Fix model namings

app.py

@@ -41,9 +41,9 @@ tfms = transforms.Compose([
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
 ])
-model = DeePixBiS(pretrained=False)
-model.load_state_dict(torch.load('./DeePixBiS/DeePixBiS.pth'))
-model.eval()
+deepix_model = DeePixBiS(pretrained=False)
+deepix_model.load_state_dict(torch.load('./DeePixBiS/DeePixBiS.pth'))
+deepix_model.eval()
 
 
 depth_config_path = 'tddfa/configs/mb05_120x120.yml'  # 'tddfa/configs/mb1_120x120.yml
@@ -51,12 +51,12 @@ cfg = yaml.load(open(depth_config_path), Loader=yaml.SafeLoader)
 tddfa = TDDFA(gpu_mode=False, **cfg)
 
 
-model = CDCN_u(basic_conv=Conv2d_cd, theta=0.7)
-model = model.to(device)
+cdcn_model = CDCN_u(basic_conv=Conv2d_cd, theta=0.7)
+cdcn_model = cdcn_model.to(device)
 weights = torch.load('./DSDG/DUM/checkpoint/CDCN_U_P1_updated.pkl', map_location=device)
-model.load_state_dict(weights)
-optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=0.00005)
-model.eval()
+cdcn_model.load_state_dict(weights)
+optimizer = optim.Adam(cdcn_model.parameters(), lr=0.001, weight_decay=0.00005)
+cdcn_model.eval()
 
 
 class Normaliztion_valtest(object):
@@ -119,7 +119,7 @@ def inference(img, model_name):
         faceRegion = faceRegion.unsqueeze(0)
 
         if model_name == 'DeePixBiS':
-            mask, binary = model.forward(faceRegion)
+            mask, binary = deepix_model.forward(faceRegion)
             res = torch.mean(mask).item()
             cls = 'Real' if res >= pix_threshhold else 'Spoof'
             res = 1 - res
@@ -144,7 +144,7 @@ def inference(img, model_name):
                 
                 map_score = 0.0
                 for frame_t in range(inputs.shape[1]):
-                    mu, logvar, map_x, x_concat, x_Block1, x_Block2, x_Block3, x_input = model(inputs[:, frame_t, :, :, :])
+                    mu, logvar, map_x, x_concat, x_Block1, x_Block2, x_Block3, x_input = cdcn_model(inputs[:, frame_t, :, :, :])
 
                     score_norm = torch.sum(mu) / torch.sum(test_maps[:, frame_t, :, :])
                     map_score += score_norm