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未验证 提交 1a74e9cb 编写于 作者: C cuicheng01 提交者： GitHub
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Merge pull request #756 from RainFrost1/develop_reg 

legendary models v0.1
上级 4d246c20 2fa80851
展开全部 隐藏空白更改
内联 并排
Showing with 1549 addition and 839 deletion
ppcls/arch/backbone/legendary_models/__init__.py
浏览文件 @ 1a74e9cb
from .resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet101_vd, ResNet152_vd
from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W64_C
from .mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75, MobileNetV1
from .mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
from .inception_v3 import InceptionV3
from .vgg import VGG11, VGG13, VGG16, VGG19
ppcls/arch/backbone/legendary_models/mobilenet_v1.py
浏览文件 @ 1a74e9cb
......@@ -14,8 +14,6 @@
from __future__ import absolute_import, division, print_function
import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2D, BatchNorm, Linear, ReLU, Flatten
......@@ -23,19 +21,22 @@ from paddle.nn import AdaptiveAvgPool2D
from paddle.nn.initializer import KaimingNormal
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain_from, load_dygraph_pretrain_from_url
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"MobileNetV1_x0_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_25_pretrained.pdparams",
"MobileNetV1_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_5_pretrained.pdparams",
"MobileNetV1_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_75_pretrained.pdparams",
"MobileNetV1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_pretrained.pdparams",
"MobileNetV1_x0_25":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams",
"MobileNetV1_x0_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams",
"MobileNetV1_x0_75":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams",
"MobileNetV1":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams"
}
__all__ = MODEL_URLS.keys()
class ConvBNLayer(TheseusLayer):
def __init__(self,
num_channels,
......@@ -44,7 +45,7 @@ class ConvBNLayer(TheseusLayer):
stride,
padding,
num_groups=1):
super(ConvBNLayer, self).__init__()
super().__init__()
self.conv = Conv2D(
in_channels=num_channels,
......@@ -55,9 +56,7 @@ class ConvBNLayer(TheseusLayer):
groups=num_groups,
weight_attr=ParamAttr(initializer=KaimingNormal()),
bias_attr=False)
self.bn = BatchNorm(num_filters)
self.relu = ReLU()
def forward(self, x):
......@@ -68,14 +67,9 @@ class ConvBNLayer(TheseusLayer):
class DepthwiseSeparable(TheseusLayer):
def __init__(self,
num_channels,
num_filters1,
num_filters2,
num_groups,
stride,
scale):
super(DepthwiseSeparable, self).__init__()
def __init__(self, num_channels, num_filters1, num_filters2, num_groups,
stride, scale):
super().__init__()
self.depthwise_conv = ConvBNLayer(
num_channels=num_channels,
......@@ -99,10 +93,18 @@ class DepthwiseSeparable(TheseusLayer):
class MobileNet(TheseusLayer):
def __init__(self, scale=1.0, class_num=1000, pretrained=False):
super(MobileNet, self).__init__()
"""
MobileNet
Args:
scale: float=1.0. The coefficient that controls the size of network parameters.
class_num: int=1000. The number of classes.
Returns:
model: nn.Layer. Specific MobileNet model depends on args.
"""
def __init__(self, scale=1.0, class_num=1000):
super().__init__()
self.scale = scale
self.pretrained = pretrained
self.conv = ConvBNLayer(
num_channels=3,
......@@ -110,30 +112,31 @@ class MobileNet(TheseusLayer):
num_filters=int(32 * scale),
stride=2,
padding=1)
#num_channels, num_filters1, num_filters2, num_groups, stride
self.cfg = [[int(32 * scale), 32, 64, 32, 1],
[int(64 * scale), 64, 128, 64, 2],
[int(128 * scale), 128, 128, 128, 1],
[int(128 * scale), 128, 256, 128, 2],
[int(256 * scale), 256, 256, 256, 1],
[int(256 * scale), 256, 512, 256, 2],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 1024, 512, 2],
self.cfg = [[int(32 * scale), 32, 64, 32, 1],
[int(64 * scale), 64, 128, 64, 2],
[int(128 * scale), 128, 128, 128, 1],
[int(128 * scale), 128, 256, 128, 2],
[int(256 * scale), 256, 256, 256, 1],
[int(256 * scale), 256, 512, 256, 2],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 512, 512, 1],
[int(512 * scale), 512, 1024, 512, 2],
[int(1024 * scale), 1024, 1024, 1024, 1]]
self.blocks = nn.Sequential(*[
DepthwiseSeparable(
num_channels=params[0],
num_filters1=params[1],
num_filters2=params[2],
num_groups=params[3],
stride=params[4],
scale=scale) for params in self.cfg])
DepthwiseSeparable(
num_channels=params[0],
num_filters1=params[1],
num_filters2=params[2],
num_groups=params[3],
stride=params[4],
scale=scale) for params in self.cfg
])
self.avg_pool = AdaptiveAvgPool2D(1)
self.flatten = Flatten(start_axis=1, stop_axis=-1)
......@@ -142,7 +145,7 @@ class MobileNet(TheseusLayer):
int(1024 * scale),
class_num,
weight_attr=ParamAttr(initializer=KaimingNormal()))
def forward(self, x):
x = self.conv(x)
x = self.blocks(x)
......@@ -152,91 +155,77 @@ class MobileNet(TheseusLayer):
return x
def MobileNetV1_x0_25(**args):
"""
MobileNetV1_x0_25
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_num: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_25` model depends on args.
