a Sic@sLdZddlZddlZddlZddlmmZddl m Z ddl m Z ddl mZddlmZddlmZddlmZdd lmZdd lmZdd lmZzddlZd ZWneydZYn0ed e dZ!d-ddZ"d.ddZ#d/ddZ$ddZ%ddZ&ddZ'ddZ(ddZ)d d!Z*d"d#Z+d0d%d&Z,d'd(Z-d)d*Z.d+d,Z/dS)1z@Functions for saving and loading a Keras Model from HDF5 format.N)backend) optimizer_v1) optimizer) model_config) saving_utils) json_utils) LazyLoader)ask_to_proceed_with_overwrite) tf_loggingisequential_libzkeras.engine.sequentialTc Cs|tdurtdt|jt|jkr.tdt|tjs|sZt j |rZt |}|sZdSt j |}t j |stjj|tj|dd}d}n|}d}zt||}|D]@\} } t| tttfrtj| tjdd |j| <q| |j| <q|d } t | |t|j!t"j#r$td n*|rN|j!rNt|j!t$j%sNt&||j!|'W|rx|(n|rv|(0dS) a@Saves a model to a HDF5 file. The saved model contains: - the model's configuration (topology) - the model's weights - the model's optimizer's state (if any) Thus the saved model can be reinstantiated in the exact same state, without any of the code used for model definition or training. Args: model: Keras model instance to be saved. filepath: One of the following: - String, path where to save the model - `h5py.File` object where to save the model overwrite: Whether we should overwrite any existing model at the target location, or instead ask the user with a manual prompt. include_optimizer: If True, save optimizer's state together. Raises: ImportError: if h5py is not available. NzD`save_model()` using h5 format requires h5py. Could not import h5py.aFound duplicated `Variable`s in Model's `weights`. This is usually caused by `Variable`s being shared by Layers in the Model. These `Variable`s will be treated as separate `Variable`s when the Model is restored. To avoid this, please save with `save_format="tf"`.wmodeTF)defaultutf8 model_weightsz{HDF5 format does not save weights of `optimizer_experimental.Optimizer`, your optimizer will be recompiled at loading time.))h5py ImportErrorlenweights_undeduplicated_weightsloggingwarning isinstanceFileospathisfiler dirnameexiststfiogfilemakedirsrmodel_metadataitemsdictlisttuplejsondumpsr get_json_typeencodeattrs create_groupsave_weights_to_hdf5_grouproptimizer_experimental Optimizerr TFOptimizer$save_optimizer_weights_to_hdf5_groupflushclose) modelfilepath overwriteinclude_optimizerproceeddirpathfopened_new_filer$kvmodel_weights_grouprAT/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/saving/hdf5_format.pysave_model_to_hdf55s`            rCc Cstdurtd|si}t|tj }|r:tj|dd}n|}d}z|jd}|durjtd|dt|dr~|d }t |}t j ||d }t |d ||r|jd }t|dr|d }|durt d |W|r|St |}|jfit||ddit|t|jtjr>t dnxd|vrz|j|jWn"ttfy|t dYn0t|}z|j|Wntyt dYn0W|r|n|r|0|S)avLoads a model saved via `save_model_to_hdf5`. Args: filepath: One of the following: - String, path to the saved model - `h5py.File` object from which to load the model custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. compile: Boolean, whether to compile the model after loading. Returns: A Keras model instance. If an optimizer was found as part of the saved model, the model is already compiled. Otherwise, the model is uncompiled and a warning will be displayed. When `compile` is set to False, the compilation is omitted without any warning. Raises: ImportError: if h5py is not available. ValueError: In case of an invalid