Merge branch 'gpu_buffer' into 'master'

Don't quantize non_zero for dsp

See merge request !933

mace/python/tools/converter_tool/hexagon_converter.py

@@ -116,7 +116,7 @@ class HexagonConverter(base_converter.ConverterInterface):
         for op in self._model.op:
             if not self._hexagon_ops.has_op(op.type):
                 raise Exception('Unsupported op: ', op)
-            print('Op: ', op.name, op.type)
+            print('Op: %s (%s)' % (op.name, op.type))
             for i in range(len(op.input)):
                 if ':' not in op.input[i]:
                     node_name = op.input[i]

mace/python/tools/converter_tool/transformer.py

@@ -1482,7 +1482,9 @@ class Transformer(base_converter.ConverterInterface):
                     mace_check(False, "wrong device.")
                 tensor.data_type = mace_pb2.DT_INT32
             else:
-                quantized_tensor = quantize_util.quantize(tensor.float_data)
+                non_zero = self._option.device == DeviceType.CPU.value
+                quantized_tensor = quantize_util.quantize(tensor.float_data,
+                                                          non_zero)
                 tensor.data_type = mace_pb2.DT_UINT8
 
             del tensor.float_data[:]
@@ -1718,6 +1720,11 @@ class Transformer(base_converter.ConverterInterface):
                 and op.type != MaceOp.Dequantize.name):  # noqa
                 mace_check(len(op.output) == len(op.quantize_info),
                            "missing quantize info: %s" % op)
+            for i in six.moves.range(len(op.quantize_info)):
+                print("Op output %s range: [%f, %f]" % (
+                    op.output[i],
+                    op.quantize_info[i].minval,
+                    op.quantize_info[i].maxval))
 
     def add_opencl_informations(self):
         print("Add OpenCL informations")

mace/python/tools/quantization/quantize_util.py

@@ -108,11 +108,12 @@ def quantize_with_scale_and_zero(data, scale, zero):
     return quantized_data
 
 
-def quantize(data):
+def quantize(data, 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, non_zero=True)
+    scale, zero, out_min, out_max = adjust_range(in_min, in_max,
+                                                 non_zero=non_zero)
     output = np.clip((np.round(zero + data / scale).astype(int)), 0, 255)
 
     quantized_data = QuantizedData()