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import torch
import torch.nn as nn
from .lcnn_hourglass import MultitaskHead, hg
class HourglassBackbone(nn.Module):
"""Hourglass backbone."""
def __init__(
self, input_channel=1, depth=4, num_stacks=2, num_blocks=1, num_classes=5
):
super(HourglassBackbone, self).__init__()
self.head = MultitaskHead
self.net = hg(
**{
"head": self.head,
"depth": depth,
"num_stacks": num_stacks,
"num_blocks": num_blocks,
"num_classes": num_classes,
"input_channels": input_channel,
}
)
def forward(self, input_images):
return self.net(input_images)[1]
class SuperpointBackbone(nn.Module):
"""SuperPoint backbone."""
def __init__(self):
super(SuperpointBackbone, self).__init__()
self.relu = torch.nn.ReLU(inplace=True)
self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
c1, c2, c3, c4 = 64, 64, 128, 128
# Shared Encoder.
self.conv1a = torch.nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1)
self.conv1b = torch.nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1)
self.conv2a = torch.nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1)
self.conv2b = torch.nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1)
self.conv3a = torch.nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1)
self.conv3b = torch.nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1)
self.conv4a = torch.nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1)
self.conv4b = torch.nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1)
def forward(self, input_images):
# Shared Encoder.
x = self.relu(self.conv1a(input_images))
x = self.relu(self.conv1b(x))
x = self.pool(x)
x = self.relu(self.conv2a(x))
x = self.relu(self.conv2b(x))
x = self.pool(x)
x = self.relu(self.conv3a(x))
x = self.relu(self.conv3b(x))
x = self.pool(x)
x = self.relu(self.conv4a(x))
x = self.relu(self.conv4b(x))
return x
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