savefile. NzD`load_model()` using h5 format requires h5py. Could not import h5py.rr rz%No model config found in the file at .decodezutf-8)custom_objectsrtraining_configzgNo training configuration found in the save file, so the model was *not* compiled. Compile it manually.from_serializedTzLoading model from HDF5 will not restore the optimizer's weights, since the optimizer is an instance of `optimizer_experimental.Optimizer`optimizer_weightszError when creating the weights of optimizer {}, making it impossible to restore the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.zuError in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.)rrrrr-get ValueErrorhasattrrFrmodel_config_libmodel_from_configload_weights_from_hdf5_grouprrr5compiler!compile_args_from_training_configtry_build_compiled_argumentsrr0r1_create_all_weightstrainable_variablesNotImplementedErrorAttributeError&load_optimizer_weights_from_hdf5_group set_weights) r7rGrQr=r<r6rrHoptimizer_weight_valuesrArArBload_model_from_hdf5s        -       r[c s4fdd}fdd}fdd}jjdkrD||}jjdkrZ||}njjd vrn||}d krˆjjdkrtj|}jjd kr>|d j}|d djd dfks|djkr|d jkr|dd jd dfksJt|d d|d <|d d d d d d d d f|d <jjdkrlj dkrlt|d d|d <jjdkrj dkrt|d d|d <j dkrt|d d|d <jjdkrj dkrt|d d|d <jjdkrnt |dkrntj |d |d|dgdd}tj |d|d|d gdd} tj |d|d!|d"gdd} || | g}jjd#krt |d$krtj |d |d|d|dgdd}tj |d|d |d|d%gdd} tj |d|d"|d!|d&gdd} || | g}jjd'krt |d$krtj |d |d|d|dgdd}tj |d|d |d|d%gdd} tj |d|d"|d!|d&gdd} j dkrt|d}t| d} || | g}gd(} jj| vr*t jd |d jkr*t|d d)|d <jjd'kr*t|dd)|d<t|S)*aPreprocess layer weights between different Keras formats. Converts layers weights from Keras 1 format to Keras 2 and also weights of cuDNN layers in Keras 2. Args: layer: Layer instance. weights: List of weights values (Numpy arrays). original_keras_version: Keras version for the weights, as a string. original_backend: Keras backend the weights were trained with, as a string. Returns: A list of weights values (Numpy arrays). csDt|d}tj|d|}tj||d}||S)a+Converts layers nested in `Bidirectional` wrapper. This function uses `preprocess_weights_for_loading()` for converting layers. Args: weights: List of weights values (Numpy arrays). Returns: A list of weights values (Numpy arrays). N)rpreprocess_weights_for_loading forward_layerbackward_layer)rnum_weights_per_layerforward_weightsbackward_weightslayeroriginal_backendoriginal_keras_versionrArBconvert_nested_bidirectionals   zDpreprocess_weights_for_loading..convert_nested_bidirectionalcstj|S)a4Converts layers nested in `TimeDistributed` wrapper. This function uses `preprocess_weights_for_loading()` for converting nested layers. Args: weights: List of weights values (Numpy arrays). Returns: A list of weights values (Numpy arrays). )r]rd)rrcrArBconvert_nested_time_distributed9s  zGpreprocess_weights_for_loading..convert_nested_time_distributedc s|dtj}|tjd}g}g}jD]~}t|j}t|j}|jr2t||d||d|d}||d||||d||d}||d}q2|j7}|j7}||S)a2Converts layers nested in `Model` or `Sequential`. This function uses `preprocess_weights_for_loading()` for converting nested layers. Args: weights: List of weights values (Numpy arrays). Returns: A list of weights values (Numpy