Switch TensorBoard to VisualDL 2.0 (#242)

* add_vdl

* Update docs for visualdl.

contrib/LaneNet/requirements.txt

@@ -2,8 +2,6 @@ pre-commit
 yapf == 0.26.0
 flake8
 pyyaml >= 5.1
-tb-paddle
-tensorboard >= 1.15.0
 Pillow
 numpy
 six
@@ -11,3 +9,4 @@ opencv-python
 tqdm
 requests
 sklearn
+visualdl == 2.0.0-alpha.2

contrib/LaneNet/train.py

@@ -78,14 +78,14 @@ def parse_args():
         help='debug mode, display detail information of training',
         action='store_true')
     parser.add_argument(
-        '--use_tb',
-        dest='use_tb',
-        help='whether to record the data during training to Tensorboard',
+        '--use_vdl',
+        dest='use_vdl',
+        help='whether to record the data during training to VisualDL',
         action='store_true')
     parser.add_argument(
-        '--tb_log_dir',
-        dest='tb_log_dir',
-        help='Tensorboard logging directory',
+        '--vdl_log_dir',
+        dest='vdl_log_dir',
+        help='VisualDL logging directory',
         default=None,
         type=str)
     parser.add_argument(
@@ -327,17 +327,17 @@ def train(cfg):
         fetch_list.extend([pred.name, grts.name, masks.name])
         # cm = ConfusionMatrix(cfg.DATASET.NUM_CLASSES, streaming=True)
 
-    if args.use_tb:
-        if not args.tb_log_dir:
-            print_info("Please specify the log directory by --tb_log_dir.")
+    if args.use_vdl:
+        if not args.vdl_log_dir:
+            print_info("Please specify the log directory by --vdl_log_dir.")
             exit(1)
 
-        from tb_paddle import SummaryWriter
-        log_writer = SummaryWriter(args.tb_log_dir)
+        from visualdl import LogWriter
+        log_writer = LogWriter(args.vdl_log_dir)
 
     # trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
     # num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
-    global_step = 0
+    step = 0
     all_step = cfg.DATASET.TRAIN_TOTAL_IMAGES // cfg.BATCH_SIZE
     if cfg.DATASET.TRAIN_TOTAL_IMAGES % cfg.BATCH_SIZE and drop_last != True:
         all_step += 1
@@ -377,9 +377,9 @@ def train(cfg):
                 avg_acc += np.mean(out_acc)
                 avg_fp += np.mean(out_fp)
                 avg_fn += np.mean(out_fn)
-                global_step += 1
+                step += 1
 
-                if global_step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
+                if step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
                     avg_loss /= args.log_steps
                     avg_seg_loss /= args.log_steps
                     avg_emb_loss /= args.log_steps
@@ -389,14 +389,14 @@ def train(cfg):
                     speed = args.log_steps / timer.elapsed_time()
                     print((
                         "epoch={} step={} lr={:.5f} loss={:.4f} seg_loss={:.4f} emb_loss={:.4f} accuracy={:.4} fp={:.4} fn={:.4} step/sec={:.3f} | ETA {}"
-                    ).format(epoch, global_step, lr[0], avg_loss, avg_seg_loss,
+                    ).format(epoch, step, lr[0], avg_loss, avg_seg_loss,
                              avg_emb_loss, avg_acc, avg_fp, avg_fn, speed,
-                             calculate_eta(all_step - global_step, speed)))
-                    if args.use_tb:
+                             calculate_eta(all_step - step, speed)))
+                    if args.use_vdl:
                         log_writer.add_scalar('Train/loss', avg_loss,
-                                              global_step)
-                        log_writer.add_scalar('Train/lr', lr[0], global_step)
-                        log_writer.add_scalar('Train/speed', speed, global_step)
+                                              step)
+                        log_writer.add_scalar('Train/lr', lr[0], step)
+                        log_writer.add_scalar('Train/speed', speed, step)
                     sys.stdout.flush()
                     avg_loss = 0.0
                     avg_seg_loss = 0.0
@@ -422,14 +422,14 @@ def train(cfg):
                     ckpt_dir=ckpt_dir,
                     use_gpu=args.use_gpu,
                     use_mpio=args.use_mpio)
-                if args.use_tb:
+                if args.use_vdl:
                     log_writer.add_scalar('Evaluate/accuracy', accuracy,
-                                          global_step)
-                    log_writer.add_scalar('Evaluate/fp', fp, global_step)
-                    log_writer.add_scalar('Evaluate/fn', fn, global_step)
+                                          step)
+                    log_writer.add_scalar('Evaluate/fp', fp, step)
+                    log_writer.add_scalar('Evaluate/fn', fn, step)
 
