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MMf  JJ ! ! * *? ; JJ ! ! 0 0 AcPs2KcURS:XaURRS:Xa[R"5(ah[ R RURRURRSS9uURlURl URS:waSURl gg)Nrarrr) rrrr rr3rrrrr1rs r.rDynamicGraphAdapter.prepares OOt # 7***,,8> 8K8Kzz))::009L9 5DJJ  5 ??d "!%DJJ  #r0) rrrr&rrrrr)rrrr))rrrrrrrrrrrrr2rRrrrrs@r.r$r$sbO> [[  5 n<| 7> 8KBZ & &r0r$c\rSrSr%SrS\S'S\S'S\S'S"S#S jjrS$S%S jjr\"5S&S'S jj5r \"5S(S j5r S)S*Sjjr S+S,Sjjr S-Sjr SrS.S/SjjrS0S1SjjrS2S3Sjjr\S4S5Sjj5r\S4S6Sjj5r\S4S7Sjj5rS8SjrS9Sjr04SjrS"S:SjjrS;SjrSrSrSrS rS!rg )<ModeliaX A Model object is a network with training and inference features. Dynamic graph and static graph are supported at the same time, switched by `paddle.enable_static()`. The usage is as follows. But note, the switching between dynamic and static should be before instantiating a Model. The input description, i.e, paddle.static.InputSpec, must be required for static graph. When training on GPU, auto mixed precision (AMP O1) and pure float16 (AMP O2) training are both supported in static graph mode and dynamic mode. In static graph mode, before training with pure float16 (AMP O2), `multi_precision` could be set to True when creating optimizer, which can avoid poor accuracy or slow convergence in a way, and inputs of dtype float should be cast to float16 by users. `paddle.static.amp.fp16_guard` API should be also used to limit the range of pure float16 training, otherwise, 'use_fp16_guard' should be set to False by users. However, limiting the range of is not supported during training using AMP. Args: network (paddle.nn.Layer): The network is an instance of paddle.nn.Layer. inputs (InputSpec|list|tuple|dict|None, optional): `inputs`, entry points of network, could be a InputSpec instance, or list/tuple of InputSpec instances, or dict ({name: InputSpec}), and it couldn't be None in static graph. Default: None. labels (InputSpec|list|tuple|None, optional): `labels`, entry points of network, could be a InputSpec instance or list/tuple of InputSpec instances, or None. For static graph, if labels is required in loss, labels must be set. Otherwise, it could be None. Default: None. Examples: 1. A common example .. code-block:: python :name: code-example1 >>> import paddle >>> import paddle.nn as nn >>> import paddle.vision.transforms as T >>> from paddle.static import InputSpec >>> device = paddle.set_device('cpu') # or 'gpu' >>> net = nn.Sequential( ... nn.Flatten(1), ... nn.Linear(784, 200), ... nn.Tanh(), ... nn.Linear(200, 10)) ... >>> # inputs and labels are not required for dynamic graph. >>> input = InputSpec([None, 784], 'float32', 'x') >>> label = InputSpec([None, 1], 'int64', 'label') >>> model = paddle.Model(net, input, label) >>> optim = paddle.optimizer.SGD(learning_rate=1e-3, ... parameters=model.parameters()) ... >>> model.prepare(optim, ... paddle.nn.CrossEntropyLoss(), ... paddle.metric.Accuracy()) ... >>> transform = T.Compose([ ... T.Transpose(), ... T.Normalize([127.5], [127.5]) >>> ]) >>> data = paddle.vision.datasets.MNIST(mode='train', transform=transform) >>> model.fit(data, epochs=2, batch_size=32, verbose=1) 2. An example using mixed precision training. .. code-block:: python :name: code-example2 >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> import paddle.nn as nn >>> import paddle.vision.transforms as T >>> def run_example_code(): ... device = paddle.set_device('gpu') ... ... net = nn.Sequential(nn.Flatten(1), nn.Linear(784, 200), nn.Tanh(), ... nn.Linear(200, 10)) ... ... model = paddle.Model(net) ... optim = paddle.optimizer.SGD(learning_rate=1e-3, parameters=model.parameters()) ... ... amp_configs = { ... "level": "O1", ... "custom_white_list": {'conv2d'}, ... "use_dynamic_loss_scaling": True ... } ... model.prepare(optim, ... paddle.nn.CrossEntropyLoss(), ... paddle.metric.Accuracy(), ... amp_configs=amp_configs) ... ... transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])]) ... data = paddle.vision.datasets.MNIST(mode='train', transform=transform) ... model.fit(data, epochs=2, batch_size=32, verbose=1) ... >>> # mixed precision training is only supported on GPU now. >>> if paddle.is_compiled_with_cuda(): ... run_example_code() ... z Literal['train', 'eval', 'test']rpaddle.nn.Layerrbool stop_trainingNc@SUlXlSUlSUlSUlSUlSUlSUlSUlSUl SUl [5(d1[U[[[[ 45(d [#S5eOU(a[%U5UlUR'USS9UlUR'U5Ul[5(a[)U5Ulg[-5(a[/U5Ulg[1U5Ulg)NrFz='inputs' must be list or tuple or dict, and couldn't be None.T)is_input)rrrrr _loss_weightsrr&_is_shape_inferredrr\rr*r+r,rr TypeErrorr _verify_specr$_adapterrrr)rrrzrs r.rModel.