fix xmap_readers and refine flowers dataset

python/paddle/v2/dataset/__init__.py

@@ -25,8 +25,9 @@
 import sentiment
 import wmt14
 import mq2007
+import flowers
 
 __all__ = [
     'mnist', 'imikolov', 'imdb', 'cifar', 'movielens', 'conll05', 'sentiment'
-    'uci_housing', 'wmt14', 'mq2007'
+    'uci_housing', 'wmt14', 'mq2007', 'flowers'
 ]

python/paddle/v2/dataset/flowers.py

@@ -13,18 +13,18 @@
 # limitations under the License.
 """
 This module will download dataset from
-http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html 
+http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html
 and parse train/test set intopaddle reader creators.
 
-This set contains images of flowers belonging to 102 different categories. 
+This set contains images of flowers belonging to 102 different categories.
 The images were acquired by searching the web and taking pictures. There are a
 minimum of 40 images for each category.
 
 The database was used in:
 
 Nilsback, M-E. and Zisserman, A. Automated flower classification over a large
- number of classes.Proceedings of the Indian Conference on Computer Vision, 
-Graphics and Image Processing (2008) 
+ number of classes.Proceedings of the Indian Conference on Computer Vision,
+Graphics and Image Processing (2008)
 http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
 
 """
@@ -34,9 +34,9 @@
 import tarfile
 import scipy.io as scio
 from paddle.v2.image import *
+from paddle.v2.reader import *
 import os
 import numpy as np
-import paddle.v2 as paddle
 from multiprocessing import cpu_count
 __all__ = ['train', 'test', 'valid']
 
@@ -46,15 +46,21 @@
 DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
 LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
 SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
+# In official 'readme', tstid is the flag of test data
+# and trnid is the flag of train data. But test data is more than train data.
+# So we exchange the train data and test data.
+TRAIN_FLAG = 'tstid'
+TEST_FLAG = 'trnid'
+VALID_FLAG = 'valid'
 
 
 def default_mapper(sample):
     '''
     map image bytes data to type needed by model input layer
     '''
     img, label = sample
-    img = paddle.image.load_image_bytes(img)
-    img = paddle.image.simple_transform(img, 256, 224, True)
+    img = load_image_bytes(img)
+    img = simple_transform(img, 256, 224, True)
     return img.flatten().astype('float32'), label
 
 
@@ -63,22 +69,23 @@ def reader_creator(data_file,
                    setid_file,
                    dataset_name,
                    mapper=default_mapper,
-                   buffered_size=1024):
+                   buffered_size=1024,
+                   use_xmap=True):
     '''
-    1. read images from tar file and 
+    1. read images from tar file and
         merge images into batch files in 102flowers.tgz_batch/
     2. get a reader to read sample from batch file
-    
-    :param data_file: downloaded data file 
+
+    :param data_file: downloaded data file
     :type data_file: string
-    :param label_file: downloaded label file 
+    :param label_file: downloaded label file
     :type label_file: string
     :param setid_file: downloaded setid file containing information
                         about how to split dataset
     :type setid_file: string
     :param dataset_name: data set name (tstid|trnid|valid)
     :type dataset_name: string
-    :param mapper: a function to map image bytes data to type 
+    :param mapper: a function to map image bytes data to type
                     needed by model input layer
     :type mapper: callable
     :param buffered_size: the size of buffer used to process images
@@ -105,15 +112,17 @@ def reader():
             for sample, label in itertools.izip(data, batch['label']):
                 yield sample, int(label)
 
-    return paddle.reader.xmap_readers(mapper, reader,
-                                      cpu_count(), buffered_size)
+    if use_xmap:
+        return xmap_readers(mapper, reader, cpu_count(), buffered_size)
+    else:
+        return map_readers(mapper, reader)
 
