Merge pull request #2149 from daemonmaker/issue1477

theano/sandbox/cuda/basic_ops.py

@@ -2312,6 +2312,31 @@ class GpuReshape(tensor.Reshape, GpuOp):
             raise ValueError('shape argument to Reshape.perform'
                              ' has incorrect length %i'
                              ', should be %i' % (len(shp), self.ndim), shp)
+
+        if shp.prod() != x.size:
+            # We need to do check here to raise the same error as NumPy.
+            # We should make pygpu do the same.
+            ss = 1
+            nb_m1 = 0
+            m1_idx = -1
+            for idx, i in enumerate(shp):
+                if i == -1:
+                    nb_m1 += 1
+                    m1_idx = idx
+                else:
+                    ss *= i
+            if nb_m1 > 1:
+                raise ValueError("Only one -1 is accepted in the new shape")
+            elif nb_m1 == 1:
+                if (x.size % ss) != 0:
+                    raise ValueError("When using -1 in new shape, the computed new shape must be an multiple of the original shape.")
+                shp_new = numpy.copy(shp)
+                shp_new[m1_idx] = x.size/ss
+                shp = shp_new
+
+            else:
+                raise ValueError("total size of new array must be unchanged")
+
         out[0] = x.reshape(tuple(shp))
 
 

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -10,6 +10,7 @@ import theano.tensor as T
 
 # Skip test if cuda_ndarray is not available.
 from nose.plugins.skip import SkipTest
+from nose.tools import assert_raises
 import theano.sandbox.cuda as cuda_ndarray
 if cuda_ndarray.cuda_available == False:
     raise SkipTest('Optional package cuda disabled')
@@ -336,6 +337,25 @@ def test_reshape():
         return T.Reshape(2)(v, theano._asarray([2, 3], dtype='int32'))
     utt.verify_grad(just_vals, [a_val])
 
+    # Test for appropriate handling of -1 indices
+    x = T.tensor3('x')
+    reshp_val = numpy.array([[[1, 0], [0, 1]], [[0, 1], [1, 0]]], dtype='float32')
+    f_reshp = theano.function([x], x.reshape((-1, 1, 1)), mode=mode_with_gpu)
+    y = f_reshp(reshp_val)
+    assert y.shape == (8, 1, 1)
+
+    dim = T.scalar('dim_val', dtype='int32')
+    f_reshp=theano.function(
+        [x, dim],
+        x.reshape((dim, dim, 1)),
+        mode=mode_with_gpu
+    )
+    try:
+        f_reshp(reshp_val, 4)
+        raise('Only one -1 is accepted in the new shape')
+    except ValueError:
+        pass
+
 
 def test_elemwise_empty():
     #test with 0 element