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Merge pull request #32 from LuxDL/ap/docs
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Move the docs
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@avik-pal
avik-pal authored Jun 8, 2024
2 parents 69d20bb + 4de9308 commit e8ab38f
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2 changes: 1 addition & 1 deletion Project.toml
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name = "Boltz"
uuid = "4544d5e4-abc5-4dea-817f-29e4c205d9c8"
authors = ["Avik Pal <[email protected]> and contributors"]
version = "0.3.4"
version = "0.3.5"

[deps]
ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
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151 changes: 11 additions & 140 deletions ext/BoltzMetalheadExt.jl
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module BoltzMetalheadExt

using ArgCheck: @argcheck
using Boltz: Boltz, __maybe_initialize_model, Vision, INITIALIZE_KWARGS
using Lux: FromFluxAdaptor
using Boltz: Boltz, __maybe_initialize_model, Vision
using Lux: Lux, FromFluxAdaptor
using Metalhead: Metalhead

"""
AlexNet(; kwargs...)
Boltz._is_extension_loaded(::Val{:Metalhead}) = true

Create an AlexNet model [1]
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with
deep convolutional neural networks." Advances in neural information processing systems 25
(2012): 1097-1105.
"""
function Vision.AlexNet(; pretrained=false, kwargs...)
function Vision.__AlexNet(; pretrained=false, kwargs...)
model = FromFluxAdaptor()(Metalhead.AlexNet().layers)
# Compatibility with pretrained weights
pretrained && (model = Chain(model[1], model[2]))
pretrained && (model = Lux.Chain(model[1], model[2])) # Compatibility with pretrained weights
return __maybe_initialize_model(:alexnet, model; pretrained, kwargs...)
end

"""
ResNet(depth::Int; kwargs...)
Create a ResNet model [1].
## Arguments
* `depth::Int`: The depth of the ResNet model. Must be one of 18, 34, 50, 101, or 152.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the
IEEE conference on computer vision and pattern recognition. 2016.
"""
function Vision.ResNet(depth::Int; kwargs...)
function Vision.__ResNet(depth::Int; kwargs...)
@argcheck depth in (18, 34, 50, 101, 152)
model = FromFluxAdaptor()(Metalhead.ResNet(depth).layers)
return __maybe_initialize_model(Symbol(:resnet, depth), model; kwargs...)
end

"""
ResNeXt(depth::Int; kwargs...)
Create a ResNeXt model [1].
## Arguments
* `depth::Int`: The depth of the ResNeXt model. Must be one of 50, 101, or 152.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He, Ross Gorshick, and
Piotr Dollár. "Aggregated residual transformations for deep neural networks."
Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
"""
function Vision.ResNeXt(depth::Int; kwargs...)
function Vision.__ResNeXt(depth::Int; kwargs...)
@argcheck depth in (50, 101, 152)
model = FromFluxAdaptor()(Metalhead.ResNeXt(depth).layers)
return __maybe_initialize_model(Symbol(:resnext, depth), model; kwargs...)
end

"""
GoogLeNet(; kwargs...)
Create a GoogLeNet model [1].
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov,
Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. "Going deeper with
convolutions." Proceedings of the IEEE conference on computer vision and pattern
recognition. 2015.
"""
function Vision.GoogLeNet(; kwargs...)
function Vision.__GoogLeNet(; kwargs...)
model = FromFluxAdaptor()(Metalhead.GoogLeNet().layers)
return __maybe_initialize_model(:googlenet, model; kwargs...)
end

"""
DenseNet(depth::Int; kwargs...)
Create a DenseNet model [1].
## Arguments
* `depth::Int`: The depth of the DenseNet model. Must be one of 121, 161, 169, or 201.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger. "Densely connected
convolutional networks." Proceedings of the IEEE conference on computer vision and
pattern recognition. 2016.
"""
function Vision.DenseNet(depth::Int; kwargs...)
function Vision.__DenseNet(depth::Int; kwargs...)
@argcheck depth in (121, 161, 169, 201)
model = FromFluxAdaptor()(Metalhead.DenseNet(depth).layers)
return __maybe_initialize_model(Symbol(:densenet, depth), model; kwargs...)
end

"""
MobileNet(name::Symbol; kwargs...)
Create a MobileNet model [1, 2, 3].
## Arguments
* `name::Symbol`: The name of the MobileNet model. Must be one of `:v1`, `:v2`,
`:v3_small`, or `:v3_large`.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for
mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
[2] Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks."
Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[3] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias
Weyand, Marco Andreetto, Hartwig Adam. "Searching for MobileNetV3." arXiv preprint
arXiv:1905.02244. 2019.
"""
function Vision.MobileNet(name::Symbol; kwargs...)
function Vision.__MobileNet(name::Symbol; kwargs...)
@argcheck name in (:v1, :v2, :v3_small, :v3_large)
model = if name == :v1
FromFluxAdaptor()(Metalhead.MobileNetv1().layers)
Expand All @@ -160,26 +50,7 @@ function Vision.MobileNet(name::Symbol; kwargs...)
return __maybe_initialize_model(Symbol(:mobilenet, "_", name), model; kwargs...)
end

