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提交 47c31c15 编写于 作者: S sunyanfang01
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add ssld

上级 c5a1dc18
隐藏空白更改
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Showing with 226 addition and 139 deletion
paddlex/cls.py
浏览文件 @ 47c31c15
......@@ -21,11 +21,15 @@ ResNet50 = cv.models.ResNet50
ResNet101 = cv.models.ResNet101
ResNet50_vd = cv.models.ResNet50_vd
ResNet101_vd = cv.models.ResNet101_vd
ResNet50_vd_ssld = cv.models.ResNet50_vd_ssld
ResNet101_vd_ssld = cv.models.ResNet101_vd_ssld
DarkNet53 = cv.models.DarkNet53
MobileNetV1 = cv.models.MobileNetV1
MobileNetV2 = cv.models.MobileNetV2
MobileNetV3_small = cv.models.MobileNetV3_small
MobileNetV3_large = cv.models.MobileNetV3_large
MobileNetV3_small_ssld = cv.models.MobileNetV3_small_ssld
MobileNetV3_large_ssld = cv.models.MobileNetV3_large_ssld
Xception41 = cv.models.Xception41
Xception65 = cv.models.Xception65
DenseNet121 = cv.models.DenseNet121
......
paddlex/cv/models/__init__.py
浏览文件 @ 47c31c15
......@@ -19,11 +19,15 @@ from .classifier import ResNet50
from .classifier import ResNet101
from .classifier import ResNet50_vd
from .classifier import ResNet101_vd
from .classifier import ResNet50_vd_ssld
from .classifier import ResNet101_vd_ssld
from .classifier import DarkNet53
from .classifier import MobileNetV1
from .classifier import MobileNetV2
from .classifier import MobileNetV3_small
from .classifier import MobileNetV3_large
from .classifier import MobileNetV3_small_ssld
from .classifier import MobileNetV3_large_ssld
from .classifier import Xception41
from .classifier import Xception65
from .classifier import DenseNet121
......
paddlex/cv/models/classifier.py
浏览文件 @ 47c31c15
......@@ -300,6 +300,17 @@ class ResNet101_vd(BaseClassifier):
def __init__(self, num_classes=1000):
super(ResNet101_vd, self).__init__(
model_name='ResNet101_vd', num_classes=num_classes)
class ResNet50_vd_ssld(BaseClassifier):
def __init__(self, num_classes=1000):
super(ResNet50_vd_ssld, self).__init__(model_name='ResNet50_vd_ssld',
num_classes=num_classes)
class ResNet101_vd_ssld(BaseClassifier):
def __init__(self, num_classes=1000):
super(ResNet101_vd_ssld, self).__init__(model_name='ResNet101_vd_ssld',
num_classes=num_classes)
class DarkNet53(BaseClassifier):
......@@ -330,6 +341,19 @@ class MobileNetV3_large(BaseClassifier):
def __init__(self, num_classes=1000):
super(MobileNetV3_large, self).__init__(
model_name='MobileNetV3_large', num_classes=num_classes)
class MobileNetV3_small_ssld(BaseClassifier):
def __init__(self, num_classes=1000):
super(MobileNetV3_small_ssld, self).__init__(model_name='MobileNetV3_small_ssld',
num_classes=num_classes)
class MobileNetV3_large_ssld(BaseClassifier):
def __init__(self, num_classes=1000):
super(MobileNetV3_large_ssld, self).__init__(model_name='MobileNetV3_large_ssld',
num_classes=num_classes)
class Xception65(BaseClassifier):
......
