validate.py

import torch
import numpy as np
from tqdm import tqdm
from utils import AverageMeter
from config import CFG

# Define your other necessary imports here


def valid_fn(valid_loader, model, criterion, device, valid_xyxys, valid_mask_gt):
    mask_pred = np.zeros(valid_mask_gt.shape)
    mask_count = np.zeros(valid_mask_gt.shape)

    model.eval()
    losses = AverageMeter()

    for step, (images, labels) in tqdm(enumerate(valid_loader), total=len(valid_loader)):
        images = images.to(device)
        labels = labels.to(device)
        batch_size = labels.size(0)

        with torch.no_grad():
            y_preds = model(images)
            loss = criterion(y_preds, labels)
        losses.update(loss.item(), batch_size)

        # make whole mask
        y_preds = torch.sigmoid(y_preds).to('cpu').numpy()
        start_idx = step * CFG.valid_batch_size
        end_idx = start_idx + batch_size
        for i, (x1, y1, x2, y2) in enumerate(valid_xyxys[start_idx:end_idx]):
            mask_pred[y1:y2, x1:x2] += y_preds[i].squeeze(0)
            mask_count[y1:y2, x1:x2] += np.ones((CFG.tile_size, CFG.tile_size))

    print(f'mask_count_min: {mask_count.min()}')
    mask_pred /= mask_count
    return losses.avg, mask_pred