Add clip models #636
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Add clip models #636
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* Also added a missing type hint & updated citation.
* Also made improvements to top channel section of the notebook.
Optim-wip: Add model linearization, and expanded weights spatial positions
Optim-wip: Move inception test helpers to separate file
…del-layers-bug Optim-wip: Fix issue with `get_model_layers`
…rget-bug Optim-wip: Fix bug with nn.Sequential targets
…#850) * Add class activation atlas tutorial notebook * Changes based on feedback * Changes based on feedback * More changes based on feedback * TSNE -> t-SNE * Added axes labels to second xy graph. * Changes to first graph based on feedback
* Fixed the WeightVisualization notebook so that it works with the latest version of the optim module. * Updated the WeightVisualization notebook to use loss comprehension for faster rendering times.
…ations (meta-pytorch#821) * Add JIT support to most transforms * Additional improvements * JIT support for `center_crop`. * Improve some transform tests. * Fix `RandomCrop` transform bug. * Fix Mypy bug * Interpolation based RandomScale & Other Improvements * Replace Affine `RandomScale` with Interpolation based variant. Renamed old variant to `RandomScaleAffine`. * `CenterCrop` & `center_crop` now use padding if the crop size is larger than the input dimensions. * Add distributions support to both versions of `RandomScale`. * Improve transform tests. * NumSeqOrTensorType -> NumSeqOrTensorOrProbDistType * Add `torch.distributions.distribution.Distribution` to `NumSeqOrTensorType` type hint. * Add TransformationRobustness transform& fix bug * Added `TransformationRobustness()` transform. * Fixed bug with `center_crop` padding code, and added related tests to `center_crop` & `CenterCrop`. * Fix center crop JIT tests * Add asserts & more tests for RandomScale transforms * Add JIT support for ToRGB, NaturalImage, & FFTImage * Add JIT support `NaturalImage`, `FFTImage`, & `PixelImage`. * Added proper JIT support for `ToRGB`. * Improved `NaturalImage` & `FFTImage` tests, and test coverage. * Add ImageParameterization Instance support for NaturalImage * Added `ImageParameterization` instance support for `NaturalImage`. This improvement should make it easier to use parameterization enhancements like SharedImage, and will be helpful for custom parameterizations that don't use the standard input variable set (size, channels, batch, & init). * Added asserts to verify `NaturalImage` parameterization inputs are instances or types of `ImageParameterization`. * Support ToRGB with no named dimensions This should make it easier to work with the ToRGB module as many PyTorch functions still don't work with named dimensions yet. * Allow more than 4 channels in ToRGB * The maximum of 4 channels isn't required as we ignore all channels after 3. * Add assert check to `RandomScale`'s mode variable The `linear` mode only supports 3D inputs, and `trilinear` only supports 5D inputs. RandomScale only uses 4D inputs, so only `nearest`, `bilinear`, `bicubic`, & `area` are supported. * Change assert to check for unsupported RandomScale mode options * Change `RandomRotation` type hint & add `RandomRotation` to `TransformationRobustness` * Change `RandomRotation` type hint from `NumSeqOrTensorType` to `NumSeqOrTensorOrProbDistType`. * Uncomment `RandomRotation` from `TransformationRobustness` & tests.
Merge master branch into optim-wip
* Removed test version checks for versions below 1.6.0. * `AssertArrayAlmostEqual` -> `AssertTensorAlmostEqual` * General linting changes / fixes.
Optim-wip: Merge master branch into optim-wip
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