Disable BN fold if conv weights are shared

mace/python/tools/converter_tool/transformer.py

@@ -623,9 +623,9 @@ class Transformer(base_converter.ConverterInterface):
                     and self.consumer_count(op.output[0]) == 1:
                 consumer_op = self._consumers[op.output[0]][0]
                 input_len = len(op.input)
-                if consumer_op.type == MaceOp.BatchNorm.name and \
-                        (input_len == 2 or
-                         (input_len == 3 and op.input[-1] in self._consts)):
+                if (consumer_op.type == MaceOp.BatchNorm.name
+                        and (input_len == 2 or (input_len == 3 and op.input[-1] in self._consts))  # noqa
+                        and len(self._consumers[op.input[1]]) == 1):
                     print("Fold conv and bn: %s(%s)" % (op.name, op.type))
                     filter = self._consts[op.input[1]]
                     scale = self._consts[consumer_op.input[1]]
@@ -678,13 +678,14 @@ class Transformer(base_converter.ConverterInterface):
                 framework = ConverterUtil.get_arg(
                         op, MaceKeyword.mace_framework_type_str).i
                 input_len = len(op.input)
-                if consumer_op.type == MaceOp.BatchNorm.name and (
+                if (consumer_op.type == MaceOp.BatchNorm.name and (
                         (framework == FrameworkType.CAFFE.value and
                          (input_len == 2 or (input_len == 3 and
                                              op.input[-1] in self._consts))) or
                         (framework == FrameworkType.TENSORFLOW.value and
                          (input_len == 3 or (input_len == 4 and
-                                             op.input[-1] in self._consts)))):
+                                             op.input[-1] in self._consts))))
+                        and len(self._consumers[op.input[1]]) == 1):
                     print("Fold deconv and bn: %s(%s)" % (op.name, op.type))
                     filter = self._consts[op.input[1]]
                     scale = self._consts[consumer_op.input[1]]
@@ -745,9 +746,9 @@ class Transformer(base_converter.ConverterInterface):
                     and self.consumer_count(op.output[0]) == 1:
                 consumer_op = self._consumers[op.output[0]][0]
                 input_len = len(op.input)
-                if consumer_op.type == MaceOp.BatchNorm.name and \
-                        (input_len == 2 or
-                         (input_len == 3 and op.input[-1] in self._consts)):
+                if (consumer_op.type == MaceOp.BatchNorm.name
+                        and (input_len == 2 or (input_len == 3 and op.input[-1] in self._consts))  # noqa
+                        and len(self._consumers[op.input[1]]) == 1):
                     print("Fold depthwise conv and bn: %s(%s)"
                           % (op.name, op.type))
                     filter = self._consts[op.input[1]]