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PaddleSeg
提交 fad18563 编写于 作者: C chenguowei01
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update api to 2.0beta

上级 a960fe3e
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Showing with 121 addition and 213 deletion
dygraph/paddleseg/models/backbones/hrnet.py
浏览文件 @ fad18563
......@@ -16,16 +16,17 @@ import math
import os
import paddle
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 Conv2D, Pool2D, Linear
from paddle.fluid.initializer import Normal
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import SyncBatchNorm as BatchNorm
from paddle.nn import Conv2d, Linear
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddleseg.cvlibs import manager
from paddleseg.utils import utils
from paddleseg.cvlibs import param_init
from paddleseg.models.common import layer_libs
__all__ = [
"HRNet_W18_Small_V1", "HRNet_W18_Small_V2", "HRNet_W18", "HRNet_W30",
......@@ -33,7 +34,7 @@ __all__ = [
]
class HRNet(fluid.dygraph.Layer):
class HRNet(nn.Layer):
"""
HRNet：Deep High-Resolution Representation Learning for Visual Recognition
https://arxiv.org/pdf/1908.07919.pdf.
......@@ -85,21 +86,21 @@ class HRNet(fluid.dygraph.Layer):
self.stage4_num_channels = stage4_num_channels
self.has_se = has_se
self.conv_layer1_1 = ConvBNLayer(
num_channels=3,
num_filters=64,
filter_size=3,
self.conv_layer1_1 = layer_libs.ConvBNReLU(
in_channels=3,
out_channels=64,
kernel_size=3,
stride=2,
act='relu',
name="layer1_1")
padding='same',
bias_attr=False)
self.conv_layer1_2 = ConvBNLayer(
num_channels=64,
num_filters=64,
filter_size=3,
self.conv_layer1_2 = layer_libs.ConvBNReLU(
in_channels=64,
out_channels=64,
kernel_size=3,
stride=2,
act='relu',
name="layer1_2")
padding='same',
bias_attr=False)
self.la1 = Layer1(
num_channels=64,
......@@ -162,45 +163,15 @@ class HRNet(fluid.dygraph.Layer):
st4 = self.st4(tr3)
x0_h, x0_w = st4[0].shape[2:]
x1 = fluid.layers.resize_bilinear(st4[1], out_shape=(x0_h, x0_w))
x2 = fluid.layers.resize_bilinear(st4[2], out_shape=(x0_h, x0_w))
x3 = fluid.layers.resize_bilinear(st4[3], out_shape=(x0_h, x0_w))
x = fluid.layers.concat([st4[0], x1, x2, x3], axis=1)
x1 = F.resize_bilinear(st4[1], out_shape=(x0_h, x0_w))
x2 = F.resize_bilinear(st4[2], out_shape=(x0_h, x0_w))
x3 = F.resize_bilinear(st4[3], out_shape=(x0_h, x0_w))
x = paddle.concat([st4[0], x1, x2, x3], axis=1)
return [x]
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act="relu",
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
bias_attr=False)
self._batch_norm = BatchNorm(num_filters)
self.act = act
def forward(self, input):
y = self._conv(input)
y = self._batch_norm(y)
if self.act == 'relu':
y = fluid.layers.relu(y)
return y
class Layer1(fluid.dygraph.Layer):
class Layer1(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -230,7 +201,7 @@ class Layer1(fluid.dygraph.Layer):
return conv
class TransitionLayer(fluid.dygraph.Layer):
class TransitionLayer(nn.Layer):
def __init__(self, in_channels, out_channels, name=None):
super(TransitionLayer, self).__init__()
......@@ -243,20 +214,22 @@ class TransitionLayer(fluid.dygraph.Layer):
if in_channels[i] != out_channels[i]:
residual = self.add_sublayer(
"transition_{}_layer_{}".format(name, i + 1),
ConvBNLayer(
num_channels=in_channels[i],
num_filters=out_channels[i],
filter_size=3,
name=name + '_layer_' + str(i + 1)))
layer_libs.ConvBNReLU(
in_channels=in_channels[i],
out_channels=out_channels[i],
kernel_size=3,
padding='same',
bias_attr=False))
else:
residual = self.add_sublayer(
"transition_{}_layer_{}".format(name, i + 1),
ConvBNLayer(
num_channels=in_channels[-1],
num_filters=out_channels[i],
filter_size=3,
layer_libs.ConvBNReLU(
in_channels=in_channels[-1],
out_channels=out_channels[i],
kernel_size=3,
stride=2,
name=name + '_layer_' + str(i + 1)))
padding='same',
bias_attr=False))
self.conv_bn_func_list.append(residual)
def forward(self, input):
......@@ -272,7 +245,7 @@ class TransitionLayer(fluid.dygraph.Layer):
return outs
class Branches(fluid.dygraph.Layer):
class Branches(nn.Layer):
def __init__(self,
num_blocks,
in_channels,
