fix deconv fold activation

mace/ops/deconv_2d.h

@@ -16,6 +16,7 @@
 #define MACE_OPS_DECONV_2D_H_
 
 #include <memory>
+#include <string>
 
 #include "mace/core/operator.h"
 #include "mace/kernels/deconv_2d.h"
@@ -34,8 +35,10 @@ class Deconv2dOp : public Operator<D, T> {
                      "padding", static_cast<int>(SAME))),
                  OperatorBase::GetRepeatedArgs<int>("padding_values"),
                  OperatorBase::GetRepeatedArgs<index_t>("output_shape"),
-                 kernels::ActivationType::NOOP,
-                 0.0f) {}
+                 kernels::StringToActivationType(
+                     OperatorBase::GetOptionalArg<std::string>("activation",
+                                                               "NOOP")),
+                 OperatorBase::GetOptionalArg<float>("max_limit", 0.0f)) {}
 
   MaceStatus Run(StatsFuture *future) override {
     const Tensor *input = this->Input(INPUT);

mace/python/tools/converter_tool/tensorflow_converter.py

@@ -403,6 +403,12 @@ class TensorflowConverter(base_converter.ConverterInterface):
                 dilation_val = [1, 1]
             dilation_arg.ints.extend(dilation_val)
         else:
+            try:
+                dilation_val = tf_op.get_attr(tf_dilations_str)[1:3]
+            except ValueError:
+                dilation_val = [1, 1]
+            mace_check(dilation_val[0] == 1 and dilation_val[1] == 1,
+                       "Mace only supports dilation == 1 conv2d_transpose.")
             mace_check(len(tf_op.inputs) >= 3,
                        "deconv should have (>=) 3 inputs.")
             output_shape_arg = op.arg.add()

mace/python/tools/converter_tool/transformer.py

@@ -950,13 +950,13 @@ class Transformer(base_converter.ConverterInterface):
         return False
 
     def reshape_fc_weight(self):
-        print("Reshape fully connected weight shape")
         net = self._model
         filter_format = self.filter_format()
         for op in net.op:
             if op.type == MaceOp.FullyConnected.name:
                 weight = self._consts[op.input[1]]
                 if len(weight.dims) == 2:
+                    print("Reshape fully connected weight shape")
                     input_op = self._producer[op.input[0]]
                     input_shape = list(input_op.output_shape[0].dims)
                     weight.dims[:] = [weight.dims[0]] + input_shape[1:]