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dla.py
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dla.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
from os.path import join
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
BatchNorm = nn.BatchNorm2d
__all__ = ['res2net_dla60']
model_urls = {
'res2net_dla60': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net_dla60_4s-d88db7f9.pth',
'res2next_dla60': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2next_dla60_4s-d327927b.pth',
}
class Bottle2neck(nn.Module):
"""
RexNeXt bottleneck type C
"""
expansion = 2
def __init__(self, inplanes, planes, stride=1, dilation=1, baseWidth=28, scale = 4):
""" Constructor
Args:
inplanes: input channel dimensionality
planes: output channel dimensionality
stride: conv stride. Replaces pooling layer.
downsample: None when stride = 1
baseWidth: basic width of conv3x3
scale: number of scale.
type: 'normal': normal set. 'stage': frist blokc of a new stage.
"""
super(Bottle2neck, self).__init__()
if stride != 1:
stype = 'stage'
else:
stype = 'normal'
width = int(math.floor(planes * (baseWidth/128.0)))
self.conv1 = nn.Conv2d(inplanes, width*scale, kernel_size=1, bias=False)
self.bn1 = BatchNorm(width*scale)
if scale == 1:
self.nums = 1
else:
self.nums = scale -1
if stype == 'stage':
self.pool = nn.AvgPool2d(kernel_size=3, stride = stride, padding=1)
convs = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, stride = stride,
padding=dilation, dilation=dilation, bias=False))
bns.append(BatchNorm(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = nn.Conv2d(width*scale, planes, kernel_size=1, bias=False)
self.bn3 = BatchNorm(planes)
self.relu = nn.ReLU(inplace=True)
self.stype = stype
self.scale = scale
self.width = width
def forward(self, x, residual=None):
if residual is None:
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0 or self.stype=='stage':
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
out = sp
else:
out = torch.cat((out, sp), 1)
if self.scale != 1 and self.stype=='normal':
out = torch.cat((out, spx[self.nums]),1)
elif self.scale != 1 and self.stype=='stage':
out = torch.cat((out, self.pool(spx[self.nums])),1)
out = self.conv3(out)
out = self.bn3(out)
out += residual
out = self.relu(out)
return out
class Bottle2neckX(nn.Module):
"""
RexNeXt bottleneck type C
"""
expansion = 2
cardinality = 8
def __init__(self, inplanes, planes, stride=1, dilation=1, scale = 4):
""" Constructor
Args:
inplanes: input channel dimensionality
planes: output channel dimensionality
stride: conv stride. Replaces pooling layer.
downsample: None when stride = 1
baseWidth: basic width of conv3x3
scale: number of scale.
type: 'normal': normal set. 'stage': frist blokc of a new stage.
"""
super(Bottle2neckX, self).__init__()
if stride != 1:
stype = 'stage'
else:
stype = 'normal'
cardinality = Bottle2neckX.cardinality
width = bottle_planes = planes * cardinality // 32
self.conv1 = nn.Conv2d(inplanes, width*scale, kernel_size=1, bias=False)
self.bn1 = BatchNorm(width*scale)
if scale == 1:
self.nums = 1
else:
self.nums = scale -1
if stype == 'stage':
self.pool = nn.AvgPool2d(kernel_size=3, stride = stride, padding=1)
convs = []
bns = []
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, stride = stride,
padding=dilation, dilation=dilation, groups=cardinality, bias=False))
bns.append(BatchNorm(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = nn.Conv2d(width*scale, planes, kernel_size=1, bias=False)
self.bn3 = BatchNorm(planes)
self.relu = nn.ReLU(inplace=True)
self.stype = stype
self.scale = scale
self.width = width
def forward(self, x, residual=None):
if residual is None:
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i==0 or self.stype=='stage':
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
out = sp
else:
out = torch.cat((out, sp), 1)
if self.scale != 1 and self.stype=='normal':
out = torch.cat((out, spx[self.nums]),1)
elif self.scale != 1 and self.stype=='stage':
out = torch.cat((out, self.pool(spx[self.nums])),1)
out = self.conv3(out)
out = self.bn3(out)
out += residual
out = self.relu(out)
return out
class Root(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, residual):
super(Root, self).__init__()
self.conv = nn.Conv2d(
in_channels, out_channels, 1,
stride=1, bias=False, padding=(kernel_size - 1) // 2)
self.bn = BatchNorm(out_channels)
self.relu = nn.ReLU(inplace=True)
self.residual = residual
def forward(self, *x):
children = x
x = self.conv(torch.cat(x, 1))
x = self.bn(x)
if self.residual:
x += children[0]
x = self.relu(x)
return x
class Tree(nn.Module):
def __init__(self, levels, block, in_channels, out_channels, stride=1,