__init__3s      !"' $"  ftUD%&@AASB1&9D (($(? ((0    /5DM ]]1$7DM.t4DMr0cURRXU5n[5(aURcUR 5 U$)aG Run one training step on one batch of data. And using `update` indicates whether optimizer update gradients computing by this batch. Args: inputs (numpy.ndarray|Tensor|list): Batch of input data. It could be a numpy array or paddle.Tensor, or a list of arrays or tensors (in case the model has multiple inputs). labels (numpy.ndarray|Tensor|list, optional): Batch of labels. It could be a numpy array or paddle.Tensor, or a list of arrays or tensors (in case the model has multiple labels). If has no labels, set None. Default: None. update (bool, optional): Whether update parameters after loss.backward() computing. Set it to False to accumulate gradients. Default: True. Returns: A list of scalar training loss if the model has no metrics, or a tuple (list of scalar loss, list of metrics) if the model set metrics. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> from paddle.static import InputSpec >>> paddle.seed(2023) >>> device = paddle.set_device('cpu') # or 'gpu' >>> net = nn.Sequential( ... nn.Linear(784, 200), ... nn.Tanh(), ... nn.Linear(200, 10)) ... >>> input = InputSpec([None, 784], 'float32', 'x') >>> label = InputSpec([None, 1], 'int64', 'label') >>> model = paddle.Model(net, input, label) >>> optim = paddle.optimizer.SGD(learning_rate=1e-3, ... parameters=model.parameters()) >>> model.prepare(optim, paddle.nn.CrossEntropyLoss()) >>> data = paddle.rand((4, 784), dtype="float32") >>> label = paddle.randint(0, 10, (4, 1), dtype="int64") >>> loss = model.train_batch([data], [label]) >>> print(loss) [array(3.0039132, dtype=float32)] )rcrrr&_update_inputs)rrzrrrds r.rModel.train_batchXs@p}}((@   !1!1!9    ! r0cURRX5n[5(aURcUR 5 U$)a= Run one evaluating step on a batch of data. Args: inputs (numpy.ndarray|Tensor|list): Batch of input data. It could be a numpy array or paddle.Tensor, or a list of arrays or tensors (in case the model has multiple inputs). labels (numpy.ndarray|Tensor|list, optional): Batch of labels. It could be a numpy array or paddle.Tensor, or a list of arrays or tensors (in case the model has multiple labels). If has no labels, set None. Default: None. Returns: A list of scalar testing loss if the model has no metrics, or a tuple (list of scalar loss, list of metrics) if the model set metrics. Examples: .. code-block:: pycon >>> import paddle >>> import paddle.nn as nn >>> from paddle.static import InputSpec >>> paddle.seed(2023) >>> device = paddle.set_device('cpu') # or 'gpu' >>> net = nn.Sequential( ... nn.Linear(784, 200), ... nn.Tanh(), ... nn.Linear(200, 10), ... ) >>> input = InputSpec([None, 784], 'float32', 'x') >>> label = InputSpec([None, 1], 'int64', 'label') >>> model = paddle.Model(net, input, label) >>> optim = paddle.optimizer.SGD( ... learning_rate=1e-3, ... parameters=model.parameters(), ... ) >>> model.prepare( ... optim, ... paddle.nn.CrossEntropyLoss(), ... metrics=paddle.metric.Accuracy(), ... ) >>> data = paddle.rand((4, 784), dtype="float32") >>> label = paddle.randint(0, 10, (4, 1), dtype="int64") >>> loss, acc = model.eval_batch([data], [label]) >>> print(loss, acc) >>> # doctest: +SKIP("Random output") [array(3.0039132, dtype=float32)] [np.float64(0.0)] >>> # doctest: -SKIP )rcrrr&rf)rrzrrds r.rModel.eval_batchs>v}}''7   !1!1!9    ! r0cURRU5n[5(aURcUR 5 U$)a Run one predicting step on a batch of data. Args: inputs (numpy.ndarray|Tensor|list): Batch of input data. It could be a numpy array or paddle.Tensor, or a list of arrays or tensors (in case the model has multiple inputs). Returns: A list of numpy.ndarray of predictions, that is the outputs of Model forward. