Merge pull request #4343 from taesupkim/issue_4303

theano/tensor/nnet/abstract_conv.py

@@ -567,7 +567,7 @@ def bilinear_upsampling(input,
                                                subsample=(ratio, ratio),
                                                filter_flip=True)
 
-    return upsampled_mat.reshape((batch_size, num_input_channels,
+    return upsampled_mat.reshape((input.shape[0], input.shape[1],
                                   row * ratio, col * ratio))
 
 

theano/tensor/nnet/tests/test_abstract_conv.py

@@ -687,6 +687,23 @@ class TestBilinearUpsampling(unittest.TestCase):
             up_mat_2d = self.get_upsampled_twobytwo_mat(input_x, ratio)
             utt.assert_allclose(f(), up_mat_2d, rtol=1e-06)
 
+    def test_bilinear_upsampling_reshaping(self):
+        # Test bilinear upsampling without giving shape information
+        # This method tests the bilinear_upsampling method
+        # without giving batch_size and num_input_channels
+        # upsampling for a ratio of two
+        input_x = np.array([[[[1, 2], [3, 4]]]], dtype=theano.config.floatX)
+
+        for ratio in [2, 3]:
+            for use_1D_kernel in [True, False]:
+                bilin_mat = bilinear_upsampling(input=input_x, ratio=ratio,
+                                                batch_size=None,
+                                                num_input_channels=None,
+                                                use_1D_kernel=use_1D_kernel)
+                f = theano.function([], bilin_mat, mode=self.compile_mode)
+                up_mat_2d = self.get_upsampled_twobytwo_mat(input_x, ratio)
+                utt.assert_allclose(f(), up_mat_2d, rtol=1e-06)
+
     def test_compare_1D_and_2D_upsampling_values(self):
         """Compare 1D and 2D upsampling