Training FlowerPhoto dataset using PyCallChainRule.jl

Manifest.toml

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

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photo_flower_pccr.jl

# -*- coding: utf-8 -*-
# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .jl
#       format_name: light
#       format_version: '1.5'
#       jupytext_version: 1.13.8
#   kernelspec:
#     display_name: Julia 1.7.2
#     language: julia
#     name: julia-1.7
# ---

using Random
Random.seed!(2022)

using PyCallChainRules.Torch: TorchModuleWrapper, torch
using PyCall
using ProgressMeter
using BSON
using Optimisers
using Metalhead
using Flux
using DataLoaders

using CUDA, PyCall, DLPack, Functors

dlpack = pyimport("torch.utils.dlpack")

pyto_dlpack(x) = @pycall dlpack.to_dlpack(x)::PyObject
pyfrom_dlpack(x) = @pycall dlpack.from_dlpack(x)::PyObject


struct TorchModel
    fn::PyObject
end

function (wrap::TorchModel)(args...; kwargs...)
    return wrap.fn(fmap(x -> DLPack.share(x, PyObject, pyfrom_dlpack), args)...; kwargs...)
end

py"""
import numpy as np

import os
from glob import glob
import torch
import torch.nn as nn
from tqdm import tqdm
from PIL import Image
import imageio
import torchvision
import albumentations as A
from albumentations.pytorch import ToTensorV2
import albumentations.augmentations.functional as F

torch.manual_seed(2022)

# https://github.com/pytorch/pytorch/issues/11201

sharing_strategy = "file_system"
torch.multiprocessing.set_sharing_strategy(sharing_strategy)

def set_worker_sharing_strategy(worker_id: int) -> None:
    torch.multiprocessing.set_sharing_strategy(sharing_strategy)

LABELS = ["daisy", "dandelion", "roses", "sunflowers", "tulips"]

LABEL2INDEX = {lab:idx for (idx,lab) in enumerate(LABELS)}

class FlowerDataset(torch.utils.data.Dataset):
    def __init__(self, dataset_dir):
        self.dataset_dir = dataset_dir
        self.anns = self.load_annotations()
    def load_annotations(self):
        train_anns=[]
        val_anns = []
        test_anns = []
        for label in LABELS:
            files = sorted(glob(os.path.join(dataset_dir, label, "*.jpg")))
            train_size = int(0.8*len(files))
            val_size = int(0.1*len(files))
            test_size = len(files) - train_size - val_size
            train, val, test = torch.utils.data.random_split(files, [train_size, val_size, test_size])
            train_anns.extend([(f, LABEL2INDEX[label]) for f in train])
            val_anns.extend([(f, LABEL2INDEX[label]) for f in val])
            test_anns.extend([(f, LABEL2INDEX[label]) for f in test])
        return dict(train=train_anns, val=val_anns, test=test_anns)

dataset_dir = os.path.expanduser("~/dataset/flower_photos")

train_transform = A.Compose(
    [
        A.Resize(128,128),
        #A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
        ToTensorV2(),
    ]
)

test_transform = val_transform = A.Compose(
    [
        A.Resize(128,128),
        #A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
        ToTensorV2(),
    ]
)

tfm_recipe = dict(train=train_transform, val=val_transform, test=test_transform)

class TransformDataset(torch.utils.data.Dataset):
    def __init__(self, base_set, phase):
        self.base_set = base_set
        self.phase = phase
        self.tfm = tfm_recipe[phase]
    def __getitem__(self, idx):
        f, class_id = self.base_set[idx]
        img = imageio.imread(f)
        tfmed = self.tfm(image=img)["image"]
        return tfmed/255, class_id

    def __len__(self):
        return len(self.base_set)
"""

py"""
dset = FlowerDataset(dataset_dir)
train_set = TransformDataset(dset.anns["train"],phase="train")
val_set = TransformDataset(dset.anns["val"],phase="val")
test_set = TransformDataset(dset.anns["test"],phase="test")

train_loader = torch.utils.data.DataLoader(
    train_set,
    batch_size=128,
    shuffle=True,
    num_workers=20,
    worker_init_fn=set_worker_sharing_strategy
)

val_loader = torch.utils.data.DataLoader(
    val_set,
    batch_size=128,
    shuffle=False,
    num_workers=4,
    worker_init_fn=set_worker_sharing_strategy
)
"""

py"""
def conv_bn(inC, outC, use_bn=True):
    bias = False if use_bn else True
    layers = [nn.Conv2d(inC, outC, kernel_size=3, stride=1,padding=1, bias=bias)]
    if use_bn:
        layers.append(nn.BatchNorm2d(outC))
    layers.append(nn.ReLU(inplace=False))
    return nn.Sequential(*layers)

torch_model = torch.nn.Sequential(
 conv_bn(3,16,use_bn=True),
 nn.MaxPool2d(2),
 conv_bn(16,32,use_bn=True),
 nn.MaxPool2d(2),
 conv_bn(32,64,use_bn=True),
 nn.MaxPool2d(2),
 nn.Flatten(),
 nn.Linear(64*16*16,512),
 nn.ReLU(inplace=False),
 nn.Linear(512,5),
)


import functorch.experimental as fexp

torch_model = fexp.replace_all_batch_norm_modules_(torch_model)
torch_model = torch_model.to(device=torch.device("cuda"))

resnet = torchvision.models.resnet18(pretrained=True)
resnet = fexp.replace_all_batch_norm_modules_(resnet)
resnet.fc = torch.nn.Linear(512,5)
resnet = resnet.to(device=torch.device("cuda"))

