[Relay] add ClipByValue and Neg in tf frontend converter (#3211)

python/tvm/relay/frontend/tensorflow.py

@@ -941,6 +941,13 @@ def _where():
         return AttrCvt(op_name="where")(inputs, attr)
     return _impl
 
+def _clip_by_value():
+    def _impl(inputs, attr, params):
+        a_min = params.pop(inputs[1].name_hint).asnumpy()[0]
+        a_max = params.pop(inputs[2].name_hint).asnumpy()[0]
+        return _op.clip(inputs[0], a_min=a_min, a_max=a_max)
+    return _impl
+
 def _reverse_v2():
     def _impl(inputs, attr, params):
         axis = _get_num_param(params, inputs[1])
@@ -1212,6 +1219,7 @@ _convert_map = {
     'Cast'                              : _cast(),
     'Ceil'                              : AttrCvt('ceil'),
     'CheckNumerics'                     : _check_numerics(),
+    'ClipByValue'                       : _clip_by_value(),
     'Concat'                            : _concat(),
     'ConcatV2'                          : _concatV2(),
     'Conv2D'                            : _conv('conv'),
@@ -1245,6 +1253,7 @@ _convert_map = {
     'Mean'                              : _mean(),
     'Minimum'                           : _elemwise('minimum'),
     'Mul'                               : _elemwise('multiply'),
+    'Neg'                               : AttrCvt('negative'),
     'NotEqual'                          : _broadcast('not_equal'),
     'Pack'                              : _pack(),
     'Pad'                               : _pad('Pad'),

tests/python/frontend/tensorflow/test_forward.py

@@ -834,6 +834,23 @@ def test_forward_tile():
 
 
 #######################################################################
+# ClipByValue
+# -----------
+
+def _test_forward_clip_by_value(ip_shape, clip_value_min, clip_value_max, dtype):
+    tf.reset_default_graph()
+    in_data = tf.placeholder(dtype, ip_shape, name="in_data")
+    tf.clip_by_value(in_data, clip_value_min, clip_value_max, name="ClipByValue")
+    np_data = np.random.uniform(-100, 100, size=ip_shape).astype(dtype)
+    compare_tf_with_tvm([np_data], ['in_data:0'], 'ClipByValue:0')
+
+def test_forward_clip_by_value():
+    '''test ClipByValue op'''
+    if tf.__version__ < LooseVersion('1.9'):
+        _test_forward_clip_by_value((4,), .1, 5., 'float32')
+        _test_forward_clip_by_value((4, 4), 1, 5, 'int32')
+
+#######################################################################
 # Multi Input to graph
 # --------------------
 
@@ -1591,6 +1608,14 @@ def test_forward_log():
     tf.log(in_data, name="log")
     compare_tf_with_tvm([np_data], ['in_data:0'], 'log:0')
 
+def test_forward_negative():
+    """test tf operator Neg """
+    np_data = np.random.uniform(-100, 255, size=(224, 224, 3)).astype(np.float32)
+    tf.reset_default_graph()
+    in_data = tf.placeholder(tf.float32, (224, 224, 3), name="in_data")
+    tf.negative(in_data, name="negative")
+    compare_tf_with_tvm([np_data], ['in_data:0'], 'negative:0')
+
 def test_forward_softplus():
     """test operator Softplus"""
     np_data = np.random.uniform(1, 10, size=(2, 3, 5)).astype(np.float32)
@@ -1738,6 +1763,7 @@ if __name__ == '__main__':
     test_forward_unstack()
     test_forward_tile()
     test_forward_top_k_v2()
+    test_forward_clip_by_value()
 
     # Activations
     test_forward_sigmoid()
@@ -1753,6 +1779,7 @@ if __name__ == '__main__':
     test_forward_pow_exp()
     test_forward_sign()
     test_forward_log()
+    test_forward_negative()
     test_forward_softplus()
     test_forward_sqrt()
     test_forward_rsqrt()