Merge pull request #493 from NarineK/optim-wip

Optim wip

Author: NarineK

.circleci/config.yml

@@ -43,7 +43,7 @@ commands:
     steps:
       - run:
           name: "Check import order with isort"
-          command: isort --check-only -v
+          command: isort --check-only -v .
 
   mypy_check:
     description: "Static type checking with mypy"
@@ -121,10 +121,11 @@ commands:
       - run:
           name: "Install CUDA"
           command: |
-            wget http://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda-repo-ubuntu1604-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb
-            sudo apt-get update
-            export OS_RELEASE=$(uname -r)
-            sudo dpkg -i cuda-repo-ubuntu1604-10-1-local-10.1.243-418.87.00_1.0-1_amd64.deb
+            wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-ubuntu1604.pin
+            sudo mv cuda-ubuntu1604.pin /etc/apt/preferences.d/cuda-repository-pin-600
+            wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-ubuntu1604-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
+            sudo dpkg -i cuda-repo-ubuntu1604-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
+            sudo apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub
             sudo apt-get update
             sudo apt-get --yes --force-yes install cuda
 
@@ -182,7 +183,6 @@ jobs:
       - conda_install:
           args: "-n"
       - lint_flake8
-      - lint_black
       - isort
       - unit_tests
       - sphinx

.github/ISSUE_TEMPLATE/---bug-report.md

@@ -0,0 +1,44 @@
+---
+name: "\U0001F41B Bug report"
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+## 🐛 Bug
+
+<!-- A clear and concise description of what the bug is. -->
+
+## To Reproduce
+
+Steps to reproduce the behavior:
+
+1.
+1.
+1.
+
+<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
+
+## Expected behavior
+
+<!-- A clear and concise description of what you expected to happen. -->
+
+## Environment
+Describe the environment used for Captum
+
+```
+
+ - Captum / PyTorch Version (e.g., 1.0 / 0.4.0):
+ - OS (e.g., Linux):
+ - How you installed Captum / PyTorch (`conda`, `pip`, source):
+ - Build command you used (if compiling from source):
+ - Python version:
+ - CUDA/cuDNN version:
+ - GPU models and configuration:
+ - Any other relevant information:
+
+## Additional context
+
+<!-- Add any other context about the problem here. -->

.github/ISSUE_TEMPLATE/---documentation.md

@@ -0,0 +1,14 @@
+---
+name: "\U0001F4DA Documentation"
+about: Describe this issue template's purpose here.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+## 📚 Documentation
+
+<!-- A clear and concise description of what content in https://captum.ai/docs, https://captum.ai/tutorials or https://captum.ai/api  is an issue. 
+Feel free also to file an issue if you see any problems in the README.md or in the code documentation
+ -->

.github/ISSUE_TEMPLATE/---feature-request.md

@@ -0,0 +1,27 @@
+---
+name: "\U0001F680 Feature request"
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+## 🚀 Feature
+<!-- A clear and concise description of the feature proposal -->
+
+## Motivation
+
+<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
+
+## Pitch
+
+<!-- A clear and concise description of what you want to happen. -->
+
+## Alternatives
+
+<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
+
+## Additional context
+
+<!-- Add any other context or screenshots about the feature request here. -->

.github/ISSUE_TEMPLATE/--questions-and-help.md

@@ -0,0 +1,14 @@
+---
+name: "❓ Questions and Help"
+about: Describe this issue template's purpose here.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+## ❓ Questions and Help
+
+We have a set of listed resources available on the website and FAQ: https://captum.ai/ and https://captum.ai/docs/faq . Feel free to open an issue here on the github or in our discussion forums:
+
+- [Discussion Forum](https://discuss.pytorch.org/c/captum)

AWESOME_LIST.md

@@ -18,3 +18,11 @@ Score-CAM is a gradient-free visualization method extended from Grad-CAM and Gra
 White noise stimuli is fed to a classifier and the ones that are categorized into a particular class are averaged. It gives an estimate of the templates a classifier uses for classification, and is based on two popular and related methods in psychophysics and neurophysiology namely classification images and spike triggered analysis.
 - [Paper](https://arxiv.org/abs/1912.12106)
 - [https://github.com/aliborji/WhiteNoiseAnalysis.git](https://github.com/aliborji/WhiteNoiseAnalysis.git)
+
+
+#### FastCAM: Multiscale Saliency Map with SMOE scale
+An attribution method that uses information at the end of each network scale which is then combined into a single saliency map. 
+- [Paper](https://arxiv.org/abs/1911.11293)
+- [https://github.com/LLNL/fastcam](https://github.com/LLNL/fastcam)
+- [pull request](https://github.com/pytorch/captum/pull/442)
+- [jupyter notebook demo](https://github.com/LLNL/fastcam/blob/captum/demo-captum.ipynb)