paddlex/cv/models/utils/pretrain_weights.py
浏览文件 @ 47c31c15
......@@ -16,6 +16,10 @@ image_pretrain = {
'https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar',
'ResNet101_vd':
'https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar',
'ResNet50_vd_ssld':
'https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar',
'ResNet101_vd_ssld':
'https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_ssld_pretrained.tar',
'MobileNetV1':
'http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar',
'MobileNetV2_x1.0':
......@@ -32,6 +36,10 @@ image_pretrain = {
'https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_pretrained.tar',
'MobileNetV3_large':
'https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar',
'MobileNetV3_small_x1_0_ssld':
'https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar',
'MobileNetV3_large_x1_0_ssld':
'https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar',
'DarkNet53':
'https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_ImageNet1k_pretrained.tar',
'DenseNet121':
......@@ -68,6 +76,10 @@ def get_pretrain_weights(flag, model_type, backbone, save_dir):
backbone = 'Seg{}'.format(backbone)
elif backbone == 'MobileNetV2':
backbone = 'MobileNetV2_x1.0'
elif backbone == 'MobileNetV3_small_ssld':
backbone = 'MobileNetV3_small_x1_0_ssld'
elif backbone == 'MobileNetV3_large_ssld':
backbone = 'MobileNetV3_large_x1_0_ssld'
if model_type == 'detector':
if backbone == 'ResNet50':
backbone = 'DetResNet50'
......
paddlex/cv/nets/__init__.py
浏览文件 @ 47c31c15
......@@ -50,6 +50,18 @@ def resnet50_vd(input, num_classes=1000):
return model(input)
def resnet50_vd_ssld(input, num_classes=1000):
model = ResNet(layers=50, num_classes=num_classes,
variant='d', lr_mult_list=[1.0, 0.1, 0.2, 0.2, 0.3])
return model(input)
def resnet101_vd_ssld(input, num_classes=1000):
model = ResNet(layers=101, num_classes=num_classes,
variant='d', lr_mult_list=[1.0, 0.1, 0.2, 0.2, 0.3])
return model(input)
def resnet101_vd(input, num_classes=1000):
model = ResNet(layers=101, num_classes=num_classes, variant='d')
return model(input)
......@@ -80,6 +92,18 @@ def mobilenetv3_large(input, num_classes=1000):
return model(input)
def mobilenetv3_small_ssld(input, num_classes=1000):
model = MobileNetV3(num_classes=num_classes, model_name='small',
lr_mult_list=[0.25, 0.25, 0.5, 0.5, 0.75])
return model(input)
def mobilenetv3_large_ssld(input, num_classes=1000):
model = MobileNetV3(num_classes=num_classes, model_name='large',
lr_mult_list=[0.25, 0.25, 0.5, 0.5, 0.75])
return model(input)
def xception65(input, num_classes=1000):
model = Xception(layers=65, num_classes=num_classes)
return model(input)
......@@ -109,7 +133,6 @@ def densenet201(input, num_classes=1000):
model = DenseNet(layers=201, num_classes=num_classes)
return model(input)
def shufflenetv2(input, num_classes=1000):
model = ShuffleNetV2(num_classes=num_classes)
return model(input)
paddlex/cv/nets/mobilenet_v3.py
浏览文件 @ 47c31c15
......@@ -31,7 +31,6 @@ class MobileNetV3():
with_extra_blocks (bool): if extra blocks should be added.
extra_block_filters (list): number of filter for each extra block.
"""
def __init__(self,
scale=1.0,
model_name='small',
......@@ -41,7 +40,11 @@ class MobileNetV3():
norm_decay=0.0,
extra_block_filters=[[256, 512], [128, 256], [128, 256],
[64, 128]],
num_classes=None):
num_classes=None,
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
assert len(lr_mult_list) == 5, \
"lr_mult_list length in MobileNetV3 must be 5 but got {}!!".format(
len(lr_mult_list))
self.scale = scale
self.with_extra_blocks = with_extra_blocks
self.extra_block_filters = extra_block_filters
......@@ -51,6 +54,8 @@ class MobileNetV3():
self.end_points = []
self.block_stride = 1
self.num_classes = num_classes
self.lr_mult_list = lr_mult_list
self.curr_stage = 0
if model_name == "large":
self.cfg = [
# kernel_size, expand, channel, se_block, act_mode, stride
......@@ -72,6 +77,7 @@ class MobileNetV3():
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
self.lr_interval = 3
elif model_name == "small":
