Merge branch 'quantize' into 'master'

Move quantize info into op's output

See merge request !790

mace/core/tensor.h

@@ -109,7 +109,9 @@ class Tensor {
         name_(""),
         is_weight_(is_weight),
         scale_(0.f),
-        zero_point_(0) {}
+        zero_point_(0),
+        minval_(0.f),
+        maxval_(0.f) {}
 
   Tensor(BufferBase *buffer, DataType dtype,
          bool is_weight = false)
@@ -120,7 +122,9 @@ class Tensor {
       name_(""),
       is_weight_(is_weight),
       scale_(0.f),
-      zero_point_(0) {}
+      zero_point_(0),
+      minval_(0.f),
+      maxval_(0.f) {}
 
   Tensor(const BufferSlice &buffer_slice,
          DataType dtype,
@@ -132,7 +136,9 @@ class Tensor {
         name_(""),
         is_weight_(is_weight),
         scale_(0.f),
-        zero_point_(0) {
+        zero_point_(0),
+        minval_(0.f),
+        maxval_(0.f) {
     buffer_ = &buffer_slice_;
   }
 
@@ -391,6 +397,15 @@ class Tensor {
     return zero_point_;
   }
 
+  // hexagon now uses min/max instead of scale and zero
+  inline float minval() const {
+    return minval_;
+  }
+
+  inline float maxval() const {
+    return maxval_;
+  }
+
   inline void SetScale(float scale) {
     scale_ = scale;
   }
@@ -403,6 +418,14 @@ class Tensor {
     is_weight_ = is_weight;
   }
 
+  inline void SetMinVal(float minval) {
+    minval_ = minval;
+  }
+
+  inline void SetMaxVal(float maxval) {
+    maxval_ = maxval;
+  }
+
  private:
   Allocator *allocator_;
   DataType dtype_;
@@ -416,6 +439,8 @@ class Tensor {
   bool is_weight_;
   float scale_;
   int32_t zero_point_;
+  float minval_;
+  float maxval_;
 
   MACE_DISABLE_COPY_AND_ASSIGN(Tensor);
 };

mace/core/workspace.cc

@@ -178,16 +178,19 @@ MaceStatus Workspace::LoadModelTensor(const NetDef &net_def,
     if (status != MaceStatus::MACE_SUCCESS) return status;
   }
 
-  if (device_type == DeviceType::CPU && net_def.has_quantize_info()) {
-    for (const auto
-          &activation_info: net_def.quantize_info().activation_info()) {
-      if (HasTensor(activation_info.tensor_name())) {
-        Tensor *tensor = GetTensor(activation_info.tensor_name());
-        tensor->SetScale(activation_info.scale());
-        tensor->SetZeroPoint(activation_info.zero_point());
-      } else {
-        LOG(WARNING) << "Quantize info exists for non-existed tensor: "
-            << activation_info.tensor_name();
+  if (device_type == DeviceType::CPU) {
+    for (const auto &op : net_def.op()) {
+      VLOG(2) << "Add quantize info for op: " << op.name();
+      MACE_CHECK(op.quantize_info().empty()
+                     || op.quantize_info().size() == op.output().size(),
+                 "quantize info size must be equal to output size or empty");
+      for (int i = 0; i < op.quantize_info().size(); ++i) {
+        auto &quantize_info = op.quantize_info(i);
+        Tensor *tensor = GetTensor(op.output(i));
+        tensor->SetScale(quantize_info.scale());
+        tensor->SetZeroPoint(quantize_info.zero_point());
+        tensor->SetMinVal(quantize_info.minval());
+        tensor->SetMaxVal(quantize_info.maxval());
       }
     }
   }
@@ -233,8 +236,7 @@ MaceStatus Workspace::CreateOutputTensorBuffer(const NetDef &net_def,
         std::unique_ptr<BufferBase> tensor_buf(
             new Buffer(GetCPUAllocator()));
         MACE_RETURN_IF_ERROR(tensor_buf->Allocate(
-            mem_block.x() * GetEnumTypeSize(dtype)
-                + MACE_EXTRA_BUFFER_PAD_SIZE));
+            mem_block.x() + MACE_EXTRA_BUFFER_PAD_SIZE));
         preallocated_allocator_.SetBuffer(mem_block.mem_id(),
                                           std::move(tensor_buf));
       } else if (mem_block.mem_type() == MemoryType::GPU_IMAGE) {

mace/kernels/conv_2d.h

@@ -879,6 +879,17 @@ struct Conv2dFunctor<DeviceType::CPU, uint8_t> : Conv2dFunctorBase {
     const index_t depth = input_channels * filter_h * filter_w;
     const index_t columns = batch * height * width;
 
