\;jO2"ddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl m Z m Z m Z mZmZmZmZddlmZmZddlmZmZmZddlmZddlmZmZmZmZm Z m!Z!m"Z"m#Z#gZ$ee%ejLdZ'd"d Z(d Z)d Z*d#d Z+d Z, d$dZ- d%dZ.dZ/edZ0d&dZ1edZ2dZ3dZ4edZ5edZ6edZ7dZ8edZ9e d'dZ: d'dZ;ed(dZed Z?d#d!Z@y))N)CompiledProgramProgramVariablecoredefault_main_program program_guard unique_name)Executor global_scope) Parameterdygraph_not_support static_only) get_logger)_clone_var_in_block__load_program_scope_pack_loaded_dict_pickle_loads_mac_unpack_saved_dictis_belong_to_optimizer is_parameteris_persistablez&%(asctime)s-%(levelname)s: %(message)s)fmtc|gn|}|gn|}|D]B}||vrtdj|||||vs(tdj|||y)NzDargument '{}' in function '{}' is deprecated, only {} are supported.zDfunction '{}' doesn't support argument '{}', only {} are supported.) ValueErrorformat)callerargssupported_argsdeprecated_argsargs YG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/static/io.py _check_argsr"8sz)1R~N+3bO / !V]]   &W^^C clt|ts|g}td|Dstd|dy)Nc3<K|]}t|tywN isinstancer).0vars r! z_check_vars..Ms=HSz#x(H'z-' should be a Variable or a list of Variable.)r(listallr)namevar_lists r! _check_varsr2Js@ h %: =H= =vB C   >r#ct|ts td|jdr tdtj j |}tj j|}|S)z/ convert path_prefix to absolute path. z!'path_prefix' should be a string./z''path_prefix' should not be a directory)r(strrendswithospathnormpathabspath) path_prefixs r!_normalize_path_prefixr<Ss` k3 '<==C BCC''"";/K''//+.K r#c| t}n>t|tr.|j}| t dt j dt|tst dt|z|S)z9 return default main program if program is None. zQThe type of input program is invalid, expected tyep is Program, but received Nonez8The input is a CompiledProgram, this is not recommended.zTThe type of input program is invalid, expected type is base.Program, but received %s) rr(r_program TypeErrorwarningswarnrtypeprograms r!_get_valid_programrE`s{&( G_ -"" ?c   F  gw ' b7m    Nr#ct|tsJ|jjtj j jk(rI|j|j|j|j|j|jdS|j|j|j|j|jdS)NTr0shapedtyperB lod_level persistable)r0rHrIrBrK) r(rdescrBrVarDescVarType LOD_TENSOR create_varr0rHrIrJblockr*s r!_clone_var_in_blockrSws c8 $$ $ xx}}$,,..999))))mm    ))))    r#ct|dk(ry|j}|j|tjj j d}t|D]`\}}|j|stdj|||j|}|jdd|gid|gid |i by) NrTr0rBrKzThe feeded_var_names[{i}]: '{name}' doesn't exist in pruned inference program. Please check whether '{name}' is a valid feed_var name, or remove it from feeded_var_names if '{name}' is not involved in the target_vars calculation.)ir0feedXOutcolrBinputsoutputsattrs) len global_blockrPrrMrNFEED_MINIBATCH enumeratehas_varrrr* _prepend_op)inference_programfeed_target_namesfeed_holder_namer`feed_varrVr0outs r!prepend_feed_opsrjs "$113L&&  \\ ! ! 0 0'H ./4##D)NNTfdOUO t$  ($SEN!* ! 0r#c|j}|j|tjjj d}t |D]"\}}|jdd|gid|gid|i$y)NTrUfetchrXrYrZr[)r`rPrrMrN FETCH_LISTrb append_op)refetch_target_namesfetch_holder_namer` fetch_varrVr0s r!append_fetch_opsrrs%113L''  \\ ! ! , ,(I /04$=YK(!*  1r#c t|tstdt|zt|ts|g}t d|Ds tdt|ts|g}t d|Ds td|j jD]X}tjj}|j|d|jdk(sCtjdnt|5g}t|D]N\}}|j t"j$k7rt#j&|d d | }|j)|P|}d d d |j+} | j } g} t| jD]\}}|j,j/d |jdk(s|jdk(r| j)||jdk(s`|j1d} t3| dkDs| d} | j4j7| g}| d dD]"}|j)| j9|$|j;d|| d d dD]}| j=|| j,j?|Dcgc]}|j@}}|jCdd }|s| jE||} | jGd} |Dcgc]}|j@}}tI| |tK| || j,jM| S#1swYxYwcc}wcc}w)a Normalize/Optimize a program according to feed_vars and fetch_vars. Args: program(Program): Specify a program you want to optimize. feed_vars(Tensor | list[Tensor]): Variables needed by inference. fetch_vars(Tensor | list[Tensor]): Variables returned by inference. kwargs: Supported keys including ``skip_prune_program``. - skip_prune_program(bool): whether to skip prunning program. Defaults to False. Returns: Program: Normalized/Optimized 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()) # normalize main