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train.py
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train.py
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import torch
from itertools import chain
from src.callbacks.gan import (
CycleGANLoss,
GANLoss,
IdenticalGANLoss,
PrepareGeneratorPhase,
GeneratorOptimizerCallback,
PrepareDiscriminatorPhase,
DiscriminatorLoss,
DiscriminatorOptimizerCallback
)
from src.callbacks.visualization import LogImageCallback
from src.dataset import UnpairedDataset
from src.modules.generator import Generator
from src.modules.discriminator import NLayerDiscriminator, PixelDiscriminator
from src.runner import CycleGANRunner
from src.modules.loss import LSGanLoss
from torchvision import transforms as T
train_ds = UnpairedDataset(
"./datasets/monet2photo/trainA_preprocessed",
"./datasets/monet2photo/trainB_preprocessed",
transforms=T.Compose([
T.Resize((300, 300)),
T.RandomCrop((256, 256)),
T.RandomHorizontalFlip(),
T.ToTensor(),
])
)
train_dl = torch.utils.data.DataLoader(
train_ds,
batch_size=1,
shuffle=True
)
from PIL import Image
tr = transforms=T.Compose([
T.Resize((256,256)),
T.ToTensor(),
])
mipt_photo = tr(Image.open("./datasets/mipt.jpg"))
zinger_photo = tr(Image.open("./datasets/vk.jpg"))
model = {
"generator_ab": Generator(3, 3, n_blocks=9),
"generator_ba": Generator(3, 3, n_blocks=9),
"discriminator_a": PixelDiscriminator(3),
"discriminator_b": PixelDiscriminator(3),
}
optimizer = {
"generator": torch.optim.Adam(
chain(
model["generator_ab"].parameters(),
model["generator_ba"].parameters()
),
lr=2e-4
),
"discriminator": torch.optim.Adam(
chain(
model["discriminator_a"].parameters(),
model["discriminator_b"].parameters()
),
lr=2e-4
)
}
callbacks = [
PrepareGeneratorPhase(),
GANLoss(),
CycleGANLoss(),
IdenticalGANLoss(),
GeneratorOptimizerCallback(
weights=[1, 10, 5],
),
PrepareDiscriminatorPhase(),
DiscriminatorLoss(),
DiscriminatorOptimizerCallback(),
LogImageCallback(log_period=5000),
LogImageCallback(log_period=5000, key="mipt", img=mipt_photo),
LogImageCallback(log_period=5000, key="vk", img=zinger_photo),
]
criterion = {
"gan": LSGanLoss(),
"cycle": torch.nn.L1Loss(reduction="mean"),
"identical": torch.nn.L1Loss(reduction="mean"),
}
runner = CycleGANRunner(buffer_size=50)
runner.train(
model=model,
optimizer=optimizer,
loaders={"train": train_dl},
callbacks=callbacks,
criterion=criterion,
num_epochs=100,
verbose=False,
logdir="teacher",
main_metric="identical_loss"
)