"""
model = MobileNet(scale=0.25, **args)
if isinstance(model.pretrained, bool):
if model.pretrained is True:
load_dygraph_pretrain_from_url(model, MODEL_URLS["MobileNetV1_x0_25"])
elif isinstance(model.pretrained, str):
load_dygraph_pretrain(model, model.pretrained)
def _load_pretrained(pretrained, model, model_url, use_ssld):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type")
return model
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MobileNetV1_x0_5(**args):
def MobileNetV1_x0_25(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV1_x0_5
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_num: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_5` model depends on args.
MobileNetV1_x0_25
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_25` model depends on args.
"""
model = MobileNet(scale=0.5, **args)
if isinstance(model.pretrained, bool):
if model.pretrained is True:
load_dygraph_pretrain_from_url(model, MODEL_URLS["MobileNetV1_x0_5"])
elif isinstance(model.pretrained, str):
load_dygraph_pretrain(model, model.pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type")
model = MobileNet(scale=0.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_25"],
use_ssld)
return model
def MobileNetV1_x0_75(**args):
def MobileNetV1_x0_5(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV1_x0_75
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_num: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_75` model depends on args.
MobileNetV1_x0_5
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_5` model depends on args.
"""
model = MobileNet(scale=0.75, **args)
if isinstance(model.pretrained, bool):
if model.pretrained is True:
load_dygraph_pretrain_from_url(model, MODEL_URLS["MobileNetV1_x0_75"])
elif isinstance(model.pretrained, str):
load_dygraph_pretrain(model, model.pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type")
model = MobileNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_5"],
use_ssld)
return model
def MobileNetV1(**args):
def MobileNetV1_x0_75(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV1
Args:
pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
kwargs:
class_num: int=1000. Output dim of last fc layer.
Returns:
model: nn.Layer. Specific `MobileNetV1` model depends on args.
MobileNetV1_x0_75
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV1_x0_75` model depends on args.
"""
model = MobileNet(scale=1.0, **args)
if isinstance(model.pretrained, bool):
if model.pretrained is True:
load_dygraph_pretrain_from_url(model, MODEL_URLS["MobileNetV1"])
elif isinstance(model.pretrained, str):
load_dygraph_pretrain(model, model.pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type")
model = MobileNet(scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_75"],
use_ssld)
return model
def MobileNetV1(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV1
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV1` model depends on args.
"""
model = MobileNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1"], use_ssld)
return model
ppcls/arch/backbone/legendary_models/mobilenet_v3.py 0 → 100644
浏览文件 @ 1a74e9cb
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import, division, print_function
import paddle
import paddle.nn as nn
from paddle import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
from paddle.regularizer import L2Decay
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"MobileNetV3_small_x0_35":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams",
"MobileNetV3_small_x0_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams",
"MobileNetV3_small_x0_75":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams",
"MobileNetV3_small_x1_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams",
"MobileNetV3_small_x1_25":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams",
"MobileNetV3_large_x0_35":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams",
"MobileNetV3_large_x0_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams",
"MobileNetV3_large_x0_75":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams",
"MobileNetV3_large_x1_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams",
"MobileNetV3_large_x1_25":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams",
}
__all__ = MODEL_URLS.keys()
# "large", "small" is just for MobinetV3_large, MobileNetV3_small respectively.
# The type of "large" or "small" config is a list. Each element(list) represents a depthwise block, which is composed of k, exp, se, act, s.
