More minor tweaks

1. Support `pretrain` for the Inception model APIs
2. Group deprecations in a single source file to make stuff more organised
3. Random formatting nitpicks
4. Use a plain broadcast instead of `applyactivation`

Author: theabhirath

src/convnets/densenet.jl

@@ -72,7 +72,6 @@ function densenet(inplanes, growth_rates; reduction = 0.5, nclasses = 1000)
     layers = []
     append!(layers, conv_bn((7, 7), 3, inplanes; stride = 2, pad = (3, 3), bias = false))
     push!(layers, MaxPool((3, 3); stride = 2, pad = (1, 1)))
-
     outplanes = 0
     for (i, rates) in enumerate(growth_rates)
         outplanes = inplanes + sum(rates)
@@ -82,7 +81,6 @@ function densenet(inplanes, growth_rates; reduction = 0.5, nclasses = 1000)
         inplanes = floor(Int, outplanes * reduction)
     end
     push!(layers, BatchNorm(outplanes, relu))
-
     return Chain(Chain(layers),
                  Chain(AdaptiveMeanPool((1, 1)),
                        MLUtils.flatten,
@@ -131,7 +129,6 @@ function DenseNet(nblocks::NTuple{N, <:Integer};
     layers = densenet(nblocks; growth_rate = growth_rate,
                       reduction = reduction,
                       nclasses = nclasses)
-
     return DenseNet(layers)
 end
 
@@ -164,13 +161,6 @@ See also [`Metalhead.densenet`](#).
 function DenseNet(config::Integer = 121; pretrain = false, nclasses = 1000)
     @assert config in keys(densenet_config) "`config` must be one out of $(sort(collect(keys(densenet_config))))."
     model = DenseNet(densenet_config[config]; nclasses = nclasses)
-
     pretrain && loadpretrain!(model, string("DenseNet", config))
     return model
 end
-
-# deprecations
-@deprecate DenseNet121(; kw...) DenseNet(121; kw...)
-@deprecate DenseNet161(; kw...) DenseNet(161; kw...)
-@deprecate DenseNet169(; kw...) DenseNet(169; kw...)
-@deprecate DenseNet201(; kw...) DenseNet(201; kw...)

src/convnets/inception.jl

@@ -134,10 +134,6 @@ Create an Inception-v3 model ([reference](https://arxiv.org/abs/1512.00567v3)).
 # Arguments
 
   - `nclasses`: the number of output classes
-
-!!! warning
-    
-    `inceptionv3` does not currently support pretrained weights.
 """
 function inceptionv3(; nclasses = 1000)
     layer = Chain(Chain(conv_bn((3, 3), 3, 32; stride = 2)...,
@@ -197,9 +193,6 @@ end
 backbone(m::Inceptionv3) = m.layers[1]
 classifier(m::Inceptionv3) = m.layers[2]
 
-@deprecate Inception3 Inceptionv3
-@deprecate inception3 inceptionv3
-
 ## Inceptionv4
 
 function mixed_3a()
@@ -325,23 +318,29 @@ function inceptionv4(; inchannels = 3, dropout = 0.0, nclasses = 1000)
 end
 
 """
-    Inceptionv4(; inchannels = 3, dropout = 0.0, nclasses = 1000)
+    Inceptionv4(; pretrain = false, inchannels = 3, dropout = 0.0, nclasses = 1000)
 
 Creates an Inceptionv4 model.
 ([reference](https://arxiv.org/abs/1602.07261))
 
 # Arguments
 
+  - `pretrain`: set to `true` to load the pre-trained weights for ImageNet
   - inchannels: number of input channels.
   - dropout: rate of dropout in classifier head.
   - nclasses: the number of output classes.
+
+!!! warning
+    
+        `Inceptionv4`` does not currently support pretrained weights.
 """
 struct Inceptionv4
     layers::Any
 end
 
-function Inceptionv4(; inchannels = 3, dropout = 0.0, nclasses = 1000)
+function Inceptionv4(; pretrain = false, inchannels = 3, dropout = 0.0, nclasses = 1000)
     layers = inceptionv4(; inchannels, dropout, nclasses)
+    pretrain && loadpretrain!(layers, "Inceptionv4")
     return Inceptionv4(layers)
 end
 
@@ -452,23 +451,30 @@ function inceptionresnetv2(; inchannels = 3, dropout = 0.0, nclasses = 1000)
 end
 
 """
-    InceptionResNetv2(; inchannels = 3, dropout = 0.0, nclasses = 1000)
+    InceptionResNetv2(; pretrain = false, inchannels = 3, dropout = 0.0, nclasses = 1000)
 
 Creates an InceptionResNetv2 model.
 ([reference](https://arxiv.org/abs/1602.07261))
 
 # Arguments
 
+  - `pretrain`: set to `true` to load the pre-trained weights for ImageNet
   - inchannels: number of input channels.
   - dropout: rate of dropout in classifier head.
   - nclasses: the number of output classes.
+
+!!! warning
+    
+        `InceptionResNetv2` does not currently support pretrained weights.
 """
 struct InceptionResNetv2
     layers::Any
 end
 
