load data from train file

Author: StephanieLarocque

code/fcn_2D_segm/data_loader.py

@@ -3,7 +3,6 @@
 
 from dataset_loaders.images.polyps912 import Polyps912Dataset
 from dataset_loaders.images.camvid import CamvidDataset
-from dataset_loaders.images.polyps912 import Polyps912Dataset
 from dataset_loaders.images.isbi_em_stacks import IsbiEmStacksDataset
 
 

code/fcn_2D_segm/train_fcn8.py

@@ -13,7 +13,7 @@
 import lasagne
 from lasagne.regularization import regularize_network_params
 
-from data_loader import load_data
+from dataset_loaders.images.polyps912 import Polyps912Dataset
 from fcn8 import buildFCN8
 
 
@@ -156,10 +156,41 @@ def train(dataset, learn_step=0.005,
         bs = batch_size
     else:
         bs = [10, 1, 1]
+    train_iter = Polyps912Dataset(which_set='train',
+                                  batch_size=batch_size[0],
+                                  seq_per_subset=0,
+                                  seq_length=0,
+                                  data_augm_kwargs=train_data_augm_kwargs,
+                                  return_one_hot=one_hot,
+                                  return_01c=False,
+                                  overlap=0,
+                                  use_threads=False,
+                                  shuffle_at_each_epoch=shuffle_train,
+                                  return_list=True,
+                                  return_0_255=return_0_255)
+    val_iter = Polyps912Dataset(which_set='val',
+                                batch_size=batch_size[1],
+                                seq_per_subset=0,
+                                seq_length=0,
+                                return_one_hot=one_hot,
+                                return_01c=False,
+                                overlap=0,
+                                use_threads=False,
+                                shuffle_at_each_epoch=False,
+                                return_list=True,
+                                return_0_255=return_0_255)
+    test_iter = Polyps912Dataset(which_set='test',
+                                 batch_size=batch_size[2],
+                                 seq_per_subset=0,
+                                 seq_length=0,
+                                 return_one_hot=one_hot,
+                                 return_01c=False,
+                                 overlap=0,
+                                 use_threads=False,
+                                 shuffle_at_each_epoch=False,
+                                 return_list=True,
+                                 return_0_255=return_0_255)
 
-    train_iter, val_iter, test_iter = \
-        load_data(dataset, data_augmentation,
-                  one_hot=False, batch_size=bs, return_0_255=train_from_0_255)
 
     n_batches_train = train_iter.nbatches
     n_batches_val = val_iter.nbatches

code/unet/data_loader.py

@@ -1,7 +1,6 @@
 import os
 import time
 
-from dataset_loaders.images.polyps912 import Polyps912Dataset
 from dataset_loaders.images.camvid import CamvidDataset
 from dataset_loaders.images.polyps912 import Polyps912Dataset
 from dataset_loaders.images.isbi_em_stacks import IsbiEmStacksDataset

code/unet/train_unet.py

@@ -13,7 +13,8 @@
 import lasagne
 from lasagne.regularization import regularize_network_params
 
-from data_loader import load_data
+
+from dataset_loaders.images.isbi_em_stacks import IsbiEmStacksDataset
 from Unet_lasagne_recipes import build_UNet
 # from metrics import jaccard, accuracy, crossentropy
 
@@ -159,9 +160,31 @@ def train(dataset, learn_step=0.005,
     else:
         bs = [10, 1, 1]
 
-    train_iter, val_iter, test_iter = \
-        load_data(dataset, data_augmentation,
-                  one_hot=False, batch_size=bs, return_0_255=train_from_0_255)
+
+    train_iter = IsbiEmStacksDataset(which_set='train',
+                                     batch_size=batch_size[0],
+                                     seq_per_subset=0,
+                                     seq_length=0,
+                                     data_augm_kwargs=data_augmentation,
+                                     return_one_hot=one_hot,
+                                     return_01c=False,
+                                     overlap=0,
+                                     use_threads=True,
+                                     shuffle_at_each_epoch=shuffle_train,
+                                     return_list=True,
+                                     return_0_255=return_0_255)
+
+    val_iter = IsbiEmStacksDataset(which_set='val',
+                                   batch_size=batch_size[1],
+                                   seq_per_subset=0,
+                                   seq_length=0,
+                                   return_one_hot=one_hot,
+                                   return_01c=False,
+                                   use_threads=True,
+                                   shuffle_at_each_epoch=False,
+                                   return_list=True,
+                                   return_0_255=return_0_255)
+    test_iter = None
 
     batch = train_iter.next()
     input_dim = (np.shape(batch[0])[2], np.shape(batch[0])[3]) #(x,y) image shape