use eps instald of 0.001

ppocr/losses/det_pse_loss.py

@@ -25,7 +25,7 @@ class PSELoss(nn.Layer):
                  ohem_ratio=3,
                  kernel_sample_mask='pred',
                  reduction='sum',
-                 **kwargs):
+                 eps=1e-6**kwargs):
         """Implement PSE Loss.
         """
         super(PSELoss, self).__init__()
@@ -34,6 +34,7 @@ class PSELoss(nn.Layer):
         self.ohem_ratio = ohem_ratio
         self.kernel_sample_mask = kernel_sample_mask
         self.reduction = reduction
+        self.eps = eps
 
     def forward(self, outputs, labels):
         predicts = outputs['maps']
@@ -92,8 +93,8 @@ class PSELoss(nn.Layer):
         target = target * mask
 
         a = paddle.sum(input * target, 1)
-        b = paddle.sum(input * input, 1) + 0.001
-        c = paddle.sum(target * target, 1) + 0.001
+        b = paddle.sum(input * input, 1) + self.eps
+        c = paddle.sum(target * target, 1) + self.eps
         d = (2 * a) / (b + c)
         return 1 - d
 
@@ -104,7 +105,6 @@ class PSELoss(nn.Layer):
                 .astype('float32')))
 
         if pos_num == 0:
-            # selected_mask = gt_text.copy() * 0 # may be not good
             selected_mask = training_mask
             selected_mask = selected_mask.reshape(
                 [1, selected_mask.shape[0], selected_mask.shape[1]]).astype(