Add visualdl. (#745)

Add image for visualdl board.

docs/tutorials/GETTING_STARTED.md

@@ -41,8 +41,8 @@ list below can be viewed by `--help`
 |    --draw_threshold      |      infer     |  Threshold to reserve the result for visualization  |  0.5  |  `--draw_threshold 0.7`  |
 |      --infer_dir         |       infer     |  Directory for images to perform inference on  |  None  |    |
 |      --infer_img         |       infer     |  Image path  |  None  |  higher priority over --infer_dir  |
-|        --use_tb          |   train/infer   |  Whether to record the data with [tb-paddle](https://github.com/linshuliang/tb-paddle), so as to display in Tensorboard  |  False  |      |
-|        --tb\_log_dir     |   train/infer   |  tb-paddle logging directory for image  |  train:`tb_log_dir/scalar` infer: `tb_log_dir/image`  |     |
+|        --use_vdl          |   train/infer   |  Whether to record the data with [VisualDL](https://github.com/paddlepaddle/visualdl), so as to display in VisualDL  |  False  |      |
+|        --vdl\_log_dir     |   train/infer   |  VisualDL logging directory for image  |  train:`vdl_log_dir/scalar` infer: `vdl_log_dir/image`  |     |
 
 
 ## Examples
@@ -163,7 +163,7 @@ moment, but it is a planned feature
                       --output_dir=infer_output/ \
                       --draw_threshold=0.5 \
                       -o weights=output/faster_rcnn_r50_1x/model_final \
-                      --use_tb=Ture
+                      --use_vdl=Ture
   ```
 
   `--draw_threshold` is an optional argument. Default is 0.5.

docs/tutorials/GETTING_STARTED_cn.md

@@ -38,8 +38,8 @@ python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_img=demo/0000005
 |    --draw_threshold      |      infer     |  可视化时分数阈值  |  0.5  |  `--draw_threshold 0.7`  |
 |      --infer_dir         |       infer     |  用于预测的图片文件夹路径  |  None  |    |
 |      --infer_img         |       infer     |  用于预测的图片路径  |  None  |  相较于`--infer_dir`具有更高优先级  |
-|        --use_tb          |   train/infer   |  是否使用[tb-paddle](https://github.com/linshuliang/tb-paddle)记录数据，进而在TensorBoard中显示  |  False  |      |
-|        --tb\_log_dir     |   train/infer   |  指定 tb-paddle 记录数据的存储路径  |  train:`tb_log_dir/scalar` infer: `tb_log_dir/image`  |     |
+|        --use_vdl          |   train/infer   |  是否使用[VisualDL](https://github.com/paddlepaddle/visualdl)记录数据，进而在VisualDL面板中显示  |  False  |      |
+|        --vdl\_log_dir     |   train/infer   |  指定 VisualDL 记录数据的存储路径  |  train:`vdl_log_dir/scalar` infer: `vdl_log_dir/image`  |     |
 
 
 ## 使用示例

docs/tutorials/QUICK_STARTED.md

@@ -22,20 +22,20 @@ Training:
 
 ```bash
 python -u tools/train.py -c configs/yolov3_mobilenet_v1_fruit.yml \
-                        --use_tb=True \
-                        --tb_log_dir=tb_fruit_dir/scalar \
+                        --use_vdl=True \
+                        --vdl_log_dir=vdl_fruit_dir/scalar \
                         --eval
 ```
 
-Use `yolov3_mobilenet_v1` to fine-tune the model from COCO dataset. Meanwhile, loss and mAP can be observed on tensorboard.  
+Use `yolov3_mobilenet_v1` to fine-tune the model from COCO dataset. Meanwhile, loss and mAP can be observed on VisualDL.
 
 ```bash
-tensorboard --logdir tb_fruit_dir/scalar/ --host <host_IP> --port <port_num>
+visualdl --logdir vdl_fruit_dir/scalar/ --host <host_IP> --port <port_num>
 ```
 
-Result on tensorboard is shown below:
+Result on VisualDL is shown below:
 
-![tensorboard_fruit.jpg](../images/tensorboard_fruit.jpg)
+![visualdl_fruit.jpg](../images/visualdl_fruit.jpg)
 
 Model can be downloaded [here](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_fruit.tar)
 

docs/tutorials/QUICK_STARTED_cn.md

@@ -24,21 +24,21 @@ python dataset/fruit/download_fruit.py
 
 ```bash
 python -u tools/train.py -c configs/yolov3_mobilenet_v1_fruit.yml \
-                        --use_tb=True \
-                        --tb_log_dir=tb_fruit_dir/scalar \
+                        --use_vdl=True \
+                        --vdl_log_dir=vdl_fruit_dir/scalar \
                         --eval
 ```
 
-训练使用`yolov3_mobilenet_v1`基于COCO数据集训练好的模型进行finetune。训练期间可以通过tensorboard实时观察loss和精度值，启动命令如下：
+训练使用`yolov3_mobilenet_v1`基于COCO数据集训练好的模型进行finetune。训练期间可以通过VisualDL实时观察loss和精度值，启动命令如下：
 
