def setUpClass(cls) -> None:
cls._tmpdir = tempfile.mkdtemp()
args = argparse.Namespace()
args.opts = [
f"env.save_dir={cls._tmpdir}", f"model=cnn_lstm", f"dataset=clevr"
]
args.config_override = None
configuration = Configuration(args)
configuration.freeze()
cls.config = configuration.get_config()
registry.register("config", cls.config)
setup_output_folder.cache_clear()
setup_logger.cache_clear()
cls.writer = setup_logger()
def setUp(self):
self.tmpdir = tempfile.mkdtemp()
self.trainer = argparse.Namespace()
self.config = OmegaConf.create({
"model": "simple",
"model_config": {},
"training": {
"checkpoint_interval": 1,
"evaluation_interval": 10,
"early_stop": {
"criteria": "val/total_loss"
},
"batch_size": 16,
"log_interval": 10,
"logger_level": "info",
},
"env": {
"save_dir": self.tmpdir
},
})
# Keep original copy for testing purposes
self.trainer.config = deepcopy(self.config)
registry.register("config", self.trainer.config)
setup_logger.cache_clear()
setup_logger()
self.report = Mock(spec=Report)
self.report.dataset_name = "abcd"
self.report.dataset_type = "test"
self.trainer.model = SimpleModule()
self.trainer.val_loader = torch.utils.data.DataLoader(
NumbersDataset(), batch_size=self.config.training.batch_size)
self.trainer.optimizer = torch.optim.Adam(
self.trainer.model.parameters(), lr=1e-01)
self.trainer.device = "cpu"
self.trainer.num_updates = 0
self.trainer.current_iteration = 0
self.trainer.current_epoch = 0
self.trainer.max_updates = 0
self.trainer.meter = Meter()
self.cb = LogisticsCallback(self.config, self.trainer)