Add tensorflow-like slice in numpy backend (keras-team#11682)

* tensorflow-like slice in numpy backend

* tiny commit

* fix pep8

* Fix test_slice using pytest.parametreized

* Simplify test_slice function

* Simplify test_slice function

keras/backend/numpy_backend.py

@@ -585,6 +585,14 @@ def reverse(x, axes):
     return np.flip(x, axes)
 
 
+py_slice = slice
+
+
+def slice(x, start, size):
+    slices = [py_slice(i, i + j) for i, j in zip(start, size)]
+    return x[tuple(slices)]
+
+
 def variable(value, dtype=None, name=None, constraint=None):
     if constraint is not None:
         raise TypeError("Constraint must be None when "

keras/backend/tensorflow_backend.py

@@ -2564,6 +2564,8 @@ def slice(x, start, size):
         ```python
         new_x = x[start[0]: start[0] + size[0], ..., start[-1]: start[-1] + size[-1]]
         ```
+
+    {{np_implementation}}
     """
     return tf.slice(x, start, size)
 

tests/keras/backend/backend_test.py

@@ -1731,6 +1731,24 @@ def test_ctc_decode_greedy(self):
         assert np.alltrue(decode_pred_np == decode_pred)
         assert np.allclose(log_prob_pred_np, log_prob_pred)
 
+    @pytest.mark.skipif(K.backend() != 'tensorflow',
+                        reason='tensorflow-way slice is '
+                        'only supported in tensorflow.')
+    @pytest.mark.parametrize('x_size', [
+        [1, 1, 3],
+        [1, 2, 3],
+        [2, 1, 3]
+    ])
+    def test_slice(self, x_size):
+        npt = np.array([[[1, 1, 1], [2, 2, 2]],
+                       [[3, 3, 3], [4, 4, 4]],
+                       [[5, 5, 5], [6, 6, 6]]])
+        x_start = [1, 0, 0]
+        tft = K.constant(npt)
+        test_input = K.eval(K.slice(tft, x_start, x_size))
+        expected = KNP.slice(npt, x_start, x_size)
+        assert np.allclose(test_input, expected)
+
     @pytest.mark.skipif(K.backend() != 'tensorflow',
                         reason='Beam search is only implemented with '
                                'the TensorFlow backend.')