Fixed the decision logic of INT8 inverse quantization

Author: PINTO0309

Dockerfile

@@ -7,7 +7,7 @@ ARG CPVER=cp38
 ARG OPENVINOVER=2021.4.582
 ARG OPENVINOROOTDIR=/opt/intel/openvino_2021
 ARG TENSORRTVER=cuda11.3-trt8.0.1.6-ga-20210626
-ARG APPVER=v1.11.3
+ARG APPVER=v1.11.4
 ARG wkdir=/home/user
 
 # dash -> bash

setup.py

@@ -11,7 +11,7 @@
     setup(
         name="tflite2tensorflow",
         scripts=scripts,
-        version="1.11.3",
+        version="1.11.4",
         description="Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite.",
         long_description=long_description,
         long_description_content_type="text/markdown",

tflite2tensorflow/tflite2tensorflow.py

@@ -495,9 +495,13 @@ def get_op_name(name):
         return op_name
 
     def backward_quantization(op_detail, op):
-        scales = np.asarray(op_detail["quantization"][0], dtype=np.float32)
-        zero_points = np.asarray(op_detail["quantization"][1], dtype=np.float32)
-        return (op - zero_points) * scales
+        if not 'quantization' in op_detail or \
+            (input_detail['quantization'][0] == 0.0 and input_detail['quantization'][1] == 0):
+            return op
+        else:
+            scales = np.asarray(op_detail['quantization'][0], dtype=np.float32)
+            zero_points = np.asarray(op_detail['quantization'][1], dtype=np.float32)
+            return (op - zero_points) * scales
 
     def searh_json_tensor_detail(name):
         tensor_detail_dict = None
@@ -518,16 +522,16 @@ def searh_json_tensor_detail(name):
 
     for input_detail in input_details:
         if not 'quantization' in input_detail or \
-            (input_detail["quantization"][0] == 0.0 and input_detail["quantization"][1] == 0):
+            (input_detail['quantization'][0] == 0.0 and input_detail['quantization'][1] == 0):
 
             tensors[input_detail['index']] = tf.placeholder(
                 dtype=input_detail['dtype'],
                 shape=input_detail['shape'],
                 name=get_op_name(input_detail['name']))
 
         else:
-            scales = np.asarray(input_detail["quantization"][0], dtype=np.float32)
-            zero_points = np.asarray(input_detail["quantization"][1], dtype=np.float32)
+            scales = np.asarray(input_detail['quantization'][0], dtype=np.float32)
+            zero_points = np.asarray(input_detail['quantization'][1], dtype=np.float32)
             inp = tf.placeholder(
                 dtype=np.float32,
                 shape=input_detail['shape'],