-            # Use Tensorboard to visualize results
-            if args.use_tb and cfg.DATASET.VIS_FILE_LIST is not None:
+            # Use VisualDL to visualize results
+            if args.use_vdl and cfg.DATASET.VIS_FILE_LIST is not None:
                 visualize(
                     cfg=cfg,
                     use_gpu=args.use_gpu,

contrib/LaneNet/utils/config.py

@@ -68,7 +68,7 @@ cfg.DATASET.VAL_TOTAL_IMAGES = 500
 cfg.DATASET.TEST_FILE_LIST = './dataset/cityscapes/test.list'
 # 测试数据数量
 cfg.DATASET.TEST_TOTAL_IMAGES = 500
-# Tensorboard 可视化的数据集
+# VisualDL 可视化的数据集
 cfg.DATASET.VIS_FILE_LIST = None
 # 类别数(需包括背景类)
 cfg.DATASET.NUM_CLASSES = 19

docs/config.md

@@ -27,10 +27,10 @@ python pdseg/train.py BATCH_SIZE 1 --cfg configs/unet_optic.yaml
 |--cfg|配置文件路径|ALL|None||
 |--use_gpu|是否使用GPU进行训练|train/eval/vis|False||
 |--use_mpio|是否使用多进程进行IO处理|train/eval|False|打开该开关会占用一定量的CPU内存，但是可以提高训练速度。</br> **NOTE：** windows平台下不支持该功能, 建议使用自定义数据初次训练时不打开，打开会导致数据读取异常不可见。 |
-|--use_tb|是否使用TensorBoard记录训练数据|train|False||
+|--use_vdl|是否使用VisualDL记录训练数据|train|False||
 |--log_steps|训练日志的打印周期（单位为step）|train|10||
 |--debug|是否打印debug信息|train|False|IOU等指标涉及到混淆矩阵的计算，会降低训练速度|
-|--tb_log_dir &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|TensorBoard的日志路径|train|None||
+|--vdl_log_dir &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|VisualDL的日志路径|train|None||
 |--do_eval|是否在保存模型时进行效果评估   &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|train|False||
 |--vis_dir|保存可视化图片的路径|vis|"visual"||
 
@@ -80,7 +80,7 @@ DATASET:
     VAL_FILE_LIST: './dataset/cityscapes/val.list'
     # 测试数据列表
     TEST_FILE_LIST: './dataset/cityscapes/test.list'
-    # Tensorboard 可视化的数据集
+    # VisualDL 可视化的数据集
     VIS_FILE_LIST: None
     # 类别数(需包括背景类)
     NUM_CLASSES: 19

docs/configs/dataset_group.md

@@ -62,7 +62,7 @@ DATASET Group存放所有与数据集相关的配置
 
 ## `VIS_FILE_LIST`
 
-可视化列表，调用`pdseg/train.py`进行训练时，如果打开了--use_tb开关，则在每次模型保存的时候，会读取该列表中的图片进行可视化
+可视化列表，调用`pdseg/train.py`进行训练时，如果打开了--use_vdl开关，则在每次模型保存的时候，会读取该列表中的图片进行可视化
 
 文件列表由多行组成，每一行的格式为
 ```

docs/usage.md

@@ -49,8 +49,8 @@ export CUDA_VISIBLE_DEVICES=0
 python pdseg/train.py --cfg configs/unet_optic.yaml \
                       --use_gpu \
                       --do_eval \
-                      --use_tb \
-                      --tb_log_dir train_log \
+                      --use_vdl \
+                      --vdl_log_dir train_log \
                       BATCH_SIZE 4 \
                       SOLVER.LR 0.001
                       
@@ -70,22 +70,22 @@ export CUDA_VISIBLE_DEVICES=0,1,2
 
 ## 5.训练过程可视化
 
-当打开do_eval和use_tb两个开关后，我们可以通过TensorBoard查看边训练边评估的效果。
+当打开do_eval和use_vdl两个开关后，我们可以通过VisualDL查看边训练边评估的效果。
 