-function InceptionResNetv2(; inchannels = 3, dropout = 0.0, nclasses = 1000)
+function InceptionResNetv2(; pretrain = false, inchannels = 3, dropout = 0.0,
+                           nclasses = 1000)
     layers = inceptionresnetv2(; inchannels, dropout, nclasses)
+    pretrain && loadpretrain!(layers, "InceptionResNetv2")
     return InceptionResNetv2(layers)
 end
 
@@ -515,7 +521,7 @@ function xception_block(inchannels, outchannels, nrepeats; stride = 1,
             inc = inchannels
             outc = i == nrepeats ? outchannels : inchannels
         end
-        push!(layers, Base.Fix1(applyactivation, relu))
+        push!(layers, x -> relu.(x))
         append!(layers,
                 depthwise_sep_conv_bn((3, 3), inc, outc; pad = 1, bias = false,
                                       use_bn1 = false, use_bn2 = false))
@@ -557,15 +563,21 @@ struct Xception
 end
 
 """
-    Xception(; inchannels = 3, dropout = 0.0, nclasses = 1000)
+    Xception(; pretrain = false, inchannels = 3, dropout = 0.0, nclasses = 1000)
 
 Creates an Xception model.
 ([reference](https://arxiv.org/abs/1610.02357))
 
 # Arguments
+
+  - pretrain: set to `true` to load the pre-trained weights for ImageNet.
   - inchannels: number of input channels.
   - dropout: rate of dropout in classifier head.
-  - nclasses: the number of output classes. 
+  - nclasses: the number of output classes.
+
+!!! warning
+    
+        `Xception` does not currently support pretrained weights.
 """
 function Xception(; inchannels = 3, dropout = 0.0, nclasses = 1000)
     layers = xception(; inchannels, dropout, nclasses)

src/convnets/resnet.jl

@@ -256,16 +256,8 @@ resnet50_v1 = ResNet([1, 1, 4], [3, 4, 6, 3], :B; block = Metalhead.bottleneck_v
 """
 function ResNet(depth::Integer = 50; pretrain = false, nclasses = 1000)
     @assert depth in keys(resnet_config) "`depth` must be one of $(sort(collect(keys(resnet_config))))"
-
     config, block = resnet_config[depth]
     model = ResNet(config...; block = block, nclasses = nclasses)
     pretrain && loadpretrain!(model, string("ResNet", depth))
     return model
 end
-
-# Compat with Metalhead 0.6; remove in 0.7
-@deprecate ResNet18(; kw...) ResNet(18; kw...)
-@deprecate ResNet34(; kw...) ResNet(34; kw...)
-@deprecate ResNet50(; kw...) ResNet(50; kw...)
-@deprecate ResNet101(; kw...) ResNet(101; kw...)
-@deprecate ResNet152(; kw...) ResNet(152; kw...)

src/convnets/vgg.jl

@@ -177,9 +177,3 @@ function VGG(depth::Integer = 16; pretrain = false, batchnorm = false, nclasses
     end
     return model
 end
-
-# deprecations
-@deprecate VGG11(; kw...) VGG(11; kw...)
-@deprecate VGG13(; kw...) VGG(13; kw...)
-@deprecate VGG16(; kw...) VGG(16; kw...)
-@deprecate VGG19(; kw...) VGG(19; kw...)

src/deprecations.jl

@@ -0,0 +1,22 @@
+## Deprecated in 0.7.0
+
+@deprecate DenseNet121(; kw...) DenseNet(121; kw...)
+@deprecate DenseNet161(; kw...) DenseNet(161; kw...)
+@deprecate DenseNet169(; kw...) DenseNet(169; kw...)
+@deprecate DenseNet201(; kw...) DenseNet(201; kw...)
+
+@deprecate VGG11(; kw...) VGG(11; kw...)
+@deprecate VGG13(; kw...) VGG(13; kw...)
+@deprecate VGG16(; kw...) VGG(16; kw...)
+@deprecate VGG19(; kw...) VGG(19; kw...)
+
+@deprecate ResNet18(; kw...) ResNet(18; kw...)
+@deprecate ResNet34(; kw...) ResNet(34; kw...)
+@deprecate ResNet50(; kw...) ResNet(50; kw...)
+@deprecate ResNet101(; kw...) ResNet(101; kw...)
+@deprecate ResNet152(; kw...) ResNet(152; kw...)
+
+# Deprecated in 0.7.3
+
+@deprecate Inception3 Inceptionv3
+@deprecate inception3 inceptionv3

src/utilities.jl

@@ -55,14 +55,6 @@ Equivalent to `permutedims(x, perm)`.
 """
 swapdims(perm) = Base.Fix2(permutedims, perm)
 
-"""
-    applyactivation(activation, x)
-
-Apply an activation function to a given input.
-Equivalent to `activation.(x)`.
-"""
-applyactivation(activation, x) = activation.(x)
-
 # Utility function for pretty printing large models
 function _maybe_big_show(io, model)
     if isdefined(Flux, :_big_show)

test/convnets.jl

@@ -108,6 +108,13 @@ GC.gc()
         @test size(m(x_299)) == (1000, 2)
         @test gradtest(m, x_299)
     end
+    GC.safepoint()
+    GC.gc()
+    @testset "Xception" begin
+        m = Xception()
+        @test size(m(x_299)) == (1000, 2)
+        @test gradtest(m, x_299)
+    end
 end
 
 GC.safepoint()