engine_finetune.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# MAE: https://github.com/facebookresearch/mae
# --------------------------------------------------------

import math
import sys
from typing import Iterable, Optional

import torch

from timm.data import Mixup
from timm.utils import accuracy

import util.misc as misc
import util.lr_sched as lr_sched
from sklearn.metrics import roc_auc_score
from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix
import torch.nn.functional as F
import numpy
import numpy as np
from torchmetrics import Specificity, AUROC



def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
                    mixup_fn: Optional[Mixup] = None, log_writer=None,
                    args=None):
    model.train(True)
    metric_logger = misc.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    header = 'Epoch: [{}]'.format(epoch)
    print_freq = 20

    accum_iter = args.accum_iter

    optimizer.zero_grad()

    if log_writer is not None:
        print('log_dir: {}'.format(log_writer.log_dir))

    for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):

        # we use a per iteration (instead of per epoch) lr scheduler
        if data_iter_step % accum_iter == 0:
            lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)

        samples = samples.to(device, non_blocking=True)
        targets = targets.to(device, non_blocking=True)

        if mixup_fn is not None:
            samples, targets = mixup_fn(samples, targets)

        with torch.cuda.amp.autocast():
            outputs = model(samples)
            loss = criterion(outputs, targets)

        loss_value = loss.item()

        if not math.isfinite(loss_value):
            print("Loss is {}, stopping training".format(loss_value))
            sys.exit(1)

        loss /= accum_iter
        loss_scaler(loss, optimizer, clip_grad=max_norm,
                    parameters=model.parameters(), create_graph=False,
                    update_grad=(data_iter_step + 1) % accum_iter == 0)
        if (data_iter_step + 1) % accum_iter == 0:
            optimizer.zero_grad()

        torch.cuda.synchronize()

        metric_logger.update(loss=loss_value)
        min_lr = 10.
        max_lr = 0.
        for group in optimizer.param_groups:
            min_lr = min(min_lr, group["lr"])
            max_lr = max(max_lr, group["lr"])

        metric_logger.update(lr=max_lr)

        loss_value_reduce = misc.all_reduce_mean(loss_value)
        if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
            """ We use epoch_1000x as the x-axis in tensorboard.
            This calibrates different curves when batch size changes.
            """
            epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
            log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
            log_writer.add_scalar('lr', max_lr, epoch_1000x)

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}

def confusion_m(y_true, y_pred):
    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
    return tn, fp, fn, tp

def compute_metrics_binary(probs, preds, targets):
    auc = roc_auc_score(targets, probs) * 100  
    precision = precision_score(targets, preds) * 100
    recall = recall_score(targets, preds) * 100
    f1 = f1_score(targets, preds) * 100
    tn, fp, fn, tp = confusion_m(targets, preds)
    specificity = (tn / float(tn+fp)) * 100
    return auc, precision, recall, f1, specificity

def compute_metrics_multiclass(probs, preds, targets, nb_classes):
    preds_tensor, probs_tensor, targets_tensor = torch.tensor(preds), torch.tensor(probs), torch.tensor(targets)
    auroc = AUROC(average='macro', num_classes=nb_classes)
    auc = auroc(probs_tensor, targets_tensor) * 100
    precision = precision_score(targets, preds, average='macro') * 100
    recall = recall_score(targets, preds, average='macro') * 100
    f1 = f1_score(targets, preds, average='macro') * 100
    speci = Specificity(average='macro', num_classes=nb_classes)
    specificity = speci(preds_tensor, targets_tensor) * 100
    return auc, precision, recall, f1, specificity

@torch.no_grad()
def evaluate(data_loader, model, device, nb_classes):
    criterion = torch.nn.CrossEntropyLoss()
    m = torch.nn.Softmax(dim=1)
    metric_logger = misc.MetricLogger(delimiter="  ")
    header = 'Test:'

    # switch to evaluation mode
    model.eval()
    probs = []
    targets = []
    preds = []

    for batch in metric_logger.log_every(data_loader, 10, header):
        images = batch[0]
        target = batch[-1]
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        # compute output
        with torch.cuda.amp.autocast():
            output = model(images)
            loss = criterion(output, target)
        output = m(output)
        score, pred = output.topk(1, 1, True, True)
        if nb_classes == 2:
            prob = output[:, 1]
        elif nb_classes > 2:
            prob = output
            
        probs.extend(prob.detach().cpu().numpy())
        targets.extend(target.detach().cpu().numpy())
        preds.extend(pred.tolist())
        if nb_classes < 5:
            acc1, acc5 = accuracy(output, target, topk=(1, 1))
        else:
            acc1, acc5 = accuracy(output, target, topk=(1, 5))

        batch_size = images.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
    if nb_classes == 2:
        print("binary class metrics!")
        auc, precision, recall, f1, specificity = compute_metrics_binary(probs, preds, targets)

    elif nb_classes > 2:
        print("multi_class metrics!")
        auc, precision, recall, f1, specificity = compute_metrics_multiclass(probs, preds, targets, nb_classes)

    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}, auc, precision, recall, f1, specificity