Visualization toolkit for neural networks in PyTorch! Demo -->
pip install flashtorch
pip install flashtorch -U
flashtorch.saliency.Backprop can now handle models with mono-channel/grayscale input imagespip install flashtorch
pip install flashtorch -U
flashtorch.saliency.Backprop.visualize now correctly passes use_gpu flag down to the calculate_gradient.pip install flashtorch
pip install flashtorch -U
README.md in setup.py: this is to avoid getting unicode decoding error (reported by #14). setup.py now gets the long_description from its docstring.pip install flashtorch
pip install flashtorch -U
flashtorch.utils.visualize: This functionality was specific for creating saliency maps, and therefore has been moved as a class method for flashtorch.saliency.Backprop
Refer to the notebooks below for details and how to use it:
flashtorch.activmax.GradientAscent: This is a new API which implements activation maximization via gradient ascent. It has three public facing APIs:
GradientAscent.optimize: Generates an image that maximally activates the target filter.GradientAscent.visualize: Optimizes for the target layer/filter and visualizes the output.GradientAscent.deepdream: Creates DeepDream.Refer to the notebooks below for details and how to use it:
flashtorch.utils.standardize_and_clip: Users can optionally set the saturation and brightness.pip install flashtorch
pip install flashtorch -U
Users can explicitly set a device to use when calculating gradients when using an instance of Backprop, by setting use_gpu=True. If it's True and torch.cuda.is_available, the computation will be moved to GPU. It defaults to False if not provided.
from flashtorch.saliency import Backprop
... # Prepare input and target_class
model = model()
backprop = Backprop(model)
gradients = backprop. calculate_gradients(input, target_class, use_gpu=True)
setup.py has better indications of supported Python versions