+    VLOG(2) << "input scale/zero: " << input->scale() << ", "
+            << input->zero_point();
+    VLOG(2) << "filter scale/zero: " << filter->scale() << ", "
+            << filter->zero_point();
+    if (bias) {
+      VLOG(2) << "bias scale/zero: " << bias->scale() << ", "
+              << bias->zero_point();
+    }
+    VLOG(2) << "output scale/zero: " << output->scale() << ", "
+            << output->zero_point();
+
     MACE_CHECK(filter->dim(0) == channels, filter->dim(0), " != ", channels);
     MACE_CHECK(filter->dim(3) == input_channels, filter->dim(3), " != ",
                input_channels);

mace/proto/mace.proto

@@ -59,6 +59,13 @@ message OutputShape {
   repeated int64 dims = 1;
 }
 
+message QuantizeActivationInfo {
+  optional float scale = 1;
+  optional int32 zero_point = 2;
+  optional float minval = 3;  // hexagon uses min/max
+  optional float maxval = 4;
+}
+
 message OperatorDef {
   repeated string input = 1;
   repeated string output = 2;
@@ -67,6 +74,7 @@ message OperatorDef {
   repeated Argument arg = 5;
   repeated OutputShape output_shape = 6;
   repeated DataType output_type = 7;
+  repeated QuantizeActivationInfo quantize_info = 8;
 
   repeated int32 mem_id = 10;
 
@@ -106,23 +114,12 @@ message OutputInfo {
   optional DataType data_type = 5 [default = DT_FLOAT];
 }
 
-message QuantizeActivationInfo {
-  optional string tensor_name = 1;
-  optional float scale = 2;
-  optional int32 zero_point = 3;
-}
-
-message QuantizeInfo {
-  repeated QuantizeActivationInfo activation_info = 1;
-}
-
 message NetDef {
   optional string name = 1;
   repeated OperatorDef op = 2;
   optional string version = 3;
   repeated Argument arg = 4;
   repeated ConstTensor tensors = 5;
-  optional QuantizeInfo quantize_info = 6;
 
   // for mem optimization
   optional MemoryArena mem_arena = 10;

mace/python/tools/converter.py

@@ -64,6 +64,10 @@ def parse_int_array_from_str(ints_str):
     return [int(int_str) for int_str in ints_str.split(',')]
 
 
+def parse_float_array_from_str(ints_str):
+    return [float(int_str) for int_str in ints_str.split(',')]
+
+
 def main(unused_args):
     if not os.path.isfile(FLAGS.model_file):
         print("Input graph file '" + FLAGS.model_file + "' does not exist!")
@@ -105,12 +109,18 @@ def main(unused_args):
 
     input_node_names = FLAGS.input_node.split(',')
     input_node_shapes = FLAGS.input_shape.split(':')
+    if FLAGS.input_range:
+        input_node_ranges = FLAGS.input_range.split(':')
+    else:
+        input_node_ranges = []
     if len(input_node_names) != len(input_node_shapes):
         raise Exception('input node count and shape count do not match.')
     for i in xrange(len(input_node_names)):
         input_node = cvt.NodeInfo()
         input_node.name = input_node_names[i]
         input_node.shape = parse_int_array_from_str(input_node_shapes[i])
+        if len(input_node_ranges) > i:
+            input_node.range = parse_float_array_from_str(input_node_ranges[i])
         option.add_input_node(input_node)
 