 
-def train(mapper=default_mapper, buffered_size=1024):
+def train(mapper=default_mapper, buffered_size=1024, use_xmap=True):
     '''
-    Create flowers training set reader. 
-    It returns a reader, each sample in the reader is   
-    image pixels in [0, 1] and label in [1, 102] 
+    Create flowers training set reader.
+    It returns a reader, each sample in the reader is
+    image pixels in [0, 1] and label in [1, 102]
     translated from original color image by steps:
     1. resize to 256*256
     2. random crop to 224*224
@@ -128,15 +137,15 @@ def train(mapper=default_mapper, buffered_size=1024):
     return reader_creator(
         download(DATA_URL, 'flowers', DATA_MD5),
         download(LABEL_URL, 'flowers', LABEL_MD5),
-        download(SETID_URL, 'flowers', SETID_MD5), 'trnid', mapper,
-        buffered_size)
+        download(SETID_URL, 'flowers', SETID_MD5), TRAIN_FLAG, mapper,
+        buffered_size, use_xmap)
 
 
-def test(mapper=default_mapper, buffered_size=1024):
+def test(mapper=default_mapper, buffered_size=1024, use_xmap=True):
     '''
-    Create flowers test set reader. 
-    It returns a reader, each sample in the reader is   
-    image pixels in [0, 1] and label in [1, 102] 
+    Create flowers test set reader.
+    It returns a reader, each sample in the reader is
+    image pixels in [0, 1] and label in [1, 102]
     translated from original color image by steps:
     1. resize to 256*256
     2. random crop to 224*224
@@ -151,15 +160,15 @@ def test(mapper=default_mapper, buffered_size=1024):
     return reader_creator(
         download(DATA_URL, 'flowers', DATA_MD5),
         download(LABEL_URL, 'flowers', LABEL_MD5),
-        download(SETID_URL, 'flowers', SETID_MD5), 'tstid', mapper,
-        buffered_size)
+        download(SETID_URL, 'flowers', SETID_MD5), TEST_FLAG, mapper,
+        buffered_size, use_xmap)
 
 
-def valid(mapper=default_mapper, buffered_size=1024):
+def valid(mapper=default_mapper, buffered_size=1024, use_xmap=True):
     '''
-    Create flowers validation set reader. 
-    It returns a reader, each sample in the reader is   
-    image pixels in [0, 1] and label in [1, 102] 
+    Create flowers validation set reader.
+    It returns a reader, each sample in the reader is
+    image pixels in [0, 1] and label in [1, 102]
     translated from original color image by steps:
     1. resize to 256*256
     2. random crop to 224*224
@@ -174,8 +183,8 @@ def valid(mapper=default_mapper, buffered_size=1024):
     return reader_creator(
         download(DATA_URL, 'flowers', DATA_MD5),
         download(LABEL_URL, 'flowers', LABEL_MD5),
-        download(SETID_URL, 'flowers', SETID_MD5), 'valid', mapper,
-        buffered_size)
+        download(SETID_URL, 'flowers', SETID_MD5), VALID_FLAG, mapper,
+        buffered_size, use_xmap)
 
 
 def fetch():

python/paddle/v2/dataset/tests/flowers_test.py

@@ -31,13 +31,13 @@ def check_reader(self, reader):
     def test_train(self):
         instances, max_label_value = self.check_reader(
             paddle.v2.dataset.flowers.train())
-        self.assertEqual(instances, 1020)
+        self.assertEqual(instances, 6149)
         self.assertEqual(max_label_value, 102)
 
     def test_test(self):
         instances, max_label_value = self.check_reader(
             paddle.v2.dataset.flowers.test())
-        self.assertEqual(instances, 6149)
+        self.assertEqual(instances, 1020)
         self.assertEqual(max_label_value, 102)
 
     def test_valid(self):

python/paddle/v2/reader/decorator.py

@@ -166,12 +166,12 @@ def buffered(reader, size):
     The buffered data reader will read and save data entries into a
     buffer. Reading from the buffered data reader will proceed as long
     as the buffer is not empty.
-    
+
     :param reader: the data reader to read from.
     :type reader: callable
     :param size: max buffer size.
     :type size: int
-    
+
     :returns: the buffered data reader.
     """
 
