tf.image.resize_bilinear gradient support for float16

The required kernel changes were implemented almost 2 years ago
in 80da0a63200cb7c9c449188620992c7a8d18c8b9, but we forgot to
change the Python gradient registry.

PiperOrigin-RevId: 185790703

diff --git a/tensorflow/python/ops/image_grad.py b/tensorflow/python/ops/image_grad.py
index d17f1a87d9759d5e83393f40e9e027dee8c15979..093843cd5bc0b7c2281a0c9ddf52d93ea3faede3 100644 (file)
--- a/tensorflow/python/ops/image_grad.py
+++ b/tensorflow/python/ops/image_grad.py
@@ -61,15 +61,10 @@ def _ResizeBilinearGrad(op, grad):
   Returns:
     The gradients w.r.t. the input.
   """
-  allowed_types = [dtypes.float32, dtypes.float64]
-  grad0 = None
-  if op.inputs[0].dtype in allowed_types:
-    # pylint: disable=protected-access
-    grad0 = gen_image_ops._resize_bilinear_grad(
-        grad,
-        op.inputs[0],
-        align_corners=op.get_attr("align_corners"))
-    # pylint: enable=protected-access
+  # pylint: disable=protected-access
+  grad0 = gen_image_ops._resize_bilinear_grad(
+      grad, op.inputs[0], align_corners=op.get_attr("align_corners"))
+  # pylint: enable=protected-access
   return [grad0, None]
 
 

diff --git a/tensorflow/python/ops/image_grad_test.py b/tensorflow/python/ops/image_grad_test.py
index 05e8fa1d72851caee522bba470bb40f430152464..75d00c8ed17c26c2c1acb4d92961a2206d959ebb 100644 (file)
--- a/tensorflow/python/ops/image_grad_test.py
+++ b/tensorflow/python/ops/image_grad_test.py
@@ -142,18 +142,6 @@ class ResizeBilinearOpTest(test.TestCase):
           input_tensor, in_shape, resize_out, out_shape, x_init_value=x)
     self.assertLess(err, 1e-3)
 
-  def testGradOnUnsupportedType(self):
-    in_shape = [1, 4, 6, 1]
-    out_shape = [1, 2, 3, 1]
-
-    x = np.arange(0, 24).reshape(in_shape).astype(np.uint8)
-
-    with self.test_session():
-      input_tensor = constant_op.constant(x, shape=in_shape)
-      resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
-      grad = gradients_impl.gradients(input_tensor, [resize_out])
-      self.assertEqual([None], grad)
-
   def testCompareGpuVsCpu(self):
     in_shape = [2, 4, 6, 3]
     out_shape = [2, 8, 16, 3]
@@ -172,6 +160,26 @@ class ResizeBilinearOpTest(test.TestCase):
 
       self.assertAllClose(grad[False], grad[True], rtol=1e-4, atol=1e-4)
 
+  def testTypes(self):
+    in_shape = [1, 4, 6, 1]
+    out_shape = [1, 2, 3, 1]
+    x = np.arange(0, 24).reshape(in_shape)
+
+    with self.test_session() as sess:
+      for dtype in [np.float16, np.float32, np.float64]:
+        input_tensor = constant_op.constant(x.astype(dtype), shape=in_shape)
+        resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
+        grad = sess.run(gradients_impl.gradients(resize_out, input_tensor))[0]
+        self.assertAllEqual(in_shape, grad.shape)
+        # Not using gradient_checker.compute_gradient as I didn't work out
+        # the changes required to compensate for the lower precision of
+        # float16 when computing the numeric jacobian.
+        # Instead, we just test the theoretical jacobian.
+        self.assertAllEqual([[[[1.], [0.], [1.], [0.], [1.], [0.]], [[0.], [
+            0.
+        ], [0.], [0.], [0.], [0.]], [[1.], [0.], [1.], [0.], [1.], [0.]],
+                              [[0.], [0.], [0.], [0.], [0.], [0.]]]], grad)
+
 
 class ResizeBicubicOpTest(test.TestCase):