Some tests cleanup

theano/sandbox/gpuarray/tests/test_dnn.py

@@ -2,8 +2,9 @@ from __future__ import absolute_import, print_function, division
 import logging
 
 from nose.plugins.skip import SkipTest
+from nose_parameterized import parameterized
 import numpy
-from itertools import product
+from itertools import product, chain
 
 import theano
 from six import StringIO
@@ -392,6 +393,9 @@ def test_dnn_tag():
 
 class TestDnnInferShapes(utt.InferShapeTester):
 
+    border_modes = ['valid', 'full', 'half']
+    conv_modes = ['conv', 'cross']
+
     def setUp(self):
         super(TestDnnInferShapes, self).setUp()
         self.mode = mode_with_gpu
@@ -426,27 +430,23 @@ class TestDnnInferShapes(utt.InferShapeTester):
             dnn.GpuDnnSoftmaxGrad
         )
 
-    def _test_conv(self, img, kerns, out, img_val, kern_vals, subsample):
+    def _test_conv(self, img, kerns, out, img_val, kern_vals, border_mode, conv_mode, subsamples):
         if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)
 
         img_val = numpy.asarray(img_val, dtype='float32')
         kern_vals = numpy.asarray(kern_vals, dtype='float32')
 
-        for params in product(
-            ['valid', 'full', 'half'],
-            subsample,
-            ['conv', 'cross']
-        ):
+        for subsample in subsamples:
             out_vals = numpy.zeros(
                 dnn.GpuDnnConv.get_out_shape(img_val.shape, kern_vals.shape,
-                                             border_mode=params[0],
-                                             subsample=params[1]),
+                                             border_mode=border_mode,
+                                             subsample=subsample),
                 dtype='float32')
             desc = dnn.GpuDnnConvDesc(
-                border_mode=params[0],
-                subsample=params[1],
-                conv_mode=params[2]
+                border_mode=border_mode,
+                subsample=subsample,
+                conv_mode=conv_mode
             )(kerns.shape)
             conv = dnn.GpuDnnConv()(img, kerns, out, desc)
             self._compile_and_check(
@@ -456,46 +456,63 @@ class TestDnnInferShapes(utt.InferShapeTester):
                 dnn.GpuDnnConv
             )
 
-    def test_conv(self):
+    @parameterized.expand(chain(product([SUPPORTED_DNN_CONV_ALGO_FWD[0]],
+                                        border_modes,
+                                        conv_modes),
+                                product(SUPPORTED_DNN_CONV_ALGO_FWD[1:],
+                                        [border_modes[0]],
+                                        [conv_modes[0]])),
+                          testcase_func_name=utt.custom_name_func)
+    def test_conv(self, algo, border_mode, conv_mode):
+        try:
+            default_algo = theano.config.dnn.conv.algo_fwd
+            theano.config.dnn.conv.algo_fwd = algo
+
             self._test_conv(T.ftensor4('img'),
                             T.ftensor4('kerns'),
                             T.ftensor4('out'),
-                        numpy.random.rand(7, 2, 6, 4),
+                            numpy.random.rand(7, 2, 8, 4),
                             numpy.random.rand(8, 2, 4, 3),
+                            border_mode,
+                            conv_mode,
                             [(1, 1), (2, 2)])
+        finally:
+            theano.config.dnn.conv.algo_fwd = default_algo
+
+    @parameterized.expand(product(border_modes, conv_modes), utt.custom_name_func)
+    def test_conv3d_none(self, border_mode, conv_mode):
+        try:
+            default_algo = theano.config.dnn.conv.algo_fwd
+            theano.config.dnn.conv.algo_fwd = 'none'
 
-    def test_conv3d(self):
             ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
             self._test_conv(ftensor5('img'),
                             ftensor5('kerns'),
                             ftensor5('out'),
                             numpy.random.rand(10, 2, 6, 4, 11),
                             numpy.random.rand(8, 2, 4, 3, 1),
+                            border_mode,
+                            conv_mode,
                             [(1, 1, 1), (2, 2, 2)])
+        finally:
+            theano.config.dnn.conv.algo_fwd = default_algo
 
-    def test_conv_gradw(self):
+    def _test_conv_gradw(self, img, kerns, out, img_val, kern_vals, border_mode, conv_mode, subsample):
         if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)
-        img = T.ftensor4('img')
-        kerns = T.ftensor4('kerns')
-        out = T.ftensor4('out')
+
         img_val = numpy.asarray(
-            numpy.random.rand(2, 5, 6, 8),
+            img_val,
             dtype='float32'
         )
         kern_vals = numpy.asarray(
-            numpy.random.rand(2, 1, 5, 6),
+            kern_vals,
             dtype='float32'
         )
 
-        for params in product(
-            ['valid', 'full', 'half'],
-            [(1, 1)],  # strides besides (1, 1)
-            ['conv', 'cross']
-        ):
         temp_img = img.dimshuffle(1, 0, 2, 3)
         temp_kerns = kerns
-            if params[2] == 'conv':
+        if conv_mode == 'conv':
             temp_kerns = temp_kerns[:, :, ::-1, ::-1]
         temp_kerns = temp_kerns.dimshuffle(1, 0, 2, 3)
         shape = (
@@ -505,9 +522,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
         )
         out_vals = numpy.zeros(shape, dtype='float32')
         desc = dnn.GpuDnnConvDesc(
-                border_mode=params[0],
-                subsample=params[1],
-                conv_mode=params[2]
+            border_mode=border_mode,
+            subsample=subsample,
+            conv_mode=conv_mode
         )(out.shape)
         conv_grad_w = dnn.GpuDnnConvGradW()(
             temp_img,
@@ -522,6 +539,17 @@ class TestDnnInferShapes(utt.InferShapeTester):
             dnn.GpuDnnConvGradW
         )
 
+    @parameterized.expand(product(border_modes, conv_modes), utt.custom_name_func)
+    def test_conv_gradw(self, border_mode, conv_mode):
+        self._test_conv_gradw(T.ftensor4('img'),
+                              T.ftensor4('kerns'),
+                              T.ftensor4('out'),
+                              numpy.random.rand(2, 5, 6, 8),
+                              numpy.random.rand(2, 1, 5, 6),
+                              border_mode,
+                              conv_mode,
+                              (1, 1))
+
     def test_conv_gradi(self):
         if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)