Recent Releases of torchcam

torchcam - v0.4.0: Evaluation metrics and PyTorch 2.0

This minor release adds evaluation metrics to the package and bumps PyTorch to version 2.0

Note: TorchCAM 0.4.0 requires PyTorch 2.0.0 or higher.

Highlights

Evaluation metrics

This release comes with a standard way to evaluate interpretability methods. This allows users to better evaluate models' robustness:

python from functools import partial from torchcam.metrics import ClassificationMetric metric = ClassificationMetric(cam_extractor, partial(torch.softmax, dim=-1)) metric.update(input_tensor) metric.summary()

What's Changed

New Features 🚀

feat: Added CAM evaluation metric by @frgfm in https://github.com/frgfm/torch-cam/pull/172
ci: Added FUNDING button by @frgfm in https://github.com/frgfm/torch-cam/pull/182
feat: Added new TV models to demo by @frgfm in https://github.com/frgfm/torch-cam/pull/184
ci: Added precommits, bandit & autoflake by @frgfm in https://github.com/frgfm/torch-cam/pull/192
feat: Removes model hooks when the context manager exits by @frgfm in https://github.com/frgfm/torch-cam/pull/198 ### Bug Fixes 🐛
chore: Applied post-release modifications by @frgfm in https://github.com/frgfm/torch-cam/pull/180
fix: Fixed division by zero during normalization by @frgfm in https://github.com/frgfm/torch-cam/pull/185
fix: Fixed zero division for weight computation in gradient based methods by @frgfm in https://github.com/frgfm/torch-cam/pull/187
docs: Fixes README badges by @frgfm in https://github.com/frgfm/torch-cam/pull/194
ci: Fixes issue templates by @frgfm in https://github.com/frgfm/torch-cam/pull/196
fix: Fixes SmoothGradCAMpp by @frgfm in https://github.com/frgfm/torch-cam/pull/204 ### Improvements
docs: Improved documentation build script by @frgfm in https://github.com/frgfm/torch-cam/pull/183
docs: Updates README & CONTRIBUTING by @frgfm in https://github.com/frgfm/torch-cam/pull/193
feat: Removed param grad computation in scripts by @frgfm in https://github.com/frgfm/torch-cam/pull/201
style: Updates precommit hooks and mypy config by @frgfm in https://github.com/frgfm/torch-cam/pull/203
refactor: Replaces flake8 by ruff and updates python version by @frgfm in https://github.com/frgfm/torch-cam/pull/211
style: Bumps ruff & black, removes isort & pydocstyle by @frgfm in https://github.com/frgfm/torch-cam/pull/216
style: Bumps ruff and updates torch & torchvision version specifiers by @frgfm in https://github.com/frgfm/torch-cam/pull/219
docs: Updates CONTRIBUTING & README by @frgfm in https://github.com/frgfm/torch-cam/pull/220
test: Speeds up test suite using plugins by @frgfm in https://github.com/frgfm/torch-cam/pull/222
ci: Adds multiple build CI jobs by @frgfm in https://github.com/frgfm/torch-cam/pull/223
feat: Removes warnings for torchvision and matplotlib by @frgfm in https://github.com/frgfm/torch-cam/pull/224

Full Changelog: https://github.com/frgfm/torch-cam/compare/v0.3.2...v0.4.0

- Python
Published by frgfm over 2 years ago

torchcam - v0.3.2: Some CAM fixes and support of batch processing

This patch release fixes the Score-CAM methods and improves the base API for CAM computation.

Note: TorchCAM 0.3.2 requires PyTorch 1.7.0 or higher.

Highlights

:hushed: Batch processing

CAM computation now supports batch sizes larger than 1 (#143) ! Practically, this means that you can compute CAMs for multiple samples at the same time, which will let you make the most of your GPU as well :zap:

The following snippet: ```python import torch from torchcam.methods import LayerCAM from torchvision.models import resnet18

A preprocessed (resized & normalized) tensor

img_tensor = torch.rand((2, 3, 224, 224)) model = resnet18(pretrained=True).eval()

Hook your model before inference

camextractor = LayerCAM(model) out = model(imgtensor)

Compute the CAM

activationmap = camextractor(out[0].argmax().item(), out) print(activation_map[0].ndim) ``will yield3` as the batch dimension is now also used.