# k: kernel_size
# exp: middle channel number in depthwise block
# c: output channel number in depthwise block
# se: whether to use SE block
# act: which activation to use
# s: stride in depthwise block
NET_CONFIG = {
"large": [
# k, exp, c, se, act, s
[3, 16, 16, False, "relu", 1],
[3, 64, 24, False, "relu", 2],
[3, 72, 24, False, "relu", 1],
[5, 72, 40, True, "relu", 2],
[5, 120, 40, True, "relu", 1],
[5, 120, 40, True, "relu", 1],
[3, 240, 80, False, "hardswish", 2],
[3, 200, 80, False, "hardswish", 1],
[3, 184, 80, False, "hardswish", 1],
[3, 184, 80, False, "hardswish", 1],
[3, 480, 112, True, "hardswish", 1],
[3, 672, 112, True, "hardswish", 1],
[5, 672, 160, True, "hardswish", 2],
[5, 960, 160, True, "hardswish", 1],
[5, 960, 160, True, "hardswish", 1],
],
"small": [
# k, exp, c, se, act, s
[3, 16, 16, True, "relu", 2],
[3, 72, 24, False, "relu", 2],
[3, 88, 24, False, "relu", 1],
[5, 96, 40, True, "hardswish", 2],
[5, 240, 40, True, "hardswish", 1],
[5, 240, 40, True, "hardswish", 1],
[5, 120, 48, True, "hardswish", 1],
[5, 144, 48, True, "hardswish", 1],
[5, 288, 96, True, "hardswish", 2],
[5, 576, 96, True, "hardswish", 1],
[5, 576, 96, True, "hardswish", 1],
]
}
# first conv output channel number in MobileNetV3
STEM_CONV_NUMBER = 16
# last second conv output channel for "small"
LAST_SECOND_CONV_SMALL = 576
# last second conv output channel for "large"
LAST_SECOND_CONV_LARGE = 960
# last conv output channel number for "large" and "small"
LAST_CONV = 1280
def _make_divisible(v, divisor=8, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
def _create_act(act):
if act == "hardswish":
return nn.Hardswish()
elif act == "relu":
return nn.ReLU()
elif act is None:
return None
else:
raise RuntimeError(
"The activation function is not supported: {}".format(act))
class MobileNetV3(TheseusLayer):
"""
MobileNetV3
Args:
config: list. MobileNetV3 depthwise blocks config.
scale: float=1.0. The coefficient that controls the size of network parameters.
class_num: int=1000. The number of classes.
inplanes: int=16. The output channel number of first convolution layer.
class_squeeze: int=960. The output channel number of penultimate convolution layer.
class_expand: int=1280. The output channel number of last convolution layer.
dropout_prob: float=0.2. Probability of setting units to zero.
Returns:
model: nn.Layer. Specific MobileNetV3 model depends on args.
"""
def __init__(self,
config,
scale=1.0,
class_num=1000,
inplanes=STEM_CONV_NUMBER,
class_squeeze=LAST_SECOND_CONV_LARGE,
class_expand=LAST_CONV,
dropout_prob=0.2):
super().__init__()
self.cfg = config
self.scale = scale
self.inplanes = inplanes
self.class_squeeze = class_squeeze
self.class_expand = class_expand
self.class_num = class_num
self.conv = ConvBNLayer(
in_c=3,
out_c=_make_divisible(self.inplanes * self.scale),
filter_size=3,
stride=2,
padding=1,
num_groups=1,
if_act=True,
act="hardswish")
self.blocks = nn.Sequential(*[
ResidualUnit(
in_c=_make_divisible(self.inplanes * self.scale if i == 0 else
self.cfg[i - 1][2] * self.scale),
mid_c=_make_divisible(self.scale * exp),
out_c=_make_divisible(self.scale * c),
filter_size=k,
stride=s,
use_se=se,
act=act) for i, (k, exp, c, se, act, s) in enumerate(self.cfg)
])
self.last_second_conv = ConvBNLayer(
in_c=_make_divisible(self.cfg[-1][2] * self.scale),
out_c=_make_divisible(self.scale * self.class_squeeze),
filter_size=1,
stride=1,
padding=0,
num_groups=1,
if_act=True,
act="hardswish")
self.avg_pool = AdaptiveAvgPool2D(1)
self.last_conv = Conv2D(
in_channels=_make_divisible(self.scale * self.class_squeeze),
out_channels=self.class_expand,
kernel_size=1,
stride=1,
padding=0,
bias_attr=False)
self.hardswish = nn.Hardswish()
