Merge pull request #5942 from botev/master

theano/tensor/nnet/tests/test_neighbours.py

@@ -236,6 +236,57 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
                 # TODO: why this is commented?
                 # assert numpy.allclose(images.get_value(borrow=True), g())
 
+    def test_neibs_half_step_by_valid(self):
+        neib_shapes = ((3, 3), (3, 5), (5, 3))
+        for shp_idx, (shape, neib_step) in enumerate([
+            [(7, 8, 5, 5), (1, 1)],
+            [(7, 8, 5, 5), (2, 2)],
+            [(7, 8, 5, 5), (4, 4)],
+            [(7, 8, 5, 5), (1, 4)],
+            [(7, 8, 5, 5), (4, 1)],
+            [(80, 90, 5, 5), (1, 2)],
+            [(1025, 9, 5, 5), (2, 1)],
+            [(1, 1, 5, 1037), (2, 4)],
+            [(1, 1, 1045, 5), (4, 2)]]
+        ):
+            for neib_shape in neib_shapes:
+                for dtype in self.dtypes:
+                    x = theano.shared(np.random.randn(*shape).astype(dtype))
+                    extra = (neib_shape[0] // 2, neib_shape[1] // 2)
+                    padded_shape = (x.shape[0], x.shape[1], x.shape[2] + 2 * extra[0], x.shape[3] + 2 * extra[1])
+                    padded_x = T.zeros(padded_shape)
+                    padded_x = T.set_subtensor(padded_x[:, :, extra[0]:-extra[0], extra[1]:-extra[1]], x)
+                    x_using_valid = images2neibs(padded_x, neib_shape, neib_step, mode="valid")
+                    x_using_half = images2neibs(x, neib_shape, neib_step, mode="half")
+                    close = T.allclose(x_using_valid, x_using_half)
+                    f = theano.function([], close, mode=self.mode)
+                    assert f()
+
+    def test_neibs_full_step_by_valid(self):
+        for shp_idx, (shape, neib_step, neib_shapes) in enumerate([
+            [(7, 8, 5, 5), (1, 1), ((3, 3), (3, 5), (5, 3))],
+            [(7, 8, 5, 5), (2, 2), ((3, 3), (3, 5), (5, 3))],
+            [(7, 8, 6, 6), (3, 3), ((2, 2), (2, 5), (5, 2))],
+            [(7, 8, 6, 6), (1, 3), ((2, 2), (2, 5), (5, 2))],
+            [(7, 8, 6, 6), (3, 1), ((2, 2), (2, 5), (5, 2))],
+            [(80, 90, 5, 5), (1, 2), ((3, 3), (3, 5), (5, 3))],
+            [(1025, 9, 5, 5), (2, 1), ((3, 3), (3, 5), (5, 3))],
+            [(1, 1, 11, 1037), (2, 3), ((3, 3), (5, 3))],
+            [(1, 1, 1043, 11), (3, 2), ((3, 3), (3, 5))]]
+        ):
+            for neib_shape in neib_shapes:
+                for dtype in self.dtypes:
+                    x = theano.shared(np.random.randn(*shape).astype(dtype))
+                    extra = (neib_shape[0] - 1, neib_shape[1] - 1)
+                    padded_shape = (x.shape[0], x.shape[1], x.shape[2] + 2 * extra[0], x.shape[3] + 2 * extra[1])
+                    padded_x = T.zeros(padded_shape)
+                    padded_x = T.set_subtensor(padded_x[:, :, extra[0]:-extra[0], extra[1]:-extra[1]], x)
+                    x_using_valid = images2neibs(padded_x, neib_shape, neib_step, mode="valid")
+                    x_using_full = images2neibs(x, neib_shape, neib_step, mode="full")
+                    close = T.allclose(x_using_valid, x_using_full)
+                    f = theano.function([], close, mode=self.mode)
+                    assert f()
+
     def test_neibs_bad_shape_wrap_centered(self):
         shape = (2, 3, 10, 10)
 
@@ -281,6 +332,28 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
         self.assertRaises(TypeError, unittest_tools.verify_grad,
                           fn, [images_val], mode=self.mode)
 
+    def test_grad_half(self):
+        # It is not implemented for now. So test that we raise an error.
+        shape = (2, 3, 6, 6)
+        images_val = np.random.rand(*shape).astype('float32')
+
+        def fn(images):
+            return images2neibs(images, (3, 3), mode='half')
+
+        self.assertRaises(TypeError, unittest_tools.verify_grad,
+                          fn, [images_val], mode=self.mode)
+
+    def test_grad_full(self):
+        # It is not implemented for now. So test that we raise an error.
+        shape = (2, 3, 6, 6)
+        images_val = np.random.rand(*shape).astype('float32')
+
+        def fn(images):
+            return images2neibs(images, (3, 3), mode='full')
+
+        self.assertRaises(TypeError, unittest_tools.verify_grad,
+                          fn, [images_val], mode=self.mode)
+
     def test_grad_valid(self):
         shape = (2, 3, 6, 6)
         images_val = np.random.rand(*shape).astype('float32')
@@ -330,15 +403,33 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
         images_val = np.arange(np.prod(shape),
                                dtype='float32').reshape(shape)
 
