also support vallist

crnn_main.py

@@ -18,7 +18,8 @@ import models.crnn as crnn
 parser = argparse.ArgumentParser()
 parser.add_argument('--trainroot', default="", help='path to dataset')
 parser.add_argument('--trainlist', default="", help='path to train_list')
-parser.add_argument('--valroot', required=True, help='path to dataset')
+parser.add_argument('--valroot', default="", help='path to dataset')
+parser.add_argument('--vallist', default="", help='path to val_list')
 parser.add_argument('--workers', type=int, help='number of data loading workers', default=2)
 parser.add_argument('--batchSize', type=int, default=64, help='input batch size')
 parser.add_argument('--imgH', type=int, default=32, help='the height of the input image to network')
@@ -65,6 +66,7 @@ elif opt.trainlist != "":
 else:
     print("no train data, exit")
     exit(0)
+
 assert train_dataset
 if not opt.random_sample:
     sampler = dataset.randomSequentialSampler(train_dataset, opt.batchSize)
@@ -75,8 +77,15 @@ train_loader = torch.utils.data.DataLoader(
     shuffle=False, sampler=sampler,
     num_workers=int(opt.workers),
     collate_fn=dataset.alignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio=opt.keep_ratio))
-test_dataset = dataset.lmdbDataset(
-    root=opt.valroot, transform=dataset.resizeNormalize((100, 32)))
+
+if opt.valroot != "":
+    test_dataset = dataset.lmdbDataset(
+        root=opt.valroot, transform=dataset.resizeNormalize((100, 32)))
+elif opt.vallist != "":
+    test_dataset = dataset.listDataset(list_file =opt.vallist, transform=dataset.resizeNormalize((100, 32)))
+else:
+    print("no val data, exit")
+    exit(0)
 
 nclass = len(opt.alphabet) + 1
 nc = 1