:paintbrush: Documentation theme

New year, new documentation theme! For clarity and improved interface, the documentation theme was changed from Read the Docs to Furo (#162)

This comes with nice features like dark mode and edit button!

:computer_mouse: Contribution process

Contributions are important to OSS projects, and for this reason, a few improvements were made to the contribution process: - added a Makefile for easier development (#109) - added a dedicated README for the documentation (#109) - updated CONTRIBUTING (#109, #166)

Breaking changes

CAM signature

CAM extractors now outputs a list of tensors. The size of the list is equal to the number of target layers and ordered the same way. Each of these elements used to be a 2D spatial tensor, and is now a 3D tensor to include the batch dimension:

```python

Model was hooked and a tensor of shape (2, 3, 224, 224) was forwarded to it

amaps = cam_extractor(0, out) for elt in amaps: print(elt.shape) will, from now on, yield torch.Size([2, 7, 7]) ```

What's Changed

Breaking Changes 🛠

feat: Adds support for batch processing and fixes ScoreCAMs by @frgfm in https://github.com/frgfm/torch-cam/pull/143 ### New Features 🚀
ci: Added release note template and a job to check PR labels by @frgfm in https://github.com/frgfm/torch-cam/pull/138
docs: Added CITATION file by @frgfm in https://github.com/frgfm/torch-cam/pull/144 ### Bug Fixes 🐛
fix: Updated headers and added pydocstyle by @frgfm in https://github.com/frgfm/torch-cam/pull/137
chore: Updated PyTorch version specifier by @frgfm in https://github.com/frgfm/torch-cam/pull/149
docs: Fixed deprecated method call by @frgfm in https://github.com/frgfm/torch-cam/pull/158
chore: Fixed jinja2 deps (subdep of sphinx) by @frgfm in https://github.com/frgfm/torch-cam/pull/159
docs: Fixed docstring of ISCAM by @frgfm in https://github.com/frgfm/torch-cam/pull/160
docs: Fixed multi-version build by @frgfm in https://github.com/frgfm/torch-cam/pull/163
docs: Fixed codacy badge by @frgfm in https://github.com/frgfm/torch-cam/pull/164
docs: Fixed typo in CONTRIBUTING by @frgfm in https://github.com/frgfm/torch-cam/pull/166
docs: Fixed author entry in pyproject by @frgfm in https://github.com/frgfm/torch-cam/pull/168
style: Fixed import order by @frgfm in https://github.com/frgfm/torch-cam/pull/175 ### Improvements
docs: Added PR template and tools for contributing by @frgfm in https://github.com/frgfm/torch-cam/pull/109
refactor: Removed unused import by @frgfm in https://github.com/frgfm/torch-cam/pull/110
feat: Added text strip for multiple target selection in demo by @frgfm in https://github.com/frgfm/torch-cam/pull/111
refactor: Updated environment collection script by @frgfm in https://github.com/frgfm/torch-cam/pull/112
style: Updated flake8 config by @frgfm in https://github.com/frgfm/torch-cam/pull/115
ci: Updated isort config and related CI job by @frgfm in https://github.com/frgfm/torch-cam/pull/118
ci: Speeded up the example script CI check by @frgfm in https://github.com/frgfm/torch-cam/pull/130
refactor: Updated the timing function for latency eval by @frgfm in https://github.com/frgfm/torch-cam/pull/129
docs: Updated TOC of documentation by @frgfm in https://github.com/frgfm/torch-cam/pull/161
