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GeoTree Model Architecture Definition for Hugging Face Loading
"""
import torch
import torch.nn as nn
class ConvResidualBlock(nn.Module):
def __init__(self, channels: int):
super().__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(channels)
self.act = nn.SiLU(inplace=True)
self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(channels)
def forward(self, x: torch.Tensor) -> torch.Tensor:
res = x
out = self.act(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
return self.act(out + res)
class TreeDetectorModel(nn.Module):
"""GeoTree Residual Convolutional Neural Network for Tree Crown Detection."""
def __init__(self, num_classes: int = 1):
super().__init__()
self.backbone = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.SiLU(inplace=True),
nn.MaxPool2d(2, 2),
ConvResidualBlock(32),
nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.SiLU(inplace=True),
nn.MaxPool2d(2, 2),
ConvResidualBlock(64),
nn.Conv2d(64, 128, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.SiLU(inplace=True),
nn.MaxPool2d(2, 2)
)
self.detector = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.SiLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(256, 128),
nn.SiLU(inplace=True),
nn.Dropout(0.1),
nn.Linear(128, 5)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
features = self.backbone(x)
detections = self.detector(features)
return torch.cat([
detections[:, :1],
torch.sigmoid(detections[:, 1:])
], dim=1)
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