arrays). N)rdrrfre) rtrainable_weightslayersnon_trainable_weightsrr]extend_trainable_weights_non_trainable_weights) rrirknew_trainable_weightsnew_non_trainable_weightssublayernum_trainable_weightsnum_non_trainable_weights preprocessedrcrArBconvert_nested_modelIs:            z.convert_nested_model BidirectionalTimeDistributed)Model Sequential Functional1Conv1DrNr\)r\r~r}rConv2Dchannels_firstConv2DTranspose channels_last)rr}r~r\)r\r~rr}Conv3D)r\r~r}rGRU axisrLSTM ConvLSTM2D)r|rrrr)r~r\rr}) __class____name__r]rdshape kernel_sizefiltersnp transpose data_formatr concatenater int_shaper_convert_rnn_weights) rdrrfrergrhrurkernelrecurrent_kernelbias conv_layersrArcrBr] s1          $         r]c sdddd|jj}|dvrt|dkr|djd}|d j}d |d |fkr`d }n$||fkrtd }ntd t|dfdd }||kr|||d kd}|dvrt|dkr|djd}|d j}ddfdd }|d |fkrd}n>|d |fkr&d}n&||fkrfuncrArB zC_convert_rnn_weights..transform_kernels..)rhstackhsplit)kernelsrn_gatesrArrBtransform_kernelss z/_convert_rnn_weights..transform_kernelscs|rdndfdd}|S)a@Makes a function that transforms input kernels from/to cuDNN format. It keeps the shape, but changes between the layout (Fortran/C). Eg.: ``` Keras cuDNN [[0, 1, 2], <---> [[0, 2, 4], [3, 4, 5]] [1, 3, 5]] ``` It can be passed to `transform_kernels()`. Args: from_cudnn: `True` if source weights are in cuDNN format, `False` if they're in plain Keras format. Returns: Function that converts input kernel to the other format. FCcs|jj|jdS)Norder)Treshaper)rrrArB transform*sz@_convert_rnn_weights..transpose_input..transformrA) from_cudnnrrArrBtranspose_inputs  z-_convert_rnn_weights..transpose_input)r CuDNNLSTMr~r}rr\rrrzInvalid bias shape: Tcsj|d|}|ddd}|rLtjtj|dddddd}ntd|dd}|||gS)a&Converts the weights between CuDNNLSTM and LSTM. Args: weights: Original weights. from_cudnn: Indicates whether original weights are from cuDNN layer. Returns: Updated weights compatible with LSTM. rr}cSs|jSNrr>rArArBSrzD_convert_rnn_weights..convert_lstm_weights..r\rg?)rsumsplittilerrrrecurrent_kernelsbiasesrrrrArBconvert_lstm_weightsBs  z2_convert_rnn_weights..convert_lstm_weights)r)rCuDNNGRUcsN|d|}|ddd}t|d|r>dnd}|||gS)a#Converts the weights between CuDNNGRU and GRU. Args: weights: Original weights. from_cudnn: Indicates whether original weights are from cuDNN layer. Returns: Updated weights compatible with GRU. rr}cSs|jSrrrrArArBr}rzC_convert_rnn_weights..convert_gru_weights..r\)r\rr)rarrayrrrrArBconvert_gru_weightsms z1_convert_rnn_weights..convert_gru_weightsrzGRU(reset_after=True)zGRU(reset_after=False)z%s is not compatible with %sF)T)T)rrrrrLstr reset_after) rdr target_classunits bias_shapesourcerrtargettypesrArrBrsV          rc Cst|d}|r|d}dd|D}t|d|t|}t||D]8\}}|j||j|jd}|jsr||d<qF||dd<qFdS) zSaves optimizer weights of a optimizer to a HDF5 group. Args: hdf5_group: HDF5 group. optimizer: optimizer instance. rrJcSsg|]}t|jdqSr)rnamer,rr rArArBrrz8save_optimizer_weights_to_hdf5_group.. weight_namesdtyperAN) getattrr.save_attributes_to_hdf5_grouprbatch_get_valuezipcreate_datasetrr) hdf5_grouprsymbolic_weights weights_groupr weight_valuesrval param_dsetrArArBr3s     r3cs$|dtd}fdd|DS)zLoad optimizer weights from a HDF5 group. Args: hdf5_group: A pointer to a HDF5 group. Returns: data: List of optimizer weight names. rJrcsg|] }|qSrArAr weight_namerrArBrsz:load_optimizer_weights_from_hdf5_group..load_attributes_from_hdf5_group)roptimizer_weight_namesrArrBrXs  rXcCslt|}dd|D}t|d|t||D]8\}}|j||j|jd}|jsZ||d<q.