This project provide a script of class activation map (CAM) visualizations, which can be used for explaining predictions and model interpretability, etc.
- Install via pip
$ pip install torchcam
- Install from source
$ pip install --upgrade git+https://github.com/Tramac/pytorch-cam.git
from torchcam import open_image, image2batch, int2tensor, getCAM from torchvision.models import resnet18 img = open_image('./data/cat.jpg', (224, 224), convert_mode='RGB') input = image2batch(img) image_class = 284 # cat class in imagenet target = int2tensor(image_class) model = resnet18(pretrained=True)
- Basic
# gradcam cam = getCAM(model, img, input, target, display=True, save=False)
- Adavanced
Besides the default gradcam method, these following additional methods are alse available: vanilla_grad, grad_x_input, saliency, integrate_grad, deconv, smooth_grad.
from torchcam import saliency results = saliency.get_image_saliency_result(model, img, input, target, methods=['smooth_grad', 'vanilla_grad', 'grad_x_input', 'saliency']) figure = saliency.get_image_saliency_plot(results, display=True, save=False)
- Yours own model
model = YourModel()
cam = getCAM(model, img, input, target, layer_path=['xxx']) # The end backprop layer key in your model
- support more explainer
- optim code
- test your own model