import PyTorch weights for VGG11/13/16/19 and ResNet50/101/152

pytorch2flux.jl

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+# 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 Images
+using Test
+
+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),
+]
+
+
+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 $modelname"
+    BSON.@save joinpath(@__DIR__,"weights","$(modelname).bson") model
+end

test/compare_pytorch.jl

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+# 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"))
+
+weightsdir = joinpath(@__DIR__,"..","weights")
+
+for (modelname,jlmodel,pymodel) in modellib
+    println(modelname)
+
+    model = jlmodel()
+
+    saved_model = BSON.load(joinpath(weightsdir,"$(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