From 35d0371955e24f638202356013726b0cb9ec976b Mon Sep 17 00:00:00 2001
From: yh_cc <poemsmileyh@gmail.com>
Date: Sat, 5 Dec 2020 13:33:02 +0800
Subject: [PATCH] update DistTrainer

---
 fastNLP/core/dist_trainer.py | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/fastNLP/core/dist_trainer.py b/fastNLP/core/dist_trainer.py
index a76d0a05..bd7ba423 100644
--- a/fastNLP/core/dist_trainer.py
+++ b/fastNLP/core/dist_trainer.py
@@ -109,6 +109,7 @@ class DistTrainer():
         :param kwargs: 支持配置可选参数
             bool test_use_tqdm: 在dev上验证的时候是否开启tqdm
             Sampler test_sampler: 在evaluate的时候使用的sampler
+            int dev_batch_size: 在evaluate时，使用的evaluate的batch大小
         """
         assert device in ['auto', 'cuda', 'cpu'], "Please set correct device in [auto', 'cuda', 'cpu']"
         if device == 'auto':
@@ -172,17 +173,14 @@ class DistTrainer():
         self.batch_size = self.world_size * self.batch_size_per_gpu
         self.n_steps = self._get_n_steps()
 
-        if 'test_use_tqdm' in kwargs:
-            test_use_tqdm = kwargs.get('test_use_tqdm')
-        else:
-            test_use_tqdm = self.use_tqdm
-
+        self.test_use_tqdm = kwargs.get('test_use_tqdm', self.use_tqdm)
+        dev_batch_size = kwargs.get('dev_batch_size', batch_size_per_gpu)
         # for evaluation, only run eval on master proc
         if dev_data and metrics:
             cb = _TesterCallback(
                 dev_data, model, metrics,
-                batch_size=batch_size_per_gpu, num_workers=num_workers, sampler=kwargs.get('test_sampler', None),
-                use_tqdm=test_use_tqdm)
+                batch_size=dev_batch_size, num_workers=num_workers, sampler=kwargs.get('test_sampler', None),
+                use_tqdm=self.test_use_tqdm)
             self.test_manager.add_callback([cb], master=True)
 
         # Setup logging
@@ -379,11 +377,11 @@ class DistTrainer():
 
                 self.callback_manager.on_batch_end()
 
-                if (self.validate_every > 0 and self.step % self.validate_every == 0):
+                if (self.validate_every > 0 and self.step % self.validate_every == 0) and len(self.test_manager.callbacks):
                     self._do_validation()
 
             # ================= mini-batch end ==================== #
-            if self.validate_every < 0:
+            if self.validate_every < 0 and len(self.test_manager.callbacks):
                 self._do_validation()
 
             # lr decay; early stopping