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()