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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
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