From 2942cfe616aa2f27500e195b6cb18a6c27da4087 Mon Sep 17 00:00:00 2001
From: chenguowei01 <chenguowei01@baidu.com>
Date: Tue, 12 May 2020 16:34:28 +0800
Subject: [PATCH] update visualdl

---
 contrib/HumanSeg/models/humanseg.py | 26 +++++++++-----------------
 contrib/HumanSeg/requirements.txt   |  8 +-------
 2 files changed, 10 insertions(+), 24 deletions(-)

diff --git a/contrib/HumanSeg/models/humanseg.py b/contrib/HumanSeg/models/humanseg.py
index cca23954..028fab16 100644
--- a/contrib/HumanSeg/models/humanseg.py
+++ b/contrib/HumanSeg/models/humanseg.py
@@ -493,10 +493,7 @@ class SegModel(object):
         if use_vdl:
             from visualdl import LogWriter
             vdl_logdir = osp.join(save_dir, 'vdl_log')
-            log_writer = LogWriter(vdl_logdir, sync_cycle=20)
-            train_step_component = OrderedDict()
-            eval_component = OrderedDict()
-
+            log_writer = LogWriter(vdl_logdir)
         best_miou = -1.0
         best_model_epoch = 1
         for i in range(self.begin_epoch, num_epochs):
@@ -527,13 +524,10 @@ class SegModel(object):
 
                     if use_vdl:
                         for k, v in step_metrics.items():
-                            if k not in train_step_component.keys():
-                                with log_writer.mode('Each_step_while_Training'
-                                                     ) as step_logger:
-                                    train_step_component[
-                                        k] = step_logger.scalar(
-                                            'Training: {}'.format(k))
-                            train_step_component[k].add_record(num_steps, v)
+                            log_writer.add_scalar(
+                                step=num_steps,
+                                tag='train/{}'.format(k),
+                                value=v)
 
                     # 计算剩余时间
                     avg_step_time = np.mean(time_stat)
@@ -587,12 +581,10 @@ class SegModel(object):
                             if isinstance(v, np.ndarray):
                                 if v.size > 1:
                                     continue
-                            if k not in eval_component:
-                                with log_writer.mode('Each_Epoch_on_Eval_Data'
-                                                     ) as eval_logger:
-                                    eval_component[k] = eval_logger.scalar(
-                                        'Evaluation: {}'.format(k))
-                            eval_component[k].add_record(i + 1, v)
+                            log_writer.add_scalar(
+                                step=num_steps,
+                                tag='evaluate/{}'.format(k),
+                                value=v)
                 self.save_model(save_dir=current_save_dir)
                 time_eval_one_epoch = time.time() - eval_epoch_start_time
                 if eval_dataset is not None:
diff --git a/contrib/HumanSeg/requirements.txt b/contrib/HumanSeg/requirements.txt
index e90d7dab..ab6d60ab 100644
--- a/contrib/HumanSeg/requirements.txt
+++ b/contrib/HumanSeg/requirements.txt
@@ -1,10 +1,4 @@
-pre-commit
-yapf == 0.26.0
-flake8
 pyyaml >= 5.1
-visualdl >= 1.3.0
+visualdl == 2.0.0-alpha.1
 Pillow
-numpy
-six
-opencv-python
 tqdm
-- 
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