add dataflow module

tensorlayer/dataflow/__init__.py

@@ -0,0 +1,4 @@
+from .base import Dataset
+from .base import Transform
+from .common import Dataloader
+from .common import TFDataloader

tensorlayer/dataflow/base.py

@@ -0,0 +1,73 @@
+class Dataset(object):
+
+    def __getitem__(self, index):
+        raise NotImplementedError("A Dataset must implement __getitem__(index) method.")
+
+    def __len__(self):
+        raise NotImplementedError("A Dataset must implement __len__() method.")
+
+    def __iter__(self):
+        for i in range(self.__len__()):
+            yield self.__getitem__(i)
+
+    def __call__(self, *args, **kwargs):
+        return self.__iter__()
+
+
+class DatasetWrapper(object):
+    def __init__(self, ds):
+        self.ds = ds
+        self.ds_len = len(ds)
+
+    def __len__(self):
+        return len(self.ds)
+
+    def __iter__(self):
+        for dp in self.ds:
+            yield dp
+
+    def __call__(self, *args, **kwargs):
+        return self.__iter__()
+
+
+class IndexableDatasetWrapper(object):
+    def __init__(self, ds):
+        self.ds = ds
+        self.ds_len = len(ds)
+
+    def __getitem__(self, index):
+        return self.ds.__getitem__(index)
+
+    def __len__(self):
+        return len(self.ds)
+
+    def __call__(self, *args, **kwargs):
+        return self
+
+
+class Transform(object):
+    def __call__(self, *args, **kwargs):
+        raise NotImplementedError("Transform must implement __call__() method.")
+
+
+class _Transforms_for_tf_dataset(object):
+    """
+    This class aggregate Transforms into one object in order to use tf.data.Dataset.map API
+    """
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, *args):
+        # assert len(args) == len(self.transforms)
+        # data_list = [None] * len(args)
+        # for i in range(len(args)):
+        #     data = args[i]
+        #     for transform in self.transforms[i]:
+        #         data = transform(data)
+        #     data_list[i] = data
+        # return data_list
+        data_list = list(args)
+        for transform in self.transforms:
+            data_list = transform(*data_list)
+        return data_list