refactor to OcrModel

tests/test_trainer.py

@@ -13,10 +13,9 @@ EXAMPLE_DIR = Path(__file__).parent.parent / 'examples'
 INDEX_DIR = Path(__file__).parent.parent / 'data/test'
 IMAGE_DIR = Path(__file__).parent.parent / 'data/images'
 
+from cnocr import gen_model
 from cnocr.data_utils.aug import NormalizeAug
 from cnocr.dataset import OcrDataModule
-from cnocr.models.densenet import DenseNet
-from cnocr.models.crnn import CRNN
 from cnocr.trainer import PlTrainer
 
 train_transform = transforms.Compose(
@@ -31,12 +30,6 @@ train_transform = transforms.Compose(
 val_transform = NormalizeAug()
 
 
-def gen_model(vocab):
-    net = DenseNet(32, [2, 2, 2, 2], 64)
-    crnn = CRNN(net, vocab=vocab, lstm_features=512, rnn_units=128)
-    return crnn
-
-
 def test_trainer():
     data_mod = OcrDataModule(
         index_dir=INDEX_DIR,
@@ -48,7 +41,6 @@ def test_trainer():
         num_workers=0,
         pin_memory=False,
     )
-    # data_mod.setup()
 
     config = {
         'epochs': 2,
@@ -64,5 +56,5 @@ def test_trainer():
         "pl_checkpoint_mode": "max",
     }
     trainer = PlTrainer(config)
-    model = gen_model(data_mod.vocab)
+    model = gen_model('densenet-s-lstm', data_mod.vocab)
     trainer.fit(model, datamodule=data_mod)