Polish codes and comments

paddle/fluid/recordio/chunk.cc

@@ -24,13 +24,21 @@ namespace paddle {
 namespace recordio {
 constexpr size_t kMaxBufSize = 1024;
 
+/**
+ * Read Stream by a fixed sized buffer.
+ * @param in input stream
+ * @param limit read at most `limit` bytes from input stream. 0 means no limit
+ * @param callback A function object with (const char* buf, size_t size) -> void
+ * as its type.
+ */
 template <typename Callback>
 static void ReadStreamByBuf(std::istream& in, size_t limit, Callback callback) {
   char buf[kMaxBufSize];
   std::streamsize actual_size;
   size_t counter = 0;
   size_t actual_max;
-  while (!in.eof() || (limit != 0 && counter >= limit)) {
+  while (!in.eof() ||
+         (limit != 0 && counter >= limit)) {  // End of file or reach limit
     actual_max =
         limit != 0 ? std::min(limit - counter, kMaxBufSize) : kMaxBufSize;
     in.read(buf, actual_max);
@@ -46,10 +54,17 @@ static void ReadStreamByBuf(std::istream& in, size_t limit, Callback callback) {
   in.clear();  // unset eof state
 }
 
+/**
+ * Copy stream in to another stream
+ */
 static void PipeStream(std::istream& in, std::ostream& os) {
   ReadStreamByBuf(
       in, 0, [&os](const char* buf, size_t len) { os.write(buf, len); });
 }
+
+/**
+ * Calculate CRC32 from an input stream.
+ */
 static uint32_t Crc32Stream(std::istream& in, size_t limit = 0) {
   uint32_t crc = static_cast<uint32_t>(crc32(0, nullptr, 0));
   ReadStreamByBuf(in, limit, [&crc](const char* buf, size_t len) {
@@ -89,7 +104,9 @@ bool Chunk::Write(std::ostream& os, Compressor ct) const {
     compressed_stream.reset();
   }
 
-  uint32_t len = static_cast<uint32_t>(sout.str().size());
+  sout.seekg(0, std::ios::end);
+  uint32_t len = static_cast<uint32_t>(sout.tellg());
+  sout.seekg(0, std::ios::beg);
   uint32_t crc = Crc32Stream(sout);
   Header hdr(static_cast<uint32_t>(records_.size()), crc, ct, len);
   hdr.Write(os);

python/paddle/fluid/layers/io.py

@@ -272,7 +272,7 @@ def read_file(file_obj):
     out = [
         helper.create_tmp_variable(
             stop_gradient=True, dtype='float32')
-        for i in range(len(file_obj.desc.shapes()))
+        for _ in range(len(file_obj.desc.shapes()))
     ]
     helper.append_op(
         type='read', inputs={'Reader': [file_obj]}, outputs={'Out': out})

python/paddle/fluid/recordio_writer.py

@@ -13,33 +13,18 @@
 # limitations under the License.
 
 import core
+import contextlib
 
+__all__ = ['convert_reader_to_recordio_file']
 
-class RecordIOWriter(object):
-    def __init__(self,
-                 filename,
+
+@contextlib.contextmanager
+def create_recordio_writer(filename,
                            compressor=core.RecordIOWriter.Compressor.Snappy,
                            max_num_records=1000):
-        self.filename = filename
-        self.compressor = compressor
-        self.max_num_records = max_num_records
-        self.writer = None
-
-    def __enter__(self):
-        self.writer = core.RecordIOWriter(self.filename, self.compressor,
-                                          self.max_num_records)
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if exc_type is not None:
-            return False
-        else:
-            self.writer.close()
-
-    def append_tensor(self, tensor):
-        self.writer.append_tensor(tensor)
-
-    def complete_append_tensor(self):
-        self.writer.complete_append_tensor()
+    writer = core.RecordIOWriter(filename, compressor, max_num_records)
+    yield writer
+    writer.close()
 
 
 def convert_reader_to_recordio_file(
@@ -49,14 +34,12 @@ def convert_reader_to_recordio_file(
         compressor=core.RecordIOWriter.Compressor.Snappy,
         max_num_records=1000,
         feed_order=None):
-    writer = RecordIOWriter(filename, compressor, max_num_records)
-    with writer:
+    if feed_order is None:
+        feed_order = feeder.feed_names
+    with create_recordio_writer(filename, compressor,
+                                max_num_records) as writer:
         for batch in reader_creator():
             res = feeder.feed(batch)
-            if feed_order is None:
-                for each in res:
-                    writer.append_tensor(res[each])
-            else:
             for each in feed_order:
                 writer.append_tensor(res[each])
             writer.complete_append_tensor()

python/paddle/fluid/tests/unittests/.gitignore

+mnist.recordio

python/paddle/fluid/tests/unittests/test_recordio_reader.py

@@ -20,22 +20,21 @@ import paddle.v2 as paddle
 
 class TestRecordIO(unittest.TestCase):
     def setUp(self):
+        # Convert mnist to recordio file
         with fluid.program_guard(fluid.Program()):
             reader = paddle.batch(mnist.train(), batch_size=32)
             feeder = fluid.DataFeeder(
-                feed_list=[
+                feed_list=[  # order is image and label
                     fluid.layers.data(
-                        name='image', shape=[784]), fluid.layers.data(
-                            name='label', shape=[1], dtype='int64')
+                        name='image', shape=[784]),
+                    fluid.layers.data(
+                        name='label', shape=[1], dtype='int64'),
                 ],
                 place=fluid.CPUPlace())
             fluid.recordio_writer.convert_reader_to_recordio_file(
-                './mnist.recordio',
-                reader,
-                feeder,
-                feed_order=['image', 'label'])
+                './mnist.recordio', reader, feeder)
 
-    def testMain(self):
+    def test_main(self):
         data_file = fluid.layers.open_recordio_file(
             './mnist.recordio',
             shapes=[[-1, 784], [-1, 1]],
@@ -48,9 +47,12 @@ class TestRecordIO(unittest.TestCase):
         loss = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_loss = fluid.layers.mean(loss)
 
-        fluid.optimizer.SGD(learning_rate=1e-3).minimize(avg_loss)
+        fluid.optimizer.Adam(learning_rate=1e-3).minimize(avg_loss)
 
         exe = fluid.Executor(fluid.CPUPlace())
         exe.run(fluid.default_startup_program())
-        avg_loss_np, = exe.run(fetch_list=[avg_loss])
-        print avg_loss_np
+        avg_loss_np = []
+        for i in xrange(100):  # train 100 mini-batch
+            tmp, = exe.run(fetch_list=[avg_loss])
+            avg_loss_np.append(tmp)
+        self.assertLess(avg_loss_np[-1], avg_loss_np[0])