program to bytes. serialized_program = paddle.static.serialize_program([image], [predict]) # deserialize bytes to program deserialized_program = paddle.static.deserialize_program(serialized_program) rZr[r6N)r&r7getr__serialize_program)rZr[kwargsr6rrrserialize_program s +  rdcCs |jS)z+ serialize given program to bytes. )r<Zserialize_to_stringr5rrrrb?s rbcKs:td|td|t|dd}t|||}t||S)aD :api_attr: Static Graph Serialize parameters using given executor and default main program according to feed_vars and fetch_vars. Args: feed_vars(Variable | list[Variable]): Variables needed by inference. fetch_vars(Variable | list[Variable]): Variables returned by inference. kwargs: Supported keys including 'program'.Attention please, kwargs is used for backward compatibility mainly. - program(Program): specify a program if you don't want to use default main program. Returns: bytes: serialized program. Examples: .. code-block:: python import paddle paddle.enable_static() path_prefix = "./infer_model" # User defined network, here a softmax regession example image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') predict = paddle.static.nn.fc(image, 10, activation='softmax') loss = paddle.nn.functional.cross_entropy(predict, label) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) # serialize parameters to bytes. serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # deserialize bytes to parameters. main_program = paddle.static.default_main_program() deserialized_params = paddle.static.deserialize_persistables(main_program, serialized_params, exe) rZr[r6N)r&r7rar__serialize_persistables)rZr[executorrcr6rrrserialize_persistablesFs ,   rgc Csttt|}t|dkrtddSt}|}i}|D]}|j t j j j kr5t||}|||j<q!g}t|D] } ||| q>td} |jt j j j | d} | jd|jdd|id | id dd d |||t| S) z@ Serialize parameters using given program and executor. rzVno variable in your model, please ensure there are any variables in your model to saveNout_var)r4r%TZ save_combineXYrI) file_pathZsave_to_memoryr4ZinputsZoutputsattrs)r#filterr` list_varslenr2r3rrQr4rr=r>RAWrAr%sortedkeysrWr generater?r<Zset_persistable append_opZ_sync_with_cpprunrfind_var get_bytes) r6rfvars_Z save_programZ save_blockZ save_var_mapr!var_copyZin_varsr%Z out_var_namerhrrrre{s>       recCsLt|ts tdt|d}||WddS1swYdS)ue Save content to given path. Args: path(str): Path to write content to. content(bytes): Content to write. Returns: None Examples: .. code-block:: python import paddle paddle.enable_static() path_prefix = "./infer_model" # 用户自定义网络，此处用 softmax 回归为例。 image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') predict = paddle.static.nn.fc(image, 10, activation='softmax') loss = paddle.nn.functional.cross_entropy(predict, label) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) # 序列化参数 serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # 将序列化之后的参数保存到文件 params_path = path_prefix + ".params" paddle.static.save_to_file(params_path, serialized_params) z'content' type should be bytes.wbN)rbytesropenwrite)r,contentfrrr save_to_files   "rc Kst|}z tj|}t|Wnty*}z |jtjkr WYd}~nd}~ww|d}|d}tj|r@t d |tj|rMt d |t d|t d|t | dd} | dd } t| ||} t| j| d } t|| t| |} | durt|| dSdS) ad Save current model and its parameters to given path. i.e. Given path_prefix = "/path/to/modelname", after invoking save_inference_model(path_prefix, feed_vars, fetch_vars, executor), you will find two files named modelname.pdmodel and modelname.pdiparams under "/path/to", which represent your model and parameters respectively. Args: path_prefix(str): Directory path to save model + model name without suffix. feed_vars(Variable | list[Variable]): Variables needed by inference. fetch_vars(Variable | list[Variable]): Variables returned by inference. executor(Executor): The executor that saves the inference model. You can refer to :ref:`api_guide_executor_en` for more details. kwargs: Supported keys including 'program' and "clip_extra". Attention please, kwargs is used for backward compatibility mainly. - program(Program): specify a program if you don't want to use default main program. - clip_extra(bool): the flag indicating whether to clip extra information for every operator. Default: True. Returns: None Examples: .. code-block:: python import paddle paddle.enable_static() path_prefix = "./infer_model" # User defined network, here a softmax regession example image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') predict = paddle.static.nn.fc(image, 10, activation='softmax') loss = paddle.nn.functional.cross_entropy(predict, label) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) # Feed data and train process # Save inference model. Note we don't save label and loss in this example paddle.static.save_inference_model(path_prefix, [image], [predict], exe) # In this example, the save_inference_mode inference will prune the default # main program according to the network's input node (img) and output node(predict). # The pruned inference program is going to be saved in file "./infer_model.pdmodel" # and parameters are going to be saved in file "./infer_model.pdiparams". N.pdmodel .pdiparamsz'{}' is an existing directory.rZr[r6 clip_extraT)r)r0r+r,dirnamemakedirsOSErrorerrnoEEXISTisdirrrr&r7rar_rbZ_remove_training_inforre) r/rZr[rfrcre model_path params_pathr6r program_bytes params_bytesrrrsave_inference_models::            rcCs,t|}t|std||S)ad :api_attr: Static Graph Deserialize given data to a program. Args: data(bytes): serialized program. Returns: Program: deserialized program. Examples: .. code-block:: python import paddle paddle.enable_static() path_prefix = "./infer_model" # User defined network, here a softmax regession example image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') predict = paddle.static.nn.fc(image, 10, activation='softmax') loss = paddle.nn.functional.cross_entropy(predict, label) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) # serialize the default main program to bytes. serialized_program = paddle.static.serialize_program([image], [predict]) # deserialize bytes to program deserialized_program = paddle.static.deserialize_program(serialized_program) z Unsupported program version: %d )rZparse_from_stringrZ_is_program_version_supported_versionr)datar6rrrdeserialize_program*s ' rcCst|ts tdt|t}|}ttt|}i}i}g}g} |D]>} t| t s0J| jt j j j kr9q't| trHt| j|| j<| jt j j jkrV| | q't|| } || | || j<q'|durvt|dkstJddSg} t|D] } | || q~|jdid| i|ddd |||D]D} t| tsqtj| j}|dksJd | jt |!j"}| j|vsJ| jd |#| j}||krt$d %|| j|qdS) ar :api_attr: Static Graph Deserialize given data to parameters according to given program and executor. Args: program(Program): program that contains parameter names (to deserialize). data(bytes): serialized parameters. executor(Executor): executor used to run load op. Returns: Program: deserialized program. Examples: .. code-block:: python import paddle paddle.enable_static() path_prefix = "./infer_model" # User defined network, here a softmax regession example image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') predict = paddle.static.nn.fc(image, 10, activation='softmax') loss = paddle.nn.functional.cross_entropy(predict, label) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) # serialize parameters to bytes. serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # deserialize bytes to parameters. main_program = paddle.static.default_main_program() deserialized_params = paddle.static.deserialize_persistables(main_program, serialized_params, exe) rBNrz]Required 'data' shall be not None if program contains parameter, but received 'data' is None.Z load_combineZOutT)rkZmodel_from_memoryrlzcan't not find var: z MUST in var list.zoShape mismatch, program needs a parameter with shape ({}), but the loaded parameter ('{}') has a shape of ({}).)&rrr1r4rQr#rnr`rorrr=r>rqrtupler< get_shaper%Z SELECTED_ROWSrWrArprrrsrurvrTZfluidrrwnparrayZ get_tensorr8ra RuntimeErrorr)r6rrfZ load_programZ load_blockryZorigin_shape_mapZ load_var_mapZ check_varsZ sparse_varsr!rzZ load_var_listr%Zvar_tmpZ new_shapeZ origin_shaperrrdeserialize_persistablesYsr +        rcCs8t|d }|}Wd|S1swY|S)u Load file in binary mode. Args: path(str): Path of an existed file. Returns: bytes: Content of file. Examples: .. code-block:: python import paddle paddle.enable_static() path_prefix = "./infer_model" # 用户自定义网络，此处用 softmax 回归为例。 