Merge branch 'Chazzz-master'

src/Model.py

@@ -27,6 +27,9 @@ class Model:
 		self.mustRestore = mustRestore
 		self.snapID = 0
 
+		# Whether to use normalization over a batch or a population
+		self.is_train = tf.placeholder(tf.bool, name="is_train");
+
 		# input image batch
 		self.inputImgs = tf.placeholder(tf.float32, shape=(None, Model.imgSize[0], Model.imgSize[1]))
 
@@ -38,6 +41,8 @@ class Model:
 		# setup optimizer to train NN
 		self.batchesTrained = 0
 		self.learningRate = tf.placeholder(tf.float32, shape=[])
+		self.update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) 
+		with tf.control_dependencies(self.update_ops):
 			self.optimizer = tf.train.RMSPropOptimizer(self.learningRate).minimize(self.loss)
 
 		# initialize TF
@@ -59,7 +64,8 @@ class Model:
 		for i in range(numLayers):
 			kernel = tf.Variable(tf.truncated_normal([kernelVals[i], kernelVals[i], featureVals[i], featureVals[i + 1]], stddev=0.1))
 			conv = tf.nn.conv2d(pool, kernel, padding='SAME',  strides=(1,1,1,1))
-			relu = tf.nn.relu(conv)
+			conv_norm = tf.layers.batch_normalization(conv, training=self.is_train)
+			relu = tf.nn.relu(conv_norm)
 			pool = tf.nn.max_pool(relu, (1, poolVals[i][0], poolVals[i][1], 1), (1, strideVals[i][0], strideVals[i][1], 1), 'VALID')
 
 		self.cnnOut4d = pool
@@ -205,19 +211,19 @@ class Model:
 		sparse = self.toSparse(batch.gtTexts)
 		rate = 0.01 if self.batchesTrained < 10 else (0.001 if self.batchesTrained < 10000 else 0.0001) # decay learning rate
 		evalList = [self.optimizer, self.loss]
-		feedDict = {self.inputImgs : batch.imgs, self.gtTexts : sparse , self.seqLen : [Model.maxTextLen] * numBatchElements, self.learningRate : rate}
+		feedDict = {self.inputImgs : batch.imgs, self.gtTexts : sparse , self.seqLen : [Model.maxTextLen] * numBatchElements, self.learningRate : rate, self.is_train: True}
 		(_, lossVal) = self.sess.run(evalList, feedDict)
 		self.batchesTrained += 1
 		return lossVal
 
 
 	def inferBatch(self, batch, calcProbability=False, probabilityOfGT=False):
-		"feed a batch into the NN to recngnize the texts"
+		"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 [])
-		feedDict = {self.inputImgs : batch.imgs, self.seqLen : [Model.maxTextLen] * numBatchElements}
+		feedDict = {self.inputImgs : batch.imgs, self.seqLen : [Model.maxTextLen] * numBatchElements, self.is_train: False}
 		evalRes = self.sess.run([self.decoder, self.ctcIn3dTBC], feedDict)
 		decoded = evalRes[0]
 		texts = self.decoderOutputToText(decoded, numBatchElements)
@@ -228,7 +234,7 @@ class Model:
 			sparse = self.toSparse(batch.gtTexts) if probabilityOfGT else self.toSparse(texts)
 			ctcInput = evalRes[1]
 			evalList = self.lossPerElement
-			feedDict = {self.savedCtcInput : ctcInput, self.gtTexts : sparse, self.seqLen : [Model.maxTextLen] * numBatchElements}
+			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)
 		return (texts, probs)