     output_node_names = FLAGS.output_node.split(',')
@@ -276,6 +286,8 @@ def parse_args():
         "--dsp_mode", type=int, default=0, help="dsp run mode, defalut=0")
     parser.add_argument(
         "--input_shape", type=str, default="", help="input shape.")
+    parser.add_argument(
+        "--input_range", type=str, default="", help="input range.")
     parser.add_argument(
         "--platform", type=str, default="tensorflow", help="tensorflow/caffe")
     parser.add_argument(

mace/python/tools/converter_tool/base_converter.py

@@ -205,6 +205,8 @@ class TransformerRule(Enum):
     QUANTIZE_WEIGHTS = 25
     TRANSFORM_LSTMCELL_ZEROSTATE = 26
     TRANSFORM_BASIC_LSTMCELL = 27
+    TRANSFORM_FAKE_QUANTIZE = 28
+    CHECK_QUANTIZE_INFO = 29
 
 
 class ConverterInterface(object):
@@ -220,6 +222,7 @@ class NodeInfo(object):
     def __init__(self):
         self._name = None
         self._shape = []
+        self._range = [-1.0, 1.0]
 
     @property
     def name(self):
@@ -229,6 +232,10 @@ class NodeInfo(object):
     def shape(self):
         return self._shape
 
+    @property
+    def range(self):
+        return self._range
+
     @name.setter
     def name(self, name):
         self._name = name
@@ -237,6 +244,10 @@ class NodeInfo(object):
     def shape(self, shape):
         self._shape = shape
 
+    @range.setter
+    def range(self, range):
+        self._range = range
+
     def __str__(self):
         return '%s %s' % (self._name, str(self._shape))
 
@@ -341,6 +352,7 @@ class ConverterOption(object):
         else:
             self._transformer_option = [
                 # Model structure related transformation
+                TransformerRule.TRANSFORM_FAKE_QUANTIZE,
                 TransformerRule.REMOVE_IDENTITY_OP,
                 TransformerRule.TRANSFORM_GLOBAL_POOLING,
                 TransformerRule.TRANSFORM_LSTMCELL_ZEROSTATE,
@@ -370,15 +382,17 @@ class ConverterOption(object):
                 # Transform finalization
                 TransformerRule.ADD_MACE_INPUT_AND_OUTPUT_NODES,
                 # for quantization entropy calibration use
-                TransformerRule.ADD_QUANTIZE_TENSOR_RANGE,
                 TransformerRule.SORT_BY_EXECUTION,
+                # Need to be put after SORT_BY_EXECUTION
+                TransformerRule.ADD_QUANTIZE_TENSOR_RANGE,
             ]
             if self._quantize:
-                self._transformer_option = self._transformer_option[:-1] + [
+                self._transformer_option = self._transformer_option + [
+                    # need to be put after ADD_QUANTIZE_TENSOR_RANGE
                     TransformerRule.QUANTIZE_NODES,
-                    TransformerRule.ADD_QUANTIZE_TENSOR_RANGE,
                     TransformerRule.QUANTIZE_WEIGHTS,
                     TransformerRule.SORT_BY_EXECUTION,
+                    TransformerRule.CHECK_QUANTIZE_INFO,
                 ]
 
 

mace/python/tools/converter_tool/tensorflow_converter.py

@@ -104,6 +104,7 @@ TFSupportedOps = [
     'Cast',
     'ArgMax',
     'Split',
+    'FakeQuantWithMinMaxVars',
 ]
 
 TFOpType = Enum('TFOpType', [(op, op) for op in TFSupportedOps], type=str)
@@ -209,6 +210,7 @@ class TensorflowConverter(base_converter.ConverterInterface):
             TFOpType.Cast.name: self.convert_cast,
             TFOpType.ArgMax.name: self.convert_argmax,
             TFOpType.Split.name: self.convert_split,
+            TFOpType.FakeQuantWithMinMaxVars.name: self.convert_fake_quantize,
         }
         self._option = option
         self._mace_net_def = mace_pb2.NetDef()
@@ -874,3 +876,21 @@ class TensorflowConverter(base_converter.ConverterInterface):
         num_split_arg.i = tf_op.get_attr('num_split')
 
         self._skip_tensor.add(tf_op.inputs[0].name)
+
+    def convert_fake_quantize(self, tf_op):
+        op = self.convert_general_op(tf_op)
+        min_arg = op.arg.add()
+        min_arg.name = 'min'
+        min_arg.f = tf_op.inputs[1].eval()
+        max_arg = op.arg.add()
+        max_arg.name = 'max'
+        max_arg.f = tf_op.inputs[2].eval()
+        narrow_range_arg = op.arg.add()
+        narrow_range_arg.name = 'narrow_range'
+        narrow_range_arg.i = int(tf_op.get_attr('narrow_range'))
+        num_bits_arg = op.arg.add()
+        num_bits_arg.name = 'num_bits'
+        num_bits_arg.i = int(tf_op.get_attr('num_bits'))
+
+        self._skip_tensor.add(tf_op.inputs[1].name)
+        self._skip_tensor.add(tf_op.inputs[2].name)

mace/python/tools/converter_tool/transformer.py

@@ -47,6 +47,8 @@ class Transformer(base_converter.ConverterInterface):
         # Dependencies
         # (TRANSFORM_MATMUL_TO_FC, TRANSFORM_GLOBAL_CONV_TO_FC) -> RESHAPE_FC_WEIGHT  # noqa
         self._registered_transformers = {
+            TransformerRule.TRANSFORM_FAKE_QUANTIZE:
+                self.transform_fake_quantize,
             TransformerRule.REMOVE_IDENTITY_OP: self.remove_identity_op,
             TransformerRule.TRANSFORM_GLOBAL_POOLING:
                 self.transform_global_pooling,
@@ -91,6 +93,8 @@ class Transformer(base_converter.ConverterInterface):
             TransformerRule.ADD_MACE_INPUT_AND_OUTPUT_NODES:
                 self.add_mace_input_and_output_nodes,
             TransformerRule.SORT_BY_EXECUTION: self.sort_by_execution,
+            TransformerRule.CHECK_QUANTIZE_INFO:
+                self.check_quantize_info,
         }
 
         self._option = option
@@ -774,16 +778,22 @@ class Transformer(base_converter.ConverterInterface):
     def transform_add_to_biasadd(self):
         net = self._model
         for op in net.op:
-            if op.type == 'Add' \
-                    and len(op.input) == 2 \
-                    and op.input[1] in self._consts \
-                    and len(self._consts[op.input[1]].dims) == 1:
+            if (op.type == 'Eltwise'
+                    and ConverterUtil.get_arg(op, MaceKeyword.mace_element_type_str).i == EltwiseType.SUM.value  # noqa
+                    and len(op.input) == 2
+                    and op.input[1] in self._consts
+                    and len(self._consts[op.input[1]].dims) == 1):
                 print("Transform add to biasadd: %s(%s)" % (op.name, op.type))
                 op.type = MaceOp.BiasAdd.name
                 return True
 
         return False
 
+    def replace_quantize_info(self, op, replace_op):
+        if len(replace_op.quantize_info) > 0:
+            del op.quantize_info[:]
+            op.quantize_info.extend(replace_op.quantize_info)
+
     def fold_biasadd(self):
         net = self._model
         for op in net.op:
@@ -799,6 +809,7 @@ class Transformer(base_converter.ConverterInterface):
                 if consumer_op.type == MaceOp.BiasAdd.name:
                     print("Fold biasadd: %s(%s)" % (op.name, op.type))
                     op.input.append(consumer_op.input[1])
+                    self.replace_quantize_info(op, consumer_op)
                     self.safe_remove_node(consumer_op, op)
                     return True
 
@@ -886,6 +897,7 @@ class Transformer(base_converter.ConverterInterface):
                                 or arg.name == MaceKeyword.mace_activation_max_limit_str:  # noqa
                             op.arg.extend([arg])
 
+                    self.replace_quantize_info(op, consumer_op)
                     self.safe_remove_node(consumer_op, op)
                     return True
 
@@ -1163,7 +1175,8 @@ class Transformer(base_converter.ConverterInterface):
         transposed_filter = set()
         transposed_deconv_filter = set()
 
-        if self._option.quantize:
+        if self._option.quantize and \
+                self._option.device == DeviceType.CPU.value:
             print("Transpose filters to OHWI")
             if filter_format == FilterFormat.HWIO:
                 transpose_order = [3, 0, 1, 2]
@@ -1601,6 +1614,9 @@ class Transformer(base_converter.ConverterInterface):
         return False
 
     def quantize_nodes(self):
+        if not self._option.quantize:
+            return False
+
         print("Add mace quantize and dequantize nodes")
 
         for op in self._model.op:
@@ -1647,28 +1663,13 @@ class Transformer(base_converter.ConverterInterface):
 
         self._input_output_added = True
 
-    def add_quantize_tensor_range(self):
-        print("Add quantize tensor range")
-        net = self._model
-        range_file = self._option.quantize_range_file
-        if not range_file:
-            return
-
-        with open(range_file) as f:
-            for line in f:
-                tensor_name, minmax = line.split("@@")
-                min_val, max_val = [float(i) for i in
-                                    minmax.strip().split(",")]
-                scale, zero = quantize_util.adjust_range(min_val, max_val,
-                                                         non_zero=False)
-                activation_info = net.quantize_info.activation_info.add()
-                activation_info.tensor_name = tensor_name
-                activation_info.scale = scale
-                activation_info.zero_point = zero
-                self._quantize_activation_info[tensor_name] = activation_info
+        return False
 
     def quantize_tensor(self, tensor):
         """Assume biasadd has been already folded with convolution and fc"""
+        if not self._option.quantize:
+            return False
+
         if tensor.data_type == mace_pb2.DT_FLOAT:
             ops = self._consumers.get(tensor.name, None)
             if len(ops) == 1 and ops[0].type in [MaceOp.Conv2D.name,
@@ -1698,8 +1699,131 @@ class Transformer(base_converter.ConverterInterface):
             tensor.zero_point = quantized_tensor.zero
             self._quantized_tensor.update([tensor.name])
 
