fix a bug of train with test

opengait/modeling/base_model.py

@@ -144,7 +144,7 @@ class BaseModel(MetaModel, nn.Module):
 
         self.build_network(cfgs['model_cfg'])
         self.init_parameters()
-        self.seq_trfs = get_transform(self.engine_cfg['transform'])
+        self.trainer_trfs = get_transform(cfgs['trainer_cfg']['transform'])
 
         self.msg_mgr.log_info(cfgs['data_cfg'])
         if training:
@@ -153,6 +153,8 @@ class BaseModel(MetaModel, nn.Module):
         if not training or self.engine_cfg['with_test']:
             self.test_loader = self.get_loader(
                 cfgs['data_cfg'], train=False)
+            self.evaluator_trfs = get_transform(
+                cfgs['evaluator_cfg']['transform'])
 
         self.device = torch.distributed.get_rank()
         torch.cuda.set_device(self.device)
@@ -300,7 +302,7 @@ class BaseModel(MetaModel, nn.Module):
             tuple: training data including inputs, labels, and some meta data.
         """
         seqs_batch, labs_batch, typs_batch, vies_batch, seqL_batch = inputs
-        seq_trfs = self.seq_trfs
+        seq_trfs = self.trainer_trfs if self.training else self.evaluator_trfs
         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)))