Merge pull request #65 from nouiz/gnumpy

theano/misc/tests/test_gnumpy_utils.py

+import numpy
+
 import theano
 from theano.misc.gnumpy_utils import gnumpy_available
 
 if not gnumpy_available:
     from nose.plugins.skip import SkipTest
-    raise SkipTest("gnumpy not installed. Skip test of theano op with pycuda code.")
+    raise SkipTest("gnumpy not installed. Skip test related to it.")
 
-from theano.misc.gnumpy_utils import garray_to_cudandarray, cudandarray_to_garray
+from theano.misc.gnumpy_utils import (garray_to_cudandarray,
+                                      cudandarray_to_garray)
 
 import gnumpy
 
-def test(shape=(3,4,5)):
+
+def test(shape=(3, 4, 5)):
+    """
+    Make sure that the gnumpy conversion is exact from garray to
+    CudaNdarray back to garray.
     """
-Make sure that the gnumpy conversion is exact.
-"""
     gpu = theano.sandbox.cuda.basic_ops.gpu_from_host
     U = gpu(theano.tensor.ftensor3('U'))
-    ii = theano.function([U], gpu(U+1))
-
+    ii = theano.function([U], gpu(U + 1))
 
     A = gnumpy.rand(*shape)
     A_cnd = garray_to_cudandarray(A)
+    assert A_cnd.shape == A.shape
+    # dtype always float32
+    # garray don't have strides
     B_cnd = ii(A_cnd)
     B = cudandarray_to_garray(B_cnd)
+    assert A_cnd.shape == A.shape
     from numpy import array
-    B2 = array(B_cnd)
 
-    u = (A+1).asarray()
+    u = (A + 1).asarray()
     v = B.asarray()
-    w = B2
-    assert abs(u-v).max() == 0
-    assert abs(u-w).max() == 0
+    w = array(B_cnd)
+    assert (u == v).all()
+    assert (u == w).all()
+
+
+def test2(shape=(3, 4, 5)):
+    """
+    Make sure that the gnumpy conversion is exact from CudaNdarray to
+    garray back to CudaNdarray.
+    """
+    gpu = theano.sandbox.cuda.basic_ops.gpu_from_host
+    U = gpu(theano.tensor.ftensor3('U'))
+    ii = theano.function([U], gpu(U + 1))
+
+    A = numpy.random.rand(*shape).astype('float32')
+    A_cnd = theano.sandbox.cuda.CudaNdarray(A)
+    A_gar = cudandarray_to_garray(A_cnd)
+    assert A_cnd.shape == A_gar.shape
+    # dtype always float32
+    # garray don't have strides
+
+    B = garray_to_cudandarray(A_gar)
+
+    assert A_cnd.shape == B.shape
+    # dtype always float32
+    assert A_cnd._strides == B._strides
+    assert A_cnd.gpudata == B.gpudata
+    v = numpy.asarray(B)
+    assert (v == A).all()