"""

import DataLoaders.LearnBase: getobs, nobs

struct PyDataset
    dataset::PyObject
end

function Base.getindex(dset::PyDataset, idx)
    t, classid = dset.dataset[idx-1]
    (t.numpy(), classid)
end

getobs(dset::PyDataset, idx) = dset[idx]
nobs(dset::PyDataset) = length(dset.dataset)

pydata = PyDataset(py"train_set")

function torchlike_uniform(sz::Integer...; kwargs...)
    Flux.kaiming_uniform(sz...; gain=inv(sqrt(3)), kwargs...)
end

function main()
    # +
    device = gpu
    #=
    model = Chain(
                Chain(Metalhead.conv_bn((3, 3), 3, 16, relu, pad=SamePad(), bias=false, init=torchlike_uniform)),
                #Conv((3, 3), 3 => 16, relu),
                MaxPool((2, 2)),
                Chain(Metalhead.conv_bn((3, 3), 16, 32, relu, pad=SamePad(), bias=false, init=torchlike_uniform)),
                #Conv((3, 3), 16 => 32, relu),
                MaxPool((2, 2)),
                Chain(Metalhead.conv_bn((3, 3), 32, 64, relu, pad=SamePad(), bias=false, init=torchlike_uniform)),
                #Conv((3, 3), 32 => 64, relu),
                MaxPool((2, 2)),
                Flux.flatten,
                Dense(16 * 16 * 64, 512, relu, init=init = torchlike_uniform),
                Dense(512, 5, init=init = torchlike_uniform)
            ) |> f32 |> device

    Flux.trainmode!(model)
    =#

    model = Chain(TorchModuleWrapper(py"resnet")) |> f32 |> device
    Flux.trainmode!(model)


    #model = ResNet18(nclasses=5) |> f32 |> device
    #Flux.trainmode!(model)
    # -

    train_loader = py"train_loader"
    val_loader = py"train_loader"

    opt = Optimisers.ADAM(0.001)
    state = Optimisers.setup(opt, model)

    @info "start training ... wait for seconds due to JIT compilation"
    epochs = 10
    for e = 1:epochs
        @info "epoch $(e)/$(epochs)"
        Flux.trainmode!(model)

        train_loss_tot = Float32(0)

        sz_train = 0
        train_acc = 0
        @showprogress for (py_x, py_y) in train_loader
            x = permutedims(py_x.numpy(), (4, 3, 2, 1)) |> f32 |> device
            sz_train += size(x)[end]
            y = Flux.onehotbatch(py_y.numpy(), 0:4) |> device
            ret = Flux.withgradient(model, x, y) do m, x, y
                ŷ = m(x)
                train_acc += sum(cpu(Flux.onecold(ŷ, 0:4)) .== cpu(Flux.onecold(y, 0:4)))
                Flux.Losses.logitcrossentropy(ŷ, y)
            end
            gs, = ret.grad
            state, model = Optimisers.update(state, model, gs)
            train_loss_tot += ret.val |> cpu
        end

        train_loss = train_loss_tot / length(train_loader)
        @info "train: " train_acc = 100train_acc / sz_train train_loss = train_loss

        Flux.testmode!(model)

        val_loss_tot = Float32(0)
        val_acc = 0
        sz_val = 0

        @showprogress for (py_x, py_y) in val_loader
            x = permutedims(py_x.numpy(), (4, 3, 2, 1)) |> f32 |> device
            sz_val += size(x)[end]
            y = Flux.onehotbatch(py_y.numpy(), 0:4) |> device
            ŷ = model(x)
            val_loss_tot += Flux.Losses.logitcrossentropy(ŷ, y) |> cpu
            val_acc += sum(cpu(Flux.onecold(ŷ, 0:4)) .== cpu(Flux.onecold(y, 0:4)))
        end
        @show "val:" val_accuracy = 100val_acc / sz_val val_loss = val_loss_tot / length(val_loader)
    end

    @info "save model"
    trained_model = model |> cpu
    BSON.@save "trained_model.bson" trained_model
    @info "Done"
    return trained_model
end

main()

Project.toml

[deps]
BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
DLPack = "53c2dc0f-f7d5-43fd-8906-6c0220547083"
DataLoaders = "2e981812-ef13-4a9c-bfa0-ab13047b12a9"
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
Metalhead = "dbeba491-748d-5e0e-a39e-b530a07fa0cc"
Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
PyCallChainRules = "b12ccfe2-7326-416f-9f4f-cd3183bd9fe8"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"