program. >>> program = paddle.static.default_main_program() >>> normalized_program = paddle.static.normalize_program(program, [image], [predict]) 6program type must be `base.Program`, but received `%s`c3<K|]}t|tywr&r'r)vs r!r+z$normalize_program..s: 1z!X& r,z8feed_vars type must be a Variable or a list of Variable.c3<K|]}t|tywr&r'rvs r!r+z$normalize_program..s; 1z!X& r,z9fetch_vars type must be a Variable or a list of Variable.auczHBe sure that you have set auc states to 0 before saving inference model.g?zsave_infer_model/scale_)r0NFrWrlpylayerblocksskip_prune_program)feeded_var_namestargetsT) prune_read_op)'r(rr?rBr.r/r`opsrop_proto_and_checker_makerkOpDeviceAttrName _set_attrr@rArrbrIpaddleboolscaleappendclonerL set_is_target_blocks_attr_idsr_r|poprR_update_desc_attr _remove_opflushr0get_prune_with_input_inference_optimizerjrr _set_version)rD feed_vars fetch_varskwargsopdevice_attr_nameuniq_fetch_varsrVr* copy_programr` remove_op_idxsub_blocks_idsbackward_block_idreserverd_blocksblock_ididxfeed_var_namesrfetch_var_namess r!normalize_programrs5N gw ' D7m    i &K : : : F   j$ ' \ ; ; ; G  ""$((::LLN %r* 77e  MMZ  ) w  +FAsyyFKK'll34KA32OP  " "3 ',% ==?L,,.LM<++,2 e$ 77f 7 2   # 77i 00:N>"Q&$22$6!##''(9:#% .s 3H$++L,>,>x,HI!4$$X/?@!-$TrT"$#*34)3chh)N4$8%@ #55+Z6  33$3GL+56:Csxx:O6\>2\?3""$ ] D57sA!MM,M1M)c td|td|t|jdd}t|||}|jdd}t ||S)a< Serialize default main program according to feed_vars and fetch_vars. Args: feed_vars(Tensor | list[Tensor]): Tensor needed by inference. fetch_vars(Tensor | list[Tensor]): Tensor 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. - legacy_format(bool): whether to save inference program in legacy format. Defaults to False. 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) rrrDN legacy_formatFr)r2rErr_serialize_program)rrrrDrs r!serialize_programr6sYV Y' j) It!<=GJ?GJJ6M g] CCr#c:|jj|S)z+ serialize given program to bytes. r)rLserialize_to_string)rDrs r!rrks << + +- + HHr#c td|td|t|jdd}t|||}t ||S)aO Serialize parameters using given executor and default main program according to feed_vars and fetch_vars. Args: feed_vars(Tensor | list[Tensor]): Tensor needed by inference. fetch_vars(Tensor | list[Tensor]): Tensor 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) rrrDN)r2rErr_serialize_persistables)rrexecutorrrDs r!serialize_persistablesrrsIV Y' j) It!<=GJ?G "7H 55r#crttt|j}t |dk(rt j dyt}|j}i}|D]O}|jtjjjk7s5t||}|||j<Qg}t!|j#D]} |j%|| t'j(d} |j+tjjj| } | j,j/d|j1dd|id | id dd  |j3|j5|t7j9| j;S) z@ Serialize parameters using given program and executor. rVno variable in your model, please ensure there are any variables in your model to saveNout_varrBr0T save_combinerXYry file_pathsave_to_memoryr[)r.filterr list_varsr_r@rArr`rBrrMrNRAWrSr0sortedkeysrr generaterPrLset_persistablern_sync_with_cpprunr find_var get_bytes) rDrvars_ save_program save_block save_var_mapr*var_copyin_varsr0 out_var_namers r!rrs{ (9(9(;< =E 5zQ  . 9L**,JL 88t||++// /*:s;H*-L ' G|((*+|D)*,'' 2L## \\ ! ! % %L$G LL  & W~g$7 ! LL > " "< 0 : : <>> 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)r(bytesropenwrite)r8contentfs r! save_to_filers?D gu %:;; dD Q    s AA c `t|} tjj|}tj||dz}|dz}tjj|rtd|dtjj|rtd|dtd|td|t|jdd} |jd d } t| |||jd d  } |jdd } t| j| | } t!|| t#t%t&| j)} t+t#| dk(rt-j.dt+| dkDrStjj|}tjj1|}t3||| t&|yy#t $r)}|j t jk7rYd}~d}~wwxYw)a Save current model and its parameters to given path. i.e. Given ``path_prefix = "PATH/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``, which represent your model and parameters respectively. Args: path_prefix(str): Directory path to save model + model name without suffix. feed_vars(Tensor | list[Tensor]): Variables needed by inference. fetch_vars(Tensor | list[Tensor]): 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. - legacy_format(bool): whether to save inference model in legacy format. Default: False. 