def __init__(self, work_dir, config, rank=0):
self.rank = rank
self.config = config
if self.rank == 0:
seed_worker(self.config.get('seed'))
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.loss_type = config.get('loss_type', 'NLL')
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'))
if self.rank == 0:
self.writer = SummaryWriter(self.logs_path)
self.writer.add_text('General info', pformat(config))
self.device = config.get('device', 'cpu')
self.multi_gpu = config.get('multi_gpu')
if self.multi_gpu:
torch.distributed.init_process_group("nccl", init_method="env://")
self.device = torch.device(f'cuda:{self.rank}')
self.create_dirs()
self.load_dataset()
self.loss = torch.nn.CTCLoss(blank=0, zero_infinity=self.config.get(
'CTCLossZeroInf', False)) if self.loss_type == 'CTC' else None
self.out_size = len(self.vocab) + 1 if self.loss_type == 'CTC' else len(self.vocab)
self.model = TextRecognitionModel(config.get('backbone_config'), self.out_size,
config.get('head', {}), config.get('transformation', {}))
print(self.model)
if self.model_path is not None:
self.model.load_weights(self.model_path, map_location=self.device)
self.model = self.model.to(self.device)
if self.multi_gpu:
if torch.cuda.device_count() > 1:
self.model = torch.nn.parallel.DistributedDataParallel(
self.model, device_ids=[self.rank], output_device=self.rank)
self.optimizer = getattr(optim, config.get('optimizer', 'Adam'))(self.model.parameters(), self.learing_rate)
self.lr_scheduler = getattr(optim.lr_scheduler, self.config.get('scheduler', 'ReduceLROnPlateau'))(
self.optimizer, **self.config.get('scheduler_params', {}))
self.time = get_timestamp()
self.use_lang_model = self.config.get("head").get("use_semantics")
if self.use_lang_model:
self.fasttext_model = fasttext.load_model(self.config.get("language_model_path"))
def __init__(self, config, runner_type=RunnerType.PyTorch):
self.config = deepcopy(config)
self.runner = create_runner(self.config, runner_type)
self.vocab = read_vocab(self.config.get('vocab_path'))
self.render = self.config.get('render')
self.load_dataset()
self.runner.load_model()
self.read_expected_outputs()
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.loss_type = config.get('loss_type', 'NLL')
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.loss = torch.nn.CTCLoss(
blank=0, zero_infinity=self.config.get(
'CTCLossZeroInf', False)) if self.loss_type == 'CTC' else None
self.out_size = len(
self.vocab) + 1 if self.loss_type == 'CTC' else len(self.vocab)
self.model = TextRecognitionModel(config.get('backbone_config'),
self.out_size,
config.get('head', {}))
print(self.model)
if self.model_path is not None:
self.model.load_weights(self.model_path, map_location=self.device)
self.model = self.model.to(self.device)
self.optimizer = getattr(optim,
config.get('optimizer',
'Adam'))(self.model.parameters(),
self.learing_rate)
self.lr_scheduler = getattr(
optim.lr_scheduler,
self.config.get('scheduler', 'ReduceLROnPlateau'))(
self.optimizer, **self.config.get('scheduler_params', {}))
self.time = get_timestamp()
def load_model(self):
self.vocab_len = len(read_vocab(self.config.get('vocab_path')))
self.use_ctc = self.config.get('use_ctc')
out_size = self.vocab_len + 1 if self.use_ctc else self.vocab_len
self.model = TextRecognitionModel(
self.config.get('backbone_config'), out_size,
self.config.get('head', {}), self.config.get('transformation', {}))
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.use_ctc = self.config.get('use_ctc')
self.model = TextRecognitionModel(config.get('backbone_config'), len(
self.vocab), config.get('head', {}), config.get('transformation', {}))
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.use_ctc = self.config.get('use_ctc')
self.out_size = len(self.vocab) + 1 if self.use_ctc else len(self.vocab)
self.model = TextRecognitionModel(config.get('backbone_config'), self.out_size, 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_encoder', self.config.get('input_shape')))
if not self.use_ctc:
self.encoder = self.model.get_encoder_wrapper(self.model)
self.encoder.eval()
self.decoder = self.model.get_decoder_wrapper(self.model)
self.decoder.eval()