import torch

import torch.nn.functional as F

from tqdm import tqdm

from utils.dice_score import multiclass_dice_coeff, dice_coeff

@torch.inference_mode()

def evaluate(net, dataloader, device, amp):

    net.eval()

    num_val_batches = len(dataloader)

    dice_score = 0

    # iterate over the validation set

    with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp):

        for batch in tqdm(dataloader, total=num_val_batches, desc='Validation round', unit='batch', leave=False):

            image, mask_true = batch['image'], batch['mask']

            # move images and labels to correct device and type

            image = image.to(device=device, dtype=torch.float32, memory_format=torch.channels_last)

            mask_true = mask_true.to(device=device, dtype=torch.long)

            # predict the mask

            mask_pred = net(image)

            if net.n_classes == 1:

                assert mask_true.min() >= 0 and mask_true.max() <= 1, 'True mask indices should be in [0, 1]'

                mask_pred = (F.sigmoid(mask_pred) > 0.5).float()

                # compute the Dice score

                dice_score += dice_coeff(mask_pred, mask_true, reduce_batch_first=False)

            else:

                assert mask_true.min() >= 0 and mask_true.max() < net.n_classes, 'True mask indices should be in [0, n_classes['

                # convert to one-hot format

                mask_true = F.one_hot(mask_true, net.n_classes).permute(0, 3, 1, 2).float()

                mask_pred = F.one_hot(mask_pred.argmax(dim=1), net.n_classes).permute(0, 3, 1, 2).float()

                # compute the Dice score, ignoring background

                dice_score += multiclass_dice_coeff(mask_pred[:, 1:], mask_true[:, 1:], reduce_batch_first=False)

    net.train()

    return dice_score / max(num_val_batches, 1)