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import torch
import torch.nn as nn
from typing import Dict, List, Tuple
class Exp:
"""
Configuration class for the page element model.
This class contains all configuration parameters for the YOLOX-based
page element detection model, including architecture settings, inference
parameters, and class-specific thresholds.
"""
def __init__(self) -> None:
"""Initialize the configuration with default parameters."""
self.name: str = "page-element-v3"
self.ckpt: str = "weights.pth"
self.device: str = "cuda:0" if torch.cuda.is_available() else "cpu"
# YOLOX architecture parameters
self.act: str = "silu"
self.depth: float = 1.00
self.width: float = 1.00
self.labels: List[str] = [
"table",
"chart",
"title",
"infographic",
"text",
"header_footer",
]
self.num_classes: int = len(self.labels)
# Inference parameters
self.size: Tuple[int, int] = (1024, 1024)
self.min_bbox_size: int = 0
self.normalize_boxes: bool = True
# NMS & thresholding. These can be updated
self.conf_thresh: float = 0.01
self.iou_thresh: float = 0.5
self.class_agnostic: bool = True
self.thresholds_per_class: Dict[str, float] = {
"table": 0.1,
"chart": 0.01,
"infographic": 0.01,
"title": 0.1,
"text": 0.1,
"header_footer": 0.1,
}
def get_model(self) -> nn.Module:
"""
Get the YOLOX model.
Builds and returns a YOLOX model with the configured architecture.
Also updates batch normalization parameters for optimal inference.
Returns:
nn.Module: The YOLOX model with configured parameters.
"""
from yolox import YOLOX, YOLOPAFPN, YOLOXHead
# Build model
if getattr(self, "model", None) is None:
in_channels = [256, 512, 1024]
backbone = YOLOPAFPN(
self.depth, self.width, in_channels=in_channels, act=self.act
)
head = YOLOXHead(
self.num_classes, self.width, in_channels=in_channels, act=self.act
)
self.model = YOLOX(backbone, head)
# Update batch-norm parameters
def init_yolo(M: nn.Module) -> None:
for m in M.modules():
if isinstance(m, nn.BatchNorm2d):
m.eps = 1e-3
m.momentum = 0.03
self.model.apply(init_yolo)
return self.model
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