diff --git a/ppocr/losses/rec_aster_loss.py b/ppocr/losses/rec_aster_loss.py
index fbb99d29a..52605e46d 100644
--- a/ppocr/losses/rec_aster_loss.py
+++ b/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(
diff --git a/ppocr/losses/table_att_loss.py b/ppocr/losses/table_att_loss.py
index d7fd99e69..51377efa2 100644
--- a/ppocr/losses/table_att_loss.py
+++ b/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
diff --git a/ppocr/modeling/backbones/kie_unet_sdmgr.py b/ppocr/modeling/backbones/kie_unet_sdmgr.py
index 545e4e751..793c68c6d 100644
--- a/ppocr/modeling/backbones/kie_unet_sdmgr.py
+++ b/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,
diff --git a/ppocr/modeling/backbones/rec_resnet_fpn.py b/ppocr/modeling/backbones/rec_resnet_fpn.py
index a7e876a2b..79efd6e41 100644
--- a/ppocr/modeling/backbones/rec_resnet_fpn.py
+++ b/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
diff --git a/ppocr/modeling/heads/rec_srn_head.py b/ppocr/modeling/heads/rec_srn_head.py
index 8d59e4711..1070d8cd6 100644
--- a/ppocr/modeling/heads/rec_srn_head.py
+++ b/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
diff --git a/ppocr/modeling/heads/self_attention.py b/ppocr/modeling/heads/self_attention.py
index 6c27fdbe4..6e4c65e39 100644
--- a/ppocr/modeling/heads/self_attention.py
+++ b/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)