Bugreport PyCallChainRules.jl Issue 29

Manifest.toml

# This file is machine-generated - editing it directly is not advised

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Project.toml

name = "DisentanglingVAE"
uuid = "1739f45c-9d92-4b55-89bf-86046b80dc74"
authors = ["Romeo Valentin <romeov@stanford.edu>"]
version = "0.1.0"

[deps]
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train_model.jl

using Statistics: mean
using FastAI
using FastAI.FluxTraining
using FastAI.Flux
using FastAI: encodedblock, encodedblockfilled, decodedblockfilled
import FastAI.Flux.MLUtils: _default_executor
import FastAI.MLUtils.Transducers: ThreadedEx
using FastVision
import FastVision: RGB
using FastTimm
using Metalhead
using PyCall, PyNNTraining
# ThreadPoolEx gave me problems, see https://github.com/JuliaML/MLUtils.jl/issues/142
_default_executor() = ThreadedEx()
DEVICE = gpu

#### Prepare dataset and task ######################################
path = load(datasets()["mnist_tiny"])
data = Datasets.loadfolderdata(
    path,
    filterfn = FastVision.isimagefile,
    loadfn = loadfile,
)


function EmbeddingTask(block, enc)
  sample = block
  encodedsample = x = y = ŷ = encodedblockfilled(enc, block)
  blocks = (; sample, x, y, ŷ, encodedsample)
  return BlockTask(blocks, enc)
end

task = EmbeddingTask(Image{2}(),
                     (ProjectiveTransforms((64, 64)),
                      ImagePreprocessing(means = FastVision.IMAGENET_MEANS,
                                         stds = FastVision.IMAGENET_STDS,
                                         C = RGB{Float32},
                                         buffered=false,
                                        ),))
BATCHSIZE=2
dl, dl_val = taskdataloaders(data, task, BATCHSIZE, pctgval=0.1;
                             buffer=true, partial=false);
####################################################################

##### Construct model #####
struct AE{E, B, D}
  encoder::E  # this will be a timm model
  bridge ::B  # to bridge the timm model to the latent space
  decoder::D
end
Flux.@functor AE

# image size is (64, 64)
Dhidden_effnet = 1792
Dlatent = 64

# here we load the timm model and remove the classification part
torch, functorch = pyimport("torch"), pyimport("functorch.experimental")
backbone = load(models()["timm/efficientnetv2_rw_s"], pretrained=true);
backbone.classifier = torch.nn.Identity()  # 1792 dimensions
functorch.replace_all_batch_norm_modules_(backbone);

bridge = Dense(Dhidden_effnet, Dlatent) |> DEVICE

decoder = Chain(Dense(Dlatent, 16*16*32),
                xs -> reshape(xs, 16, 16, 32, :),
                Metalhead.basicblock(32, 128),
                PixelShuffle(2),
                Metalhead.basicblock(32, 128),
                PixelShuffle(2),
                Metalhead.basicblock(32, 3),
               ) |> DEVICE

model = AE(backbone, bridge, decoder);
###########################

#### Use FluxTraining to define how this model is run. #####
struct AETrainingPhase <: FluxTraining.AbstractTrainingPhase end
function FluxTraining.on(
    ::FluxTraining.StepBegin,
    ::AETrainingPhase,
    cb::ToDevice,
    learner,
  )
  learner.step.x = cb.movedatafn(learner.step.x)
end

function FluxTraining.step!(learner, phase::AETrainingPhase, batch)
  FluxTraining.runstep(learner, phase, (x = batch,)) do handle, state
    gs = gradient(learner.params) do
      intermediate   = learner.model.encoder(state.x)
      state.z        = learner.model.bridge(intermediate)
      state.x̄        = learner.model.decoder(state.z)

      handle(FluxTraining.LossBegin())
      state.loss = learner.lossfn(state.x, state.x̄)

      handle(FluxTraining.BackwardBegin())
      return state.loss
    end
    handle(FluxTraining.BackwardEnd())
    Flux.Optimise.update!(learner.optimizer, learner.params, gs)
  end
end
############################################################

#### Try to run the training. #######################
reconstruction_error(x, x̄) = mean(sum(@. ((x̄ - x)^2); dims=1))

# This seems to segfault randomly after a while.
learner = Learner(model, reconstruction_error, callbacks=[ToPyTorch()])
fitonecycle!(learner, 10, 1e-4; phases=(AETrainingPhase() => dl,))
#####################################################