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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)
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