self.cfg = [
# kernel_size, expand, channel, se_block, act_mode, stride
......@@ -89,6 +95,7 @@ class MobileNetV3():
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
self.lr_interval = 2
else:
raise NotImplementedError
......@@ -103,30 +110,32 @@ class MobileNetV3():
act=None,
name=None,
use_cudnn=True):
conv_param_attr = ParamAttr(
name=name + '_weights', regularizer=L2Decay(self.conv_decay))
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=conv_param_attr,
bias_attr=False)
lr_idx = self.curr_stage // self.lr_interval
lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
lr_mult = self.lr_mult_list[lr_idx]
conv_param_attr = ParamAttr(name=name + '_weights',
learning_rate=lr_mult,
regularizer=L2Decay(self.conv_decay))
conv = fluid.layers.conv2d(input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=conv_param_attr,
bias_attr=False)
bn_name = name + '_bn'
bn_param_attr = ParamAttr(
name=bn_name + "_scale", regularizer=L2Decay(self.norm_decay))
bn_bias_attr = ParamAttr(
name=bn_name + "_offset", regularizer=L2Decay(self.norm_decay))
bn = fluid.layers.batch_norm(
input=conv,
param_attr=bn_param_attr,
bias_attr=bn_bias_attr,
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
bn_param_attr = ParamAttr(name=bn_name + "_scale",
regularizer=L2Decay(self.norm_decay))
bn_bias_attr = ParamAttr(name=bn_name + "_offset",
regularizer=L2Decay(self.norm_decay))
bn = fluid.layers.batch_norm(input=conv,
param_attr=bn_param_attr,
bias_attr=bn_bias_attr,
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
if if_act:
if act == 'relu':
bn = fluid.layers.relu(bn)
......@@ -140,23 +149,33 @@ class MobileNetV3():
return x * fluid.layers.relu6(x + 3) / 6.
def _se_block(self, input, num_out_filter, ratio=4, name=None):
lr_idx = self.curr_stage // self.lr_interval
lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
lr_mult = self.lr_mult_list[lr_idx]
num_mid_filter = int(num_out_filter // ratio)
pool = fluid.layers.pool2d(
input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
pool = fluid.layers.pool2d(input=input,
pool_type='avg',
global_pooling=True,
use_cudnn=False)
conv1 = fluid.layers.conv2d(
input=pool,
filter_size=1,
num_filters=num_mid_filter,
act='relu',
param_attr=ParamAttr(name=name + '_1_weights'),
bias_attr=ParamAttr(name=name + '_1_offset'))
param_attr=ParamAttr(
name=name + '_1_weights', learning_rate=lr_mult),
bias_attr=ParamAttr(
name=name + '_1_offset', learning_rate=lr_mult))
conv2 = fluid.layers.conv2d(
input=conv1,
filter_size=1,
num_filters=num_out_filter,
act='hard_sigmoid',
param_attr=ParamAttr(name=name + '_2_weights'),
bias_attr=ParamAttr(name=name + '_2_offset'))
param_attr=ParamAttr(
name=name + '_2_weights', learning_rate=lr_mult),
bias_attr=ParamAttr(
name=name + '_2_offset', learning_rate=lr_mult))
scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
return scale
......@@ -172,46 +191,43 @@ class MobileNetV3():
use_se=False,
name=None):
input_data = input
conv0 = self._conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_mid_filter,
stride=1,
padding=0,
if_act=True,
act=act,
name=name + '_expand')
conv0 = self._conv_bn_layer(input=input,
filter_size=1,
num_filters=num_mid_filter,
stride=1,
padding=0,
if_act=True,
act=act,
name=name + '_expand')
if self.block_stride == 16 and stride == 2:
self.end_points.append(conv0)
conv1 = self._conv_bn_layer(
input=conv0,
filter_size=filter_size,
num_filters=num_mid_filter,
stride=stride,
padding=int((filter_size - 1) // 2),
if_act=True,
act=act,
num_groups=num_mid_filter,
use_cudnn=False,
name=name + '_depthwise')
conv1 = self._conv_bn_layer(input=conv0,
filter_size=filter_size,
num_filters=num_mid_filter,
stride=stride,
padding=int((filter_size - 1) // 2),
if_act=True,
act=act,
num_groups=num_mid_filter,
use_cudnn=False,
name=name + '_depthwise')
if use_se:
conv1 = self._se_block(
input=conv1, num_out_filter=num_mid_filter, name=name + '_se')
conv1 = self._se_block(input=conv1,
num_out_filter=num_mid_filter,
name=name + '_se')
conv2 = self._conv_bn_layer(
input=conv1,
filter_size=1,
