Merge pull request #1136 from will-jl944/develop_jf

update link

docs/apis/datasets.md

@@ -117,7 +117,7 @@ anchors = train_dataset.cluster_yolo_anchor(num_anchors=9, image_size=608)
 anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLO(num_classes=num_classes,
                        backbone='ResNet50_vd_dcn',
@@ -226,7 +226,7 @@ anchors = train_dataset.cluster_yolo_anchor(num_anchors=9, image_size=608)
 anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLO(num_classes=num_classes,
                        backbone='ResNet50_vd_dcn',

docs/apis/tools/anchor_clustering.md

@@ -75,7 +75,7 @@ anchors = cluster()
 anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLO(num_classes=num_classes,
                        backbone='ResNet50_vd_dcn',

tutorials/slim/prune/image_classification/mobilenetv2_train.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.MobileNetV2(num_classes=num_classes)
 

tutorials/slim/prune/object_detection/yolov3_train.py

@@ -39,7 +39,7 @@ eval_dataset = pdx.datasets.VOCDetection(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
 

tutorials/slim/prune/semantic_segmentation/unet_train.py

@@ -37,7 +37,7 @@ eval_dataset = pdx.datasets.SegDataset(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.UNet(num_classes=num_classes)
 

tutorials/slim/quantize/semantic_segmentation/unet_train.py

@@ -37,7 +37,7 @@ eval_dataset = pdx.datasets.SegDataset(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.UNet(num_classes=num_classes)
 

tutorials/train/image_classification/alexnet.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.AlexNet(num_classes=num_classes)
 

tutorials/train/image_classification/darknet53.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.DarkNet53(num_classes=num_classes)
 

tutorials/train/image_classification/densenet121.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.DenseNet121(num_classes=num_classes)
 

tutorials/train/image_classification/hrnet_w18_c.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.HRNet_W18_C(num_classes=num_classes)
 

tutorials/train/image_classification/mobilenetv3_large_w_custom_optimizer.py

@@ -31,7 +31,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.MobileNetV3_large(num_classes=num_classes)
 

tutorials/train/image_classification/mobilenetv3_small.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.MobileNetV3_small(num_classes=num_classes)
 

tutorials/train/image_classification/resnet50_vd_ssld.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.ResNet50_vd_ssld(num_classes=num_classes)
 

tutorials/train/image_classification/shufflenetv2.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.ShuffleNetV2(num_classes=num_classes)
 

tutorials/train/image_classification/xception41.py

@@ -30,7 +30,7 @@ eval_dataset = pdx.datasets.ImageNet(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.Xception41(num_classes=num_classes)
 

tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py

@@ -34,7 +34,7 @@ eval_dataset = pdx.datasets.CocoDetection(
     transforms=eval_transforms)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.MaskRCNN(
     num_classes=num_classes, backbone='ResNet50', with_fpn=True)

tutorials/train/object_detection/faster_rcnn_hrnet_w18.py

@@ -38,7 +38,7 @@ eval_dataset = pdx.datasets.VOCDetection(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.FasterRCNN(num_classes=num_classes, backbone='HRNet_W18')
 

tutorials/train/object_detection/faster_rcnn_r50_fpn.py

@@ -38,7 +38,7 @@ eval_dataset = pdx.datasets.VOCDetection(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.FasterRCNN(
     num_classes=num_classes, backbone='ResNet50', with_fpn=True)

tutorials/train/object_detection/ppyolo.py

@@ -39,7 +39,7 @@ eval_dataset = pdx.datasets.VOCDetection(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLO(num_classes=num_classes, backbone='ResNet50_vd_dcn')
 

tutorials/train/object_detection/ppyolotiny.py

@@ -39,7 +39,7 @@ eval_dataset = pdx.datasets.VOCDetection(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLOTiny(num_classes=num_classes)
 

tutorials/train/object_detection/ppyolov2.py

@@ -42,7 +42,7 @@ eval_dataset = pdx.datasets.VOCDetection(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLOv2(num_classes=num_classes, backbone='ResNet50_vd_dcn')
 

tutorials/train/object_detection/yolov3_darknet53.py

@@ -39,7 +39,7 @@ eval_dataset = pdx.datasets.VOCDetection(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
 

tutorials/train/semantic_segmentation/bisenetv2.py

@@ -37,7 +37,7 @@ eval_dataset = pdx.datasets.SegDataset(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.BiSeNetV2(num_classes=num_classes)
 

tutorials/train/semantic_segmentation/deeplabv3p_resnet50_vd.py

@@ -37,7 +37,7 @@ eval_dataset = pdx.datasets.SegDataset(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.DeepLabV3P(num_classes=num_classes, backbone='ResNet50_vd')
 

tutorials/train/semantic_segmentation/fastscnn.py

@@ -37,7 +37,7 @@ eval_dataset = pdx.datasets.SegDataset(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.FastSCNN(num_classes=num_classes)
 

tutorials/train/semantic_segmentation/hrnet.py

@@ -37,7 +37,7 @@ eval_dataset = pdx.datasets.SegDataset(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.HRNet(num_classes=num_classes, width=48)
 

tutorials/train/semantic_segmentation/unet.py

@@ -37,7 +37,7 @@ eval_dataset = pdx.datasets.SegDataset(
     shuffle=False)
 
 # 初始化模型，并进行训练
-# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.UNet(num_classes=num_classes)