Make LocallyConnected1D layer respect the data_format parameter.

PiperOrigin-RevId: 199879521

1 files changed, 47 insertions, 0 deletions

diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py
index 58df263a4f..53e30e0e4a 100644
--- a/tensorflow/python/keras/backend_test.py
+++ b/tensorflow/python/keras/backend_test.py
@@ -810,6 +810,53 @@ class BackendNNOpsTest(test.TestCase):
                              padding='same', data_format='channels_last')
     self.assertEqual(y.get_shape().as_list(), [10, 5, 5])
 
+  def test_local_conv1d_channels_dim(self):
+    input_length = 5
+    input_dim = 3
+    batch_size = 2
+
+    inputs = np.random.normal(0, 1, (batch_size, input_dim, input_length))
+    inputs_cf = keras.backend.variable(inputs)
+
+    filters = 4
+    for kernel_size in [(1,), (2,), (3,)]:
+      for strides in [(1,), (2,), (3,)]:
+        output_length = (input_length - kernel_size[0]
+                         + strides[0]) // strides[0]
+
+        kernel_shape = (output_length, kernel_size[0] * input_dim, filters)
+        kernel = np.random.normal(0, 1, (output_length,
+                                         input_dim,
+                                         kernel_size[0],
+                                         filters))
+        kernel_cf = np.reshape(kernel, kernel_shape)
+        kernel_cf = keras.backend.variable(kernel_cf)
+
+        conv_cf = keras.backend.local_conv1d(inputs_cf,
+                                             kernel_cf,
+                                             kernel_size,
+                                             strides,
+                                             'channels_first')
+
+        inputs_cl = np.transpose(inputs, (0, 2, 1))
+        inputs_cl = keras.backend.variable(inputs_cl)
+
+        kernel_cl = np.reshape(np.transpose(kernel, (0, 2, 1, 3)),
+                               kernel_shape)
+        kernel_cl = keras.backend.variable(kernel_cl)
+
+        conv_cl = keras.backend.local_conv1d(inputs_cl,
+                                             kernel_cl,
+                                             kernel_size,
+                                             strides,
+                                             'channels_last')
+        with self.test_session():
+          conv_cf = keras.backend.eval(conv_cf)
+          conv_cl = keras.backend.eval(conv_cl)
+
+        self.assertAllCloseAccordingToType(conv_cf,
+                                           np.transpose(conv_cl, (0, 2, 1)))
+
   def test_conv2d(self):
     val = np.random.random((10, 4, 10, 10))
     x = keras.backend.variable(val)