198 lines
7.9 KiB
Python
198 lines
7.9 KiB
Python
import torch
|
||
import torch.nn as nn
|
||
import torch.nn.functional as F
|
||
|
||
from core.models.base_models.densenet import *
|
||
from core.models.fcn import _FCNHead
|
||
|
||
__all__ = ['DenseASPP', 'get_denseaspp', 'get_denseaspp_densenet121_citys',
|
||
'get_denseaspp_densenet161_citys', 'get_denseaspp_densenet169_citys', 'get_denseaspp_densenet201_citys']
|
||
|
||
|
||
class DenseASPP(nn.Module):
|
||
def __init__(self, nclass, backbone='densenet121', aux=False, jpu=False,
|
||
pretrained_base=True, dilate_scale=8, **kwargs):
|
||
super(DenseASPP, self).__init__()
|
||
self.nclass = nclass
|
||
self.aux = aux
|
||
self.dilate_scale = dilate_scale
|
||
if backbone == 'densenet121':
|
||
self.pretrained = dilated_densenet121(dilate_scale, pretrained=pretrained_base, **kwargs)
|
||
elif backbone == 'densenet161':
|
||
self.pretrained = dilated_densenet161(dilate_scale, pretrained=pretrained_base, **kwargs)
|
||
elif backbone == 'densenet169':
|
||
self.pretrained = dilated_densenet169(dilate_scale, pretrained=pretrained_base, **kwargs)
|
||
elif backbone == 'densenet201':
|
||
self.pretrained = dilated_densenet201(dilate_scale, pretrained=pretrained_base, **kwargs)
|
||
else:
|
||
raise RuntimeError('unknown backbone: {}'.format(backbone))
|
||
in_channels = self.pretrained.num_features
|
||
|
||
self.head = _DenseASPPHead(in_channels, nclass)
|
||
|
||
if aux:
|
||
self.auxlayer = _FCNHead(in_channels, nclass, **kwargs)
|
||
|
||
self.__setattr__('exclusive', ['head', 'auxlayer'] if aux else ['head'])
|
||
|
||
def forward(self, x):
|
||
size = x.size()[2:]
|
||
#print('size', size) #torch.Size([512, 512])
|
||
features = self.pretrained.features(x)
|
||
#print('22',features.shape) #torch.Size([2, 1024, 64, 64])
|
||
if self.dilate_scale > 8:
|
||
features = F.interpolate(features, scale_factor=2, mode='bilinear', align_corners=True)
|
||
outputs = []
|
||
x = self.head(features) #torch.Size([2, 4, 64, 64])
|
||
#print('x.shape',x.shape)
|
||
x = F.interpolate(x, size, mode='bilinear', align_corners=True)#直接64到512。。。。效果还这么好！
|
||
outputs.append(x)
|
||
|
||
if self.aux:
|
||
auxout = self.auxlayer(features)
|
||
auxout = F.interpolate(auxout, size, mode='bilinear', align_corners=True)
|
||
outputs.append(auxout)
|
||
#return tuple(outputs)
|
||
return outputs[0]
|
||
|
||
class _DenseASPPHead(nn.Module):
|
||
def __init__(self, in_channels, nclass, norm_layer=nn.BatchNorm2d, norm_kwargs=None, **kwargs):
|
||
super(_DenseASPPHead, self).__init__()
|
||
self.dense_aspp_block = _DenseASPPBlock(in_channels, 256, 64, norm_layer, norm_kwargs)
|
||
self.block = nn.Sequential(
|
||
nn.Dropout(0.1),
|
||
nn.Conv2d(in_channels + 5 * 64, nclass, 1)
|
||
)
|
||
|
||
def forward(self, x):
|
||
x = self.dense_aspp_block(x)
|
||
return self.block(x)
|
||
|
||
|
||
class _DenseASPPConv(nn.Sequential):
|
||
def __init__(self, in_channels, inter_channels, out_channels, atrous_rate,
|
||
drop_rate=0.1, norm_layer=nn.BatchNorm2d, norm_kwargs=None):
|
||
super(_DenseASPPConv, self).__init__()
|
||
self.add_module('conv1', nn.Conv2d(in_channels, inter_channels, 1)),
|
||
self.add_module('bn1', norm_layer(inter_channels, **({} if norm_kwargs is None else norm_kwargs))),
|
||
self.add_module('relu1', nn.ReLU(True)),
|
||
self.add_module('conv2', nn.Conv2d(inter_channels, out_channels, 3, dilation=atrous_rate, padding=atrous_rate)),
|
||
self.add_module('bn2', norm_layer(out_channels, **({} if norm_kwargs is None else norm_kwargs))),
|
||
self.add_module('relu2', nn.ReLU(True)),
|
||
self.drop_rate = drop_rate
|
||
|
||
def forward(self, x):
|
||
features = super(_DenseASPPConv, self).forward(x)
|
||
if self.drop_rate > 0:
|
||
features = F.dropout(features, p=self.drop_rate, training=self.training)
|
||
return features
|
||
|
||
|
||
class _DenseASPPBlock(nn.Module):
|
||
def __init__(self, in_channels, inter_channels1, inter_channels2,
