File size: 6,201 Bytes
a80d6bb
 
 
 
84efff1
a80d6bb
c74a070
a80d6bb
 
 
c74a070
a80d6bb
c74a070
 
 
 
 
 
 
 
a80d6bb
 
c74a070
 
 
 
 
 
 
 
a80d6bb
c74a070
a80d6bb
c74a070
a80d6bb
 
 
c74a070
a80d6bb
 
 
c74a070
 
 
 
 
 
 
 
 
 
a80d6bb
c74a070
a80d6bb
 
 
 
 
 
 
 
 
 
c74a070
a80d6bb
 
 
 
c74a070
a80d6bb
c74a070
a80d6bb
c74a070
 
 
a80d6bb
 
 
c74a070
a80d6bb
c74a070
 
 
a80d6bb
 
 
 
 
 
 
 
 
c74a070
a80d6bb
c74a070
a80d6bb
 
 
 
 
 
 
 
 
 
 
 
c74a070
a80d6bb
 
 
 
c74a070
a80d6bb
 
 
 
 
 
 
c74a070
 
a80d6bb
 
c74a070
a80d6bb
 
c74a070
 
 
 
 
 
 
 
 
 
 
 
 
a80d6bb
 
 
c74a070
 
a80d6bb
 
 
 
 
 
 
 
 
c74a070
 
a80d6bb
 
c74a070
a80d6bb
 
c74a070
 
 
 
 
 
 
a80d6bb
 
 
 
 
 
 
c74a070
a80d6bb
 
 
c74a070
a80d6bb
 
 
 
c74a070
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a80d6bb
 
 
c74a070
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
import torch
import torch.nn as nn
import torch.nn.functional as F

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

        self.conv1 = ConvBlock(3, 32)
        self.conv2 = ConvBlock(32, 64)
        self.conv3 = ConvBlock(64, 128)
        self.conv4 = ConvBlock(128, 256)

        self.maxpool2x2 = nn.MaxPool2d(2, 2)

        # score head
        self.score_conv = nn.Conv2d(
            256, 256, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.score_norm = nn.BatchNorm2d(256)
        self.score_out = nn.Conv2d(256, 3, kernel_size=3, stride=1, padding=1)
        self.softmax = nn.Softmax(dim=1)

        # location head
        self.loc_conv = nn.Conv2d(
            256, 256, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.loc_norm = nn.BatchNorm2d(256)
        self.loc_out = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)

        # descriptor out
        self.des_conv2 = DilationConv3x3(64, 256)
        self.des_conv3 = DilationConv3x3(128, 256)

        # cross_head:
        self.shift_out = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1)

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        B, _, H, W = x.shape

        x = self.conv1(x)
        x = self.maxpool2x2(x)
        x2 = self.conv2(x)
        x = self.maxpool2x2(x2)
        x3 = self.conv3(x)
        x = self.maxpool2x2(x3)
        x = self.conv4(x)

        B, _, Hc, Wc = x.shape

        # score head
        score_x = self.score_out(self.relu(self.score_norm(self.score_conv(x))))
        aware = self.softmax(score_x[:, 0:2, :, :])
        score = score_x[:, 2, :, :].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.relu(self.loc_norm(self.loc_conv(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_conv2(x2))
        desc_block.append(self.des_conv3(x3))
        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)
            aware = desc_block[2]

            desc = torch.mul(desc2, aware[:, 0, :, :]) + torch.mul(
                desc3, aware[:, 1, :, :]
            )
            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