 ```shell
-tensorboard --logdir train_log --host {$HOST_IP} --port {$PORT}
+visualdl --logdir train_log --host {$HOST_IP} --port {$PORT}
 ```
 
 NOTE:
 1. 上述示例中，$HOST\_IP为机器IP地址，请替换为实际IP，$PORT请替换为可访问的端口。
 2. 数据量较大时，前端加载速度会比较慢，请耐心等待。
 
-启动TensorBoard命令后，我们可以在浏览器中查看对应的训练数据。
+启动VisualDL命令后，我们可以在浏览器中查看对应的训练数据。
 在`SCALAR`这个tab中，查看训练loss、iou、acc的变化趋势。
-![](./imgs/tensorboard_scalar.JPG)
+![](./imgs/visualdl_scalar.png)
 
 在`IMAGE`这个tab中，查看样本图片。
-![](./imgs/tensorboard_image.JPG)
+![](./imgs/visualdl_image.png)
 
 ## 6.模型评估
 训练完成后，我们可以通过eval.py来评估模型效果。由于我们设置的训练EPOCH数量为10，保存间隔为5，因此一共会产生2个定期保存的模型，加上最终保存的final模型，一共有3个模型。我们选择最后保存的模型进行效果的评估：

pdseg/train.py

@@ -77,14 +77,14 @@ def parse_args():
         help='debug mode, display detail information of training',
         action='store_true')
     parser.add_argument(
-        '--use_tb',
-        dest='use_tb',
-        help='whether to record the data during training to Tensorboard',
+        '--use_vdl',
+        dest='use_vdl',
+        help='whether to record the data during training to VisualDL',
         action='store_true')
     parser.add_argument(
-        '--tb_log_dir',
-        dest='tb_log_dir',
-        help='Tensorboard logging directory',
+        '--vdl_log_dir',
+        dest='vdl_log_dir',
+        help='VisualDL logging directory',
         default=None,
         type=str)
     parser.add_argument(
@@ -354,17 +354,17 @@ def train(cfg):
         fetch_list.extend([pred.name, grts.name, masks.name])
         cm = ConfusionMatrix(cfg.DATASET.NUM_CLASSES, streaming=True)
 
-    if args.use_tb:
-        if not args.tb_log_dir:
-            print_info("Please specify the log directory by --tb_log_dir.")
+    if args.use_vdl:
+        if not args.vdl_log_dir:
+            print_info("Please specify the log directory by --vdl_log_dir.")
             exit(1)
 
-        from tb_paddle import SummaryWriter
-        log_writer = SummaryWriter(args.tb_log_dir)
+        from visualdl import LogWriter
+        log_writer = LogWriter(args.vdl_log_dir)
 
     # trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
     # num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
-    global_step = 0
+    step = 0
     all_step = cfg.DATASET.TRAIN_TOTAL_IMAGES // cfg.BATCH_SIZE
     if cfg.DATASET.TRAIN_TOTAL_IMAGES % cfg.BATCH_SIZE and drop_last != True:
         all_step += 1
@@ -398,9 +398,9 @@ def train(cfg):
                         return_numpy=True)
                     cm.calculate(pred, grts, masks)
                     avg_loss += np.mean(np.array(loss))
-                    global_step += 1
+                    step += 1
 
-                    if global_step % args.log_steps == 0:
+                    if step % args.log_steps == 0:
                         speed = args.log_steps / timer.elapsed_time()
                         avg_loss /= args.log_steps
                         category_acc, mean_acc = cm.accuracy()
@@ -408,22 +408,22 @@ def train(cfg):
 
                         print_info((
                             "epoch={} step={} lr={:.5f} loss={:.4f} acc={:.5f} mIoU={:.5f} step/sec={:.3f} | ETA {}"
-                        ).format(epoch, global_step, lr[0], avg_loss, mean_acc,
+                        ).format(epoch, step, lr[0], avg_loss, mean_acc,
                                  mean_iou, speed,
-                                 calculate_eta(all_step - global_step, speed)))
+                                 calculate_eta(all_step - step, speed)))
                         print_info("Category IoU: ", category_iou)
                         print_info("Category Acc: ", category_acc)
-                        if args.use_tb:
+                        if args.use_vdl:
                             log_writer.add_scalar('Train/mean_iou', mean_iou,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/mean_acc', mean_acc,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/loss', avg_loss,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/lr', lr[0],
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/step/sec', speed,
-                                                  global_step)
+                                                  step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         cm.zero_matrix()
@@ -435,30 +435,30 @@ def train(cfg):
                         fetch_list=fetch_list,
                         return_numpy=True)
                     avg_loss += np.mean(np.array(loss))
-                    global_step += 1
+                    step += 1
 
-                    if global_step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
+                    if step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
