evaluate.py

import torch
from datasets.dataset import PositioningDataset
from torch.utils.data import DataLoader
from models.model import PositioningModel
import yaml

with open("config.yaml", "r") as f:
    config = yaml.safe_load(f)

dataset = PositioningDataset('data/choords.xlsx', 'data/images')
loader = DataLoader(dataset, batch_size=1, shuffle=False)

model = PositioningModel()
model.eval()

total_error = torch.zeros(3)
count = 0

with torch.no_grad():
    for images, targets in loader:
        outputs = model(images)
        total_error += torch.sum(torch.abs(outputs - targets), dim=0)
        count += images.size(0)

avg_error = total_error / count
print(f"Средняя ошибка: X={avg_error[0]:.3f}, Y={avg_error[1]:.3f}, Угол={avg_error[2]:.3f}")