added dump option for NN output

.gitignore

@@ -6,4 +6,5 @@ model/snapshot-*
 notes/
 *.so
 *.pyc
-.idea/
+.idea/
+dump/

README.md

@@ -39,6 +39,7 @@ Tested with:
 * `--validate`: validate the NN, details see below.
 * `--beamsearch`: use vanilla beam search decoding (better, but slower) instead of best path decoding.
 * `--wordbeamsearch`: use word beam search decoding (only outputs words contained in a dictionary) instead of best path decoding. This is a custom TF operation and must be compiled from source, more information see corresponding section below. It should **not** be used when training the NN.
+* `--dump`: dumps the output of the NN to CSV file(s) saved in the `dump/` folder. Can be used as input for the [CTCDecoder](https://github.com/githubharald/CTCDecoder).
 
 If neither `--train` nor `--validate` is specified, the NN infers the text from the test image (`data/test.png`).
 Two examples: if you want to infer using beam search, execute `python main.py --beamsearch`, while you have to execute `python main.py --train --beamsearch` if you want to train the NN and do the validation using beam search.

src/Model.py

@@ -4,6 +4,7 @@
 import sys
 import numpy as np
 import tensorflow as tf
+import os
 
 
 class DecoderType:
@@ -20,8 +21,9 @@ class Model:
 	imgSize = (128, 32)
 	maxTextLen = 32
 
-	def __init__(self, charList, decoderType=DecoderType.BestPath, mustRestore=False):
+	def __init__(self, charList, decoderType=DecoderType.BestPath, mustRestore=False, dump=False):
 		"init model: add CNN, RNN and CTC and initialize TF"
+		self.dump = dump
 		self.charList = charList
 		self.decoderType = decoderType
 		self.mustRestore = mustRestore
@@ -217,14 +219,35 @@ def trainBatch(self, batch):
 		return lossVal
 
 
+	def dumpNNOutput(self, rnnOutput):
+		"dump the output of the NN to CSV file(s)"
+		dumpDir = '../dump/'
+		if not os.path.isdir(dumpDir):
+			os.mkdir(dumpDir)
+
+		# iterate over all batch elements and create a CSV file for each one
+		maxT, maxB, maxC = rnnOutput.shape
+		for b in range(maxB):
+			csv = ''
+			for t in range(maxT):
+				for c in range(maxC):
+					csv += str(rnnOutput[t, b, c]) + ';'
+				csv += '\n'
+			fn = dumpDir + 'rnnOutput_'+str(b)+'.csv'
+			print('Write dump of NN to file: ' + fn)
+			with open(fn, 'w') as f:
+				f.write(csv)
+
+
 	def inferBatch(self, batch, calcProbability=False, probabilityOfGT=False):
 		"feed a batch into the NN to recognize the texts"
 
 		# decode, optionally save RNN output
 		numBatchElements = len(batch.imgs)
-		evalList = [self.decoder] + ([self.ctcIn3dTBC] if calcProbability else [])
+		evalRnnOutput = self.dump or calcProbability
+		evalList = [self.decoder] + ([self.ctcIn3dTBC] if evalRnnOutput else [])
 		feedDict = {self.inputImgs : batch.imgs, self.seqLen : [Model.maxTextLen] * numBatchElements, self.is_train: False}
-		evalRes = self.sess.run([self.decoder, self.ctcIn3dTBC], feedDict)
+		evalRes = self.sess.run(evalList, feedDict)
 		decoded = evalRes[0]
 		texts = self.decoderOutputToText(decoded, numBatchElements)
 
@@ -237,6 +260,11 @@ def inferBatch(self, batch, calcProbability=False, probabilityOfGT=False):
 			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)
+
+		# dump the output of the NN to CSV file(s)
+		if self.dump:
+			self.dumpNNOutput(evalRes[1])
+
 		return (texts, probs)
 
 

src/main.py

@@ -105,6 +105,8 @@ def main():
 	parser.add_argument('--validate', help='validate the NN', action='store_true')
 	parser.add_argument('--beamsearch', help='use beam search instead of best path decoding', action='store_true')
 	parser.add_argument('--wordbeamsearch', help='use word beam search instead of best path decoding', action='store_true')
+	parser.add_argument('--dump', help='dump output of NN to CSV file(s)', action='store_true')
+
 	args = parser.parse_args()
 
 	decoderType = DecoderType.BestPath
@@ -135,7 +137,7 @@ def main():
 	# infer text on test image
 	else:
 		print(open(FilePaths.fnAccuracy).read())
-		model = Model(open(FilePaths.fnCharList).read(), decoderType, mustRestore=True)
+		model = Model(open(FilePaths.fnCharList).read(), decoderType, mustRestore=True, dump=args.dump)
 		infer(model, FilePaths.fnInfer)