mindspore
MindSpore package.
- class mindspore.DatasetHelper(dataset, dataset_sink_mode=True)[source]
Help function to use the Minddata dataset.
According to different context, change the iter of dataset, to use the same for loop in different context.
Note
The iter of DatasetHelper will give one epoch data.
- Parameters
dataset (DataSet) – The dataset.
dataset_sink_mode (bool) – If true use GetNext to fetch the data, or else feed the data from host. Default: True.
Examples
>>> dataset_helper = DatasetHelper(dataset) >>> for inputs in dataset_helper: >>> outputs = network(*inputs)
- class mindspore.Model(network, loss_fn=None, optimizer=None, metrics=None, eval_network=None, eval_indexes=None, amp_level='O0', **kwargs)[source]
High-Level API for Training or Testing.
Model groups layers into an object with training and inference features.
- Parameters
network (Cell) – The training or testing network.
loss_fn (Cell) – Objective function, if loss_fn is None, the network should contain the logic of loss and grads calculation, and the logic of parallel if needed. Default: None.
optimizer (Cell) – Optimizer for updating the weights. Default: None.
metrics (Union[dict, set]) – Dict or set of metrics to be evaluated by the model during training and testing. eg: {‘accuracy’, ‘recall’}. Default: None.
eval_network (Cell) – Network for evaluation. If not defined, network and loss_fn would be wrapped as eval_network. Default: None.
eval_indexes (list) – In case of defining the eval_network, if eval_indexes is None, all outputs of eval_network would be passed to metrics, otherwise eval_indexes must contain three elements, representing the positions of loss value, predict value and label, the loss value would be passed to Loss metric, predict value and label would be passed to other metric. Default: None.
amp_level (str) –
Option for argument level in mindspore.amp.build_train_network, level for mixed precision training. Supports [O0, O2]. Default: “O0”.
O0: Do not change.
O2: Cast network to float16, keep batchnorm run in float32, using dynamic loss scale.
loss_scale_manager (Union[None, LossScaleManager]) – If None, not scale the loss, or else scale the loss by LossScaleManager. If it is set, overwrite the level setting. It’s a eyword argument. e.g. Use loss_scale_manager=None to set the value.
Examples
>>> class Net(nn.Cell): >>> def __init__(self): >>> super(Net, self).__init__() >>> self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal') >>> self.bn = nn.BatchNorm2d(64) >>> self.relu = nn.ReLU() >>> self.flatten = nn.Flatten() >>> self.fc = nn.Dense(64*222*222, 3) # padding=0 >>> >>> def construct(self, x): >>> x = self.conv(x) >>> x = self.bn(x) >>> x = self.relu(x) >>> x = self.flatten(x) >>> out = self.fc(x) >>> return out >>> >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) >>> dataset = get_dataset() >>> model.train(2, dataset)
- eval(valid_dataset, callbacks=None, dataset_sink_mode=True)[source]
Evaluation API where the iteration is controlled by python front-end.
Configure to pynative mode, the evaluation will be performed with dataset non-sink mode.
Note
CPU is not supported when dataset_sink_mode is true.
- Parameters
- Returns
Dict, returns the loss value & metrics values for the model in test mode.
Examples
>>> dataset = get_dataset() >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> model = Model(net, loss_fn=loss, optimizer=None, metrics={'acc'}) >>> model.eval(dataset)
- predict(*predict_data)[source]
Generates output predictions for the input samples.
Data could be single tensor, or list of tensor, tuple of tensor.
Note
Batch data should be put together in one tensor.
- Parameters
predict_data (Tensor) – Tensor of predict data. can be array, list or tuple.
- Returns
Tensor, array(s) of predictions.
Examples
>>> input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]), mstype.float32) >>> model = Model(Net()) >>> model.predict(input_data)
- train(epoch, train_dataset, callbacks=None, dataset_sink_mode=True)[source]
Training API where the iteration is controlled by python front-end.
Configure to pynative mode, the training will be performed with dataset non-sink mode.
Note
CPU is not supported when dataset_sink_mode is true.
- Parameters
epoch (int) – Total number of iterations on the data.
train_dataset (Dataset) – A training dataset iterator. If there is no loss_fn, a tuple with multiply data (data1, data2, data3, …) should be returned and passed to the network. Otherwise, a tuple (data, label) should be returned, and the data and label are passed to the network and loss function respectively.
callbacks (list) – List of callback object. Callbacks which should be excuted while training. Default: None.
dataset_sink_mode (bool) – Determines whether to pass the data through dataset channel. Default: True.
Examples
>>> dataset = get_dataset() >>> net = Net() >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> loss_scale_manager = FixedLossScaleManager() >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None, loss_scale_manager=loss_scale_manager) >>> model.train(2, dataset)
- class mindspore.ParallelMode[source]
Parallel mode options.
There are five kinds of parallel modes, “STAND_ALONE”, “DATA_PARALLEL”, “HYBRID_PARALLEL”, “SEMI_AUTO_PARALLEL” and “AUTO_PARALLEL”. Default: “STAND_ALONE”.
STAND_ALONE: Only one processor working.
DATA_PARALLEL: Distributing the data across different processors.
HYBRID_PARALLEL: Achieving data parallelism and model parallelism manually.
SEMI_AUTO_PARALLEL: Achieving data parallelism and model parallelism by setting parallel strategies.
AUTO_PARALLEL: Achieving parallelism automatically.
