Merge pull request #1168 from WenmuZhou/dygraph_rc

delete fluid

ppocr/modeling/heads/rec_ctc_head.py

@@ -24,7 +24,7 @@ from paddle.nn import functional as F
 
 
 def get_para_bias_attr(l2_decay, k, name):
-    regularizer = paddle.fluid.regularizer.L2Decay(l2_decay)
+    regularizer = paddle.regularizer.L2Decay(l2_decay)
     stdv = 1.0 / math.sqrt(k * 1.0)
     initializer = nn.initializer.Uniform(-stdv, stdv)
     weight_attr = ParamAttr(
@@ -33,6 +33,7 @@ def get_para_bias_attr(l2_decay, k, name):
         regularizer=regularizer, initializer=initializer, name=name + "_b_attr")
     return [weight_attr, bias_attr]
 
+
 class CTCHead(nn.Layer):
     def __init__(self, in_channels, out_channels, fc_decay=0.0004, **kwargs):
         super(CTCHead, self).__init__()

ppocr/optimizer/regularizer.py

@@ -17,7 +17,7 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
-from paddle import fluid
+import paddle
 
 
 class L1Decay(object):
@@ -32,8 +32,7 @@ class L1Decay(object):
         self.regularization_coeff = factor
 
     def __call__(self):
-        reg = fluid.regularizer.L1Decay(
-            regularization_coeff=self.regularization_coeff)
+        reg = paddle.regularizer.L1Decay(self.regularization_coeff)
         return reg
 
 
@@ -49,6 +48,5 @@ class L2Decay(object):
         self.regularization_coeff = factor
 
     def __call__(self):
-        reg = fluid.regularizer.L2Decay(
-            regularization_coeff=self.regularization_coeff)
+        reg = paddle.regularizer.L2Decay(self.regularization_coeff)
         return reg

ppocr/postprocess/rec_postprocess.py

@@ -102,7 +102,6 @@ class CTCLabelDecode(BaseRecLabelDecode):
     def __call__(self, preds, label=None, *args, **kwargs):
         if isinstance(preds, paddle.Tensor):
             preds = preds.numpy()
-        # out = self.decode_preds(preds)
 
         preds_idx = preds.argmax(axis=2)
         preds_prob = preds.max(axis=2)
@@ -116,27 +115,6 @@ class CTCLabelDecode(BaseRecLabelDecode):
         dict_character = ['blank'] + dict_character
         return dict_character
 
-    def decode_preds(self, preds):
-        probs_ind = np.argmax(preds, axis=2)
-
-        B, N, _ = preds.shape
-        l = np.ones(B).astype(np.int64) * N
-        length = paddle.to_tensor(l)
-        out = paddle.fluid.layers.ctc_greedy_decoder(preds, 0, length)
-        batch_res = [
-            x[:idx[0]] for x, idx in zip(out[0].numpy(), out[1].numpy())
-        ]
-
-        result_list = []
-        for sample_idx, ind, prob in zip(batch_res, probs_ind, preds):
-            char_list = [self.character[idx] for idx in sample_idx]
-            valid_ind = np.where(ind != 0)[0]
-            if len(valid_ind) == 0:
-                continue
-            conf_list = prob[valid_ind, ind[valid_ind]]
-            result_list.append((''.join(char_list), conf_list))
-        return result_list
-
 
 class AttnLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """