[Docathon] Fix en api_label

python/paddle/base/lod_tensor.py

@@ -46,7 +46,6 @@ def create_lod_tensor(data, recursive_seq_lens, place):
     would be converted to offset-based LoD [[0, 2, 5]] inside the function
     call.
 
-    Please reference :ref:`user_guide_lod_tensor` for more details regarding LoD.
 
     Args:
         data (numpy.ndarray|list|LoDTensor): a numpy array, a list or ad LoDTensor

python/paddle/jit/dy2static/utils.py

@@ -137,7 +137,7 @@ def data_layer_not_check(name, shape, dtype='float32', lod_level=0):
     data can be various-length. This API is used in translating dygraph into
     static graph.
 
-     Note:
+    Note:
         The default :code:`stop_gradient` attribute of the Tensor created by
         this API is true, which means the gradient won't be passed backward
         through the data Tensor. Set :code:`var.stop_gradient = False` If
@@ -153,8 +153,7 @@ def data_layer_not_check(name, shape, dtype='float32', lod_level=0):
            dtype: bool, float16, float32, float64, int8, int16, int32, int64,
            uint8. Default: float32
        lod_level (int, optional): The LoD level of the LoDTensor. Usually users
-           don't have to set this value. For more details about when and how to
-           use LoD level, see :ref:`user_guide_lod_tensor` . Default: 0
+           don't have to set this value. Default: 0
 
     Returns:
         Tensor: The global Tensor that gives access to the data.

python/paddle/nn/functional/activation.py

@@ -319,7 +319,7 @@ def hardtanh(x, min=-1.0, max=1.0, name=None):
 def hardtanh_(x, min=-1.0, max=1.0, name=None):
     r"""
     Inplace version of ``hardtanh`` API, the output Tensor will be inplaced with input ``x``.
-    Please refer to :ref:`paddle_nn_functional_hardtanh`.
+    Please refer to :ref:`api_paddle_nn_functional_hardtanh`.
     """
     if in_dynamic_mode():
         return _C_ops.hardtanh_(x, min, max)
@@ -495,7 +495,7 @@ def leaky_relu(x, negative_slope=0.01, name=None):
 def leaky_relu_(x, negative_slope=0.01, name=None):
     r"""
     Inplace version of ``leaky_relu`` API, the output Tensor will be inplaced with input ``x``.
-    Please refer to :ref:`paddle_nn_functional_leaky_relu`.
+    Please refer to :ref:`api_paddle_nn_functional_leaky_relu`.
     """
     if in_dynamic_mode():
         return _C_ops.leaky_relu_(x, negative_slope)
@@ -1594,7 +1594,7 @@ def thresholded_relu(x, threshold=1.0, name=None):
 def thresholded_relu_(x, threshold=1.0, name=None):
     r"""
     Inplace version of ``thresholded_relu`` API, the output Tensor will be inplaced with input ``x``.
-    Please refer to :ref:`paddle_nn_functional_thresholded_relu`.
+    Please refer to :ref:`api_paddle_nn_functional_thresholded_relu`.
     """
     if in_dynamic_mode():
         return _C_ops.thresholded_relu_(x, threshold)

python/paddle/nn/functional/vision.py

@@ -338,7 +338,7 @@ def grid_sample(
 def pixel_shuffle(x, upscale_factor, data_format="NCHW", name=None):
     """
     This API implements pixel shuffle operation.
-    See more details in :ref:`PixelSuffle <api_paddle_nn_PixelSuffle>` .
+    See more details in :ref:`PixelSuffle <api_paddle_nn_PixelShuffle>` .
 
 
     Parameters:
@@ -392,7 +392,7 @@ def pixel_shuffle(x, upscale_factor, data_format="NCHW", name=None):
 def pixel_unshuffle(x, downscale_factor, data_format="NCHW", name=None):
     """
     This API implements pixel unshuffle operation.
-    See more details in :ref:`PixelUnSuffle <api_paddle_nn_PixelUnSuffle>` .
+    See more details in :ref:`PixelUnSuffle <api_paddle_nn_PixelUnshuffle>` .
 