MODE_LIST: The list for all supported parallel modes.
- class mindspore.Parameter(default_input, name, requires_grad=True, layerwise_parallel=False)[source]
Parameter types of cell models.
Note
Each parameter of Cell is represented by Parameter class.
- Parameters
default_input (Tensor) – A parameter tensor.
name (str) – Name of the child parameter.
requires_grad (bool) – True if the parameter requires gradient. Default: True.
layerwise_parallel (bool) – A kind of model parallel mode. When layerwise_parallel is true in paralle mode, broadcast and gradients communication would not be applied on parameters. Default: False.
- property is_init
Get init status of the parameter.
- property name
Get the name of the parameter.
- property requires_grad
Return whether the parameter requires gradient.
- class mindspore.ParameterTuple(iterable)[source]
Class for storing tuple of parameters.
Note
Used to store the parameters of the network into the parameter tuple collection.
- class mindspore.Tensor(input_data, dtype=None)[source]
Tensor for data storage.
Tensor inherits tensor object in C++ side, some functions are implemented in C++ side and some functions are implemented in Python layer.
- Parameters
input_data (Tensor, float, int, bool, tuple, list, numpy.ndarray) – Input data of the tensor.
dtype (
mindspore.dtype) – Should be None, bool or numeric type defined in mindspore.dtype. The argument is used to define the data type of the output tensor. If it is None, the data type of the output tensor will be as same as the input_data. Default: None.
- Outputs:
Tensor, with the same shape as input_data.
Examples
>>> # init a tensor with input data >>> t1 = mindspore.Tensor(np.zeros([1, 2, 3]), mindspore.float32) >>> assert isinstance(t1, mindspore.Tensor) >>> assert t1.shape() == (1, 2, 3) >>> assert t1.dtype() == mindspore.float32 >>> >>> # init a tensor with a float scalar >>> t2 = mindspore.Tensor(0.1) >>> assert isinstance(t2, mindspore.Tensor) >>> assert t2.dtype() == mindspore.float64
- property virtual_flag
Mark tensor is virtual.
- mindspore.dtype_to_nptype(type_)[source]
Get numpy data type corresponding to MindSpore dtype.
- Parameters
type (
mindspore.dtype) – MindSpore’s dtype.- Returns
The data type of numpy.
- mindspore.dtype_to_pytype(type_)[source]
Get python type corresponding to MindSpore dtype.
- Parameters
type (
mindspore.dtype) – MindSpore’s dtype.- Returns
Type of python.
- mindspore.get_level()[source]
Get the logger level.
- Returns
str, the Log level includes 3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).
Examples
>>> import os >>> os.environ['GLOG_v'] = '0' >>> from mindspore import log as logger >>> logger.get_level()
- mindspore.get_log_config()[source]
Get logger configurations.
- Returns
Dict, the dictionary of logger configurations.
Examples
>>> import os >>> os.environ['GLOG_v'] = '1' >>> os.environ['GLOG_logtostderr'] = '0' >>> os.environ['GLOG_log_dir'] = '/var/log/mindspore' >>> os.environ['logger_maxBytes'] = '5242880' >>> os.environ['logger_backupCount'] = '10' >>> from mindspore import log as logger >>> logger.get_log_config()
- mindspore.get_py_obj_dtype(obj)[source]
Get the corresponding MindSpore data type by python type or variable.
- Parameters
obj – An object of python type, or a variable in python type.
- Returns
Type of MindSpore type.
- mindspore.issubclass_(type_, dtype)[source]
Determine whether type_ is a subclass of dtype.
- Parameters
type (
mindspore.dtype) – Target MindSpore dtype.dtype (
mindspore.dtype) – Compare MindSpore dtype.
- Returns
bool, True or False.
- mindspore.ms_function(fn=None, obj=None, input_signature=None)[source]
Creates a callable MindSpore graph from a python function.
This allows the MindSpore runtime to apply optimizations based on graph.
- Parameters
fn (Function) – The Python function that will be run as a graph. Default: None.
obj (Object) – The Python Object that provide information for identify compiled function. Default: None.
input_signature (MetaTensor) – The MetaTensor to describe the input arguments. The MetaTensor specifies the shape and dtype of the Tensor and they will be supplied to this function. If input_signature is specified, every input to fn must be a Tensor. And the input parameters of fn cannot accept **kwargs. The shape and dtype of actual inputs should keep same with input_signature, or TypeError will be raised. Default: None.
- Returns
Function, if fn is not None, returns a callable that will execute the compiled function; If fn is None, returns a decorator and when this decorator invokes with a single fn argument, the callable is equal to the case when fn is not None.
Examples
>>> def tensor_add(x, y): >>> z = F.tensor_add(x, y) >>> return z >>> >>> @ms_function >>> def tensor_add_with_dec(x, y): >>> z = F.tensor_add(x, y) >>> return z >>> >>> @ms_function(input_signature=(MetaTensor(mstype.float32, (1, 1, 3, 3)), >>> MetaTensor(mstype.float32, (1, 1, 3, 3)))) >>> def tensor_add_with_sig(x, y): >>> z = F.tensor_add(x, y) >>> return z >>> >>> x = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> y = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> >>> tensor_add_graph = ms_function(fn=tensor_add) >>> out = tensor_add_graph(x, y) >>> out = tensor_add_with_dec(x, y) >>> out = tensor_add_with_sig(x, y)