def __init__(self, config, pretrained=True): self.config = config self.model, self.vocab = build_model(config) self.device = config['device'] self.num_iters = config['trainer']['iters'] self.beamsearch = config['predictor']['beamsearch'] self.data_root = config['dataset']['data_root'] self.train_annotation = config['dataset']['train_annotation'] self.valid_annotation = config['dataset']['valid_annotation'] self.dataset_name = config['dataset']['name'] self.batch_size = config['trainer']['batch_size'] self.print_every = config['trainer']['print_every'] self.valid_every = config['trainer']['valid_every'] self.checkpoint = config['trainer']['checkpoint'] self.export_weights = config['trainer']['export'] self.metrics = config['trainer']['metrics'] logger = config['trainer']['log'] if logger: self.logger = Logger(logger) if pretrained: weight_file = download_weights(**config['pretrain'], quiet=config['quiet']) self.load_weights(weight_file) self.iter = 0 self.optimizer = AdamW(self.model.parameters(), betas=(0.9, 0.98), eps=1e-09) self.scheduler = OneCycleLR(self.optimizer, **config['optimizer']) # self.optimizer = ScheduledOptim( # Adam(self.model.parameters(), betas=(0.9, 0.98), eps=1e-09), # #config['transformer']['d_model'], # 512, # **config['optimizer']) self.criterion = LabelSmoothingLoss(len(self.vocab), padding_idx=self.vocab.pad, smoothing=0.1) transforms = ImgAugTransform() self.train_gen = self.data_gen('train_{}'.format(self.dataset_name), self.data_root, self.train_annotation, transform=transforms) if self.valid_annotation: self.valid_gen = self.data_gen( 'valid_{}'.format(self.dataset_name), self.data_root, self.valid_annotation) self.train_losses = []
def __init__(self, config, pretrained=True, augmentor=ImgAugTransform()): self.config = config self.model, self.vocab = build_model(config) self.device = config['device'] self.num_iters = config['trainer']['iters'] self.beamsearch = config['predictor']['beamsearch'] self.data_root = config['dataset']['data_root'] self.train_annotation = config['dataset']['train_annotation'] self.valid_annotation = config['dataset']['valid_annotation'] self.train_lmdb = config['dataset']['train_lmdb'] self.valid_lmdb = config['dataset']['valid_lmdb'] self.dataset_name = config['dataset']['name'] self.batch_size = config['trainer']['batch_size'] self.print_every = config['trainer']['print_every'] self.valid_every = config['trainer']['valid_every'] self.image_aug = config['aug']['image_aug'] self.masked_language_model = config['aug']['masked_language_model'] self.metrics = config['trainer']['metrics'] self.is_padding = config['dataset']['is_padding'] self.tensorboard_dir = config['monitor']['log_dir'] if not os.path.exists(self.tensorboard_dir): os.makedirs(self.tensorboard_dir, exist_ok=True) self.writer = SummaryWriter(self.tensorboard_dir) # LOGGER self.logger = Logger(config['monitor']['log_dir']) self.logger.info(config) self.iter = 0 self.best_acc = 0 self.scheduler = None self.is_finetuning = config['trainer']['is_finetuning'] if self.is_finetuning: self.logger.info("Finetuning model ---->") if self.model.seq_modeling == 'crnn': self.optimizer = Adam(lr=0.0001, params=self.model.parameters(), betas=(0.5, 0.999)) else: self.optimizer = AdamW(lr=0.0001, params=self.model.parameters(), betas=(0.9, 0.98), eps=1e-09) else: self.optimizer = AdamW(self.model.parameters(), betas=(0.9, 0.98), eps=1e-09) self.scheduler = OneCycleLR(self.optimizer, total_steps=self.num_iters, **config['optimizer']) if self.model.seq_modeling == 'crnn': self.criterion = torch.nn.CTCLoss(self.vocab.pad, zero_infinity=True) else: self.criterion = LabelSmoothingLoss(len(self.vocab), padding_idx=self.vocab.pad, smoothing=0.1) # Pretrained model if config['trainer']['pretrained']: self.load_weights(config['trainer']['pretrained']) self.logger.info("Loaded trained model from: {}".format( config['trainer']['pretrained'])) # Resume elif config['trainer']['resume_from']: self.load_checkpoint(config['trainer']['resume_from']) for state in self.optimizer.state.values(): for k, v in state.items(): if torch.is_tensor(v): state[k] = v.to(torch.device(self.device)) self.logger.info("Resume training from {}".format( config['trainer']['resume_from'])) # DATASET transforms = None if self.image_aug: transforms = augmentor train_lmdb_paths = [ os.path.join(self.data_root, lmdb_path) for lmdb_path in self.train_lmdb ] self.train_gen = self.data_gen( lmdb_paths=train_lmdb_paths, data_root=self.data_root, annotation=self.train_annotation, masked_language_model=self.masked_language_model, transform=transforms, is_train=True) if self.valid_annotation: self.valid_gen = self.data_gen( lmdb_paths=[os.path.join(self.data_root, self.valid_lmdb)], data_root=self.data_root, annotation=self.valid_annotation, masked_language_model=False) self.train_losses = [] self.logger.info("Number batch samples of training: %d" % len(self.train_gen)) self.logger.info("Number batch samples of valid: %d" % len(self.valid_gen)) config_savepath = os.path.join(self.tensorboard_dir, "config.yml") if not os.path.exists(config_savepath): self.logger.info("Saving config file at: %s" % config_savepath) Cfg(config).save(config_savepath)