                         avg_loss /= args.log_steps
                         speed = args.log_steps / timer.elapsed_time()
                         print((
                             "epoch={} step={} lr={:.5f} loss={:.4f} step/sec={:.3f} | ETA {}"
-                        ).format(epoch, global_step, lr[0], avg_loss, speed,
-                                 calculate_eta(all_step - global_step, speed)))
-                        if args.use_tb:
+                        ).format(epoch, step, lr[0], avg_loss, speed,
+                                 calculate_eta(all_step - step, speed)))
+                        if args.use_vdl:
                             log_writer.add_scalar('Train/loss', avg_loss,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/lr', lr[0],
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/speed', speed,
-                                                  global_step)
+                                                  step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         timer.restart()
 
                     # NOTE : used for benchmark, profiler tools
-                    if args.is_profiler and epoch == 1 and global_step == args.log_steps:
+                    if args.is_profiler and epoch == 1 and step == args.log_steps:
                         profiler.start_profiler("All")
-                    elif args.is_profiler and epoch == 1 and global_step == args.log_steps + 5:
+                    elif args.is_profiler and epoch == 1 and step == args.log_steps + 5:
                         profiler.stop_profiler("total", args.profiler_path)
                         return
 
@@ -479,11 +479,11 @@ def train(cfg):
                     ckpt_dir=ckpt_dir,
                     use_gpu=args.use_gpu,
                     use_mpio=args.use_mpio)
-                if args.use_tb:
+                if args.use_vdl:
                     log_writer.add_scalar('Evaluate/mean_iou', mean_iou,
-                                          global_step)
+                                          step)
                     log_writer.add_scalar('Evaluate/mean_acc', mean_acc,
-                                          global_step)
+                                          step)
 
                 if mean_iou > best_mIoU:
                     best_mIoU = mean_iou
@@ -493,8 +493,8 @@ def train(cfg):
                         os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, 'best_model'),
                         mean_iou))
 
-            # Use Tensorboard to visualize results
-            if args.use_tb and cfg.DATASET.VIS_FILE_LIST is not None:
+            # Use VisualDL to visualize results
+            if args.use_vdl and cfg.DATASET.VIS_FILE_LIST is not None:
                 visualize(
                     cfg=cfg,
                     use_gpu=args.use_gpu,

pdseg/utils/config.py

@@ -56,7 +56,7 @@ cfg.DATASET.VAL_TOTAL_IMAGES = 500
 cfg.DATASET.TEST_FILE_LIST = './dataset/cityscapes/test.list'
 # 测试数据数量
 cfg.DATASET.TEST_TOTAL_IMAGES = 500
-# Tensorboard 可视化的数据集
+# VisualDL 可视化的数据集
 cfg.DATASET.VIS_FILE_LIST = None
 # 类别数(需包括背景类)
 cfg.DATASET.NUM_CLASSES = 19

pdseg/vis.py

@@ -162,18 +162,17 @@ def visualize(cfg,
             img_cnt += 1
             print("#{} visualize image path: {}".format(img_cnt, vis_fn))
 
-            # Use Tensorboard to visualize image
+            # Use VisualDL to visualize image
             if log_writer is not None:
                 # Calulate epoch from ckpt_dir folder name
                 epoch = int(os.path.split(ckpt_dir)[-1])
-                print("Tensorboard visualization epoch", epoch)
+                print("VisualDL visualization epoch", epoch)
 
                 pred_mask_np = np.array(pred_mask.convert("RGB"))
                 log_writer.add_image(
                     "Predict/{}".format(img_name),
                     pred_mask_np,
-                    epoch,
-                    dataformats='HWC')
+                    epoch)
                 # Original image
                 # BGR->RGB
                 img = cv2.imread(
@@ -181,8 +180,7 @@ def visualize(cfg,
                 log_writer.add_image(
                     "Images/{}".format(img_name),
                     img,
-                    epoch,
-                    dataformats='HWC')
+                    epoch)
                 # add ground truth (label) images
                 grt = grts[i]
                 if grt is not None:
@@ -194,8 +192,7 @@ def visualize(cfg,
                     log_writer.add_image(
                         "Label/{}".format(img_name),
                         grt,
