replace fluid to paddle

ppocr/losses/rec_aster_loss.py

@@ -27,12 +27,12 @@ class CosineEmbeddingLoss(nn.Layer):
         self.epsilon = 1e-12
 
     def forward(self, x1, x2, target):
-        similarity = paddle.fluid.layers.reduce_sum(
+        similarity = paddle.sum(
             x1 * x2, dim=-1) / (paddle.norm(
                 x1, axis=-1) * paddle.norm(
                     x2, axis=-1) + self.epsilon)
         one_list = paddle.full_like(target, fill_value=1)
-        out = paddle.fluid.layers.reduce_mean(
+        out = paddle.mean(
             paddle.where(
                 paddle.equal(target, one_list), 1. - similarity,
                 paddle.maximum(

ppocr/losses/table_att_loss.py

@@ -19,7 +19,6 @@ from __future__ import print_function
 import paddle
 from paddle import nn
 from paddle.nn import functional as F
-from paddle import fluid
 
 class TableAttentionLoss(nn.Layer):
     def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs):
@@ -36,13 +35,13 @@ class TableAttentionLoss(nn.Layer):
         :param bbox:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,]
         :return: loss
         '''
-        ix1 = fluid.layers.elementwise_max(preds[:, 0], bbox[:, 0])
-        iy1 = fluid.layers.elementwise_max(preds[:, 1], bbox[:, 1])
-        ix2 = fluid.layers.elementwise_min(preds[:, 2], bbox[:, 2])
-        iy2 = fluid.layers.elementwise_min(preds[:, 3], bbox[:, 3])
+        ix1 = paddle.maximum(preds[:, 0], bbox[:, 0])
+        iy1 = paddle.maximum(preds[:, 1], bbox[:, 1])
+        ix2 = paddle.minimum(preds[:, 2], bbox[:, 2])
+        iy2 = paddle.minimum(preds[:, 3], bbox[:, 3])
 
-        iw = fluid.layers.clip(ix2 - ix1 + 1e-3, 0., 1e10)
-        ih = fluid.layers.clip(iy2 - iy1 + 1e-3, 0., 1e10)
+        iw = paddle.clip(ix2 - ix1 + 1e-3, 0., 1e10)
+        ih = paddle.clip(iy2 - iy1 + 1e-3, 0., 1e10)
 
         # overlap
         inters = iw * ih
@@ -55,12 +54,12 @@ class TableAttentionLoss(nn.Layer):
         # ious
         ious = inters / uni
 
-        ex1 = fluid.layers.elementwise_min(preds[:, 0], bbox[:, 0])
-        ey1 = fluid.layers.elementwise_min(preds[:, 1], bbox[:, 1])
-        ex2 = fluid.layers.elementwise_max(preds[:, 2], bbox[:, 2])
-        ey2 = fluid.layers.elementwise_max(preds[:, 3], bbox[:, 3])
-        ew = fluid.layers.clip(ex2 - ex1 + 1e-3, 0., 1e10)
-        eh = fluid.layers.clip(ey2 - ey1 + 1e-3, 0., 1e10)
+        ex1 = paddle.minimum(preds[:, 0], bbox[:, 0])
+        ey1 = paddle.minimum(preds[:, 1], bbox[:, 1])
+        ex2 = paddle.maximum(preds[:, 2], bbox[:, 2])
+        ey2 = paddle.maximum(preds[:, 3], bbox[:, 3])
+        ew = paddle.clip(ex2 - ex1 + 1e-3, 0., 1e10)
+        eh = paddle.clip(ey2 - ey1 + 1e-3, 0., 1e10)
 
         # enclose erea
         enclose = ew * eh + eps

ppocr/modeling/backbones/kie_unet_sdmgr.py

@@ -175,7 +175,7 @@ class Kie_backbone(nn.Layer):
             img, relations, texts, gt_bboxes, tag, img_size)
         x = self.img_feat(img)
         boxes, rois_num = self.bbox2roi(gt_bboxes)
-        feats = paddle.fluid.layers.roi_align(
+        feats = paddle.vision.ops.roi_align(
             x,
             boxes,
             spatial_scale=1.0,

ppocr/modeling/backbones/rec_resnet_fpn.py

@@ -18,7 +18,6 @@ from __future__ import print_function
 
 from paddle import nn, ParamAttr
 from paddle.nn import functional as F
-import paddle.fluid as fluid
 import paddle
 import numpy as np
 

ppocr/modeling/heads/rec_srn_head.py

@@ -20,13 +20,11 @@ import math
 import paddle
 from paddle import nn, ParamAttr
 from paddle.nn import functional as F
-import paddle.fluid as fluid
 import numpy as np
 from .self_attention import WrapEncoderForFeature
 from .self_attention import WrapEncoder
 from paddle.static import Program
 from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
-import paddle.fluid.framework as framework
 
 from collections import OrderedDict
 gradient_clip = 10

ppocr/modeling/heads/self_attention.py

@@ -22,7 +22,6 @@ import paddle
 from paddle import ParamAttr, nn
 from paddle import nn, ParamAttr
 from paddle.nn import functional as F
-import paddle.fluid as fluid
 import numpy as np
 gradient_clip = 10
 
@@ -288,10 +287,10 @@ class PrePostProcessLayer(nn.Layer):
                         "layer_norm_%d" % len(self.sublayers()),
                         paddle.nn.LayerNorm(
                             normalized_shape=d_model,
-                            weight_attr=fluid.ParamAttr(
-                                initializer=fluid.initializer.Constant(1.)),
-                            bias_attr=fluid.ParamAttr(
-                                initializer=fluid.initializer.Constant(0.)))))
+                            weight_attr=paddle.ParamAttr(
+                                initializer=paddle.nn.initializer.Constant(1.)),
+                            bias_attr=paddle.ParamAttr(
+                                initializer=paddle.nn.initializer.Constant(0.)))))
             elif cmd == "d":  # add dropout
                 self.functors.append(lambda x: F.dropout(
                     x, p=dropout_rate, mode="downscale_in_infer")
@@ -324,7 +323,7 @@ class PrepareEncoder(nn.Layer):
 
     def forward(self, src_word, src_pos):
         src_word_emb = src_word
-        src_word_emb = fluid.layers.cast(src_word_emb, 'float32')
+        src_word_emb = paddle.cast(src_word_emb, 'float32')
         src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)
         src_pos = paddle.squeeze(src_pos, axis=-1)
         src_pos_enc = self.emb(src_pos)
@@ -367,7 +366,7 @@ class PrepareDecoder(nn.Layer):
         self.dropout_rate = dropout_rate
 
     def forward(self, src_word, src_pos):
-        src_word = fluid.layers.cast(src_word, 'int64')
+        src_word = paddle.cast(src_word, 'int64')
         src_word = paddle.squeeze(src_word, axis=-1)
         src_word_emb = self.emb0(src_word)
         src_word_emb = paddle.scale(x=src_word_emb, scale=self.src_emb_dim**0.5)