update readme!

Author: yifanfeng97

HyperG/utils/meter/transductive.py

@@ -18,7 +18,6 @@ def trans_iou_socre(pred, target, mask):
         target_idx = target == _c
         intersection = (pred_idx & target_idx).sum().float().item()
         union = (pred_idx | target_idx).sum().float().item()
-        ious.append((intersection + 1e-6)/(union + 1e-6))
+        ious.append((intersection + 1e-6) / (union + 1e-6))
 
     return ious
-

README.md

@@ -1,2 +1,42 @@
 #Hypergraph Toolbox: HyperG
-A toolbox for hypergraph including edge construction, graph construction and graph/hypergraph convolution.
+**HyperG** is a python toolbox for hypergraph-based deep learning, which is built upon [pytorch](https://pytorch.org/). 
+ Edge in hypergraph named hyperedge can link more than two nodes, which allows hyperedge to express more than pair-wise 
+ relation(like: entity-attribute relation, group relation, hierarchical relation and so on.). Thus, hypergraph owns more 
+ powerful model ability than common graph. 
+ 
+ It consists of sparse hypergraph construction, fusion, convolution operations, convenient util functions for medical
+ image(MRI, Pathology, etc.), 3D(point cloud, view-based graph, etc.) and other hypergraph applications(to be continue...).
+ Hypergrpah inductive learning and hypergraph transductive learning examples is also included in this toolbox. What's more,
+ we write several examples that deploy hypergraph in different tasks like: Classification, Segmentation and Regression.    
+ 
+ The supported operations include:
+ 
+ * **Hyperedge base operations**: compute hyperedge/node degree, add/remove hypergraph self loop, count hyperedge/node number,
+ 
+ * **Hyperedge construction operations**: construct hyperedge group from grid-like structure (image) with spatial neighbors, 
+ construct hyperedge group from feature spatial neighbors. K Nearest Neighbors algorithm is supported here.
+ 
+ * **Hyperedge group/Hypergraph fusion operations**: fusion hypergraphs with concatenate constructed hypergraph incidence matrix.
+ 
+ * **Hypergraph Convolution**: the common hyconv(hypergrpah convolution) ([Feng et al. AAAI2019](https://github.com/iMoonLab/HGNN)) 
+ is implemented here.
+ 
+ * **models**: HGNN([Feng et al. AAAI2019](https://github.com/iMoonLab/HGNN)) with two hyconv layers, ResNet(18, 34, 50, 101, 152)
+ ([He et al.](https://arxiv.org/abs/1512.03385)), and ResNet_HGNN a combination of ResNet and HGNN for image input and real-time
+ construct hypergraph supported.
+ 
+ * **utils**: some convenient util functions(to be continue... ):
+    * **data**: multiple modality data supported (to be continue...)
+        * **mri**: mri series read and write functions.
+        * **pathology**: sample patches from WSI slide return patch coordinates(left top point) and patch width and height. 
+        draw sampled patches on WSI slide function for overview or visualization.
+    * **meter**: evaluate meters in hypergraph learning.
+        * **inductive**:  *C-Index Meter* for survival prediction.
+        * **transductive**: compute class accuracy in classification task for transductive learning, compute IOU Score for 
+        segmentation task in transductive learning. 
+    * **visualization**: some visualization functions.
+        * **transductive**: visualize segmentation result in transductive learning. 
+    
+ 
+  
+ including edge construction, graph construction and graph/hypergraph convolution.

dist/HyperG-yifanfeng-0.0.1.tar.gz

@@ -13,7 +13,7 @@
 from HyperG.utils.data.pathology import sample_patch_coors, draw_patches_on_slide
 
 
-def split_train_val(data_root, ratio=0.8, save_split_dir=None, resplit=False):
+def split_train_val(data_root, ratio=0.8, save_split_dir=None, resplit=True):
     if not resplit and save_split_dir is not None and osp.exists(save_split_dir):
         with open(save_split_dir, 'rb') as f:
             result = pickle.load(f)