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from glob import glob
import fire
import lightning as pl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import rich
import torch
import torch.nn as nn
import yaml
from lightning import Trainer
from lightning.pytorch import callbacks as pl_callbacks
from lightning.pytorch import loggers as pl_loggers
from rich import traceback
from src import dataset as datasets
from src import model as models
from src.config import Backbone, Config, Head, load_config
from src.utils import files
from src.utils.checks import checks
from src.utils.model_checkpoint import ModelCheckpointParallel
traceback.install()
def main(config: Config, train: bool):
checks(config)
torch.set_float32_matmul_precision("high") # Set the precision for matmul operations
model = models.DeepfakeDetectionModel(config, verbose=True)
if config.checkpoint:
model.load_state_dict(torch.load(config.checkpoint, map_location="cpu", weights_only=True)["state_dict"])
data_module = datasets.DeepfakeDataModule(config, model.get_preprocessing())
loggers: list = [pl_loggers.CSVLogger(config.run_dir, name=config.run_name, version="")]
if config.wandb:
wandb_logger = pl_loggers.WandbLogger(
project="deepfake",
name=config.run_name,
save_dir=f"{config.run_dir}/{config.run_name}",
tags=config.wandb_tags,
)
loggers.append(wandb_logger)
callbacks = [
pl_callbacks.RichProgressBar(),
ModelCheckpointParallel(filename="best_mAP", monitor="val/mAP_video", mode="max"),
]
trainer = Trainer(
devices=config.devices,
max_epochs=config.max_epochs,
precision=config.precision,
accumulate_grad_batches=config.batch_size // config.mini_batch_size,
fast_dev_run=config.fast_dev_run,
log_every_n_steps=100,
overfit_batches=config.overfit_batches,
limit_train_batches=config.limit_train_batches,
limit_val_batches=config.limit_val_batches,
limit_test_batches=config.limit_test_batches,
deterministic=config.deterministic,
detect_anomaly=config.detect_anomaly,
logger=loggers,
callbacks=callbacks,
default_root_dir=config.run_dir,
)
if train:
trainer.fit(model, data_module)
ckpt_path = f"{config.run_dir}/{config.run_name}/checkpoints/{config.checkpoint_for_testing}.ckpt"
trainer.test(model, data_module, ckpt_path=ckpt_path)
else:
assert config.checkpoint is not None, "Checkpoint is required for testing"
trainer.test(model, data_module)
if config.wandb:
wandb_logger.finalize("success")
wandb_logger.experiment.finish()
def get_train_config() -> Config:
config = Config()
config.run_name = "example-run"
config.run_dir = "runs/train"
config.wandb = False
config.num_workers = 12
config.devices = [2]
config.backbone = Backbone.CLIP_L_14
config.freeze_feature_extractor = True
config.peft.enabled = True
config.peft.ln_tuning.enabled = True
config.head = Head.LinearNorm
config.num_classes = 2
config.loss.ce_labels = 1.0
config.slerp_feature_augmentation = True
config.batch_size = config.mini_batch_size = 128
config.lr_scheduler = "cosine"
config.lr = 8e-5
config.min_lr = 5e-5
config.weight_decay = 0
config.max_epochs = 10
limit_val_files = 16384
config.limit_val_files = limit_val_files
config.limit_val_batches = limit_val_files // config.mini_batch_size
config.binary_labels = True
config.trn_files = [
"config/datasets/FF/test/DF.txt",
"config/datasets/FF/test/F2F.txt",
"config/datasets/FF/test/FS.txt",
"config/datasets/FF/test/NT.txt",
"config/datasets/FF/test/real.txt",
]
config.val_files = [
"config/datasets/CDFv2/test/Celeb-synthesis.txt",
"config/datasets/CDFv2/test/Celeb-real.txt",
"config/datasets/CDFv2/test/YouTube-real.txt",
]
config.tst_files = {
"CDF": [
"config/datasets/CDFv2/test/Celeb-synthesis.txt",
"config/datasets/CDFv2/test/Celeb-real.txt",
"config/datasets/CDFv2/test/YouTube-real.txt",
]
}
return config
def get_test_config() -> Config:
config_path = "runs/train/example-run/hparams.yaml"
new_run_name = "example-run"
config = load_config(config_path)
config.run_name = new_run_name
config.run_dir = "runs/test"
config.checkpoint = config_path.replace("hparams.yaml", "checkpoints/best_mAP.ckpt")
config.wandb = False
config.wandb_tags.extend(["test"])
config.num_workers = 12
config.batch_size = config.mini_batch_size = 512
config.devices = [0]
config.tst_files = {
"CDF": [
"config/datasets/CDFv2/test/Celeb-synthesis.txt",
"config/datasets/CDFv2/test/Celeb-real.txt",
"config/datasets/CDFv2/test/YouTube-real.txt",
]
}
return config
def get_debug_config(config: Config) -> Config:
#! Debug
config.run_name = "tmp"
config.devices = [2]
config.num_workers = 8
# config.batch_size = config.mini_batch_size = 512
config.max_epochs = 1
config.limit_train_batches = 12
config.limit_val_batches = 12
config.limit_test_batches = 12
config.deterministic = True
config.detect_anomaly = True
return config
def entry(train: bool = False, test: bool = False, debug: bool = False, **kwargs):
if train:
config = get_train_config()
elif test:
config = get_test_config()
else:
raise ValueError("Either --train or --test must be provided")
# Overwrite config with debug values
if debug:
config = config.model_copy(update=dict(get_debug_config(config)))
# Parse command line arguments
config = config.model_copy(update=kwargs)
# Revalidate the config - checks if user provided valid values
config = Config(**dict(config))
main(config, train)
if __name__ == "__main__":
fire.Fire(entry)
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