+        return False
+
     def quantize_weights(self):
         print("Quantize weights")
         net = self._model
         for tensor in net.tensors:
             self.quantize_tensor(tensor)
+
+        return False
+
+    def add_quantize_info(self, op, minval, maxval):
+        scale, zero = quantize_util.adjust_range(minval, maxval,
+                                                 non_zero=False)
+        quantize_info = op.quantize_info.add()
+        quantize_info.minval = minval
+        quantize_info.maxval = maxval
+        quantize_info.scale = scale
+        quantize_info.zero_point = zero
+
+        return quantize_info
+
+    def transform_fake_quantize(self):
+        if not self._option.quantize:
+            return False
+
+        # Quantize info from fixpoint fine tune
+        print("Transform fake quantize")
+        range_file = self._option.quantize_range_file
+        if range_file:
+            return
+
+        net = self._model
+        for op in net.op:
+            if op.type == 'FakeQuantWithMinMaxVars':
+                producer_op = self._producer[op.input[0]]
+                minval = ConverterUtil.get_arg(op, 'min').f
+                maxval = ConverterUtil.get_arg(op, 'max').f
+                quantize_info = \
+                    self.add_quantize_info(producer_op, minval, maxval)
+                self._quantize_activation_info[op.input[0]] = quantize_info
+                op.type = MaceOp.Identity.name
+
+        return False
+
+    def add_quantize_tensor_range(self):
+        if not self._option.quantize:
+            return False
+
+        # Quantize info from range statistics
+        print("Add quantize tensor range")
+        range_file = self._option.quantize_range_file
+        if range_file:
+            with open(range_file) as f:
+                for line in f:
+                    tensor_name, minmax = line.split("@@")
+                    min_val, max_val = [float(i) for i in
+                                        minmax.strip().split(",")]
+                    scale, zero = quantize_util.adjust_range(min_val, max_val,
+                                                             non_zero=False)
+                    activation_info = mace_pb2.QuantizeActivationInfo()
+                    activation_info.minval = min_val
+                    activation_info.maxval = max_val
+                    activation_info.scale = scale
+                    activation_info.zero_point = zero
+                    self._quantize_activation_info[tensor_name] = activation_info  # noqa
+
+            for op in self._model.op:
+                if op.name.find(MaceKeyword.mace_output_node_name) >= 0:
+                    continue
+                for output in op.output:
+                    mace_check(output in self._quantize_activation_info,
+                               "%s does not have quantize activation info"
+                               % op)
+                    op.quantize_info.extend([
+                        self._quantize_activation_info[output]
+                        for output in op.output])
+
+        print ("Add default quantize info for ops like Pooling, Softmax")
+        for op in self._model.op:
+            if op.type in [MaceOp.Pooling.name,
+                           MaceOp.Squeeze.name,
+                           MaceOp.Concat.name,
+                           MaceOp.ResizeBilinear.name,
+                           MaceOp.BatchToSpaceND.name,
+                           MaceOp.SpaceToBatchND.name]:
+                del op.quantize_info[:]
+                producer_op = self._producer[op.input[0]]
+                quantize_info = op.quantize_info.add()
+                quantize_info.minval = producer_op.quantize_info[0].minval
+                quantize_info.maxval = producer_op.quantize_info[0].maxval
+                quantize_info.scale = producer_op.quantize_info[0].scale
+                quantize_info.zero_point = \
+                    producer_op.quantize_info[0].zero_point
+                self._quantize_activation_info[op.output[0]] = quantize_info
+            elif op.type == MaceOp.Softmax.name:
+                del op.quantize_info[:]
+                quantize_info = \
+                    self.add_quantize_info(op, 0.0, 1.0)
+                self._quantize_activation_info[op.output[0]] = quantize_info
+
+        print ("Add default quantize info for input")
+        for input_node in self._option.input_nodes.values():
+            if input_node.name not in self._quantize_activation_info:
+                print("Input range %s: %s" % (input_node.name,
+                                              str(input_node.range)))
+                scale, zero = quantize_util.adjust_range(input_node.range[0],
+                                                         input_node.range[1],
+                                                         non_zero=False)
+                quantize_info = mace_pb2.QuantizeActivationInfo()
+                quantize_info.minval = input_node.range[0]
+                quantize_info.maxval = input_node.range[1]
+                quantize_info.scale = scale
+                quantize_info.zero_point = zero
+                self._quantize_activation_info[input_node.name] = quantize_info
+
+        return False
+
+    def check_quantize_info(self):
+        if not self._option.quantize:
+            return False
+
+        for op in self._model.op:
+            if (op.name.find(MaceKeyword.mace_input_node_name) == -1
+                and op.name.find(MaceKeyword.mace_output_node_name) == -1
+                and op.type != MaceOp.Quantize.name
+                and op.type != MaceOp.Dequantize.name):  # noqa
+                mace_check(len(op.output) == len(op.quantize_info),
+                           "missing quantize info: %s" % op)

mace/python/tools/memory_optimizer.py

@@ -80,9 +80,13 @@ class MemoryOptimizer(object):
     def op_need_optimize_memory(self, op):
         return True
 
-    def get_op_mem_block(self, op_type, output_shape):
+    def get_op_mem_block(self, op_type, output_shape, output_type):
+        data_type_size = 4
+        if output_type == mace_pb2.DT_UINT8:
+            data_type_size = 1
         return MemoryBlock(mace_pb2.CPU_BUFFER,
-                           [reduce(operator.mul, output_shape, 1)])
+                           [reduce(operator.mul, output_shape, 1) *
+                            data_type_size])
 
     def mem_size(self, memory_block):
         return memory_block.block[0]
@@ -143,9 +147,13 @@ class MemoryOptimizer(object):
                     # make these ops reuse memory of input tensor
                     mem_id = self.op_mem.get(op.input[0], -1)
                 else:
+                    output_type = mace_pb2.DT_FLOAT
+                    if len(op.output_type) > i:
+                        output_type = op.output_type[i]
                     op_mem_block = self.get_op_mem_block(
                         op.type,
-                        op.output_shape[i].dims)
+                        op.output_shape[i].dims,
+                        output_type)
                     mem_id = -1
                     if len(self.idle_mem) > 0:
                         best_mem_add_size = sys.maxint
@@ -221,7 +229,7 @@ class GPUMemoryOptimizer(MemoryOptimizer):
                     return False
         return op.type != 'ImageToBuffer'
 
