diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py index c78c89964c92eafa25b69a1df0456b6099784672..b5662f9ecf4f9df055b02117288fcdff57855d93 100644 --- a/python/paddle/hapi/model.py +++ b/python/paddle/hapi/model.py @@ -934,89 +934,91 @@ class Model(object): Args: network (paddle.nn.Layer): The network is an instance of paddle.nn.Layer. - inputs (InputSpec|list|tuple|dict|None): `inputs`, entry points of network, + 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. - labels (InputSpec|list|tuple|None): `labels`, entry points of network, + graph. Default: None. + labels (InputSpec|list|tuple|None, optional): `labels`, entry points of network, could be a InputSpec instnace 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. + 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, + 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) + + 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 - - # required: gpu - import paddle - import paddle.nn as nn - import paddle.vision.transforms as T + :name: code-example2 - def run_example_code(): - device = paddle.set_device('gpu') + # required: gpu + import paddle + import paddle.nn as nn + import paddle.vision.transforms as T - net = nn.Sequential(nn.Flatten(1), nn.Linear(784, 200), nn.Tanh(), - nn.Linear(200, 10)) + def run_example_code(): + device = paddle.set_device('gpu') - model = paddle.Model(net) - optim = paddle.optimizer.SGD(learning_rate=1e-3, parameters=model.parameters()) + net = nn.Sequential(nn.Flatten(1), nn.Linear(784, 200), nn.Tanh(), + nn.Linear(200, 10)) - 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) + model = paddle.Model(net) + optim = paddle.optimizer.SGD(learning_rate=1e-3, parameters=model.parameters()) - 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) + 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) - # mixed precision training is only supported on GPU now. - if paddle.is_compiled_with_cuda(): - run_example_code() + 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() """ @@ -1059,12 +1061,12 @@ class Model(object): 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): Batch of labels. It could be + 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 is None. - update (bool): Whether update parameters after loss.backward() computing. - Using it to accumulate gradients. Default is True. + 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, @@ -1074,29 +1076,30 @@ class Model(object): Examples: .. code-block:: python + :name: code-example-train-batch - import numpy as np - import paddle - import paddle.nn as nn - from paddle.static import InputSpec - - 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 = np.random.random(size=(4,784)).astype(np.float32) - label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64) - loss = model.train_batch([data], [label]) - print(loss) + import paddle + import paddle.nn as nn + from paddle.static import InputSpec + + 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([2.192784], dtype=float32)] """ loss = self._adapter.train_batch(inputs, labels, update) if fluid._non_static_mode() and self._input_info is None: @@ -1112,10 +1115,10 @@ class Model(object): 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): Batch of labels. It could be + 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 is None. + set None. Default: None. Returns: A list of scalar testing loss if the model has no metrics, @@ -1125,30 +1128,31 @@ class Model(object): Examples: .. code-block:: python - - import numpy as np - import paddle - import paddle.nn as nn - from paddle.static import InputSpec - - 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 = np.random.random(size=(4,784)).astype(np.float32) - label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64) - loss = model.eval_batch([data], [label]) - print(loss) + :name: code-example-eval-batch + + import paddle + import paddle.nn as nn + from paddle.static import InputSpec + + 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) + # [array([2.8825705], dtype=float32)] [0.0] """ loss = self._adapter.eval_batch(inputs, labels) if fluid._non_static_mode() and self._input_info is None: @@ -1172,28 +1176,31 @@ class Model(object): Examples: .. code-block:: python - - import numpy as np - import paddle - import paddle.nn as nn - from paddle.static import InputSpec - - 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 = np.random.random(size=(4,784)).astype(np.float32) - out = model.predict_batch([data]) - print(out) + :name: code-example-predict-batch + + import paddle + import paddle.nn as nn + from paddle.static import InputSpec + + 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([[0.08189095, 0.16740078, 0.06889386, 0.05085445, 0.10729759, + # 0.02217775, 0.14518553, 0.1591538 , 0.01808308, 0.17906217]], + # dtype=float32)] """ loss = self._adapter.predict_batch(inputs) if fluid._non_static_mode() and self._input_info is None: @@ -1229,6 +1236,7 @@ class Model(object): Examples: .. code-block:: python + :name: code-example-save import paddle import paddle.nn as nn @@ -1259,7 +1267,7 @@ class Model(object): optim = paddle.optimizer.SGD(learning_rate=1e-3, parameters=model.parameters()) model.prepare(optim, paddle.nn.CrossEntropyLoss()) - + transform = T.Compose([ T.Transpose(), T.Normalize([127.5], [127.5]) @@ -1294,14 +1302,14 @@ class Model(object): optimizer states. The files would be `path.pdparams` and `path.pdopt` separately, and the latter is not necessary when no need to restore. - skip_mismatch (bool): Whether to skip the loading of mismatch + skip_mismatch (bool, optional): Whether to skip the loading of mismatch parameter or raise an error when mismatch happens (not found the parameter in file storing model states of or receives a - mismatch shape). - reset_optimizer (bool): If True, ignore the providing file storing + mismatch shape). Default: False. + reset_optimizer (bool, optional): If True, ignore the providing file storing optimizer states and initialize optimizer states from scratch. Otherwise, restore optimizer states from `path.pdopt` if - a optimizer has been set to the model. Default False. + a optimizer has been set to the model. Default: False. Returns: None @@ -1309,23 +1317,24 @@ class Model(object): Examples: .. code-block:: python - - import paddle - import paddle.nn as nn - from paddle.static import InputSpec + :name: code-example-load + + import paddle + import paddle.nn as nn + from paddle.static import InputSpec - device = paddle.set_device('cpu') + device = paddle.set_device('cpu') - input = InputSpec([None, 784], 'float32', 'x') + 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 = 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') + model.save('checkpoint/test') + model.load('checkpoint/test') """ def _load_state_from_path(path): @@ -1395,19 +1404,20 @@ class Model(object): Examples: .. code-block:: python + :name: code-example-parameters + + import paddle + import paddle.nn as nn + from paddle.static import InputSpec - import paddle - import paddle.nn as nn - from paddle.static import InputSpec - - input = InputSpec([None, 784], 'float32', 'x') - - model = paddle.Model(nn.Sequential( - nn.Linear(784, 200), - nn.Tanh(), - nn.Linear(200, 10)), input) + 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() + params = model.parameters() """ return self._adapter.parameters() @@ -1501,16 +1511,16 @@ class Model(object): Configures the model before runing. Args: - optimizer (Optimizer|None): Optimizer must be set in training + 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. - loss (Loss|callable function|None): Loss function can + 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. - metrics (Metric|list of Metric|None): If metrics is set, all - metrics will be calculated and output in train/eval mode. - amp_configs (str|dict|None): AMP configurations. If AMP or pure + 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 @@ -1526,6 +1536,7 @@ class Model(object): 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 """ @@ -1587,133 +1598,133 @@ class Model(object): evaluation will be done at the end of each epoch. Args: - train_data (Dataset|DataLoader): An iterable data loader is used for + 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 recomended. Default: None. - eval_data (Dataset|DataLoader): An iterable data loader is used for + 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 recomended. Default: None. - batch_size (int): Integer number. 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): Integer number. The number of epochs to train - the model. Default: 1. - eval_freq (int): The frequency, in number of epochs, an evalutation + batch_size (int, 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 evalutation is performed. Default: 1. - log_freq (int): The frequency, in number of steps, the training logs + log_freq (int, optional): The frequency, in number of steps, the training logs are printed. Default: 10. - save_dir(str|None): The directory to save checkpoint during training. + save_dir(str|None, optional): The directory to save checkpoint during training. If None, will not save checkpoint. Default: None. - save_freq (int): The frequency, in number of epochs, to save + save_freq (int, optional): The frequency, in number of epochs, to save checkpoint. Default: 1. - verbose (int): The verbosity mode, should be 0, 1, or 2. 