From 92b4f31c0c261f1143c62f2b7ef40d54d91c3231 Mon Sep 17 00:00:00 2001
From: Nishant <getrooted0019@hotmail.com>
Date: Tue, 8 Dec 2020 22:47:57 +0530
Subject: [PATCH] Fixed inferBatch ValueError.
---
src/Model.py | 6 +++---
src/main.py | 13 ++++++-------
2 files changed, 9 insertions(+), 10 deletions(-)
diff --git a/src/Model.py b/src/Model.py
index 1d12c81..e987acf 100644
--- a/src/Model.py
+++ b/src/Model.py
@@ -260,14 +260,14 @@ class Model:
ctcInput = evalRes[1]
evalList = self.lossPerElement
feedDict = {self.savedCtcInput : ctcInput, self.gtTexts : sparse, self.seqLen : [Model.maxTextLen] * numBatchElements, self.is_train: False}
- #lossVals = self.sess.run(evalList, feedDict)
- #probs = np.exp(-lossVals)
+ lossVals = self.sess.run(evalList, feedDict)
+ probs = np.exp(-lossVals)
# dump the output of the NN to CSV file(s)
if self.dump:
self.dumpNNOutput(evalRes[1])
- return (texts)
+ return (texts, probs)
def save(self):
diff --git a/src/main.py b/src/main.py
index 7645320..1e71283 100644
--- a/src/main.py
+++ b/src/main.py
@@ -40,7 +40,7 @@ def train(model, loader):
# validate
charErrorRate = validate(model, loader)
-
+
# if best validation accuracy so far, save model parameters
if charErrorRate < bestCharErrorRate:
print('Character error rate improved, save model')
@@ -71,8 +71,8 @@ def validate(model, loader):
print('Batch:', iterInfo[0],'/', iterInfo[1])
batch = loader.getNext()
(recognized, _) = model.inferBatch(batch)
-
- print('Ground truth -> Recognized')
+
+ print('Ground truth -> Recognized')
for i in range(len(recognized)):
numWordOK += 1 if batch.gtTexts[i] == recognized[i] else 0
numWordTotal += 1
@@ -80,7 +80,7 @@ def validate(model, loader):
numCharErr += dist
numCharTotal += len(batch.gtTexts[i])
print('[OK]' if dist==0 else '[ERR:%d]' % dist,'"' + batch.gtTexts[i] + '"', '->', '"' + recognized[i] + '"')
-
+
# print validation result
charErrorRate = numCharErr / numCharTotal
wordAccuracy = numWordOK / numWordTotal
@@ -115,14 +115,14 @@ def main():
elif args.wordbeamsearch:
decoderType = DecoderType.WordBeamSearch
- # train or validate on IAM dataset
+ # train or validate on IAM dataset
if args.train or args.validate:
# load training data, create TF model
loader = DataLoader(FilePaths.fnTrain, Model.batchSize, Model.imgSize, Model.maxTextLen)
# save characters of model for inference mode
open(FilePaths.fnCharList, 'w').write(str().join(loader.charList))
-
+
# save words contained in dataset into file
open(FilePaths.fnCorpus, 'w').write(str(' ').join(loader.trainWords + loader.validationWords))
@@ -143,4 +143,3 @@ def main():
if __name__ == '__main__':
main()
-
--
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