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PaddleRS/paddlers/models/ppcls/arch/backbone/model_zoo/dla.py

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# 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.
# Code was based on https://github.com/ucbdrive/dla
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import Normal, Constant
from ppcls.arch.backbone.base.theseus_layer import Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DLA34":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams",
"DLA46_c":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams",
"DLA46x_c":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46x_c_pretrained.pdparams",
"DLA60":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams",
"DLA60x":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams",
"DLA60x_c":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams",
"DLA102":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams",
"DLA102x":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams",
"DLA102x2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams",
"DLA169":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams"
}
__all__ = MODEL_URLS.keys()
zeros_ = Constant(value=0.)
ones_ = Constant(value=1.)
class DlaBasic(nn.Layer):
def __init__(self, inplanes, planes, stride=1, dilation=1, **cargs):
super(DlaBasic, self).__init__()
self.conv1 = nn.Conv2D(
inplanes,
planes,
kernel_size=3,
stride=stride,
padding=dilation,
bias_attr=False,
dilation=dilation)
self.bn1 = nn.BatchNorm2D(planes)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2D(
planes,
planes,
kernel_size=3,
stride=1,
padding=dilation,
bias_attr=False,
dilation=dilation)
self.bn2 = nn.BatchNorm2D(planes)
self.stride = stride
def forward(self, x, residual=None):
if residual is None:
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += residual
out = self.relu(out)
return out
class DlaBottleneck(nn.Layer):
expansion = 2
def __init__(self,
inplanes,
outplanes,
stride=1,
dilation=1,
cardinality=1,
base_width=64):
super(DlaBottleneck, self).__init__()
self.stride = stride
mid_planes = int(
math.floor(outplanes * (base_width / 64)) * cardinality)
mid_planes = mid_planes // self.expansion
self.conv1 = nn.Conv2D(
inplanes, mid_planes, kernel_size=1, bias_attr=False)
self.bn1 = nn.BatchNorm2D(mid_planes)
self.conv2 = nn.Conv2D(
mid_planes,
mid_planes,
kernel_size=3,
stride=stride,
padding=dilation,
bias_attr=False,
dilation=dilation,
groups=cardinality)
self.bn2 = nn.BatchNorm2D(mid_planes)
self.conv3 = nn.Conv2D(
mid_planes, outplanes, kernel_size=1, bias_attr=False)
self.bn3 = nn.BatchNorm2D(outplanes)
self.relu = nn.ReLU()
def forward(self, x, residual=None):
if residual is None:
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
out += residual
out = self.relu(out)
return out
class DlaRoot(nn.Layer):
def __init__(self, in_channels, out_channels, kernel_size, residual):
super(DlaRoot, self).__init__()
self.conv = nn.Conv2D(
in_channels,
out_channels,
1,
stride=1,
bias_attr=False,
padding=(kernel_size - 1) // 2)
self.bn = nn.BatchNorm2D(out_channels)
self.relu = nn.ReLU()
self.residual = residual
def forward(self, *x):
children = x
x = self.conv(paddle.concat(x, 1))
x = self.bn(x)
if self.residual:
x += children[0]
x = self.relu(x)
return x
class DlaTree(nn.Layer):
def __init__(self,
levels,
block,
in_channels,
out_channels,
stride=1,
dilation=1,
cardinality=1,
base_width=64,
level_root=False,
root_dim=0,
root_kernel_size=1,
root_residual=False):
super(DlaTree, self).__init__()
if root_dim == 0:
root_dim = 2 * out_channels
if level_root:
root_dim += in_channels
self.downsample = nn.MaxPool2D(
stride, stride=stride) if stride > 1 else Identity()
self.project = Identity()
cargs = dict(
dilation=dilation, cardinality=cardinality, base_width=base_width)
if levels == 1:
self.tree1 = block(in_channels, out_channels, stride, **cargs)
self.tree2 = block(out_channels, out_channels, 1, **cargs)
if in_channels != out_channels:
self.project = nn.Sequential(
nn.Conv2D(
in_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=False),
nn.BatchNorm2D(out_channels))
else:
cargs.update(
dict(
root_kernel_size=root_kernel_size,
root_residual=root_residual))
self.tree1 = DlaTree(
levels - 1,
block,
in_channels,
out_channels,
stride,
root_dim=0,
**cargs)
self.tree2 = DlaTree(
levels - 1,
block,
out_channels,
out_channels,
root_dim=root_dim + out_channels,
**cargs)
if levels == 1:
self.root = DlaRoot(root_dim, out_channels, root_kernel_size,
root_residual)
self.level_root = level_root
self.root_dim = root_dim
self.levels = levels
def forward(self, x, residual=None, children=None):
children = [] if children is None else children
bottom = self.downsample(x)
residual = self.project(bottom)
if self.level_root:
children.append(bottom)
x1 = self.tree1(x, residual)
if self.levels == 1:
x2 = self.tree2(x1)
x = self.root(x2, x1, *children)
else:
children.append(x1)
x = self.tree2(x1, children=children)
return x
class DLA(nn.Layer):
def __init__(self,
levels,
channels,
in_chans=3,
cardinality=1,
base_width=64,
block=DlaBottleneck,
residual_root=False,
drop_rate=0.0,
class_num=1000,
with_pool=True):
super(DLA, self).__init__()
self.channels = channels
self.class_num = class_num
self.with_pool = with_pool
self.cardinality = cardinality
self.base_width = base_width
self.drop_rate = drop_rate
self.base_layer = nn.Sequential(
nn.Conv2D(
in_chans,
channels[0],
kernel_size=7,
stride=1,
padding=3,
bias_attr=False),
nn.BatchNorm2D(channels[0]),
nn.ReLU())
self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
self.level1 = self._make_conv_level(
channels[0], channels[1], levels[1], stride=2)
cargs = dict(
cardinality=cardinality,
base_width=base_width,
root_residual=residual_root)
self.level2 = DlaTree(
levels[2],
block,
channels[1],
channels[2],
2,
level_root=False,
**cargs)
self.level3 = DlaTree(
levels[3],
block,
channels[2],
channels[3],
2,
level_root=True,
**cargs)
self.level4 = DlaTree(
levels[4],
block,
channels[3],
channels[4],
2,
level_root=True,
**cargs)
self.level5 = DlaTree(
levels[5],
block,
channels[4],
channels[5],
2,
level_root=True,
**cargs)
self.feature_info = [
# rare to have a meaningful stride 1 level
dict(
num_chs=channels[0], reduction=1, module='level0'),
dict(
num_chs=channels[1], reduction=2, module='level1'),
dict(
num_chs=channels[2], reduction=4, module='level2'),
dict(
num_chs=channels[3], reduction=8, module='level3'),
dict(
num_chs=channels[4], reduction=16, module='level4'),
dict(
num_chs=channels[5], reduction=32, module='level5'),
]
self.num_features = channels[-1]
if with_pool:
self.global_pool = nn.AdaptiveAvgPool2D(1)
if class_num > 0:
self.fc = nn.Conv2D(self.num_features, class_num, 1)
for m in self.sublayers():
if isinstance(m, nn.Conv2D):
n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
normal_ = Normal(mean=0.0, std=math.sqrt(2. / n))
normal_(m.weight)
elif isinstance(m, nn.BatchNorm2D):
ones_(m.weight)
zeros_(m.bias)
def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):
modules = []
for i in range(convs):
modules.extend([
nn.Conv2D(
inplanes,
planes,
kernel_size=3,
stride=stride if i == 0 else 1,
padding=dilation,
bias_attr=False,
dilation=dilation), nn.BatchNorm2D(planes), nn.ReLU()
])
inplanes = planes
return nn.Sequential(*modules)
def forward_features(self, x):
x = self.base_layer(x)
x = self.level0(x)
x = self.level1(x)
x = self.level2(x)
x = self.level3(x)
x = self.level4(x)
x = self.level5(x)
return x
def forward(self, x):
x = self.forward_features(x)
if self.with_pool:
x = self.global_pool(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
if self.class_num > 0:
x = self.fc(x)
x = x.flatten(1)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
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 DLA34(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 128, 256, 512),
block=DlaBasic,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA34"])
return model
def DLA46_c(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA46_c"])
return model
def DLA46x_c(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA46x_c"])
return model
def DLA60(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA60"])
return model
def DLA60x(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA60x"])
return model
def DLA60x_c(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA60x_c"])
return model
def DLA102(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
residual_root=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA102"])
return model
def DLA102x(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=32,
base_width=4,
residual_root=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA102x"])
return model
def DLA102x2(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=64,
base_width=4,
residual_root=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA102x2"])
return model
def DLA169(pretrained=False, **kwargs):
model = DLA(levels=(1, 1, 2, 3, 5, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
residual_root=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA169"])
return model