self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
self.fc = Linear(self.class_expand, class_num)
def forward(self, x):
x = self.conv(x)
x = self.blocks(x)
x = self.last_second_conv(x)
x = self.avg_pool(x)
x = self.last_conv(x)
x = self.hardswish(x)
x = self.dropout(x)
x = self.flatten(x)
x = self.fc(x)
return x
class ConvBNLayer(TheseusLayer):
def __init__(self,
in_c,
out_c,
filter_size,
stride,
padding,
num_groups=1,
if_act=True,
act=None):
super().__init__()
self.conv = Conv2D(
in_channels=in_c,
out_channels=out_c,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
bias_attr=False)
self.bn = BatchNorm(
num_channels=out_c,
act=None,
param_attr=ParamAttr(regularizer=L2Decay(0.0)),
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
self.if_act = if_act
self.act = _create_act(act)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.if_act:
x = self.act(x)
return x
class ResidualUnit(TheseusLayer):
def __init__(self,
in_c,
mid_c,
out_c,
filter_size,
stride,
use_se,
act=None):
super().__init__()
self.if_shortcut = stride == 1 and in_c == out_c
self.if_se = use_se
self.expand_conv = ConvBNLayer(
in_c=in_c,
out_c=mid_c,
filter_size=1,
stride=1,
padding=0,
if_act=True,
act=act)
self.bottleneck_conv = ConvBNLayer(
in_c=mid_c,
out_c=mid_c,
filter_size=filter_size,
stride=stride,
padding=int((filter_size - 1) // 2),
num_groups=mid_c,
if_act=True,
act=act)
if self.if_se:
self.mid_se = SEModule(mid_c)
self.linear_conv = ConvBNLayer(
in_c=mid_c,
out_c=out_c,
filter_size=1,
stride=1,
padding=0,
if_act=False,
act=None)
def forward(self, x):
identity = x
x = self.expand_conv(x)
x = self.bottleneck_conv(x)
if self.if_se:
x = self.mid_se(x)
x = self.linear_conv(x)
if self.if_shortcut:
x = paddle.add(identity, x)
return x
# nn.Hardsigmoid can't transfer "slope" and "offset" in nn.functional.hardsigmoid
class Hardsigmoid(TheseusLayer):
def __init__(self, slope=0.2, offset=0.5):
super().__init__()
self.slope = slope
self.offset = offset
def forward(self, x):
return nn.functional.hardsigmoid(
x, slope=self.slope, offset=self.offset)
class SEModule(TheseusLayer):
def __init__(self, channel, reduction=4):
super().__init__()
self.avg_pool = AdaptiveAvgPool2D(1)
self.conv1 = Conv2D(
in_channels=channel,
out_channels=channel // reduction,
kernel_size=1,
stride=1,
padding=0)
self.relu = nn.ReLU()
self.conv2 = Conv2D(
in_channels=channel // reduction,
out_channels=channel,
kernel_size=1,
stride=1,
padding=0)
self.hardsigmoid = Hardsigmoid(slope=0.2, offset=0.5)
def forward(self, x):
identity = x
x = self.avg_pool(x)
x = self.conv1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.hardsigmoid(x)
return paddle.multiply(x=identity, y=x)
def _load_pretrained(pretrained, model, model_url, use_ssld):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MobileNetV3_small_x0_35(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_small_x0_35
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x0_35` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["small"],
scale=0.35,
class_squeeze=LAST_SECOND_CONV_SMALL,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_35"],
use_ssld)
return model
def MobileNetV3_small_x0_5(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_small_x0_5
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x0_5` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["small"],
scale=0.5,
class_squeeze=LAST_SECOND_CONV_SMALL,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_5"],
use_ssld)
return model
def MobileNetV3_small_x0_75(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_small_x0_75
Args:
pretrained: bool=false or str. if `true` load pretrained parameters, `false` otherwise.
