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BoTNet.py
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import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
#===========================================================================================
## resnet50
## 主体替换Bottleneck的部件
class MHSA(nn.Module):
def __init__(self, n_dims, width=14, height=14):
super(MHSA, self).__init__()
self.query = nn.Conv2d(n_dims, n_dims, kernel_size=1)
self.key = nn.Conv2d(n_dims, n_dims, kernel_size=1)
self.value = nn.Conv2d(n_dims, n_dims, kernel_size=1)
self.rel_h = nn.Parameter(torch.randn([1, n_dims, height, 1]), requires_grad=True)
self.rel_w = nn.Parameter(torch.randn([1, n_dims, 1, width]), requires_grad=True)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
n_batch, C, height, width = x.size()
q = self.query(x).view(n_batch, C, -1)
k = self.key(x).view(n_batch, C, -1)
v = self.value(x).view(n_batch, C, -1)
content_content = torch.bmm(q.permute(0, 2, 1), k)
content_position = (self.rel_h + self.rel_w).view(1, C, -1).permute(0, 2, 1)
content_position = torch.matmul(content_position, q)
energy = content_content + content_position
attention = self.softmax(energy)
out = torch.bmm(v, attention.permute(0, 2, 1))
out = out.view(n_batch, C, height, width)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1, mhsa=False):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
### 添加的transformer部件
if not mhsa:
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, stride=stride, bias=False)
else:
if stride == 2:
self.conv2 = nn.Sequential(
MHSA(planes, width=64, height=32), # for myself
nn.AvgPool2d(2, 2)
)
else:
self.conv2 = nn.Sequential(
MHSA(planes, width=32, height=16) # for myself -- 是stride==2的一半
)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
#===========================================================================================
class ResNetBase(nn.Module):
def __init__(self, block, num_blocks, pretrained_model=None):
super(ResNetBase, self).__init__()
self.in_planes = 64
if pretrained_model is None:
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
# Bottom-up layers
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, mhsa=True) ## 仅仅替换最好一层是最小的计算代价
else:
self.conv1 = pretrained_model.conv1
self.bn1 = pretrained_model.bn1
self.layer1 = pretrained_model.layer1
self.layer2 = pretrained_model.layer2
self.layer3 = pretrained_model.layer3
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, mhsa=True)
# # Lateral layers --- resnet50之后
# self.latlayer1 = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0)
# self.latlayer2 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
# self.latlayer3 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
# self.latlayer4 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
# # # Lateral layers --- resnet18,34
# # self.latlayer1 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
# # self.latlayer2 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
# # self.latlayer3 = nn.Conv2d(128, 256, kernel_size=1, stride=1, padding=0)
# # self.latlayer4 = nn.Conv2d(64, 256, kernel_size=1, stride=1, padding=0)
# # Top-down layers
# self.toplayer1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
# self.toplayer2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
# self.toplayer3 = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
def _make_layer(self, block, planes, num_blocks, stride, mhsa=False):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
### 修改是否需要mhsa
layers.append(block(self.in_planes, planes, stride, mhsa))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def _upsample_add(self, x, y):
_,_,H,W = y.size()
## return F.upsample(x, size=(H,W), mode='bilinear') + y
return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=False) + y
def forward(self, x):
# Bottom-up
c1 = F.relu(self.bn1(self.conv1(x)))
c1 = F.max_pool2d(c1, kernel_size=3, stride=2, padding=1) ## 128, 256
## 使用的原始输出--encoder
c2 = self.layer1(c1) ### [b, 256, 128, 256] ## 若是18则换成[b, 64, 16, 32]
c3 = self.layer2(c2) ### [b, 512, 64, 128] ## 若是18则换成[b, 128, 16, 32]
c4 = self.layer3(c3) ### [b, 1024, 32, 64] ## 若是18则换成[b, 256, 16, 32]
c5 = self.layer4(c4) ### [b, 2048, 16, 32] ## 若是18则换成[b, 512, 16, 32]
return c5
# # Top-down -- decoder
# p5 = self.latlayer1(c5)
# p4 = self._upsample_add(p5, self.latlayer2(c4))
# p4 = self.toplayer1(p4)
#
# p3 = self._upsample_add(p4, self.latlayer3(c3))
# p3 = self.toplayer2(p3)
#
#
# c2up = F.upsample(c2, size=(512, 1024), mode='bilinear')
# p2_in = self._upsample_add(p3, self.latlayer4(c2up))
# p2 = self.toplayer3(p2_in)
#
# return p2, p3, p4, p5
def ResNet50_BoT(pretrained=False):
model = ResNetBase(Bottleneck, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(torch.load('./pth/resnet50.pth'), strict=False)
return model
if __name__ == '__main__':
input = torch.randn(1, 3, 512, 1024)
net = ResNet50_BoT(pretrained=True)
out = net(input)
print(out.shape) ## (1, 3, 512, 1024)