Update README.md

README.md

@@ -1,3 +1,60 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+To use Sum-of-Parts(SOP), you would need to install exlib. Currently SOP is only available on the dev branch https://github.com/BrachioLab/exlib/tree/dev
+
+To use SOP trained for `google/vit-base-patch16-224`, follow the following code.
+
+### Load the model
+```
+import torch
+import os
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+import sys
+from exlib.modules.sop import WrappedModel, SOPConfig, SOPImageCls, get_chained_attr
+
+
+# init backbone model
+backbone_model = AutoModelForImageClassification.from_pretrained('google/vit-base-patch16-224')
+processor = AutoImageProcessor.from_pretrained('google/vit-base-patch16-224')
+
+# get needed wrapped models
+original_model = WrappedModel(backbone_model, output_type='logits')
+wrapped_backbone_model = WrappedModel(backbone_model, output_type='tuple')
+projection_layer = WrappedModel(wrapped_backbone_model, output_type='hidden_states')
+
+# load trained sop model
+model = SOPImageCls.from_pretrained('BrachioLab/sop-vit-base-patch16-224', 
+                                    blackbox_model=wrapped_backbone_model, 
+                                    projection_layer=projection_layer)
+model.eval();
+```
+
+### Open an image
+```
+from PIL import Image
+
+# Open an example image
+# image_path = '../../examples/ILSVRC2012_val_00000873.JPEG'
+image_path = '../../examples/ILSVRC2012_val_00000247.JPEG'
+image = Image.open(image_path)
+image.show()
+image_rgb = image.convert("RGB")
+inputs = torch.tensor(processor(image_rgb)['pixel_values'])
+inputs.shape # (1, 3, 224, 224)
+```
+
+### Get the output from SOP
+```
+# Get the outputs from the model
+outputs = model(inputs, return_tuple=True)
+```
+
+### Show the groups
+```
+from exlib.modules.sop import show_masks_weights
+
+show_masks_weights(inputs, outputs, i=0) # This allows you to see the group masks with group attribution scores.
+```