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> from paddle.static import InputSpec >>> paddle.seed(2023) >>> device = paddle.set_device('cpu') # or 'gpu' >>> input = InputSpec([None, 784], 'float32', 'x') >>> label = InputSpec([None, 1], 'int64', 'label') >>> net = nn.Sequential( ... nn.Linear(784, 200), ... nn.Tanh(), ... nn.Linear(200, 10), ... nn.Softmax()) ... >>> model = paddle.Model(net, input, label) >>> model.prepare() >>> data = paddle.rand((1, 784), dtype="float32") >>> out = model.predict_batch([data]) >>> print(out) [array([[0.10844935, 0.04650883, 0.11790176, 0.04962315, 0.10899059, 0.08197589, 0.03125402, 0.03232312, 0.3786293 , 0.04434395]], dtype=float32)] )rcrrr&rf)rrzrds r.rModel.predict_batchs>Z}}**62   !1!1!9    ! r0c[RR5RS:Xa5U(dUR U5 gUR R U5 gg)a This function saves parameters, optimizer information or model and parameters only for inference to path. It depends on the parameter `training`. If `training` is set to True, the parameters saved contain all the trainable Variable, will save to a file with suffix ".pdparams". The optimizer information contains all the variable 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 variable need to save (like SGD), the fill will not generated). This function will silently overwrite existing file at the target location. If `training` is set to False, only inference model will be saved. Args: path (str): The file prefix to save model. The format is 'dirname/file_prefix' or 'file_prefix'. if empty str. A exception will be raised. training (bool, optional): Whether to save for training. If not, save for inference only. Default: True. Returns: None Examples: .. code-block:: python >>> # doctest: +TIMEOUT(80) >>> import paddle >>> import paddle.nn as nn >>> import paddle.vision.transforms as T >>> from paddle.static import InputSpec >>> from paddle.vision.datasets import MNIST >>> dynamic = True # False >>> # If use static graph, do not set >>> if not dynamic: ... paddle.enable_static() >>> transform = T.Compose([T.Transpose(), ... T.Normalize([127.5], [127.5])]) >>> train_dataset = MNIST(mode='train', transform=transform) >>> train_loader = paddle.io.DataLoader(train_dataset, batch_size=64) >>> val_dataset = MNIST(mode='test', transform=transform) >>> val_loader = paddle.io.DataLoader(val_dataset, batch_size=64) >>> input = InputSpec([None, 1, 28, 28], 'float32', 'image') >>> label = InputSpec([None, 1], 'int64', 'label') >>> model = paddle.Model(paddle.vision.models.LeNet(), input, label) >>> optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) >>> model.prepare(optim, paddle.nn.CrossEntropyLoss(), paddle.metric.Accuracy(topk=(1, 2))) >>> model.fit(train_loader, val_loader, epochs=2, verbose=0) >>> model.save('checkpoint/test') # save for training >>> model.save('inference_model', False) # save for inference rN)r3rrr_save_inference_modelrcr2)rrtrainings r.r2 Model.savesJ|    ) ) + 6 6! ;**40 ""4( >> import paddle >>> import paddle.nn as nn >>> from paddle.static import InputSpec >>> device = paddle.set_device('cpu') >>> input = InputSpec([None, 784], 'float32', 'x') >>> model = paddle.Model(nn.Sequential( ... nn.Linear(784, 200), ... nn.Tanh(), ... nn.Linear(200, 10), ... nn.Softmax()), input) ... >>> model.save('checkpoint/test') >>> model.load('checkpoint/test') c[RRU5(dg[US5n[R "USS9sSSS5 $!,(df  g=f)Nrblatin1)encoding)r#rr&rrrR)rrs r._load_state_from_path)Model.load.._load_state_from_paths>77>>$''dD!Q{{1x8"!!s A Ac >TRUS5nUc[US35e[UR5[UR5:wa:[US[UR5S[UR5S35eX4$)Nz$ is not found in the providing file.z receives a shape z, but the expected shape is .)r) ValueErrorr+r)rrQr param_states r. _check_match Model.load.._check_matchsOOC.E} C5(L!MNNEKK D$55 e-d5;;.?-@@\]abgbmbm]n\oopq< r0cj[RRU5upUS;d SUS35eU$)N)rr!r"z.pdmodelzUnknown postfix z from weights)r#rsplitext)rexts r._strip_postfix"Model.load.._strip_postfixsI((.ID 5 "#m4  5 Kr0r!z-Failed to load parameters, please check path.zSkip loading for z. TNr"r1rD)rr(rryrOrPr]rArrEr1r#rr&r3rRrc)rr skip_mismatchreset_optimizerrur{rmatched_param_staterrQ match_reserrrOrRrzs @r.rR Model.loadKs^t 9   d#+D;,>? KKK{ ,,11399;JC (4  & &y 1<$D)>th)O    LtY''DLL,D77>>$"455#);;tk/A#BL==%%#, ==%%&9G G1  MM$5cU""=C"HI&*OI  sD66 E</E75E77E<c6URR5$)a, Returns a list of parameters of the model. Returns: A list of Parameter in static graph. A list of ParamBase in dynamic graph. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> from paddle.static import InputSpec >>> paddle.seed(2023) >>> input = InputSpec([None, 784], 'float32', 'x') >>> model = paddle.Model(nn.Sequential( ... nn.Linear(784, 200), ... nn.Tanh(), ... nn.Linear(200, 10)), input) ... >>> params = model.parameters() >>> print(params) [Parameter containing: Tensor(shape=[784, 200], dtype=float32, place=Place(cpu), stop_gradient=False, [[ 0.05713400, 0.00314646, -0.03754271, ..., -0.02529256, 0.04872842, -0.06670858], ..., [ 0.06268418, 0.06550254, -0.02103353, ..., 0.06395906, 0.05509177, -0.06355451]]), Parameter containing: Tensor(shape=[200], dtype=float32, place=Place(cpu), stop_gradient=False, [0., 0., 0., ..., 0., 0.]), Parameter containing: Tensor(shape=[200, 10], dtype=float32, place=Place(cpu), stop_gradient=False, [[ 0.12933084, 0.07726504, 0.05336720, ..., 0.10865459, 0.06605886, 0.13684085], ..., [-0.10171061, -0.01649965, -0.13420501, ..., 0.11190581, -0.12700224, 0.02916957]]), Parameter containing: Tensor(shape=[10], dtype=float32, place=Place(cpu), stop_gradient=False, [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])] )rcrr s r.rModel.parameterssZ}}''))r0cB^^U4Sjn0TRl0TRlT(dSTRlg[ T[ 5(a*TS;a [ S5eTTRlU"5 gST;aSTRlO(TSS;a [ S5eTSTRl[TR55S1- nU(aTRRS:XagST;a [ S 5eU"5 TRRS:wa8U(a1S H+nXC;dM TUTRRU'X41-nM- UU4S jnU"U5nUHnTUTRRU'M! g) Nc0>TRRS:Xa{TRR(a_[ TRR[ R R[ R R45(dS5eggg)NrazcOnly ClipGradByNorm and ClipGradByGlobalNorm are supported in amp training with level=O2 currently.) rcrr _grad_clipr*r3nnClipGradByGlobalNormClipGradByNormrsr._check_pure_fp16_configs4Model._prepare_amp.._check_pure_fp16_configssu}}''4/DOO4N4N!OO..YY33VYY5M5MNz 5O/r0r)rO1raz6The level of amp_configs should be 'O0', 'O1' or 'O2'.rrz2amp_configs['level'] should be 'O0', 'O1' or 'O2'.rzl'use_pure_fp16' is an invalid parameter, the level of mixed precision training only depends on 'O1' or 'O2'.)custom_white_listcustom_black_listcustom_black_varnamesc>1SknX- (a[S[X- 5S35eSU;a?[5(a [S5eTSTRlUR S5 U$)N> decr_ratio incr_ratiorinit_loss_scalingincr_every_n_stepsdecr_every_n_nan_or_infuse_dynamic_loss_scalingz\Except for 'level', the keys of 'amp_configs' must be accepted by mixed precision APIs, but z could not be recognized.rz8'use_fp16_guard' is supported in static graph mode only.)ryr,rrcrr)amp_config_key_setaccepted_param_setrrs r._check_amp_configs.Model._prepare_amp.._check_amp_configs,s" "6 rsxzLzatbscc|} #55"$$$R1<DMM & &s +#r0c<[5Ul[UR[R5(Ga[ R R5RS:a[(d[5(a[R"5Rn[R"5Rn[R"5 [ R"UR5 U[R"5l U[R"5l O[!UR5 Sq XlUbD[U[ R$R&5(d[)U5(d [+S5eX lU=(d /n[/U5H6n[U[05(aMUR2R4S35e [/U5UlUR9U5 UR:R=5 g)a Configures the model before running. Args: optimizer (Optimizer|None, optional): Optimizer must be set in training and should be a Optimizer instance. It can be None in eval and test mode. Default: None. loss (Loss|Callable|None, optional): Loss function can be a `paddle.nn.Layer` instance or any callable function taken the predicted values and ground truth values as input. It can be None when there is no loss. Default: None. metrics (Metric|list[Metric]|None, optional): If metrics is set, all metrics will be calculated and output in train/eval mode. Default: None. amp_configs (str|dict|None, optional): AMP configurations. If AMP or pure float16 training is used, the key 'level' of 'amp_configs' should be set to 'O1' or 'O2' respectively. Otherwise, the value of 'level' defaults to 'O0', which means float32 training. In addition to 'level', parameters consistent with mixed precision API could also be passed in. The supported keys are: 'init_loss_scaling', 'incr_ratio', 'decr_ratio', 'incr_every_n_steps', 'decr_every_n_nan_or_inf', 'use_dynamic_loss_scaling', 'custom_white_list', 'custom_black_list', and 'custom_black_varnames'or 'use_fp16_guard' is only supported in static graph mode. Mixed precision API documentations :ref:`api_paddle_amp_auto_cast` and :ref:`api_paddle_amp_GradScaler` could be referenced for details. For convenience, 'amp_configs' could be set to 'O1' or 'O2' if no more parameters are needed. 'amp_configs' could be None in float32 training. Default: None. Returns: None rTNzI'loss' must be sub classes of `paddle.nn.Layer` or any callable function.z is not sub class of Metric) _get_devicer5r*r rr3rrrrrr random_seedrrdisable_staticrrrLayercallablerarr/rrrrrrrcr)r optimizerrdrrmain_prog_seedstartup_prog_seedrcs r.r Model.prepareIsX"m dkk4>> 2 2""..077!;55"$$%)%>%>%@%L%LN446BB&((*))$++6?MD--/;)002>0 <04-#  dFIIOO44Xd^^_ -Rg&Fff-- ##,,--HI -' (  +& r0c LUcS5e[U[[45(a3[SU55(a[ U5S:XdS5eUunnO[U[ 5(aX3nn[U[ 5(a$[UWU U S9n[UUURU SS9nOUnUb7[U[ 5(a"[UWS 9n[UUURU SS9nOUbUnOSnUSLnUUl Xl URU5nXl UbK[U[ 5(a6[U[ 5(a!US :dS 5eUU-S -n[UU5n[U UUUUUUU UR!5S 9 n[#SU55(aU(d[$R&"S5 UR)S5 [+U5HnUR-U5 UR/UUS5nUR1UU5 