fix bug of freeze_bn default value

mindspore/train/quant/quant.py

@@ -476,7 +476,7 @@ def export(network, *inputs, file_name, mean=127.5, std_dev=127.5, file_format='
 
 def convert_quant_network(network,
                           bn_fold=True,
-                          freeze_bn=1e7,
+                          freeze_bn=10000000,
                           quant_delay=(0, 0),
                           num_bits=(8, 8),
                           per_channel=(False, False),

model_zoo/official/cv/lenet_quant/eval_quant.py

@@ -50,7 +50,8 @@ if __name__ == "__main__":
     # define fusion network
     network = LeNet5Fusion(cfg.num_classes)
     # convert fusion network to quantization aware network
-    network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000)
+    network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000,
+                                          per_channel=[True, False])
 
     # define loss
     net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")