[MXNET-145] Remove the dependences of mx.io and mx.initializer on the…

… numpy's global random number generator (apache#10260)

* Remove the dependence of mx.io on the global random number generator of numpy

* Remove the dependence of mx.initializer on the global random number generator of numpy

python/mxnet/initializer.py

@@ -530,9 +530,9 @@ def _init_weight(self, _, arr):
         nout = arr.shape[0]
         nin = np.prod(arr.shape[1:])
         if self.rand_type == "uniform":
-            tmp = np.random.uniform(-1.0, 1.0, (nout, nin))
+            tmp = random.uniform(-1.0, 1.0, shape=(nout, nin)).asnumpy()
         elif self.rand_type == "normal":
-            tmp = np.random.normal(0.0, 1.0, (nout, nin))
+            tmp = random.normal(0.0, 1.0, shape=(nout, nin)).asnumpy()
         u, _, v = np.linalg.svd(tmp, full_matrices=False) # pylint: disable=invalid-name
         if u.shape == tmp.shape:
             res = u

python/mxnet/io.py

@@ -39,6 +39,8 @@
 from .ndarray import _ndarray_cls
 from .ndarray import array
 from .ndarray import concatenate
+from .ndarray import arange
+from .ndarray.random import shuffle as random_shuffle
 
 class DataDesc(namedtuple('DataDesc', ['name', 'shape'])):
     """DataDesc is used to store name, shape, type and layout
@@ -651,12 +653,14 @@ def __init__(self, data, label=None, batch_size=1, shuffle=False,
             raise NotImplementedError("`NDArrayIter` only supports ``CSRNDArray``" \
                                       " with `last_batch_handle` set to `discard`.")
 
-        self.idx = np.arange(self.data[0][1].shape[0])
         # shuffle data
         if shuffle:
-            np.random.shuffle(self.idx)
+            tmp_idx = arange(self.data[0][1].shape[0], dtype=np.int32)
+            self.idx = random_shuffle(tmp_idx, out=tmp_idx).asnumpy()
             self.data = _shuffle(self.data, self.idx)
             self.label = _shuffle(self.label, self.idx)
+        else:
+            self.idx = np.arange(self.data[0][1].shape[0])
 
         # batching
         if last_batch_handle == 'discard':