add api docs (#3483)

* add api docs

* fix docs

* add api doc in math_ops

* Change the theme to read_the_docs

* add equation docs in math_add

* add equation docs in sub, mul, addn

* add mod div tanh docs

* add gelu, relu docs

* add sigmoid unsorted_segment_sum api docs

* add unsorted_segment example docs

* add equal to greate_equal example docs

* add logical and example docs

* add other example docs, include squared difference

* add requirements of sphinx_rtd_theme

* delete the requirement of sphinx-material

* use make of format

Co-authored-by: doombeaker <[email protected]>
Co-authored-by: Shenghang Tsai <[email protected]>
Co-authored-by: oneflow-bot <[email protected]>
Former-commit-id: 3a1ef10

docker/ci/test/requirements.txt

@@ -1,7 +1,7 @@
 pycocotools
 opencv-python==4.2.0.34
 sphinx
-sphinx-material
 recommonmark
 scipy
 pillow
+sphinx_rtd_theme

docs/source/conf.py

@@ -74,7 +74,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "sphinx_material"
+html_theme = "sphinx_rtd_theme"
 
 # Material theme options (see theme.conf for more information)
 html_theme_options = {

oneflow/python/ops/layers.py

@@ -49,8 +49,8 @@ def dense(
         activation (Optional[remote_blob_util.BlobDef], optional):  Activation function. Defaults to None.
         use_bias (bool, optional): A boolean specifies whether to use a bias vector. Defaults to True.
         kernel_initializer (Optional[op_conf_util.InitializerConf], optional): Initializer for the kernel weights matrix. Defaults to None.
-        bias_initializer (Optional[op_conf_util.InitializerConf], optional): [description]. Defaults to None.
-        kernel_regularizer (Optional[op_conf_util.RegularizerConf], optional): [description]. Defaults to None.
+        bias_initializer (Optional[op_conf_util.InitializerConf], optional): Initializer for the bias vector. Defaults to None.
+        kernel_regularizer (Optional[op_conf_util.RegularizerConf], optional): Regularizer function applied to the kernel weights matrix. Defaults to None.
         bias_regularizer (Optional[op_conf_util.RegularizerConf], optional): Regularizer for the bias vector. Defaults to None.
         trainable (bool, optional): A boolean specifies whether to train the variables. Defaults to True.
         name (Optional[str], optional): This layer's name. Defaults to None.
@@ -646,7 +646,7 @@ def layer_norm(
         begin_norm_axis (int, optional): An integer specifies which axis to normalize at first. Defaults to 1.
         begin_params_axis (int, optional):  An integer specifies which axis params at . Defaults to -1.
         epsilon (float, optional): A small float is added to avoid division by zero. Defaults to 1e-5.
-        name (Optional[str], optional):  This layer's name. Defaults to None.
+        name (Optional[str], optional): This layer's name. Defaults to None.
 
     Returns:
         remote_blob_util.BlobDef: A normalized `Blob` with same shape of input.
@@ -1004,18 +1004,18 @@ def upsample(
 
     Args:
         x ([type]): Input `Blob`.
-        size (tuple, optional): (height_scale,width_scale)  Defaults to (2, 2).
+        size (tuple, optional): (height_scale, width_scale)  Defaults to (2, 2).
         data_format (str, optional): A string specifies the format of the input `Blob`, one of "NCHW" or "NHWC" (default: "NCHW"). "NCHW" cooresponds to channels_first, i.e. the input `Blob` with shape (batch_size, channels, height, width).
                         "NHWC" cooresponds to channels_last, i.e. the input `Blob` with shape (batch_size, height, width, channels).. Defaults to "NCHW".
-        interpolation (str, optional): Image interpolation algorithm to enlarge the image size. Defaults to "nearest".
+        interpolation (str, optional): Image interpolation algorithm to enlarge the image size. Defaults to "nearest". "nearest" and "bilinear" are available now.
         name ([type], optional): This layer's name. Defaults to None.
 
     Raises:
         ValueError: interpolation must be "nearest" or "bilinear".
         ValueError: data_format must be "NHWC" or "NCHW"
 
     Returns:
-        [type]: remote_blob_util.BlobDef:  A `Blob` with new shape of input. if  input size is(2,2),then the  new shape is [N, C, 2H, 2W].
+        [type]: remote_blob_util.BlobDef:  A `Blob` which is the upsampled `x`. If `size` is (2, 2), the shape of return value is [N, C, 2H, 2W].
     """
     if isinstance(size, int):
         height_scale = size