LuxDL · avik-pal · Sep 14, 2024 · Sep 9, 2024 · Sep 9, 2024 · Sep 9, 2024
diff --git a/.github/workflows/Documentation.yml b/.github/workflows/Documentation.yml
@@ -40,7 +40,7 @@ jobs:
       - name: Pull Julia cache
         uses: julia-actions/cache@v2
       - name: Install documentation dependencies
-        run: julia --project=docs -e 'using Pkg; pkg"dev ."; Pkg.instantiate(); Pkg.precompile(); Pkg.status()'
+        run: julia --project=docs -e 'using Pkg; Pkg.develop(Pkg.PackageSpec(path=".")); Pkg.instantiate(); Pkg.precompile(); Pkg.status()'
       - name: Build the tutorials
         run: julia --project=docs docs/tutorials.jl
       - name: Build and deploy docs

diff --git a/Artifacts.toml b/Artifacts.toml
@@ -1,111 +1,71 @@
 [alexnet]
-git-tree-sha1 = "8904a6756649aa4cd264328430b829dafde95645"
+git-tree-sha1 = "f1739363d54a358cae904133699a93eca7b7a028"
 lazy = true
 
     [[alexnet.download]]
-    sha256 = "e20107404aba1c2c0ed3fad4314033a2fa600cdc0c55d03bc1bfe4f8e5031105"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/alexnet.tar.gz"
-
-# [resnet101]
-# git-tree-sha1 = "6c9143d40950726405b88db0cc021fa1dcbc0896"
-# lazy = true
-
-#     [[resnet101.download]]
-#     sha256 = "3840f05b3d996b2b3ea1e8fb6617775fd60ad6b8769402200fdc9c8b8dca246f"
-#     url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/resnet101.tar.gz"
-
-# [resnet152]
-# git-tree-sha1 = "892915c44de37537aad97da3de8a4458dfa36297"
-# lazy = true
-
-#     [[resnet152.download]]
-#     sha256 = "6033a1ecc46d7f4ed1139067c5f9f5ea0d247656e9abbbe755c4702ec5a636d6"
-#     url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/resnet152.tar.gz"
-
-# [resnet18]
-# git-tree-sha1 = "1d4a46fee1bb87eeef0ce2c85f63cfe0ff47d4de"
-# lazy = true
-
-#     [[resnet18.download]]
-#     sha256 = "f4041ea1d1ec9bba86c7a5a519daaa49bb096a55fcd4ebf74f0743c8bdcb1c35"
-#     url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/resnet18.tar.gz"
-
-# [resnet34]
-# git-tree-sha1 = "306a8055ae9207ae2a316e31b376254557e481c9"
-# lazy = true
-
-#     [[resnet34.download]]
-#     sha256 = "d62e40ee9213ea9611e3fcedc958df4011da1fa108fb1537bac91e6b7778a3c8"
-#     url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/resnet34.tar.gz"
-
-# [resnet50]
-# git-tree-sha1 = "8c5866edb29b53f581a9ed7148efa1dbccde6133"
-# lazy = true
-
-#     [[resnet50.download]]
-#     sha256 = "275365d76e592c6ea35574853a75ee068767641664e7817aedf394fcd7fea25a"
-#     url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/resnet50.tar.gz"
+    sha256 = "feb3e1600179ba00b72a68759c7f3b12f400f6d59b28ac72b48614cbafa187d8"
+    url = "https://huggingface.co/LuxDL/alexnet/resolve/2c48051ecb131d38f2209470cdda70a343289db1/alexnet.tar.gz"
 
 [vgg11]
-git-tree-sha1 = "ea7e8ef9399a0fe0aad2331781af5d6435950d36"
+git-tree-sha1 = "2468801ab9bc7343c02a141e145e95209236b218"
 lazy = true
 
