Spaces:
Sleeping
Sleeping
File size: 6,058 Bytes
2c8b554 |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class DenseFeatureExtractionModule(nn.Module):
def __init__(self, use_relu=True, use_cuda=True):
super(DenseFeatureExtractionModule, self).__init__()
self.model = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2),
nn.Conv2d(64, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, stride=2),
nn.Conv2d(128, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.AvgPool2d(2, stride=1),
nn.Conv2d(256, 512, 3, padding=2, dilation=2),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=2, dilation=2),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, 3, padding=2, dilation=2),
)
self.num_channels = 512
self.use_relu = use_relu
if use_cuda:
self.model = self.model.cuda()
def forward(self, batch):
output = self.model(batch)
if self.use_relu:
output = F.relu(output)
return output
class D2Net(nn.Module):
def __init__(self, model_file=None, use_relu=True, use_cuda=False):
super(D2Net, self).__init__()
self.dense_feature_extraction = DenseFeatureExtractionModule(
use_relu=use_relu, use_cuda=use_cuda
)
self.detection = HardDetectionModule()
self.localization = HandcraftedLocalizationModule()
if model_file is not None:
if use_cuda:
self.load_state_dict(torch.load(model_file)['model'])
else:
self.load_state_dict(torch.load(model_file, map_location='cpu')['model'])
def forward(self, batch):
_, _, h, w = batch.size()
dense_features = self.dense_feature_extraction(batch)
detections = self.detection(dense_features)
displacements = self.localization(dense_features)
return {
'dense_features': dense_features,
'detections': detections,
'displacements': displacements
}
class HardDetectionModule(nn.Module):
def __init__(self, edge_threshold=5):
super(HardDetectionModule, self).__init__()
self.edge_threshold = edge_threshold
self.dii_filter = torch.tensor(
[[0, 1., 0], [0, -2., 0], [0, 1., 0]]
).view(1, 1, 3, 3)
self.dij_filter = 0.25 * torch.tensor(
[[1., 0, -1.], [0, 0., 0], [-1., 0, 1.]]
).view(1, 1, 3, 3)
self.djj_filter = torch.tensor(
[[0, 0, 0], [1., -2., 1.], [0, 0, 0]]
).view(1, 1, 3, 3)
def forward(self, batch):
b, c, h, w = batch.size()
device = batch.device
depth_wise_max = torch.max(batch, dim=1)[0]
is_depth_wise_max = (batch == depth_wise_max)
del depth_wise_max
local_max = F.max_pool2d(batch, 3, stride=1, padding=1)
is_local_max = (batch == local_max)
del local_max
dii = F.conv2d(
batch.view(-1, 1, h, w), self.dii_filter.to(device), padding=1
).view(b, c, h, w)
dij = F.conv2d(
batch.view(-1, 1, h, w), self.dij_filter.to(device), padding=1
).view(b, c, h, w)
djj = F.conv2d(
batch.view(-1, 1, h, w), self.djj_filter.to(device), padding=1
).view(b, c, h, w)
det = dii * djj - dij * dij
tr = dii + djj
del dii, dij, djj
threshold = (self.edge_threshold + 1) ** 2 / self.edge_threshold
is_not_edge = torch.min(tr * tr / det <= threshold, det > 0)
detected = torch.min(
is_depth_wise_max,
torch.min(is_local_max, is_not_edge)
)
del is_depth_wise_max, is_local_max, is_not_edge
return detected
class HandcraftedLocalizationModule(nn.Module):
def __init__(self):
super(HandcraftedLocalizationModule, self).__init__()
self.di_filter = torch.tensor(
[[0, -0.5, 0], [0, 0, 0], [0, 0.5, 0]]
).view(1, 1, 3, 3)
self.dj_filter = torch.tensor(
[[0, 0, 0], [-0.5, 0, 0.5], [0, 0, 0]]
).view(1, 1, 3, 3)
self.dii_filter = torch.tensor(
[[0, 1., 0], [0, -2., 0], [0, 1., 0]]
).view(1, 1, 3, 3)
self.dij_filter = 0.25 * torch.tensor(
[[1., 0, -1.], [0, 0., 0], [-1., 0, 1.]]
).view(1, 1, 3, 3)
self.djj_filter = torch.tensor(
[[0, 0, 0], [1., -2., 1.], [0, 0, 0]]
).view(1, 1, 3, 3)
def forward(self, batch):
b, c, h, w = batch.size()
device = batch.device
dii = F.conv2d(
batch.view(-1, 1, h, w), self.dii_filter.to(device), padding=1
).view(b, c, h, w)
dij = F.conv2d(
batch.view(-1, 1, h, w), self.dij_filter.to(device), padding=1
).view(b, c, h, w)
djj = F.conv2d(
batch.view(-1, 1, h, w), self.djj_filter.to(device), padding=1
).view(b, c, h, w)
det = dii * djj - dij * dij
inv_hess_00 = djj / det
inv_hess_01 = -dij / det
inv_hess_11 = dii / det
del dii, dij, djj, det
di = F.conv2d(
batch.view(-1, 1, h, w), self.di_filter.to(device), padding=1
).view(b, c, h, w)
dj = F.conv2d(
batch.view(-1, 1, h, w), self.dj_filter.to(device), padding=1
).view(b, c, h, w)
step_i = -(inv_hess_00 * di + inv_hess_01 * dj)
step_j = -(inv_hess_01 * di + inv_hess_11 * dj)
del inv_hess_00, inv_hess_01, inv_hess_11, di, dj
return torch.stack([step_i, step_j], dim=1)
|