U(abUU-S :XaYURU5nUR)SUUR!5S.5 UR/UUS5nUR3SU5 UR4(dM O UR3SW5 SUl g)a Trains the model for a fixed number of epochs. If `eval_data` is set, evaluation will be done at the end of each epoch. Args: train_data (Dataset|DataLoader, optional): An iterable data loader is used for train. An instance of paddle paddle.io.Dataset or paddle.io.Dataloader is recommended. Default: None. eval_data (Dataset|DataLoader, optional): An iterable data loader is used for evaluation at the end of epoch. If None, will not do evaluation. An instance of paddle.io.Dataset or paddle.io.Dataloader is recommended. Default: None. batch_size (int|list, optional): The batch size of train_data and eval_data. When train_data and eval_data are both the instance of Dataloader, this parameter will be ignored. Default: 1. epochs (int, optional): The number of epochs to train the model. Default: 1. eval_freq (int, optional): The frequency, in number of epochs, an evaluation is performed. Default: 1. log_freq (int, optional): The frequency, in number of steps, the training logs are printed. Default: 10. save_dir(str|None, optional): The directory to save checkpoint during training. If None, will not save checkpoint. Default: None. save_freq (int, optional): The frequency, in number of epochs, to save checkpoint. Default: 1. verbose (int, optional): The verbosity mode, should be 0, 1, or 2. 0 = silent, 1 = progress bar, 2 = one line per epoch. Default: 2. drop_last (bool, optional): Whether drop the last incomplete batch of train_data when dataset size is not divisible by the batch size. When train_data is an instance of Dataloader, this parameter will be ignored. Default: False. shuffle (bool, optional): Whether to shuffle train_data. When train_data is an instance of Dataloader, this parameter will be ignored. Default: True. num_workers (int, optional): The number of subprocess to load data, 0 for no subprocess used and loading data in main process. When train_data and eval_data are both the instance of Dataloader, this parameter will be ignored. Default: 0. callbacks (Sequence[Callback]|Callback|None, optional): A list of `Callback` instances to apply during training. If None, :ref:`api_paddle_callbacks_ProgBarLogger` and :ref:`api_paddle_callbacks_ModelCheckpoint` are automatically inserted. Default: None. accumulate_grad_batches (int, optional): The number of batches to accumulate gradient during training process before optimizer updates. It can mimic large batch size. Default: 1. num_iters (int|None, optional): The number of iterations to evaluate the model. If None, evaluate on whole input dataset, otherwise, evaluate `num_iters` times. Default: None. Returns: None Examples: 1. An example use Dataset and set batch size, shuffle in fit. How to make a batch is done internally. .. code-block:: python :name: code-example3 >>> # doctest: +TIMEOUT(80) >>> import paddle >>> import paddle.vision.transforms as T >>> from paddle.vision.datasets import MNIST >>> from paddle.static import InputSpec >>> dynamic = True >>> if not dynamic: ... paddle.enable_static() ... >>> transform = T.Compose([T.Transpose(), ... T.Normalize([127.5], [127.5])]) >>> train_dataset = MNIST(mode='train', transform=transform) >>> val_dataset = MNIST(mode='test', transform=transform) >>> input = InputSpec([None, 1, 28, 28], 'float32', 'image') >>> label = InputSpec([None, 1], 'int64', 'label') >>> model = paddle.Model( ... paddle.vision.models.LeNet(), ... input, label) >>> optim = paddle.optimizer.Adam( ... learning_rate=0.001, parameters=model.parameters()) >>> model.prepare( ... optim, ... paddle.nn.CrossEntropyLoss(), ... paddle.metric.Accuracy(topk=(1, 2))) >>> model.fit(train_dataset, ... val_dataset, ... epochs=2, ... batch_size=64, ... save_dir='mnist_checkpoint') ... 