Update train.py

train.py

@@ -54,55 +54,5 @@ def main():
     fit(network=network, data_train=data_train, data_val=data_val, metrics=metrics, args=args, hp=hp, data_names=data_names)
 
 
-def main2():
-    args = parse_args()
-    hp = Hyperparams()
-
-    if args.gpu:
-        contexts = [mx.context.gpu(i) for i in range(args.gpu)]
-    else:
-        contexts = [mx.context.cpu(i) for i in range(args.cpu)]
-
-
-    init_c = [('l%d_init_c' % l, (hp.batch_size, hp.num_hidden)) for l in range(hp.num_lstm_layer * 2)]
-    init_h = [('l%d_init_h' % l, (hp.batch_size, hp.num_hidden)) for l in range(hp.num_lstm_layer * 2)]
-    init_states = init_c + init_h
-    data_names = ['data'] + [x[0] for x in init_states]
-
-    data_train = ImageIterLstm(
-        args.data_root, args.train_file, hp.batch_size, (hp.img_width, hp.img_height), hp.num_label, init_states, name="train")
-    data_val = ImageIterLstm(
-        args.data_root, args.test_file,  hp.batch_size, (hp.img_width, hp.img_height), hp.num_label, init_states, name="val")
-
-    head = '%(asctime)-15s %(message)s'
-    logging.basicConfig(level=logging.DEBUG, format=head)
-
-    symbol = crnn_lstm(hp)
-    module = mx.mod.Module(
-        symbol,
-        data_names=data_names,
-        label_names=['label'],
-        context=contexts)
-
-    module.bind(data_shapes=data_train.provide_data, label_shapes=data_train.provide_label)
-
-    metrics = CtcMetrics(hp.seq_length)
-
-    module.fit(train_data=data_train,
-               eval_data=data_val,
-               # use metrics.accuracy or metrics.accuracy_lcs
-               eval_metric=mx.metric.np(metrics.accuracy, allow_extra_outputs=True),
-               optimizer='AdaDelta',
-               optimizer_params={'learning_rate': hp.learning_rate,
-                                 # 'momentum': hp.momentum,
-                                 'wd': 0.00001,
-                                 },
-               initializer=mx.init.Xavier(factor_type="in", magnitude=2.34),
-               num_epoch=hp.num_epoch,
-               batch_end_callback=mx.callback.Speedometer(hp.batch_size, 50),
-               epoch_end_callback=mx.callback.do_checkpoint(args.prefix),
-               )
-
-
 if __name__ == '__main__':
-    main()
+    main()