|
||
norm_layer=nn.BatchNorm2d, norm_kwargs=None):
|
||
super(_DenseASPPBlock, self).__init__()
|
||
self.aspp_3 = _DenseASPPConv(in_channels, inter_channels1, inter_channels2, 3, 0.1,
|
||
norm_layer, norm_kwargs)
|
||
self.aspp_6 = _DenseASPPConv(in_channels + inter_channels2 * 1, inter_channels1, inter_channels2, 6, 0.1,
|
||
norm_layer, norm_kwargs)
|
||
self.aspp_12 = _DenseASPPConv(in_channels + inter_channels2 * 2, inter_channels1, inter_channels2, 12, 0.1,
|
||
norm_layer, norm_kwargs)
|
||
self.aspp_18 = _DenseASPPConv(in_channels + inter_channels2 * 3, inter_channels1, inter_channels2, 18, 0.1,
|
||
norm_layer, norm_kwargs)
|
||
self.aspp_24 = _DenseASPPConv(in_channels + inter_channels2 * 4, inter_channels1, inter_channels2, 24, 0.1,
|
||
norm_layer, norm_kwargs)
|
||
|
||
def forward(self, x):
|
||
aspp3 = self.aspp_3(x)
|
||
x = torch.cat([aspp3, x], dim=1)
|
||
|
||
aspp6 = self.aspp_6(x)
|
||
x = torch.cat([aspp6, x], dim=1)
|
||
|
||
aspp12 = self.aspp_12(x)
|
||
x = torch.cat([aspp12, x], dim=1)
|
||
|
||
aspp18 = self.aspp_18(x)
|
||
x = torch.cat([aspp18, x], dim=1)
|
||
|
||
aspp24 = self.aspp_24(x)
|
||
x = torch.cat([aspp24, x], dim=1)
|
||
|
||
return x
|
||
|
||
|
||
def get_denseaspp(dataset='citys', backbone='densenet121', pretrained=False,
|
||
root='~/.torch/models', pretrained_base=True, **kwargs):
|
||
r"""DenseASPP
|
||
|
||
Parameters
|
||
----------
|
||
dataset : str, default citys
|
||
The dataset that model pretrained on. (pascal_voc, ade20k)
|
||
pretrained : bool or str
|
||
Boolean value controls whether to load the default pretrained weights for model.
|
||
String value represents the hashtag for a certain version of pretrained weights.
|
||
root : str, default '~/.torch/models'
|
||
Location for keeping the model parameters.
|
||
pretrained_base : bool or str, default True
|
||
This will load pretrained backbone network, that was trained on ImageNet.
|
||
Examples
|
||
--------
|
||
# >>> model = get_denseaspp(dataset='citys', backbone='densenet121', pretrained=False)
|
||
# >>> print(model)
|
||
"""
|
||
acronyms = {
|
||
'pascal_voc': 'pascal_voc',
|
||
'pascal_aug': 'pascal_aug',
|
||
'ade20k': 'ade',
|
||
'coco': 'coco',
|
||
'citys': 'citys',
|
||
}
|
||
from ..data.dataloader import datasets
|
||
model = DenseASPP(datasets[dataset].NUM_CLASS, backbone=backbone, pretrained_base=pretrained_base, **kwargs)
|
||
if pretrained:
|
||
from .model_store import get_model_file
|
||
device = torch.device(kwargs['local_rank'])
|
||
model.load_state_dict(torch.load(get_model_file('denseaspp_%s_%s' % (backbone, acronyms[dataset]), root=root),
|
||
map_location=device))
|
||
return model
|
||
|
||
|
||
def get_denseaspp_densenet121_citys(**kwargs):
|
||
return get_denseaspp('citys', 'densenet121', **kwargs)
|
||
|
||
|
||
def get_denseaspp_densenet161_citys(**kwargs):
|
||
return get_denseaspp('citys', 'densenet161', **kwargs)
|
||
|
||
|
||
def get_denseaspp_densenet169_citys(**kwargs):
|
||
return get_denseaspp('citys', 'densenet169', **kwargs)
|
||
|
||
|
||
def get_denseaspp_densenet201_citys(**kwargs):
|
||
return get_denseaspp('citys', 'densenet201', **kwargs)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# img = torch.randn(2, 3, 480, 480)
|
||
# model = get_denseaspp_densenet121_citys()
|
||
# outputs = model(img)
|
||
input = torch.rand(2, 3, 512, 512)
|
||
model = DenseASPP(4, pretrained_base=True)
|
||
# target = torch.zeros(4, 512, 512).cuda()
|
||
# model.eval()
|
||
# print(model)
|
||
loss = model(input)
|
||
print(loss, loss.shape)
|
||
|
||
# from torchsummary import summary
|
||
#
|
||
# summary(model, (3, 224, 224)) # 打印表格，按顺序输出每层的输出形状和参数
|
||
import torch
|
||
from thop import profile
|
||
from torchsummary import summary
|
||
|
||
flop, params = profile(model, input_size=(1, 3, 512, 512))
|
||
print('flops:{:.3f}G\nparams:{:.3f}M'.format(flop / 1e9, params / 1e6)) |