Use @autodocs and fix references

Author: Saransh-cpp

docs/make.jl

@@ -11,7 +11,7 @@ makedocs(modules = [Metalhead, Artifacts, LazyArtifacts, Images, OneHotArrays, D
                    ],
                   "Developer guide" => "contributing.md",
                   "API reference" => [
-                      "api/models.md",
+                      "api/reference.md",
                    ],
                  ],
          format = Documenter.HTML(

docs/src/api/models.md

@@ -0,0 +1,14 @@
+# API Reference
+
+The API reference of `Metalhead.jl`.
+
+**Note**:
+
+```@autodocs
+Modules = [Metalhead]
+```
+
+```@docs
+Metalhead.squeeze_excite
+Metalhead.LayerScale
+```

docs/src/api/reference.md

@@ -5,7 +5,6 @@ using Flux, Metalhead
 ```
 
 Using a model from Metalhead is as simple as selecting a model from the table of [available models](@ref). For example, below we use the pre-trained ResNet-18 model.
-{cell=quickstart}
 ```julia
 using Flux, Metalhead
 

docs/src/tutorials/quickstart.md

@@ -45,7 +45,7 @@ Create a `AlexNet`.
     
     `AlexNet` does not currently support pretrained weights.
 
-See also [`Metalhead.alexnet`](@ref).
+See also [`alexnet`](@ref).
 """
 struct AlexNet
     layers::Any

src/convnets/alexnet.jl

@@ -7,8 +7,8 @@ Creates a single block of ConvNeXt.
 # Arguments
 
   - `planes`: number of input channels.
-  - `stochastic_depth_prob`: Stochastic depth probability.
-  - `layerscale_init`: Initial value for [`LayerScale`](@ref)
+  - `drop_path_rate`: Stochastic depth rate.
+  - `layerscale_init`: Initial value for [`Metalhead.LayerScale`](@ref)
 """
 function convnextblock(planes::Integer, stochastic_depth_prob = 0.0,
                        layerscale_init = 1.0f-6)
@@ -33,8 +33,13 @@ Creates the layers for a ConvNeXt model.
 
   - `depths`: list with configuration for depth of each block
   - `planes`: list with configuration for number of output channels in each block
+<<<<<<< HEAD
   - `stochastic_depth_prob`: Stochastic depth probability.
   - `layerscale_init`: Initial value for [`LayerScale`](@ref)
+=======
+  - `drop_path_rate`: Stochastic depth rate.
+  - `layerscale_init`: Initial value for [`Metalhead.LayerScale`](@ref)
+>>>>>>> 5ac81ce (Use `@autodocs` and fix references)
     ([reference](https://arxiv.org/abs/2103.17239))
   - `inchannels`: number of input channels.
   - `nclasses`: number of output classes
@@ -96,10 +101,13 @@ Creates a ConvNeXt model.
   - `inchannels`: number of input channels
   - `nclasses`: number of output classes
 
+<<<<<<< HEAD
 !!! warning
     
     `ConvNeXt` does not currently support pretrained weights.
 
+=======
+>>>>>>> 5ac81ce (Use `@autodocs` and fix references)
 See also [`Metalhead.convnext`](@ref).
 """
 struct ConvNeXt

src/convnets/convnext.jl

@@ -66,7 +66,11 @@ used to build the block for the model, see [`Metalhead.bottleneck_builder`](@ref
   - `revnorm`: set to `true` to place the normalisation layer before the convolution
   - `drop_block`: the drop block layer
   - `drop_path`: the drop path layer
+<<<<<<< HEAD
   - `attn_fn`: the attention function to use. See [`squeeze_excite`](@ref) for an example.
+=======
+  - `attn_fn`: the attention function to use. See [`Metalhead.squeeze_excite`](@ref) for an example.
+>>>>>>> 5ac81ce (Use `@autodocs` and fix references)
 """
 function bottleneck(inplanes::Integer, planes::Integer; stride::Integer,
                     cardinality::Integer = 1, base_width::Integer = 64,
@@ -231,7 +235,11 @@ const RESNET_SHORTCUTS = Dict(:A => (downsample_identity, downsample_identity),
     resnet_stem(; stem_type = :default, inchannels::Integer = 3, replace_stem_pool = false,
                   norm_layer = BatchNorm, activation = relu)
 
+<<<<<<< HEAD
 Builds a stem to be used in a ResNet model. See the `stem` argument of [`resnet`](@ref) for details
+=======
+Builds a stem to be used in a ResNet model. See the `stem` argument of [`Metalhead.resnet`](@ref) for details
+>>>>>>> 5ac81ce (Use `@autodocs` and fix references)
 on how to use this function.
 
 # Arguments

src/convnets/resnets/core.jl

@@ -48,7 +48,11 @@ The number of channels in outer 1x1 convolutions is the same.
   - `inchannels`: The number of input channels.
   - `nclasses`: The number of output classes
 
+<<<<<<< HEAD
 Advanced users who want more configuration options will be better served by using [`resnet`](@ref).
+=======
+Advanced users who want more configuration options will be better served by using [`Metalhead.resnet`](@ref).
+>>>>>>> 5ac81ce (Use `@autodocs` and fix references)
 """
 struct WideResNet
     layers::Any

src/convnets/resnets/resnet.jl

@@ -58,7 +58,11 @@ Creates a SEResNeXt model with the specified depth, cardinality, and base width.
     
     `SEResNeXt` does not currently support pretrained weights.
 
+<<<<<<< HEAD
 Advanced users who want more configuration options will be better served by using [`resnet`](@ref).
+=======
+Advanced users who want more configuration options will be better served by using [`Metalhead.resnet`](@ref).
+>>>>>>> 5ac81ce (Use `@autodocs` and fix references)
 """
 struct SEResNeXt
     layers::Any

src/convnets/resnets/seresnet.jl

@@ -63,7 +63,7 @@ Create a SqueezeNet
   - `inchannels`: number of input channels.
   - `nclasses`: the number of output classes.
 
-See also [`Metalhead.squeezenet`](@ref).
+See also [`squeezenet`](@ref).
 """
 struct SqueezeNet
     layers::Any