update api links in tutorials

dygraph/docs/apis/models/semantic_segmentation.md

@@ -76,6 +76,9 @@ predict(self, img_file, transforms=None):
 > >
 > > - **dict** ｜ **List[dict]**: 如果输入为单张图像，返回dict。包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes)。如果输入为多张图像，返回由每张图像预测结果组成的列表。
 
+
+### analyze_sensitivity
+
 ```python
 analyze_sensitivity(self, dataset, batch_size=8, criterion='l1_norm', save_dir='output')
 ```

dygraph/docs/apis/prediction.md

@@ -0,0 +1,83 @@
+# 加载模型预测
+
+PaddleX可以使用`paddlex.load_model`接口加载模型（包括训练过程中保存的模型，导出的部署模型，量化模型以及裁剪的模型）进行预测，同时PaddleX中也内置了一系列的可视化工具函数，帮助用户方便地检查模型的效果。
+
+**注意**：使用`paddlex.load_model`接口加载仅用于模型预测，如需要在此模型基础上继续训练，可以将该模型作为预训练模型进行训练，具体做法是在训练代码中，将train函数中的`pretrain_weights`参数指定为预训练模型路径。
+
+## 图像分类
+
+```
+import paddlex as pdx
+test_jpg = 'mobilenetv3_small_ssld_imagenet/test.jpg'
+model = pdx.load_model('mobilenetv3_small_ssld_imagenet')
+result = model.predict(test_jpg)
+print("Predict Result: ", result)
+```
+结果输出如下：
+```
+Predict Result: [{'category_id': 549, 'category': 'envelope', 'score': 0.29062933}]
+```
+
+测试图片如下：
+
+![](images/test.jpg)
+
+- 分类模型predict接口[说明文档](https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md#predict)
+
+
+## 目标检测
+
+
+```
+import paddlex as pdx
+test_jpg = 'yolov3_mobilenetv1_coco/test.jpg'
+model = pdx.load_model('yolov3_mobilenetv1_coco')
+
+# predict接口并未过滤低置信度识别结果，用户根据需求按score值进行过滤
+result = model.predict(test_jpg)
+
+# 可视化结果存储在./visualized_test.jpg, 见下图
+pdx.det.visualize(test_jpg, result, threshold=0.3, save_dir='./')
+```
+- YOLOv3模型predict接口[说明文档](https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md#predict)
+- 可视化pdx.det.visualize接口[说明文档](https://github.com/PaddlePaddle/PaddleX/blob/d555d26f92cd6f8d3b940636bd7cb9043de93768/dygraph/paddlex/cv/models/utils/visualize.py#L25)
+> 注意：目标检测和实例分割模型在调用`predict`接口得到的结果需用户自行过滤低置信度结果，在`paddlex.det.visualize`接口中，我们提供了`threshold`用于过滤，置信度低于此值的结果将被过滤，不会可视化。
+![](./images/yolo_predict.jpg)
+
+## 实例分割
+
+
+```
+import paddlex as pdx
+test_jpg = 'mask_r50_fpn_coco/test.jpg'
+model = pdx.load_model('mask_r50_fpn_coco')
+
+# predict接口并未过滤低置信度识别结果，用户根据需求按score值进行过滤
+result = model.predict(test_jpg)
+
+# 可视化结果存储在./visualized_test.jpg, 见下图
+pdx.det.visualize(test_jpg, result, threshold=0.5, save_dir='./')
+```
+- MaskRCNN模型predict接口[说明文档](https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/instance_segmentation.md#predict)
+- 可视化pdx.det.visualize接口[说明文档](https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/paddlex/cv/models/utils/visualize.py#L25)
+
+**注意**：目标检测和实例分割模型在调用`predict`接口得到的结果需用户自行过滤低置信度结果，在`paddlex.det.visualize`接口中，我们提供了`threshold`用于过滤，置信度低于此值的结果将被过滤，不会可视化。
+![](./images/mask_predict.jpg)
+
+## 语义分割
+
+
+```
+import paddlex as pdx
+test_jpg = './deeplabv3p_mobilenetv2_voc/test.jpg'
+model = pdx.load_model('./deeplabv3p_mobilenetv2_voc')
+result = model.predict(test_jpg)
+# 可视化结果存储在./visualized_test.jpg，见下图右(左图为原图)
+pdx.seg.visualize(test_jpg, result, weight=0.0, save_dir='./')
+```
+
+在上述示例代码中，通过调用`paddlex.seg.visualize`可以对语义分割的预测结果进行可视化，可视化的结果保存在`save_dir`下，见下图。其中`weight`参数用于调整预测结果和原图结果融合展现时的权重，0.0时只展示预测结果mask的可视化，1.0时只展示原图可视化。
+
+- DeepLabv3模型predict接口[说明文档](https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md#predict)
+- 可视化pdx.seg.visualize接口[说明文档](https://github.com/PaddlePaddle/PaddleX/blob/d555d26f92cd6f8d3b940636bd7cb9043de93768/dygraph/paddlex/cv/models/utils/visualize.py#L50)
+![](images/deeplab_predict.jpg)