     [[vgg11.download]]
-    sha256 = "a88b78c4e939138270068933f6278fa6392ce699991385000b1b9bb0ff53fa3e"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/vgg11.tar.gz"
+    sha256 = "509567b428c02f7e9fa69ad7b6e0f62a1347ce6ec7fbad4e4cb4fddbf4f66b60"
+    url = "https://huggingface.co/LuxDL/vgg/resolve/ac7292d920627cdb66caee17420bfee5dbfe0f61/vgg11.tar.gz"
 
 [vgg11_bn]
-git-tree-sha1 = "6bb8128b150ac7ca30c32e34295d8d7403471942"
+git-tree-sha1 = "e444f279f7b6641da80f4910362d0ccb37a5fe96"
 lazy = true
 
     [[vgg11_bn.download]]
-    sha256 = "159392d11cb5ced5bfe66dc9da7bd12fbbf92687bbea9e9bb96bf0c6b58c9ef2"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/vgg11_bn.tar.gz"
+    sha256 = "8ed6002ccad7e204276d638e8bc288cce85f6b26f0a6d7cb5a85d714750a0cc7"
+    url = "https://huggingface.co/LuxDL/vgg/resolve/0ecfa4aea43abc4bbf27fd7f185ffda3fdc5d31a/vgg11_bn.tar.gz"
 
 [vgg13]
-git-tree-sha1 = "260246834e15048ec128fadc6d1a5dd050dd0928"
+git-tree-sha1 = "d9b0cc88a71a8eabe3375e9bb92b601cb0c984a5"
 lazy = true
 
     [[vgg13.download]]
-    sha256 = "757c555f47e2dd4898e89fcb6fb94f1a7a9977e4cdbdba5502eacd1d3496cf3b"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/vgg13.tar.gz"
+    sha256 = "4c554fb0d7ed976019c0d03ffe9ec6ed0cc88d8fa94a3405c65b55e138c5a7b7"
+    url = "https://huggingface.co/LuxDL/vgg/resolve/f4a690e83af5a8f9e907e33de0b2d53b8a6ad1cc/vgg13.tar.gz"
 
 [vgg13_bn]
-git-tree-sha1 = "623d2ae96cbf935ae09067fb7e85ca6894b79447"
+git-tree-sha1 = "12ac8f1c9fadfcd4fb80db270ffa17ec96375fb7"
 lazy = true
 
     [[vgg13_bn.download]]
-    sha256 = "3ccfb481b79bf56e6b70fefbc727127f5eb6de6478b1c57be669e5e5bb007377"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/vgg13_bn.tar.gz"
+    sha256 = "e6f69a7580a9af6eedbbb11c1c590ad6da4e57872dfdb2ac38f2e085d50b429b"
+    url = "https://huggingface.co/LuxDL/vgg/resolve/5f6acdbc972e0455428c4618c41ebedcd7d5b14b/vgg13_bn.tar.gz"
 
 [vgg16]
-git-tree-sha1 = "0e4d8a869e2688c765a3126c8a8c59e4a58fe97d"
+git-tree-sha1 = "8b85924419036f584e32aac9500b1d198a3b748a"
 lazy = true
 
     [[vgg16.download]]
-    sha256 = "79463750e1fd2424928b21a8d5d0cb2328c46e5cf39252600e0d132570bfd931"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/vgg16.tar.gz"
+    sha256 = "9e2905a02c4f2425e199b5d7b47aad9c6d2029850d2f2fd4d9c1a9d26dafb163"
+    url = "https://huggingface.co/LuxDL/vgg/resolve/d05baefb64db70c66ccfe6daa6e2a3b7ce8b3021/vgg16.tar.gz"
 
 [vgg16_bn]
-git-tree-sha1 = "b93f35dddcaec69444e0cfb709a19be4fb6be53f"
+git-tree-sha1 = "46d8299cc071102ceaf9e7d84699a7029dc0293a"
 lazy = true
 
