update

app.py

@@ -19,14 +19,14 @@ with open('shape_names.txt') as f:
 
 model_gda = GDANET()
 model_gda = nn.DataParallel(model_gda)
-model_gda.load_state_dict(torch.load('./GDANet_WOLFMix.t7', map_location=torch.device('cpu')))
-# model_gda.load_state_dict(torch.load('/Users/renjiawei/Downloads/pretrained_models/GDANet_WOLFMix.t7', map_location=torch.device('cpu')))
+# model_gda.load_state_dict(torch.load('./GDANet_WOLFMix.t7', map_location=torch.device('cpu')))
+model_gda.load_state_dict(torch.load('/Users/renjiawei/Downloads/pretrained_models/GDANet_WOLFMix.t7', map_location=torch.device('cpu')))
 model_gda.eval()
 
 model_dgcnn = DGCNN()
 model_dgcnn = nn.DataParallel(model_dgcnn)
-model_dgcnn.load_state_dict(torch.load('./dgcnn.t7', map_location=torch.device('cpu')))
-# model_dgcnn.load_state_dict(torch.load('/Users/renjiawei/Downloads/pretrained_models/dgcnn.t7', map_location=torch.device('cpu')))
+# model_dgcnn.load_state_dict(torch.load('./dgcnn.t7', map_location=torch.device('cpu')))
+model_dgcnn.load_state_dict(torch.load('/Users/renjiawei/Downloads/pretrained_models/dgcnn.t7', map_location=torch.device('cpu')))
 model_dgcnn.eval()
 
 def pyplot_draw_point_cloud(points, corruption):
@@ -68,11 +68,11 @@ def load_dataset(corruption_idx, severity):
     ]
     corruption_type = corruptions[corruption_idx]
     if corruption_type == 'clean':
-        f = h5py.File(osp.join('modelnet_c', corruption_type + '.h5'))
-        # f = h5py.File(osp.join('/Users/renjiawei/Downloads/modelnet_c', corruption_type + '.h5'))
+        # f = h5py.File(osp.join('modelnet_c', corruption_type + '.h5'))
+        f = h5py.File(osp.join('/Users/renjiawei/Downloads/modelnet_c', corruption_type + '.h5'))
     else:
-        f = h5py.File(osp.join('modelnet_c', corruption_type + '_{}'.format(severity-1) + '.h5'))
-        # f = h5py.File(osp.join('/Users/renjiawei/Downloads/modelnet_c', corruption_type + '_{}'.format(severity - 1) + '.h5'))
+        # f = h5py.File(osp.join('modelnet_c', corruption_type + '_{}'.format(severity-1) + '.h5'))
+        f = h5py.File(osp.join('/Users/renjiawei/Downloads/modelnet_c', corruption_type + '_{}'.format(severity - 1) + '.h5'))
     data = f['data'][:].astype('float32')
     label = f['label'][:].astype('int64')
     f.close()
@@ -98,16 +98,15 @@ def run(seed, corruption_idx, severity):
 
 description = """
 
-Welcome to the demo of PointCloud-C! PointCloud-C is the very first test-suite for point cloud robustness analysis under corruptions.
-
-In this demo, you may:
+Welcome to the demo of PointCloud-C! [PointCloud-C](https://pointcloud-c.github.io/home.html) is a test-suite for point cloud robustness analysis under corruptions. In this demo, you may:
 
 - __Visualize__ various types of corrupted point clouds in [ModelNet-C](https://github.com/jiawei-ren/ModelNet-C).
 
 - __Compare__ our proposed techniques to the baseline in terms of prediction robustness.
 
-For more details, checkout our paper [Benchmarking and Analyzing Point Cloud Classification under Corruptions, __ICML 2022__](https://arxiv.org/abs/2202.03377) and our [project page](https://pointcloud-c.github.io/home.html)!
+For more details, checkout our paper [Benchmarking and Analyzing Point Cloud Classification under Corruptions, __ICML 2022__](https://arxiv.org/abs/2202.03377)!
 
+📣 News: [The first PointCloud-C challenge](https://codalab.lisn.upsaclay.fr/competitions/6437) with Classification track and Part Segmentation track in [ECCV'22 SenseHuman workshop](https://sense-human.github.io/) is open for submission now!
 """