This release of ART 1.6.2 provides updates to ART 1.6.

Added

Added targeted option to RobustDpatch (#1069)
Added option standardise_output to define provided label format (#1069)
Added property native_label_is_pytorch_format to object detectors to define label format expected by the model (#1069)

Changed

Changed Dpatch and RobustDpatch to work internally with PyTorchFasterRCNN's object detection label format and convert labels if provided in TensorFlowFasterRCNN's format accordingly using option standardise_output (#1069)
Change setup.py to only contain core dependencies in install_requires and added additional install options tensorflow_image, tensorflow_audio, pytorch_image, and pytorch_audio (#1116)
Changed check for version of torch and torchvision in AdversarialPatchPyTorch to account for suffixes like +cu102 (#1115)
Changed art.utils.load_iris to use sklearn.datasets.load_iris instead of download from https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data (#1097)

Removed unnecessary requirement for scores in labels y for TensorFlowFasterRCNN.loss_gradient and PyTorchFasterRCNN.loss_gradient (#1069)

Fixed docstrings of methods predict and loss_gradient to correctly describe the expected and provided label format (#1069)
Fixed bug of missing transfer of tensor to device ProjectedGradientDescentPyTorch (#1076)
Fixed bug resulting in wrong loss gradients calculated with ScikitlearnLogisticRegression.loss_gradient (#1065)