def __init__(self, config, runner_type=RunnerType.PyTorch):
self.config = config
self.runner = create_runner(self.config, runner_type)
self.vocab = read_vocab(self.config.get("vocab_path"))
self.load_dataset()
self.runner.load_model()
self.read_expected_outputs()
def load_model(self):
vocab_len = len(read_vocab(self.config.get('vocab_path')))
self.model = Im2latexModel(self.config.get('backbone_type', 'resnet'),
self.config.get('backbone_config'),
vocab_len, self.config.get('head', {}))
self.device = self.config.get('device', 'cpu')
self.model.load_weights(self.config.get("model_path"),
map_location=self.device)
self.model = self.model.to(self.device)
self.model.eval()
def __init__(self, config):
self.config = config
self.model_path = config.get('model_path')
self.vocab = read_vocab(config.get('vocab_path'))
self.transform = create_list_of_transforms(config.get('transforms_list'))
self.model = Im2latexModel(config.get('backbone_type', 'resnet'), config.get(
'backbone_config'), len(self.vocab), config.get('head', {}))
if self.model_path is not None:
self.model.load_weights(self.model_path, map_location=config.get('map_location', 'cpu'))
self.model.eval()
self.device = config.get('device', 'cpu')
self.model = self.model.to(self.device)
def __init__(self, config):
self.config = config
self.model_path = config.get('model_path')
self.vocab = read_vocab(config.get('vocab_path'))
self.model = Im2latexModel(config.get('backbone_type', 'resnet'),
config.get('backbone_config'),
len(self.vocab), config.get('head', {}))
self.model.eval()
if self.model_path is not None:
self.model.load_weights(self.model_path)
self.img_for_export = torch.rand(
self.config.get("input_shape_decoder"))
self.encoder = self.model.get_encoder_wrapper(self.model)
self.encoder.eval()
self.decoder = self.model.get_decoder_wrapper(self.model)
self.decoder.eval()
def __init__(self, work_dir, config):
self.config = config
self.model_path = config.get('model_path')
self.train_paths = config.get('train_paths')
self.val_path = config.get('val_path')
self.vocab = read_vocab(config.get('vocab_path'))
self.train_transforms_list = config.get('train_transforms_list')
self.val_transforms_list = config.get('val_transforms_list')
self.total_epochs = config.get('epochs', 30)
self.learing_rate = config.get('learning_rate', 1e-3)
self.clip = config.get('clip_grad', 5.0)
self.work_dir = os.path.abspath(work_dir)
self.save_dir = os.path.join(
self.work_dir, config.get("save_dir", "model_checkpoints"))
self.val_results_path = os.path.join(self.work_dir, "val_results")
self.step = 0
self.global_step = 0
self._test_steps = config.get('_test_steps', 1e18)
self.epoch = 1
self.best_val_loss = 1e18
self.best_val_accuracy = 0.0
self.best_val_loss_test = 1e18
self.best_val_accuracy_test = 0.0
self.print_freq = config.get('print_freq', 16)
self.save_freq = config.get('save_freq', 2000)
self.val_freq = config.get('val_freq', 5000)
self.logs_path = os.path.join(self.work_dir,
config.get("log_path", "logs"))
self.writer = SummaryWriter(self.logs_path)
self.device = config.get('device', 'cpu')
self.writer.add_text("General info", pformat(config))
self.create_dirs()
self.load_dataset()
self.model = Im2latexModel(config.get('backbone_type', 'resnet'),
config.get('backbone_config'),
len(self.vocab), config.get('head', {}))
if self.model_path is not None:
self.model.load_weights(self.model_path, map_location=self.device)
self.optimizer = getattr(optim,
config.get('optimizer',
"Adam"))(self.model.parameters(),
self.learing_rate)
self.lr_scheduler = ReduceLROnPlateau(self.optimizer)
self.model = self.model.to(self.device)
self.time = get_timestamp()