mindspore_xai.tool

CV tools.

class mindspore_xai.tool.cv.OoDNet(underlying, num_classes)

Out of distribution network.

OoDNet takes an underlying classifier and outputs the out of distribution scores of samples.

Note

A training of OoDNet is required with the classifier’s training dataset inorder to give the correct OoD scores.

Parameters

• underlying (Cell) – The underlying classifier, it must has the num_features (int) and output_features (bool) attributes, please check the example code for the details.

• num_classes (int) – The number of classes for the classifier.

Returns

Tensor, classification logits (if set_train(True) was called) or OoD scores (if set_train(False) was called). In the shape of \((N, L)\) (L is number of classes).

Raises

• TypeError – Be raised for any argument or input type problem.

• ValueError – Be raised for any input value problem.

• AttributeError – Be raised for underlying is missing any required attribute.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import nn, set_context, PYNATIVE_MODE
>>> from mindspore_xai.tool.cv import OoDNet
>>> from mindspore.common.initializer import Normal
>>>
>>>
>>> class MyLeNet5(nn.Cell):
...    def __init__(self, num_class, num_channel):
...        super(MyLeNet5, self).__init__()
...
...        # must add the following 2 attributes to your model
...        self.num_features = 84 # no. of features, int
...        self.output_features = False # output features flag, bool
...
...        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
...        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
...        self.relu = nn.ReLU()
...        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
...        self.flatten = nn.Flatten()
...        self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
...        self.fc2 = nn.Dense(120, self.num_features, weight_init=Normal(0.02))
...        self.fc3 = nn.Dense(self.num_features, num_class, weight_init=Normal(0.02))
...
...    def construct(self, x):
...        x = self.conv1(x)
...        x = self.relu(x)
...        x = self.max_pool2d(x)
...        x = self.conv2(x)
...        x = self.relu(x)
...        x = self.max_pool2d(x)
...        x = self.flatten(x)
...        x = self.relu(self.fc1(x))
...        x = self.relu(self.fc2(x))
...
...        # return the features tensor if output_features is True
...        if self.output_features:
...            return x
...
...        x = self.fc3(x)
...        return x
>>>
>>> set_context(mode=PYNATIVE_MODE)
>>> # prepare classifier
>>> net = MyLeNet5(10, num_channel=3)
>>> # prepare OoD network
>>> ood_net = OoDNet(net, 10)
>>> inputs = ms.Tensor(np.random.rand(1, 3, 32, 32), ms.float32)
>>> ood_map = ood_net(inputs)
>>> print(ood_map.shape)
(1, 10)

construct(x)

Forward inferences the classification logits or OOD scores.

Parameters

x (Tensor) – Input tensor for the underlying classifier.

Returns

Tensor, logits of softmax with temperature (if set_train(True) was called) or OOD scores (if set_train(False) was called). In the shape of \((N, L)\) (L is number of classes).

get_train_parameters(train_underlying=False)

Get the training parameters.

Parameters

train_underlying (bool, optional) – Set to True to include the underlying classifier parameters. Default: False.

Returns

list[Parameter], parameters.

property num_classes

Get the number of classes.

Returns

int, the number of classes.

prepare_train(learning_rate=0.1, momentum=0.9, weight_decay=0.0001, lr_base_factor=0.1, lr_epoch_denom=30, train_underlying=False)

Creates necessities for training.

Parameters

• learning_rate (float, optional) – The optimizer learning rate. Default: 0.1.

• momentum (float, optional) – The optimizer momentum. Default: 0.9.

• weight_decay (float, optional) – The optimizer weight decay. Default: 0.0001.

• lr_base_factor (float, optional) – The base scaling factor of learning rate scheduler. Default: 0.1.

• lr_epoch_denom (int, optional) – The epoch denominator of learning rate scheduler. Default: 30.

• train_underlying (bool, optional) – True to train the underlying classifier as well.Default: False.

Returns

• Optimizer, optimizer.

• LearningRateScheduler, learning rate scheduler.

set_train(mode=True)

Set training mode.

Parameters

mode (bool, optional) – It is in training mode. Default: True.

train(dataset, loss_fn, callbacks=None, epoch=90, optimizer=None, scheduler=None, **kwargs)

Trains this OoD net.

Parameters

• dataset (Dataset) – The training dataset, expecting (data, one-hot label) items.

• loss_fn (Cell) – The loss function, if the classifier’s activation function is nn.Softmax, then use nn.SoftmaxCrossEntropyWithLogits, if the activation function is nn.Sigmoid, then use nn.BCEWithLogitsLoss.

• callbacks (Callback, optional) – The train callbacks. Default: None.

• epoch (int, optional) – The number of epochs to be trained. Default: 90.

• optimizer (Optimizer, optional) – The optimizer. The one from prepare_train() will be used if which is set to None. Default: None.

• scheduler (LearningRateScheduler, optional) – The learning rate scheduler. The one from prepare_train() will be used if which is set to None. Default: None.

• **kwargs (any, optional) – Keyword arguments for prepare_train().

property underlying

Get the underlying classifier.

Returns

nn.Cell, the underlying classifier.