2. An example use DataLoader, batch size and shuffle is set in DataLoader. .. code-block:: python :name: code-example4 >>> # doctest: +TIMEOUT(80) >>> import paddle >>> import paddle.vision.transforms as T >>> from paddle.vision.datasets import MNIST >>> from paddle.static import InputSpec >>> dynamic = True >>> if not dynamic: ... paddle.enable_static() ... >>> transform = T.Compose([T.Transpose(), ... T.Normalize([127.5], [127.5])]) >>> train_dataset = MNIST(mode='train', transform=transform) >>> train_loader = paddle.io.DataLoader(train_dataset, ... batch_size=64) >>> val_dataset = MNIST(mode='test', transform=transform) >>> val_loader = paddle.io.DataLoader(val_dataset, ... batch_size=64) ... >>> input = InputSpec([None, 1, 28, 28], 'float32', 'image') >>> label = InputSpec([None, 1], 'int64', 'label') >>> model = paddle.Model( ... paddle.vision.models.LeNet(), input, label) >>> optim = paddle.optimizer.Adam( ... learning_rate=0.001, parameters=model.parameters()) >>> model.prepare( ... optim, ... paddle.nn.CrossEntropyLoss(), ... paddle.metric.Accuracy(topk=(1, 2))) >>> model.fit(train_loader, ... val_loader, ... epochs=2, ... save_dir='mnist_checkpoint') ... Nztrain_data must be given!c3B# UHn[U[5v M g7fr))r*re).0rVs r. Model.fit..9 s9 (21Jq#   r[zGbatch_size length error, expected train_batch_size and eval_batch_size.) batch_sizeshuffle drop_lastT batch_samplerplaces num_workers return_listrr!num_iters must be greater than 0!r)repochsstepslog_freq save_freqsave_dirverboserc3B# UHn[U[5v M g7fr))r*r)rrs r.rr s:Tz!]++Trz$EarlyStopping needs validation data.rrrr)r*r,r+allrBrerrrr5r _accumulate_len_data_loader num_itersminr _metrics_nameanyrOrPon_beginrangeon_epoch_begin_run_one_epoch on_epoch_endon_endr\)r train_data eval_datarr eval_freqrrrrrrr callbacksaccumulate_grad_batchesrtrain_batch_sizeeval_batch_size train_sampler train_loader eval_sampler eval_loaderdo_evalrcbksepochlogs eval_steps eval_logss r.fit Model.fitsp%B'BB% j5$- 0 0S9 (29 6 6 z?a' Y '1; - o  C ( (0:o  j' * *3+# M &+{{' L&L  Z 7%C%C2oL%*{{' K "#KKT) +2%%l3"  !9c**5#&&q= E"E E=5(A-F 5)E &&(   :T: : :7 MM@ A g6]E    &&&|T7CD   eT *59,1!22;?  (T5G5G5IJ !// T6J  FI.!!!!#$ GT" $r0cZUb6[U[5(a![XS9n[UUURUSS9n OUn Xl[ UUUUUR5S9n URU 5n Xpl UbH[U[5(a3[U [5(aUS:dS5e[X{5n Xl U RSXR5S .5 URXS5n U RSU 5 SUl0n UR5H nXX'M U $) a6 Evaluate the loss and metrics of the model on input dataset. Args: eval_data (Dataset|DataLoader): An iterable data loader is used for evaluation. An instance of paddle.io.Dataset or paddle.io.Dataloader is recommended. batch_size (int, optional): The batch size of train_data and eval_data. When eval_data is the instance of Dataloader, this argument will be ignored. Default: 1. log_freq (int, optional): The frequency, in number of steps, the eval logs are printed. Default: 10. verbose (int, optional): The verbosity mode, should be 0, 1, or 2. 0 = silent, 1 = progress bar, 2 = one line per epoch. Default: 2. num_workers (int, optional): The number of subprocess to load data, 0 for no subprocess used and loading data in main process. When train_data and eval_data are both the instance of Dataloader, this parameter will be ignored. Default: 0. callbacks (Sequence[Callback]|Callback|None, optional): A list of `Callback` instances to apply during training. If None, `ProgBarLogger` and `ModelCheckpoint` are automatically inserted. Default: None. num_iters (int|None, optional): The number of iterations to evaluate the model. If None, evaluate on whole input dataset, otherwise, evaluate `num_iters` times. Default: None. Returns: dict: Result of metric. The key is the names of Metric, value is a scalar or numpy.array. Examples: .. code-block:: python >>> # doctest: +SKIP('Cause each step's acc and using time are not same when repeat running') >>> import paddle >>> import paddle.vision.transforms as T >>> from paddle.static import InputSpec >>> # declarative mode >>> transform = T.Compose([T.Transpose(), ... T.Normalize([127.5], [127.5])]) >>> val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform) >>> input = InputSpec([-1, 1, 28, 28], 'float32', 'image') >>> label = InputSpec([None, 1], 'int64', 'label') >>> model = paddle.Model(paddle.vision.models.LeNet(), input, label) >>> model.prepare(metrics=paddle.metric.Accuracy()) >>> result = model.evaluate(val_dataset, batch_size=64) >>> print(result) {'acc': 0.0699} NrTr)rrrrrrrr)r*rrrr5rrrrrrerrrr)rrrrrrrrrrrrr eval_resultrs r.evaluateModel.evaluate s:z  Z 7%C%C2L%*{{' K$K + &&(  **;7 "  !9c**:s++q= E"E E=Y3J'N j5G5G5IJ "";f= FD! $ ##%A!WKN&r0cgr)rr test_datarr stack_outputsrrs r.predict Model.predict s"%r0cgr)rrs r.rr s.1r0cgr)rrs r.rr s ADr0cUb6[U[5(a![XS9n[UUURUSS9nOUnXl[ X`US9n URU5n SU 0n U RSU 5 /n URXS5up[[U 65n U(a&U V s/sHn [R"U 5PM n n SUlU RSU 5 U $s sn f)a Compute the output predictions on testing data. Args: test_data (Dataset|DataLoader): An iterable data