The backbone class that contains encode and decode function
| 3 | import torch.nn as nn |
| 4 | |
| 5 | class Backbone(nn.Module): |
| 6 | """The backbone class that contains encode and decode function""" |
| 7 | |
| 8 | def __init__(self, height_feat_size, compress_level=0, train_completion=False): |
| 9 | super().__init__() |
| 10 | self.conv_pre_1 = nn.Conv2d( |
| 11 | height_feat_size, 32, kernel_size=3, stride=1, padding=1 |
| 12 | ) |
| 13 | self.conv_pre_2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1) |
| 14 | self.bn_pre_1 = nn.BatchNorm2d(32) |
| 15 | self.bn_pre_2 = nn.BatchNorm2d(32) |
| 16 | |
| 17 | self.conv3d_1 = Conv3D( |
| 18 | 64, 64, kernel_size=(1, 1, 1), stride=1, padding=(0, 0, 0) |
| 19 | ) |
| 20 | self.conv3d_2 = Conv3D( |
| 21 | 128, 128, kernel_size=(1, 1, 1), stride=1, padding=(0, 0, 0) |
| 22 | ) |
| 23 | |
| 24 | self.conv1_1 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1) |
| 25 | self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) |
| 26 | |
| 27 | self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) |
| 28 | self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) |
| 29 | |
| 30 | self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) |
| 31 | self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) |
| 32 | |
| 33 | self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) |
| 34 | self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) |
| 35 | |
| 36 | self.conv5_1 = nn.Conv2d(512 + 256, 256, kernel_size=3, stride=1, padding=1) |
| 37 | self.conv5_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) |
| 38 | |
| 39 | self.conv6_1 = nn.Conv2d(256 + 128, 128, kernel_size=3, stride=1, padding=1) |
| 40 | self.conv6_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) |
| 41 | |
| 42 | self.conv7_1 = nn.Conv2d(128 + 64, 64, kernel_size=3, stride=1, padding=1) |
| 43 | self.conv7_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) |
| 44 | |
| 45 | self.conv8_1 = nn.Conv2d(64 + 32, 32, kernel_size=3, stride=1, padding=1) |
| 46 | self.conv8_2 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1) |
| 47 | |
| 48 | self.bn1_1 = nn.BatchNorm2d(64) |
| 49 | self.bn1_2 = nn.BatchNorm2d(64) |
| 50 | |
| 51 | self.bn2_1 = nn.BatchNorm2d(128) |
| 52 | self.bn2_2 = nn.BatchNorm2d(128) |
| 53 | |
| 54 | self.bn3_1 = nn.BatchNorm2d(256) |
| 55 | self.bn3_2 = nn.BatchNorm2d(256) |
| 56 | |
| 57 | self.bn4_1 = nn.BatchNorm2d(512) |
| 58 | self.bn4_2 = nn.BatchNorm2d(512) |
| 59 | |
| 60 | self.bn5_1 = nn.BatchNorm2d(256) |
| 61 | self.bn5_2 = nn.BatchNorm2d(256) |
| 62 |
nothing calls this directly
no outgoing calls
no test coverage detected