Merge branch 'activation' into 'master'

Add activations support

See merge request !3

python/tools/tf_converter_lib.py

@@ -216,6 +216,9 @@ class TFConverter(object):
         and self.tf_graph[final_op.name][0].type == 'Relu':
       relu_op = self.tf_graph[final_op.name][0]
       op_def.type = "FusedConv2D"
+      fused_relu_arg = op_def.arg.add()
+      fused_relu_arg.name = 'activation'
+      fused_relu_arg.s = "RELU"
       final_op = relu_op
       self.resolved_ops[relu_op.name] = 1
 
@@ -273,8 +276,8 @@ class TFConverter(object):
       relu_op = self.tf_graph[op.name][0]
       final_op = relu_op
       fused_relu_arg = op_def.arg.add()
-      fused_relu_arg.name = 'fused_relu'
-      fused_relu_arg.i = 1
+      fused_relu_arg.name = 'activation'
+      fused_relu_arg.s = "RELU"
       self.resolved_ops[relu_op.name] = 1
 
     op_def.output.extend([final_op.outputs[0].name])
@@ -362,16 +365,34 @@ class TFConverter(object):
     data_format_arg.s = 'NHWC'
     self.resolved_ops[op.name] = 1
 
+  def convert_relu(self, op):
+    op_def = self.net_def.op.add()
+    arg = op_def.arg.add()
+    arg.name = 'T'
+    arg.i = self.dt
+    op_def.name = op.name
+    op_def.type = 'Activation'
+    activation_arg = op_def.arg.add()
+    activation_arg.name = 'activation'
+    activation_arg.s = "RELU"
+    op_def.input.extend([input.name for input in op.inputs])
+    op_def.output.extend([output.name for output in op.outputs])
+    self.add_output_shape(op.outputs, op_def)
+    self.resolved_ops[op.name] = 1
+
   def convert_relu6(self, op):
     op_def = self.net_def.op.add()
     arg = op_def.arg.add()
     arg.name = 'T'
     arg.i = self.dt
     op_def.name = op.name
-    op_def.type = 'Relu'
+    op_def.type = 'Activation'
     op_def.input.extend([input.name for input in op.inputs])
     op_def.output.extend([output.name for output in op.outputs])
     self.add_output_shape(op.outputs, op_def)
+    activation_arg = op_def.arg.add()
+    activation_arg.name = 'activation'
+    activation_arg.s = "RELUX"
     max_limit_arg = op_def.arg.add()
     max_limit_arg.name = 'max_limit'
     max_limit_arg.f = 6
@@ -531,6 +552,9 @@ class TFConverter(object):
         and self.tf_graph[final_op.name][0].type == 'Relu':
       relu_op = self.tf_graph[final_op.name][0]
       op_def.type = "FusedConv2D"
+      fused_relu_arg = op_def.arg.add()
+      fused_relu_arg.name = 'activation'
+      fused_relu_arg.s = "RELU"
       final_op = relu_op
       self.resolved_ops[relu_op.name] = 1
 
@@ -602,6 +626,8 @@ class TFConverter(object):
         self.convert_batchnorm(op)
       elif op.type == 'AvgPool' or op.type == 'MaxPool':
         self.convert_pooling(op)
+      elif op.type == 'Relu':
+        self.convert_relu(op)
       elif op.type == 'Relu6':
         self.convert_relu6(op)
       elif op.type == 'Add':
@@ -618,8 +644,8 @@ class TFConverter(object):
         self.convert_space_to_batch(op, True)
       elif self.is_softmax(op):
         self.convert_softmax(op)
-      elif op.type in ['Relu']:
-        self.convert_normal_op(op)
+      #elif op.type in ['']:
+      #  self.convert_normal_op(op)
       else:
         raise Exception('Unknown Op: %s, type: %s' % (op.name, op.type))