Readme

README.md

@@ -111,7 +111,7 @@ This can be useful if you're not sure what layer will perform best.
 
 ----------
 
-# Using from code as a library
+# Usage examples
 
 ```python
 from pytorch_grad_cam import GradCAM, HiResCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, FullGrad
@@ -124,34 +124,27 @@ target_layers = [model.layer4[-1]]
 input_tensor = # Create an input tensor image for your model..
 # Note: input_tensor can be a batch tensor with several images!
 
-# Construct the CAM object once, and then re-use it on many images:
-cam = GradCAM(model=model, target_layers=target_layers)
-
-# You can also use it within a with statement, to make sure it is freed,
-# In case you need to re-create it inside an outer loop:
-# with GradCAM(model=model, target_layers=target_layers) as cam:
-#   ...
 
 # We have to specify the target we want to generate
 # the Class Activation Maps for.
-# If targets is None, the highest scoring category
-# will be used for every image in the batch.
-# Here we use ClassifierOutputTarget, but you can define your own custom targets
-# That are, for example, combinations of categories, or specific outputs in a non standard model.
-
 targets = [ClassifierOutputTarget(281)]
 
-# You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.
-grayscale_cam = cam(input_tensor=input_tensor, targets=targets)
+# Construct the CAM object once, and then re-use it on many images.
+with GradCAM(model=model, target_layers=target_layers) as cam:
 
-# In this example grayscale_cam has only one image in the batch:
-grayscale_cam = grayscale_cam[0, :]
-visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
+  # You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.
+  grayscale_cam = cam(input_tensor=input_tensor, targets=targets)
 
-# You can also get the model outputs without having to re-inference
-model_outputs = cam.outputs
+  # In this example grayscale_cam has only one image in the batch:
+  grayscale_cam = grayscale_cam[0, :]
+  visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
+
+  # You can also get the model outputs without having to redo inference
+  model_outputs = cam.outputs
 ```
 
+Cam.py has a more detailed usage example.
+
 ----------
 
 # Metrics and evaluating the explanations
@@ -179,18 +172,42 @@ from pytorch_grad_cam.metrics.road import ROADMostRelevantFirstAverage,
 cam_metric = ROADCombined(percentiles=[20, 40, 60, 80])
 scores = cam_metric(input_tensor, grayscale_cams, targets, model)
 ```
+
 ----------
 
 
 # Advanced use cases and tutorials:
 
-You can use this package for "custom" deep learning models, for example Object Detection or Semantic Segmentation.
+Methods like GradCAM were designed for and were originally mostly applied on classification models, 
+and specifically CNN classification models.
+However you can also use this package on new architectures like Vision Transformers, and on non classification tasks like Object Detection or Semantic Segmentation.
+
+The be able to adapt to non standard cases, we have two concepts.
+- The reshape transform - how do we convert activations to represent spatial images ?
+- The model targets - What exactly should the explainability method try to explain ?
+
+## The reshape transform
+In a CNN the intermediate activations in the model are a mult-channel image that have the dimensions channel x rows x cols,
+and the various explainabiltiy methods work with these to produce a new image.
+
+In case of another architecture, like the Vision Transformer, the shape might be different, like (rows x cols + 1) x channels, or something else.
+The reshape transform converts the activations back into a multi-channel image, for example by removing the class token in a vision transformer. 
+For examples, check [here](https://github.com/jacobgil/pytorch-grad-cam/blob/master/pytorch_grad_cam/utils/reshape_transforms.py)
+
+## Model Targets
+The model target is just a callable that is able to get the model output, and filter it out for the specific scalar output we want to explain.
+
+For classification tasks, the model target will typically be the output from a specific category.
+The `targets` parameter passed to the CAM method can then use `ClassifierOutputTarget`:
+```python
+targets = [ClassifierOutputTarget(281)]
+```
 
+However more advanced cases, you might want another behaviour.
+Check [here](https://github.com/jacobgil/pytorch-grad-cam/blob/master/pytorch_grad_cam/utils/model_targets.py) for more examples.
 
-You will have to define objects that you can then pass to the CAM algorithms:
-1. A reshape_transform, that aggregates the layer outputs into 2D tensors that will be displayed.
-2. Model Targets, that define what target do you want to compute the visualizations for, for example a specific category, or a list of bounding boxes.
 
+# Tutorials
 Here you can find detailed examples of how to use this for various custom use cases like object detection:
 
 These point to the new documentation jupter-book for fast rendering.