361 lines
13 KiB
Python
361 lines
13 KiB
Python
""" Object Context Network for Scene Parsing"""
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
from core.models.segbase import SegBaseModel
|
|
from core.models.fcn import _FCNHead
|
|
|
|
__all__ = ['OCNet', 'get_ocnet', 'get_base_ocnet_resnet101_citys',
|
|
'get_pyramid_ocnet_resnet101_citys', 'get_asp_ocnet_resnet101_citys']
|
|
|
|
|
|
class OCNet(SegBaseModel):
|
|
r"""OCNet
|
|
|
|
Parameters
|
|
----------
|
|
nclass : int
|
|
Number of categories for the training dataset.
|
|
backbone : string
|
|
Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
|
|
'resnet101' or 'resnet152').
|
|
norm_layer : object
|
|
Normalization layer used in backbone network (default: :class:`nn.BatchNorm`;
|
|
for Synchronized Cross-GPU BachNormalization).
|
|
aux : bool
|
|
Auxiliary loss.
|
|
Reference:
|
|
Yuhui Yuan, Jingdong Wang. "OCNet: Object Context Network for Scene Parsing."
|
|
arXiv preprint arXiv:1809.00916 (2018).
|
|
"""
|
|
|
|
def __init__(self, nclass, backbone='resnet101', oc_arch='base', aux=False, pretrained_base=True, **kwargs):
|
|
super(OCNet, self).__init__(nclass, aux, backbone, pretrained_base=pretrained_base, **kwargs)
|
|
self.head = _OCHead(nclass, oc_arch, **kwargs)
|
|
if self.aux:
|
|
self.auxlayer = _FCNHead(1024, nclass, **kwargs)
|
|
|
|
self.__setattr__('exclusive', ['head', 'auxlayer'] if aux else ['head'])
|
|
|
|
def forward(self, x):
|
|
size = x.size()[2:]
|
|
_, _, c3, c4 = self.base_forward(x)
|
|
outputs = []
|
|
x = self.head(c4)
|
|
x = F.interpolate(x, size, mode='bilinear', align_corners=True)
|
|
outputs.append(x)
|
|
|
|
if self.aux:
|
|
auxout = self.auxlayer(c3)
|
|
auxout = F.interpolate(auxout, size, mode='bilinear', align_corners=True)
|
|
outputs.append(auxout)
|
|
#return tuple(outputs)
|
|
return outputs[0]
|
|
|
|
class _OCHead(nn.Module):
|
|
def __init__(self, nclass, oc_arch, norm_layer=nn.BatchNorm2d, **kwargs):
|
|
super(_OCHead, self).__init__()
|
|
if oc_arch == 'base':
|
|
self.context = nn.Sequential(
|
|
nn.Conv2d(2048, 512, 3, 1, padding=1, bias=False),
|
|
norm_layer(512),
|
|
nn.ReLU(True),
|
|
BaseOCModule(512, 512, 256, 256, scales=([1]), norm_layer=norm_layer, **kwargs))
|
|
elif oc_arch == 'pyramid':
|
|
self.context = nn.Sequential(
|
|
nn.Conv2d(2048, 512, 3, 1, padding=1, bias=False),
|
|
norm_layer(512),
|
|
nn.ReLU(True),
|
|
PyramidOCModule(512, 512, 256, 512, scales=([1, 2, 3, 6]), norm_layer=norm_layer, **kwargs))
|
|
elif oc_arch == 'asp':
|
|
self.context = ASPOCModule(2048, 512, 256, 512, norm_layer=norm_layer, **kwargs)
|
|
else:
|
|
raise ValueError("Unknown OC architecture!")
|
|
|
|
self.out = nn.Conv2d(512, nclass, 1)
|
|
|
|
def forward(self, x):
|
|
x = self.context(x)
|
|
return self.out(x)
|
|
|
|
|
|
class BaseAttentionBlock(nn.Module):
|
|
"""The basic implementation for self-attention block/non-local block."""
