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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 | |