loader is used for predict. An instance of paddle.io.Dataset or paddle.io.Dataloader is recommended. batch_size (int, optional): The batch size of test_data. When test_data is the instance of Dataloader, this argument will be ignored. Default: 1. num_workers (int, optional): The number of subprocess to load data, 0 for no subprocess used and loading data in main process. When test_data is the instance of Dataloader, this argument will be ignored. Default: 0. stack_outputs (bool, optional): Whether stack output field like a batch, as for an output field of a sample is in shape [X, Y], test_data contains N samples, predict output field will be in shape [N, X, Y] if stack_output is True, and will be a length N list in shape [[X, Y], [X, Y], ..., [X, Y]] if stack_outputs is False. stack_outputs as False is used for DenseTensor output situation, it is recommended set as True if outputs contains no DenseTensor. Default: False. verbose (int, optional): The verbosity mode, should be 0, 1, or 2. 0 = silent, 1 = progress bar, 2 = one line per batch. Default: 1. callbacks(Sequence[Callback]|Callback|None, optional): A Callback instance, Default: None. Returns: list: output of models. Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> from paddle.static import InputSpec >>> class MnistDataset(paddle.vision.datasets.MNIST): ... def __init__(self, mode, return_label=True): ... super().__init__(mode=mode) ... self.return_label = return_label ... ... def __getitem__(self, idx): ... img = np.reshape(self.images[idx], [1, 28, 28]) ... if self.return_label: ... return img, np.array(self.labels[idx]).astype('int64') ... return img ... ... def __len__(self): ... return len(self.images) ... >>> test_dataset = MnistDataset(mode='test', return_label=False) >>> # imperative mode >>> input = InputSpec([-1, 1, 28, 28], 'float32', 'image') >>> model = paddle.Model(paddle.vision.models.LeNet(), input) >>> model.prepare() >>> result = model.predict(test_dataset, batch_size=64) >>> print(len(result[0]), result[0][0].shape) 157 (64, 10) >>> # declarative mode >>> device = paddle.set_device('cpu') >>> paddle.enable_static() >>> input = InputSpec([-1, 1, 28, 28], 'float32', 'image') >>> model = paddle.Model(paddle.vision.models.LeNet(), input) >>> model.prepare() >>> result = model.predict(test_dataset, batch_size=64) >>> print(len(result[0]), result[0][0].shape) 157 (64, 10) NrTr)rrrr)r*rrrr5rrrrrr+rqr7vstackr)rrrrrrr test_sampler test_loaderr test_stepsrr{outss r.rr( sX  Z 7%C%C2L%*{{' K$K + wG**;7 $ i&++KyI sG}% 3:;74ryy7G; $ It$ RAUU U UR.RBU S9 g!,(df  g=f)z Save inference model can be used in static or dynamic mode. Args: path (str): The path prefix to save model. The format is ``dirname/file_prefix`` or ``file_prefix``. Returns: None NzSaving inference model needs 'inputs' or running before saving. Please specify 'inputs' in Model initialization or input training data and perform a training for shape derivation.z'inputs' was not specified when Model initialization, so the input shape to be saved will be the shape derived from the user's actual inputs. The input shape to be saved is rzT. For saving correct input shapes, please provide 'inputs' for Model initialization.) input_specrzThe input path MUST be format of dirname/file_prefix [dirname\file_prefix in Windows system], but received file_prefix is empty string.rr_TrrW)r)"rr framework_dygraph_guardrr& RuntimeErrorr`rOrPr3jitr2rr#rr$ryr%r&r'rrrcrr)rrr+rrrsave_inference_modelr) rrlayer file_prefixr% model_pathmodel_filenameparams_filenamer. infer_progrzrhs r.rmModel._save_inference_model s   ..t4 ##+&N**MMHIMIYIYZ[I\H]]qr  E54''**40Kb  3 ggood+Grww~~g66 G$ J(+==N),??O==''++FD9D I 4}}! !ZZZ6 $--33F;DAqDG?<<+--d3FS5F.GHD--d3Dt$L==--11$/1EJ!3!3!?!?!A!H!HH\"&*]]%?%?x%P\"  " "4t 4t[))dnn.H!#>>Q&)-D&U1V  9 =  Y ;7s =M8,M=c|UcURcS5eUbUnO URn[URX2S9$)a4 Prints a string summary of the network. Args: input_size (tuple|InputSpec|list[tuple|InputSpec], optional): Size of input tensor. if not set, input_size will get from ``self._inputs`` if network only have one input, input_size can be tuple or InputSpec. if model have multiple input, input_size must be a list which contain every input's shape. Default: None. dtype (str, optional): If dtype is None, 'float32' will be used, Default: None. Returns: Dict: A summary of the network including total params and total trainable params. Examples: .. code-block:: pycon >>> import paddle >>> from paddle.static import InputSpec >>> input = InputSpec([None, 1, 28, 28], 'float32', 'image') >>> label = InputSpec([None, 1], 'int64', 'label') >>> model = paddle.Model(paddle.vision.models.LeNet(), input, label) >>> optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) >>> model.prepare(optim, paddle.nn.CrossEntropyLoss()) >>> params_info = model.summary() >>> print(params_info) --------------------------------------------------------------------------- Layer (type) Input Shape Output Shape Param # =========================================================================== Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60 ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0 MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0 Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416 ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0 MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0 Linear-1 [[1, 400]] [1, 120] 48,120 Linear-2 [[1, 120]] [1, 84] 10,164 Linear-3 [[1, 84]] [1, 10] 850 =========================================================================== Total params: 61,610 Trainable params: 61,610 Non-trainable params: 0 --------------------------------------------------------------------------- Input size (MB): 0.00 Forward/backward pass size (MB): 0.11 Params size (MB): 0.24 Estimated Total Size (MB): 0.35 --------------------------------------------------------------------------- {'total_params': np.int64(61610), 'trainable_params': np.int64(61610)} z)'input_size' or 'self._input' must be set)r)rr r)r input_sizer _input_sizes r.r Model.summary4 sKr%)A 7 A  !$K,,Kt||[??r0c /nUcU(a[URR5SSnUb?Ub<[5(a-[ U5VVs/sHupx[ XUX'S9PM nnnOUVs/sHn[ US/S9PM nnOm[ U5nOa[U[5(aAUSLde[URR5Vs/sHnUS:wdM XPM nnO [ U5nUbJ[ U5H;upy[U [ 5(deU RbM+[SUSU S35e U$s snnfs snfs snf) Nr)r:rr)r:rFrzRequires Input[z(].name != None, but receive `None` with rx) rPrrrrirr/r*rr:ry) rspecsrrr^ out_specs arg_namesr}rwspecs r.rbModel._verify_specv sz =()=)=>qrB &*'))%.i$8!$8DA11IVYG$8!I GP PiAdV!#'::x2 A    1 2r0cD[U5nU$![a SnU$f=fr))rB Exception)rrrs r.rModel._len_data_loader s3  $E  E  s  cURRUlURbU[UR5S:Xa;URSURSURSS5UlSUlggg)z.Update self._inputs according to given inputs.Nr[rrT)rcr&rBrbrr`rs r.rfModel._update_inputs sv==44    'C0@0@,AQ,F,,d&&q)4+;+;A+>DL'+D # -G 'r0)rrcr&rr`rrr_rrrr5rrrrr\)NN)rrZrzz1Input | Sequence[Input] | dict[str, Input] | NonerzInput | Sequence[Input] | NonereturnNoner)rzr'r_InputBatch | Nonerr[r*8list[float] | tuple[list[npt.NDArray[Any]], list[float]]r))rzr'rr,r*r-)rzr'r*list[npt.NDArray[Any]])T)rr]rnr[r*r+)FF)rr]rr[rr[r*r+)rNrr rr*z list[Tensor])NNNN) rz!paddle.optimizer.Optimizer | Nonerdz;paddle.nn.Layer | Callable[[Tensor, Tensor], Tensor] | NonerzMetric | list[Metric] | Nonerzstr | dict[str, Any] | Noner*r+)NNrrr Nrr[FTrNrN) rDataset | DataLoader | Nonerr0rzint | list[int]rrerrerrerz str | Nonerrerrerr[rr[rrer$Sequence[Callback] | Callback | Nonerrer int | Noner*r+)rr/r[rNN)rDataset | DataLoaderrrerrerrerrerr1rr2r*z#dict[str, float | npt.NDArray[Any]]).....)rr3rrerrerz Literal[True]rrerr1r*r.)rr3rrerrerzLiteral[False]rrerr1r*z"list[tuple[npt.NDArray[Any], ...]])rr3rrerrerr[rrerr1r*z5list[npt.NDArray[Any] | tuple[npt.NDArray[Any], ...]])rrFrN)rr]r*r+)rz>tuple[int, ...] | Input | list[tuple[int, ...] | Input] | Nonerz_DTypeLiteral | Noner*r&)NNF)rrrrr__annotations__rrr rrr2rRrrrrrrrrmrr rbrrrrfrrr0r.rYrYsm^ +*  EI15 #5 #5B#5/ #5  #5P&* ;;#; ; B ;z Y@D=!=+== A==~ Y//bB)N$ % tHtHtH tH  tHl-*^W?v8< 0437T 4T  H T . T 1T  T p3715&'#:>'( $!w%/w%/w%$ w%  w%  w%w%w%w%w%w%w%w%8w%"%w% !w%" #w%x:> $m'mm m  m  m8mm -m^'*:=%'%% % % %  %8% %%(+:=1'11 1 & 1  181 ,11!:=D'DD D  D  D8D ?DDqf>J Wz &* @@ K@@ $ @@  @@D)V +r0rYr))[ __future__rr^rLr#rr`rfrOtypingrrrrrnumpyr7typing_extensionsr r3paddle.distributedrrSpaddle.optimizerr paddle.autogradr paddle.baser paddle.base.executorr paddle.base.frameworkrrrrrpaddle.distributed.fleet.baserpaddle.frameworkrrpaddle.framework.io_utilsr paddle.iorrrpaddle.jit.translated_layerrr paddle.metricr paddle.staticrrrrr model_summaryr collections.abcr!r" numpy.typingnptr#paddle._typing.dtype_liker$r%r&NDArrayr+r'r4__all__rr/r?rGrJrPrXrorrrrrr$rYrr0r.rMs:# ' !#- %49<BBO ,6"27#+" C V  S[[  K  %-*d N0f$A*A*H `3`3FQ&Q&hz+z+r0