added cli utils

Author: divamgupta

LoadBatches.py

@@ -66,7 +66,7 @@ def imageSegmentationGenerator( images_path , segs_path ,  batch_size,  n_classe
 
 	assert len( images ) == len(segmentations)
 	for im , seg in zip(images,segmentations):
-		assert(  im.split('/')[-1] ==  seg.split('/')[-1] )
+		assert(  im.split('/')[-1].split(".")[0] ==  seg.split('/')[-1].split(".")[0] )
 
 	zipped = itertools.cycle( zip(images,segmentations) )
 

train.py

@@ -1,20 +1,66 @@
+import argparse
+import Models , LoadBatches
 
 
+"""
 
+THEANO_FLAGS=device=gpu0,floatX=float32  python train.py \
+ --save_weights_path=weights/ex1 \
+ --train_images="data/clothes_seg/prepped/images_prepped_train/" \
+ --train_annotations="data/clothes_seg/prepped/annotations_prepped_train/" \
+ --n_classes=10 \
+ --input_height=800 \
+ --input_width=550 
 
-import Models , LoadBatches
 
+"""
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--save_weights_path", type = str  )
+parser.add_argument("--train_images", type = str  )
+parser.add_argument("--train_annotations", type = str  )
+parser.add_argument("--n_classes", type=int )
+parser.add_argument("--input_height", type=int , default = 224  )
+parser.add_argument("--input_width", type=int , default = 224 )
+
+parser.add_argument('--validate',action='store_false')
+parser.add_argument("--val_images", type = str , default = "")
+parser.add_argument("--val_annotations", type = str , default = "")
+parser.add_argument("--epochs", type = int, default = 5 )
+parser.add_argument("--batch_size", type = int, default = 2 )
+parser.add_argument("--val_batch_size", type = int, default = 2 )
+parser.add_argument("--load_weights", type = str , default = "")
+
+args = parser.parse_args()
+
+train_images_path = args.train_images
+train_segs_path = args.train_annotations
+train_batch_size = args.batch_size
+n_classes = args.n_classes
+input_height = args.input_height
+input_width = args.input_width
+validate = args.validate
+save_weights_path = args.save_weights_path
+epochs = args.epochs
+
+if validate:
+	val_images_path = args.val_images
+	val_segs_path = args.val_annotations
+	val_batch_size = args.val_batch_size
 
 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 )
+if validate:
+	G2  = LoadBatches.imageSegmentationGenerator( val_images_path , val_segs_path ,  val_batch_size,  n_classes , input_height , input_width , output_height , output_width   )
+
 
+for ep in range( epochs ):
+	m.fit_generator( G , 512  , nb_epoch=1 )
+	m.save_weights( save_weights_path + "." + str( ep ) )