fix boardcasting superlink

paddle/fluid/operators/controlflow/bitwise_op.cc

@@ -48,7 +48,9 @@ It operates ``%s`` on Tensor ``X`` and ``Y`` .
         %s
 
 .. note::
-    ``paddle.%s`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
+    ``paddle.%s`` supports broadcasting. If you want know more about broadcasting, please refer to please refer to `Introduction to Tensor`_ .
+
+    .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor.
 )DOC",
                                comment.type,
                                comment.equation,

python/paddle/distribution/uniform.py

@@ -49,7 +49,12 @@ class Uniform(distribution.Distribution):
     * :math:`Z`: is the normalizing constant.
 
     The parameters `low` and `high` must be shaped in a way that supports
-    :ref:`user_guide_broadcasting` (e.g., `high - low` is a valid operation).
+    `Boardcasting` (e.g., `high - low` is a valid operation).
+
+    Note:
+        If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         low(int|float|list|tuple|numpy.ndarray|Tensor): The lower boundary of

python/paddle/tensor/creation.py

@@ -2005,8 +2005,10 @@ def complex(real, imag, name=None):
     Returns:
         Tensor: The output tensor. The data type is 'complex64' or 'complex128', with the same precision as ``real`` and ``imag``.
 
-    **Note**:
-        ``paddle.complex`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+    Note:
+        ``paddle.complex`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Examples:
         .. code-block:: python

python/paddle/tensor/logic.py

@@ -96,7 +96,9 @@ def logical_and(x, y, out=None, name=None):
         out = x \&\& y
 
     Note:
-        ``paddle.logical_and`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
+        ``paddle.logical_and`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64.
@@ -136,7 +138,9 @@ def logical_or(x, y, out=None, name=None):
         out = x || y
 
     Note:
-        ``paddle.logical_or`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
+        ``paddle.logical_or`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64.
@@ -178,7 +182,9 @@ def logical_xor(x, y, out=None, name=None):
         out = (x || y) \&\& !(x \&\& y)
 
     Note:
-        ``paddle.logical_xor`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
+        ``paddle.logical_xor`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be one of bool, int8, int16, in32, in64, float32, float64.
@@ -221,6 +227,11 @@ def logical_not(x, out=None, name=None):
 
         out = !x
 
+    Note:
+        ``paddle.logical_not`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
+
     Args:
         x(Tensor):  Operand of logical_not operator. Must be a Tensor of type bool, int8, int16, in32, in64, float32, or float64.
         out(Tensor): The ``Tensor`` that specifies the output of the operator, which can be any ``Tensor`` that has been created in the program. The default value is None, and a new ``Tensor` will be created to save the output.

python/paddle/tensor/math.py

@@ -420,7 +420,9 @@ def pow(x, y, name=None):
         out = x^{y}
 
     Note:
-        ``paddle.pow`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.pow`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensors
 
 
     Args:
@@ -679,7 +681,9 @@ def subtract(x, y, name=None):
         out = x - y
 
     Note:
-        ``paddle.subtract`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.subtract`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
@@ -773,7 +777,9 @@ def divide(x, y, name=None):
         out = x / y
 
     Note:
-        ``paddle.divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.divide`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
@@ -817,7 +823,10 @@ def floor_divide(x, y, name=None):
         out = trunc(x / y)
 
     Note:
-        ``paddle.floor_divide`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.floor_divide`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
+
         Also note that the name ``floor_divide`` can be misleading, as the quotinents are actually rounded toward zero, not toward negative infinite.
 
     Args:
@@ -859,7 +868,9 @@ def remainder(x, y, name=None):
         out = x \% y
 
     Note:
-        ``paddle.remainder`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.remainder`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
@@ -924,7 +935,9 @@ def multiply(x, y, name=None):
         out = x * y
 
     Note:
-        ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
+        ``paddle.multiply`` supports broadcasting. If you would like to know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, its data type should be one of float32, float64, int32, int64, bool.
@@ -980,7 +993,9 @@ def maximum(x, y, name=None):
         out = max(x, y)
 
     Note:
-        ``paddle.maximum`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.maximum`` supports broadcasting. If you want know more about broadcasting, please refer to  `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
@@ -1046,7 +1061,9 @@ def minimum(x, y, name=None):
         out = min(x, y)
 
     Note:
-        ``paddle.minimum`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.minimum`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
@@ -1114,7 +1131,9 @@ def fmax(x, y, name=None):
         out = fmax(x, y)
 
     Note:
-        ``paddle.fmax`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.fmax`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
@@ -1182,7 +1201,9 @@ def fmin(x, y, name=None):
         out = fmin(x, y)
 
     Note:
-        ``paddle.fmin`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
+        ``paddle.fmin`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
@@ -3962,7 +3983,12 @@ def any(x, axis=None, keepdim=False, name=None):
 
 def broadcast_shape(x_shape, y_shape):
     """
-    The function returns the shape of doing operation with broadcasting on tensors of x_shape and y_shape, please refer to :ref:`user_guide_broadcasting` for more details.
+    The function returns the shape of doing operation with broadcasting on tensors of x_shape and y_shape.
+
+    Note:
+        If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x_shape (list[int]|tuple[int]): A shape of tensor.
@@ -4991,7 +5017,9 @@ def heaviside(x, y, name=None):
             \right.
 
     Note:
-        ``paddle.heaviside`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
+        ``paddle.heaviside`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
+
+        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
 
     Args:
         x (Tensor): The input tensor of Heaviside step function, it's data type should be float16, float32, float64, int32 or int64.

python/paddle/vision/image.py

@@ -23,8 +23,8 @@
 
 def set_image_backend(backend):
     """
-    Specifies the backend used to load images in class ``paddle.vision.datasets.ImageFolder``
-    and ``paddle.vision.datasets.DatasetFolder`` . Now support backends are pillow and opencv.
+    Specifies the backend used to load images in class :ref:`api_paddle_datasets_ImageFolder`
+    and :ref:`api_paddle_datasets_DatasetFolder` . Now support backends are pillow and opencv.
     If backend not set, will use 'pil' as default.
 
     Args:
@@ -117,7 +117,7 @@ def image_load(path, backend=None):
         path (str): Path of the image.
         backend (str, optional): The image decoding backend type. Options are
             `cv2`, `pil`, `None`. If backend is None, the global _imread_backend
-            specified by ``paddle.vision.set_image_backend`` will be used. Default: None.
+            specified by :ref:`api_paddle_vision_set_image_backend` will be used. Default: None.
 
     Returns:
         PIL.Image or np.array: Loaded image.

python/paddle/vision/transforms/functional.py

@@ -42,7 +42,18 @@ def _is_numpy_image(img):
 def to_tensor(pic, data_format='CHW'):
     """Converts a ``PIL.Image`` or ``numpy.ndarray`` to paddle.Tensor.
 
-    See ``ToTensor`` for more details.
+    Converts a PIL.Image or numpy.ndarray (H x W x C) to a paddle.Tensor of shape (C x H x W).
+
+    If input is a grayscale image (H x W), it will be converted to an image of shape (H x W x 1).
+    And the shape of output tensor will be (1 x H x W).
+
+    If you want to keep the shape of output tensor as (H x W x C), you can set data_format = ``HWC`` .
+
+    Converts a PIL.Image or numpy.ndarray in the range [0, 255] to a paddle.Tensor in the
+    range [0.0, 1.0] if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr,
+    RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8.
+
+    In the other cases, tensors are returned without scaling.
 
     Args:
         pic (PIL.Image|np.ndarray): Image to be converted to tensor.
@@ -764,7 +775,7 @@ def rotate(
         center (2-list|2-tuple, optional): Optional center of rotation.
             Origin is the upper left corner.
             Default is the center of the image.
-        fill (3-list|3-tuple or int): RGB pixel fill value for area outside the rotated image.
+        fill (3-list|3-tuple or int, optional): RGB pixel fill value for area outside the rotated image.
             If int, it is used for all channels respectively. Default value is 0.
 
 

python/paddle/vision/transforms/transforms.py

@@ -136,6 +136,8 @@ class BaseTransform:
 
     calling logic:
 
+    .. code-block:: text
+
         if keys is None:
             _get_params -> _apply_image()
         else:
@@ -153,14 +155,11 @@ class BaseTransform:
 
             Current available strings & data type are describe below:
 
-            - "image": input image, with shape of (H, W, C)
-            - "coords": coordinates, with shape of (N, 2)
-            - "boxes": bounding boxes, with shape of (N, 4), "xyxy" format,
-
-                       the 1st "xy" represents top left point of a box,
-                       the 2nd "xy" represents right bottom point.
-
-            - "mask": map used for segmentation, with shape of (H, W, 1)
+                - "image": input image, with shape of (H, W, C)
+                - "coords": coordinates, with shape of (N, 2)
+                - "boxes": bounding boxes, with shape of (N, 4), "xyxy" format,the 1st "xy" represents
+                  top left point of a box,the 2nd "xy" represents right bottom point.
+                - "mask": map used for segmentation, with shape of (H, W, 1)
 
