diff --git a/docs/Project.toml b/docs/Project.toml index 3aac24093..ad0d342b8 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -1,7 +1,6 @@ [deps] ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b" Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -Boltz = "4544d5e4-abc5-4dea-817f-29e4c205d9c8" ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66" Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" @@ -30,7 +29,6 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" [compat] ADTypes = "1.3" Adapt = "4" -Boltz = "0.3.8" ChainRulesCore = "1.24" ComponentArrays = "0.15" Documenter = "1.4" diff --git a/docs/make.jl b/docs/make.jl index 4c3077534..08e4be0f8 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -1,5 +1,5 @@ using Documenter, DocumenterVitepress, Pkg -using Lux, LuxCore, LuxLib, WeightInitializers, Boltz +using Lux, LuxCore, LuxLib, WeightInitializers using LuxTestUtils, LuxDeviceUtils using MLDataDevices using LuxCUDA @@ -63,12 +63,6 @@ pages = [ "api/Building_Blocks/LuxLib.md", "api/Building_Blocks/WeightInitializers.md" ], - "Domain Specific Modeling" => [ - "api/Domain_Specific_Modeling/Boltz.md", - "api/Domain_Specific_Modeling/Boltz_Layers.md", - "api/Domain_Specific_Modeling/Boltz_Vision.md", - "api/Domain_Specific_Modeling/Boltz_Private.md" - ], "Testing Functionality" => [ "api/Testing_Functionality/LuxTestUtils.md" ] @@ -85,7 +79,7 @@ makedocs(; sitename="Lux.jl Docs", authors="Avik Pal et al.", clean=true, doctest=false, # We test it in the CI, no need to run it here - modules=[Lux, LuxCore, LuxLib, WeightInitializers, Boltz, + modules=[Lux, LuxCore, LuxLib, WeightInitializers, LuxTestUtils, LuxDeviceUtils, MLDataDevices], linkcheck=true, repo="https://github.com/LuxDL/Lux.jl/blob/{commit}{path}#{line}", diff --git a/docs/src/.vitepress/config.mts b/docs/src/.vitepress/config.mts index f2c068919..b111a3bf3 100644 --- a/docs/src/.vitepress/config.mts +++ b/docs/src/.vitepress/config.mts @@ -92,11 +92,6 @@ export default defineConfig({ { text: 'Activation Functions', link: 'https://fluxml.ai/NNlib.jl/dev/reference/#Activation-Functions' } ] }, - { - text: 'Domain Specific Modeling', items: [ - { text: 'Boltz', link: '/api/Domain_Specific_Modeling/Boltz' } - ] - }, { text: 'Testing Functionality', items: [ { text: 'LuxTestUtils', link: '/api/Testing_Functionality/LuxTestUtils' } @@ -232,12 +227,6 @@ export default defineConfig({ { text: 'NNlib', link: 'https://fluxml.ai/NNlib.jl/dev/' }, { text: 'Activation Functions', link: 'https://fluxml.ai/NNlib.jl/dev/reference/#Activation-Functions' }] }, - { - text: 'Domain Specific Modeling', collapsed: false, items: [ - { text: 'Boltz', link: '/api/Domain_Specific_Modeling/Boltz' }, - { text: 'Computer Vision', link: '/api/Domain_Specific_Modeling/Boltz_Vision' }, - { text: 'Boltz Layers', link: '/api/Domain_Specific_Modeling/Boltz_Layers' }] - }, { text: 'Testing Functionality', collapsed: false, items: [ { text: 'LuxTestUtils', link: '/api/Testing_Functionality/LuxTestUtils' }] diff --git a/docs/src/api/Domain_Specific_Modeling/Boltz.md b/docs/src/api/Domain_Specific_Modeling/Boltz.md deleted file mode 100644 index f55d26c33..000000000 --- a/docs/src/api/Domain_Specific_Modeling/Boltz.md +++ /dev/null @@ -1,9 +0,0 @@ -# Boltz - -Accelerate ⚡ your ML research using pre-built Deep Learning Models with Lux. - -## Index - -```@index -Pages = ["Boltz.md", "Boltz_Layers.md", "Boltz_Vision.md"] -``` diff --git a/docs/src/api/Domain_Specific_Modeling/Boltz_Layers.md b/docs/src/api/Domain_Specific_Modeling/Boltz_Layers.md deleted file mode 100644 index 9711509fc..000000000 --- a/docs/src/api/Domain_Specific_Modeling/Boltz_Layers.md +++ /dev/null @@ -1,33 +0,0 @@ -# `Boltz.Layers` and `Boltz.Basis` API Reference - -## `Layers` API - -```@docs -Layers.ConvBatchNormActivation -Layers.ConvNormActivation -Layers.ClassTokens -Layers.HamiltonianNN -Layers.MultiHeadSelfAttention -Layers.MLP -Layers.SplineLayer -Layers.TensorProductLayer -Layers.ViPosEmbedding -Layers.VisionTransformerEncoder -``` - -## Basis Functions - -!!! warning - - The function calls for these basis functions should be considered experimental and are - subject to change without deprecation. However, the functions themselves are stable - and can be freely used in combination with the other Layers and Models. - -```@docs -Basis.Cos -Basis.Chebyshev -Basis.Fourier -Basis.Legendre -Basis.Polynomial -Basis.Sin -``` diff --git a/docs/src/api/Domain_Specific_Modeling/Boltz_Private.md b/docs/src/api/Domain_Specific_Modeling/Boltz_Private.md deleted file mode 100644 index 718ddcf49..000000000 --- a/docs/src/api/Domain_Specific_Modeling/Boltz_Private.md +++ /dev/null @@ -1,8 +0,0 @@ -# Boltz.jl Private API - -```@docs -Boltz._seconddimmean -Boltz._should_type_assert -Boltz._fast_chunk -Boltz._flatten_spatial -``` diff --git a/docs/src/api/Domain_Specific_Modeling/Boltz_Vision.md b/docs/src/api/Domain_Specific_Modeling/Boltz_Vision.md deleted file mode 100644 index e3f979db4..000000000 --- a/docs/src/api/Domain_Specific_Modeling/Boltz_Vision.md +++ /dev/null @@ -1,48 +0,0 @@ -# Computer Vision Models (`Vision` API) - -## Native Lux Models - -```@docs -Vision.VGG -Vision.VisionTransformer -``` - -## Imported from Metalhead.jl - -!!! tip - - You need to load `Flux` and `Metalhead` before using these models. - -```@docs -Vision.AlexNet -Vision.ConvMixer -Vision.DenseNet -Vision.GoogLeNet -Vision.MobileNet -Vision.ResNet -Vision.ResNeXt -``` - -## Pretrained Models - -!!! tip - - 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 | - -### Preprocessing - -All the pretrained models require that the images be normalized with the parameters -`mean = [0.485f0, 0.456f0, 0.406f0]` and `std = [0.229f0, 0.224f0, 0.225f0]`.