-                        epoch,
-                        dataformats='HWC')
+                        epoch)
 
         # If in local_test mode, only visualize 5 images just for testing
         # procedure

requirements.txt

@@ -2,11 +2,10 @@ pre-commit
 yapf == 0.26.0
 flake8
 pyyaml >= 5.1
-tb-paddle
-tensorboard >= 1.15.0
 Pillow
 numpy
 six
 opencv-python
 tqdm
 requests
+visualdl == 2.0.0-alpha.2

slim/distillation/train_distill.py

@@ -87,14 +87,14 @@ def parse_args():
         help='debug mode, display detail information of training',
         action='store_true')
     parser.add_argument(
-        '--use_tb',
-        dest='use_tb',
-        help='whether to record the data during training to Tensorboard',
+        '--use_vdl',
+        dest='use_vdl',
+        help='whether to record the data during training to VisualDL',
         action='store_true')
     parser.add_argument(
-        '--tb_log_dir',
-        dest='tb_log_dir',
-        help='Tensorboard logging directory',
+        '--vdl_log_dir',
+        dest='vd;_log_dir',
+        help='VisualDL logging directory',
         default=None,
         type=str)
     parser.add_argument(
@@ -409,17 +409,17 @@ def train(cfg):
         fetch_list.extend([pred.name, grts.name, masks.name])
         cm = ConfusionMatrix(cfg.DATASET.NUM_CLASSES, streaming=True)
 
-    if args.use_tb:
-        if not args.tb_log_dir:
-            print_info("Please specify the log directory by --tb_log_dir.")
+    if args.use_vdl:
+        if not args.vdl_log_dir:
+            print_info("Please specify the log directory by --vdl_log_dir.")
             exit(1)
 
-        from tb_paddle import SummaryWriter
-        log_writer = SummaryWriter(args.tb_log_dir)
+        from visualdl import LogWriter
+        log_writer = LogWriter(args.vdl_log_dir)
 
     # trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
     # num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
-    global_step = 0
+    step = 0
     all_step = cfg.DATASET.TRAIN_TOTAL_IMAGES // cfg.BATCH_SIZE
     if cfg.DATASET.TRAIN_TOTAL_IMAGES % cfg.BATCH_SIZE and drop_last != True:
         all_step += 1
@@ -455,9 +455,9 @@ def train(cfg):
                         return_numpy=True)
                     cm.calculate(pred, grts, masks)
                     avg_loss += np.mean(np.array(loss))
-                    global_step += 1
+                    step += 1
 
-                    if global_step % args.log_steps == 0:
+                    if step % args.log_steps == 0:
                         speed = args.log_steps / timer.elapsed_time()
                         avg_loss /= args.log_steps
                         category_acc, mean_acc = cm.accuracy()
@@ -465,22 +465,22 @@ def train(cfg):
 
                         print_info((
                             "epoch={} step={} lr={:.5f} loss={:.4f} acc={:.5f} mIoU={:.5f} step/sec={:.3f} | ETA {}"
-                        ).format(epoch, global_step, lr[0], avg_loss, mean_acc,
+                        ).format(epoch, step, lr[0], avg_loss, mean_acc,
                                  mean_iou, speed,
-                                 calculate_eta(all_step - global_step, speed)))
+                                 calculate_eta(all_step - step, speed)))
                         print_info("Category IoU: ", category_iou)
                         print_info("Category Acc: ", category_acc)
-                        if args.use_tb:
+                        if args.use_vdl:
                             log_writer.add_scalar('Train/mean_iou', mean_iou,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/mean_acc', mean_acc,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/loss', avg_loss,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/lr', lr[0],
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/step/sec', speed,
-                                                  global_step)
+                                                  step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         cm.zero_matrix()
@@ -494,25 +494,25 @@ def train(cfg):
                     avg_loss += np.mean(np.array(loss))
                     avg_t_loss += np.mean(np.array(t_loss))
                     avg_d_loss += np.mean(np.array(d_loss))
-                    global_step += 1
+                    step += 1
 
-                    if global_step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
+                    if step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
                         avg_loss /= args.log_steps
                         avg_t_loss /= args.log_steps
                         avg_d_loss /= args.log_steps
