python3 compatibile, update env requirment

.gitignore

@@ -21,3 +21,4 @@ mace/examples/android/macelibrary/src/main/cpp/mace/
 \.project/
 *swp
 *~
+.python-version

docs/installation/env_requirement.rst

@@ -14,7 +14,7 @@ Required dependencies
       - Tested version
     * - Python
       -
-      - 2.7
+      - 2.7 or 3.6
     * - Bazel
       - `bazel installation guide <https://docs.bazel.build/versions/master/install.html>`__
       - 0.13.0

mace/python/tools/converter_tool/caffe_converter.py

@@ -194,7 +194,7 @@ class CaffeConverter(base_converter.ConverterInterface):
         caffe_weights = caffe_pb2.NetParameter()
 
         # parse prototxt
-        with open(src_model_file, 'rb') as f:
+        with open(src_model_file, 'r') as f:
             google.protobuf.text_format.Merge(
                 str(f.read()), self._caffe_layers)
             self.filter_test_layers(self._caffe_layers)
@@ -471,7 +471,7 @@ class CaffeConverter(base_converter.ConverterInterface):
 
         type_arg = op.arg.add()
         type_arg.name = MaceKeyword.mace_activation_type_str
-        type_arg.s = self.activation_type[caffe_op.type].name
+        type_arg.s = six.b(self.activation_type[caffe_op.type].name)
 
         if caffe_op.type == 'PReLU':
             alpha_tensor_name = caffe_op.name + '_alpha'

mace/python/tools/converter_tool/tensorflow_converter.py

@@ -122,10 +122,13 @@ class TensorflowConverter(base_converter.ConverterInterface):
         'SAME': PaddingMode.SAME,
         'FULL': PaddingMode.FULL
     }
+    padding_mode = {six.b(k): v for k, v in six.iteritems(padding_mode)}
+
     pooling_type_mode = {
         TFOpType.AvgPool.name: PoolingType.AVG,
         TFOpType.MaxPool.name: PoolingType.MAX
     }
+
     eltwise_type = {
         TFOpType.Add.name: EltwiseType.SUM,
         TFOpType.Sub.name: EltwiseType.SUB,
@@ -144,6 +147,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
         TFOpType.Rsqrt.name: EltwiseType.POW,
         TFOpType.Equal.name: EltwiseType.EQUAL,
     }
+
     activation_type = {
         TFOpType.Relu.name: ActivationType.RELU,
         TFOpType.Relu6.name: ActivationType.RELUX,
@@ -245,13 +249,13 @@ class TensorflowConverter(base_converter.ConverterInterface):
 
     def replace_input_output_tensor_name(self):
         for op in self._mace_net_def.op:
-            for i in xrange(len(op.input)):
+            for i in six.moves.range(len(op.input)):
                 if op.input[i][-2:] == ':0':
                     op_name = op.input[i][:-2]
                     if op_name in self._option.input_nodes \
                             or op_name in self._option.output_nodes:
                         op.input[i] = op_name
-            for i in xrange(len(op.output)):
+            for i in six.moves.range(len(op.output)):
                 if op.output[i][-2:] == ':0':
                     op_name = op.output[i][:-2]
                     if op_name in self._option.output_nodes:
@@ -432,7 +436,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
             if len(tf_op.inputs) == 1:
                 return len(tf_op.inputs[0].shape) == 0
             elif len(tf_op.inputs) == 2:
-                return len(tf_op.inputs[0].shape) == 0 and\
+                return len(tf_op.inputs[0].shape) == 0 and \
                        len(tf_op.inputs[1].shape) == 0
 
         if check_is_scalar(tf_op):
@@ -456,8 +460,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
                         EltwiseType.SUM, EltwiseType.PROD,
                         EltwiseType.MAX, EltwiseType.MIN]
 