num_filters=num_out_filter,
stride=1,
padding=0,
if_act=False,
name=name + '_linear')
conv2 = self._conv_bn_layer(input=conv1,
filter_size=1,
num_filters=num_out_filter,
stride=1,
padding=0,
if_act=False,
name=name + '_linear')
if num_in_filter != num_out_filter or stride != 1:
return conv2
else:
return fluid.layers.elementwise_add(
x=input_data, y=conv2, act=None)
return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None)
def _extra_block_dw(self,
input,
......@@ -219,32 +235,29 @@ class MobileNetV3():
num_filters2,
stride,
name=None):
pointwise_conv = self._conv_bn_layer(
input=input,
filter_size=1,
num_filters=int(num_filters1),
stride=1,
padding="SAME",
act='relu6',
name=name + "_extra1")
depthwise_conv = self._conv_bn_layer(
input=pointwise_conv,
filter_size=3,
num_filters=int(num_filters2),
stride=stride,
padding="SAME",
num_groups=int(num_filters1),
act='relu6',
use_cudnn=False,
name=name + "_extra2_dw")
normal_conv = self._conv_bn_layer(
input=depthwise_conv,
filter_size=1,
num_filters=int(num_filters2),
stride=1,
padding="SAME",
act='relu6',
name=name + "_extra2_sep")
pointwise_conv = self._conv_bn_layer(input=input,
filter_size=1,
num_filters=int(num_filters1),
stride=1,
padding="SAME",
act='relu6',
name=name + "_extra1")
depthwise_conv = self._conv_bn_layer(input=pointwise_conv,
filter_size=3,
num_filters=int(num_filters2),
stride=stride,
padding="SAME",
num_groups=int(num_filters1),
act='relu6',
use_cudnn=False,
name=name + "_extra2_dw")
normal_conv = self._conv_bn_layer(input=depthwise_conv,
filter_size=1,
num_filters=int(num_filters2),
stride=1,
padding="SAME",
act='relu6',
name=name + "_extra2_sep")
return normal_conv
def __call__(self, input):
......@@ -269,38 +282,36 @@ class MobileNetV3():
self.block_stride *= layer_cfg[5]
if layer_cfg[5] == 2:
blocks.append(conv)
conv = self._residual_unit(
input=conv,
num_in_filter=inplanes,
num_mid_filter=int(scale * layer_cfg[1]),
num_out_filter=int(scale * layer_cfg[2]),
act=layer_cfg[4],
stride=layer_cfg[5],
filter_size=layer_cfg[0],
use_se=layer_cfg[3],
name='conv' + str(i + 2))
conv = self._residual_unit(input=conv,
num_in_filter=inplanes,
num_mid_filter=int(scale * layer_cfg[1]),
num_out_filter=int(scale * layer_cfg[2]),
act=layer_cfg[4],
stride=layer_cfg[5],
filter_size=layer_cfg[0],
use_se=layer_cfg[3],
name='conv' + str(i + 2))
inplanes = int(scale * layer_cfg[2])
i += 1
self.curr_stage = i
blocks.append(conv)
if self.num_classes:
conv = self._conv_bn_layer(
input=conv,
filter_size=1,
num_filters=int(scale * self.cls_ch_squeeze),
stride=1,
padding=0,
num_groups=1,
if_act=True,
act='hard_swish',
name='conv_last')
conv = fluid.layers.pool2d(
input=conv,
pool_type='avg',
global_pooling=True,
use_cudnn=False)
conv = self._conv_bn_layer(input=conv,
filter_size=1,
num_filters=int(scale * self.cls_ch_squeeze),
stride=1,
padding=0,
num_groups=1,
if_act=True,
act='hard_swish',
name='conv_last')
conv = fluid.layers.pool2d(input=conv,
pool_type='avg',
global_pooling=True,
use_cudnn=False)
conv = fluid.layers.conv2d(
input=conv,
num_filters=self.cls_ch_expand,
......@@ -312,27 +323,25 @@ class MobileNetV3():
bias_attr=False)
conv = self._hard_swish(conv)
drop = fluid.layers.dropout(x=conv, dropout_prob=0.2)
out = fluid.layers.fc(
input=drop,
size=self.num_classes,
param_attr=ParamAttr(name='fc_weights'),
bias_attr=ParamAttr(name='fc_offset'))
out = fluid.layers.fc(input=drop,
size=self.num_classes,
param_attr=ParamAttr(name='fc_weights'),
bias_attr=ParamAttr(name='fc_offset'))
return out
if not self.with_extra_blocks:
return blocks
# extra block
conv_extra = self._conv_bn_layer(
conv,
filter_size=1,
num_filters=int(scale * cfg[-1][1]),
stride=1,
padding="SAME",
num_groups=1,
if_act=True,
act='hard_swish',
name='conv' + str(i + 2))
conv_extra = self._conv_bn_layer(conv,
filter_size=1,
num_filters=int(scale * cfg[-1][1]),
stride=1,
padding="SAME",
num_groups=1,
if_act=True,
act='hard_swish',
name='conv' + str(i + 2))
self.end_points.append(conv_extra)
i += 1
for block_filter in self.extra_block_filters:
......