                         speed = args.log_steps / timer.elapsed_time()
                         print((
                             "epoch={} step={} lr={:.5f} loss={:.4f} teacher loss={:.4f} distill loss={:.4f} step/sec={:.3f} | ETA {}"
-                        ).format(epoch, global_step, lr[0], avg_loss,
+                        ).format(epoch, step, lr[0], avg_loss,
                                  avg_t_loss, avg_d_loss, speed,
-                                 calculate_eta(all_step - global_step, speed)))
-                        if args.use_tb:
+                                 calculate_eta(all_step - step, speed)))
+                        if args.use_vdl:
                             log_writer.add_scalar('Train/loss', avg_loss,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/lr', lr[0],
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/speed', speed,
-                                                  global_step)
+                                                  step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         avg_t_loss = 0.0
@@ -536,11 +536,11 @@ def train(cfg):
                     ckpt_dir=ckpt_dir,
                     use_gpu=args.use_gpu,
                     use_mpio=args.use_mpio)
-                if args.use_tb:
+                if args.use_vdl:
                     log_writer.add_scalar('Evaluate/mean_iou', mean_iou,
-                                          global_step)
+                                          step)
                     log_writer.add_scalar('Evaluate/mean_acc', mean_acc,
-                                          global_step)
+                                          step)
 
                 if mean_iou > best_mIoU:
                     best_mIoU = mean_iou
@@ -550,8 +550,8 @@ def train(cfg):
                         os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, 'best_model'),
                         mean_iou))
 
-            # Use Tensorboard to visualize results
-            if args.use_tb and cfg.DATASET.VIS_FILE_LIST is not None:
+            # Use VisualDL to visualize results
+            if args.use_vdl and cfg.DATASET.VIS_FILE_LIST is not None:
                 visualize(
                     cfg=cfg,
                     use_gpu=args.use_gpu,

slim/nas/train_nas.py

@@ -87,14 +87,14 @@ def parse_args():
         help='debug mode, display detail information of training',
         action='store_true')
     parser.add_argument(
-        '--use_tb',
-        dest='use_tb',
-        help='whether to record the data during training to Tensorboard',
+        '--use_vdl',
+        dest='use_vdl',
+        help='whether to record the data during training to VisualDL',
         action='store_true')
     parser.add_argument(
-        '--tb_log_dir',
-        dest='tb_log_dir',
-        help='Tensorboard logging directory',
+        '--vdl_log_dir',
+        dest='vdl_log_dir',
+        help='VisualDL logging directory',
         default=None,
         type=str)
     parser.add_argument(

slim/prune/train_prune.py

@@ -83,14 +83,14 @@ def parse_args():
         help='debug mode, display detail information of training',
         action='store_true')
     parser.add_argument(
-        '--use_tb',
-        dest='use_tb',
-        help='whether to record the data during training to Tensorboard',
+        '--use_vdl',
+        dest='use_vdl',
+        help='whether to record the data during training to VisualDL',
         action='store_true')
     parser.add_argument(
-        '--tb_log_dir',
-        dest='tb_log_dir',
-        help='Tensorboard logging directory',
+        '--vdl_log_dir',
+        dest='vdl_log_dir',
+        help='VisualDL logging directory',
         default=None,
         type=str)
     parser.add_argument(
@@ -335,13 +335,13 @@ def train(cfg):
         fetch_list.extend([pred.name, grts.name, masks.name])
         cm = ConfusionMatrix(cfg.DATASET.NUM_CLASSES, streaming=True)
 
-    if args.use_tb:
-        if not args.tb_log_dir:
-            print_info("Please specify the log directory by --tb_log_dir.")
+    if args.use_vdl:
+        if not args.vdl_log_dir:
+            print_info("Please specify the log directory by --vdl_log_dir.")
             exit(1)
 
-        from tb_paddle import SummaryWriter
-        log_writer = SummaryWriter(args.tb_log_dir)
+        from visualdl import LogWriter
+        log_writer = LogWriter(args.vdl_log_dir)
 
     pruner = Pruner()
     train_prog = pruner.prune(
@@ -357,7 +357,7 @@ def train(cfg):
         exec_strategy=exec_strategy,
         build_strategy=build_strategy)
 
-    global_step = 0
+    step = 0