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 .pdiparamsr-z' is an existing directory.rrrD clip_extraTrF)rr)rrrrdirname main_program predicatefilename)r<r7r8rmakedirsOSErrorerrnoEEXISTisdirrr2rErrr_remove_training_inforr.rrrr_r@rAbasename save_vars)r;rrrrre model_path params_pathrDrr program_bytesvars save_dirnameparams_filenames r!save_inference_modelrsx)5K''//+. Gz)J ,K ww}}Z 1ZL(CDEE ww}}[!1[M)DEFF Y' j) It!<=GL$/J!::&:EB GJJ6M&%%%<#M ]+ ~w'8'8':; >> 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 )rparse_from_stringr_is_program_version_supported_versionr)datarDs r!deserialize_programrrsQL''-G  - -g.>.>.@ A /'2B2B2D D   Nr#ct|tstdt|zt}|j }t t t|j}i}i}g}g} |D]} t| tsJ| jtjjjk(rGt| tr0t| j j#|| j$<| jtjjj&k(r| j)| t+|| } |j)| | || j$<|t-|dk(sJdyg} t/|j1D]} | j)|| |j3did| i|dd |j5||D]} t| tst6j8j;j=| j$}|Jd | j$zt?j@|jCjD}| j$|vsJ| j$d z|jG| j$}||k7stId jK|| j$|y) a 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) rtNrz]Required 'data' shall be not None if program contains parameter, but received 'data' is None. load_combinerYTrmodel_from_memoryr[can'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 ({}).)&r(rr?rBr`r.rrrrrrMrNrr tuplerL get_shaper0 SELECTED_ROWSrrSr_rrrnrrbaser rnparray get_tensorrHr RuntimeErrorr)rDrr load_program load_blockrorigin_shape_map load_var_map check_vars sparse_varsr*r load_var_listr0var_tmp new_shape origin_shapes r!deserialize_persistablesr sT gw ' D7m   9L**,J (9(9(;< =ELJK#x((( 88t||++// /  c9 %).sxx/A/A/C)D SXX & 88t||++99 9   s # &z37#&. X]]# |  !Q & k j k &M|((*+\$/0,  & t<  LL#y) ++**,55chh?"E$:SXX$EE"XXg0023:: xx++LSXX8L-LL+'++CHH5  $GGMv #((IH r#cjt|d5}|j}ddd|S#1swYSxYw)u  Load file in binary mode. Args: path(str): Path of an existed file. Returns: bytes: Content of file. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # 用户自定义网络，此处用 softmax 回归为例。 >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # 序列化参数 >>> serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # 将序列化之后的参数保存到文件 >>> params_path = path_prefix + ".params" >>> paddle.static.save_to_file(params_path, serialized_params) # 从文件加载序列化之后的参数 >>> serialized_params_copy = paddle.static.load_from_file(params_path) rbN)rread)r8rrs r!load_from_filer s1H dD Qvvx  K  Ks(2c "d}d}tjjj}t |||||t j d|jdd}|jdd}| td|}t|} ttt| j} t| dkDrat|d| t| nKt!|}t"j$j'|} t"j$j)| std | |s |d z} |d z} nI|jdd}|jdd}|!t"j$j+|d } nbt"j$j+||d z} t"j$j-| s t"j$j+||} |!t"j$j+|d} nbt"j$j+||d z} t"j$j-| s t"j$j+||} t j d| d| t/| }t|} ttt| j} t| dkDrRt"j$j'| }t"j$j1| }t||| t| | j2j5}| j2j7}|Dcgc]!}| j9j;|#}}| ||gScc}w)a 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_filenamer)pserver_endpointsNzKLoad inference model from memory is deprecated. Please specify path_prefix.rrz8params_filename cannot be None when path_prefix is None.rrzThere is no directory named rr __model__ryz[The old way to load inference model is deprecated. Please specify path_prefix. model path: z, params path: )inspect currentframef_codeco_namer"_loggerwarningrrrr.rrrr_ load_varsr<r7r8rrjoinexistsrrrLget_feed_target_namesget_fetch_target_namesr`r*)r;rrrrrrrrrDrdir_pathrr load_dirnamerfror0 fetch_targetss r!load_inference_modelr!3sF;N,O  ! ! # * * 2 2F@ Y  $4d; **%6=  "J ' %m4F>7+<+<+>?@ t9q= $((  -[9 77??;/ww}}X&;H:FG G$z1J% 4K$ZZ(8$?N$jj):DAO%WW\\+{C WW\\*!< ww~~j1!#k>!JJ& ggll;;  ggll7+<+<+>?@ t9q=77??;7L gg..{;O $$((   ::< <<>5G5GT""4(5G & 66 s&N c Zd}||d}t|}|1t|||tt||j |Sd}t t|dk(rt jdyt}|j} i} |D]} | jtjjjk(r5t| | } ||durt j"j%t j"j'|| j(} | j+dd | giid t j"j'| i | | | j(<||rg}t-| j/D]}|j1| |d }|dur=t j"j%t j"j'||}| j3tjjj| }|j4j7d| j+dd |id|i||d |j9|j;|j=||r't?jA|jCSy)a Save specific variables in the `Program` to files. There are two ways to specify the variables to be saved: set variables in a list and assign it to the `vars`, or use the `predicate` function to select variables that make `predicate(variable) == True`. The first way has a higher priority. The `dirname` is used to specify the folder where to save variables. If you prefer to save variables in separate files in the `dirname` folder, do not set `filename`. If you prefer to save all variables in a single file, use `filename` to specify it. Args: executor(Executor): The executor to run for saving variables. dirname(str, optional): The folder where to save variables. When you need to save the parameter to the memory, set it to None. main_program(Program, optional): The program whose variables will be saved. If it is None, the default main program will be used automatically. Default: None vars(list[Variable], optional): The list contains all variables to be saved. Default: None predicate(function, optional): The function selects the variables that make `predicate(variable) == True`. Default: None filename(str, optional): If you prefer to save all variables in a single file, use `filename` to specify it. Otherwise, let `filename` be None. Default: None Returns: str: When saving parameters to a file, returns None. When saving parameters to memory, returns a binary string containing parameters. Raises: TypeError: If `main_program` is not an instance of Program nor None. Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> main_prog = static.Program() >>> startup_prog = static.Program() >>> with static.program_guard(main_prog, startup_prog): ... data = paddle.static.data(name="img", shape=[64, 784]) ... w = paddle.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') ... b = paddle.create_parameter(shape=[200], dtype='float32', name='fc_b') ... hidden_w = paddle.matmul(x=data, y=w) ... hidden_b = paddle.add(hidden_w, b) >>> place = static.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(startup_prog) # The first usage: use `vars` to set the saved variables. >>> var_list = [w, b] >>> path = "./my_paddle_vars" # w and b will be save in a file named "var_file". >>> paddle.static.io.save_vars(executor=exe, dirname=path, vars=var_list, ... filename="vars_file") # The second usage: use `predicate` to select the saved variable. >>> def name_has_fc(var): ... res = "fc" in var.name ... return res >>> param_path = "./my_paddle_model" # all variables whose names contain "fc " are saved. >>> paddle.static.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, vars=None, predicate = name_has_fc) FNT)rrrr saved_paramsrrsaverXrr[ryrrrr)"rErr.rrr_r@rArr`rBrrMrNrrr7r8rr9r0rnrrrrPrLrrrrr rr)rrrrrrrparams_var_namerrreach_varnew_varsave_file_path save_var_listr0 save_pathr#s r!rrs{fN8+%l3L | %fY (>(>(@AB   ) tDz?a  MMh y !..0  H}} 4 4 8 88*:x@GNe$;!#GG$$W-w||"$$'+&(8(8(HI %.5 W\\*!$  >M|0023$$\$%784I&GGLL)9)9')BHM %00\\))--O1L    - -d 3  #]+l+!*&4 !  ##% \" >**?;EEG G r#c @ d}| tjj|}nd}|dk(rd}|e| t}t |t st dt|zt|||tt||j|yt }|j}| t}t |t st dt|zi} i} g} g} |D]I} t | tsJ| jtjj j"k(rHt | t$r0t'| j(j+| | j,<| jtjj j.k(r| j1| t3|| }| j1| |Q| t5d|j7did |gid tjj9||j,i ;|| |j,<L| D]} t | tsJ| t5d t3|| }tjj9||j,}tjj;|st5d |j,d|tjj=|rC|j7did |gid tjj9||j,i g}tj>|}|D]/}|jA|j,s|j1|1g}|D]}|jC||j|jD|jFd}|j1|tjj9||d}|j7did |gid |i |j7dd|id |ii |rg}tI| jKD]}|j1| ||dur tjj9||}|j7did |i||d |jM|| D]} t | t$stNjPjSjU| j,}|Jd| j,ztWjX|j[jD}| j,| vsJ| j,dz| j]| j,}||k7st_dja|| j,|y)a :api_attr: Static Graph This API loads variables from files by executor. There are two ways to specify the variables to be loaded: the first way, set variables in a list and assign it to the `vars`; the second way, use the `predicate` function to select variables that make `predicate(variable) == True`. The first way has a higher priority. The `dirname` is used to specify the folder where to load variables. If variables were saved in separate files in the folder `dirname`, set `filename` None. If all variables were saved in a single file, use `filename` to specify it. Args: executor(Executor): The executor to run for loading variables. dirname(str): The folder where to load the variables. main_program(Program, optional): The program whose variables will be loaded. If it is None, the default main program will be used automatically. Default: None vars(list[Variable], optional): The list that contains all variables to be loaded. Default: None predicate(function, optional): The function selects variables that make `predicate(variable) == True`. Default: None filename(str, optional): The file which saved all required variables. If variables were saved in separate files, set it to be None. Default: None Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> main_prog = static.Program() >>> startup_prog = static.Program() >>> with static.program_guard(main_prog, startup_prog): ... data = paddle.static.data(name="img", shape=[64, 784]) ... w = paddle.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') ... b = paddle.create_parameter(shape=[200], dtype='float32', name='fc_b') ... hidden_w = paddle.matmul(x=data, y=w) ... hidden_b = paddle.add(hidden_w, b) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(startup_prog) # The first usage: using `vars` to specify the variables. >>> path = "./my_paddle_vars" >>> var_list = [w, b] >>> paddle.static.io.save_vars(executor=exe, dirname=path, vars=var_list, ... filename="vars_file") >>> paddle.static.io.load_vars(executor=exe, dirname=path, vars=var_list, ... filename="vars_file") # w and b will be loaded, and they are supposed to # be saved in the same file named 'var_file' in the path "./my_paddle_vars". # The second usage: using the `predicate` function to select variables >>> param_path = "./my_paddle_model" >>> def name_has_fc(var): ... res = "fc" in var.name ... return res >>> paddle.static.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, ... vars=None, predicate=name_has_fc) >>> paddle.static.io.load_vars(executor=exe, dirname=param_path, main_program=main_prog, ... vars=None, predicate=name_has_fc) # Load All variables in the `main_program` whose name includes "fc". # And all the variables are supposed to be saved in separate files. FNTryzYThe type of input main_program is invalid, expected type is base.Program, but received %s)rrrrz>The directory path and params cannot be None at the same time.loadrYrr[z.SelectedRows can not be load with load_combinezSelectedRows var z can not find at )r0rBrHrIrKParamlookup_sparse_table_mergerXrrrzMUST in var listzVariable's shape does not match, the Program requires a parameter with the shape of ({}), while the loaded parameter (namely [ {} ]) has a shape of ({}).)1r7r8r9rr(rr?rBrr.rrr`rrrMrNrr rrLrr0rrrrrnrrisfilelistdir startswithrPrHrIrrrrrr rrrrrrr)rrrrrrvars_from_memory load_progrorig_para_shaperrrr&r'var_pathr| block_pathsrRslicesslicerrr0var_tempr orig_shapes r!rrys_l''""7+2~ |  /1L,0k|$%   %fY (>(>(@AB  I ++-  /1L,0k|$%     Hh1 11}} 4 4 8 88(I.16MM++-2 .}} 4 4 B BB""8,*:x@G   h '?$X$$"WI.& Wgll(KL %.5 W\\*=@$Hh1 11# D+:x@Gww||GW\\:H77>>(+ ' ~5FxjQww~~h'$$"WI.& Wgll(KL % jj2 (E'' 5 e,)#E&11"$\\%mm%mm$) 2EMM%( " Xug FI((#!!