level_root=False, root_dim=0, root_kernel_size=1,
dilation=1, root_residual=False):
super(Tree, self).__init__()
if root_dim == 0:
root_dim = 2 * out_channels
if level_root:
root_dim += in_channels
if levels == 1:
self.tree1 = block(in_channels, out_channels, stride,
dilation=dilation)
self.tree2 = block(out_channels, out_channels, 1,
dilation=dilation)
else:
self.tree1 = Tree(levels - 1, block, in_channels, out_channels,
stride, root_dim=0,
root_kernel_size=root_kernel_size,
dilation=dilation, root_residual=root_residual)
self.tree2 = Tree(levels - 1, block, out_channels, out_channels,
root_dim=root_dim + out_channels,
root_kernel_size=root_kernel_size,
dilation=dilation, root_residual=root_residual)
if levels == 1:
self.root = Root(root_dim, out_channels, root_kernel_size,
root_residual)
self.level_root = level_root
self.root_dim = root_dim
self.downsample = None
self.project = None
self.levels = levels
if stride > 1:
self.downsample = nn.MaxPool2d(stride, stride=stride)
if in_channels != out_channels:
self.project = nn.Sequential(
nn.Conv2d(in_channels, out_channels,
kernel_size=1, stride=1, bias=False),
BatchNorm(out_channels)
)
def forward(self, x, residual=None, children=None):
children = [] if children is None else children
bottom = self.downsample(x) if self.downsample else x
residual = self.project(bottom) if self.project else 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.Module):
def __init__(self, levels, channels, num_classes=1000,
block=Bottle2neck, residual_root=False, return_levels=False,
pool_size=7, linear_root=False):
super(DLA, self).__init__()
self.channels = channels
self.return_levels = return_levels
self.num_classes = num_classes
self.base_layer = nn.Sequential(
nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
padding=3, bias=False),
BatchNorm(channels[0]),
nn.ReLU(inplace=True))
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)
self.level2 = Tree(levels[2], block, channels[1], channels[2], 2,
level_root=False,
root_residual=residual_root)
self.level3 = Tree(levels[3], block, channels[2], channels[3], 2,
level_root=True, root_residual=residual_root)
self.level4 = Tree(levels[4], block, channels[3], channels[4], 2,
level_root=True, root_residual=residual_root)
self.level5 = Tree(levels[5], block, channels[4], channels[5], 2,
level_root=True, root_residual=residual_root)
self.avgpool = nn.AvgPool2d(pool_size)
self.fc = nn.Conv2d(channels[-1], num_classes, kernel_size=1,
stride=1, padding=0, bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, BatchNorm):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_level(self, block, inplanes, planes, blocks, stride=1):
downsample = None
if stride != 1 or inplanes != planes:
downsample = nn.Sequential(
nn.MaxPool2d(stride, stride=stride),
nn.Conv2d(inplanes, planes,
kernel_size=1, stride=1, bias=False),
BatchNorm(planes),
)
layers = []
layers.append(block(inplanes, planes, stride, downsample=downsample))
for i in range(1, blocks):
layers.append(block(inplanes, planes))
return nn.Sequential(*layers)
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=False, dilation=dilation),
BatchNorm(planes),
nn.ReLU(inplace=True)])
inplanes = planes
return nn.Sequential(*modules)
def forward(self, x):
y = []
x = self.base_layer(x)
for i in range(6):
x = getattr(self, 'level{}'.format(i))(x)
y.append(x)
if self.return_levels:
return y
else:
x = self.avgpool(x)
x = self.fc(x)
x = x.view(x.size(0), -1)
return x
def load_pretrained_model(self, data_name, name):
assert data_name in dataset.__dict__, \
'No pretrained model for {}'.format(data_name)
data = dataset.__dict__[data_name]
fc = self.fc
if self.num_classes != data.classes:
self.fc = nn.Conv2d(
self.channels[-1], data.classes,
kernel_size=1, stride=1, padding=0, bias=True)
try:
model_url = get_model_url(data, name)
except KeyError:
raise ValueError(
'{} trained on {} does not exist.'.format(data.name, name))
self.load_state_dict(model_zoo.load_url(model_url))
self.fc = fc
def res2net_dla60(pretrained=None, **kwargs):
Bottle2neck.expansion = 2
model = DLA([1, 1, 1, 2, 3, 1],
[16, 32, 128, 256, 512, 1024],
block=Bottle2neck, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['res2net_dla60']))
return model
def res2next_dla60(pretrained=None, **kwargs):
Bottle2neckX.expansion = 2
model = DLA([1, 1, 1, 2, 3, 1],
[16, 32, 128, 256, 512, 1024],
block=Bottle2neckX, **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['res2next_dla60']))
return model
if __name__ == '__main__':
images = torch.rand(1, 3, 224, 224).cuda(0)
model = res2next_dla60(pretrained=True)
model = model.cuda(0)
print(model(images).size())