 ```bash
-tensorboard --logdir tb_fruit_dir/scalar/ --host <host_IP> --port <port_num>
+visualdl --logdir vdl_fruit_dir/scalar/ --host <host_IP> --port <port_num>
 ```
 
-tensorboard结果显示如下：
+VisualDL结果显示如下：
 
 
-![](../images/tensorboard_fruit.jpg)
+![](../images/visualdl_fruit.jpg)
 
 训练模型[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_fruit.tar)
 

requirements.txt

 tqdm
 docstring_parser @ http://github.com/willthefrog/docstring_parser/tarball/master
 typeguard ; python_version >= '3.4'
-tb-paddle
-tensorboard >= 1.15
+visualdl>=2.0.0b
 opencv-python
 PyYAML
 shapely

slim/prune/prune.py

@@ -244,12 +244,12 @@ def main():
     time_stat = deque(maxlen=cfg.log_smooth_window)
     best_box_ap_list = [0.0, 0]  #[map, iter]
 
-    # use tb-paddle to log data
-    if FLAGS.use_tb:
-        from tb_paddle import SummaryWriter
-        tb_writer = SummaryWriter(FLAGS.tb_log_dir)
-        tb_loss_step = 0
-        tb_mAP_step = 0
+    # use VisualDL to log data
+    if FLAGS.use_vdl:
+        from visualdl import LogWriter
+        vdl_writer = LogWriter(FLAGS.vdl_log_dir)
+        vdl_loss_step = 0
+        vdl_mAP_step = 0
 
     if FLAGS.eval:
         resolution = None
@@ -286,12 +286,12 @@ def main():
         outs = exe.run(compiled_train_prog, fetch_list=train_values)
         stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
 
-        # use tb-paddle to log loss
-        if FLAGS.use_tb:
+        # use VisualDL to log loss
+        if FLAGS.use_vdl:
             if it % cfg.log_iter == 0:
                 for loss_name, loss_value in stats.items():
-                    tb_writer.add_scalar(loss_name, loss_value, tb_loss_step)
-                tb_loss_step += 1
+                    vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step)
+                vdl_loss_step += 1
 
         train_stats.update(stats)
         logs = train_stats.log()
@@ -329,10 +329,10 @@ def main():
                     map_type,
                     dataset=dataset)
 
-                # use tb_paddle to log mAP
-                if FLAGS.use_tb:
-                    tb_writer.add_scalar("mAP", box_ap_stats[0], tb_mAP_step)
-                    tb_mAP_step += 1
+                # use VisualDL to log mAP
+                if FLAGS.use_vdl:
+                    vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step)
+                    vdl_mAP_step += 1
 
                 if box_ap_stats[0] > best_box_ap_list[0]:
                     best_box_ap_list[0] = box_ap_stats[0]
@@ -374,15 +374,15 @@ if __name__ == '__main__':
         type=str,
         help="Evaluation directory, default is current directory.")
     parser.add_argument(
-        "--use_tb",
+        "--use_vdl",
         type=bool,
         default=False,
-        help="whether to record the data to Tensorboard.")
+        help="whether to record the data to VisualDL.")
     parser.add_argument(
-        '--tb_log_dir',
+        '--vdl_log_dir',
         type=str,
-        default="tb_log_dir/scalar",
-        help='Tensorboard logging directory for scalar.')
+        default="vdl_log_dir/scalar",
+        help='VisualDL logging directory for scalar.')
 
     parser.add_argument(
         "-p",

tools/infer.py

@@ -158,12 +158,12 @@ def main():
             callable(model.is_bbox_normalized):
         is_bbox_normalized = model.is_bbox_normalized()
 
-    # use tb-paddle to log image
-    if FLAGS.use_tb:
-        from tb_paddle import SummaryWriter
-        tb_writer = SummaryWriter(FLAGS.tb_log_dir)
-        tb_image_step = 0
-        tb_image_frame = 0  # each frame can display ten pictures at most. 
+    # use VisualDL to log image
+    if FLAGS.use_vdl:
+        from visualdl import LogWriter
+        vdl_writer = LogWriter(FLAGS.vdl_log_dir)
+        vdl_image_step = 0
+        vdl_image_frame = 0  # each frame can display ten pictures at most.
 
     imid2path = dataset.get_imid2path()
     for iter_id, data in enumerate(loader()):
@@ -191,32 +191,27 @@ def main():
             image_path = imid2path[int(im_id)]
             image = Image.open(image_path).convert('RGB')
 
-            # use tb-paddle to log original image           
-            if FLAGS.use_tb:
+            # use VisualDL to log original image
+            if FLAGS.use_vdl:
                 original_image_np = np.array(image)
-                tb_writer.add_image(
-                    "original/frame_{}".format(tb_image_frame),
-                    original_image_np,
-                    tb_image_step,
-                    dataformats='HWC')
+                vdl_writer.add_image(
+                    "original/frame_{}".format(vdl_image_frame),
+                    original_image_np, vdl_image_step)
 