refactor: Updated build config and documentation theme by @frgfm in https://github.com/frgfm/torch-cam/pull/162
style: Updated mypy and isort configs by @frgfm in https://github.com/frgfm/torch-cam/pull/167
chore: Improved version specifiers and fixed conda recipe by @frgfm in https://github.com/frgfm/torch-cam/pull/169
docs: Fixed README badge and updated documentation by @frgfm in https://github.com/frgfm/torch-cam/pull/170
ci: Updated release job by @frgfm in https://github.com/frgfm/torch-cam/pull/173
refactor: Improved target resolution by @frgfm in https://github.com/frgfm/torch-cam/pull/174
ci: Updated the trigger for the release job by @frgfm in https://github.com/frgfm/torch-cam/pull/176
docs: Updated landing page screenshot by @frgfm in https://github.com/frgfm/torch-cam/pull/179

Full Changelog: https://github.com/frgfm/torch-cam/compare/v0.3.1...v0.3.2

- Python
Published by frgfm almost 4 years ago

torchcam - v0.3.1: Improved demo & reorganized package

This patch release adds new features to the demo and reorganizes the package for a clearer hierarchy.

Note: TorchCAM 0.3.1 requires PyTorch 1.5.1 or higher.

Highlights

CAM fusion is coming to the demo :rocket:

With release 0.3.0, the support of multiple target layers was added as well as CAM fusion. The demo was updated to automatically fuse CAMs when you hooked multiple layers (add a "+" separator between each layer name):

demo

Breaking changes

Submodule renaming

To anticipate further developments of the library, modules were renamed: - torchcam.cams was renamed into torchcam.methods - torchcam.cams.utils was renamed and made private (torchcam.methods._utils) since it's API may evolve quickly - activation-based CAM methods are now implemented in torchcam.methods.activation rather than torchcam.cams.cam - gradient-based CAM methods are now implemented in torchcam.methods.gradient rather than torchcam.cams.gradcam

0.3.0 | 0.3.1 -- | -- >>> from torchcam.cams import LayerCAM | >>> from torchcam.methods import LayerCAM |

What's Changed

chore: Made post release modifications by @frgfm in https://github.com/frgfm/torch-cam/pull/103
docs: Updated changelog by @frgfm in https://github.com/frgfm/torch-cam/pull/104
feat: Added possibility to retrieve multiple CAMs in demo by @frgfm in https://github.com/frgfm/torch-cam/pull/105
refactor: Reorganized package hierarchy by @frgfm in https://github.com/frgfm/torch-cam/pull/106
docs: Fixed LaTeX syntax in docstrings by @frgfm in https://github.com/frgfm/torch-cam/pull/107

Full Changelog: https://github.com/frgfm/torch-cam/compare/v0.3.0...v0.3.1

- Python
Published by frgfm over 4 years ago

torchcam - v0.3.0: Support of Layer-CAM & multi-layer CAM computation

This release extends CAM methods with Layer-CAM, greatly improves the core features (CAM computation for multiple layers at once, CAM fusion, support of torch.nn.Module), while improving accessibility for entry users.

Note: TorchCAM 0.3.0 requires PyTorch 1.5.1 or higher.

Highlights

Enters Layer-CAM

The previous release saw the introduction of Score-CAM variants, and this one introduces you to Layer-CAM, which is meant to be considerably faster, while offering very competitive localization cues!

Just like any other CAM methods, you can now use it as follows:

```python from torchcam.cams import LayerCAM

model = ....

Hook the model

cam_extractor = LayerCAM(model) ```

Consequently, the illustration of visual outputs for all CAM methods has been updated so that you can better choose the option that suits you:

cam_example

Computing CAMs for multiple layers & CAM fusion

A class activation map is specific to a given layer in a model. To fully capture the influence of visual traits on your classification output, you might want to explore the CAMs for multiple layers.

For instance, here are the CAMs on the layers "layer2", "layer3" and "layer4" of a resnet18:

```python from torchvision.io.image import readimage from torchvision.models import resnet18 from torchvision.transforms.functional import normalize, resize, topil_image import matplotlib.pyplot as plt

from torchcam.cams import LayerCAM from torchcam.utils import overlay_mask

Download an image

!wget https://www.woopets.fr/assets/races/000/066/big-portrait/border-collie.jpg

Set this to your image path if you wish to run it on your own data

img_path = "border-collie.jpg"

Get your input

img = readimage(imgpath)

Preprocess it for your chosen model

input_tensor = normalize(resize(img, (224, 224)) / 255., [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

Get your model

model = resnet18(pretrained=True).eval()

Hook the model

cam_extractor = LayerCAM(model, ["layer2", "layer3", "layer4"])

out = model(inputtensor.unsqueeze(0)) cams = camextractor(out.squeeze(0).argmax().item(), out)

Plot the CAMs

, axes = plt.subplots(1, len(camextractor.targetnames)) for idx, name, cam in zip(range(len(camextractor.targetnames)), camextractor.targetnames, cams): axes[idx].imshow(cam.numpy()); axes[idx].axis('off'); axes[idx].settitle(name); plt.show() ```

multi_cams

Now, the way you would combine those together is up to you. By default, most approaches use an element-wise maximum. But, LayerCAM has its own fusion method:

```python

Let's fuse them

fusedcam = camextractor.fuse_cams(cams)

Plot the raw version

plt.imshow(fusedcam.numpy()); plt.axis('off'); plt.title(" + ".join(camextractor.target_names)); plt.show() ```

