Fix tests, hopefully

Author: theabhirath

.github/workflows/CI.yml

@@ -27,15 +27,14 @@ jobs:
           - x64
         suite:
           - '["AlexNet", "VGG"]'
-          - '["GoogLeNet", "SqueezeNet"]'
-          - '["EfficientNet", "MobileNet"]'
-          - '[r"/*/ResNet*", "ResNeXt"]'
-          - 'r"/*/Inception/Inceptionv*"'
-          - '["InceptionResNetv2", "Xception"]'
+          - '["GoogLeNet", "SqueezeNet", "MobileNet"]'
+          - '["EfficientNet"]'
+          - '[r"ResNet", r"ResNeXt"]'
+          - '"Inception"'
           - '"DenseNet"'
           - '["ConvNeXt", "ConvMixer"]'
-          - '"ViT"'
-          - '"Mixers"'
+          - 'r"ViTs"'
+          - 'r"Mixers"'
     steps:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@v1

test/convnets.jl

@@ -107,7 +107,7 @@ end
 end
 
 @testset "EfficientNet" begin
-    @testset "EfficientNet($name)" for name in [:b0, :b1, :b2, :b3, :b4] #, :b5, :b6, :b7, :b8]
+    @testset "EfficientNet($name)" for name in [:b0, :b1, :b2, :b3, :b4, :b5, :b6, :b7, :b8]
         # preferred image resolution scaling
         r = Metalhead.efficientnet_global_configs[name][1]
         x = rand(Float32, r, r, 3, 1)

test/mixers.jl

@@ -10,14 +10,14 @@
 end
 
 @testset "ResMLP" begin
-  @testset for mode in [:small, :base, :large, :huge]
-		@testset for drop_path_rate in [0.0, 0.5]
-			m = ResMLP(mode; drop_path_rate)
-			@test size(m(x_224)) == (1000, 1)
-			@test gradtest(m, x_224)
-			_gc()
-		end
-  end
+    @testset for mode in [:small, :base, :large, :huge]
+        @testset for drop_path_rate in [0.0, 0.5]
+            m = ResMLP(mode; drop_path_rate)
+            @test size(m(x_224)) == (1000, 1)
+            @test gradtest(m, x_224)
+            _gc()
+        end
+    end
 end
 
 @testset "gMLP" begin

test/runtests.jl

@@ -29,16 +29,16 @@ function gradtest(model, input)
 end
 
 function normalize_imagenet(data)
-    cmean = reshape(Float32[0.485, 0.456, 0.406],(1,1,3,1))
-    cstd = reshape(Float32[0.229, 0.224, 0.225],(1,1,3,1))
+    cmean = reshape(Float32[0.485, 0.456, 0.406], (1, 1, 3, 1))
+    cstd = reshape(Float32[0.229, 0.224, 0.225], (1, 1, 3, 1))
     return (data .- cmean) ./ cstd
 end
 
 # test image
 const TEST_PATH = download("https://cdn.pixabay.com/photo/2015/05/07/11/02/guitar-756326_960_720.jpg")
 const TEST_IMG = imresize(Images.load(TEST_PATH), (224, 224))
 # CHW -> WHC
-const TEST_X = permutedims(convert(Array{Float32}, channelview(TEST_IMG)), (3,2,1)) |> normalize_imagenet
+const TEST_X = permutedims(convert(Array{Float32}, channelview(TEST_IMG)), (3, 2, 1)) |> normalize_imagenet
 
 # image net labels
 const TEST_LBLS = readlines(download("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"))
@@ -58,17 +58,11 @@ x_256 = rand(Float32, 256, 256, 3, 1)
     include("convnets.jl")
 end
 
-GC.safepoint()
-GC.gc()
-
 # Mixer tests
 @testset verbose = true "Mixers" begin
     include("mixers.jl")
 end
 
-GC.safepoint()
-GC.gc()
-
 # ViT tests
 @testset verbose = true "ViTs" begin
     include("vits.jl")