def _convert_merge(inexpr, keras_layer, _):
merge_type = type(keras_layer).__name__
ret = inexpr[0]
- if merge_type == 'Subtract':
+ if merge_type == 'Dot':
+ axes = keras_layer.axes
+ if isinstance(keras_layer.axes, int):
+ axes = [keras_layer.axes, keras_layer.axes]
+ if isinstance(axes, list):
+ if len(axes) != 2:
+ 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(
+ 'Dot with axes {} is not supported.'.format(keras_layer.axes))
+ if axes[i] == 2:
+ inexpr[i] = _op.transpose(inexpr[i], axes=[0, 2, 1])
+ else:
+ raise tvm.error.OpAttributeUnImplemented(
+ 'Dot with axes {} is not supported.'.format(keras_layer.axes))
+ ret_dot = _op.nn.batch_matmul(inexpr[0], inexpr[1])
+ ret = _op.transpose(ret_dot, axes=[0, 2, 1])
+ elif merge_type == 'Subtract':
assert len(inexpr) == 2, "Subtract merge takes 2 inputs."
ret = _op.subtract(ret, inexpr[1])
elif merge_type in ['Add', 'Multiply', 'Maximum']:
'Average' : _convert_merge,
'Maximum' : _convert_merge,
- # 'Dot' : _convert_merge,
+ 'Dot' : _convert_merge,
'Permute' : _convert_permute,
# 'Embedding' : _convert_embedding,
# 'RepeatVector' : _convert_repeat_vector,
keras.layers.Average(),
keras.layers.Concatenate()]
for merge_func in merge_funcs:
- if isinstance(merge_func, keras.layers.merge.Subtract):
+ if isinstance(merge_func, (keras.layers.merge.Subtract, keras.layers.merge.Dot)):
out = merge_func([x, y])
else:
out = merge_func([x, y, z])
keras_model = keras.models.Model(data, out)
verify_keras_frontend(keras_model)
+def test_forward_merge_dot():
+ data1 = keras.layers.Input(shape=(2, 2))
+ data2 = keras.layers.Input(shape=(2, 2))
+ merge_funcs = [keras.layers.Dot(axes=[1, 2]),
+ keras.layers.Dot(axes=[2, 1]),
+ keras.layers.Dot(axes=[1, 1]),
+ keras.layers.Dot(axes=[2, 2]),
+ keras.layers.Dot(axes=1),
+ keras.layers.Dot(axes=2)]
+ for merge_func in merge_funcs:
+ out = merge_func([data1, data2])
+ keras_model = keras.models.Model([data1, data2], out)
+ verify_keras_frontend(keras_model)
def test_forward_activations():
data = keras.layers.Input(shape=(32, 32, 3))
if __name__ == '__main__':
test_forward_merge()
+ test_forward_merge_dot()
test_forward_activations()
test_forward_dense()
test_forward_permute()