fused_cams

```python

Overlay it on the image

result = overlaymask(topilimage(img), topilimage(fusedcam, mode='F'), alpha=0.5)

Plot the result

plt.imshow(result); plt.axis('off'); plt.title(" + ".join(camextractor.targetnames)); plt.show() ```

fused_overlay

Support of `torch.nn.Module` as `target_layer`

While making the API more robust, CAM constructors now also accept torch.nn.Module as target_layer. Previously, you had to pass the name of the layer as string, but you can now pass the object reference directly if you prefer: ```python from torchcam.cams import LayerCAM

model = ....

Hook the model

cam_extractor = LayerCAM(model, model.layer4) ```

:zap: Latency benchmark :zap:

Since CAMs can be used from localization or production pipelines, it is important to consider latency along with pure visual output quality. For this reason, a latency evaluation script has been included in this release along with a full benchmark table.

Should you wish to have latency metrics on your dedicated hardware, you can run the script on your own: shell python scripts/eval_latency.py SmoothGradCAMpp --size 224

Notebooks :playorpause_button:

Do you prefer to only run code rather than write it? Perhaps you only want to tweak a few things? Then enjoy the brand new Jupyter notebooks than you can either run locally or on Google Colab!

:hugs: Live demo :hugs:

The ML community was recently blessed by HuggingFace with their beta of Spaces, which let you host free-of-charge your ML demos!

Previously, you were able to run the demo locally on deploy it on your own, but now, you can enjoy the live demo of TorchCAM :art:

Breaking changes

Multiple CAM output

Since CAM extractor can now compute the resulting maps for multiple layer at a time, the return type of all CAMs has been changed from torch.Tensor to List[torch.Tensor] with N elements, where N is the number of target layers.

0.2.0 | 0.3.0 -- | -- >>> from torchcam.cams import SmoothGradCAMpp
>>> extractor = SmoothGradCAMpp(model)
>>> out = model(input_tensor.unsqueeze(0))
>>> print(type(cam_extractor(out.squeeze(0).argmax().item(), out)))
<class 'torch.Tensor'> | >>> from torchcam.cams import SmoothGradCAMpp
>>> extractor = SmoothGradCAMpp(model)
>>> out = model(input_tensor.unsqueeze(0))
>>> print(type(cam_extractor(out.squeeze(0).argmax().item(), out)))
<class 'list'> |

New features

CAMs

Implementations of CAM method - Added support of conv1x1 as FC candidate in base CAM #69 (@frgfm) - Added support of LayerCAM #77 (@frgfm) - Added support of torch.nn.Module as target_layer or fc_layer #83 (@frgfm) - Added support of multiple target layers for all CAM methods #89 #92 (@frgfm) - Added layer-specific CAM fusion method #93 (@frgfm)

Scripts

Side scripts to make the most out of TorchCAM - Added latency evaluation script #95 (@frgfm)

Test

Verifications of the package well-being before release - Added unittests to verify that conv1x1 can be used as FC in base CAM #69 (@frgfm) - Added unittest for LayerCAM #77 (@frgfm) - Added unittest for gradient-based CAM method for models with in-place ops #80 (@frgfm) - Added unittest to check support of torch.nn.Module as target_layer in CAM constructor #83 #88 (@frgfm) - Added unittest for CAM fusion #93 (@frgfm)

Documentation

Online resources for potential users - Added LayerCAM ref in the README and in the documentation #77 (@frgfm) - Added CODEOFCONDUCT #86 (@frgfm) - Added changelog to the documentation #91 (@frgfm) - Added latency benchmark & GIF illustration of CAM on a video in README #95 (@frgfm) - Added documentation of .fuse_cams method #93 (@frgfm) - Added ref to HF Space demo in README and documentation #96 (@frgfm) - Added tutorial notebooks and reference page in the documentation #99 #100 #101 #102 (@frgfm)

Others

Other tools and implementations - Added class_idx & target_layer selection in the demo #67 (@frgfm) - Added CI jobs to build on different OS & Python versions, to validate the demo, and the example script #73 #74 (@frgfm) - Added LayerCAM to the demo #77 (@frgfm) - Added an environment collection script #78 (@frgfm) - Added CI check for the latency evaluation script #95 (@frgfm)

Bug fixes

CAMs

Fixes backward hook mechanism for in-place operations #80 (@frgfm)

Documentation

Fixed docutils version constraint for documentation building #98 (@frgfm)

Others

Fixed CI job to build documentation #64 (@frgfm)
Fixed Pillow version constraint #73 #84 (@frgfm)

Improvements

CAMs

Improved weight broadcasting for all CAMs #77 (@frgfm)
Refactored hook enabling #80 (@frgfm)
Improved the warning message for target automatic resolution #87 #92 (@frgfm)
Improved arg type checking for CAM constructor #88 (@frgfm)

Scripts

Improved the layout option of the example script #66 (@frgfm)
Refactored example script #80 #94 (@frgfm)