& 0*I6 )$$$$4="G, %e$r  M|0023$$\$%784 5(77<<:  # .!))9 !   Y#Hh 2{{//1::8==IH' O)?(--)O O'("5"5"78??I==O3  22 3),,X]];JJ&"WW]W]"HMM9X#r#c ,tjj|}|dk7sJdd|vr|d}tjdt |t stdt||dks|dkDrtd|tjj|}|r4tjj|stj|d }ttt|j}|Dcic]}|j ||} }t#| |} t$j&d k(rt$j(j*d k(ret-j.| | } t1|d zd5} d} t3dt5| | D]} | j7| | | | z dddn0t1|d zd5} t-j8| | | dddttt:|j}|Dcic]}|j ||}}t1|dzd5} t-j8|| | ddd|j=}|j>jAt1|dzd5} | j7|j>jCdddycc}w#1swYxYw#1swYxYwcc}w#1swYxYw#1swYyxYw)a~ This function save parameters, optimizer information and network description to model_path. The parameters contains all the trainable Tensor, will save to a file with suffix ".pdparams". The optimizer information contains all the Tensor used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. All the information will save to a file with suffix ".pdopt". (If the optimizer have no Tensor need to save (like SGD), the fill will not generated). The network description is the description of the program. It's only used for deployment. The description will save to a file with a suffix ".pdmodel". Args: program(Program) : The program to saved. model_path(str): the file prefix to save the program. The format is "dirname/file_prefix". If file_prefix is empty str. A exception will be raised protocol(int, optional): The protocol version of pickle module must be greater than 1 and less than 5. Default: 4 configs(dict, optional) : optional keyword arguments. Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> x = static.data(name="x", shape=[10, 10], dtype='float32') >>> y = static.nn.fc(x, 10) >>> z = static.nn.fc(y, 10) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(static.default_startup_program()) >>> prog = static.default_main_program() >>> static.save(prog, "./temp") ryzThe input model_path MUST be format of dirname/filename [dirname\filename in Windows system], but received model_path is empty string.pickle_protocolzJ'pickle_protocol' is a deprecated argument. Please use 'protocol' instead.z+The 'protocol' MUST be `int`, but received z/Expected 1<'protocol'<5, but received protocol=ctj|jj}t j |Sr&)r rr0rrr)r*ts r!rzsave..get_tensors/ N # #CHH - 8 8 :xx{r#darwin)protocol .pdparamsri@rN.pdoptr)"r7r8rr@rAr(intrrBrrrr.rrrr0rsysplatform version_infomajorpickledumpsrranger_rdumprrrLrr)rDrrCconfigs base_namedir_namerparameter_listp param_dict pickle_bytesr max_bytesrVoptimizer_var_listopt_dictrs r!r$r$|sP  ,IRQQQG#,- X  h $9$x.9I J  !|x!|=hZ H  wwz*Hx0 H&w/@/@/BCDN1?@A!&&*Q-'J@#J9J ||xC$4$4$:$:a$?||JB *{*D 1QI1c,/; QY78<2 1 *{*D 1Q KK A 92%w'8'8':;0BB/A! 1 %/AHB j8#T *a Ha(3 +==?L LL j:%t ,  0023 - ,5A2 1 2 1C * * - ,s< K 5K!K-$K9K>)*L !K*-K6>L Lc |t|tsJ|}|jdr|dd}n-|jdr|dd}n|jdr|dd}|dz}tjj |st jdj|| td ||Dcgc]}|j}}nd}tjj|rrt}tj|d D]L\} } } | D]A} |jtjj| | j!d d CNt#|j%} g}| D]z}tjj||jj!d d }|duxs|j|v}||vsV|sY|j'||j)||t+|dkDrJdjt#|}t j-ddjt#|z t/|||ytjj5|r| td|j%} | Dchc]}|j}}|D]}|j|vst7dtjj9|\}} t/||||yd}t#t;t<|j%}|r=t>j@jBjE|tG|jHtK|d5} tLjNdk(r*tLjPjRdk(r tU|| }ntWjX| d}t[|}ddd|D]>}|jvsJd|jd|d||||j@t#t;t\|j%}t+|dkDr|dz}tjj |s Jd |d!|r=t>j@jBjE|tG|jHtK|d5} tWjX| d}ddd|D]>}|jvsJd|jd|d||||j@yycc}w#t0$r}t j3||d}~wt1dxYwcc}w#t0$r}t j3||d}~wt1dxYw#1swYxYw#1swYxYw)"aU :api_attr: Static Graph This function get parameters and optimizer information from program, and then get corresponding value from file. An exception will throw if shape or dtype of the parameters is not match. This function can also load model file saved with [ save_params, save_persistables, save_vars ]. var_list can not be None when load single model file ( filename is not None When save_params, save_persistables or save_vars is called ). Args: program(Program): The program will