dygraph/docs/parameters.md

@@ -0,0 +1,43 @@
+# 训练参数调整
+
+PaddleX所有训练接口中，内置的参数均为根据单GPU卡相应batch_size下的较优参数，用户在自己的数据上训练模型，涉及到参数调整时，如无太多参数调优经验，则可参考如下方式
+
+## 1.num_epochs的调整
+num_epochs是模型训练迭代的总轮数(模型对训练集全部样本过一遍即为一个epoch)，用户可以设置较大的数值，根据模型迭代过程在验证集上的指标表现，来判断模型是否收敛，进而提前终止训练。此外也可以使用`train`接口中的`early_stop`策略，模型在训练过程会自动判断模型是否收敛自动中止。
+
+## 2.batch_size和learning_rate
+
+> - Batch Size指模型在训练过程中，前向计算一次(即为一个step)所用到的样本数量
+> - 如若使用多卡训练， batch_size会均分到各张卡上（因此需要让batch size整除卡数）
+> - Batch Size跟机器的显存/内存高度相关，`batch_size`越高，所消耗的显存/内存就越高
+> - PaddleX在各个`train`接口中均配置了默认的batch size(默认针对单GPU卡)，如若训练时提示GPU显存不足，则相应调低BatchSize，如若GPU显存高或使用多张GPU卡时，可相应调高BatchSize。
+> - **如若用户调整batch size，则也注意需要对应调整其它参数，特别是train接口中默认的learning_rate值**。如在YOLOv3模型中，默认`train_batch_size`为8，`learning_rate`为0.000125，当用户将模型在2卡机器上训练时，可以将`train_batch_size`调整为16, 那么同时`learning_rate`也可以对应调整为0.000125 * 2 = 0.00025
+
+## 3.warmup_steps和warmup_start_lr
+
+在训练模型时，一般都会使用预训练模型，例如检测模型在训练时使用backbone在ImageNet数据集上的预训练权重。但由于在自行训练时，自己的数据与ImageNet数据集存在较大的差异，可能会一开始由于梯度过大使得训练出现问题，这种情况下可以在刚开始训练时，让学习率以一个较小的值，慢慢增长到设定的学习率。`warmup_steps`和`warmup_start_lr`就是起到这个作用，模型开始训练时，学习率会从`warmup_start_lr`开始，在`warmup_steps`个batch数据迭代后线性增长到设定的学习率。
+
+> 例如YOLOv3的train接口，默认`train_batch_size`为8，`learning_rate`为0.000125, `warmup_steps`为1000， `warmup_start_lr`为0.0；在此参数配置下表示，模型在启动训练后，在前1000个step(每个step使用一个batch的数据，即8个样本)内，学习率会从0.0开始线性增长到设定的0.000125。
+
+## 4.lr_decay_epochs和lr_decay_gamma
+
+`lr_decay_epochs`用于让学习率在模型训练后期逐步衰减，它一般是一个list，如[6, 8, 10]，表示学习率在第6个epoch时衰减一次，第8个epoch时再衰减一次，第10个epoch时再衰减一次。每次学习率衰减为之前的学习率*lr_decay_gamma。
+
+> 例如YOLOv3的train接口，默认`num_epochs`为270,`learning_rate`为0.000125， `lr_decay_epochs`为[213, 240]，`lr_decay_gamma`为0.1;在此参数配置下表示，模型在启动训练后，在前213个epoch中，训练时使用的学习率为0.000125，在第213至240个epoch之间，训练使用的学习率为0.000125x0.1=0.0000125，在240个epoch之后，使用的学习率为0.000125x0.1x0.1=0.00000125
+
+## 5.参数设定时的约束
+根据上述几个参数，可以了解到学习率的变化分为WarmUp热身阶段和Decay衰减阶段，
+> - Wamup热身阶段：随着训练迭代，学习率从较低的值逐渐线性增长至设定的值，以step为单位
+> - Decay衰减阶段：随着训练迭代，学习率逐步衰减，如每次衰减为之前的0.1， 以epoch为单位  
+> - step与epoch的关系：1个epoch由多个step组成，例如训练样本有800张图像，`train_batch_size`为8, 那么每个epoch都要完整用这800张图片训一次模型，而每个epoch总共包含800//8即100个step
+
+在PaddleX中，约束warmup必须在Decay之前结束，因此各参数设置需要满足下面条件
+```
+warmup_steps <= lr_decay_epochs[0] * num_steps_each_epoch
+```
+其中`num_steps_each_epoch`计算方式如下,
+```
+num_steps_each_eposh = num_samples_in_train_dataset // train_batch_size
+```
+
+因此，如若你在启动训练时，被提示`warmup_steps should be less than...`时，即表示需要根据上述公式调整你的参数啦，可以调整`lr_decay_epochs`或者是`warmup_steps`。

dygraph/tutorials/slim/prune/image_classification/mobilenetv2_prune.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -33,17 +33,17 @@ eval_dataset = pdx.datasets.ImageNet(
 model = pdx.load_model('output/mobilenet_v2/best_model')
 