             image = visualize_results(image,
                                       int(im_id), catid2name,
                                       FLAGS.draw_threshold, bbox_results,
                                       mask_results)
 
-            # use tb-paddle to log image with bbox
-            if FLAGS.use_tb:
+            # use VisualDL to log image with bbox
+            if FLAGS.use_vdl:
                 infer_image_np = np.array(image)
-                tb_writer.add_image(
-                    "bbox/frame_{}".format(tb_image_frame),
-                    infer_image_np,
-                    tb_image_step,
-                    dataformats='HWC')
-                tb_image_step += 1
-                if tb_image_step % 10 == 0:
-                    tb_image_step = 0
-                    tb_image_frame += 1
+                vdl_writer.add_image("bbox/frame_{}".format(vdl_image_frame),
+                                     infer_image_np, vdl_image_step)
+                vdl_image_step += 1
+                if vdl_image_step % 10 == 0:
+                    vdl_image_step = 0
+                    vdl_image_frame += 1
 
             save_name = get_save_image_name(FLAGS.output_dir, image_path)
             logger.info("Detection bbox results save in {}".format(save_name))
@@ -246,14 +241,14 @@ if __name__ == '__main__':
         default=0.5,
         help="Threshold to reserve the result for visualization.")
     parser.add_argument(
-        "--use_tb",
+        "--use_vdl",
         type=bool,
         default=False,
-        help="whether to record the data to Tensorboard.")
+        help="whether to record the data to VisualDL.")
     parser.add_argument(
-        '--tb_log_dir',
+        '--vdl_log_dir',
         type=str,
-        default="tb_log_dir/image",
-        help='Tensorboard logging directory for image.')
+        default="vdl_log_dir/image",
+        help='VisualDL logging directory for image.')
     FLAGS = parser.parse_args()
     main()

tools/train.py

@@ -222,12 +222,12 @@ def main():
     time_stat = deque(maxlen=cfg.log_smooth_window)
     best_box_ap_list = [0.0, 0]  #[map, iter]
 
-    # use tb-paddle to log data
-    if FLAGS.use_tb:
-        from tb_paddle import SummaryWriter
-        tb_writer = SummaryWriter(FLAGS.tb_log_dir)
-        tb_loss_step = 0
-        tb_mAP_step = 0
+    # use VisualDL to log data
+    if FLAGS.use_vdl:
+        from visualdl import LogWriter
+        vdl_writer = LogWriter(FLAGS.vdl_log_dir)
+        vdl_loss_step = 0
+        vdl_mAP_step = 0
 
     for it in range(start_iter, cfg.max_iters):
         start_time = end_time
@@ -239,12 +239,12 @@ def main():
         outs = exe.run(compiled_train_prog, fetch_list=train_values)
         stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
 
-        # use tb-paddle to log loss
-        if FLAGS.use_tb:
+        # use vdl-paddle to log loss
+        if FLAGS.use_vdl:
             if it % cfg.log_iter == 0:
                 for loss_name, loss_value in stats.items():
-                    tb_writer.add_scalar(loss_name, loss_value, tb_loss_step)
-                tb_loss_step += 1
+                    vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step)
+                vdl_loss_step += 1
 
         train_stats.update(stats)
         logs = train_stats.log()
@@ -287,10 +287,10 @@ def main():
                     is_bbox_normalized, FLAGS.output_eval, map_type,
                     cfg['EvalReader']['dataset'])
 
-                # use tb_paddle to log mAP
-                if FLAGS.use_tb:
-                    tb_writer.add_scalar("mAP", box_ap_stats[0], tb_mAP_step)
-                    tb_mAP_step += 1
+                # use vdl_paddle to log mAP
+                if FLAGS.use_vdl:
+                    vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step)
+                    vdl_mAP_step += 1
 
                 if box_ap_stats[0] > best_box_ap_list[0]:
                     best_box_ap_list[0] = box_ap_stats[0]
@@ -335,15 +335,15 @@ if __name__ == '__main__':
         type=str,
         help="Evaluation directory, default is current directory.")
     parser.add_argument(
-        "--use_tb",
+        "--use_vdl",
         type=bool,
         default=False,
-        help="whether to record the data to Tensorboard.")
+        help="whether to record the data to VisualDL.")
     parser.add_argument(
-        '--tb_log_dir',
+        '--vdl_log_dir',
         type=str,
-        default="tb_log_dir/scalar",
-        help='Tensorboard logging directory for scalar.')
+        default="vdl_log_dir/scalar",
+        help='VisualDL logging directory for scalar.')
     parser.add_argument(
         "--enable_ce",
         type=bool,