be loaded model_path(str): The file prefix store the program executor(Executor, optional): The executor used for initialize the parameter When startup program is not run. var_list(list|tuple, optional): The Tensor list/tuple to load single model file saved with [ save_params, save_persistables, save_vars ]. Default: None Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> x = static.data(name="x", shape=[10, 10], dtype='float32') >>> y = static.nn.fc(x, 10) >>> z = static.nn.fc(y, 10) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(static.default_startup_program()) >>> prog = static.default_main_program() >>> static.save(prog, "./temp") >>> static.load(prog, "./temp") NrDrEr]{} not found, try to load model file saved with [ save_params, save_persistables, save_vars ]zeexecutor is required when loading model file saved with [ save_params, save_persistables, save_vars ]Ftopdown\r4r zvariable file [ %s ] not used)rrrzFailed to load model file, please make sure model file is saved with the following APIs: save_params, save_persistables, save_varszevar_list is required when loading model file saved with [ save_params, save_persistables, save_vars ]z/loaded var [{}] is not in program variable listrrrrzFailed to load model file , please make sure model file is saved with the the following APIs: [ save_params, save_persistables, save_vars ]. When these API called, filename CANNOT be Nonectj|jj}|j }|j r t jj}n|jr t jj}n|jrvt jjj}|j|j t jj|j!}n(|j#rt jjj}|j|j t jj%t j&j)j+dd|j-}ntt jjj}|j|j t jj/|j1}|j3||y)N:r)r rr0r_place is_cpu_placerrCPUPlaceis_cuda_pinned_placeCUDAPinnedPlace is_xpu_placerPlace set_placeXPUPlace xpu_device_idis_custom_place CustomPlacedevice get_devicesplitcustom_device_id CUDAPlace gpu_device_idset)r*ndarrayr@rSplaces r!set_varzload..set_varrs N # #CHH - 8 8 : HHJ >> KK((*E  # # %KK//1E ^^    &&(A KK #KK(():;E       &&(A KK #KK++ ((*005a8!:L:L:NE   &&(A KK #KK))!//*;>- .  k r r#   w   2:;(3chh(N;!N 77== $!eO%'WWZ%G!dEA#'' T1-55dC@&H $G$5$5$78  O'77<< CHH=EEdCP"d*Hchh..H.>#**3/#**84(?#a'"%((4+@"A3xx_ 568 %z  WW^^J ' { '002 8H#I8HCHH8H #I 88#77%I #%''-- "; Hi %$!&  0&w/@/@/BCDN 11 LNH,F,F  !4 (A <<8 #(8(8(>(>!(C)*=qAI A9I%i0 ) FFi  L AFF8#45H4I K L 9QVV$%  %w'8'8':; "$x/ ww~~   9 m_K 8 9   KK   5 5"LNH4N4N - &! A9I'#A)# Jx'8qI J# Ay( ) $#w<>   a  "P$J"   a  "EL ) (8' &sU7T=U!U59U:>A V-V: U2 U""U2: V*VV*-V7:Wct|}ttt|j }i}|D]}t j jj|j}|Jd|jd|j|vsatj|j}||j}|j|jk(s6Jdj|j|j|j|j|jk(s6Jdj|j|j|j|j}|j!} t j j#} | j%rt j j'} n| j)rgt j j*j-} | j/| t j j1| j3} nv| j5rft j j*j-} | j/| t j j7| j9} |j;|| d||j<g} |j=D]\}}||vs | j?|tA| dkDr4tCjDdjd jG| yy) a Set program parameter from state_dict An exception will throw if shape or dtype of the parameters is not match. NOTICE: This function MUST called after run start_up_program Args: program(Program): The program to be set state_dict(dict): the dict store Parameter and optimizer information Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> x = static.data(name="x", shape=[10, 10], dtype='float32') >>> y = static.nn.fc(x, 10) >>> z = static.nn.fc(y, 10) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(static.default_startup_program()) >>> prog = static.default_main_program() >>> static.save(prog, "./temp") >>> program_state = static.load_program_state("./temp") >>> static.set_program_state(prog, program_state) Nz Variable [ z2 ] Not found, Please make sure run startup programzParameter's shape does not match, the Program requires a parameter with the shape of ({}), while the loaded parameter (namely [ {} ]) has a shape of ({}).zParameter's data type does not match, the Program requires a parameter with a dtype of ({}), while the loaded parameter (namely [ {} ]) has a dtype of ({}).r}rzOThis list is not set, Because of Paramerter not found in program. There are: {}ra)$rr.rrrrrr rr0rrrrHrrIrergrhri is_gpu_placerrkrlrurvrjrmrnrwitemsrr_r@rAr)rD state_dictrRused_para_listparar9 orig_para_np new_para_npten ten_placepy_placeryrSunused_para_listkrws r!set_program_statersJ#:.J&1B1B1DEFNN;;++-66tyyA   W  #U V W 99 "88H$7$7$9:L$TYY/K%%):):: SSYSY && ;3D3DT :  %%):):: SSYSY && ;3D3DT :%%'C I{{++-H--/ 335'')KK$$**, I&!;;001BC'')KK$$**, I&!;;//0AB GGK *()N499 %SV  "1 N "  # #A &# q  ] d d)*  !r#cPttt|jS)a Get all the persistable vars from Program. Args: var(Program): The Program to get persistable vars Returns: list: The list contains all persistable vars in the program Examples: .. code-block:: python >>> import paddle >>> import paddle.static.io as io >>> paddle.enable_static() >>> data = paddle.static.data(name="img", shape=[64, 784]) >>> w = paddle.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') >>> b = paddle.create_parameter(shape=[200], dtype='float32', name='fc_b') >>> list_para = io.get_program_persistable_vars( paddle.static.default_main_program() ) )r.rrrrCs r!get_program_persistable_varsrs& ~w'8'8':; <dk(r*t<j@jBdk(r tE||}ntGjH|d}dddtK}|dz}tjj|r=t;|d5}tGjH|d}ddd|jM|S#1swY xYw#1swYxYw#1swY5xYw)a Load program state from local file Args: model_path(str): The file prefix store the program var_list(list|tuple, optional): The Tensor list/tuple to load saved with [ save_params, save_persistables, save_vars ]. Default: None. The var_list is only used to get name, will not be modified. Returns: state_dict(dict): the dict store Parameter and optimizer information Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> x = static.data(name="x", shape=[10, 10], dtype='float32') >>> y = static.nn.fc(x, 10) >>> z = static.nn.fc(y, 10) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(static.default_startup_program()) >>> prog = static.default_main_program() >>> static.save(prog, "./temp") >>> program_state = static.load_program_state("./temp") rDNrZrEr[rr\r]z7var_list can not be None when model_path is a file typeFr^r`r4cXt|ts td|j|j|j |j |j|jjtjjjk(r|jdSddS)Nz"value in var_list must be variableTrG)r(rr?rPr0rHrIrBrLrrMrNrOrJrQs r!clone_var_to_blockz.load_program_state..clone_var_to_blockrs!#x0#$HII''))))xx}}$,,*>*>*I*II"mm!%(   $(  r#Tc t||||y#d}| |Dcgc]}|jncc}wc}n|}|rt||ztj||zt YyxYw)NrbTzFailed to load model/variables `%s`, please make sure model/variables file is saved with the following APIs: save_params, save_persistables, save_vars.F)rr0rr@rARuntimeWarning)exerrr raise_error error_strr* filenamess r!_load_vars_with_try_catchz5load_program_state.._load_vars_with_try_catchsM!$ '!!)   ME$+.22TcT2% #*9y+@AA i)&;^Ls A)08A))r0rKzParameter file [rr rArBr{r|)T)'r6r7r8rrrrr/rrrrelpathrrrrr`rrrgr rsrPrasarrayr rr0rrrGrHrIrJrrKr,rupdate)rr1rr var_name_listrrrrr var_temp_namer3rrrryrrrQrr*var_nametemp_varres_dict para_dictr opti_dicts r!load_program_stater+sHL[)#CR(   x (#CR(   z *#CR( &4 77>>- .  k r r#    z :I "$U!C D$GGLLq1 " : F - 5 5dC @ $$]3 "D! " I"//1J ;? 8KK((*E++&&u-C Oww~~j)&(ggmmJ&?#)#C#**+=j#+NO$)?I '''...z3? (.Z$%2 $.#8#8!)t$9$5hZu,228<%2H&%'ZZKK,,.77ALLN&"' u# "x 77>> ; -.k:;  !4 (A <<8 #(8(8(>(>!(C)*=qAI A9I ) "),I 8+M ww~~m$ - &! A9I' # _# "@ ) (' &s-D P3BP5AP  PP  PP")NNr&)rW)rl)F)NNNN)r>)Arrloggingr7rKrGr@numpyrr paddle.baserrrrrrr paddle.base.executorr r paddle.base.frameworkr r rpaddle.base.log_helperrpaddle.framework.io_utilsrrrrrrrr__all____name__INFOrr"r2r<rErSrjrrrrrrrrrrr rr!rrr$r,rrrr#r!rs   8MM-      gll H  $  . ,E &up 1D 1DhI 06 06f,=^%P q  q h * *\ e eP&R `7 `7F   ^H^HH   @F _4 _4D R* R*j \  \ ~==*rr#