-                if len(tf_op.inputs) > 1 and\
-                        len(tf_op.inputs[1].shape) == 0 and\
+                if len(tf_op.inputs) > 1 and \
+                        len(tf_op.inputs[1].shape) == 0 and \
                         tf_op.inputs[1].op.type == TFOpType.Const.name:
                     scalar = tf_op.inputs[1].eval().astype(np.float32)
                     value_arg = op.arg.add()
@@ -465,20 +469,20 @@ class TensorflowConverter(base_converter.ConverterInterface):
                     value_arg.f = scalar
                     self._skip_tensor.add(tf_op.inputs[1].name)
                     value_index_arg = op.arg.add()
-                    value_index_arg.name =\
+                    value_index_arg.name = \
                         MaceKeyword.mace_scalar_input_index_str
                     value_index_arg.i = 1
                     self._skip_tensor.add(tf_op.inputs[1].name)
                     del op.input[1]
-                elif len(tf_op.inputs[0].shape) == 0 and\
-                        tf_op.inputs[0].op.type == TFOpType.Const.name and\
+                elif len(tf_op.inputs[0].shape) == 0 and \
+                        tf_op.inputs[0].op.type == TFOpType.Const.name and \
                         is_commutative(type_arg.i):
                     scalar = tf_op.inputs[0].eval().astype(np.float32)
                     value_arg = op.arg.add()
                     value_arg.name = MaceKeyword.mace_scalar_input_str
                     value_arg.f = scalar
                     value_index_arg = op.arg.add()
-                    value_index_arg.name =\
+                    value_index_arg.name = \
                         MaceKeyword.mace_scalar_input_index_str
                     value_index_arg.i = 0
                     self._skip_tensor.add(tf_op.inputs[0].name)
@@ -503,7 +507,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
 
         type_arg = op.arg.add()
         type_arg.name = MaceKeyword.mace_activation_type_str
-        type_arg.s = self.activation_type[tf_op.type].name
+        type_arg.s = six.b(self.activation_type[tf_op.type].name)
 
         if tf_op.type == TFOpType.Relu6.name:
             limit_arg = op.arg.add()
@@ -520,7 +524,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
 
         is_training = tf_op.get_attr(tf_is_training_str)
         assert is_training is False, 'Only support batch normalization ' \
-            'with is_training False, but got %s' % is_training
+                                     'with is_training False, but got %s' % \
+                                     is_training
 
         gamma_value = tf_op.inputs[1].eval().astype(np.float32)
         beta_value = tf_op.inputs[2].eval().astype(np.float32)
@@ -531,8 +536,8 @@ class TensorflowConverter(base_converter.ConverterInterface):
         scale_name = self.get_scope(tf_op.name) + '/scale:0'
         offset_name = self.get_scope(tf_op.name) + '/offset:0'
         scale_value = (
-            (1.0 / np.vectorize(math.sqrt)(
-                var_value + epsilon_value)) * gamma_value)
+                (1.0 / np.vectorize(math.sqrt)(
+                    var_value + epsilon_value)) * gamma_value)
         offset_value = (-mean_value * scale_value) + beta_value
         self.add_tensor(scale_name, scale_value.shape, mace_pb2.DT_FLOAT,
                         scale_value)

mace/python/tools/memory_optimizer.py

@@ -94,7 +94,7 @@ class MemoryOptimizer(object):
         if output_type == mace_pb2.DT_UINT8:
             data_type_size = 1
         return MemoryBlock(mace_pb2.CPU_BUFFER,
-                           [reduce(operator.mul, output_shape, 1) *
+                           [six.moves.reduce(operator.mul, output_shape, 1) *
                             data_type_size])
 
     def mem_size(self, memory_block):
@@ -123,7 +123,9 @@ class MemoryOptimizer(object):
         for op in self.net_def.op:
             if not self.op_need_optimize_memory(op):
                 continue
-            origin_mem_size += reduce(operator.mul, op.output_shape[0].dims, 1)
+            origin_mem_size += six.moves.reduce(operator.mul,
+                                                op.output_shape[0].dims,
+                                                1)
         return origin_mem_size
 
     def get_total_optimized_mem_size(self):
@@ -170,8 +172,8 @@ class MemoryOptimizer(object):
                         output_type)
                     mem_id = -1
                     if len(self.idle_mem) > 0:
-                        best_mem_add_size = sys.maxint
-                        best_mem_waste_size = sys.maxint
+                        best_mem_add_size = six.MAXSIZE
+                        best_mem_waste_size = six.MAXSIZE
                         for mid in self.idle_mem:
                             old_mem_block = self.mem_block[mid]
                             if old_mem_block.mem_type != op_mem_block.mem_type:

mace/python/tools/model_saver.py

@@ -191,7 +191,7 @@ def save_model_to_proto(net_def, model_tag, output_dir):
     proto_file_path = output_dir + model_tag + '.pb'
     with open(proto_file_path, "wb") as f:
         f.write(net_def.SerializeToString())
-    with open(proto_file_path + '_txt', "wb") as f:
+    with open(proto_file_path + '_txt', "w") as f:
         f.write(str(net_def))
 
 

mace/python/tools/str2vec_maps.cc.jinja2

@@ -22,7 +22,7 @@ namespace mace {
 
 extern const std::map<std::string, std::vector<{{data_type}}>> {{variable_name}} =
 {
-  {% for key, value in maps.iteritems() %}
+  {% for key, value in maps.items() %}
   {
     "{{key}}",
     {