[Relay][Frontend][Keras] Fix ReLU in Keras Converter missed the case (#3917)

* [Relay][Frontend][Keras] Fix ReLU in Keras Converter missed the case

* [Relay][Frontend][Keras] Add test case for ReLU in Keras Converter missed the case

* [Relay][Frontend][Keras] Add test case for ReLU in Keras Converter missed the case

diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py
index e5b70a4..1b8fe12 100644 (file)
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -128,8 +128,15 @@ def _convert_advanced_activation(inexpr, keras_layer, etab):
             axis = axis + 1 if axis < dims - 1 else 1
         return _op.nn.softmax(inexpr, axis=axis)
     if act_type == 'ReLU':
-        if keras_layer.max_value:
+        threshold = _expr.const(keras_layer.threshold, dtype='float32')
+        if keras_layer.max_value and float(keras_layer.threshold) == 0:
+            # f(x) = max_value, for x >= max_value
+            # f(x) = x,         for threshold <= x < max_value
             return _op.clip(inexpr, a_min=0., a_max=float(keras_layer.max_value))
+        elif keras_layer.max_value and _op.greater(threshold, inexpr).astype('float32'):
+            # f(x) = negative_slope * (inexpr - threshold)
+            negative_slope = _expr.const(keras_layer.negative_slope, dtype='float32')
+            return _op.multiply(negative_slope, _op.subtract(inexpr, threshold))
         return _op.nn.relu(inexpr)
     if act_type == 'LeakyReLU':
         return _op.nn.leaky_relu(inexpr, alpha=float(keras_layer.alpha))
@@ -162,11 +169,11 @@ def _convert_merge(inexpr, keras_layer, _):
             axes = [keras_layer.axes, keras_layer.axes]
         if isinstance(axes, list):
             if len(axes) != 2:
-                raise tvm.error.OpAttributeUnimplemented(
+                raise tvm.error.OpAttributeUnImplemented(
                     'Dot with axes {} is not supported.'.format(keras_layer.axes))
             for i, axis in enumerate(axes):
                 if axis not in [1, 2]:
-                    raise tvm.error.OpAttributeUnimplemented(
+                    raise tvm.error.OpAttributeUnImplemented(
                         'Dot with axes {} is not supported.'.format(keras_layer.axes))
                 if axes[i] == 2:
                     inexpr[i] = _op.transpose(inexpr[i], axes=[0, 2, 1])
@@ -191,9 +198,11 @@ def _convert_merge(inexpr, keras_layer, _):
             'Operator {} is not supported in frontend Keras.'.format(merge_type))
     return ret
 
+
 def _convert_permute(inexpr, keras_layer, _):
     return _op.transpose(inexpr, axes=(0,) + keras_layer.dims)
 
+
 def _convert_dense(inexpr, keras_layer, etab):
     weightList = keras_layer.get_weights()
     weight = etab.new_const(weightList[0].transpose([1, 0]))

diff --git a/tests/python/frontend/keras/test_forward.py b/tests/python/frontend/keras/test_forward.py
index 3cc4ac5..1017e3a 100644 (file)
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -123,6 +123,11 @@ def test_forward_activations():
                  keras.layers.Activation('selu'),
                  keras.layers.ReLU(),
                  keras.layers.ReLU(max_value=6.),
+                 keras.layers.ReLU(max_value=6., threshold=0.),
+                 keras.layers.ReLU(max_value=6., threshold=1.),
+                 keras.layers.ReLU(max_value=6., threshold=1., negative_slope=0.),
+                 keras.layers.ReLU(max_value=6., threshold=1., negative_slope=0.5),
+                 keras.layers.ReLU(max_value=6., threshold=1., negative_slope=1.),
                  keras.layers.LeakyReLU(alpha=0.3),
                  keras.layers.PReLU(weights=np.random.rand(1, 32, 32, 3)),
                  keras.layers.ELU(alpha=0.5),