paddlex/cv/nets/resnet.py
浏览文件 @ 47c31c15
......@@ -65,7 +65,8 @@ class ResNet(object):
nonlocal_stages=[],
gcb_stages=[],
gcb_params=dict(),
num_classes=None):
num_classes=None,
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
super(ResNet, self).__init__()
if isinstance(feature_maps, Integral):
......@@ -79,6 +80,10 @@ class ResNet(object):
assert norm_type in ['bn', 'sync_bn', 'affine_channel']
assert not (len(nonlocal_stages)>0 and layers<50), \
"non-local is not supported for resnet18 or resnet34"
assert len(
lr_mult_list
) == 5, "lr_mult_list length in ResNet must be 5 but got {}!!".format(
len(lr_mult_list))
self.layers = layers
self.freeze_at = freeze_at
......@@ -113,6 +118,8 @@ class ResNet(object):
self.gcb_stages = gcb_stages
self.gcb_params = gcb_params
self.num_classes = num_classes
self.lr_mult_list = lr_mult_list
self.curr_stage = 0
def _conv_offset(self,
input,
......@@ -128,8 +135,7 @@ class ResNet(object):
filter_size=filter_size,
stride=stride,
padding=padding,
param_attr=ParamAttr(
initializer=Constant(0.0), name=name + ".w_0"),
param_attr=ParamAttr(initializer=Constant(0.0), name=name + ".w_0"),
bias_attr=ParamAttr(initializer=Constant(0.0), name=name + ".b_0"),
act=act,
name=name)
......@@ -143,7 +149,9 @@ class ResNet(object):
groups=1,
act=None,
name=None,
dcn_v2=False):
dcn_v2=False,
use_lr_mult_list=False):
lr_mult = self.lr_mult_list[self.curr_stage] if use_lr_mult_list else 1.0
_name = self.prefix_name + name if self.prefix_name != '' else name
if not dcn_v2:
conv = fluid.layers.conv2d(
......@@ -154,7 +162,8 @@ class ResNet(object):
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=_name + "_weights"),
param_attr=ParamAttr(name=_name + "_weights",
learning_rate=lr_mult),
bias_attr=False,
name=_name + '.conv2d.output.1')
else:
......@@ -191,7 +200,7 @@ class ResNet(object):
bn_name = self.na.fix_conv_norm_name(name)
bn_name = self.prefix_name + bn_name if self.prefix_name != '' else bn_name
norm_lr = 0. if self.freeze_norm else 1.
norm_lr = 0. if self.freeze_norm else lr_mult
norm_decay = self.norm_decay
pattr = ParamAttr(
name=bn_name + '_scale',
......@@ -253,7 +262,8 @@ class ResNet(object):
pool_padding=0,
ceil_mode=True,
pool_type='avg')
return self._conv_norm(input, ch_out, 1, 1, name=name)
return self._conv_norm(input, ch_out, 1, 1, name=name,
use_lr_mult_list=True)
return self._conv_norm(input, ch_out, 1, stride, name=name)
else:
return input
......@@ -448,6 +458,7 @@ class ResNet(object):
feature_maps = range(2, max(self.feature_maps) + 1)
for i in feature_maps:
self.curr_stage += 1
res = self.layer_warp(res, i)
if i in self.feature_maps:
res_endpoints.append(res)
......
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