works for mnt data

README.md

@@ -36,3 +36,9 @@ Train a new model
 Stable commits
 --------------
 dbe73da0dd7efb8bd76dbd7f0ac3856e742b98d4: support image list with label and alphabet
+
+Train for VGG text data
+--------------
+1. create a link to mnt folder
+2. python data/create_mnt_list.py
+3. python main.py --trainlist data/train_list.txt --vallist data/test_list.txt --cuda --adam --lr=0.001

data/create_mnt_list.py

+with open('data/mnt/ramdisk/max/90kDICT32px/annotation_train.txt') as fp:
+    lines = fp.readlines()
+
+train_fp = open('data/train_list.txt', 'w')
+for line in lines:
+    imgpath = line.strip().split(' ')[0]
+    label = imgpath.split('/')[-1].split('_')[1].lower()
+    label = ':'.join(label)
+    imgpath = 'data/mnt/ramdisk/max/90kDICT32px/%s' % imgpath
+    output = ' '.join([imgpath, label])
+    print >> train_fp, output
+
+train_fp.close()
+
+
+with open('data/mnt/ramdisk/max/90kDICT32px/annotation_test.txt') as fp:
+    lines = fp.readlines()
+
+test_fp = open('data/test_list.txt', 'w')
+for line in lines:
+    imgpath = line.strip().split(' ')[0]
+    label = imgpath.split('/')[-1].split('_')[1].lower()
+    label = ':'.join(label)
+    imgpath = 'data/mnt/ramdisk/max/90kDICT32px/%s' % imgpath
+    output = ' '.join([imgpath, label])
+    print >> test_fp, output
+
+test_fp.close()

dataset.py

@@ -46,59 +46,6 @@ class listDataset(Dataset):
 
         return (img, label)
 
-class lmdbDataset(Dataset):
-
-    def __init__(self, root=None, transform=None, target_transform=None):
-        self.env = lmdb.open(
-            root,
-            max_readers=1,
-            readonly=True,
-            lock=False,
-            readahead=False,
-            meminit=False)
-
-        if not self.env:
-            print('cannot creat lmdb from %s' % (root))
-            sys.exit(0)
-
-        with self.env.begin(write=False) as txn:
-            nSamples = int(txn.get('num-samples'))
-            self.nSamples = nSamples
-
-        self.transform = transform
-        self.target_transform = target_transform
-
-    def __len__(self):
-        return self.nSamples
-
-    def __getitem__(self, index):
-        assert index <= len(self), 'index range error'
-        index += 1
-        with self.env.begin(write=False) as txn:
-            img_key = 'image-%09d' % index
-            imgbuf = txn.get(img_key)
-
-            buf = six.BytesIO()
-            buf.write(imgbuf)
-            buf.seek(0)
-            try:
-                img = Image.open(buf).convert('L')
-            except IOError:
-                print('Corrupted image for %d' % index)
-                return self[index + 1]
-
-            if self.transform is not None:
-                img = self.transform(img)
-
-            label_key = 'label-%09d' % index
-            label = str(txn.get(label_key))
-
-            if self.target_transform is not None:
-                label = self.target_transform(label)
-
-        return (img, label)
-
-
 class resizeNormalize(object):
 
     def __init__(self, size, interpolation=Image.BILINEAR):

run.sh

@@ -2,14 +2,4 @@
 # imagepath label
 # /ab/cd/image.jpg a:b:c:d 
 
-#python main.py --trainlist train_list2.txt --vallist test_list2.txt --cuda --adam --lr=0.001
-
-
-
-#python main_for_music.py --trainlist data/train_list2.txt --vallist data/test_list2.txt --cuda --adam --lr=0.001
-# loss ~ 2
-
-
-
-nohup python main_for_music.py --trainlist data/train_list3.txt --vallist data/test_list3.txt --cuda --adam --lr=0.001 > log3.txt &
-# loss ~ 2
+python main.py --trainlist data/train_list.txt --vallist data/test_list.txt --cuda --adam --lr=0.001