"""
ConvMixer(name::Symbol; kwargs...)
Create a ConvMixer model [1].
## Arguments
* `name::Symbol`: The name of the ConvMixer model. Must be one of `:base`, `:small`, or
`:large`.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Zhu, Zhuoyuan, et al. "ConvMixer: A Convolutional Neural Network with Faster
Depth-wise Convolutions for Computer Vision." arXiv preprint arXiv:1911.11907 (2019).
"""
function Vision.ConvMixer(name::Symbol; kwargs...)
function Vision.__ConvMixer(name::Symbol; kwargs...)
@argcheck name in (:base, :large, :small)
model = FromFluxAdaptor()(Metalhead.ConvMixer(name).layers)
return __maybe_initialize_model(Symbol(:convmixer, "_", name), model; kwargs...)
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2 changes: 2 additions & 0 deletions src/Boltz.jl
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Expand Up @@ -16,6 +16,8 @@ end

@reexport using Lux

@inline _is_extension_loaded(::Val) = false

# Utility Functions
include("utils.jl")
include("initialize.jl")
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10 changes: 3 additions & 7 deletions src/vision/Vision.jl
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Expand Up @@ -4,19 +4,15 @@ using PrecompileTools: @recompile_invalidations

@recompile_invalidations begin
using ArgCheck: @argcheck
using ..Boltz: _flatten_spatial, _seconddimmean, __maybe_initialize_model, Layers,
INITIALIZE_KWARGS
using ..Boltz: _is_extension_loaded, _flatten_spatial, _seconddimmean,
__maybe_initialize_model, Layers, INITIALIZE_KWARGS
using Lux: Lux
using LuxCore: LuxCore
using NNlib: relu
using Random: Xoshiro
end

# Define functions. Methods defined in extensions later
for f in (:AlexNet, :ConvMixer, :DenseNet, :GoogLeNet, :MobileNet, :ResNet, :ResNeXt)
@eval function $(f) end
end

include("extensions.jl")
include("vit.jl")
include("vgg.jl")

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157 changes: 157 additions & 0 deletions src/vision/extensions.jl
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"""
AlexNet(; kwargs...)
Create an AlexNet model [1]
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with
deep convolutional neural networks." Advances in neural information processing systems 25
(2012): 1097-1105.
"""
function AlexNet end

"""
ResNet(depth::Int; kwargs...)
Create a ResNet model [1].
## Arguments
* `depth::Int`: The depth of the ResNet model. Must be one of 18, 34, 50, 101, or 152.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the
IEEE conference on computer vision and pattern recognition. 2016.
"""
function ResNet end

"""
ResNeXt(depth::Int; kwargs...)
Create a ResNeXt model [1].
## Arguments
* `depth::Int`: The depth of the ResNeXt model. Must be one of 50, 101, or 152.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He, Ross Gorshick, and
Piotr Dollár. "Aggregated residual transformations for deep neural networks."
Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
"""
function ResNeXt end

"""
GoogLeNet(; kwargs...)
Create a GoogLeNet model [1].
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov,
Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. "Going deeper with
convolutions." Proceedings of the IEEE conference on computer vision and pattern
recognition. 2015.
"""
function GoogLeNet end

"""
DenseNet(depth::Int; kwargs...)
Create a DenseNet model [1].
## Arguments
* `depth::Int`: The depth of the DenseNet model. Must be one of 121, 161, 169, or 201.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger. "Densely connected
convolutional networks." Proceedings of the IEEE conference on computer vision and
pattern recognition. 2016.
"""
function DenseNet end

"""
MobileNet(name::Symbol; kwargs...)
Create a MobileNet model [1, 2, 3].
## Arguments
* `name::Symbol`: The name of the MobileNet model. Must be one of `:v1`, `:v2`,
`:v3_small`, or `:v3_large`.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for
mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
[2] Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks."
Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[3] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias
Weyand, Marco Andreetto, Hartwig Adam. "Searching for MobileNetV3." arXiv preprint
arXiv:1905.02244. 2019.
"""
function MobileNet end

"""
ConvMixer(name::Symbol; kwargs...)
Create a ConvMixer model [1].
## Arguments
* `name::Symbol`: The name of the ConvMixer model. Must be one of `:base`, `:small`, or
`:large`.
## Keyword Arguments
$(INITIALIZE_KWARGS)
## References
[1] Zhu, Zhuoyuan, et al. "ConvMixer: A Convolutional Neural Network with Faster
Depth-wise Convolutions for Computer Vision." arXiv preprint arXiv:1911.11907 (2019).
"""
function ConvMixer end

for f in [:AlexNet, :ResNet, :ResNeXt, :GoogLeNet, :DenseNet, :MobileNet, :ConvMixer]
f_inner = Symbol("__", f)
@eval begin
function $(f_inner) end
function $f(args...; kwargs...)
if !_is_extension_loaded(Val(:Metalhead))
error("Metalhead.jl is not loaded. Please load Metalhead.jl to use this \
function.")
end
$(f_inner)(args...; kwargs...)
end
end
end
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Registration pull request created: JuliaRegistries/General/108542

Tip: Release Notes

Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.

@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.3.5 -m "<description of version>" e8ab38ffb5e7a2fb9a8983e810f50ceb73cc36b0
git push origin v0.3.5

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