@@ -238,7 +238,7 @@ def xmap_readers(mapper, reader, process_num, buffer_size, order=False):
     :type mapper: callable
     :param reader: the data reader to read from
     :type reader: callable
-    :param process_num: process number to handle original sample 
+    :param process_num: process number to handle original sample
     :type process_num: int
     :param buffer_size: max buffer size
     :type buffer_size: int
@@ -248,9 +248,6 @@ def xmap_readers(mapper, reader, process_num, buffer_size, order=False):
     :rtype: callable
     """
     end = XmapEndSignal()
-    in_queue = Queue(buffer_size)
-    out_queue = Queue(buffer_size)
-    out_order = [0]
 
     # define a worker to read samples from reader to in_queue
     def read_worker(reader, in_queue):
@@ -266,12 +263,6 @@ def order_read_worker(reader, in_queue):
             in_order += 1
         in_queue.put(end)
 
-    # start a read worker in a thread
-    target = order_read_worker if order else read_worker
-    t = Thread(target=target, args=(reader, in_queue))
-    t.daemon = True
-    t.start()
-
     # define a worker to handle samples from in_queue by mapper
     # and put mapped samples into out_queue
     def handle_worker(in_queue, out_queue, mapper):
@@ -298,19 +289,27 @@ def order_handle_worker(in_queue, out_queue, mapper, out_order):
         in_queue.put(end)
         out_queue.put(end)
 
-    # start several handle_workers
-    target = order_handle_worker if order else handle_worker
-    args = (in_queue, out_queue, mapper, out_order) if order else (
-        in_queue, out_queue, mapper)
-    workers = []
-    for i in xrange(process_num):
-        worker = Thread(target=target, args=args)
-        worker.daemon = True
-        workers.append(worker)
-    for w in workers:
-        w.start()
-
     def xreader():
+        in_queue = Queue(buffer_size)
+        out_queue = Queue(buffer_size)
+        out_order = [0]
+        # start a read worker in a thread
+        target = order_read_worker if order else read_worker
+        t = Thread(target=target, args=(reader, in_queue))
+        t.daemon = True
+        t.start()
+        # start several handle_workers
+        target = order_handle_worker if order else handle_worker
+        args = (in_queue, out_queue, mapper, out_order) if order else (
+            in_queue, out_queue, mapper)
+        workers = []
+        for i in xrange(process_num):
+            worker = Thread(target=target, args=args)
+            worker.daemon = True
+            workers.append(worker)
+        for w in workers:
+            w.start()
+
         sample = out_queue.get()
         while not isinstance(sample, XmapEndSignal):
             yield sample

python/paddle/v2/reader/tests/decorator_test.py

@@ -132,15 +132,17 @@ def mapper(x):
         for order in orders:
             for tNum in thread_nums:
                 for size in buffered_size:
-                    result = []
-                    for i in paddle.v2.reader.xmap_readers(mapper,
+                    reader = paddle.v2.reader.xmap_readers(mapper,
                                                            reader_creator_10(0),
-                                                           tNum, size, order)():
-                        result.append(i)
-                    if not order:
-                        result.sort()
-                    for idx, e in enumerate(result):
-                        self.assertEqual(e, mapper(idx))
+                                                           tNum, size, order)
+                    for n in xrange(3):
+                        result = []
+                        for i in reader():
+                            result.append(i)
+                        if not order:
+                            result.sort()
+                        for idx, e in enumerate(result):
+                            self.assertEqual(e, mapper(idx))
 
 
 if __name__ == '__main__':

python/setup.py.in

@@ -15,7 +15,8 @@ setup_requires=["requests",
                 "protobuf==3.1",
                 "recordio",
                 "matplotlib",
-                "rarfile"]
+                "rarfile",
+                "scipy>=0.19.0"]
 
 if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']:
     setup_requires+=["opencv-python"]