Use TestImages to avoid flaky download

We can guarantee these test images will always be available, which is not the case for the current sample image.

Author: ToucheSir

test/Project.toml

@@ -4,6 +4,7 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
 ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
 TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
@@ -13,5 +14,6 @@ Flux = "0.13.16, 0.14"
 Images = "0.26"
 ReTestItems = "1"
 Test = "1"
+TestImages = "1.8"
 TestItemRunner = "0.2"
 cuDNN = "1"

test/model_tests.jl

@@ -1,5 +1,5 @@
 @testsetup module TestModels
-using Metalhead, Images
+using Metalhead, Images, TestImages
 using Flux: gradient, gpu
 
 export PRETRAINED_MODELS,
@@ -62,8 +62,7 @@ function normalize_imagenet(data)
 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))
+const TEST_IMG = imresize(testimage("monarch_color_256"), (224, 224))
 # CHW -> WHC
 const TEST_X = let img_array = convert(Array{Float32}, channelview(TEST_IMG))
     permutedims(img_array, (3, 2, 1)) |> normalize_imagenet |> gpu
@@ -75,7 +74,7 @@ const TEST_LBLS = readlines(download("https://raw.githubusercontent.com/pytorch/
 function acctest(model)
     ypred = gpu(model)(TEST_X) |> vec
     top5 = TEST_LBLS[sortperm(ypred; rev = true)]
-    return "acoustic guitar" in top5
+    return "monarch" in top5
 end
 
 const x_224 = rand(Float32, 224, 224, 3, 1) |> gpu