||dd<q.dS)zSave top-level weights of a model to a HDF5 group. Args: f: HDF5 group. weights: List of weight variables. cSsg|]}|jdqSrrr,rrArArBrrz5save_subset_weights_to_hdf5_group..rrrAN)rrrrrrr)r<rrrrrrrArArB!save_subset_weights_to_hdf5_groups   rcCsddlm}t|ddd|jDtd|jd<t|d|jd<t|jd d d D]"}| |j }t |}t ||q^|j |j}| d }t ||d S)z|Saves the weights of a list of layers to a HDF5 group. Args: f: HDF5 group. model: Model instance. r) __version__ layer_namescSsg|]}|jdqSrr)rrdrArArBrrz.save_weights_to_hdf5_group..rr keras_versioncSs|jSr)r)xrArArBrrz,save_weights_to_hdf5_group..)keytop_level_model_weightsN)kerasrrrjrr,r-rsortedr.r_legacy_weightsrrmrn)r<r6rrdgrrArArBr/s     r/cstd}fdd|DS)a Load layer weights of a model from hdf5. Args: f: A pointer to a HDF5 group. Returns: List of NumPy arrays of the weight values. Raises: ValueError: in case of mismatch between provided model and weights file. rcsg|]}t|qSrA)rasarrayrr<rArBrrz7load_subset_weights_from_hdf5_group..r)r<rrArrB#load_subset_weights_from_hdf5_groups rc Csd|jvr*|jd}t|dr.|d}nd}d|jvrX|jd}t|dr\|d}nd}g}|jD]}t|}|rf||qft|d}g}|D]$} || } t| d} | r|| q|}t|t|krtd t|d t|d g} t |D]\} } || } || }t|}t | }t ||||}t|t|krrtd | d |j d| dt|dt|d | t ||7} qd|vr|j|j}t |d}t|t|krtdt|dt|d| t ||7} t| |D]}|qdS)zImplements topological (order-based) weight loading. Args: f: A pointer to a HDF5 group. model: Model instance. Raises: ValueError: in case of mismatch between provided layers and weights file. rrFrr{rNrrzDLayer count mismatch when loading weights from file. Model expected z layers, found z saved layers.!Weight count mismatch for layer # (named z in the current model, z" in the save file). Layer expects  weight(s). Received  saved weight(s)rzZWeight count mismatch for top-level weights when loading weights from file. Model expects  top-level weight(s). Received  saved top-level weight(s))r-rMrFrjrappendrrrL enumeraterr]rrrmrnrbatch_set_value_flatten_layersfinalize_state)r<r6rfrefiltered_layersrdrrfiltered_layer_namesrrrweight_value_tuplesr>rrrArArBrP s                 rPFcCsFd|jvr*|jd}t|dr.|d}nd}d|jvrX|jd}t|dr\|d}nd}t|d}i}|jD]}|jrp||jg|qpg}t|D]l\} } || } t | } | | gD]D}t |} t || ||} t | t | krR|r&td| d |jd t | d t | d qtd | d |jdt | d t | d tt | D]}t| |}| |j}||kr|rtd| d |jd| |jd|d| q^td| d |jd| |jd|d| n|| || |fq^qqd|vr |j|j} t |d} t | t | kr|rhtdt | dt | dntdt | dt | dntt | D]}t| |}| |j}||kr|rtd| |jd|d|n td| |jd|d|n|| || |fqt||D]}|q2dS)aImplements name-based weight loading (instead of topological loading). Layers that have no matching name are skipped. Args: f: A pointer to a HDF5 group. model: Model instance. skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weights. Raises: ValueError: in case of mismatch between provided layers and weights file and skip_match=False. rrFrr{rNrz'Skipping loading of weights for layer #rz6) due to mismatch in number of weights. Layer expects rrrz). Layer expects z$Skipping loading weights for layer #z&) due to mismatch in shape for weight z. Weight expects shape z#. Received saved weight with shape zShape mismatch in layer #z ) for weight rzaSkipping loading top-level weights for model due to mismatch in number of weights. Model expects rrzDWeight count mismatch for top-level weights of model. Model expects zPSkipping loading top-level weight for model due to mismatch in shape for weight z-Shape mismatch in model for top-level weight )r-rMrFrrjr setdefaultrrrrKrr]rrrrLrangerrrrmrnrrr)r<r6 skip_mismatchrfrerindexrdrr>rrrriexpected_shapeZreceived_shaperArArB$load_weights_from_hdf5_group_by_nameas                    rc Csdd|D}|r&tdtd|t|}d}t||}tdd|Drh|d7}t||}q@|dkrt|D]\}}||jd||f<qxn ||j|<d S) aSaves attributes (data) of the specified name into the HDF5 group. This method deals with an inherent problem of HDF5 file which is not able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes. Args: group: A pointer to a HDF5 group. name: A name of the attributes to save. data: Attributes data to store. Raises: RuntimeError: If any single attribute is too large to be saved. cSsg|]}t|tkr|qSrA)rHDF5_OBJECT_HEADER_LIMITrrrArArBrrz1save_attributes_to_hdf5_group..zSThe following attributes cannot be saved to HDF5 file because they are larger than z bytes: r}css|]}|jtkVqdSr)nbytesrr rArArB rz0save_attributes_to_hdf5_group..%s%dN) RuntimeErrorrrr array_splitanyrr-) grouprdatabad_attributesdata_npy num_chunks chunked_datachunk_id chunk_datarArArBrs&  rcCsj||jvr dd|j|D}nFg}d}d||f|jvrf|dd|jd||fD|d7}q(|S)acLoads attributes of the specified name from the HDF5 group. This method deals with an inherent problem of HDF5 file which is not able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes. Args: group: A pointer to a HDF5 group. name: A name of the attributes to load. Returns: data: Attributes data. cSs$g|]}t|dr|dn|qSrFrrMrFrnrArArBr1sz3load_attributes_from_hdf5_group..rr cSs$g|]}t|dr|dn|qSrrrrArArBr:sr})r-rl)rrrrrArArBr"s  rcCs:|j|j}tdd|Dr6td|jjd||S)aDO NOT USE. For legacy reason, the layer.weights was in the order of [self.trainable_weights + self.non_trainable_weights], and this order was used for preserving the weights in h5 format. The new order of layer.weights are the same as layer.get_weights() which is more intuitive for user. To keep supporting the existing saved h5 file, this method should be used to save/load weights. In future version, we will delete this method and introduce a breaking change for h5 and stay with the new order for weights. Args: layer: a `tf.keras.Model` or `tf.keras.layers.Layer` instance. Returns: A list of variables with the order of trainable_weights, followed by non_trainable_weights. css|]}t|tj VqdSr)rr VariablerrArArBr Vrz"_legacy_weights..zSave or restore weights that is not an instance of `tf.Variable` is not supported in h5, use `save_format='tf'` instead. Received a model or layer z with weights )rirkrrVrr)rdrrArArBrCs r)TT)NT)NN)F)0__doc__r)rnumpyrtensorflow.compat.v2compatv2r rrkeras.optimizersr'keras.optimizers.optimizer_experimentalrr0 keras.savingrrNrkeras.saving.saved_modelrkeras.utils.generic_utilsrZkeras.utils.io_utilsr tensorflow.python.platformr rrrrglobalsr rCr[r]rr3rXrr/rrPrrrrrArArArBsN            ` w d2W ,!