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

😯 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 ⚡

The following snippet:

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
cam_extractor = LayerCAM(model)
out = model(img_tensor)
# Compute the CAM
activation_map = cam_extractor(out[0].argmax().item(), out)
print(activation_map[0].ndim)

will yield 3 as the batch dimension is now also used.

🖌️ 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!

🖱️ 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:

# 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 #143

New Features 🚀

• ci: Added release note template and a job to check PR labels by @frgfm in #138
• docs: Added CITATION file by @frgfm in #144

Bug Fixes 🐛

• fix: Updated headers and added pydocstyle by @frgfm in #137
• chore: Updated PyTorch version specifier by @frgfm in #149
• docs: Fixed deprecated method call by @frgfm in #158
• chore: Fixed jinja2 deps (subdep of sphinx) by @frgfm in #159
• docs: Fixed docstring of ISCAM by @frgfm in #160
• docs: Fixed multi-version build by @frgfm in #163
• docs: Fixed codacy badge by @frgfm in #164
• docs: Fixed typo in CONTRIBUTING by @frgfm in #166
• docs: Fixed author entry in pyproject by @frgfm in #168
• style: Fixed import order by @frgfm in #175

Improvements

• docs: Added PR template and tools for contributing by @frgfm in #109
• refactor: Removed unused import by @frgfm in #110
• feat: Added text strip for multiple target selection in demo by @frgfm in #111
• refactor: Updated environment collection script by @frgfm in #112
• style: Updated flake8 config by @frgfm in #115
• ci: Updated isort config and related CI job by @frgfm in #118
• ci: Speeded up the example script CI check by @frgfm in #130
• refactor: Updated the timing function for latency eval by @frgfm in #129
• docs: Updated TOC of documentation by @frgfm in #161
• refactor: Updated build config and documentation theme by @frgfm in #162
• style: Updated mypy and isort configs by @frgfm in #167
• chore: Improved version specifiers and fixed conda recipe by @frgfm in #169
• docs: Fixed README badge and updated documentation by @frgfm in #170
• ci: Updated release job by @frgfm in #173
• refactor: Improved target resolution by @frgfm in #174
• ci: Updated the trigger for the release job by @frgfm in #176
• docs: Updated landing page screenshot by @frgfm in #179

Full Changelog: v0.3.1...v0.3.2