AbstractConv tests for empty dimensions.

theano/tensor/nnet/tests/test_abstract_conv.py

@@ -289,7 +289,7 @@ class BaseTestConv(object):
         res_ref = numpy.array(f_ref())
         res = numpy.array(f())
         utt.assert_allclose(res_ref, res)
-        if verify_grad:
+        if verify_grad and inputs_val.size > 0 and filters_val.size > 0 and res.size > 0:
             utt.verify_grad(conv_op(border_mode=border_mode,
                                     imshp=imshp, kshp=kshp,
                                     subsample=subsample,
@@ -355,7 +355,7 @@ class BaseTestConv(object):
                                      filter_dilation=filter_dilation)
             return conv_op(inputs_val, output_val, filters_shape[2:])
 
-        if verify_grad:
+        if verify_grad and inputs_val.size > 0 and output_val.size > 0 and res.size > 0:
             utt.verify_grad(abstract_conv_gradweight,
                             [inputs_val, output_val],
                             mode=mode, eps=1)
@@ -421,7 +421,7 @@ class BaseTestConv(object):
                                     filter_dilation=filter_dilation)
             return conv_op(filters_val, output_val, inputs_shape[2:])
 
-        if verify_grad:
+        if verify_grad and filters_val.size > 0 and output_val.size > 0 and res.size > 0:
             utt.verify_grad(abstract_conv_gradinputs,
                             [filters_val, output_val],
                             mode=mode, eps=1)
@@ -436,13 +436,14 @@ class BaseTestConv(object):
         for (i, f) in zip(self.inputs_shapes, self.filters_shapes):
             for provide_shape in self.provide_shape:
                 yield (self.tcase, i, f, ds, db, dflip, provide_shape)
-            for fd in self.filters_dilations:
-                for s in self.subsamples:
-                    for b in self.border_modes:
-                        yield (self.tcase, i, f, s, b, dflip,
-                               dprovide_shape, fd)
-            for flip in self.filter_flip:
-                yield (self.tcase, i, f, ds, db, flip, dprovide_shape)
+            if min(i) > 0 and min(f) > 0:
+                for fd in self.filters_dilations:
+                    for s in self.subsamples:
+                        for b in self.border_modes:
+                            yield (self.tcase, i, f, s, b, dflip,
+                                   dprovide_shape, fd)
+                for flip in self.filter_flip:
+                    yield (self.tcase, i, f, ds, db, flip, dprovide_shape)
 
 
 class BaseTestConv2d(BaseTestConv):
@@ -450,9 +451,11 @@ class BaseTestConv2d(BaseTestConv):
     def setup_class(cls):
         # This tests can run even when theano.config.blas.ldflags is empty.
         cls.inputs_shapes = [(8, 1, 6, 6), (8, 1, 8, 8), (2, 1, 7, 7),
-                             (6, 1, 10, 11), (2, 1, 6, 5), (1, 5, 9, 9)]
+                             (6, 1, 10, 11), (2, 1, 6, 5), (1, 5, 9, 9),
+                             (0, 1, 6, 6), (1, 0, 6, 6), (1, 1, 6, 6)]
         cls.filters_shapes = [(5, 1, 2, 2), (4, 1, 3, 3), (2, 1, 3, 3),
-                              (1, 1, 2, 3), (4, 1, 1, 3), (4, 5, 3, 2)]
+                              (1, 1, 2, 3), (4, 1, 1, 3), (4, 5, 3, 2),
+                              (1, 1, 2, 2), (1, 0, 2, 2), (0, 1, 2, 2)]
         cls.subsamples = [(1, 1), (2, 2), (2, 4)]
         cls.default_subsamples = (1, 1)
         cls.filters_dilations = [(1, 1), (1, 2), (2, 1)]
@@ -806,8 +809,10 @@ class BaseTestConv3d(BaseTestConv):
     @classmethod
     def setup_class(cls):
         # This tests can run even when theano.config.blas.ldflags is empty.
-        cls.inputs_shapes = [(2, 1, 5, 5, 5), (1, 2, 7, 5, 6)]
-        cls.filters_shapes = [(2, 1, 2, 2, 2), (1, 2, 2, 1, 3)]
+        cls.inputs_shapes = [(2, 1, 5, 5, 5), (1, 2, 7, 5, 6),
+                             (0, 1, 5, 5, 5), (1, 0, 5, 5, 5), (1, 1, 5, 5, 5)]
+        cls.filters_shapes = [(2, 1, 2, 2, 2), (1, 2, 2, 1, 3),
+                              (1, 1, 2, 2, 2), (1, 0, 2, 2, 2), (0, 1, 2, 2, 2)]
         cls.subsamples = [(1, 1, 1), (2, 2, 2), (1, 2, 3)]
         cls.default_subsamples = (1, 1, 1)
         cls.filters_dilations = [(1, 1, 1), (1, 2, 1), (2, 1, 2)]
@@ -975,6 +980,9 @@ class TestCpuConv3d(BaseTestConv3d):
             raise SkipTest("No dilation implementation for basic cpu Conv3D.")
         if not theano.config.cxx:
             raise SkipTest("Need cxx to test conv2d")
+        if min(i) == 0 or min(f) == 0:
+            raise SkipTest('Not tested for old cpu Conv3D.')
+
         mode = self.mode
         o = self.get_output_shape(i, f, s, b, fd)
         fwd_OK = True
@@ -1062,6 +1070,8 @@ class TestCpuConv3d(BaseTestConv3d):
         if fd != (1, 1, 1):
             raise SkipTest("No dilation implementation for basic cpu Conv3D.")
         mode = self.mode
+        if min(i) == 0 or min(f) == 0 or min(o) == 0:
+            raise SkipTest('Not tested for old cpu Conv3D.')
 
         if b not in ((0, 0, 0), 'valid'):
             return