change nd -> np in imagenet_gen_qsym_onedenn.py (#20399)

also few tiny formatting fixes

example/quantization/imagenet_gen_qsym_onednn.py

@@ -39,6 +39,7 @@ def download_calib_dataset(dataset_url, calib_dataset, logger=None):
         logger.info('Downloading calibration dataset from %s to %s' % (dataset_url, calib_dataset))
     mx.test_utils.download(dataset_url, calib_dataset)
 
+
 def get_from_gluon(model_name, classes=1000, logger=None):
     dir_path = os.path.dirname(os.path.realpath(__file__))
     model_path = os.path.join(dir_path, 'model')
@@ -48,12 +49,14 @@ def get_from_gluon(model_name, classes=1000, logger=None):
     prefix = os.path.join(model_path, model_name)
     return net, prefix
 
+
 def regex_find_excluded_symbols(patterns_dict, model_name):
     for key, value in patterns_dict.items():
         if re.search(key, model_name) is not None:
             return value
     return None
 
+
 def get_exclude_symbols(model_name, exclude_first_conv):
     """Grouped supported models at the time of commit:
     - alexnet
@@ -95,6 +98,7 @@ def get_exclude_symbols(model_name, exclude_first_conv):
         excluded_sym_names += excluded_first_conv_sym_names
     return excluded_sym_names
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Generate a calibrated quantized model from a FP32 model with Intel oneDNN support')
     parser.add_argument('--model', type=str, default='resnet50_v1',
@@ -116,7 +120,7 @@ def get_exclude_symbols(model_name, exclude_first_conv):
                         help='number of batches for calibration')
     parser.add_argument('--exclude-first-conv', action='store_true', default=False,
                         help='excluding quantizing the first conv layer since the'
-                             ' input data may have negative value which doesn\'t support at moment' )
+                             ' input data may have negative value which doesn\'t support at moment')
     parser.add_argument('--shuffle-dataset', action='store_true',
                         help='shuffle the calibration dataset')
     parser.add_argument('--calib-mode', type=str, default='entropy',
@@ -170,8 +174,7 @@ def get_exclude_symbols(model_name, exclude_first_conv):
     dir_path = os.path.dirname(os.path.realpath(__file__))
     dir_path = os.path.join(dir_path, 'model')
     if not os.path.exists(dir_path):
-        os.mkdir(dir_path) # without try catch block as we expect to finish
-                           # script if it fail
+        os.mkdir(dir_path)  # without try catch block as we expect to finish script if it fail
 
     # download model
     if not args.no_pretrained:
@@ -191,7 +194,6 @@ def get_exclude_symbols(model_name, exclude_first_conv):
         epoch = args.epoch
         net = gluon.SymbolBlock.imports("{}-symbol.json".format(prefix), ['data'], "{}-0000.params".format(prefix))
 
-
     # get batch size
     batch_size = args.batch_size
     if logger:

python/mxnet/contrib/quantization.py

@@ -32,6 +32,7 @@
 from ..context import cpu, Context
 from ..util import is_np_array
 
+
 def _quantize_params(qsym, params, min_max_dict):
     """Given a quantized symbol and a dict of params that have not been quantized,
     generate quantized params. Currently only supports quantizing the arg_params
@@ -86,6 +87,7 @@ def _quantize_params(qsym, params, min_max_dict):
                 quantized_params[name] = array_cls.array([min_max_dict[output][1]])
     return quantized_params
 
+
 def _quantize_symbol(sym, ctx, excluded_symbols=None, excluded_operators=None,
                      offline_params=None, quantized_dtype='int8', quantize_mode='smart',
                      quantize_granularity='tensor-wise'):
@@ -291,6 +293,7 @@ def get_optimal_thresholds(hist_dict, quantized_dtype, num_quantized_bins=255, l
                 logger.debug(f"layer={name}, min_val={min_val}, max_val={max_val}, th={th}, divergence={divergence}")
         return th_dict
 
+
 class _LayerOutputMinMaxCollector(CalibrationCollector):
     """Saves layer output min and max values in a dict with layer names as keys.
     The collected min and max values will be directly used as thresholds for quantization.
@@ -319,11 +322,12 @@ def collect(self, name, op_name, arr):
             self.logger.debug("Collecting layer %s min_range=%f, max_range=%f"
                               % (name, min_range, max_range))
 
+
 def _calibrate_quantized_sym(qsym, min_max_dict):
     """Given a dictionary containing the thresholds for quantizing the layers,
     set the thresholds into the quantized symbol as the params of requantize operators.
     """
-    if min_max_dict  is None or len(min_max_dict) == 0:
+    if min_max_dict is None or len(min_max_dict) == 0:
         return qsym
     num_layer_outputs = len(min_max_dict)
     layer_output_names = []
@@ -363,8 +367,6 @@ def _collect_layer_statistics(sym_block, data, collector, num_inputs, num_calib_
     return num_batches
 
 
-
-
 def _generate_list_of_data_desc(data_shapes, data_types):
     """"Convert list ot tuples to list of DataDesc."""
     if isinstance(data_shapes, list):
@@ -527,6 +529,7 @@ def quantize_model(sym, arg_params, aux_params, data_names=('data',),
 
     return qsym, qarg_params, aux_params
 
+
 def quantize_model_mkldnn(sym, arg_params, aux_params, data_names=('data',),
                           ctx=cpu(), excluded_sym_names=None, excluded_op_names=None,
                           calib_mode='entropy', calib_data=None, num_calib_batches=None,