art.estimators.object_detection
¶
Module containing estimators for object detection.
Mixin Base Class Object Detector¶

class
art.estimators.object_detection.
ObjectDetectorMixin
¶ Mixin Base class for ART object detectors.
Object Detector PyTorch FasterRCNN¶

class
art.estimators.object_detection.
PyTorchFasterRCNN
(**kwargs)¶ This class implements a modelspecific object detector using FasterRCNN and PyTorch.

__init__
(model: Optional[torchvision.models.detection.fasterrcnn_resnet50_fpn] = None, clip_values: Optional[CLIP_VALUES_TYPE] = None, channel_index=<art.utils._Deprecated object>, channels_first: Optional[bool] = None, preprocessing_defences: Optional[Union[Preprocessor, List[Preprocessor]]] = None, postprocessing_defences: Optional[Union[Postprocessor, List[Postprocessor]]] = None, preprocessing: PREPROCESSING_TYPE = None, attack_losses: Tuple[str, ...] = ('loss_classifier', 'loss_box_reg', 'loss_objectness', 'loss_rpn_box_reg'), device_type: str = 'gpu')¶ Initialization.
 Parameters
model –
FasterRCNN model. The output of the model is List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction
clip_values – Tuple of the form (min, max) of floats or np.ndarray representing the minimum and maximum values allowed for features. If floats are provided, these will be used as the range of all features. If arrays are provided, each value will be considered the bound for a feature, thus the shape of clip values needs to match the total number of features.
channel_index (int) – Index of the axis in data containing the color channels or features.
channels_first – Set channels first or last.
preprocessing_defences – Preprocessing defence(s) to be applied by the classifier.
postprocessing_defences – Postprocessing defence(s) to be applied by the classifier.
preprocessing – Tuple of the form (subtractor, divider) of floats or np.ndarray of values to be used for data preprocessing. The first value will be subtracted from the input. The input will then be divided by the second one.
attack_losses (
Tuple
) – Tuple of any combination of strings of loss components: ‘loss_classifier’, ‘loss_box_reg’, ‘loss_objectness’, and ‘loss_rpn_box_reg’.device_type (
str
) – Type of device to be used for model and tensors, if cpu run on CPU, if gpu run on GPU if available otherwise run on CPU.

property
channel_index
¶  Returns
Index of the axis containing the color channels in the samples x.

property
channels_first
¶  Returns
Boolean to indicate index of the color channels in the sample x.

property
clip_values
¶ Return the clip values of the input samples.
 Returns
Clip values (min, max).

fit
(x: numpy.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) → None¶ Fit the model of the estimator on the training data x and y.
 Parameters
x (
ndarray
) – Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).y (Format as expected by the model) – Target values.
batch_size (
int
) – Batch size.nb_epochs (
int
) – Number of training epochs.

fit_generator
(generator: DataGenerator, nb_epochs: int = 20, **kwargs) → None¶ Fit the estimator using a generator yielding training batches. Implementations can provide frameworkspecific versions of this function to speedup computation.
 Parameters
generator – Batch generator providing (x, y) for each epoch.
nb_epochs (
int
) – Number of training epochs.

get_activations
(x: numpy.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False) → numpy.ndarray¶ Return the output of a specific layer for samples x where layer is the index of the layer between 0 and nb_layers  1 or the name of the layer. The number of layers can be determined by counting the results returned by calling `layer_names.
 Return type
ndarray
 Parameters
x (
ndarray
) – Sampleslayer – Index or name of the layer.
batch_size (
int
) – Batch size.framework (
bool
) – If true, return the intermediate tensor representation of the activation.
 Returns
The output of layer, where the first dimension is the batch size corresponding to x.

get_params
() → Dict[str, Any]¶ Get all parameters and their values of this estimator.
 Returns
A dictionary of string parameter names to their value.

property
input_shape
¶ Return the shape of one input sample.
 Returns
Shape of one input sample.

property
layer_names
¶ Return the names of the hidden layers in the model, if applicable.
 Returns
The names of the hidden layers in the model, input and output layers are ignored.
Warning
layer_names tries to infer the internal structure of the model. This feature comes with no guarantees on the correctness of the result. The intended order of the layers tries to match their order in the model, but this is not guaranteed either.

property
learning_phase
¶ The learning phase set by the user. Possible values are True for training or False for prediction and None if it has not been set by the library. In the latter case, the library does not do any explicit learning phase manipulation and the current value of the backend framework is used. If a value has been set by the user for this property, it will impact all following computations for model fitting, prediction and gradients.
 Returns
Learning phase.

loss_gradient
(x: numpy.ndarray, y: numpy.ndarray, **kwargs) → numpy.ndarray¶ Compute the gradient of the loss function w.r.t. x.
 Return type
ndarray
 Parameters
x (
ndarray
) – Samples of shape (nb_samples, height, width, nb_channels).y (
ndarray
) –Target values of format List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction.
 Returns
Loss gradients of the same shape as x.

property
model
¶ Return the model.
 Returns
The model.

predict
(x: numpy.ndarray, batch_size: int = 128, **kwargs) → numpy.ndarray¶ Perform prediction for a batch of inputs.
 Return type
ndarray
 Parameters
x (
ndarray
) – Samples of shape (nb_samples, height, width, nb_channels).batch_size (
int
) – Batch size.
 Returns
Predictions of format List[Dict[Tensor]], one for each input image. The fields of the Dict are as follows:
boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W
labels (Int64Tensor[N]): the predicted labels for each image
scores (Tensor[N]): the scores or each prediction.

set_learning_phase
(train: bool) → None¶ Set the learning phase for the backend framework.
 Parameters
train (
bool
) – True if the learning phase is training, otherwise False.

set_params
(**kwargs) → None¶ Take a dictionary of parameters and apply checks before setting them as attributes.
 Parameters
kwargs – A dictionary of attributes.
