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

@@ -16,7 +16,7 @@ To add a new model architecture to Metalhead.jl, you can [open a PR](https://git
 
 - reuse layers from Flux as much as possible (e.g. use `Parallel` before defining a `Bottleneck` struct)
 - adhere as closely as possible to a reference such as a published paper (i.e. the structure of your model should follow intuitively from the paper)
-- use generic functional builders (e.g. [`resnet`](#) is the core function that builds "ResNet-like" models)
+- use generic functional builders (e.g. [`Metalhead.resnet`](@ref) is the core function that builds "ResNet-like" models)
 - use multiple dispatch to add convenience constructors that wrap your functional builder
 
 When in doubt, just open a PR! We are more than happy to help review your code to help it align with the rest of the library. After adding a model, you might consider adding some pre-trained weights (see below).

docs/src/contributing.md

@@ -4,7 +4,7 @@
 using Flux, Metalhead
 ```
 
-Using a model from Metalhead is as simple as selecting a model from the table of [available models](#). For example, below we use the pre-trained ResNet-18 model.
+Using a model from Metalhead is as simple as selecting a model from the table of [available models](@ref API-Reference). For example, below we use the pre-trained ResNet-18 model.
 ```julia
 using Flux, Metalhead
 

docs/src/tutorials/quickstart.md

@@ -44,7 +44,7 @@ Create a `AlexNet`.
     
     `AlexNet` does not currently support pretrained weights.
 
-See also [`alexnet`](#).
+See also [`alexnet`](@ref).
 """
 struct AlexNet
     layers::Any

src/convnets/alexnet.jl

@@ -8,7 +8,7 @@ Creates a single block of ConvNeXt.
 
   - `planes`: number of input channels.
   - `drop_path_rate`: Stochastic depth rate.
-  - `layerscale_init`: Initial value for [`LayerScale`](#)
+  - `layerscale_init`: Initial value for [`Metalhead.LayerScale`](@ref)
 """
 function convnextblock(planes::Integer, drop_path_rate = 0.0, layerscale_init = 1.0f-6)
     layers = SkipConnection(Chain(DepthwiseConv((7, 7), planes => planes; pad = 3),
@@ -34,7 +34,7 @@ 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
   - `drop_path_rate`: Stochastic depth rate.
-  - `layerscale_init`: Initial value for [`LayerScale`](#)
+  - `layerscale_init`: Initial value for [`Metalhead.LayerScale`](@ref)
     ([reference](https://arxiv.org/abs/2103.17239))
   - `inchannels`: number of input channels.
   - `nclasses`: number of output classes
@@ -87,7 +87,7 @@ Creates a ConvNeXt model.
   - `inchannels`: The number of channels in the input.
   - `nclasses`: number of output classes
 
-See also [`Metalhead.convnext`](#).
+See also [`Metalhead.convnext`](@ref).
 """
 struct ConvNeXt
     layers::Any

src/convnets/convnext.jl

@@ -64,7 +64,7 @@ Create a DenseNet model
 
   - `inplanes`: the number of input feature maps to the first dense block
   - `growth_rates`: the growth rates of output feature maps within each
-    [`dense_block`](#) (a vector of vectors)
+    [`dense_block`](@ref) (a vector of vectors)
   - `reduction`: the factor by which the number of feature maps is scaled across each transition
   - `nclasses`: the number of output classes
 """
@@ -122,7 +122,7 @@ Set `pretrain = true` to load the model with pre-trained weights for ImageNet.
     
     `DenseNet` does not currently support pretrained weights.
 
-See also [`Metalhead.densenet`](#).
+See also [`Metalhead.densenet`](@ref).
 """
 struct DenseNet
     layers::Any

src/convnets/densenet.jl

@@ -88,7 +88,7 @@ const EFFICIENTNET_GLOBAL_CONFIGS = Dict(:b0 => (224, (1.0, 1.0)),
     EfficientNet(config::Symbol; pretrain::Bool = false)
 
 Create an EfficientNet model ([reference](https://arxiv.org/abs/1905.11946v5)).
-See also [`efficientnet`](#).
+See also [`efficientnet`](@ref).
 
 # Arguments
 

src/convnets/efficientnet.jl

@@ -71,7 +71,7 @@ Create an Inception-v1 model (commonly referred to as `GoogLeNet`)
     
     `GoogLeNet` does not currently support pretrained weights.
 
-See also [`googlenet`](#).
+See also [`googlenet`](@ref).
 """
 struct GoogLeNet
     layers::Any