Implemented a workaround to deal with the problem that padding with t…

…he minimum value causes the output error of `MaxPool2D` to be maximized only when quantizing with INT8 quantization. #444

README.md

@@ -255,15 +255,15 @@ Video speed is adjusted approximately 50 times slower than actual speed.
   $ docker run --rm -it \
   -v `pwd`:/workdir \
   -w /workdir \
-  ghcr.io/pinto0309/onnx2tf:1.15.9
+  ghcr.io/pinto0309/onnx2tf:1.15.10
 
   or
 
   # Authentication is not required for pulls from Docker Hub.
   $ docker run --rm -it \
   -v `pwd`:/workdir \
   -w /workdir \
-  docker.io/pinto0309/onnx2tf:1.15.9
+  docker.io/pinto0309/onnx2tf:1.15.10
 
   or
 

onnx2tf/__init__.py

@@ -1,3 +1,3 @@
 from onnx2tf.onnx2tf import convert, main
 
-__version__ = '1.15.9'
+__version__ = '1.15.10'

onnx2tf/onnx2tf.py

@@ -765,6 +765,7 @@ def sanitizing(node):
         'mvn_epsilon': mvn_epsilon,
         'output_signaturedefs': output_signaturedefs,
         'output_nms_with_dynamic_tensor': output_nms_with_dynamic_tensor,
+        'output_integer_quantized_tflite': output_integer_quantized_tflite,
         'use_cuda': use_cuda,
     }
 

onnx2tf/ops/MaxPool.py

@@ -65,6 +65,8 @@ def make_node(
         if isinstance(graph_node_input, gs.Variable) else graph_node_input
     input_tensor_shape = input_tensor.shape
 
+    output_integer_quantized_tflite = bool(kwargs['output_integer_quantized_tflite'])
+
     # Pre-process transpose
     input_tensor = pre_process_transpose(
         value_before_transpose=input_tensor,
@@ -221,11 +223,15 @@ def make_node(
             [[0, 0]]
 
         # use minimum limit value of data type for explicit padding value since this is max pooling
+        # https://github.com/PINTO0309/onnx2tf/issues/444
+        # Implemented a workaround to deal with the problem that padding with the minimum value causes
+        # the output error of `MaxPool2D` to be maximized only when quantizing with INT8 quantization.
         padded_tensor = tf.pad(
             tensor=input_tensor,
             paddings=tf_pads,
             mode='CONSTANT',
-            constant_values=input_tensor.dtype.min
+            constant_values=input_tensor.dtype.min \
+                if not output_integer_quantized_tflite else 0.0
         )
 
     else: