update code

dataset.py

@@ -29,7 +29,8 @@ class lmdbDataset(Dataset):
             sys.exit(0)
 
         with self.env.begin(write=False) as txn:
-            nSamples = int(txn.get('num-samples'))
+            # nSamples = int(txn.get('num-samples'))
+            nSamples = int(txn.get('num-samples'.encode()).decode('utf-8'))
             self.nSamples = nSamples
 
         self.transform = transform
@@ -42,7 +43,8 @@ class lmdbDataset(Dataset):
         assert index <= len(self), 'index range error'
         index += 1
         with self.env.begin(write=False) as txn:
-            img_key = 'image-%09d' % index
+            # img_key = 'image-%09d' % index
+            img_key = 'image-%09d'.encode() % index
             imgbuf = txn.get(img_key)
 
             buf = six.BytesIO()
@@ -57,8 +59,10 @@ class lmdbDataset(Dataset):
             if self.transform is not None:
                 img = self.transform(img)
 
-            label_key = 'label-%09d' % index
-            label = str(txn.get(label_key))
+            # label_key = 'label-%09d' % index
+            label_key = 'label-%09d'.encode() % index
+            # label = str(txn.get(label_key))
+            label = str(txn.get(label_key).decode('utf-8'))
 
             if self.target_transform is not None:
                 label = self.target_transform(label)

models/crnn.py

 import torch.nn as nn
-
+import torch.nn.functional as F
 
 class BidirectionalLSTM(nn.Module):
 
     def __init__(self, nIn, nHidden, nOut):
         super(BidirectionalLSTM, self).__init__()
 
-        self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
+        self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True, batch_first=True)
         self.embedding = nn.Linear(nHidden * 2, nOut)
 
     def forward(self, input):
         recurrent, _ = self.rnn(input)
-        T, b, h = recurrent.size()
-        t_rec = recurrent.view(T * b, h)
+        # T, b, h = recurrent.size()
+        b, T, h = recurrent.size()
+        # t_rec = recurrent.view(T * b, h)
+        t_rec = recurrent.reshape(T * b, h)
 
         output = self.embedding(t_rec)  # [T * b, nOut]
-        output = output.view(T, b, -1)
+        # output = output.view(T, b, -1)
+        output = output.view(b, T, -1)
 
         return output
 
@@ -71,9 +74,13 @@ class CRNN(nn.Module):
         b, c, h, w = conv.size()
         assert h == 1, "the height of conv must be 1"
         conv = conv.squeeze(2)
-        conv = conv.permute(2, 0, 1)  # [w, b, c]
+        # conv = conv.permute(2, 0, 1)  # [w, b, c]
+        conv = conv.permute(0, 2, 1)  # [b, w, c]
 
         # rnn features
         output = self.rnn(conv)
 
+        # add log_softmax to converge output
+        output = F.log_softmax(output, dim=2)
+
         return output

utils.py

@@ -131,7 +131,9 @@ def oneHot(v, v_length, nc):
 
 
 def loadData(v, data):
-    v.data.resize_(data.size()).copy_(data)
+    # v.data.resize_(data.size()).copy_(data)
+    with torch.no_grad():
+        v.resize_(data.size()).copy_(data)
 
 
 def prettyPrint(v):
@@ -147,3 +149,25 @@ def assureRatio(img):
         main = nn.UpsamplingBilinear2d(size=(h, h), scale_factor=None)
         img = main(img)
     return img
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+
+    def __init__(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
\ No newline at end of file