 # Step 1/3: 分析模型各层参数在不同的剪裁比例下的敏感度
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/base.py#L352
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md#analyze_sensitivity
 model.analyze_sensitivity(
     dataset=eval_dataset, save_dir='output/mobilenet_v2/prune')
 
 # Step 2/3: 根据选择的FLOPs减小比例对模型进行剪裁
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/base.py#L394
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md#prune
 model.prune(pruned_flops=.2)
 
 # Step 3/3: 对剪裁后的模型重新训练
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md#train
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/slim/prune/image_classification/mobilenetv2_train.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.MobileNetV2(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/slim/prune/object_detection/yolov3_prune.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.MixupImage(mixup_epoch=250), T.RandomDistort(),
     T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
@@ -23,7 +23,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -42,19 +42,19 @@ eval_dataset = pdx.datasets.VOCDetection(
 model = pdx.load_model('output/yolov3_darknet53/best_model')
 
 # Step 1/3: 分析模型各层参数在不同的剪裁比例下的敏感度
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/base.py#L352
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md#analyze_sensitivity
 model.analyze_sensitivity(
     dataset=eval_dataset,
     batch_size=1,
     save_dir='output/yolov3_darknet53/prune')
 
 # Step 2/3: 根据选择的FLOPs减小比例对模型进行剪裁
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/base.py#L394
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md#prune
 model.prune(pruned_flops=.2)
 
 # Step 3/3: 对剪裁后的模型重新训练
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L154
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md#train
 model.train(
     num_epochs=270,
     train_dataset=train_dataset,

dygraph/tutorials/slim/prune/object_detection/yolov3_train.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.MixupImage(mixup_epoch=250), T.RandomDistort(),
     T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
@@ -23,7 +23,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -39,12 +39,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L154
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=270,
     train_dataset=train_dataset,

dygraph/tutorials/slim/prune/semantic_segmentation/unet_prune.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -40,17 +40,17 @@ eval_dataset = pdx.datasets.SegDataset(
 model = pdx.load_model('output/unet/best_model')
 
 # Step 1/3: 分析模型各层参数在不同的剪裁比例下的敏感度
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/base.py#L352
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md#analyze_sensitivity
 model.analyze_sensitivity(
     dataset=eval_dataset, batch_size=1, save_dir='output/unet/prune')
 
 # Step 2/3: 根据选择的FLOPs减小比例对模型进行剪裁
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/base.py#L394
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md#prune
 model.prune(pruned_flops=.2)
 
 # Step 3/3: 对剪裁后的模型重新训练
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md#train
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/slim/prune/semantic_segmentation/unet_train.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -37,12 +37,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.UNet(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/slim/quantize/semantic_segmentation/unet_qat.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -40,6 +40,7 @@ eval_dataset = pdx.datasets.SegDataset(
 model = pdx.load_model('output/unet/best_model')
 
 # 在线量化
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md#quant_aware_train
 model.quant_aware_train(
     num_epochs=5,
     train_dataset=train_dataset,

dygraph/tutorials/slim/quantize/semantic_segmentation/unet_train.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -37,12 +37,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.UNet(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/image_classification/alexnet.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.AlexNet(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/image_classification/darknet53.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.DarkNet53(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/image_classification/densenet121.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.DenseNet121(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/image_classification/hrnet_w18_c.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.HRNet_W18_C(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/image_classification/mobilenetv3_large_w_custom_optimizer.py

@@ -7,7 +7,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -16,7 +16,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.MobileNetV3_large(num_classes=num_classes)
 

dygraph/tutorials/train/image_classification/mobilenetv3_small.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.MobileNetV3_small(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/image_classification/resnet50_vd_ssld.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.ResNet50_vd_ssld(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/image_classification/shufflenetv2.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.ShuffleNetV2(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/image_classification/xception41.py

@@ -6,7 +6,7 @@ veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
 pdx.utils.download_and_decompress(veg_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose(
     [T.RandomCrop(crop_size=224), T.RandomHorizontalFlip(), T.Normalize()])
 
