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feat: add PPLCNetV2

pull/1916/head
gaotingquan 2022-05-11 14:54:32 +00:00
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1
ppcls/arch/backbone/__init__.py
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@ -22,6 +22,7 @@ from ppcls.arch.backbone.legendary_models.vgg import VGG11, VGG13, VGG16, VGG19
from ppcls.arch.backbone.legendary_models.inception_v3 import InceptionV3
from ppcls.arch.backbone.legendary_models.hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W64_C
from ppcls.arch.backbone.legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_5, PPLCNet_x0_75, PPLCNet_x1_0, PPLCNet_x1_5, PPLCNet_x2_0, PPLCNet_x2_5
from ppcls.arch.backbone.legendary_models.pp_lcnet_v2 import PPLCNetV2_base
from ppcls.arch.backbone.legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet50_vc

396
ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py 100644
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@ -0,0 +1,396 @@
# copyright (c) 2022 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
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
from paddle.regularizer import L2Decay
from paddle.nn.initializer import KaimingNormal
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
#TODO(gaotingquan): upload pretrained to bos
MODEL_URLS = {
"PPLCNetV2_base":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
NET_CONFIG = {
# in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut
"stage1": [64, 3, False, False, False, False],
"stage2": [128, 3, False, False, False, False],
"stage3": [256, 5, True, True, True, False],
"stage4": [512, 5, False, True, False, True],
}
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
class ConvBNLayer(TheseusLayer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
groups=1,
use_act=True):
super().__init__()
self.use_act = use_act
self.conv = Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(initializer=KaimingNormal()),
bias_attr=False)
self.bn = BatchNorm(
out_channels,
param_attr=ParamAttr(regularizer=L2Decay(0.0)),
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
if self.use_act:
self.act = nn.ReLU()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.use_act:
x = self.act(x)
return x
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 = nn.Sigmoid()
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)
x = paddle.multiply(x=identity, y=x)
return x
class RepDepthwiseSeparable(TheseusLayer):
def __init__(self,
in_channels,
out_channels,
stride,
dw_size=3,
split_pw=False,
use_rep=False,
use_se=False,
use_shortcut=False):
super().__init__()
self.is_repped = False
self.dw_size = dw_size
self.split_pw = split_pw
self.use_rep = use_rep
self.use_se = use_se
self.use_shortcut = True if use_shortcut and stride == 1 and in_channels == out_channels else False
if self.use_rep:
self.dw_conv_list = nn.LayerList()
for kernel_size in range(self.dw_size, 0, -2):
if kernel_size == 1 and stride != 1:
continue
dw_conv = ConvBNLayer(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=kernel_size,
stride=stride,
groups=in_channels,
use_act=False)
self.dw_conv_list.append(dw_conv)
self.dw_conv = nn.Conv2D(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=dw_size,
stride=stride,
padding=(dw_size - 1) // 2,
groups=in_channels)
else:
self.dw_conv = ConvBNLayer(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=dw_size,
stride=stride,
groups=in_channels)
self.act = nn.ReLU()
if use_se:
self.se = SEModule(in_channels)
if self.split_pw:
pw_ratio = 0.5
self.pw_conv_1 = ConvBNLayer(
in_channels=in_channels,
kernel_size=1,
out_channels=int(out_channels * pw_ratio),
stride=1)
self.pw_conv_2 = ConvBNLayer(
in_channels=int(out_channels * pw_ratio),
kernel_size=1,
out_channels=out_channels,
stride=1)
else:
self.pw_conv = ConvBNLayer(
in_channels=in_channels,
kernel_size=1,
out_channels=out_channels,
stride=1)
def forward(self, x):
if self.use_rep:
if not self.training and not self.is_repped:
self.rep()
self.is_repped = True
if self.training and self.is_repped:
self.is_repped = False
input_x = x
if not self.training:
x = self.act(self.dw_conv(x))
else:
y = self.dw_conv_list[0](x)