     [[vgg16_bn.download]]
-    sha256 = "98f3e88c12fd07faa20505c864bfe2afc17e714ac5ddb4b05018fab25d9b3373"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/vgg16_bn.tar.gz"
+    sha256 = "b0fb3382434fd4f025f2e5517fc29b787aca093300d1a683d4dc10d380d102ed"
+    url = "https://huggingface.co/LuxDL/vgg/resolve/79b657b252c9b9a2f0fecb67c10ba4ec6c4fed0b/vgg16_bn.tar.gz"
 
 [vgg19]
-git-tree-sha1 = "8c0f1bdb7e540f1529a4038a0c73971debb1634b"
+git-tree-sha1 = "4dfc386f5da165daf80429b15a9a6bf7c2bf1b3b"
 lazy = true
 
     [[vgg19.download]]
-    sha256 = "2cedf1574207abe1989619aab292c51964dc27226b8852f0ce8a5acbc66ded31"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/vgg19.tar.gz"
+    sha256 = "662c7a63b07fdf52bb548a2019c31dd19d43e8675beb8fecf9ff3901ba36ef6e"
+    url = "https://huggingface.co/LuxDL/vgg/resolve/0268c4327e5ab4ae7172755bb767bf80dd792b0c/vgg19.tar.gz"
 
 [vgg19_bn]
-git-tree-sha1 = "fb3ec225e8a0b2eb838be682c6facceaa5b8aad3"
+git-tree-sha1 = "c0db4c840ae575da62ed28b3fcb29e3ca4da570a"
 lazy = true
 
     [[vgg19_bn.download]]
-    sha256 = "7a8d3d43ea092db4f53e721015078fcba4980dd5534a743f2b388165f8af185d"
-    url = "https://github.com/LuxDL/Lux.jl/releases/download/weights/vgg19_bn.tar.gz"
+    sha256 = "43a0005b4f4ad261773e5a9dd2241165c211be476d6bce8493fd470bfe16ef5f"
+    url = "https://huggingface.co/LuxDL/vgg/resolve/9b3f3be58a84e52b40f5489c481d954d0cf4f2b4/vgg19_bn.tar.gz"
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "Boltz"
 uuid = "4544d5e4-abc5-4dea-817f-29e4c205d9c8"
 authors = ["Avik Pal <[email protected]> and contributors"]
-version = "0.4.2"
+version = "1.0.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -11,6 +11,7 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 LazyArtifacts = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
@@ -20,6 +21,7 @@ Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
+Static = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 WeightInitializers = "d49dbf32-c5c2-4618-8acc-27bb2598ef2d"
 
@@ -51,18 +53,20 @@ ConcreteStructs = "0.2.3"
 DataInterpolations = "< 5.3"
 DynamicExpressions = "0.16, 0.17, 0.18, 0.19"
 ForwardDiff = "0.10.36"
+Functors = "0.4.12"
 GPUArraysCore = "0.1.6"
-JLD2 = "0.4.48"
+JLD2 = "0.5"
 LazyArtifacts = "1.10"
-Lux = "0.5.66"
-LuxCore = "0.1.24"
-MLDataDevices = "1.0.1"
+Lux = "1"
+LuxCore = "1"
+MLDataDevices = "1.1"
 Markdown = "1.10"
-Metalhead = "0.9"
+Metalhead = "0.9.4"
 NNlib = "0.9.21"
 Random = "1.10"
 Reexport = "1.2.2"
 ReverseDiff = "1.15"
+Static = "1.1.1"
 Statistics = "1.10"
 Tracker = "0.2.34"
 WeightInitializers = "1"