-        def fn(images):
-            return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
-                         axis=[0, 1])
-
         f = theano.function([images],
                             T.sqr(images2neibs(images, (2, 2), mode='valid')),
                             mode=self.mode)
         self.assertRaises(TypeError, f, images_val)
 
+    def test_neibs_half_with_inconsistent_borders(self):
+        shape = (2, 3, 5, 5)
+        images = T.dtensor4()
+        images_val = np.arange(np.prod(shape),
+                               dtype='float32').reshape(shape)
+
+        f = theano.function([images],
+                            T.sqr(images2neibs(images, (2, 2), mode='half')),
+                            mode=self.mode)
+        self.assertRaises(TypeError, f, images_val)
+
+    def test_neibs_full_with_inconsistent_borders(self):
+        shape = (2, 3, 5, 5)
+        images = T.dtensor4()
+        images_val = np.arange(np.prod(shape),
+                               dtype='float32').reshape(shape)
+
+        f = theano.function([images],
+                            T.sqr(images2neibs(images, (2, 2), mode='full')),
+                            mode=self.mode)
+        self.assertRaises(TypeError, f, images_val)
+
     def test_can_not_infer_nb_dim(self):
         # Was reported in gh-5613. Test that we do not crash
         # or that we crash in a few other case found while
@@ -346,7 +437,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
 
         img = T.tensor4('img')
         patches = T.nnet.neighbours.images2neibs(img, [16, 16])
-        extractPatches = theano.function([img], patches)
+        extractPatches = theano.function([img], patches, mode=self.mode)
 
         patsRecovery = T.matrix('patsRecovery')
         original_size = T.ivector('original_size')
@@ -354,7 +445,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
         for mode in ['valid', 'ignore_borders']:
             out = neibs2images(patsRecovery, (16, 16),
                                original_size, mode=mode)
-            f = theano.function([patsRecovery, original_size], out)
+            f = theano.function([patsRecovery, original_size], out,
+                                mode=self.mode)
 
             im_val = np.ones((1, 3, 320, 320), dtype=np.float32)
             neibs = extractPatches(im_val)
@@ -392,6 +484,32 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
         for i in range(1000):
             f()
 
+    def speed_neibs_half(self):
+        shape = (100, 40, 18, 18)
+        images = shared(np.arange(np.prod(shape),
+                                  dtype='float32').reshape(shape))
+        neib_shape = T.as_tensor_variable((3, 3))
+
+        f = function([],
+                     images2neibs(images, neib_shape, mode="half"),
+                     mode=self.mode)
+
+        for i in range(1000):
+            f()
+
+    def speed_neibs_full(self):
+        shape = (100, 40, 18, 18)
+        images = shared(np.arange(np.prod(shape),
+                                  dtype='float32').reshape(shape))
+        neib_shape = T.as_tensor_variable((3, 3))
+
+        f = function([],
+                     images2neibs(images, neib_shape, mode="full"),
+                     mode=self.mode)
+
+        for i in range(1000):
+            f()
+
     def test_infer_shape(self):
         shape = (100, 40, 6, 3)
         images = np.ones(shape).astype('float32')
@@ -431,6 +549,24 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
             [x], [images2neibs(
                 x, neib_shape=(3, 3), mode='wrap_centered')],
             [images], Images2Neibs)
+        shape = (100, 40, 6, 4)
+        images = np.ones(shape).astype('float32')
+        x = T.ftensor4()
+        self._compile_and_check(
+            [x], [images2neibs(x, neib_shape=(2, 1), mode='half')],
+            [images], Images2Neibs)
+        self._compile_and_check(
+            [x], [images2neibs(x, neib_shape=(2, 3), mode='half')],
+            [images], Images2Neibs)
+        shape = (100, 40, 6, 5)
+        images = np.ones(shape).astype('float32')
+        x = T.ftensor4()
+        self._compile_and_check(
+            [x], [images2neibs(x, neib_shape=(2, 1), mode='full')],
+            [images], Images2Neibs)
+        self._compile_and_check(
+            [x], [images2neibs(x, neib_shape=(2, 3), mode='full')],
+            [images], Images2Neibs)
 
 if __name__ == '__main__':
     unittest.main()