def __init__(self, globalConfig={}, config={}):
self.globalConfig = Configuration(globalConfig, GLOBAL_DEFAULTS)
self.config = Configuration(config, DEFAULTS)
self.modelConfig = Configuration.load(self.config["model_path"],
"algorithm")
self._configure_dataset()
self._configure_algorithm()
self._configure_executor()
default=MODEL_DATE)
parser.add_argument('--paper-note-path',
default='../paper-notes/data/words')
parser.add_argument('--model-epoch',
help='epoch to continue for',
default=MODEL_EPOCH,
type=int)
args = parser.parse_args()
# TRAINING
LOG_NAME = '{}-{}'.format("otf-iam-paper", args.model_date)
model_folder = os.path.join(Constants.MODELS_PATH, LOG_NAME)
models_path = os.path.join(model_folder,
'model-{}'.format(args.model_epoch))
logger = Logger()
config = Configuration.load(model_folder, "algorithm")
algorithm = HtrNet(config['algo_config'])
dataset = PreparedDataset.PreparedDataset(config['dataset'], False,
config['data_config'])
algorithm.configure(batch_size=config['batch'],
learning_rate=config['learning_rate'],
sequence_length=dataset.max_length,
image_height=dataset.meta["height"],
image_width=dataset.meta["width"],
vocab_length=dataset.vocab_length,
channels=dataset.channels,
class_learning_rate=config.default(
'class_learning_rate', config['learning_rate']))
executor = Executor(algorithm, True, config, logger=logger)
'gt.viz', False):
vizimage = self.viz(vizimage, gt, True)
if len(self.blocks) > 0:
vizimage = self.viz(vizimage, res["result"], False)
self.viz.store(vizimage, file)
res["viz"] = vizimage
if len(self.evals) > 0:
for evl in self.evals:
scores = evl(gt, res["result"])
for score_key in scores.keys():
self.scores[score_key] = [
scores[score_key]
] if score_key not in self.scores else [
scores[score_key], *self.scores[score_key]
]
return res
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--config')
parser.add_argument('--gpu',
help='Runs scripts on gpu. Default is cpu.',
default=-1,
type=int)
args = parser.parse_args()
config = Configuration.load("./config/e2e/", args.config)
e2e = E2ERunner(config, {"gpu": args.gpu})
e2e()
parser.add_argument('--logplacement',
help='Log Device placement',
action='store_true',
default=False)
parser.add_argument('--model-date',
help='date to continue for',
default='')
parser.add_argument('--model-epoch',
help='epoch to continue for',
default=0,
type=int)
args = parser.parse_args()
# TRAINING
logger = Logger()
config = Configuration.load(SEP_CONFIG_PATH, args.config)
config()
algorithm = TFUnet(config['algo_config'])
algorithm.configure(learning_rate=config['learning_rate'],
slice_width=config['data_config.slice_width'],
slice_height=config['data_config.slice_height'])
executor = Executor(algorithm, True, config, logger=logger)
dataset = PaperNoteSlices(paper_note_path=config.default(
'data_config.paper_note_path', '../paper-notes/data/final'),
filter=config['data_config.filter'],
slice_width=config['data_config.slice_width'],
slice_height=config['data_config.slice_height'],
binarize=config.default('binary', False),
config=config['data_config'])
log_name = '{}-{}'.format(config["name"],
