Fix quantize

mace/python/tools/converter_tool/transformer.py

@@ -1563,6 +1563,7 @@ class Transformer(base_converter.ConverterInterface):
             else:
                 non_zero = self._option.device == DeviceType.CPU.value
                 quantized_tensor = quantize_util.quantize(tensor.float_data,
+                                                          self._option.device,
                                                           non_zero)
                 tensor.data_type = mace_pb2.DT_UINT8
 
@@ -1587,7 +1588,8 @@ class Transformer(base_converter.ConverterInterface):
 
     def add_quantize_info(self, op, minval, maxval):
         scale, zero, minval, maxval = \
-            quantize_util.adjust_range(minval, maxval, non_zero=False)
+            quantize_util.adjust_range(minval, maxval, self._option.device,
+                                       non_zero=False)
         quantize_info = op.quantize_info.add()
         quantize_info.minval = minval
         quantize_info.maxval = maxval
@@ -1687,8 +1689,9 @@ class Transformer(base_converter.ConverterInterface):
                     min_val, max_val = [float(i) for i in
                                         minmax.strip().split(",")]
                     scale, zero, min_val, max_val = \
-                        quantize_util.adjust_range(
-                            min_val, max_val, non_zero=False)
+                        quantize_util.adjust_range(min_val, max_val,
+                                                   self._option.device,
+                                                   non_zero=False)
                     activation_info = mace_pb2.QuantizeActivationInfo()
                     activation_info.minval = min_val
                     activation_info.maxval = max_val
@@ -1703,9 +1706,8 @@ class Transformer(base_converter.ConverterInterface):
                     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])
+                    op.quantize_info.append(
+                        self._quantize_activation_info[output])
 
         if not self._option.quantize:
             return False
@@ -1719,6 +1721,7 @@ class Transformer(base_converter.ConverterInterface):
                 scale, zero, minval, maxval = \
                     quantize_util.adjust_range(input_node.range[0],
                                                input_node.range[1],
+                                               self._option.device,
                                                non_zero=False)
                 quantize_info = \
                     mace_pb2.QuantizeActivationInfo()
@@ -1995,7 +1998,7 @@ class Transformer(base_converter.ConverterInterface):
                 if input_tensor in self._consts:
                     const_tensor = self._consts[input_tensor]
                     quantized_tensor = quantize_util.quantize(
-                        const_tensor.float_data, non_zero)
+                        const_tensor.float_data, self._option.device, non_zero)
                     del const_tensor.float_data[:]
                     const_tensor.int32_data.extend(quantized_tensor.data)
                     const_tensor.data_type = mace_pb2.DT_UINT8

mace/python/tools/quantization/quantize_util.py

 import numpy as np
 import math
 
+from mace.python.tools.converter_tool.base_converter import DeviceType
+
 
 class QuantizedData(object):
     def __init__(self):
@@ -51,7 +53,10 @@ class QuantizedData(object):
         self._maxval = maxval
 
 
-def adjust_range(in_min, in_max, non_zero):
+def adjust_range(in_min, in_max, device, non_zero):
+    if device in [DeviceType.HEXAGON.value, DeviceType.HTA.value]:
+        return adjust_range_for_hexagon(in_min, in_max)
+
     out_max = max(0.0, in_max)
     out_min = min(0.0, in_min)
     if non_zero:
@@ -61,12 +66,33 @@ def adjust_range(in_min, in_max, non_zero):
     if out_min < -eps and out_max > eps:
         zero = -out_min / scale
         zero_int = int(round(zero))
-        if abs(zero - zero_int) > eps:
-            if zero < zero_int or non_zero:
-                zero_int = int(math.ceil(zero))
-                scale = out_max / (255.0 - zero_int)
-            else:
-                scale = -out_min / zero_int
+        if abs(zero - zero_int) > eps and non_zero:
+            zero_int = int(math.ceil(zero))
+    elif out_min > -eps:
+        zero_int = 0
+    else:
+        zero_int = 255
+
+    return scale, zero_int, -zero_int*scale, (255-zero_int)*scale
+
+
+def adjust_range_for_hexagon(in_min, in_max):
+    out_max = max(0.0, in_max)
+    out_min = min(0.0, in_min)
+    scale = (out_max - out_min) / 255.0
+    eps = 1e-6
+    if out_min < -eps and out_max > eps:
+        zero = -out_min / scale
+        zero_int = int(round(zero))
+        # if zero_int <=0 or >= 255, try to avoid divide by 0,
+        # else, try to make adjustment as small as possible
+        ceil = int(math.ceil(zero))
+        keep_max = (ceil - zero) / out_max < (zero + 1 - ceil) / -out_min
+        if zero_int <= 0 or (zero_int < 254 and keep_max):
+            zero_int = ceil
+            scale = out_max / (255.0 - zero_int)
+        else:
+            scale = -out_min / zero_int
     elif out_min > -eps:
         zero_int = 0
     else:
@@ -108,11 +134,11 @@ def quantize_with_scale_and_zero(data, scale, zero):
     return quantized_data
 
 
-def quantize(data, non_zero):
+def quantize(data, device, non_zero):
     np_data = np.array(data).astype(float)
     in_min = np_data.min()
     in_max = np_data.max()
-    scale, zero, out_min, out_max = adjust_range(in_min, in_max,
+    scale, zero, out_min, out_max = adjust_range(in_min, in_max, device,
                                                  non_zero=non_zero)
     output = np.clip((np.round(zero + data / scale).astype(np.int32)), 0, 255)
 

mace/utils/quantize.h

@@ -57,15 +57,8 @@ inline void AdjustRange(const float in_min_data,
     int32_t
         quantized_zero_near_int = static_cast<int32_t>(roundf(quantized_zero));
     *zero_point = quantized_zero_near_int;
-    if (fabs(quantized_zero - quantized_zero_near_int) > kEps) {
-      if (quantized_zero < quantized_zero_near_int || non_zero) {
-        // keep out_max fixed, and move out_min
-        *zero_point = static_cast<int32_t>(std::ceil(quantized_zero));
-        *scale = out_max / (quantized_max - *zero_point);
-      } else {
-        // keep out_min fixed, and move out_max
-        *scale = out_min / (quantized_min - *zero_point);
-      }
+    if (fabs(quantized_zero - quantized_zero_near_int) > kEps && non_zero) {
+      *zero_point = static_cast<int32_t>(std::ceil(quantized_zero));
     }
   } else if (out_min > -kEps) {
     *zero_point = quantized_min;