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| import torch | |
| import torch.nn as nn | |
| from .constants import * | |
| """ | |
| Class for custom activation. | |
| """ | |
| class SymReLU(nn.Module): | |
| def __init__(self, inplace: bool = False): | |
| super().__init__() | |
| self.inplace = inplace | |
| def forward(self, input): | |
| return torch.min(torch.max(input, -torch.ones_like(input)), torch.ones_like(input)) | |
| def extra_repr(self) -> str: | |
| inplace_str = 'inplace=True' if self.inplace else '' | |
| return inplace_str | |
| """ | |
| Class implementing YOLO-Stamp architecture described in https://link.springer.com/article/10.1134/S1054661822040046. | |
| """ | |
| class YOLOStamp(nn.Module): | |
| def __init__( | |
| self, | |
| anchors=ANCHORS, | |
| in_channels=3, | |
| ): | |
| super().__init__() | |
| self.register_buffer('anchors', torch.tensor(anchors)) | |
| self.act = SymReLU() | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm1 = nn.BatchNorm2d(num_features=8) | |
| self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm2 = nn.BatchNorm2d(num_features=16) | |
| self.conv3 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm3 = nn.BatchNorm2d(num_features=16) | |
| self.conv4 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm4 = nn.BatchNorm2d(num_features=16) | |
| self.conv5 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm5 = nn.BatchNorm2d(num_features=16) | |
| self.conv6 = nn.Conv2d(in_channels=16, out_channels=24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm6 = nn.BatchNorm2d(num_features=24) | |
| self.conv7 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm7 = nn.BatchNorm2d(num_features=24) | |
| self.conv8 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm8 = nn.BatchNorm2d(num_features=48) | |
| self.conv9 = nn.Conv2d(in_channels=48, out_channels=48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm9 = nn.BatchNorm2d(num_features=48) | |
| self.conv10 = nn.Conv2d(in_channels=48, out_channels=48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm10 = nn.BatchNorm2d(num_features=48) | |
| self.conv11 = nn.Conv2d(in_channels=48, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm11 = nn.BatchNorm2d(num_features=64) | |
| self.conv12 = nn.Conv2d(in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) | |
| self.norm12 = nn.BatchNorm2d(num_features=256) | |
| self.conv13 = nn.Conv2d(in_channels=256, out_channels=len(anchors) * 5, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) | |
| def forward(self, x, head=True): | |
| x = x.type(self.conv1.weight.dtype) | |
| x = self.act(self.pool(self.norm1(self.conv1(x)))) | |
| x = self.act(self.pool(self.norm2(self.conv2(x)))) | |
| x = self.act(self.pool(self.norm3(self.conv3(x)))) | |
| x = self.act(self.pool(self.norm4(self.conv4(x)))) | |
| x = self.act(self.pool(self.norm5(self.conv5(x)))) | |
| x = self.act(self.norm6(self.conv6(x))) | |
| x = self.act(self.norm7(self.conv7(x))) | |
| x = self.act(self.pool(self.norm8(self.conv8(x)))) | |
| x = self.act(self.norm9(self.conv9(x))) | |
| x = self.act(self.norm10(self.conv10(x))) | |
| x = self.act(self.norm11(self.conv11(x))) | |
| x = self.act(self.norm12(self.conv12(x))) | |
| x = self.conv13(x) | |
| nb, _, nh, nw= x.shape | |
| x = x.permute(0, 2, 3, 1).view(nb, nh, nw, self.anchors.shape[0], 5) | |
| return x | |