repair bug in latexocr cpu infer and typo in bleu score

configs/rec/PP-FormuaNet/rec_pp_formulanet_l.yml

@@ -69,7 +69,7 @@ PostProcess:
 Metric:
   name: LaTeXOCRMetric
   main_indicator:  exp_rate
-  cal_blue_score: False
+  cal_bleu_score: False
 
 Train:
   dataset:

configs/rec/PP-FormuaNet/rec_pp_formulanet_s.yml

@@ -67,7 +67,7 @@ PostProcess:
 Metric:
   name: LaTeXOCRMetric
   main_indicator:  exp_rate
-  cal_blue_score: False
+  cal_bleu_score: False
 
 Train:
   dataset:

configs/rec/rec_latex_ocr.yml

@@ -65,7 +65,7 @@ PostProcess:
 Metric:
   name: LaTeXOCRMetric
   main_indicator:  exp_rate
-  cal_blue_score: False
+  cal_bleu_score: False
 
 Train:
   dataset:

configs/rec/rec_unimernet.yml

@@ -66,7 +66,7 @@ PostProcess:
 Metric:
   name: LaTeXOCRMetric
   main_indicator:  exp_rate
-  cal_blue_score: False
+  cal_bleu_score: False
 
 Train:
   dataset:

ppocr/metrics/bleu.py

@@ -193,7 +193,7 @@ def __call__(self, line):
         return self._post_tokenizer(f" {line} ")
 
 
-def compute_blue_score(
+def compute_bleu_score(
     predictions, references, tokenizer=Tokenizer13a(), max_order=4, smooth=False
 ):
     # if only one reference is provided make sure we still use list of lists

ppocr/metrics/rec_metric.py

@@ -17,7 +17,7 @@
 
 import numpy as np
 import string
-from .bleu import compute_blue_score, compute_edit_distance
+from .bleu import compute_bleu_score, compute_edit_distance
 
 
 class RecMetric(object):
@@ -181,21 +181,21 @@ def epoch_reset(self):
 
 
 class LaTeXOCRMetric(object):
-    def __init__(self, main_indicator="exp_rate", cal_blue_score=False, **kwargs):
+    def __init__(self, main_indicator="exp_rate", cal_bleu_score=False, **kwargs):
         self.main_indicator = main_indicator
-        self.cal_blue_score = cal_blue_score
+        self.cal_bleu_score = cal_bleu_score
         self.edit_right = []
         self.exp_right = []
-        self.blue_right = []
+        self.bleu_right = []
         self.e1_right = []
         self.e2_right = []
         self.e3_right = []
         self.editdistance_total_length = 0
         self.exp_total_num = 0
         self.edit_dist = 0
         self.exp_rate = 0
-        if self.cal_blue_score:
-            self.blue_score = 0
+        if self.cal_bleu_score:
+            self.bleu_score = 0
         self.e1 = 0
         self.e2 = 0
         self.e3 = 0
@@ -227,16 +227,16 @@ def __call__(self, preds, batch, **kwargs):
 
