torchvision to Metalhead port scripts

compare_pytorch.jl

# Compare Flux model from Metalhead to PyTorch model
# for a sample image

# PyTorch need to be installed

# Tested on ResNet and VGG models

using Flux
import Metalhead
using DataStructures
using Statistics
using BSON
using PyCall
using Images
using Test

using MLUtils
using Random

torchvision = pyimport("torchvision")
torch = pyimport("torch")

modellib = [
    ("vgg11", () -> Metalhead.VGG(11), torchvision.models.vgg11),
    ("vgg13", () -> Metalhead.VGG(13), torchvision.models.vgg13),
    ("vgg16", () -> Metalhead.VGG(16), torchvision.models.vgg16),
    ("vgg19", () -> Metalhead.VGG(19), torchvision.models.vgg19),
    ("resnet18", () -> Metalhead.ResNet(18), torchvision.models.resnet18),
    ("resnet34", () -> Metalhead.ResNet(34), torchvision.models.resnet34),
    ("resnet50", () -> Metalhead.ResNet(50), torchvision.models.resnet50),
    ("resnet101",() -> Metalhead.ResNet(101),torchvision.models.resnet101),
    ("resnet152",() -> Metalhead.ResNet(152),torchvision.models.resnet152),
]


tr(tmp) = permutedims(tmp,ndims(tmp):-1:1)


function normalize(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))
    return (data .- cmean) ./ cstd
end

# test image
guitar_path = download("https://cdn.pixabay.com/photo/2015/05/07/11/02/guitar-756326_960_720.jpg")

# image net labels
labels = readlines(download("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"))

for (modelname,jlmodel,pymodel) in modellib
    println(modelname)

    model = jlmodel()

    saved_model = BSON.load(joinpath(@__DIR__, "$(modelname).bson"))
    Flux.loadmodel!(model,saved_model[:model])

    pytorchmodel = pymodel(pretrained=true)

    Flux.testmode!(model)

    sz = (224, 224)
    img = Images.load(guitar_path);
    img = imresize(img, sz);
    # CHW -> WHC
    data = permutedims(convert(Array{Float32}, channelview(img)), (3,2,1))
    data = normalize(data[:,:,:,1:1])

    out = model(data) |> softmax;
    out = out[:,1]

    println("  Flux:")

    for i in sortperm(out,rev=true)[1:5]
        println("    $(labels[i]): $(out[i])")
    end


    pytorchmodel.eval()
    output =  pytorchmodel(torch.Tensor(tr(data)));
    probabilities = torch.nn.functional.softmax(output[0], dim=0).detach().numpy();

    println("  PyTorch:")

    for i in sortperm(probabilities[:,1],rev=true)[1:5]
        println("    $(labels[i]): $(probabilities[i])")
    end

    @test maximum(out) ≈ maximum(probabilities)
    @test argmax(out) ≈ argmax(probabilities)

    println()
end

Manifest.toml

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Project.toml

[deps]
BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
Metalhead = "dbeba491-748d-5e0e-a39e-b530a07fa0cc"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

pytorch2flux.jl

# Converts the weigths of a PyTorch model to a Flux model from Metalhead

# PyTorch need to be installed

# Tested on ResNet and VGG models

using Flux
import Metalhead
using DataStructures
using Statistics
using BSON
using PyCall
using PyCall: Conda
using Images
using Test

##

Conda.add_channel("pytorch")
Conda.add("pytorch")
Conda.add("torchvision")

torch = pyimport("torch")
torchvision = pyimport("torchvision")

##

modellib = [
    ("vgg11", () -> Metalhead.VGG(11), torchvision.models.vgg11),
    ("vgg13", () -> Metalhead.VGG(13), torchvision.models.vgg13),
    ("vgg16", () -> Metalhead.VGG(16), torchvision.models.vgg16),
    ("vgg19", () -> Metalhead.VGG(19), torchvision.models.vgg19),
    ("resnet18", () -> Metalhead.ResNet(18), torchvision.models.resnet18),
    ("resnet34", () -> Metalhead.ResNet(34), torchvision.models.resnet34),
    ("resnet50", () -> Metalhead.ResNet(50), torchvision.models.resnet50),
    ("resnet101",() -> Metalhead.ResNet(101),torchvision.models.resnet101),
    ("resnet152",() -> Metalhead.ResNet(152),torchvision.models.resnet152),
]

##

function _list_state(node::Flux.BatchNorm,channel,prefix)
    # use the same order of parameters than PyTorch
    put!(channel, (prefix * ".γ", node.γ)) # weigth (learnable)
    put!(channel, (prefix * ".β", node.β)) # bias (learnable)
    put!(channel, (prefix * ".μ", node.μ))  # running mean
    put!(channel, (prefix * ".σ²", node.σ²)) # running variance
end

function _list_state(node::Union{Flux.Conv,Flux.Dense},channel,prefix)
    put!(channel, (prefix * ".weight", node.weight))

    if node.bias !== Flux.Zeros()
        put!(channel, (prefix * ".bias", node.bias))
    end
end

_list_state(node,channel,prefix) = nothing

function _list_state(node::Union{Flux.Chain,Flux.Parallel},channel,prefix)
    for (i,n) in enumerate(node.layers)
        _list_state(n,channel,prefix * ".layers[$i]")
    end
end

function list_state(node; prefix = "model")
    Channel() do channel
        _list_state(node,channel,prefix)
    end
end

for (modelname,jlmodel,pymodel) in modellib

    model = jlmodel()
    pytorchmodel = pymodel(pretrained=true)

    state = OrderedDict(list_state(model.layers))

    # pytorchmodel.state_dict() looses the order
    state_dict = OrderedDict(pycall(pytorchmodel.state_dict,PyObject).items())
    pytorch_pp = OrderedDict((k,v.numpy()) for (k,v) in state_dict if !occursin("num_batches_tracked",k))


    # loop over all parameters
    for ((flux_key,flux_param),(pytorch_key,pytorch_param)) in zip(state,pytorch_pp)
        if size(flux_param) == size(pytorch_param)
            # Dense weight and vectors
            flux_param .= pytorch_param
        elseif size(flux_param) == reverse(size(pytorch_param))
            tmp = pytorch_param
            tmp = permutedims(tmp,ndims(tmp):-1:1)

            if ndims(flux_param)  == 4
                # convolutional weights
                flux_param .= reverse(tmp,dims=(1,2))
            else
                flux_param .= tmp
            end
        else
            @debug begin
                @show size(flux_param), size(pytorch_param)
            end
            error("incompatible shape $flux_key $pytorch_key")
        end
    end

    @info "saving model $modelname"
    BSON.@save joinpath(@__DIR__,"$(modelname).bson") model
end