[Relay] add ClipByValue and Neg in tf frontend

apache · May 21, 2019 · 924245a · 924245a
1 parent 78a0f47
commit 924245a
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diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
@@ -934,6 +934,13 @@ def _impl(inputs, attr, params):
         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 = params.pop(inputs[1].name_hint).asnumpy()[0]
@@ -1190,6 +1197,7 @@ def _impl(inputs, attr, params):
     'Cast'                              : _cast(),
     'Ceil'                              : AttrCvt('ceil'),
     'CheckNumerics'                     : _check_numerics(),
+    'ClipByValue'                       : _clip_by_value(),
     'Concat'                            : _concat(),
     'ConcatV2'                          : _concatV2(),
     'Conv2D'                            : _conv('conv'),
@@ -1223,6 +1231,7 @@ def _impl(inputs, attr, params):
     'Mean'                              : _mean(),
     'Minimum'                           : _elemwise('minimum'),
     'Mul'                               : _elemwise('multiply'),
+    'Neg'                               : AttrCvt('negative'),
     'NotEqual'                          : _broadcast('not_equal'),
     'Pack'                              : _pack(),
     'Pad'                               : _pad('Pad'),

diff --git a/tests/python/frontend/tensorflow/test_forward.py b/tests/python/frontend/tensorflow/test_forward.py
@@ -1558,6 +1558,14 @@ 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 operator Neg """
+    np_data = np.random.uniform(-100, 100, 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)
@@ -1708,6 +1716,7 @@ def test_placeholder():
     test_forward_pow_exp()
     test_forward_sign()
     test_forward_log()
+    test_forward_negative()
     test_forward_softplus()
     test_forward_sqrt()
     test_forward_rsqrt()