Merge branch 'mix' into 'master'

Support mixing quantization-aware training and post-quantization ranges

See merge request deep-computing/mace!1251

docs/user_guide/quantization_usage.rst

@@ -6,9 +6,9 @@ MACE supports two kinds of quantization mechanisms, i.e.,
 * **Quantization-aware training (Recommend)**
 
 After pre-training model using float point, insert simulated quantization operations into the model. Fine tune the new model.
-Refer to `Tensorflow quantization-aware training <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize>`__.
+Refer to `Tensorflow quantization-aware training <https://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/contrib/quantize>`__.
 
-* **Post training quantization**
+* **Post-training quantization**
 
 After pre-training model using float point, estimate output range of each activation layer using sample inputs.
 
@@ -28,7 +28,7 @@ models, e.g., MobileNet. The only thing you need to make it run using MACE is to
   2. `quantize`: set `quantize` to be 1.
 
 
-Post training quantization
+Post-training quantization
 ---------------------------
 MACE supports post-training quantization if you want to take a chance to quantize model directly without fine tuning.
 This method requires developer to calculate tensor range of each activation layer statistically using sample inputs.
@@ -84,6 +84,16 @@ MACE provides tools to do statistics with following steps(using `inception-v3` f
   `quantize` to `1` and `quantize_range_file` to the overall_range file path in yaml config).
 
 
+Mixing usage
+---------------------------
+As `quantization-aware training` is still evolving, there are some operations that are not supported,
+which leaves some activation layers without tensor range. In this case, `post-training quantization`
+can be used to calculate these missing ranges. To mix the usage, just get a `quantization-aware training`
+model and then go through all the steps of `post-training quantization`. MACE will use the tensor ranges
+from the `overall_range` file of `post-training quantization` if the ranges are missing from the
+`quantization-aware training` model.
+
+
 Supported devices
 -----------------
 MACE supports running quantized models on ARM CPU and other acceleration devices, e.g., Qualcomm Hexagon DSP, MediaTek APU.

tools/python/transform/transformer.py

@@ -1758,20 +1758,14 @@ class Transformer(base_converter.ConverterInterface):
         quantize_info.zero_point = info.zero_point
 
     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' or \
                    op.type == 'FakeQuantWithMinMaxArgs':
-                if op.input[0] not in self._consts:
+                if self._option.quantize and op.input[0] not in self._consts:
                     producer_op = self._producer[op.input[0]]
                     minval = ConverterUtil.get_arg(op, 'min').f
                     maxval = ConverterUtil.get_arg(op, 'max').f
@@ -1842,6 +1836,7 @@ class Transformer(base_converter.ConverterInterface):
         range_file = self._option.quantize_range_file
         if range_file:
             print("Add quantize tensor range")
+            post_quantize_info = {}
             with open(range_file) as f:
                 for line in f:
                     tensor_name, minmax = line.split("@@")[:2]
@@ -1856,17 +1851,21 @@ class Transformer(base_converter.ConverterInterface):
                     activation_info.maxval = max_val
                     activation_info.scale = scale
                     activation_info.zero_point = zero
-                    self._quantize_activation_info[tensor_name] = activation_info  # noqa
+                    if tensor_name not in self._quantize_activation_info:
+                        post_quantize_info[tensor_name] = activation_info
 
             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]])
+                    # Prefer quantize info from quantization-aware training
+                    if output not in self._quantize_activation_info:
+                        mace_check(output in post_quantize_info,
+                                   "%s does not have quantize activation info"
+                                   % op)
+                        op.quantize_info.extend([post_quantize_info[output]])
+                        self._quantize_activation_info[output] = \
+                            post_quantize_info[output]
 
         if not self._option.quantize:
             return False
@@ -1979,6 +1978,7 @@ class Transformer(base_converter.ConverterInterface):
                     maxval = producer_op0.quantize_info[0].maxval \
                         - producer_op1.quantize_info[0].minval
                 else:
+                    print(op)
                     mace_check(False, "Quantized Elementwise only support:"
                                       " SUM and SUB without ranges now.")
                 quantize_info = \