0.1.3

Install steps

• pip install flashtorch

Upgrade steps

• pip install flashtorch -U

Breaking changes

• None

New features

• None

Bug fixes

• None

Improvements

• Requested improvement: #30
  • Implemented by #31
  • Quick summary: flashtorch.saliency.Backprop can now handle models with mono-channel/grayscale input images

Other changes

• None

0.1.2

Install steps

• pip install flashtorch

Upgrade steps

• pip install flashtorch -U

Breaking changes

• None

New features

• None

Bug fixes

• Reported bug: #18
  • Fixed by: #25
  • Quick summary: flashtorch.saliency.Backprop.visualize now correctly passes use_gpu flag down to the calculate_gradient.

Improvements

• None

Other changes

• None

0.1.1

Install steps

• pip install flashtorch

Upgrade steps

• pip install flashtorch -U

Breaking changes

• None

New features

• None

Bug fixes

• Removes a dependency on 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.

Improvements

• None

Other changes

• None

0.1.0

Install steps

• pip install flashtorch

Upgrade steps

• pip install flashtorch -U

Breaking changes

• 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:

• Image-specific class saliency map with backpropagation
• Google Colab version: best for playing around

New features

• 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:

• Activation maximization
• Google Colab version: best for playing around

Bug fixes

• None

Improvements

• flashtorch.utils.standardize_and_clip: Users can optionally set the saturation and brightness.

Other changes

• None

0.0.8

Install steps

• pip install flashtorch

Upgrade steps

• pip install flashtorch -U

Breaking changes

• None

New features

• None

Bug fixes

• Fixes #2

Improvements

• 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)
  

Other changes

• setup.py has better indications of supported Python versions