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ccnet.py
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ccnet.py
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import torch.nn as nn
from torch.nn import functional as F
import math
import torch.utils.model_zoo as model_zoo
import torch
import numpy as np
from torch.autograd import Variable
affine_par = True
import functools
import sys, os
#from cc_attention import CrissCrossAttention
from .CC import CC_module as CrissCrossAttention
#from .utils.pyt_utils import load_model
#from Synchronized.sync_batchnorm import SynchronizedBatchNorm2d as SyncBN
BatchNorm2d = nn.BatchNorm2d#SyncBN#functools.partial(InPlaceABNSync, activation='identity')
def outS(i):
i = int(i)
i = (i+1)/2
i = int(np.ceil((i+1)/2.0))
i = (i+1)/2
return i
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, fist_dilation=1, multi_grid=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=dilation*multi_grid, dilation=dilation*multi_grid, bias=False)
self.bn2 = BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=False)
self.relu_inplace = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
def forward(self, x):
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)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu_inplace(out)
return out
class RCCAModule(nn.Module):
def __init__(self, in_channels, out_channels, num_classes):
super(RCCAModule, self).__init__()
inter_channels = in_channels // 4
self.conva = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
BatchNorm2d(inter_channels),nn.ReLU(inplace=False))
self.cca = CrissCrossAttention(inter_channels)
self.convb = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False),
BatchNorm2d(inter_channels),nn.ReLU(inplace=False))
self.bottleneck = nn.Sequential(
nn.Conv2d(in_channels+inter_channels, out_channels, kernel_size=3, padding=1, dilation=1, bias=False),
BatchNorm2d(out_channels),nn.ReLU(inplace=False),
nn.Dropout2d(0.1),
nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
)
def forward(self, x, recurrence=2):
output = self.conva(x)
for i in range(recurrence):
output = self.cca(output)
output = self.convb(output)
output = self.bottleneck(torch.cat([x, output], 1))
return output
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes, recurrence):
self.inplanes = 128
super(ResNet, self).__init__()
self.conv1 = conv3x3(3, 64, stride=2)
self.bn1 = BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=False)
self.conv2 = conv3x3(64, 64)
self.bn2 = BatchNorm2d(64)
self.relu2 = nn.ReLU(inplace=False)
self.conv3 = conv3x3(64, 128)
self.bn3 = BatchNorm2d(128)
self.relu3 = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.relu = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, multi_grid=(1,1,1))
#self.layer5 = PSPModule(2048, 512)
self.head = RCCAModule(2048, 512, num_classes)
self.dsn = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
BatchNorm2d(512),nn.ReLU(inplace=False),
nn.Dropout2d(0.1),
nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
)
self.conv4 = nn.Conv2d(num_classes*2, num_classes, kernel_size=1, stride=1, bias=False)
#self.criterion = criterion
self.recurrence = recurrence
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
BatchNorm2d(planes * block.expansion,affine = affine_par))
layers = []
generate_multi_grid = lambda index, grids: grids[index%len(grids)] if isinstance(grids, tuple) else 1
layers.append(block(self.inplanes, planes, stride,dilation=dilation, downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid)))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation, multi_grid=generate_multi_grid(i, multi_grid)))
return nn.Sequential(*layers)
def forward(self, x, labels=None):
#print(111)
size = (x.shape[2], x.shape[3])
x = self.relu1(self.bn1(self.conv1(x)))
x = self.relu2(self.bn2(self.conv2(x)))
x = self.relu3(self.bn3(self.conv3(x)))
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
#print(222)
x = self.layer3(x)
#print(333)
x_dsn = self.dsn(x)
#print(x_dsn.shape)
x = self.layer4(x)
#print(x.shape)
x = self.head(x, self.recurrence)
#print(x.shape)
outs = torch.cat([x, x_dsn],1)
#print(outs.shape)
outs = self.conv4(outs)
outs = nn.Upsample(size, mode='bilinear', align_corners=True)(outs)
#print(outs)
return outs
def resnet152(num_classes=4, pretrained_model=None, recurrence=2, **kwargs):
model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes, recurrence)
return model
if __name__ == "__main__":
device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
model = resnet152()
model = model.to(device)
x = torch.rand((4,3,320,320))
#x = torch.tensor(x, dtype = torch.float)
x = x.to(device)
print(x.shape)
print('====================')
output = model(x)
print('====================')
print(output.shape)
#torch.save(model.state_dict(), 'ccnet_{}.pth'.format(0))
#torch.save(model, 'model_{}.pt'.format(0))