Updated all scripts for support of multiple target layers #89 (@frgfm)

Test

Updated unittests for multiple target layer support #89 (@frgfm)

Documentation

Added latest release doc version & updated README badge #63 (@frgfm)
Added demo screenshot in the README #67 (@frgfm)
Updated instructions in README #89 (@frgfm)
Improved documentation landing page #91 (@frgfm)
Updated contribution guidelines #94 (@frgfm)
Updated documentation requirements #99 (@frgfm)

Others

Updated package version and fixed CI jobs to validate release publish #63 (@frgfm)
Updated license from MIT to Apache 2.0 #70 (@frgfm)
Refactored CI jobs #73 (@frgfm)
Improved bug report template #78 (@frgfm)
Updated streamlit syntax in demo #94 (@frgfm)
Added isort config and CI job #97 (@frgfm)
Added CI job for sanity check of the documentation build #98 (@frgfm)

- Python
Published by frgfm over 4 years ago

torchcam - Compatibility with 3D inputs and improved documentation

This release extends TorchCAM compatibility to 3D inputs, and improves documentation.

Note: TorchCAM 0.2.0 requires PyTorch 1.5.1 or higher.

Highlights

Compatibility for inputs with more than 2 spatial dimensions

The first papers about CAM methods were built for classification models using 2D (spatially) inputs. However, the latest methods can be extrapolated to higher dimension inputs and it's now live: ```python import torch from torchcam.cams import SmoothGradCAMpp

Define your model compatible with 3D inputs

videomodel = ... extractor = SmoothGradCAMpp(videomodel)

Forward your input

scores = model(torch.rand((1, 3, 32, 224, 224)))

Retrieve the CAM

cam = extractor(scores[0].argmax().item(), scores) ```

Multi-version documentation

While documentation was up-to-date with the latest commit on the main branch, previously if you were running an older release of the library, you had no corresponding documentation.

As of now, you can select the version of the documentation you wish to access (stable releases or latest commit): torchcam_doc

Demo app

Since spatial information is at the very core of TorchCAM, a minimal Streamlit demo app was added to explore the activation of your favorite models. You can run the demo with the following commands: streamlit run demo/app.py

Here is how it renders retrieving the heatmap using SmoothGradCAMpp on a pretrained resnet18: torchcam_demo

New features

CAMs

Implementations of CAM method - Enabled CAM compatibility for inputs with more than 2 spatial dimensions #45 (@frgfm) - Added support of XGradCAM #47 (@frgfm)

Test

Verifications of the package well-being before release - Added unittests for XGradCAM #47 (@frgfm)

Documentation

Online resources for potential users - Added references to XGradCAM in README and documentation #47 (@frgfm) - Added multi-version documentation & added github star button #53, #54, #55, #56 (@frgfm) - Revamped README #59 (@frgfm) focusing on short easy code snippets - Improved documentation #60 (@frgfm)

Others

Other tools and implementations - Added issue templates for bug report and feature request #49 (@frgfm) - Added option to specify a single CAM method in example script #52 (@frgfm) - Added minimal demo app #59 (@frgfm)

Bug fixes

CAMs

Fixed automatic layer resolution on GPU #41 (@frgfm)
Fixed backward hook warnings for Pytorch >= 1.8.0 #58 (@frgfm)

Utils

Fixed RGBA -> RGB conversion in overlay_mask #38 (@alexandrosstergiou)

Test

Fixed overlay_mask unittest #38 (@alexandrosstergiou)

Documentation

Fixed codacy badge in README #46 (@frgfm)
Fixed typo in documentation #62 (@frgfm)

Others

Fixed CI job for conda build #34 (@frgfm)
Fixed model mode in example script #37 (@frgfm)
Fixed sphinx version #40 (@frgfm)
Fixed usage instructions in README #43 (@frgfm)
Fixed example script for local image input #51 (@frgfm)

Improvements

CAMs

Added NaN check in gradcams #37 (@frgfm)

Test

Added NaN check unittest for gradcam #37 (@frgfm)
Switched from unittest to pytest #45 (@frgfm) and split test files by module

Documentation

Updated README badges #34, illustration #39 and usage instructions #41 (@frgfm)
Added instructions to run all CI checks locally in CONTRIBUTING #34, #45 (@frgfm)
Updated project hierarchy description in CONTRIBUTING #43 (@frgfm)
Added minimal code snippet in documentation #41 (@frgfm)

Others

Updated version in setup #34 and requirements #61 (@frgfm)
Leveraged automatic layer resolution in example script #41 (@frgfm)
Updated CI job to run unittests #45 (@frgfm)

- Python
Published by frgfm about 5 years ago

torchcam - Automatic target layer resolution and support of IS-CAM

This release adds an implementation of IS-CAM and greatly improves interface.

Note: torchcam 0.1.2 requires PyTorch 1.1 or newer.

Highlights

CAMs