CITATION

@@ -1,8 +1,8 @@
-@misc{captum2019github,
-  author = {Kokhlikyan, Narine and Miglani, Vivek and Martin, Miguel and Wang, Edward and Reynolds, Jonathan and Melnikov, Alexander and Lunova, Natalia and Reblitz-Richardson, Orion},
-  title = {PyTorch Captum},
-  year = {2019},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/pytorch/captum}},
+@misc{kokhlikyan2020captum,
+    title={Captum: A unified and generic model interpretability library for PyTorch},
+    author={Narine Kokhlikyan and Vivek Miglani and Miguel Martin and Edward Wang and Bilal Alsallakh and Jonathan Reynolds and Alexander Melnikov and Natalia Kliushkina and Carlos Araya and Siqi Yan and Orion Reblitz-Richardson},
+    year={2020},
+    eprint={2009.07896},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
 }

README.md

@@ -415,7 +415,7 @@ The slides of our presentation from NeurIPS 2019 can be found [here](docs/presen
 ## References of Algorithms
 
 * `IntegratedGradients`, `LayerIntegratedGradients`: [Axiomatic Attribution for Deep Networks, Mukund Sundararajan et al. 2017](https://arxiv.org/abs/1703.01365) and [Did the Model Understand the Question?, Pramod K. Mudrakarta, et al. 2018](https://arxiv.org/abs/1805.05492)
-* `InputXGradient`: [Investigating the influence of noise and distractors on the interpretation of neural networks, Pieter-Jan Kindermans et al. 2016](https://arxiv.org/abs/1611.07270)
+* `InputXGradient`: [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences, Avanti Shrikumar et al. 2016](https://arxiv.org/abs/1605.01713)
 * `SmoothGrad`: [SmoothGrad: removing noise by adding noise, Daniel Smilkov et al. 2017](https://arxiv.org/abs/1706.03825)
 * `NoiseTunnel`: [Sanity Checks for Saliency Maps, Julius Adebayo et al. 2018](https://arxiv.org/abs/1810.03292)
 * `NeuronConductance`: [How Important is a neuron?, Kedar Dhamdhere et al. 2018](https://arxiv.org/abs/1805.12233)

captum/_utils/common.py

@@ -23,17 +23,17 @@ class ExpansionTypes(Enum):
 
 
 def safe_div(
-    denom: Tensor, quotient: Union[Tensor, float], default_value: Tensor
+    numerator: Tensor, denom: Union[Tensor, float], default_value: Tensor
 ) -> Tensor:
     r"""
-        A simple utility function to perform `denom / quotient`
-        if the statement is undefined => result will be `default_value`
+    A simple utility function to perform `numerator / denom`
+    if the statement is undefined => result will be `default_value`
     """
-    if isinstance(quotient, float):
-        return denom / quotient if quotient != 0.0 else default_value
+    if isinstance(denom, float):
+        return numerator / denom if denom != 0.0 else default_value
 
-    # if quotient is a tensor
-    return denom / torch.where(quotient != 0.0, quotient, default_value)
+    # if denominator is a tensor
+    return numerator / torch.where(denom != 0.0, denom, default_value)
 
 
 @typing.overload
@@ -418,9 +418,9 @@ def _select_targets(output: Tensor, target: TargetType) -> Tensor:
 
 
 def _verify_select_column(
-    output: Tensor, target: Union[int, Tuple[int, ...]]
+    output: Tensor, target: Union[int, Tuple[Union[int, slice], ...]]
 ) -> Tensor:
-    target = cast(Tuple[int, ...], (target,) if isinstance(target, int) else target)
+    target = (target,) if isinstance(target, int) else target
     assert (
         len(target) <= len(output.shape) - 1
     ), "Cannot choose target column with output shape %r." % (output.shape,)
@@ -472,8 +472,10 @@ def _reduce_list(
     val_list[0]. It is assumed that all tuples in the list have the same length
     and red_func can be applied to all elements in each corresponding position.
     """
+    assert len(val_list) > 0, "Cannot reduce empty list!"
     if isinstance(val_list[0], torch.Tensor):
-        return red_func(val_list)
+        first_device = val_list[0].device
+        return red_func([elem.to(first_device) for elem in val_list])
     elif isinstance(val_list[0], bool):
         return any(val_list)
     elif isinstance(val_list[0], tuple):