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -10,11 +10,11 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-Boltz = "0.4"
+Boltz = "1"
 Documenter = "1"
 DocumenterCitations = "1"
 DocumenterVitepress = "0.1"
 DynamicExpressions = "0.16, 0.17, 0.18, 0.19"
-Lux = "0.5.65"
+Lux = "1"
 Random = "1.10"
 Zygote = "0.6.70"
diff --git a/docs/ref.bib b/docs/ref.bib
@@ -89,3 +89,23 @@ @inproceedings{howard2019searching
   pages     = {1314--1324},
   year      = {2019}
 }
+
+@misc{iandola2016squeezenetalexnetlevelaccuracy50x,
+  title         = {SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size},
+  author        = {Forrest N. Iandola and Song Han and Matthew W. Moskewicz and Khalid Ashraf and William J. Dally and Kurt Keutzer},
+  year          = {2016},
+  eprint        = {1602.07360},
+  archiveprefix = {arXiv},
+  primaryclass  = {cs.CV},
+  url           = {https://arxiv.org/abs/1602.07360}
+}
+
+@misc{zagoruyko2017wideresidualnetworks,
+  title         = {Wide Residual Networks},
+  author        = {Sergey Zagoruyko and Nikos Komodakis},
+  year          = {2017},
+  eprint        = {1605.07146},
+  archiveprefix = {arXiv},
+  primaryclass  = {cs.CV},
+  url           = {https://arxiv.org/abs/1605.07146}
+}
diff --git a/docs/src/api/vision.md b/docs/src/api/vision.md
@@ -3,6 +3,7 @@
 ## Native Lux Models
 
 ```@docs
+Vision.AlexNet
 Vision.VGG
 Vision.VisionTransformer
 ```
@@ -14,13 +15,14 @@ Vision.VisionTransformer
     You need to load `Metalhead` before using these models.
 
 ```@docs
-Vision.AlexNet
 Vision.ConvMixer
 Vision.DenseNet
 Vision.GoogLeNet
 Vision.MobileNet
 Vision.ResNet
 Vision.ResNeXt
+Vision.SqueezeNet
+Vision.WideResNet
 ```
 
 ## Pretrained Models
@@ -33,18 +35,34 @@ Vision.ResNeXt
 
     Pass `pretrained=true` to the model constructor to load the pretrained weights.
 
-
-| MODEL                     | TOP 1 ACCURACY (%) | TOP 5 ACCURACY (%) |
-| :------------------------ | :----------------: | :----------------: |
-| `AlexNet()`               |       54.48        |       77.72        |
-| `VGG(11)`                 |       67.35        |       87.91        |
-| `VGG(13)`                 |       68.40        |       88.48        |
-| `VGG(16)`                 |       70.24        |       89.80        |
-| `VGG(19)`                 |       71.09        |       90.27        |
-| `VGG(11; batchnorm=true)` |       69.09        |       88.94        |
-| `VGG(13; batchnorm=true)` |       69.66        |       89.49        |
-| `VGG(16; batchnorm=true)` |       72.11        |       91.02        |
-| `VGG(19; batchnorm=true)` |       72.95        |       91.32        |
+| MODEL                                        | TOP 1 ACCURACY (%) | TOP 5 ACCURACY (%) |
+| :------------------------------------------- | :----------------: | :----------------: |
+| `AlexNet()`                                  |       54.48        |       77.72        |
+| `VGG(11)`                                    |       67.35        |       87.91        |
+| `VGG(13)`                                    |       68.40        |       88.48        |
+| `VGG(16)`                                    |       70.24        |       89.80        |
+| `VGG(19)`                                    |       71.09        |       90.27        |
+| `VGG(11; batchnorm=true)`                    |       69.09        |       88.94        |
+| `VGG(13; batchnorm=true)`                    |       69.66        |       89.49        |
+| `VGG(16; batchnorm=true)`                    |       72.11        |       91.02        |
+| `VGG(19; batchnorm=true)`                    |       72.95        |       91.32        |
+| `ResNet(18)`                                 |         -          |         -          |
+| `ResNet(34)`                                 |         -          |         -          |
+| `ResNet(50)`                                 |         -          |         -          |
+| `ResNet(101)`                                |         -          |         -          |
+| `ResNet(152)`                                |         -          |         -          |
+| `ResNeXt(50; cardinality=32, base_width=4)`  |         -          |         -          |
+| `ResNeXt(101; cardinality=32, base_width=8)` |         -          |         -          |
+| `ResNeXt(101; cardinality=64, base_width=4)` |         -          |         -          |
+| `SqueezeNet()`                               |         -          |         -          |
+| `WideResNet(50)`                             |         -          |         -          |
+| `WideResNet(101)`                            |         -          |         -          |
+
+!!! note "Pretrained Models from Metalhead"
+
+    For Models imported from Metalhead, the pretrained weights can be loaded if they are
+    available in Metalhead. Refer to the [Metalhead.jl docs](https://fluxml.ai/Metalhead.jl/stable/#Image-Classification)
+    for a list of available pretrained models.
 