     all_step = cfg.DATASET.TRAIN_TOTAL_IMAGES // cfg.BATCH_SIZE
     if cfg.DATASET.TRAIN_TOTAL_IMAGES % cfg.BATCH_SIZE and drop_last != True:
         all_step += 1
@@ -389,9 +389,9 @@ def train(cfg):
                         return_numpy=True)
                     cm.calculate(pred, grts, masks)
                     avg_loss += np.mean(np.array(loss))
-                    global_step += 1
+                    step += 1
 
-                    if global_step % args.log_steps == 0:
+                    if step % args.log_steps == 0:
                         speed = args.log_steps / timer.elapsed_time()
                         avg_loss /= args.log_steps
                         category_acc, mean_acc = cm.accuracy()
@@ -399,22 +399,22 @@ def train(cfg):
 
                         print_info((
                             "epoch={} step={} lr={:.5f} loss={:.4f} acc={:.5f} mIoU={:.5f} step/sec={:.3f} | ETA {}"
-                        ).format(epoch, global_step, lr[0], avg_loss, mean_acc,
+                        ).format(epoch, step, lr[0], avg_loss, mean_acc,
                                  mean_iou, speed,
-                                 calculate_eta(all_step - global_step, speed)))
+                                 calculate_eta(all_step - step, speed)))
                         print_info("Category IoU: ", category_iou)
                         print_info("Category Acc: ", category_acc)
-                        if args.use_tb:
+                        if args.use_vdl:
                             log_writer.add_scalar('Train/mean_iou', mean_iou,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/mean_acc', mean_acc,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/loss', avg_loss,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/lr', lr[0],
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/step/sec', speed,
-                                                  global_step)
+                                                  step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         cm.zero_matrix()
@@ -426,22 +426,22 @@ def train(cfg):
                         fetch_list=fetch_list,
                         return_numpy=True)
                     avg_loss += np.mean(np.array(loss))
-                    global_step += 1
+                    step += 1
 
-                    if global_step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
+                    if step % args.log_steps == 0 and cfg.TRAINER_ID == 0:
                         avg_loss /= args.log_steps
                         speed = args.log_steps / timer.elapsed_time()
                         print((
                             "epoch={} step={} lr={:.5f} loss={:.4f} step/sec={:.3f} | ETA {}"
-                        ).format(epoch, global_step, lr[0], avg_loss, speed,
-                                 calculate_eta(all_step - global_step, speed)))
-                        if args.use_tb:
+                        ).format(epoch, step, lr[0], avg_loss, speed,
+                                 calculate_eta(all_step - step, speed)))
+                        if args.use_vdl:
                             log_writer.add_scalar('Train/loss', avg_loss,
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/lr', lr[0],
-                                                  global_step)
+                                                  step)
                             log_writer.add_scalar('Train/speed', speed,
-                                                  global_step)
+                                                  step)
                         sys.stdout.flush()
                         avg_loss = 0.0
                         timer.restart()
@@ -463,14 +463,14 @@ def train(cfg):
                     ckpt_dir=ckpt_dir,
                     use_gpu=args.use_gpu,
                     use_mpio=args.use_mpio)
-                if args.use_tb:
+                if args.use_vdl:
                     log_writer.add_scalar('Evaluate/mean_iou', mean_iou,
-                                          global_step)
+                                          step)
                     log_writer.add_scalar('Evaluate/mean_acc', mean_acc,
-                                          global_step)
+                                          step)
 
-            # Use Tensorboard to visualize results
-            if args.use_tb and cfg.DATASET.VIS_FILE_LIST is not None:
+            # Use VisualDL to visualize results
+            if args.use_vdl and cfg.DATASET.VIS_FILE_LIST is not None:
                 visualize(
                     cfg=cfg,
                     use_gpu=args.use_gpu,