for dw_conv in self.dw_conv_list[1:]:
y += dw_conv(x)
x = self.act(y)
else:
x = self.dw_conv(x)
if self.use_se:
x = self.se(x)
if self.split_pw:
x = self.pw_conv_1(x)
x = self.pw_conv_2(x)
else:
x = self.pw_conv(x)
if self.use_shortcut:
x = x + input_x
return x
def rep(self):
kernel, bias = self._get_equivalent_kernel_bias()
self.dw_conv.weight.set_value(kernel)
self.dw_conv.bias.set_value(bias)
def _get_equivalent_kernel_bias(self):
kernel_sum = 0
bias_sum = 0
for dw_conv in self.dw_conv_list:
kernel, bias = self._fuse_bn_tensor(dw_conv)
kernel = self._pad_tensor(kernel, to_size=self.dw_size)
kernel_sum += kernel
bias_sum += bias
return kernel_sum, bias_sum
def _fuse_bn_tensor(self, branch):
kernel = branch.conv.weight
running_mean = branch.bn._mean
running_var = branch.bn._variance
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn._epsilon
std = (running_var + eps).sqrt()
t = (gamma / std).reshape((-1, 1, 1, 1))
return kernel * t, beta - running_mean * gamma / std
def _pad_tensor(self, tensor, to_size):
from_size = tensor.shape[-1]
if from_size == to_size:
return tensor
pad = (to_size - from_size) // 2
return F.pad(tensor, [pad, pad, pad, pad])
class PPLCNetV2(TheseusLayer):
def __init__(self,
scale=1.0,
depths=[2, 2, 6, 2],
class_num=1000,
dropout_prob=0.2,
class_expand=1280):
super().__init__()
self.scale = scale
self.class_expand = class_expand
self.stem = nn.Sequential(* [
ConvBNLayer(
in_channels=3,
kernel_size=3,
out_channels=make_divisible(32 * scale),
stride=2), RepDepthwiseSeparable(
in_channels=make_divisible(32 * scale),
out_channels=make_divisible(64 * scale),
stride=1,
dw_size=3)
])
# stage1
in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
"stage1"]
self.stage1 = nn.Sequential(* [
RepDepthwiseSeparable(
in_channels=make_divisible((in_channels if i == 0 else
in_channels * 2) * scale),
out_channels=make_divisible(in_channels * 2 * scale),
stride=2 if i == 0 else 1,
dw_size=kernel_size,
split_pw=split_pw,
use_rep=use_rep,
use_se=use_se,
use_shortcut=use_shortcut, ) for i in range(depths[0])
])
# stage2
in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
"stage2"]
self.stage2 = nn.Sequential(* [
RepDepthwiseSeparable(
in_channels=make_divisible((in_channels if i == 0 else
in_channels * 2) * scale),
out_channels=make_divisible(in_channels * 2 * scale),
stride=2 if i == 0 else 1,
dw_size=kernel_size,
split_pw=split_pw,
use_rep=use_rep,
use_se=use_se,
use_shortcut=use_shortcut, ) for i in range(depths[1])
])
# stage3
in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
"stage3"]
self.stage3 = nn.Sequential(* [
RepDepthwiseSeparable(
in_channels=make_divisible((in_channels if i == 0 else
in_channels * 2) * scale),
out_channels=make_divisible(in_channels * 2 * scale),
stride=2 if i == 0 else 1,
dw_size=kernel_size,
split_pw=split_pw,
use_rep=use_rep,
use_se=use_se,
use_shortcut=use_shortcut, ) for i in range(depths[2])
])
# stage4
in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
"stage4"]
self.stage4 = nn.Sequential(* [
RepDepthwiseSeparable(
in_channels=make_divisible((in_channels if i == 0 else
in_channels * 2) * scale),
out_channels=make_divisible(in_channels * 2 * scale),
stride=2 if i == 0 else 1,
dw_size=kernel_size,
split_pw=split_pw,
use_rep=use_rep,
use_se=use_se,
use_shortcut=use_shortcut, ) for i in range(depths[3])
])
self.avg_pool = AdaptiveAvgPool2D(1)
self.last_conv = Conv2D(
in_channels=make_divisible(NET_CONFIG["stage4"][0] * 2 * scale),
out_channels=self.class_expand,
kernel_size=1,
stride=1,
padding=0,
bias_attr=False)
self.act = nn.ReLU()
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.stem(x)
x = self.stage1(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.avg_pool(x)
x = self.last_conv(x)
x = self.act(x)
x = self.dropout(x)
x = self.flatten(x)
x = self.fc(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 PPLCNetV2_base(pretrained=False, use_ssld=False, **kwargs):
"""
PPLCNetV2_base
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 `PPLCNetV2_base` model depends on args.
"""
model = PPLCNetV2(scale=1.0, depths=[2, 2, 6, 2], **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["PPLCNetV2_base"], use_ssld)
return model