@@ -15,7 +15,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/imagenet.py#L21
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.ImageNet(
     data_dir='vegetables_cls',
     file_list='vegetables_cls/train_list.txt',
@@ -30,12 +30,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.cls.Xception41(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/classifier.py#L153
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/classification.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_ins_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.RandomResizeByShort(
         short_sizes=[640, 672, 704, 736, 768, 800],
@@ -22,7 +22,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/paddlex/cv/datasets/coco.py#L26
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.CocoDetection(
     data_dir='xiaoduxiong_ins_det/JPEGImages',
     ann_file='xiaoduxiong_ins_det/train.json',
@@ -34,13 +34,13 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.MaskRCNN(
     num_classes=num_classes, backbone='ResNet50', with_fpn=True)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/instance_segmentation.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=12,
     train_dataset=train_dataset,

dygraph/tutorials/train/object_detection/faster_rcnn_hrnet_w18.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.RandomResizeByShort(
         short_sizes=[640, 672, 704, 736, 768, 800],
@@ -22,7 +22,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -38,12 +38,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
-model = pdx.seg.FasterRCNN(num_classes=num_classes, backbone='HRNet_W18')
+model = pdx.det.FasterRCNN(num_classes=num_classes, backbone='HRNet_W18')
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=24,
     train_dataset=train_dataset,

dygraph/tutorials/train/object_detection/faster_rcnn_r50_fpn.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.RandomResizeByShort(
         short_sizes=[640, 672, 704, 736, 768, 800],
@@ -22,7 +22,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -38,13 +38,13 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.FasterRCNN(
     num_classes=num_classes, backbone='ResNet50', with_fpn=True)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=12,
     train_dataset=train_dataset,

dygraph/tutorials/train/object_detection/ppyolo.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.MixupImage(mixup_epoch=-1), T.RandomDistort(),
     T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
@@ -23,7 +23,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -39,12 +39,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLO(num_classes=num_classes, backbone='ResNet50_vd_dcn')
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=200,
     train_dataset=train_dataset,

dygraph/tutorials/train/object_detection/ppyolotiny.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.MixupImage(mixup_epoch=-1), T.RandomDistort(),
     T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
@@ -23,7 +23,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -39,12 +39,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLOTiny(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=550,
     train_dataset=train_dataset,

dygraph/tutorials/train/object_detection/ppyolov2.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.MixupImage(mixup_epoch=-1), T.RandomDistort(),
     T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
@@ -26,7 +26,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -42,12 +42,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.PPYOLOv2(num_classes=num_classes, backbone='ResNet50_vd_dcn')
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=170,
     train_dataset=train_dataset,

dygraph/tutorials/train/object_detection/yolov3_darknet53.py

@@ -6,7 +6,7 @@ dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
 pdx.utils.download_and_decompress(dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.MixupImage(mixup_epoch=250), T.RandomDistort(),
     T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
@@ -23,7 +23,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.VOCDetection(
     data_dir='insect_det',
     file_list='insect_det/train_list.txt',
@@ -39,12 +39,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.det.YOLOv3(num_classes=num_classes, backbone='DarkNet53')
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L155
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/detection.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=270,
     train_dataset=train_dataset,

dygraph/tutorials/train/semantic_segmentation/bisenetv2.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -37,12 +37,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.BiSeNetV2(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/semantic_segmentation/deeplabv3p_resnet50_vd.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -37,12 +37,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.DeepLabV3P(num_classes=num_classes, backbone='ResNet50_vd')
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/semantic_segmentation/fastscnn.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -37,12 +37,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.FastSCNN(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/semantic_segmentation/hrnet.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -37,12 +37,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.HRNet(num_classes=num_classes, width=48)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,

dygraph/tutorials/train/semantic_segmentation/unet.py

@@ -6,7 +6,7 @@ optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz'
 pdx.utils.download_and_decompress(optic_dataset, path='./')
 
 # 定义训练和验证时的transforms
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/transforms/transforms.md
 train_transforms = T.Compose([
     T.Resize(target_size=512),
     T.RandomHorizontalFlip(),
@@ -21,7 +21,7 @@ eval_transforms = T.Compose([
 ])
 
 # 定义训练和验证所用的数据集
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/seg_dataset.py#L22
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/datasets.md
 train_dataset = pdx.datasets.SegDataset(
     data_dir='optic_disc_seg',
     file_list='optic_disc_seg/train_list.txt',
@@ -37,12 +37,12 @@ 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/tree/release/2.0-rc/tutorials/train#visualdl可视化训练指标
+# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/train/visualdl.md
 num_classes = len(train_dataset.labels)
 model = pdx.seg.UNet(num_classes=num_classes)
 
-# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/segmenter.py#L150
-# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
+# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/apis/models/semantic_segmentation.md
+# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/dygraph/docs/parameters.md
 model.train(
     num_epochs=10,
     train_dataset=train_dataset,