......@@ -307,7 +280,7 @@ class Branches(fluid.dygraph.Layer):
return outs
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -320,34 +293,35 @@ class BottleneckBlock(fluid.dygraph.Layer):
self.has_se = has_se
self.downsample = downsample
self.conv1 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act="relu",
name=name + "_conv1",
)
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
self.conv1 = layer_libs.ConvBNReLU(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=1,
padding='same',
bias_attr=False)
self.conv2 = layer_libs.ConvBNReLU(
in_channels=num_filters,
out_channels=num_filters,
kernel_size=3,
stride=stride,
act="relu",
name=name + "_conv2")
self.conv3 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_conv3")
padding='same',
bias_attr=False)
self.conv3 = layer_libs.ConvBN(
in_channels=num_filters,
out_channels=num_filters * 4,
kernel_size=1,
padding='same',
bias_attr=False)
if self.downsample:
self.conv_down = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_downsample")
self.conv_down = layer_libs.ConvBN(
in_channels=num_channels,
out_channels=num_filters * 4,
kernel_size=1,
padding='same',
bias_attr=False)
if self.has_se:
self.se = SELayer(
......@@ -368,11 +342,12 @@ class BottleneckBlock(fluid.dygraph.Layer):
if self.has_se:
conv3 = self.se(conv3)
y = fluid.layers.elementwise_add(x=conv3, y=residual, act="relu")
y = conv3 + residual
y = F.relu(y)
return y
class BasicBlock(fluid.dygraph.Layer):
class BasicBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -385,28 +360,27 @@ class BasicBlock(fluid.dygraph.Layer):
self.has_se = has_se
self.downsample = downsample
self.conv1 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=3,
self.conv1 = layer_libs.ConvBNReLU(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=3,
stride=stride,
act="relu",
name=name + "_conv1")
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=1,
act=None,
name=name + "_conv2")
padding='same',
bias_attr=False)
self.conv2 = layer_libs.ConvBN(
in_channels=num_filters,
out_channels=num_filters,
kernel_size=3,
padding='same',
bias_attr=False)
if self.downsample:
self.conv_down = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
act="relu",
name=name + "_downsample")
self.conv_down = layer_libs.ConvBNReLU(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=1,
padding='same',
bias_attr=False)
if self.has_se:
self.se = SELayer(
......@@ -426,15 +400,16 @@ class BasicBlock(fluid.dygraph.Layer):
if self.has_se:
conv2 = self.se(conv2)
y = fluid.layers.elementwise_add(x=conv2, y=residual, act="relu")
y = conv2 + residual
y = F.relu(y)
return y
class SELayer(fluid.dygraph.Layer):
class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
self.pool2d_gap = Pool2D(pool_type='avg', global_pooling=True)
self.pool2d_gap = AdaptiveAvgPool2d(1)
self._num_channels = num_channels
......@@ -445,9 +420,7 @@ class SELayer(fluid.dygraph.Layer):
med_ch,
act="relu",
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
initializer=nn.initializer.Uniform(-stdv, stdv)))
stdv = 1.0 / math.sqrt(med_ch * 1.0)
self.excitation = Linear(
......@@ -455,22 +428,20 @@ class SELayer(fluid.dygraph.Layer):
num_filters,
act="sigmoid",
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset'))
initializer=nn.initializer.Uniform(-stdv, stdv)))
def forward(self, input):
pool = self.pool2d_gap(input)
pool = fluid.layers.reshape(pool, shape=[-1, self._num_channels])
pool = paddle.reshape(pool, shape=[-1, self._num_channels])
squeeze = self.squeeze(pool)
excitation = self.excitation(squeeze)
excitation = fluid.layers.reshape(
excitation = paddle.reshape(
excitation, shape=[-1, self._num_channels, 1, 1])
out = input * excitation
return out
class Stage(fluid.dygraph.Layer):
class Stage(nn.Layer):
def __init__(self,
num_channels,
num_modules,
......@@ -514,7 +485,7 @@ class Stage(fluid.dygraph.Layer):
return out
class HighResolutionModule(fluid.dygraph.Layer):
class HighResolutionModule(nn.Layer):
def __init__(self,
num_channels,
num_blocks,
......@@ -543,7 +514,7 @@ class HighResolutionModule(fluid.dygraph.Layer):
return out
class FuseLayers(fluid.dygraph.Layer):
class FuseLayers(nn.Layer):
def __init__(self,