0 = silent, + 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): Whether drop the last incomplete batch of + 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): Whther to shuffle train_data. When train_data is + shuffle (bool, optional): Whther to shuffle train_data. When train_data is an instance of Dataloader, this parameter will be ignored. Default: True. - num_workers (int): The number of subprocess to load data, 0 for no + 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 (Callback|None): A list of `Callback` instances to apply - during training. If None, `ProgBarLogger` and `ModelCheckpoint` - are automatically inserted. Default: None. - accumulate_grad_batches (int): The number of batches to accumulate gradident + callbacks (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 gradident during training process before optimizer updates. It can mimic large batch size. Default: 1. - num_iters (int|None): Integer number. The number of iterations to train - the model. If None, follow `epochs` to train the model, otherwise, train - the model `num_iters` times. 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: None Examples: - 1. An example use Dataset and set btch size, shuffle in fit. + 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-example-fit-1 - 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') + 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-example-fit-2 + + import paddle + import paddle.vision.transforms as T + from paddle.vision.datasets import MNIST + from paddle.static import InputSpec - 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') - 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') + 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') """ assert train_data is not None, \ "train_data must be given!" @@ -1809,23 +1820,23 @@ class Model(object): eval_data (Dataset|DataLoader): An iterable data loader is used for evaluation. An instance of paddle.io.Dataset or paddle.io.Dataloader is recomended. - batch_size (int): Integer number. 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): The frequency, in number of steps, the eval logs + 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): The verbosity mode, should be 0, 1, or 2. 0 = silent, + 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): The number of subprocess to load data, + 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 (Callback|None): A list of `Callback` instances to apply + callbacks (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): Integer number. The number of iterations to - evaluate the model. If None, evaluate on whole input dataset, - otherwise, evaluate `num_iters` times. 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. @@ -1833,24 +1844,26 @@ class Model(object): Examples: .. code-block:: python + :name: code-example-evaluate - import paddle - import paddle.vision.transforms as T - from paddle.static import InputSpec + 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) + # 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) + 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} """ if eval_data is not None and isinstance(eval_data, Dataset): @@ -1912,21 +1925,20 @@ class Model(object): test_data (Dataset|DataLoader): An iterable data loader is used for predict. An instance of paddle.io.Dataset or paddle.io.Dataloader is recomended. - batch_size (int): Integer number. The batch size of train_data and eval_data. - When train_data and eval_data are both the instance of Dataloader, this - argument will be ignored. Default: 1. - num_workers (int): 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 argument will be ignored. Default: 0. - stack_outputs (bool): Whether stack output field like a batch, as for an output - filed of a sample is in shape [X, Y], test_data contains N samples, predict + 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 + 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 LoDTensor output situation, it is recommended set as True if outputs contains no LoDTensor. Default: False. - verbose (int): The verbosity mode, should be 0, 1, or 2. 0 = silent, + 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(Callback): A Callback instance, default None. + callbacks(Callback, optional): A Callback instance, Default: None. Returns: list: output of models. @@ -1934,43 +1946,46 @@ class Model(object): Examples: .. code-block:: python + :name: code-example-predict - import numpy as np - import paddle - from paddle.static import InputSpec + 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(MnistDataset, self).__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) - - # 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) + class MnistDataset(paddle.vision.datasets.MNIST): + def __init__(self, mode, return_label=True): + super(MnistDataset, self).__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) """ if test_data is not None and isinstance(test_data, Dataset): @@ -2164,23 +2179,25 @@ class Model(object): Examples: .. code-block:: python + :name: code-example-summary + + import paddle + from paddle.static import InputSpec + + input = InputSpec([None, 1, 28, 28], 'float32', 'image') + label = InputSpec([None, 1], 'int64', 'label') - 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) + 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) + # {'total_params': 61610, 'trainable_params': 61610} """ assert (input_size is not None or self._inputs