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

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