diff --git a/paddle3d/apis/trainer.py b/paddle3d/apis/trainer.py
index 84358932..bfbb25a6 100644
--- a/paddle3d/apis/trainer.py
+++ b/paddle3d/apis/trainer.py
@@ -113,76 +113,79 @@ def __init__(
             **dataloader_fn) if isinstance(dataloader_fn,
                                            dict) else dataloader_fn
 
-        self.train_dataloader = _dataloader_build_fn(train_dataset, self.model)
+        self.train_dataloader = _dataloader_build_fn(
+            train_dataset, self.model) if train_dataset else None
         self.eval_dataloader = _dataloader_build_fn(
             val_dataset, self.model) if val_dataset else None
         self.val_dataset = val_dataset
 
-        self.resume = resume
-        vdl_file_name = None
-        self.iters_per_epoch = len(self.train_dataloader)
+        if train_dataset:
+            self.resume = resume
+            vdl_file_name = None
+            self.iters_per_epoch = len(self.train_dataloader)
 
-        if iters is None:
-            self.epochs = epochs
-            self.iters = epochs * self.iters_per_epoch
-            self.train_by_epoch = True
-        else:
-            self.iters = iters
-            self.epochs = (iters - 1) // self.iters_per_epoch + 1
-            self.train_by_epoch = False
-
-        self.cur_iter = 0
-        self.cur_epoch = 0
+            if iters is None:
+                self.epochs = epochs
+                self.iters = epochs * self.iters_per_epoch
+                self.train_by_epoch = True
+            else:
+                self.iters = iters
+                self.epochs = (iters - 1) // self.iters_per_epoch + 1
+                self.train_by_epoch = False
 
-        if self.optimizer.__class__.__name__ == 'OneCycleAdam':
-            self.optimizer.before_run(max_iters=self.iters)
+            self.cur_iter = 0
+            self.cur_epoch = 0
 
-        self.checkpoint = default_checkpoint_build_fn(
-            **checkpoint) if isinstance(checkpoint, dict) else checkpoint
+            if self.optimizer.__class__.__name__ == 'OneCycleAdam':
+                self.optimizer.before_run(max_iters=self.iters)
 
-        if isinstance(scheduler, dict):
-            scheduler.setdefault('train_by_epoch', self.train_by_epoch)
-            scheduler.setdefault('iters_per_epoch', self.iters_per_epoch)
-            self.scheduler = default_scheduler_build_fn(**scheduler)
-        else:
-            self.scheduler = scheduler
-
-        if self.checkpoint is None:
-            return
-
-        if not self.checkpoint.empty:
-            if not resume:
-                raise RuntimeError(
-                    'The checkpoint {} is not emtpy! Set `resume=True` to continue training or use another dir as checkpoint'
-                    .format(self.checkpoint.rootdir))
-
-            if self.checkpoint.meta.get(
-                    'train_by_epoch') != self.train_by_epoch:
-                raise RuntimeError(
-                    'Unable to resume training since the train_by_epoch is inconsistent with that saved in the checkpoint'
-                )
-
-            params_dict, opt_dict = self.checkpoint.get()
-            self.model.set_dict(params_dict)
-            self.optimizer.set_state_dict(opt_dict)
-            self.cur_iter = self.checkpoint.meta.get('iters')
-            self.cur_epoch = self.checkpoint.meta.get('epochs')
-            self.scheduler.step(self.cur_iter)
-
-            logger.info(
-                'Resume model from checkpoint {}, current iter set to {}'.
-                format(self.checkpoint.rootdir, self.cur_iter))
-            vdl_file_name = self.checkpoint.meta['vdl_file_name']
-        elif resume:
-            logger.warning(
-                "Attempt to restore parameters from an empty checkpoint")
+            self.checkpoint = default_checkpoint_build_fn(
+                **checkpoint) if isinstance(checkpoint, dict) else checkpoint
 
-        if env.local_rank == 0:
-            self.log_writer = LogWriter(
-                logdir=self.checkpoint.rootdir, file_name=vdl_file_name)
-            self.checkpoint.record('vdl_file_name',
-                                   os.path.basename(self.log_writer.file_name))
-            self.checkpoint.record('train_by_epoch', self.train_by_epoch)
+            if isinstance(scheduler, dict):
+                scheduler.setdefault('train_by_epoch', self.train_by_epoch)
+                scheduler.setdefault('iters_per_epoch', self.iters_per_epoch)
+                self.scheduler = default_scheduler_build_fn(**scheduler)
+            else:
+                self.scheduler = scheduler
+
+            if self.checkpoint is None:
+                return
+
+            if not self.checkpoint.empty:
+                if not resume:
+                    raise RuntimeError(
+                        'The checkpoint {} is not emtpy! Set `resume=True` to continue training or use another dir as checkpoint'
+                        .format(self.checkpoint.rootdir))
+
+                if self.checkpoint.meta.get(
+                        'train_by_epoch') != self.train_by_epoch:
+                    raise RuntimeError(
+                        'Unable to resume training since the train_by_epoch is inconsistent with that saved in the checkpoint'
+                    )
+
+                params_dict, opt_dict = self.checkpoint.get()
+                self.model.set_dict(params_dict)
+                self.optimizer.set_state_dict(opt_dict)
+                self.cur_iter = self.checkpoint.meta.get('iters')
+                self.cur_epoch = self.checkpoint.meta.get('epochs')
+                self.scheduler.step(self.cur_iter)
+
+                logger.info(
+                    'Resume model from checkpoint {}, current iter set to {}'.
+                    format(self.checkpoint.rootdir, self.cur_iter))
+                vdl_file_name = self.checkpoint.meta['vdl_file_name']
+            elif resume:
+                logger.warning(
+                    "Attempt to restore parameters from an empty checkpoint")
+
+            if env.local_rank == 0:
+                self.log_writer = LogWriter(
+                    logdir=self.checkpoint.rootdir, file_name=vdl_file_name)
+                self.checkpoint.record(
+                    'vdl_file_name',
+                    os.path.basename(self.log_writer.file_name))
+                self.checkpoint.record('train_by_epoch', self.train_by_epoch)
 
     def train(self):
         """
diff --git a/tools/evaluate.py b/tools/evaluate.py
index 692dfa1b..e5a124f3 100644
--- a/tools/evaluate.py
+++ b/tools/evaluate.py
@@ -80,7 +80,8 @@ def main(args):
         'dataloader_fn': {
             'batch_size': batch_size,
             'num_workers': args.num_workers
-        }
+        },
+        'train_dataset': None
     })
 
     if args.model is not None: