fixing the missing hkl import, preprocessing (#22)

in models: alexnet, googlenet, resnet50, vgg16

theanompi/models/alex_net.py

@@ -419,7 +419,16 @@ def train_iter(self, count,recorder):
 
             else:
 
-                arr = hkl.load(img[self.current_t]) #- img_mean
+                img_mean = self.data.rawdata[-1]
+                import hickle as hkl
+                arr = hkl.load(img[self.current_t]) - img_mean
+
+                from theanompi.models.data.utils import crop_and_mirror
+
+                arr = crop_and_mirror(arr, mode, 
+                                    rand_crop, 
+                                    batch_crop_mirror, 
+                                    input_width)
 
                 self.shared_x.set_value(arr)
 
@@ -510,7 +519,16 @@ def val_iter(self, count,recorder):
             else:
 
 
-                arr = hkl.load(img[self.current_v]) #- img_mean
+                img_mean = self.data.rawdata[-1]
+                import hickle as hkl
+                arr = hkl.load(img[self.current_v]) - img_mean
+
+                from theanompi.models.data.utils import crop_and_mirror
+
+                arr = crop_and_mirror(arr, mode, 
+                                    rand_crop, 
+                                    batch_crop_mirror, 
+                                    input_width)
 
                 # arr = np.rollaxis(arr,0,4)
 

theanompi/models/googlenet.py

@@ -766,7 +766,16 @@ def train_iter(self, count, recorder):
 
             else:
 
-                arr = hkl.load(img[self.current_t]) #- img_mean
+                img_mean = self.data.rawdata[-1]
+                import hickle as hkl
+                arr = hkl.load(img[self.current_t]) - img_mean
+
+                from theanompi.models.data.utils import crop_and_mirror
+
+                arr = crop_and_mirror(arr, mode, 
+                                    rand_crop, 
+                                    batch_crop_mirror, 
+                                    input_width)
 
                 self.shared_x.set_value(arr)
 
@@ -857,7 +866,16 @@ def val_iter(self, count,recorder):
             else:
 
 
-                arr = hkl.load(img[self.current_v]) #- img_mean
+                img_mean = self.data.rawdata[-1]
+                import hickle as hkl
+                arr = hkl.load(img[self.current_v]) - img_mean
+
+                from theanompi.models.data.utils import crop_and_mirror
+
+                arr = crop_and_mirror(arr, mode, 
+                                    rand_crop, 
+                                    batch_crop_mirror, 
+                                    input_width)
 
                 # arr = np.rollaxis(arr,0,4)
 

theanompi/models/lasagne_model_zoo/resnet50.py

@@ -489,7 +489,16 @@ def train_iter(self, count,recorder):
 
             else:
 
-                arr = hkl.load(img[self.current_t]) #- img_mean
+                img_mean = self.data.rawdata[-1]
+                import hickle as hkl
+                arr = hkl.load(img[self.current_t]) - img_mean
+
+                from theanompi.models.data.utils import crop_and_mirror
+
+                arr = crop_and_mirror(arr, mode, 
+                                    rand_crop, 
+                                    batch_crop_mirror, 
+                                    input_width)
 
                 self.shared_x.set_value(arr)
 
@@ -580,7 +589,17 @@ def val_iter(self, count,recorder):
             else:
 
 
-                arr = hkl.load(img[self.current_v]) #- img_mean
+                img_mean = self.data.rawdata[-1]
+
+                import hickle as hkl
+                arr = hkl.load(img[self.current_v]) - img_mean
+
+                from theanompi.models.data.utils import crop_and_mirror
+
+                arr = crop_and_mirror(arr, mode, 
+                                    rand_crop, 
+                                    batch_crop_mirror, 
+                                    input_width)
 
                 # arr = np.rollaxis(arr,0,4)
 

theanompi/models/lasagne_model_zoo/vgg16.py

@@ -393,7 +393,16 @@ def train_iter(self, count,recorder):
 
             else:
 
-                arr = hkl.load(img[self.current_t]) #- img_mean
+                img_mean = self.data.rawdata[-1]
+                import hickle as hkl
+                arr = hkl.load(img[self.current_t]) - img_mean
+
+                from theanompi.models.data.utils import crop_and_mirror
+
+                arr = crop_and_mirror(arr, mode, 
+                                    rand_crop, 
+                                    batch_crop_mirror, 
+                                    input_width)
 
                 self.shared_x.set_value(arr)
 
@@ -484,7 +493,16 @@ def val_iter(self, count,recorder):
             else:
 
 
-                arr = hkl.load(img[self.current_v]) #- img_mean
+                img_mean = self.data.rawdata[-1]
+                import hickle as hkl
+                arr = hkl.load(img[self.current_v]) - img_mean
+
+                from theanompi.models.data.utils import crop_and_mirror
+
+                arr = crop_and_mirror(arr, mode, 
+                                    rand_crop, 
+                                    batch_crop_mirror, 
+                                    input_width)
 
                 # arr = np.rollaxis(arr,0,4)