image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') predict = paddle.static.nn.fc(image, 10, activation='softmax') loss = paddle.nn.functional.cross_entropy(predict, label) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(paddle.static.default_startup_program()) # 序列化参数 serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # 将序列化之后的参数保存到文件 params_path = path_prefix + ".params" paddle.static.save_to_file(params_path, serialized_params) # 从文件加载序列化之后的参数 serialized_params_copy = paddle.static.load_from_file(params_path) rbN)r}read)r,rrrrrload_from_files   rc sd}d}tjj}t|||||dur5td|dd}|dd}|dur.tdd}|} |} nt |}|sD|d } |d } nY|dd}|dd}|dur\t j |d } nt j ||d } t j | srt j ||} |dur~t j |d} nt j ||d } t j | st j ||} td | | t| } t j | }t j | }t j ||} d} t j | rt| } t| t| |j} j}fd d|D}| |gS)aH :api_attr: Static Graph Load inference model from a given path. By this API, you can get the model structure(Inference Program) and model parameters. Args: path_prefix(str | None): One of the following: - Directory path to save model + model name without suffix. - Set to None when reading the model from memory. executor(Executor): The executor to run for loading inference model. See :ref:`api_guide_executor_en` for more details about it. kwargs: Supported keys including 'model_filename', 'params_filename'.Attention please, kwargs is used for backward compatibility mainly. - model_filename(str): specify model_filename if you don't want to use default name. - params_filename(str): specify params_filename if you don't want to use default name. Returns: list: The return of this API is a list with three elements: (program, feed_target_names, fetch_targets). The `program` is a ``Program`` (refer to :ref:`api_guide_Program_en`), which is used for inference. The `feed_target_names` is a list of ``str``, which contains names of variables that need to feed data in the inference program. The `fetch_targets` is a list of ``Variable`` (refer to :ref:`api_guide_Program_en`). It contains variables from which we can get inference results. Examples: .. code-block:: python import paddle import numpy as np paddle.enable_static() # Build the model startup_prog = paddle.static.default_startup_program() main_prog = paddle.static.default_main_program() with paddle.static.program_guard(main_prog, startup_prog): image = paddle.static.data(name="img", shape=[64, 784]) w = paddle.create_parameter(shape=[784, 200], dtype='float32') b = paddle.create_parameter(shape=[200], dtype='float32') hidden_w = paddle.matmul(x=image, y=w) hidden_b = paddle.add(hidden_w, b) exe = paddle.static.Executor(paddle.CPUPlace()) exe.run(startup_prog) # Save the inference model path_prefix = "./infer_model" paddle.static.save_inference_model(path_prefix, [image], [hidden_b], exe) [inference_program, feed_target_names, fetch_targets] = ( paddle.static.load_inference_model(path_prefix, exe)) tensor_img = np.array(np.random.random((64, 784)), dtype=np.float32) results = exe.run(inference_program, feed={feed_target_names[0]: tensor_img}, fetch_list=fetch_targets) # In this example, the inference program was saved in file # "./infer_model.pdmodel" and parameters were saved in file # " ./infer_model.pdiparams". # By the inference program, feed_target_names and # fetch_targets, we can use an executor to run the inference # program to get the inference result. )model_filenameparams_filename)Zpserver_endpointsNz/Load inference model from memory is deprecated.rrz8params_filename cannot be None when path_prefix is None.rIrrZ __model__zRThe old way to load inference model is deprecated. model path: {}, params path: {}csg|] }|qSr)rQr!)r r%r5rrr"psz(load_inference_model..)inspect currentframef_codeco_namer_loggerwarningrarr0r+r,joinexistsrrrbasenamerrr<Zget_feed_target_namesZget_fetch_target_names)r/rfrcrrrrrZ load_dirnamerrrrZfeed_target_namesZfetch_target_namesZ fetch_targetsrr5rload_inference_modelspB                 r)NNrD)4 __future__rrrloggingr+r2r(numpyrrTZ paddle.fluidrrrrrrr r Zpaddle.fluid.ior r Zpaddle.fluid.frameworkr rZpaddle.fluid.executorrrZpaddle.fluid.log_helperr__all____name__INFOrrr&r0r7rAr_r`rdrbrgrerrrrrrrrrrsR (    i 3 4/# \ . i#