Merge pull request #715 from PINTO0309/multi-batch-quant

Multi batch quant

README.md

@@ -299,15 +299,15 @@ Video speed is adjusted approximately 50 times slower than actual speed.
   docker run --rm -it \
   -v `pwd`:/workdir \
   -w /workdir \
-  ghcr.io/pinto0309/onnx2tf:1.26.1
+  ghcr.io/pinto0309/onnx2tf:1.26.2
 
   or
 
   # Authentication is not required for pulls from Docker Hub.
   docker run --rm -it \
   -v `pwd`:/workdir \
   -w /workdir \
-  docker.io/pinto0309/onnx2tf:1.26.1
+  docker.io/pinto0309/onnx2tf:1.26.2
 
   or
 

onnx2tf/__init__.py

@@ -1,3 +1,3 @@
 from onnx2tf.onnx2tf import convert, main
 
-__version__ = '1.26.1'
+__version__ = '1.26.2'

onnx2tf/onnx2tf.py

@@ -1626,6 +1626,7 @@ def sanitizing(node):
                             mean,
                             std,
                         ]
+
             elif custom_input_op_name_np_data_path is not None:
                 for param in custom_input_op_name_np_data_path:
                     if len(param) != 4:
@@ -1652,11 +1653,14 @@ def sanitizing(node):
 
             # representative_dataset_gen
             def representative_dataset_gen():
-                for idx in range(data_count):
+                batch_size = model.inputs[0].shape[0]
+                if not isinstance(batch_size, int):
+                    batch_size = 1
+                for idx in range(0, data_count, batch_size):
                     yield_data_dict = {}
                     for model_input_name in model_input_name_list:
                         calib_data, mean, std = calib_data_dict[model_input_name]
-                        normalized_calib_data: np.ndarray = (calib_data[idx] - mean) / std
+                        normalized_calib_data: np.ndarray = (calib_data[idx:idx+batch_size] - mean) / std
                         yield_data_dict[model_input_name] = tf.cast(tf.convert_to_tensor(normalized_calib_data), tf.float32)
                     yield yield_data_dict
 
@@ -1708,7 +1712,7 @@ def representative_dataset_gen():
                     inf_type_input = tf.float32
                 else:
                     inf_type_input = tf.int8
-                
+
                 if output_quant_dtype == 'int8':
                     inf_type_output = tf.int8
                 elif output_quant_dtype == 'uint8':