|
|
|
|
def __init__(self, in_channels, out_channels, key_channels, value_channels,
|
|
scale=1, norm_layer=nn.BatchNorm2d, **kwargs):
|
|
super(BaseAttentionBlock, self).__init__()
|
|
self.scale = scale
|
|
self.key_channels = key_channels
|
|
self.value_channels = value_channels
|
|
if scale > 1:
|
|
self.pool = nn.MaxPool2d(scale)
|
|
|
|
self.f_value = nn.Conv2d(in_channels, value_channels, 1)
|
|
self.f_key = nn.Sequential(
|
|
nn.Conv2d(in_channels, key_channels, 1),
|
|
norm_layer(key_channels),
|
|
nn.ReLU(True)
|
|
)
|
|
self.f_query = self.f_key
|
|
self.W = nn.Conv2d(value_channels, out_channels, 1)
|
|
nn.init.constant_(self.W.weight, 0)
|
|
nn.init.constant_(self.W.bias, 0)
|
|
|
|
def forward(self, x):
|
|
batch_size, c, w, h = x.size()
|
|
if self.scale > 1:
|
|
x = self.pool(x)
|
|
|
|
value = self.f_value(x).view(batch_size, self.value_channels, -1).permute(0, 2, 1)
|
|
query = self.f_query(x).view(batch_size, self.key_channels, -1).permute(0, 2, 1)
|
|
key = self.f_key(x).view(batch_size, self.key_channels, -1)
|
|
|
|
sim_map = torch.bmm(query, key) * (self.key_channels ** -.5)
|
|
sim_map = F.softmax(sim_map, dim=-1)
|
|
|
|
context = torch.bmm(sim_map, value).permute(0, 2, 1).contiguous()
|
|
context = context.view(batch_size, self.value_channels, *x.size()[2:])
|
|
context = self.W(context)
|
|
if self.scale > 1:
|
|
context = F.interpolate(context, size=(w, h), mode='bilinear', align_corners=True)
|
|
|
|
return context
|
|
|
|
|
|
class BaseOCModule(nn.Module):
|
|
"""Base-OC"""
|
|
|
|
def __init__(self, in_channels, out_channels, key_channels, value_channels,
|
|
scales=([1]), norm_layer=nn.BatchNorm2d, concat=True, **kwargs):
|
|
super(BaseOCModule, self).__init__()
|
|
self.stages = nn.ModuleList([
|
|
BaseAttentionBlock(in_channels, out_channels, key_channels, value_channels, scale, norm_layer, **kwargs)
|
|
for scale in scales])
|
|
in_channels = in_channels * 2 if concat else in_channels
|
|
self.project = nn.Sequential(
|
|
nn.Conv2d(in_channels, out_channels, 1),
|
|
norm_layer(out_channels),
|
|
nn.ReLU(True),
|
|
nn.Dropout2d(0.05)
|
|
)
|
|
self.concat = concat
|
|
|
|
def forward(self, x):
|
|
priors = [stage(x) for stage in self.stages]
|
|
context = priors[0]
|
|
for i in range(1, len(priors)):
|
|
context += priors[i]
|
|
if self.concat:
|
|
context = torch.cat([context, x], 1)
|
|
out = self.project(context)
|
|
return out
|
|
|
|
|
|
class PyramidAttentionBlock(nn.Module):
|
|
"""The basic implementation for pyramid self-attention block/non-local block"""
|
|
|
|
def __init__(self, in_channels, out_channels, key_channels, value_channels,
|
|
scale=1, norm_layer=nn.BatchNorm2d, **kwargs):
|
|
super(PyramidAttentionBlock, self).__init__()
|
|
self.scale = scale
|
|
self.value_channels = value_channels
|
|
self.key_channels = key_channels
|
|
|
|
self.f_value = nn.Conv2d(in_channels, value_channels, 1)
|
|
self.f_key = nn.Sequential(
|
|
nn.Conv2d(in_channels, key_channels, 1),
|
|
norm_layer(key_channels),
|
|
nn.ReLU(True)
|
|
)
|
|
self.f_query = self.f_key
|
|
self.W = nn.Conv2d(value_channels, out_channels, 1)
|
|
nn.init.constant_(self.W.weight, 0)
|
|
nn.init.constant_(self.W.bias, 0)
|
|
|
|
def forward(self, x):
|
|
batch_size, c, w, h = x.size()
|
|
|
|
local_x = list()
|
|
local_y = list()
|
|
step_w, step_h = w // self.scale, h // self.scale
|
|
for i in range(self.scale):
|
|
for j in range(self.scale):
|
|
start_x, start_y = step_w * i, step_h * j
|
|
end_x, end_y = min(start_x + step_w, w), min(start_y + step_h, h)
|
|
if i == (self.scale - 1):
|
|
end_x = w
|
|
if j == (self.scale - 1):
|
|
end_y = h
|
|
local_x += [start_x, end_x]
|
|
local_y += [start_y, end_y]
|
|
|
|
value = self.f_value(x)
|
|
query = self.f_query(x)
|
|
key = self.f_key(x)
|
|
|
|
local_list = list()
|
|
local_block_cnt = (self.scale ** 2) * 2
|
|
for i in range(0, local_block_cnt, 2):
|
|
value_local = value[:, :, local_x[i]:local_x[i + 1], local_y[i]:local_y[i + 1]]
|
|
query_local = query[:, :, local_x[i]:local_x[i + 1], local_y[i]:local_y[i + 1]]
|
|
key_local = key[:, :, local_x[i]:local_x[i + 1], local_y[i]:local_y[i + 1]]
|
|
|
|
w_local, h_local = value_local.size(2), value_local.size(3)
|
|
value_local = value_local.contiguous().view(batch_size, self.value_channels, -1).permute(0, 2, 1)
|
|
query_local = query_local.contiguous().view(batch_size, self.key_channels, -1).permute(0, 2, 1)
|
|
key_local = key_local.contiguous().view(batch_size, self.key_channels, -1)
|
|
|
|
sim_map = torch.bmm(query_local, key_local) * (self.key_channels ** -.5)
|
|
sim_map = F.softmax(sim_map, dim=-1)
|
|
|
|
context_local = torch.bmm(sim_map, value_local).permute(0, 2, 1).contiguous()
|
|
context_local = context_local.view(batch_size, self.value_channels, w_local, h_local)
|
|
local_list.append(context_local)
|
|
|
|
context_list = list()
|
|
for i in range(0, self.scale):
|
|
row_tmp = list()
|
|
for j in range(self.scale):
|
|
row_tmp.append(local_list[j + i * self.scale])
|
|
context_list.append(torch.cat(row_tmp, 3))
|
|
|
|
context = torch.cat(context_list, 2)
|
|
context = self.W(context)
|
|
|
|
return context
|
|
|
|
|
|
class PyramidOCModule(nn.Module):
|
|
"""Pyramid-OC"""
|
|
|
|
def __init__(self, in_channels, out_channels, key_channels, value_channels,
|
|
scales=([1]), norm_layer=nn.BatchNorm2d, **kwargs):
|
|
super(PyramidOCModule, self).__init__()
|
|
self.stages = nn.ModuleList([
|
|
PyramidAttentionBlock(in_channels, out_channels, key_channels, value_channels, scale, norm_layer, **kwargs)
|
|
for scale in scales])
|
|
self.up_dr = nn.Sequential(
|
|
nn.Conv2d(in_channels, in_channels * len(scales), 1),
|
|
norm_layer(in_channels * len(scales)),
|
|
nn.ReLU(True)
|
|
)
|
|
self.project = nn.Sequential(
|
|
nn.Conv2d(in_channels * len(scales) * 2, out_channels, 1),
|
|
norm_layer(out_channels),
|
|
nn.ReLU(True),
|
|
nn.Dropout2d(0.05)
|
|
)
|
|
|
|
def forward(self, x):
|
|
priors = [stage(x) for stage in self.stages]
|
|
context = [self.up_dr(x)]
|
|
for i in range(len(priors)):
