Changes to help unet run

Author: lamblin

code/unet/Unet_lasagne_recipes.py

@@ -12,7 +12,7 @@
 # end-snippet-1
 
 # start-snippet-downsampling
-def build_UNet(n_input_channels=1, BATCH_SIZE=None, num_output_classes=2, pad='same', nonlinearity=lasagne.nonlinearities.elu, input_dim=(128, 128), base_n_filters=64, do_dropout=False):
+def build_UNet(n_input_channels=1, BATCH_SIZE=None, num_output_classes=2, pad='same', nonlinearity=lasagne.nonlinearities.elu, input_dim=(None, None), base_n_filters=64, do_dropout=False):
     net = OrderedDict()
     net['input'] = InputLayer((BATCH_SIZE, n_input_channels, input_dim[0], input_dim[1]))
 

code/unet/train_unet.py

@@ -207,7 +207,7 @@ def train(dataset, learn_step=0.005,
 
     net = build_UNet(n_input_channels= nb_in_channels,# BATCH_SIZE = batch_size,
                 num_output_classes = n_classes, base_n_filters = 64, do_dropout=False,
-                input_dim =input_dim)
+                input_dim = (None, None))
 
     output_layer = net["output_flattened"]
     #
@@ -383,7 +383,8 @@ def main():
                         help='Max patience')
     parser.add_argument('-batch_size',
                         type=int,
-                        default=[10, 1, 1],
+                        nargs='+',
+                        default=[5, 1, 1],
                         help='Batch size [train, val, test]')
     parser.add_argument('-data_augmentation',
                         type=dict,
@@ -394,7 +395,8 @@ def main():
                                  'fill_mode':'reflect',
                                  'spline_warp':True,
                                  'warp_sigma':10,
-                                 'warp_grid_size':3},
+                                 'warp_grid_size':3,
+                                 'crop_size': (224, 224)},
                         help='use data augmentation')
     parser.add_argument('-early_stop_class',
                         type=int,