         self.edit_dist = sum(lev_dist)  # float
         self.exp_rate = line_right  # float
-        if self.cal_blue_score:
-            self.blue_score = compute_blue_score(word_pred, word_label)
+        if self.cal_bleu_score:
+            self.bleu_score = compute_bleu_score(word_pred, word_label)
         self.e1 = e1
         self.e2 = e2
         self.e3 = e3
         exp_length = len(word_label)
         self.edit_right.append(self.edit_dist)
         self.exp_right.append(self.exp_rate)
-        if self.cal_blue_score:
-            self.blue_right.append(self.blue_score * batch_size)
+        if self.cal_bleu_score:
+            self.bleu_right.append(self.bleu_score * batch_size)
         self.e1_right.append(self.e1)
         self.e2_right.append(self.e2)
         self.e3_right.append(self.e3)
@@ -247,21 +247,21 @@ def get_metric(self):
         """
         return {
             'edit distance': 0,
-            "blue_score": 0,
+            "bleu_score": 0,
             "exp_rate": 0,
         }
         """
         cur_edit_distance = sum(self.edit_right) / self.exp_total_num
         cur_exp_rate = sum(self.exp_right) / self.exp_total_num
-        if self.cal_blue_score:
-            cur_blue_score = sum(self.blue_right) / self.editdistance_total_length
+        if self.cal_bleu_score:
+            cur_bleu_score = sum(self.bleu_right) / self.editdistance_total_length
         cur_exp_1 = sum(self.e1_right) / self.exp_total_num
         cur_exp_2 = sum(self.e2_right) / self.exp_total_num
         cur_exp_3 = sum(self.e3_right) / self.exp_total_num
         self.reset()
-        if self.cal_blue_score:
+        if self.cal_bleu_score:
             return {
-                "blue_score": cur_blue_score,
+                "bleu_score": cur_bleu_score,
                 "edit distance": cur_edit_distance,
                 "exp_rate": cur_exp_rate,
                 "exp_rate<=1 ": cur_exp_1,
@@ -281,17 +281,17 @@ def get_metric(self):
     def reset(self):
         self.edit_dist = 0
         self.exp_rate = 0
-        if self.cal_blue_score:
-            self.blue_score = 0
+        if self.cal_bleu_score:
+            self.bleu_score = 0
         self.e1 = 0
         self.e2 = 0
         self.e3 = 0
 
     def epoch_reset(self):
         self.edit_right = []
         self.exp_right = []
-        if self.cal_blue_score:
-            self.blue_right = []
+        if self.cal_bleu_score:
+            self.bleu_right = []
         self.e1_right = []
         self.e2_right = []
         self.e3_right = []

ppocr/modeling/backbones/rec_resnetv2.py

@@ -90,16 +90,9 @@ def __init__(
 
         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())
+        self.batch_norm = paddle.nn.BatchNorm1D(
+            self._out_channels, use_global_stats=False
+        )
 
     def forward(self, x):
         if not self.training:
@@ -110,7 +103,14 @@ def forward(self, x):
             else:
                 x = pad_same(x, self._kernel_size, self._stride, self._dilation)
         if self.export:
-            weight = self.weight
+            weight = paddle.reshape(
+                self.batch_norm(
+                    self.weight.reshape([1, self._out_channels, -1]).cast(
+                        paddle.float32
+                    ),
+                ),
+                self.weight.shape,
+            )
         else:
             weight = paddle.reshape(
                 F.batch_norm(

ppocr/utils/export_model.py

@@ -99,7 +99,16 @@ def dump_infer_config(config, path, logger):
     infer_cfg["PreProcess"] = {"transform_ops": config["Eval"]["dataset"]["transforms"]}
     postprocess = OrderedDict()
     for k, v in config["PostProcess"].items():
-        postprocess[k] = v
+        if config["Architecture"].get("algorithm") in [
+            "LaTeXOCR",
+            "UniMERNet",
+            "PP-FormulaNet-L",
+            "PP-FormulaNet-S",
+        ]:
+            if k != "rec_char_dict_path":
+                postprocess[k] = v
+        else:
+            postprocess[k] = v
 
     if config["Architecture"].get("algorithm") in ["LaTeXOCR"]:
         tokenizer_file = config["Global"].get("rec_char_dict_path")

tools/eval.py

@@ -107,16 +107,16 @@ def main():
             model_type = "can"
         elif config["Architecture"]["algorithm"] == "LaTeXOCR":
             model_type = "latexocr"
-            config["Metric"]["cal_blue_score"] = True
+            config["Metric"]["cal_bleu_score"] = True
         elif config["Architecture"]["algorithm"] == "UniMERNet":
             model_type = "unimernet"
-            config["Metric"]["cal_blue_score"] = True
+            config["Metric"]["cal_bleu_score"] = True
         elif config["Architecture"]["algorithm"] in [
             "PP-FormulaNet-S",
             "PP-FormulaNet-L",
         ]:
             model_type = "pp_formulanet"
-            config["Metric"]["cal_blue_score"] = True
+            config["Metric"]["cal_bleu_score"] = True
         else:
             model_type = config["Architecture"]["model_type"]
     else: