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from curses import is_term_resized
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
from lanet_utils import image_grid
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvBlock, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(
out_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.conv(x)
class DilationConv3x3(nn.Module):
def __init__(self, in_channels, out_channels):
super(DilationConv3x3, self).__init__()
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=2,
dilation=2,
bias=False,
)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class InterestPointModule(nn.Module):
def __init__(self, is_test=False):
super(InterestPointModule, self).__init__()
self.is_test = is_test
model = models.vgg16_bn(pretrained=True)
# use the first 23 layers as encoder
self.encoder = nn.Sequential(*list(model.features.children())[:33])
# score head
self.score_head = nn.Sequential(
nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1),
)
self.softmax = nn.Softmax(dim=1)
# location head
self.loc_head = nn.Sequential(
nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
)
# location out
self.loc_out = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
self.shift_out = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)
# descriptor out
self.des_out2 = DilationConv3x3(128, 256)
self.des_out3 = DilationConv3x3(256, 256)
self.des_out4 = DilationConv3x3(512, 256)
def forward(self, x):
B, _, H, W = x.shape
x = self.encoder[2](self.encoder[1](self.encoder[0](x)))
x = self.encoder[5](self.encoder[4](self.encoder[3](x)))
x = self.encoder[6](x)
x = self.encoder[9](self.encoder[8](self.encoder[7](x)))
x2 = self.encoder[12](self.encoder[11](self.encoder[10](x)))
x = self.encoder[13](x2)
x = self.encoder[16](self.encoder[15](self.encoder[14](x)))
x = self.encoder[19](self.encoder[18](self.encoder[17](x)))
x3 = self.encoder[22](self.encoder[21](self.encoder[20](x)))
x = self.encoder[23](x3)
x = self.encoder[26](self.encoder[25](self.encoder[24](x)))
x = self.encoder[29](self.encoder[28](self.encoder[27](x)))
x = self.encoder[32](self.encoder[31](self.encoder[30](x)))
B, _, Hc, Wc = x.shape
# score head
score_x = self.score_head(x)
aware = self.softmax(score_x[:, 0:3, :, :])
score = score_x[:, 3, :, :].unsqueeze(1).sigmoid()
border_mask = torch.ones(B, Hc, Wc)
border_mask[:, 0] = 0
border_mask[:, Hc - 1] = 0
border_mask[:, :, 0] = 0
border_mask[:, :, Wc - 1] = 0
border_mask = border_mask.unsqueeze(1)
score = score * border_mask.to(score.device)
# location head
coord_x = self.loc_head(x)
coord_cell = self.loc_out(coord_x).tanh()
shift_ratio = self.shift_out(coord_x).sigmoid() * 2.0
step = ((H / Hc) - 1) / 2.0
center_base = (
image_grid(
B,
Hc,
Wc,
dtype=coord_cell.dtype,
device=coord_cell.device,
ones=False,
normalized=False,
).mul(H / Hc)
+ step
)
coord_un = center_base.add(coord_cell.mul(shift_ratio * step))
coord = coord_un.clone()
coord[:, 0] = torch.clamp(coord_un[:, 0], min=0, max=W - 1)
coord[:, 1] = torch.clamp(coord_un[:, 1], min=0, max=H - 1)
# descriptor block
desc_block = []
desc_block.append(self.des_out2(x2))
desc_block.append(self.des_out3(x3))
desc_block.append(self.des_out4(x))
desc_block.append(aware)
if self.is_test:
coord_norm = coord[:, :2].clone()
coord_norm[:, 0] = (coord_norm[:, 0] / (float(W - 1) / 2.0)) - 1.0
coord_norm[:, 1] = (coord_norm[:, 1] / (float(H - 1) / 2.0)) - 1.0
coord_norm = coord_norm.permute(0, 2, 3, 1)
desc2 = torch.nn.functional.grid_sample(desc_block[0], coord_norm)
desc3 = torch.nn.functional.grid_sample(desc_block[1], coord_norm)
desc4 = torch.nn.functional.grid_sample(desc_block[2], coord_norm)
aware = desc_block[3]
desc = (
torch.mul(desc2, aware[:, 0, :, :])
+ torch.mul(desc3, aware[:, 1, :, :])
+ torch.mul(desc4, aware[:, 2, :, :])
)
desc = desc.div(
torch.unsqueeze(torch.norm(desc, p=2, dim=1), 1)
) # Divide by norm to normalize.
return score, coord, desc
return score, coord, desc_block
class CorrespondenceModule(nn.Module):
def __init__(self, match_type="dual_softmax"):
super(CorrespondenceModule, self).__init__()
self.match_type = match_type
if self.match_type == "dual_softmax":
self.temperature = 0.1
else:
raise NotImplementedError()
def forward(self, source_desc, target_desc):
b, c, h, w = source_desc.size()
source_desc = source_desc.div(
torch.unsqueeze(torch.norm(source_desc, p=2, dim=1), 1)
).view(b, -1, h * w)
target_desc = target_desc.div(
torch.unsqueeze(torch.norm(target_desc, p=2, dim=1), 1)
).view(b, -1, h * w)
if self.match_type == "dual_softmax":
sim_mat = (
torch.einsum("bcm, bcn -> bmn", source_desc, target_desc)
/ self.temperature
)
confidence_matrix = F.softmax(sim_mat, 1) * F.softmax(sim_mat, 2)
else:
raise NotImplementedError()
return confidence_matrix
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