|
|
context += [priors[i]]
|
|
context = torch.cat(context, 1)
|
|
out = self.project(context)
|
|
return out
|
|
|
|
|
|
class ASPOCModule(nn.Module):
|
|
"""ASP-OC"""
|
|
|
|
def __init__(self, in_channels, out_channels, key_channels, value_channels,
|
|
atrous_rates=(12, 24, 36), norm_layer=nn.BatchNorm2d, **kwargs):
|
|
super(ASPOCModule, self).__init__()
|
|
self.context = nn.Sequential(
|
|
nn.Conv2d(in_channels, out_channels, 3, padding=1),
|
|
norm_layer(out_channels),
|
|
nn.ReLU(True),
|
|
BaseOCModule(out_channels, out_channels, key_channels, value_channels, ([2]), norm_layer, False, **kwargs))
|
|
|
|
rate1, rate2, rate3 = tuple(atrous_rates)
|
|
self.b1 = nn.Sequential(
|
|
nn.Conv2d(in_channels, out_channels, 3, padding=rate1, dilation=rate1, bias=False),
|
|
norm_layer(out_channels),
|
|
nn.ReLU(True))
|
|
self.b2 = nn.Sequential(
|
|
nn.Conv2d(in_channels, out_channels, 3, padding=rate2, dilation=rate2, bias=False),
|
|
norm_layer(out_channels),
|
|
nn.ReLU(True))
|
|
self.b3 = nn.Sequential(
|
|
nn.Conv2d(in_channels, out_channels, 3, padding=rate3, dilation=rate3, bias=False),
|
|
norm_layer(out_channels),
|
|
nn.ReLU(True))
|
|
self.b4 = nn.Sequential(
|
|
nn.Conv2d(in_channels, out_channels, 1, bias=False),
|
|
norm_layer(out_channels),
|
|
nn.ReLU(True))
|
|
|
|
self.project = nn.Sequential(
|
|
nn.Conv2d(out_channels * 5, out_channels, 1, bias=False),
|
|
norm_layer(out_channels),
|
|
nn.ReLU(True),
|
|
nn.Dropout2d(0.1)
|
|
)
|
|
|
|
def forward(self, x):
|
|
feat1 = self.context(x)
|
|
feat2 = self.b1(x)
|
|
feat3 = self.b2(x)
|
|
feat4 = self.b3(x)
|
|
feat5 = self.b4(x)
|
|
out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
|
|
out = self.project(out)
|
|
return out
|
|
|
|
|
|
def get_ocnet(dataset='citys', backbone='resnet50', oc_arch='base', pretrained=False, root='~/.torch/models',
|
|
pretrained_base=True, **kwargs):
|
|
acronyms = {
|
|
'pascal_voc': 'pascal_voc',
|
|
'pascal_aug': 'pascal_aug',
|
|
'ade20k': 'ade',
|
|
'coco': 'coco',
|
|
'citys': 'citys',
|
|
}
|
|
from ..data.dataloader import datasets
|
|
model = OCNet(datasets[dataset].NUM_CLASS, backbone=backbone, oc_arch=oc_arch,
|
|
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('%s_ocnet_%s_%s' % (
|
|
oc_arch, backbone, acronyms[dataset]), root=root),
|
|
map_location=device))
|
|
return model
|
|
|
|
|
|
def get_base_ocnet_resnet101_citys(**kwargs):
|
|
return get_ocnet('citys', 'resnet101', 'base', **kwargs)
|
|
|
|
|
|
def get_pyramid_ocnet_resnet101_citys(**kwargs):
|
|
return get_ocnet('citys', 'resnet101', 'pyramid', **kwargs)
|
|
|
|
|
|
def get_asp_ocnet_resnet101_citys(**kwargs):
|
|
return get_ocnet('citys', 'resnet101', 'asp', **kwargs)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
#img = torch.randn(1, 3, 256, 256)
|
|
#model = get_asp_ocnet_resnet101_citys()
|
|
# outputs = model(img)
|
|
input = torch.rand(1, 3, 224,224)
|
|
model=OCNet(4,pretrained_base=False)
|
|
#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)) |