engine.py

import math
import sys
from typing import Iterable, Optional

import torch

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

import utils
import torch.nn.functional as F


def train_one_epoch(model, criterion,
                    data_loader, optimizer,
                    device: torch.device, epoch):
    model.train()
    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Epoch: [{}]'.format(epoch)
    print_freq = 100

    for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
        samples = samples.to(device, non_blocking=True)
        targets = targets.to(device, non_blocking=True)

                    
        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)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        torch.cuda.synchronize()
        metric_logger.update(loss=loss_value)

    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}

def train_one_epoch_distillation(teacher, student, criterion,
                    data_loader, optimizer,
                    device: torch.device, epoch, alpha=1.0, temp=1.0):
    teacher.eval()
    student.train()
    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Epoch: [{}]'.format(epoch)
    print_freq = 100
    kl_loss = torch.nn.KLDivLoss(reduction="batchmean")

    for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
        samples = samples.to(device, non_blocking=True)
        targets = targets.to(device, non_blocking=True)

           
        with torch.cuda.amp.autocast():
            outputs = student(samples)
            outputs_teacher = teacher(samples)
            
            # Imeplemet knowledge distillation loss here
            
            
            # *****************************************
        loss_value = loss.item()

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

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        torch.cuda.synchronize()
        metric_logger.update(loss=loss_value)

    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def evaluate(data_loader, model, criterion, device):

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Test:'

    # switch to evaluation mode
    model.eval()

    for images, target in metric_logger.log_every(data_loader, 20, header):
        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)

        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)

    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))

    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}