Fix memory leak in image classification demo, which is caused by dataprovider

demo/image_classification/.gitignore

@@ -5,3 +5,5 @@ plot.png
 train.log
 image_provider_copy_1.py
 *pyc
+train.list
+test.list

demo/image_classification/image_provider.py

@@ -58,24 +58,29 @@ def hook(settings, img_size, mean_img_size, num_classes, color, meta, use_jpeg,
     settings.logger.info('DataProvider Initialization finished')
 
 
-@provider(init_hook=hook)
-def processData(settings, file_name):
+@provider(init_hook=hook, min_pool_size=0)
+def processData(settings, file_list):
     """
     The main function for loading data.
     Load the batch, iterate all the images and labels in this batch.
-    file_name: the batch file name.
+    file_list: the batch file list.
     """
-    data = cPickle.load(io.open(file_name, 'rb'))
-    indexes = list(range(len(data['images'])))
-    if settings.is_train:
-        random.shuffle(indexes)
-    for i in indexes:
-        if settings.use_jpeg == 1:
-            img = image_util.decode_jpeg(data['images'][i])
-        else:
-            img = data['images'][i]
-        img_feat = image_util.preprocess_img(img, settings.img_mean,
-                                             settings.img_size, settings.is_train,
-                                             settings.color)
-        label = data['labels'][i]
-        yield img_feat.tolist(), int(label)
+    with open(file_list, 'r') as fdata:
+        lines = [line.strip() for line in fdata]
+        random.shuffle(lines)
+        for file_name in lines:
+            with io.open(file_name.strip(), 'rb') as file:
+                data = cPickle.load(file)
+                indexes = list(range(len(data['images'])))
+                if settings.is_train:
+                    random.shuffle(indexes)
+                for i in indexes:
+                    if settings.use_jpeg == 1:
+                        img = image_util.decode_jpeg(data['images'][i])
+                    else:
+                        img = data['images'][i]
+                    img_feat = image_util.preprocess_img(img, settings.img_mean,
+                                                         settings.img_size, settings.is_train,
+                                                         settings.color)
+                    label = data['labels'][i]
+                    yield img_feat.astype('float32'), int(label)

demo/image_classification/preprocess.py

@@ -35,6 +35,8 @@ def option_parser():
      data_creator = ImageClassificationDatasetCreater(data_dir,
                                                       processed_image_size,
                                                       color)
+     data_creator.train_list_name = "train.txt"
+     data_creator.test_list_name = "test.txt"
      data_creator.num_per_batch = 1000
      data_creator.overwrite = True
      data_creator.create_batches()

demo/image_classification/preprocess.sh

@@ -17,3 +17,6 @@ set -e
 data_dir=./data/cifar-out
 
 python preprocess.py -i $data_dir -s 32 -c 1
+
+echo "data/cifar-out/batches/train.txt" > train.list
+echo "data/cifar-out/batches/test.txt" > test.list

demo/image_classification/vgg_16_cifar.py

@@ -25,8 +25,8 @@
           'img_size': 32,'num_classes': 10,
           'use_jpeg': 1,'color': "color"}
 
-  define_py_data_sources2(train_list=data_dir+"train.list",
-                          test_list=data_dir+'test.list',
+  define_py_data_sources2(train_list="train.list",
+                          test_list="train.list",
                           module='image_provider',
                           obj='processData',
                           args=args)