fix gaitedge config and add check

for num of transform

configs/gaitedge/phase2_e2e.yaml

@@ -70,8 +70,8 @@ trainer_cfg:
   scheduler_reset: true
   sync_BN: true
   restore_hint:
-    - /home/leeeung/workspace/OpenGait/output/CASIA-B_new/Segmentation/Segmentation/checkpoints/Segmentation-25000.pt
-    - /home/leeeung/OpenGait/output/CASIA-B_new/GaitGL/GaitGL/checkpoints/GaitGL-80000.pt
+    - Segmentation-25000.pt
+    - GaitGL-80000.pt
   save_iter: 2000
   save_name: GaitGL_E2E
   total_iter: 20000

configs/gaitedge/phase2_gaitedge.yaml

@@ -70,8 +70,8 @@ trainer_cfg:
   optimizer_reset: true
   scheduler_reset: true
   sync_BN: true
-  restore_hint:
-    - Segmentation-30000.pt
+  restore_hint: 
+    - Segmentation-25000.pt
     - GaitGL-80000.pt
   save_iter: 2000
   save_name: GaitEdge

opengait/modeling/base_model.py

@@ -301,7 +301,9 @@ class BaseModel(MetaModel, nn.Module):
         seqs_batch, labs_batch, typs_batch, vies_batch, seqL_batch = inputs
         trf_cfgs = self.engine_cfg['transform']
         seq_trfs = get_transform(trf_cfgs)
-
+        if len(seqs_batch) != len(seq_trfs):
+            raise ValueError(
+                "The number of types of input data and transform should be same. But got {} and {}".format(len(seqs_batch), len(seq_trfs)))
         requires_grad = bool(self.training)
         seqs = [np2var(np.asarray([trf(fra) for fra in seq]), requires_grad=requires_grad).float()
                 for trf, seq in zip(seq_trfs, seqs_batch)]

opengait/modeling/models/gaitedge.py

@@ -50,7 +50,7 @@ class GaitEdge(GaitGL):
         self.is_edge = model_cfg['edge']
         self.seg_lr = model_cfg['seg_lr']
         self.kernel = torch.ones(
-            (model_cfg['kernel_size'], model_cfg['kernel_size'])).cuda()
+            (model_cfg['kernel_size'], model_cfg['kernel_size']))
 
     def finetune_parameters(self):
         fine_tune_params = list()