add export model of pse

doc/doc_ch/algorithm_overview.md

@@ -24,7 +24,7 @@ PaddleOCR开源的文本检测算法列表：
 |DB|MobileNetV3|77.29%|73.08%|75.12%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
 |PSE|ResNet50_vd|85.81%|79.53%|82.55%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
-|PSE|MobileNetV3|82.20%|70.47%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 
 在Total-text文本检测公开数据集上，算法效果如下：
 

doc/doc_en/algorithm_overview_en.md

@@ -26,7 +26,7 @@ On the ICDAR2015 dataset, the text detection result is as follows:
 |DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
 |PSE|ResNet50_vd|85.81%|79.53%|82.55%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
-|PSE|MobileNetV3|82.20%|70.47%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 
 On Total-Text dataset, the text detection result is as follows:
 

ppocr/modeling/necks/fpn.py

@@ -70,33 +70,31 @@ class FPN(nn.Layer):
                 m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32',
                                                  default_initializer=paddle.nn.initializer.Constant(0.0))
 
-    def _upsample(self, x, y, scale=1):
+    def _upsample(self, x, scale=1):
-        _, _, H, W = y.shape
+        return F.upsample(x, scale_factor=scale, mode='bilinear')
-        return F.upsample(x, size=(H // scale, W // scale), mode='bilinear')
 
-    def _upsample_add(self, x, y):
+    def _upsample_add(self, x, y, scale=1):
-        _, _, H, W = y.shape
+        return F.upsample(x, scale_factor=scale, mode='bilinear') + y
-        return F.upsample(x, size=(H, W), mode='bilinear') + y
 
     def forward(self, x):
         f2, f3, f4, f5 = x
         p5 = self.toplayer_(f5)
 
         f4 = self.latlayer1_(f4)
-        p4 = self._upsample_add(p5, f4)
+        p4 = self._upsample_add(p5, f4,2)
         p4 = self.smooth1_(p4)
 
         f3 = self.latlayer2_(f3)
-        p3 = self._upsample_add(p4, f3)
+        p3 = self._upsample_add(p4, f3,2)
         p3 = self.smooth2_(p3)
 
         f2 = self.latlayer3_(f2)
-        p2 = self._upsample_add(p3, f2)
+        p2 = self._upsample_add(p3, f2,2)
         p2 = self.smooth3_(p2)
 
-        p3 = self._upsample(p3, p2)
+        p3 = self._upsample(p3, 2)
-        p4 = self._upsample(p4, p2)
+        p4 = self._upsample(p4, 4)
-        p5 = self._upsample(p5, p2)
+        p5 = self._upsample(p5, 8)
 
         fuse = paddle.concat([p2, p3, p4, p5], axis=1)
         return fuse

tools/infer/predict_det.py

@@ -89,6 +89,14 @@ class TextDetector(object):
                 postprocess_params["sample_pts_num"] = 2
                 postprocess_params["expand_scale"] = 1.0
                 postprocess_params["shrink_ratio_of_width"] = 0.3
+        elif self.det_algorithm == "PSE":
+            postprocess_params['name'] = 'PSEPostProcess'
+            postprocess_params["thresh"] = args.det_pse_thresh
+            postprocess_params["box_thresh"] = args.det_pse_box_thresh
+            postprocess_params["min_area"] = args.det_pse_min_area
+            postprocess_params["box_type"] = args.det_pse_box_type
+            postprocess_params["scale"] = args.det_pse_scale
+            self.det_pse_box_type = args.det_pse_box_type
         else:
             logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
             sys.exit(0)
@@ -207,7 +215,7 @@ class TextDetector(object):
             preds['f_score'] = outputs[1]
             preds['f_tco'] = outputs[2]
             preds['f_tvo'] = outputs[3]
-        elif self.det_algorithm == 'DB':
+        elif self.det_algorithm in ['DB','PSE']:
             preds['maps'] = outputs[0]
         else:
             raise NotImplementedError
@@ -217,6 +225,8 @@ class TextDetector(object):
         dt_boxes = post_result[0]['points']
         if self.det_algorithm == "SAST" and self.det_sast_polygon:
             dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
+        elif self.det_algorithm == "PSE" and self.det_pse_box_type=='poly':
+            dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
         else:
             dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
 

tools/infer/utility.py

@@ -62,6 +62,13 @@ def init_args():
     parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
     parser.add_argument("--det_sast_polygon", type=bool, default=False)
 
+    # PSE parmas
+    parser.add_argument("--det_pse_thresh", type=float, default=0)
+    parser.add_argument("--det_pse_box_thresh", type=float, default=0.85)
+    parser.add_argument("--det_pse_min_area", type=float, default=16)
+    parser.add_argument("--det_pse_box_type", type=str, default='poly')
+    parser.add_argument("--det_pse_scale", type=int, default=1)
+
     # params for text recognizer
     parser.add_argument("--rec_algorithm", type=str, default='CRNN')
     parser.add_argument("--rec_model_dir", type=str)