def __init__(self, config, pretrained=True): self.config = config self.model, self.vocab = build_model(config) self.device = config['device'] self.num_iters = config['trainer']['iters'] self.beamsearch = config['predictor']['beamsearch'] self.data_root = config['dataset']['data_root'] self.train_annotation = config['dataset']['train_annotation'] self.valid_annotation = config['dataset']['valid_annotation'] self.dataset_name = config['dataset']['name'] self.batch_size = config['trainer']['batch_size'] self.print_every = config['trainer']['print_every'] self.valid_every = config['trainer']['valid_every'] self.checkpoint = config['trainer']['checkpoint'] self.export_weights = config['trainer']['export'] self.metrics = config['trainer']['metrics'] logger = config['trainer']['log'] if logger: self.logger = Logger(logger) if pretrained: download_weights(**config['pretrain'], quiet=config['quiet']) state_dict = torch.load(config['pretrain']['cached'], map_location=torch.device(self.device)) for name, param in self.model.named_parameters(): if state_dict[name].shape != param.shape: print('{} missmatching shape'.format(name)) del state_dict[name] self.model.load_state_dict(state_dict, strict=False) self.iter = 0 self.optimizer = ScheduledOptim( Adam(self.model.parameters(), betas=(0.9, 0.98), eps=1e-09), config['transformer']['d_model'], **config['optimizer']) # self.criterion = nn.CrossEntropyLoss(ignore_index=0) self.criterion = LabelSmoothingLoss(len(self.vocab), padding_idx=self.vocab.pad, smoothing=0.1) # transforms = torchvision.transforms.Compose([ # torchvision.transforms.ColorJitter(brightness=.1, contrast=.1, hue=.1, saturation=.1), # torchvision.transforms.RandomAffine(degrees=0, scale=(3/4, 4/3)) # ]) transforms = ImgAugTransform() self.train_gen = self.data_gen('train_{}'.format(self.dataset_name), self.data_root, self.train_annotation, transform=transforms) if self.valid_annotation: self.valid_gen = self.data_gen( 'valid_{}'.format(self.dataset_name), self.data_root, self.valid_annotation) self.train_losses = []
def __init__(self, config, pretrained=False, augmentor=ImgAugTransform()): self.config = config self.model, self.vocab = build_model(config) # ipdb.set_trace() self.device = config['device'] self.num_iters = config['trainer']['iters'] self.beamsearch = config['predictor']['beamsearch'] self.data_root = config['dataset']['data_root'] self.train_annotation = config['dataset']['train_annotation'] self.valid_annotation = config['dataset']['valid_annotation'] self.dataset_name = config['dataset']['name'] self.batch_size = config['trainer']['batch_size'] self.print_every = config['trainer']['print_every'] self.valid_every = config['trainer']['valid_every'] self.image_aug = config['aug']['image_aug'] self.masked_language_model = config['aug']['masked_language_model'] # self.checkpoint = config['trainer']['checkpoint'] self.export_weights = config['trainer']['export'] self.metrics = config['trainer']['metrics'] logger = config['trainer']['log'] if logger: self.logger = Logger(logger) if pretrained: logging.info(f"Load weights from {config['weights']}....") # weight_file = download_weights(**config['pretrain'], quiet=config['quiet']) self.load_weights(config['weights']) self.iter = 0 self.optimizer = AdamW(self.model.parameters(), betas=(0.9, 0.98), eps=1e-09) self.scheduler = OneCycleLR(self.optimizer, total_steps=self.num_iters, **config['optimizer']) # self.optimizer = ScheduledOptim( # Adam(self.model.parameters(), betas=(0.9, 0.98), eps=1e-09), # #config['transformer']['d_model'], # 512, # **config['optimizer']) self.criterion = LabelSmoothingLoss(len(self.vocab), padding_idx=self.vocab.pad, smoothing=0.1) transforms = None if self.image_aug: transforms = augmentor self.train_gen = self.data_gen('train_{}'.format(self.dataset_name), self.data_root, self.train_annotation, self.masked_language_model, transform=transforms) if self.valid_annotation: self.valid_gen = self.data_gen('valid_{}'.format( self.dataset_name), self.data_root, self.valid_annotation, masked_language_model=False) self.train_losses = []