     Parameters:
         x (Tensor): 4-D tensor, the data type should be float32 or float64.

python/paddle/profiler/profiler.py

@@ -98,7 +98,7 @@ class ProfilerState(Enum):
 
 class ProfilerTarget(Enum):
     r"""
-    ProfilerTarget is used to specify target device for :ref:`profiling <api_paddle_profiler_Profiler>` . Only CPU, GPU and XPU are supported currently.
+    ProfilerTarget is used to specify target device for :ref:`Profiler <api_paddle_profiler_Profiler>` . Only CPU, GPU and XPU are supported currently.
 
     The meaning of each ProfilerState is as following
 
@@ -123,7 +123,7 @@ def make_scheduler(
     skip_first: int = 0,
 ) -> Callable:
     r"""
-    Return a scheduler function, which scheduler the :ref:`state <api_paddle_profiler_ProfilerState>` according to the setting.
+    Return a scheduler function, which scheduler the :ref:`ProfilerState <api_paddle_profiler_ProfilerState>` according to the setting.
     The state transform confirms to:
 
     .. code-block:: text

python/paddle/profiler/profiler_statistic.py

@@ -48,7 +48,7 @@
 
 class SortedKeys(Enum):
     r"""
-    SortedKeys is used to specify how to sort items when printing :ref:`summary <api_paddle_profiler_profiler_summary>` table.
+    SortedKeys is used to specify how to sort items when printing ``paddle.profiler.Profiler.summary`` table.
 
     The meaning of each SortedKeys is as following
 

python/paddle/static/nn/common.py

@@ -1500,7 +1500,7 @@ def conv2d_transpose(
             is not set, the parameter is initialized with Xavier. Default: None.
         bias_attr (ParamAttr|bool, optional): Specifies the object for the bias parameter attribute.
             The default value is None, which means that the default bias parameter attribute is used.
-            For detailed information, please refer to :ref:`paramattr`.
+            For detailed information, please refer to :ref:`api_paddle_ParamAttr`.
             The default bias initialisation for the conv2d_transpose operator is 0.0.
         use_cudnn(bool, optional): Use cudnn kernel or not, it is valid only when the cudnn
             library is installed. Default: True.

python/paddle/tensor/linalg.py

@@ -2581,9 +2581,9 @@ def eig(x, name=None):
     Performs the eigenvalue decomposition of a square matrix or a batch of square matrices.
 
     Note:
-        - If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.
-        - If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.
-        - If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.
+        - If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`api_paddle_linalg_eigh` instead, which is much faster.
+        - If only eigenvalues is needed, please use :ref:`api_paddle_linalg_eigvals` instead.
+        - If the matrix is of any shape, please use :ref:`api_paddle_linalg_svd`.
         - This API is only supported on CPU device.
         - The output datatype is always complex for both real and complex input.
 

python/paddle/vision/datasets/cifar.py

@@ -51,7 +51,7 @@ class Cifar10(Dataset):
         download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True.
         backend (str, optional): Specifies which type of image to be returned:
             PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
-            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_vision_image_get_image_backend>`,
+            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
             default backend is 'pil'. Default: None.
 
     Returns:
@@ -198,7 +198,7 @@ class Cifar100(Cifar10):
         download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default True.
         backend (str, optional): Specifies which type of image to be returned:
             PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
-            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_vision_image_get_image_backend>`,
+            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
             default backend is 'pil'. Default: None.
 
     Returns:

python/paddle/vision/datasets/flowers.py

@@ -55,7 +55,7 @@ class Flowers(Dataset):
         download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default: True.
         backend (str, optional): Specifies which type of image to be returned:
             PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
-            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_vision_image_get_image_backend>`,
+            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
             default backend is 'pil'. Default: None.
 
     Returns:

python/paddle/vision/datasets/mnist.py

@@ -40,7 +40,7 @@ class MNIST(Dataset):
             :attr:`image_path` :attr:`label_path` is not set. Default: True.
         backend (str, optional): Specifies which type of image to be returned:
             PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
-            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_vision_image_get_image_backend>`,
+            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
             default backend is 'pil'. Default: None.
 
