fix latex_ocr inference (#14498)

* add

* update

* add

* add

Author: vivienfanghuagood

ppocr/modeling/backbones/rec_resnetv2.py

@@ -88,6 +88,19 @@ def __init__(
         self.export = is_export
         self.eps = eps
 
+        self.running_mean = paddle.zeros([self._out_channels], dtype="float32")
+        self.running_variance = paddle.ones([self._out_channels], dtype="float32")
+        orin_shape = self.weight.shape
+        new_weight = F.batch_norm(
+            self.weight.reshape([1, self._out_channels, -1]),
+            self.running_mean,
+            self.running_variance,
+            momentum=0.0,
+            epsilon=self.eps,
+            use_global_stats=False,
+        ).reshape(orin_shape)
+        self.weight.set_value(new_weight.numpy())
+
     def forward(self, x):
         if not self.training:
             self.export = True
@@ -96,30 +109,14 @@ def forward(self, x):
                 x = pad_same_export(x, self._kernel_size, self._stride, self._dilation)
             else:
                 x = pad_same(x, self._kernel_size, self._stride, self._dilation)
-        running_mean = paddle.to_tensor([0] * self._out_channels, dtype="float32")
-        running_variance = paddle.to_tensor([1] * self._out_channels, dtype="float32")
         if self.export:
-            weight = paddle.reshape(
-                F.batch_norm(
-                    self.weight.reshape([1, self._out_channels, -1]).cast(
-                        paddle.float32
-                    ),
-                    running_mean,
-                    running_variance,
-                    momentum=0.0,
-                    epsilon=self.eps,
-                    use_global_stats=False,
-                ),
-                self.weight.shape,
-            )
+            weight = self.weight
         else:
             weight = paddle.reshape(
                 F.batch_norm(
-                    self.weight.reshape([1, self._out_channels, -1]).cast(
-                        paddle.float32
-                    ),
-                    running_mean,
-                    running_variance,
+                    self.weight.reshape([1, self._out_channels, -1]),
+                    self.running_mean,
+                    self.running_variance,
                     training=True,
                     momentum=0.0,
                     epsilon=self.eps,