             You can also customize your data types only if you implement the corresponding
             _apply_*() methods, otherwise ``NotImplementedError`` will be raised.
@@ -433,9 +432,9 @@ class RandomResizedCrop(BaseTransform):
 
     Args:
         size (int|list|tuple): Target size of output image, with (height, width) shape.
-        scale (list|tuple): Scale range of the cropped image before resizing, relatively to the origin
-            image. Default: (0.08, 1.0)
-        ratio (list|tuple): Range of aspect ratio of the origin aspect ratio cropped. Default: (0.75, 1.33)
+        scale (list|tuple, optional): Scale range of the cropped image before resizing, relatively to the origin
+            image. Default: (0.08, 1.0).
+        ratio (list|tuple, optional): Range of aspect ratio of the origin aspect ratio cropped. Default: (0.75, 1.33)
         interpolation (int|str, optional): Interpolation method. Default: 'bilinear'. when use pil backend,
             support method are as following:
             - "nearest": Image.NEAREST,
@@ -778,7 +777,7 @@ class BrightnessTransform(BaseTransform):
 
     Args:
         value (float): How much to adjust the brightness. Can be any
-            non negative number. 0 gives the original image
+            non negative number. 0 gives the original image.
         keys (list[str]|tuple[str], optional): Same as ``BaseTransform``. Default: None.
 
     Shape:
@@ -821,7 +820,7 @@ class ContrastTransform(BaseTransform):
 
     Args:
         value (float): How much to adjust the contrast. Can be any
-            non negative number. 0 gives the original image
+            non negative number. 0 gives the original image.
         keys (list[str]|tuple[str], optional): Same as ``BaseTransform``. Default: None.
 
     Shape:
@@ -866,7 +865,7 @@ class SaturationTransform(BaseTransform):
 
     Args:
         value (float): How much to adjust the saturation. Can be any
-            non negative number. 0 gives the original image
+            non negative number. 0 gives the original image.
         keys (list[str]|tuple[str], optional): Same as ``BaseTransform``. Default: None.
 
     Shape:
@@ -909,7 +908,7 @@ class HueTransform(BaseTransform):
 
     Args:
         value (float): How much to adjust the hue. Can be any number
-            between 0 and 0.5, 0 gives the original image
+            between 0 and 0.5, 0 gives the original image.
         keys (list[str]|tuple[str], optional): Same as ``BaseTransform``. Default: None.
 
     Shape:
@@ -953,14 +952,14 @@ class ColorJitter(BaseTransform):
     """Randomly change the brightness, contrast, saturation and hue of an image.
 
     Args:
-        brightness (float): How much to jitter brightness.
-            Chosen uniformly from [max(0, 1 - brightness), 1 + brightness]. Should be non negative numbers.
-        contrast (float): How much to jitter contrast.
-            Chosen uniformly from [max(0, 1 - contrast), 1 + contrast]. Should be non negative numbers.
-        saturation (float): How much to jitter saturation.
-            Chosen uniformly from [max(0, 1 - saturation), 1 + saturation]. Should be non negative numbers.
-        hue (float): How much to jitter hue.
-            Chosen uniformly from [-hue, hue]. Should have 0<= hue <= 0.5.
+        brightness (float, optional): How much to jitter brightness.
+            Chosen uniformly from [max(0, 1 - brightness), 1 + brightness]. Should be non negative numbers. Default: 0.
+        contrast (float, optional): How much to jitter contrast.
+            Chosen uniformly from [max(0, 1 - contrast), 1 + contrast]. Should be non negative numbers. Default: 0.
+        saturation (float, optional): How much to jitter saturation.
+            Chosen uniformly from [max(0, 1 - saturation), 1 + saturation]. Should be non negative numbers. Default: 0.
+        hue (float, optional): How much to jitter hue.
+            Chosen uniformly from [-hue, hue]. Should have 0<= hue <= 0.5. Default: 0.
         keys (list[str]|tuple[str], optional): Same as ``BaseTransform``. Default: None.
 
     Shape:
@@ -1665,7 +1664,7 @@ class Grayscale(BaseTransform):
     """Converts image to grayscale.
 
     Args:
-        num_output_channels (int): (1 or 3) number of channels desired for output image
+        num_output_channels (int, optional): (1 or 3) number of channels desired for output image. Default: 1.
         keys (list[str]|tuple[str], optional): Same as ``BaseTransform``. Default: None.
 
     Shape: