unet added

Author: divamgupta

Models/Segnet.py

@@ -79,6 +79,11 @@ def segnetModel(nClasses , optimizer=None , input_height=360, input_width=480 ):
 
 
 	model.add(Convolution2D( nClasses , 1, 1, border_mode='valid',))
+
+	model.outputHeight = model.output_shape[-2]
+	model.outputWidth = model.output_shape[-1]
+
+
 	model.add(Reshape(( nClasses ,  model.output_shape[-2]*model.output_shape[-1]   ), input_shape=( nClasses , model.output_shape[-2], model.output_shape[-1]  )))
 
 	model.add(Permute((2, 1)))

Models/Unet.py

@@ -0,0 +1,69 @@
+
+
+
+
+from keras.models import Sequential
+from keras.layers import Reshape
+from keras.models import Model
+from keras.layers.core import Layer, Dense, Dropout, Activation, Flatten, Reshape, Merge, Permute
+from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, Reshape, core, Dropout
+from keras.layers.normalization import BatchNormalization
+from keras.layers.convolutional import Convolution3D, MaxPooling3D, ZeroPadding3D , ZeroPadding3D , UpSampling3D
+from keras.layers.convolutional import Convolution2D, MaxPooling2D, UpSampling2D, ZeroPadding2D
+from keras.layers.convolutional import Convolution1D, MaxPooling1D
+from keras.layers.recurrent import LSTM
+from keras.layers.advanced_activations import LeakyReLU
+from keras.optimizers import Adam , SGD
+from keras.layers.embeddings import Embedding
+from keras.utils import np_utils
+from keras.regularizers import ActivityRegularizer
+from keras import backend as K
+
+
+
+
+
+def unet_2d (nClasses , optimizer=None , input_width=360 , input_height=480 , nChannels=1 ): 
+    
+    inputs = Input((nChannels, input_height, input_width))
+    conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs)
+    conv1 = Dropout(0.2)(conv1)
+    conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
+    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
+    
+    conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(pool1)
+    conv2 = Dropout(0.2)(conv2)
+    conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv2)
+    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
+    
+    conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool2)
+    conv3 = Dropout(0.2)(conv3)
+    conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv3)
+
+    up1 = merge([UpSampling2D(size=(2, 2))(conv3), conv2], mode='concat', concat_axis=1)
+    conv4 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up1)
+    conv4 = Dropout(0.2)(conv4)
+    conv4 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv4)
+    
+    up2 = merge([UpSampling2D(size=(2, 2))(conv4), conv1], mode='concat', concat_axis=1)
+    conv5 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up2)
+    conv5 = Dropout(0.2)(conv5)
+    conv5 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv5)
+    
+    conv6 = Convolution2D(nClasses, 1, 1, activation='relu',border_mode='same')(conv5)
+    conv6 = core.Reshape((nClasses,input_height*input_width))(conv6)
+    conv6 = core.Permute((2,1))(conv6)
+
+
+    conv7 = core.Activation('softmax')(conv6)
+
+    model = Model(input=inputs, output=conv7)
+
+    if not optimizer is None:
+	    model.compile(loss="categorical_crossentropy", optimizer= optimizer , metrics=['accuracy'] )
+	
+    return model
+	
+	
+	
+

train.py

@@ -3,10 +3,18 @@
 
 
 import Models , LoadBatches
-G  = LoadBatches.imageSegmentationGenerator( "data/clothes_seg/prepped/images_prepped_train/" ,  "data/clothes_seg/prepped/annotations_prepped_train/" ,  1,  10 , 800 , 550 , 400 , 272   ) 
-G2  = LoadBatches.imageSegmentationGenerator( "data/clothes_seg/prepped/images_prepped_test/" ,  "data/clothes_seg/prepped/annotations_prepped_test/" ,  1,  10 , 800 , 550 , 400 , 272   ) 
 
-m = Models.VGGSegnet.VGGSegnet( 10  , use_vgg_weights=True ,  optimizer='adadelta' , input_image_size=( 800 , 550 )  )
+
+m = Models.VGGSegnet.VGGSegnet( 10  , use_vgg_weights=True ,  optimizer='adadelta' , input_image_size=( input_height , input_width )  )
+
+output_height = m.outputHeight
+output_width = m.outputWidth
+
+
+G  = LoadBatches.imageSegmentationGenerator( train_images_path , train_segs_path ,  train_batch_size,  n_classes , input_height , input_width , output_height , output_width   )
+G2  = LoadBatches.imageSegmentationGenerator( val_images_path , val_segs_path ,  val_batch_size,  n_classes , input_height , input_width , output_height , output_width   )
+
+
 m.fit_generator( G , 512  , nb_epoch=10 )