-    def get_op_mem_block(self, op_type, output_shape):
+    def get_op_mem_block(self, op_type, output_shape, output_type):
         if op_type == 'WinogradTransform' or op_type == 'MatMul':
             buffer_shape = list(output_shape) + [1]
             mem_block = MemoryBlock(

mace/python/tools/model.jinja2

@@ -138,20 +138,6 @@ void CreateMemoryArena(mace::MemoryArena *mem_arena) {
 }
 {% endif %}
 
-void AddQuantizeInfo(NetDef *net_def) {
-  MACE_LATENCY_LOGGER(1, "Add quantize info");
-  (void) net_def;
-
-  {% for i in range(net.quantize_info.activation_info|length) %}
-  mace::QuantizeActivationInfo *activation_info{{i}} =
-      net_def->mutable_quantize_info()->add_activation_info();
-  activation_info{{i}}->set_tensor_name("{{net.quantize_info.activation_info[i].tensor_name}}");
-  activation_info{{i}}->set_scale({{net.quantize_info.activation_info[i].scale}});
-  activation_info{{i}}->set_zero_point({{net.quantize_info.activation_info[i].zero_point}});
-
-  {% endfor %}
-}
-
 }  // namespace
 
 namespace {{tag}} {
@@ -179,8 +165,6 @@ const std::shared_ptr<NetDef> CreateNet() {
   CreateOutputInfo(net_def.get());
   {% endif %}
 
-  AddQuantizeInfo(net_def.get());
-
   return net_def;
 }
 

mace/python/tools/operator.jinja2

@@ -122,6 +122,16 @@ void CreateOperator{{i}}(mace::OperatorDef *op) {
           {{ net.op[i].node_id }},
           { {{ net.op[i].mem_id | join(', ') }} });
 
+
+  op->mutable_quantize_info()->Reserve({{ net.op[i].quantize_info | length }});
+  {% for j in range(net.op[i].quantize_info|length) %}
+    auto quantize_info{{j}} = op->add_quantize_info();
+    quantize_info{{j}}->set_scale({{ net.op[i].quantize_info[j].scale }});
+    quantize_info{{j}}->set_zero_point({{ net.op[i].quantize_info[j].zero_point }});
+    quantize_info{{j}}->set_minval({{ net.op[i].quantize_info[j].minval }});
+    quantize_info{{j}}->set_maxval({{ net.op[i].quantize_info[j].maxval }});
+  {% endfor %}
+
   {% if runtime == 'dsp' %}
     op->set_padding({{ net.op[i].padding }});
     {% if net.op[i].node_input | length > 0 %}

tools/converter.py

@@ -760,6 +760,7 @@ def convert_model(configs):
             runtime,
             model_name,
             ":".join(subgraphs[0][YAMLKeyword.input_shapes]),
+            ":".join(subgraphs[0][YAMLKeyword.input_ranges]),
             model_config[YAMLKeyword.nnlib_graph_mode],
             embed_model_data,
             model_config[YAMLKeyword.winograd],

tools/sh_commands.py

@@ -551,6 +551,7 @@ def gen_model_code(model_codegen_dir,
                    runtime,
                    model_tag,
                    input_shapes,
+                   input_ranges,
                    dsp_mode,
                    embed_model_data,
                    winograd,
@@ -579,6 +580,7 @@ def gen_model_code(model_codegen_dir,
               "--template=%s" % "mace/python/tools",
               "--model_tag=%s" % model_tag,
               "--input_shape=%s" % input_shapes,
+              "--input_range=%s" % input_ranges,
               "--dsp_mode=%s" % dsp_mode,
               "--embed_model_data=%s" % embed_model_data,
               "--winograd=%s" % winograd,