Merge branch 'feature_wuch' into 'master'

revert dsp convert tool to support both nn_graph & nnlib

See merge request !158

mace/dsp/test/quantized_add_test.cc

@@ -17,6 +17,7 @@ static NetDef BuildNetDef() {
   input_op->set_type("INPUT");
   input_op->set_node_id(0);
   input_op->set_padding(0);
+  input_op->add_out_max_byte_size(1000);
 
   // add op
   OperatorDef *add_op = net.add_op();
@@ -58,6 +59,10 @@ static NetDef BuildNetDef() {
   input_node_input->set_node_id(16);
   input_node_input->set_output_port(0);
 
+  add_op->add_out_max_byte_size(1000);
+  add_op->add_out_max_byte_size(1000);
+  add_op->add_out_max_byte_size(1000);
+
   // output op
   OperatorDef *output_op = net.add_op();
   output_op->set_name("__output__");

mace/dsp/test/quantized_maxpool_test.cc

@@ -24,6 +24,7 @@ static NetDef BuildNetDef(const vector<index_t> &input_shape,
   input_op->set_type("INPUT");
   input_op->set_node_id(0);
   input_op->set_padding(0);
+  input_op->add_out_max_byte_size(1000);
 
   // maxpool op
   OperatorDef *maxpool_op = net.add_op();
@@ -57,6 +58,9 @@ static NetDef BuildNetDef(const vector<index_t> &input_shape,
   input_node_input = maxpool_op->add_node_input();
   input_node_input->set_node_id(13);
   input_node_input->set_output_port(0);
+  maxpool_op->add_out_max_byte_size(1000);
+  maxpool_op->add_out_max_byte_size(1000);
+  maxpool_op->add_out_max_byte_size(1000);
 
   // output op
   OperatorDef *output_op = net.add_op();

mace/dsp/test/quantized_relu_test.cc

@@ -16,6 +16,7 @@ static NetDef BuildNetDef() {
   input_op->set_type("INPUT");
   input_op->set_node_id(0);
   input_op->set_padding(0);
+  input_op->add_out_max_byte_size(1000);
 
   // relu op
   OperatorDef *relu_op = net.add_op();
@@ -38,6 +39,9 @@ static NetDef BuildNetDef() {
   input_node_input = relu_op->add_node_input();
   input_node_input->set_node_id(11);
   input_node_input->set_output_port(0);
+  relu_op->add_out_max_byte_size(1000);
+  relu_op->add_out_max_byte_size(1000);
+  relu_op->add_out_max_byte_size(1000);
 
   // output op
   OperatorDef *output_op = net.add_op();

mace/dsp/test/quantized_resize_bilinear_test.cc

@@ -18,6 +18,7 @@ static NetDef BuildNetDef() {
   input_op->set_type("INPUT");
   input_op->set_node_id(0);
   input_op->set_padding(0);
+  input_op->add_out_max_byte_size(1200);
 
   // relu op
   OperatorDef *resize_bilinear_op = net.add_op();
@@ -45,6 +46,9 @@ static NetDef BuildNetDef() {
   input_node_input = resize_bilinear_op->add_node_input();
   input_node_input->set_node_id(12);
   input_node_input->set_output_port(0);
+  resize_bilinear_op->add_out_max_byte_size(1200);
+  resize_bilinear_op->add_out_max_byte_size(1000);
+  resize_bilinear_op->add_out_max_byte_size(1000);
 
   // output op
   OperatorDef *output_op = net.add_op();

mace/dsp/test/supernode_test.cc

@@ -17,6 +17,7 @@ static NetDef BuildNetDef() {
   input_op->set_type("INPUT");
   input_op->set_node_id(0);
   input_op->set_padding(0);
+  input_op->add_out_max_byte_size(1000);
 
   // add op
   OperatorDef *supernode_op = net.add_op();
@@ -76,6 +77,10 @@ static NetDef BuildNetDef() {
   input_node_input->set_node_id(20);
   input_node_input->set_output_port(0);
 
+  supernode_op->add_out_max_byte_size(1000);
+  supernode_op->add_out_max_byte_size(1000);
+  supernode_op->add_out_max_byte_size(1000);
+
   // output op
   OperatorDef *output_op = net.add_op();
   output_op->set_name("__output__");

mace/proto/mace.proto

@@ -93,6 +93,7 @@ message OperatorDef {
   optional uint32 op_id = 101;
   optional uint32 padding = 102;
   repeated NodeInput node_input = 103;
+  repeated int32 out_max_byte_size = 104; // only support 32-bit len
 }
 
 // for memory optimization

mace/python/tools/tf_dsp_converter_lib.py

@@ -132,6 +132,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
       op_def.input.append(input_tensor.name)
       op_def.input.extend([t.name for t in s2b_op.inputs[1:]])
       op_def.input.extend([min_tensor.name, max_tensor.name])
+      op_def.out_max_byte_size.extend([max_elem_size(out) for out in quantize_op.outputs])
       convert_op_outputs(op_def, quantize_op)
     elif has_padding_and_strides(first_op):
       op_def.padding = padding_mode[first_op.get_attr('padding')]
@@ -143,6 +144,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
       strides = first_op.get_attr('strides')
       strides_tensor = add_shape_const_node(net_def, first_op, strides, 'strides')
       op_def.input.extend([strides_tensor])
+      op_def.out_max_byte_size.extend([max_elem_size(out) for out in first_op.outputs])
       convert_op_outputs(op_def, first_op)
     elif is_node_flatten_reshape(first_op):
       op_def.type = 'Flatten'
@@ -150,6 +152,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
       convert_op_outputs(op_def, first_op)
     elif dsp_ops.has_op(first_op.type):
       op_def.input.extend([t.name for t in first_op.inputs])
+      op_def.out_max_byte_size.extend([max_elem_size(out) for out in first_op.outputs])
       convert_op_outputs(op_def, first_op)
     else:
       raise Exception('Unsupported op: ', first_op)
@@ -201,9 +204,11 @@ def reverse_batch_to_space_and_biasadd(net_def):
             new_biasadd_op.input[0] = get_tensor_name_from_op(conv_requantize_op.name, 0)
             new_biasadd_op.input[2] = get_tensor_name_from_op(conv_requantize_op.name, 1)
             new_biasadd_op.input[3] = get_tensor_name_from_op(conv_requantize_op.name, 2)
+            new_biasadd_op.out_max_byte_size[0] = conv_requantize_op.out_max_byte_size[0] * 4
 
             new_biasadd_requantize_op = mace_pb2.OperatorDef()
             new_biasadd_requantize_op.CopyFrom(biasadd_requantize_op)
+            new_biasadd_requantize_op.out_max_byte_size[0] = new_biasadd_op.out_max_byte_size[0] / 4
 
             new_b2s_op = mace_pb2.OperatorDef()
             new_b2s_op.CopyFrom(b2s_op)
@@ -320,6 +325,7 @@ def strip_input_quantize_and_output_dequantize(net_def, input_node, output_node)
         new_input_op.name = input_op.name
         new_input_op.type = input_op.type
         new_input_op.padding = input_op.padding
+        new_input_op.out_max_byte_size.extend([input_op.out_max_byte_size[0]/4, 4, 4])
         new_ops.append(new_input_op)
         new_input_op.output_shape.extend([input_op.output_shape[0],
                                           minf_op.output_shape[0],