 ### Preprocessing
 

diff --git a/examples/GettingStarted/Project.toml b/examples/GettingStarted/Project.toml
@@ -8,10 +8,10 @@ Metalhead = "dbeba491-748d-5e0e-a39e-b530a07fa0cc"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [compat]
-Boltz = "0.4"
+Boltz = "1"
 InteractiveUtils = "<0.0.1, 1"
-JLD2 = "0.4.52"
+JLD2 = "0.4.52, 0.5"
 Literate = "2.19"
-Lux = "0.5.65"
+Lux = "1"
 Metalhead = "0.9.3"
 Random = "1.10"
diff --git a/examples/GettingStarted/main.jl b/examples/GettingStarted/main.jl
@@ -29,16 +29,10 @@ model = Layers.MLP(784, (256, 10), relu)
 
 # ## How about VGG?
 #
-# !!! warning "Returned Values"
-#
-#     The returned value from `Vision` module functions are a 3 tuple of (model, ps, st).
-#     The `ps` and `st` are the parameters and states of the model respectively.
-#
 # Let's take a look at the `Vision` module. We can construct a VGG model with the
 # following code:
 
-model, _, _ = Vision.VGG(13)
-model
+Vision.VGG(13)
 
 # We can also load pretrained ImageNet weights using
 
@@ -48,14 +42,12 @@ model
 
 using JLD2
 
-model, _, _ = Vision.VGG(13; pretrained=true)
-model
+Vision.VGG(13; pretrained=true)
 
 # ## Loading Models from Metalhead (Flux.jl)
 
 # We can load models from Metalhead (Flux.jl), just remember to load `Metalhead` before.
 
 using Metalhead
 
-model, _, _ = Vision.ResNet(18)
-model
+Vision.ResNet(18)
diff --git a/examples/SymbolicOptimalControl/Project.toml b/examples/SymbolicOptimalControl/Project.toml
@@ -11,7 +11,7 @@ MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+OrdinaryDiffEqVerner = "79d7bb75-1356-48c1-b8c0-6832512096c2"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
@@ -20,19 +20,19 @@ SymbolicRegression = "8254be44-1295-4e6a-a16d-46603ac705cb"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 
 [compat]
-Boltz = "0.4"
+Boltz = "1"
 CairoMakie = "0.12"
 ComponentArrays = "0.15.11"
 DynamicExpressions = "0.16, 0.17, 0.18, 0.19"
 Latexify = "0.16.2"
 Literate = "2"
-Lux = "0.5"
+Lux = "1"
 MLJ = "0.20.3"
 Optimization = "3.24.3"
 OptimizationOptimJL = "0.2.3, 0.3"
 OptimizationOptimisers = "0.2.1"
-OrdinaryDiffEq = "6.74.1"
+OrdinaryDiffEqVerner = "1"
 SciMLSensitivity = "7.57"
-Statistics = "1.11"
+Statistics = "1.10"
 SymbolicRegression = "0.24.1"
 SymbolicUtils = "1.5.1, 2, 3"