hardswish support for tflite (#1735)

Signed-off-by: Guenther Schmuelling <guschmue@microsoft.com>

Author: guschmue

tests/test_backend.py

@@ -5696,6 +5696,19 @@ def func(x):
         # can't check the values because in onnx they are padded with 0, in tf they are not
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, check_value=False)
 
+    @check_tf_min_version("2.5")
+    @check_opset_min_version(14, "hardswish")
+    @skip_tfjs("not supported in tfjs")
+    def test_hardswish(self):
+        def func(x):
+            # there is no hardswich in tf but toco will optimize to it
+            op_ = x * tf.nn.relu6(x + np.float32(3)) * np.float32(1. / 6.)
+            return tf.identity(op_, name=_TFOUTPUT)
+
+        # tf gets this wrong and returns fp32 instead of int
+        x_val = np.array([0.5, 1.0, -0.5, -1.0], dtype=np.float32).reshape((2, 2))
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
+
 
 if __name__ == '__main__':
     unittest_main()

tf2onnx/onnx_opset/math.py

@@ -806,3 +806,11 @@ def version_11(cls, ctx, node, **kwargs):
             shapes=[shape], dtypes=[onnx_dtype])
 
         ctx.replace_all_inputs(node.output[0], last_node.output[0])  # ops=ctx.get_nodes()
+
+
+@tf_op(["HardSwish"])
+class HardSwish:
+    # Note: this doesn't really exist in tensorflow but it does in tflite
+    @classmethod
+    def version_14(cls, ctx, node, **kwargs):
+        pass

tf2onnx/tflite_handlers/tfl_direct.py

@@ -82,6 +82,7 @@
 @tfl_op("TFL_CUMSUM", tf_op="Cumsum")
 @tfl_op("TFL_RFFT2D", tf_op="RFFT2D")
 @tfl_op("TFL_COMPLEX_ABS", tf_op="ComplexAbs")
+@tfl_op("TFL_HARD_SWISH", tf_op="HardSwish")
 class TflDirectOp:
     @classmethod
     def to_tf(cls, ctx, node, **kwargs):