Implementation of CAM extractor New - Add an IS-CAM implementation #13 (@frgfm) - Added automatic target layer resolution #32 (@frgfm)

Improvements - Added support for submodule hooking #21 (@pkmandke)

Fixes - Fixed hooking mechanism edge case #23 (@frgfm)

Test

Verifications of the package well-being before release New - Updated test for torchcam.cams #13, #30 (@frgfm)

Improvements - Removed pretrained model loading in unittests #25 (@frgfm) - Switched all models to eval, removed gradient when not required, and changed to simpler models #33 (@frgfm)

Documentation

Online resources for potential users New - Added entry for IS-CAM #13, #30 (@frgfm)

Fixes - Fixed examples in docstrings of gradient-based CAMs #28, #33 (@frgfm)

Others

Other tools and implementations New - Added annotation typing to the codebase & mypy verification CI job #19 (@frgfm) - Added package publishing verification jobs #12 (@frgfm)

Improvements - Improved example script #15 (@frgfm) - Optimized CI cache #20 (@frgfm)

Fixes - Fixed coverage upload job #16 (@frgfm) - Fixed doc deployment job #24 (@frgfm) - Fixed conda recipe #29 (@frgfm)

- Python
Published by frgfm over 5 years ago

torchcam - Support of SmoothGradCAM++, Score-CAM and SS-CAM

This release adds implementations of SmoothGradCAM++, Score-CAM and SS-CAM.

Note: torchcam 0.1.1 requires PyTorch 1.1 or newer.

brought to you by @frgfm

Highlights

CAMs

Implementation of CAM extractor New - Add a SmoothGradCAM++ implementation (#4) - Add a Score-CAM implementation (#5) - Add a SS-CAM implementation (#11).

Improvements - Refactor CAM extractor for better code reusability (#6)

Test

Verifications of the package well-being before release New - Updated test for torchcam.cams (#4, #5, #11)

Documentation

Online resources for potential users Improvements - Add detailed explanation of CAM computation (#8, #11) - Add websearch referencing of documentation (#7)

Others

Other tools and implementations - Fixed conda upload job (#3)

- Python
Published by frgfm almost 6 years ago

torchcam - Class activation maps for CNN in PyTorch

This release adds implementations of CAM, GradCAM and GradCAM++.

Note: torchcam 0.1.0 requires PyTorch 1.1 or newer.

brought to you by @frgfm

Highlights

GradCAM

Implementation of gradient-based CAM extractor New - Add a CAM implementation (#2) - Add Grad-CAM and Grad-CAM++ implementations (#1, #2).

Test

Verifications of the package well-being before release New - Add test for torchcam.cams (#1, #2) - Add test for torschscan.utils (#1)

Documentation

Online resources for potential users New - Add sphinx automatic documentation build for existing features (#1, #2) - Add contribution guidelines (#1) - Add installation, usage, and benchmark in readme (#1, #2)

Others

Other tools and implementations - Add ̀overlay_mask` to easily overlay mask on images (#1).

- Python
Published by frgfm about 6 years ago

Recent Releases of torchcam

torchcam - v0.4.0: Evaluation metrics and PyTorch 2.0

Highlights

Evaluation metrics

What's Changed

New Features 🚀

torchcam - v0.3.2: Some CAM fixes and support of batch processing

Highlights

:hushed: Batch processing

A preprocessed (resized & normalized) tensor

Hook your model before inference

Compute the CAM

:paintbrush: Documentation theme

:computer_mouse: Contribution process

Breaking changes

CAM signature

Model was hooked and a tensor of shape (2, 3, 224, 224) was forwarded to it

What's Changed

Breaking Changes 🛠

torchcam - v0.3.1: Improved demo & reorganized package

Highlights

CAM fusion is coming to the demo :rocket:

Breaking changes

Submodule renaming

What's Changed

torchcam - v0.3.0: Support of Layer-CAM & multi-layer CAM computation

Highlights

Enters Layer-CAM

model = ....

Hook the model

Computing CAMs for multiple layers & CAM fusion

Download an image

Set this to your image path if you wish to run it on your own data

Get your input

Preprocess it for your chosen model

Get your model

Hook the model

Plot the CAMs

Let's fuse them

Plot the raw version

Overlay it on the image

Plot the result

Support of torch.nn.Module as target_layer

model = ....

Hook the model

:zap: Latency benchmark :zap:

Notebooks :playorpause_button:

:hugs: Live demo :hugs:

Breaking changes

Multiple CAM output

New features

CAMs

Scripts

Test

Documentation

Others

Bug fixes

CAMs

Documentation

Others

Improvements

CAMs

Scripts

Test

Documentation

Others

torchcam - Compatibility with 3D inputs and improved documentation

Highlights

Compatibility for inputs with more than 2 spatial dimensions

Define your model compatible with 3D inputs

Forward your input

Retrieve the CAM

Multi-version documentation

Demo app

New features

CAMs

Test

Documentation

Others

Bug fixes

Support of `torch.nn.Module` as `target_layer`