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run_attack.py
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run_attack.py
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import os
import sys
import wandb
import pytorch_lightning as pl
from datasets import load_dataset
from utils.wandb import get_experiments, load_model
from attack import trainer
# args
from absl import app
from absl import flags
from ml_collections.config_flags import config_flags
FLAGS = flags.FLAGS
config_flags.DEFINE_config_file("config", default="config.py:attack")
def model_name(args):
n = args.prior + '_' + args.arc_type + '_' + str(args.z_dim)
return n
def cli_main(_):
pl.seed_everything(1234)
if "absl.logging" in sys.modules:
import absl.logging
absl.logging.set_verbosity("info")
absl.logging.set_stderrthreshold("info")
args = FLAGS.config
print(args)
# ------------
# data
# ------------
data_module, args.model = load_dataset(args.model)
data_module.setup('test')
dataloader = data_module.test_dataloader()
print(args)
# ------------
# load pretrained model
# ------------
ids = get_experiments(config=args.model)
model = load_model(ids[0]).vae
model.eval()
# ------------
# wandb
# ------------
os.environ["WANDB_API_KEY"] = ''# WAND API KEY HERE
tags = [
args.model.prior,
args.model.dataset_name,
args.attack.type
]
wandb.init(
project="adv_vae",
tags=tags,
entity='' # USER NAME HERE
)
wandb.config.update(flags.FLAGS)
# run attack
trainer.train(model, dataloader, args)
if __name__ == "__main__":
app.run(cli_main)