     Returns:
@@ -249,7 +249,7 @@ class FashionMNIST(MNIST):
             :attr:`image_path` :attr:`label_path` is not set. Default: True.
         backend (str, optional): Specifies which type of image to be returned:
             PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
-            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_vision_image_get_image_backend>`,
+            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
             default backend is 'pil'. Default: None.
 
     Returns:

python/paddle/vision/datasets/voc2012.py

@@ -48,7 +48,7 @@ class VOC2012(Dataset):
         download (bool, optional): Download dataset automatically if :attr:`data_file` is None. Default: True.
         backend (str, optional): Specifies which type of image to be returned:
             PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
-            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_vision_image_get_image_backend>`,
+            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
             default backend is 'pil'. Default: None.
 
     Returns:

python/paddle/vision/models/densenet.py

@@ -359,7 +359,7 @@ def densenet121(pretrained=False, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
             on ImageNet. Default: False.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_models_DenseNet>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of DenseNet 121-layer model.
@@ -392,7 +392,7 @@ def densenet161(pretrained=False, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
             on ImageNet. Default: False.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_models_DenseNet>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of DenseNet 161-layer model.
@@ -425,7 +425,7 @@ def densenet169(pretrained=False, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
             on ImageNet. Default: False.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_models_DenseNet>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of DenseNet 169-layer model.
@@ -458,7 +458,7 @@ def densenet201(pretrained=False, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
             on ImageNet. Default: False.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_models_DenseNet>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of DenseNet 201-layer model.
@@ -490,7 +490,7 @@ def densenet264(pretrained=False, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
             on ImageNet. Default: False.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_DenseNet>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`DenseNet <api_paddle_vision_models_DenseNet>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of DenseNet 264-layer model.

python/paddle/vision/models/googlenet.py

@@ -237,7 +237,7 @@ def googlenet(pretrained=False, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
             on ImageNet. Default: False.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`GoogLeNet <api_paddle_vision_GoogLeNet>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`GoogLeNet <api_paddle_vision_models_GoogLeNet>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) model.

python/paddle/vision/models/inceptionv3.py

@@ -592,7 +592,7 @@ def inception_v3(pretrained=False, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
             on ImageNet. Default: False.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`InceptionV3 <api_paddle_vision_InceptionV3>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`InceptionV3 <api_paddle_vision_models_InceptionV3>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of Inception v3 model.

python/paddle/vision/models/mobilenetv1.py

@@ -270,7 +270,7 @@ def mobilenet_v1(pretrained=False, scale=1.0, **kwargs):
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
             on ImageNet. Default: False.
         scale (float, optional): Scale of channels in each layer. Default: 1.0.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV1 <api_paddle_vision_MobileNetV1>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV1 <api_paddle_vision_models_MobileNetV1>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of MobileNetV1 model.

python/paddle/vision/models/mobilenetv2.py

@@ -208,7 +208,7 @@ def mobilenet_v2(pretrained=False, scale=1.0, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False.
         scale (float, optional): Scale of channels in each layer. Default: 1.0.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV2 <api_paddle_vision_MobileNetV2>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV2 <api_paddle_vision_models_MobileNetV2>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of MobileNetV2 model.

python/paddle/vision/models/mobilenetv3.py

@@ -428,7 +428,7 @@ def mobilenet_v3_small(pretrained=False, scale=1.0, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False.
         scale (float, optional): Scale of channels in each layer. Default: 1.0.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV3Small <api_paddle_vision_MobileNetV3Small>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV3Small <api_paddle_vision_models_MobileNetV3Small>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of MobileNetV3 Small architecture model.
@@ -467,7 +467,7 @@ def mobilenet_v3_large(pretrained=False, scale=1.0, **kwargs):
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False.
         scale (float, optional): Scale of channels in each layer. Default: 1.0.
-        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV3Large <api_paddle_vision_MobileNetV3Large>`.
+        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV3Large <api_paddle_vision_models_MobileNetV3Large>`.
 
     Returns:
         :ref:`api_paddle_nn_Layer`. An instance of MobileNetV3 Large architecture model.