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添加resnet backbone

This commit is contained in:
WenmuZhou 2020-11-09 18:29:33 +08:00
parent 4d775dc98f
commit b2004fe586
2 changed files with 17 additions and 11 deletions
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13
ppocr/modeling/backbones/det_resnet_vd.py
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@ -19,6 +19,7 @@ from __future__ import print_function
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
__all__ = ["ResNet"]
@ -37,9 +38,9 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2d(
self._pool2d_avg = nn.AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = nn.Conv2d(
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@ -118,7 +119,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv2, act='relu')
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
@ -165,7 +167,8 @@ class BasicBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv1, act='relu')
y = paddle.add(x=short, y=conv1)
y = F.relu(y)
return y
@ -214,7 +217,7 @@ class ResNet(nn.Layer):
stride=1,
act='relu',
name="conv1_3")
self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.stages = []
self.out_channels = []

15
ppocr/modeling/backbones/rec_resnet_vd.py
View File

@ -19,6 +19,7 @@ from __future__ import print_function
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
__all__ = ["ResNet"]
@ -37,9 +38,9 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2d(
self._pool2d_avg = nn.AvgPool2D(
kernel_size=stride, stride=stride, padding=0, ceil_mode=True)
self._conv = nn.Conv2d(
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@ -119,7 +120,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv2, act='relu')
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
@ -166,7 +168,8 @@ class BasicBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=conv1, act='relu')
y = paddle.add(x=short, y=conv1)
y = F.relu(y)
return y
@ -215,7 +218,7 @@ class ResNet(nn.Layer):
stride=1,
act='relu',
name="conv1_3")
self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
@ -270,7 +273,7 @@ class ResNet(nn.Layer):
shortcut = True
self.block_list.append(basic_block)
self.out_channels = num_filters[block]
self.out_pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.out_pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
def forward(self, inputs):
y = self.conv1_1(inputs)