diff --git a/dygraph/paddleseg/models/backbones/resnet_vd.py b/dygraph/paddleseg/models/backbones/resnet_vd.py
index 89a9048ef57bd1e060edcb33fa8bcda2a5c59a09..bddd6a1391cb2e9935a015da95b3f00efdd9d3bf 100644
--- a/dygraph/paddleseg/models/backbones/resnet_vd.py
+++ b/dygraph/paddleseg/models/backbones/resnet_vd.py
@@ -21,13 +21,11 @@ import math
import numpy as np
import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
from paddle.nn import SyncBatchNorm as BatchNorm
-from paddle.nn import Conv2d
-
-import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
-from paddle.fluid.layer_helper import LayerHelper
-from paddle.fluid.dygraph.nn import Pool2D, Linear, Dropout
+from paddle.nn import Conv2d, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddleseg.utils import utils
from paddleseg.models.common import layer_utils
@@ -38,7 +36,7 @@ __all__ = [
]
-class ConvBNLayer(fluid.dygraph.Layer):
+class ConvBNLayer(nn.Layer):
def __init__(
self,
in_channels,
@@ -54,12 +52,8 @@ class ConvBNLayer(fluid.dygraph.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
- self._pool2d_avg = Pool2D(
- pool_size=2,
- pool_stride=2,
- pool_padding=0,
- pool_type='avg',
- ceil_mode=True)
+ self._pool2d_avg = AvgPool2d(
+ kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = Conv2d(
in_channels=in_channels,
out_channels=out_channels,
@@ -68,16 +62,12 @@ class ConvBNLayer(fluid.dygraph.Layer):
padding=(kernel_size - 1) // 2 if dilation == 1 else 0,
dilation=dilation,
groups=groups,
- weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
- self._batch_norm = BatchNorm(
- out_channels,
- weight_attr=ParamAttr(name=bn_name + '_scale'),
- bias_attr=ParamAttr(bn_name + '_offset'))
+ self._batch_norm = BatchNorm(out_channels)
self._act_op = layer_utils.Activation(act=act)
def forward(self, inputs):
@@ -90,7 +80,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
-class BottleneckBlock(fluid.dygraph.Layer):
+class BottleneckBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
@@ -143,8 +133,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
# If given dilation rate > 1, using corresponding padding
if self.dilation > 1:
padding = self.dilation
- y = fluid.layers.pad(
- y, [0, 0, 0, 0, padding, padding, padding, padding])
+ y = F.pad(y, [0, 0, 0, 0, padding, padding, padding, padding])
#####################################################################
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
@@ -154,12 +143,11 @@ class BottleneckBlock(fluid.dygraph.Layer):
else:
short = self.short(inputs)
- y = fluid.layers.elementwise_add(x=short, y=conv2)
- layer_helper = LayerHelper(self.full_name(), act='relu')
- return layer_helper.append_activation(y)
+ y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+ return y
-class BasicBlock(fluid.dygraph.Layer):
+class BasicBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
@@ -202,13 +190,12 @@ class BasicBlock(fluid.dygraph.Layer):
short = inputs
else:
short = self.short(inputs)
- y = fluid.layers.elementwise_add(x=short, y=conv1)
+ y = paddle.elementwise_add(x=short, y=conv1, act='relu')
- layer_helper = LayerHelper(self.full_name(), act='relu')
- return layer_helper.append_activation(y)
+ return y
-class ResNet_vd(fluid.dygraph.Layer):
+class ResNet_vd(nn.Layer):
def __init__(self,
backbone_pretrained=None,
layers=50,
@@ -264,8 +251,7 @@ class ResNet_vd(fluid.dygraph.Layer):
stride=1,
act='relu',
name="conv1_3")
- self.pool2d_max = Pool2D(
- pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+ self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
# self.block_list = []
self.stage_list = []
@@ -330,23 +316,6 @@ class ResNet_vd(fluid.dygraph.Layer):
shortcut = True
self.stage_list.append(block_list)
- self.pool2d_avg = Pool2D(
- pool_size=7, pool_type='avg', global_pooling=True)
-
- self.pool2d_avg_channels = num_channels[-1] * 2
-
- stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
-
- self.out = Linear(
- self.pool2d_avg_channels,
- class_dim,
- param_attr=ParamAttr(
- initializer=fluid.initializer.Uniform(-stdv, stdv),
- name="fc_0.w_0"),
- bias_attr=ParamAttr(name="fc_0.b_0"))
-
- self.init_weight(backbone_pretrained)
-
def forward(self, inputs):
y = self.conv1_1(inputs)
y = self.conv1_2(y)
@@ -358,33 +327,10 @@ class ResNet_vd(fluid.dygraph.Layer):
for i, stage in enumerate(self.stage_list):
for j, block in enumerate(stage):
y = block(y)
- #print("stage {} block {}".format(i+1, j+1), y.shape)
feat_list.append(y)
- y = self.pool2d_avg(y)
- y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
- y = self.out(y)
return feat_list
- # def init_weight(self, pretrained_model=None):
-
- # if pretrained_model is not None:
- # if os.path.exists(pretrained_model):
- # utils.load_pretrained_model(self, pretrained_model)
-
- def init_weight(self, pretrained_model=None):
- """
- Initialize the parameters of model parts.
- Args:
- pretrained_model ([str], optional): the path of pretrained model. Defaults to None.
- """
- if pretrained_model is not None:
- if os.path.exists(pretrained_model):
- utils.load_pretrained_model(self, pretrained_model)
- else:
- raise Exception('Pretrained model is not found: {}'.format(
- pretrained_model))
-
@manager.BACKBONES.add_component
def ResNet18_vd(**args):
diff --git a/dygraph/paddleseg/models/fcn.py b/dygraph/paddleseg/models/fcn.py
index 12ebfed993bc584608875ac7d5aea27e2822060c..52d9c106f13e9b52cdad6bb8c9819cb34c85eef3 100644
--- a/dygraph/paddleseg/models/fcn.py
+++ b/dygraph/paddleseg/models/fcn.py
@@ -16,13 +16,9 @@ import math
import os
import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
from paddle.nn import Conv2d
-
-import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
-from paddle.fluid.layer_helper import LayerHelper
-from paddle.fluid.dygraph.nn import Pool2D, Linear
-from paddle.fluid.initializer import Normal
from paddle.nn import SyncBatchNorm as BatchNorm
from paddleseg.cvlibs import manager
@@ -39,7 +35,7 @@ __all__ = [
@manager.MODELS.add_component
-class FCN(fluid.dygraph.Layer):
+class FCN(nn.Layer):
"""
Fully Convolutional Networks for Semantic Segmentation.
https://arxiv.org/abs/1411.4038
@@ -96,7 +92,7 @@ class FCN(fluid.dygraph.Layer):
x = fea_list[self.backbone_indices[0]]
x = self.conv_last_2(x)
logit = self.conv_last_1(x)
- logit = fluid.layers.resize_bilinear(logit, input_shape)
+ logit = F.resize_bilinear(logit, input_shape)
return [logit]
def init_weight(self):
@@ -128,7 +124,7 @@ class FCN(fluid.dygraph.Layer):
logger.warning('No pretrained model to load, train from scratch')
-class ConvBNLayer(fluid.dygraph.Layer):
+class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,