if str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x0_75` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["small"],
scale=0.75,
class_squeeze=LAST_SECOND_CONV_SMALL,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_75"],
use_ssld)
return model
def MobileNetV3_small_x1_0(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_small_x1_0
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x1_0` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["small"],
scale=1.0,
class_squeeze=LAST_SECOND_CONV_SMALL,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x1_0"],
use_ssld)
return model
def MobileNetV3_small_x1_25(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_small_x1_25
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_small_x1_25` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["small"],
scale=1.25,
class_squeeze=LAST_SECOND_CONV_SMALL,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x1_25"],
use_ssld)
return model
def MobileNetV3_large_x0_35(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_large_x0_35
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x0_35` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["large"],
scale=0.35,
class_squeeze=LAST_SECOND_CONV_LARGE,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_35"],
use_ssld)
return model
def MobileNetV3_large_x0_5(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_large_x0_5
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x0_5` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["large"],
scale=0.5,
class_squeeze=LAST_SECOND_CONV_LARGE,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_5"],
use_ssld)
return model
def MobileNetV3_large_x0_75(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_large_x0_75
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x0_75` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["large"],
scale=0.75,
class_squeeze=LAST_SECOND_CONV_LARGE,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_75"],
use_ssld)
return model
def MobileNetV3_large_x1_0(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_large_x1_0
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x1_0` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["large"],
scale=1.0,
class_squeeze=LAST_SECOND_CONV_LARGE,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x1_0"],
use_ssld)
return model
def MobileNetV3_large_x1_25(pretrained=False, use_ssld=False, **kwargs):
"""
MobileNetV3_large_x1_25
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `MobileNetV3_large_x1_25` model depends on args.
"""
model = MobileNetV3(
config=NET_CONFIG["large"],
scale=1.25,
class_squeeze=LAST_SECOND_CONV_LARGE,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x1_25"],
use_ssld)
return model
ppcls/arch/backbone/legendary_models/vgg.py
浏览文件 @ 1a74e9cb
......@@ -14,16 +14,24 @@
from __future__ import absolute_import, division, print_function
import paddle
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import MaxPool2D
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain
__all__ = ["VGG11", "VGG13", "VGG16", "VGG19"]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"VGG11":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams",
"VGG13":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams",
"VGG16":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams",
"VGG19":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams",
}
__all__ = MODEL_URLS.keys()
# VGG config
# key: VGG network depth
......@@ -36,68 +44,12 @@ NET_CONFIG = {
}
def VGG11(**args):
"""
VGG11
Args:
kwargs:
class_num: int=1000. Output dim of last fc layer.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
Returns:
model: nn.Layer. Specific `VGG11` model depends on args.
"""
model = VGGNet(config=NET_CONFIG[11], **args)
return model
def VGG13(**args):
"""
VGG13
Args:
kwargs:
class_num: int=1000. Output dim of last fc layer.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
Returns:
model: nn.Layer. Specific `VGG11` model depends on args.
"""
model = VGGNet(config=NET_CONFIG[13], **args)
return model
def VGG16(**args):
"""
VGG16
Args:
kwargs:
class_num: int=1000. Output dim of last fc layer.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
Returns:
model: nn.Layer. Specific `VGG11` model depends on args.
"""
model = VGGNet(config=NET_CONFIG[16], **args)
return model
def VGG19(**args):
"""
VGG19
Args:
kwargs:
class_num: int=1000. Output dim of last fc layer.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
Returns:
model: nn.Layer. Specific `VGG11` model depends on args.
"""
model = VGGNet(config=NET_CONFIG[19], **args)
return model
class ConvBlock(TheseusLayer):
def __init__(self, input_channels, output_channels, groups):
super(ConvBlock, self).__init__()
super().__init__()
self.groups = groups
self._conv_1 = Conv2D(
self.conv1 = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=3,
......@@ -105,7 +57,7 @@ class ConvBlock(TheseusLayer):
padding=1,
bias_attr=False)
if groups == 2 or groups == 3 or groups == 4:
self._conv_2 = Conv2D(
self.conv2 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
......@@ -113,7 +65,7 @@ class ConvBlock(TheseusLayer):
padding=1,
bias_attr=False)
if groups == 3 or groups == 4:
self._conv_3 = Conv2D(
self.conv3 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
......@@ -121,7 +73,7 @@ class ConvBlock(TheseusLayer):
padding=1,
bias_attr=False)
if groups == 4:
self._conv_4 = Conv2D(
self.conv4 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
......@@ -129,73 +81,148 @@ class ConvBlock(TheseusLayer):
padding=1,
bias_attr=False)
self._pool = MaxPool2D(kernel_size=2, stride=2, padding=0)
self._relu = nn.ReLU()
self.max_pool = MaxPool2D(kernel_size=2, stride=2, padding=0)
self.relu = nn.ReLU()
def forward(self, inputs):
x = self._conv_1(inputs)
x = self._relu(x)
x = self.conv1(inputs)
x = self.relu(x)
if self.groups == 2 or self.groups == 3 or self.groups == 4:
x = self._conv_2(x)
x = self._relu(x)
x = self.conv2(x)
x = self.relu(x)
if self.groups == 3 or self.groups == 4:
x = self._conv_3(x)
x = self._relu(x)
x = self.conv3(x)
x = self.relu(x)
if self.groups == 4:
x = self._conv_4(x)
x = self._relu(x)
x = self._pool(x)
x = self.conv4(x)
x = self.relu(x)
x = self.max_pool(x)
return x
class VGGNet(TheseusLayer):
def __init__(self,
config,
stop_grad_layers=0,
class_num=1000,
pretrained=False,
**args):
"""
VGGNet
Args:
config: list. VGGNet config.
stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
class_num: int=1000. The number of classes.
Returns:
model: nn.Layer. Specific VGG model depends on args.
"""
def __init__(self, config, stop_grad_layers=0, class_num=1000):
super().__init__()
self.stop_grad_layers = stop_grad_layers
self._conv_block_1 = ConvBlock(3, 64, config[0])
self._conv_block_2 = ConvBlock(64, 128, config[1])
self._conv_block_3 = ConvBlock(128, 256, config[2])
self._conv_block_4 = ConvBlock(256, 512, config[3])
self._conv_block_5 = ConvBlock(512, 512, config[4])
self.conv_block_1 = ConvBlock(3, 64, config[0])
self.conv_block_2 = ConvBlock(64, 128, config[1])
self.conv_block_3 = ConvBlock(128, 256, config[2])
self.conv_block_4 = ConvBlock(256, 512, config[3])
self.conv_block_5 = ConvBlock(512, 512, config[4])
self._relu = nn.ReLU()
self._flatten = nn.Flatten(start_axis=1, stop_axis=-1)
self.relu = nn.ReLU()
self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
for idx, block in enumerate([
self._conv_block_1, self._conv_block_2, self._conv_block_3,
self._conv_block_4, self._conv_block_5
self.conv_block_1, self.conv_block_2, self.conv_block_3,
self.conv_block_4, self.conv_block_5
]):
if self.stop_grad_layers >= idx + 1:
for param in block.parameters():
param.trainable = False
self._drop = Dropout(p=0.5, mode="downscale_in_infer")
self._fc1 = Linear(7 * 7 * 512, 4096)
self._fc2 = Linear(4096, 4096)
self._out = Linear(4096, class_num)
if pretrained is not None:
load_dygraph_pretrain(self, pretrained)
self.drop = Dropout(p=0.5, mode="downscale_in_infer")
self.fc1 = Linear(7 * 7 * 512, 4096)
self.fc2 = Linear(4096, 4096)
self.fc3 = Linear(4096, class_num)
def forward(self, inputs):
x = self._conv_block_1(inputs)
x = self._conv_block_2(x)
x = self._conv_block_3(x)
x = self._conv_block_4(x)
x = self._conv_block_5(x)
x = self._flatten(x)
x = self._fc1(x)
x = self._relu(x)
x = self._drop(x)
x = self._fc2(x)
x = self._relu(x)
x = self._drop(x)
x = self._out(x)
x = self.conv_block_1(inputs)
x = self.conv_block_2(x)
x = self.conv_block_3(x)
x = self.conv_block_4(x)
x = self.conv_block_5(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.drop(x)
x = self.fc2(x)
x = self.relu(x)
x = self.drop(x)
x = self.fc3(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def VGG11(pretrained=False, use_ssld=False, **kwargs):
"""
VGG11
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `VGG11` model depends on args.
"""
model = VGGNet(config=NET_CONFIG[11], **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG11"], use_ssld)
return model
def VGG13(pretrained=False, use_ssld=False, **kwargs):
"""
VGG13
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `VGG13` model depends on args.
"""
model = VGGNet(config=NET_CONFIG[13], **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG13"], use_ssld)
return model
def VGG16(pretrained=False, use_ssld=False, **kwargs):
"""
VGG16
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `VGG16` model depends on args.
"""
model = VGGNet(config=NET_CONFIG[16], **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG16"], use_ssld)
return model
def VGG19(pretrained=False, use_ssld=False, **kwargs):
"""
VGG19
Args:
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
If str, means the path of the pretrained model.
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
Returns:
model: nn.Layer. Specific `VGG19` model depends on args.
"""
model = VGGNet(config=NET_CONFIG[19], **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG19"], use_ssld)
return model
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