in_channels,
out_channels,
......@@ -561,14 +532,12 @@ class FuseLayers(fluid.dygraph.Layer):
if j > i:
residual_func = self.add_sublayer(
"residual_{}_layer_{}_{}".format(name, i + 1, j + 1),
ConvBNLayer(
num_channels=in_channels[j],
num_filters=out_channels[i],
filter_size=1,
stride=1,
act=None,
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1)))
layer_libs.ConvBN(
in_channels=in_channels[j],
out_channels=out_channels[i],
kernel_size=1,
padding='same',
bias_attr=False))
self.residual_func_list.append(residual_func)
elif j < i:
pre_num_filters = in_channels[j]
......@@ -577,27 +546,25 @@ class FuseLayers(fluid.dygraph.Layer):
residual_func = self.add_sublayer(
"residual_{}_layer_{}_{}_{}".format(
name, i + 1, j + 1, k + 1),
ConvBNLayer(
num_channels=pre_num_filters,
num_filters=out_channels[i],
filter_size=3,
layer_libs.ConvBN(
in_channels=pre_num_filters,
out_channels=out_channels[i],
kernel_size=3,
stride=2,
act=None,
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1) + '_' + str(k + 1)))
padding='same',
bias_attr=False))
pre_num_filters = out_channels[i]
else:
residual_func = self.add_sublayer(
"residual_{}_layer_{}_{}_{}".format(
name, i + 1, j + 1, k + 1),
ConvBNLayer(
num_channels=pre_num_filters,
num_filters=out_channels[j],
filter_size=3,
layer_libs.ConvBNReLU(
in_channels=pre_num_filters,
out_channels=out_channels[j],
kernel_size=3,
stride=2,
act="relu",
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1) + '_' + str(k + 1)))
padding='same',
bias_attr=False))
pre_num_filters = out_channels[j]
self.residual_func_list.append(residual_func)
......@@ -612,54 +579,22 @@ class FuseLayers(fluid.dygraph.Layer):
y = self.residual_func_list[residual_func_idx](input[j])
residual_func_idx += 1
y = fluid.layers.resize_bilinear(
input=y, out_shape=residual_shape)
residual = fluid.layers.elementwise_add(
x=residual, y=y, act=None)
y = F.resize_bilinear(input=y, out_shape=residual_shape)
residual = residual + y
elif j < i:
y = input[j]
for k in range(i - j):
y = self.residual_func_list[residual_func_idx](y)
residual_func_idx += 1
residual = fluid.layers.elementwise_add(
x=residual, y=y, act=None)
residual = residual + y
layer_helper = LayerHelper(self.full_name(), act='relu')
residual = layer_helper.append_activation(residual)
residual = F.relu(residual)
outs.append(residual)
return outs
class LastClsOut(fluid.dygraph.Layer):
def __init__(self,
num_channel_list,
has_se,
num_filters_list=[32, 64, 128, 256],
name=None):
super(LastClsOut, self).__init__()
self.func_list = []
for idx in range(len(num_channel_list)):
func = self.add_sublayer(
"conv_{}_conv_{}".format(name, idx + 1),
BottleneckBlock(
num_channels=num_channel_list[idx],
num_filters=num_filters_list[idx],
has_se=has_se,
downsample=True,
name=name + 'conv_' + str(idx + 1)))
self.func_list.append(func)
def forward(self, inputs):
outs = []
for idx, input in enumerate(inputs):
out = self.func_list[idx](input)
outs.append(out)
return outs
@manager.BACKBONES.add_component
def HRNet_W18_Small_V1(**kwargs):
model = HRNet(
......
dygraph/paddleseg/models/fcn.py
浏览文件 @ fad18563
......@@ -72,10 +72,11 @@ class FCN(nn.Layer):
channels = backbone_channels[0]
self.backbone = backbone
self.conv_last_2 = ConvBNLayer(
self.conv_last_2 = layer_libs.ConvBNReLU(
in_channels=backbone_channels[0],
out_channels=channels,
kernel_size=1,
padding='same',
stride=1)
self.conv_last_1 = Conv2d(
in_channels=channels,
......@@ -124,34 +125,6 @@ class FCN(nn.Layer):
logger.warning('No pretrained model to load, train from scratch')
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act="relu"):
super(ConvBNLayer, self).__init__()
self._conv = Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
bias_attr=False)
self._batch_norm = BatchNorm(out_channels)
self.act = activation.Activation(act=act)
def forward(self, input):
y = self._conv(input)
y = self._batch_norm(y)
y = self.act(y)
return y
@manager.MODELS.add_component
def fcn_hrnet_w18_small_v1(*args, **kwargs):
return FCN(backbone='HRNet_W18_Small_V1', backbone_channels=(240), **kwargs)
......
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