engine_finetuning.py

import math
import sys
from typing import Iterable

import torch
import util.lr_sched as lr_sched
import util.misc as misc


def train_one_epoch(
    model: torch.nn.Module,
    data_loader: Iterable,
    optimizer: torch.optim.Optimizer,
    device: torch.device,
    epoch: int,
    loss_scaler,
    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 = 10

    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, (examples, labels, example_mask) 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)

        c_loss = model(examples, labels)
        loss = c_loss
        loss_value = loss.item()
        c_loss_value = c_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, parameters=model.parameters(), 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(closs=c_loss_value)

        lr = optimizer.param_groups[0]["lr"]
        metric_logger.update(lr=lr)

        misc.all_reduce_mean(loss_value)
        c_loss_value_reduce = misc.all_reduce_mean(c_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("c_train_loss", c_loss_value_reduce, epoch_1000x)
            log_writer.add_scalar("lr", 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 val_one_epoch(
    model: torch.nn.Module,
    data_loader: Iterable,
    optimizer: torch.optim.Optimizer,
    device: torch.device,
    epoch: int,
    loss_scaler,
    log_writer=None,
    args=None,
):
    model.eval()
    metric_logger = misc.MetricLogger(delimiter="  ")
    metric_logger.add_meter("lr", misc.SmoothedValue(window_size=1, fmt="{value:.6f}"))
    header = "Epoch: [{}]".format(epoch)
    print_freq = 10

    accum_iter = args.accum_iter

    if log_writer is not None:
        print("log_dir: {}".format(log_writer.log_dir))
    for data_iter_step, (examples, labels, example_mask) in enumerate(
        metric_logger.log_every(data_loader, print_freq, header)
    ):

        with torch.no_grad():
            c_loss = model(examples, labels)
        loss = c_loss
        loss_value = loss.item()

        c_loss_value = c_loss.item()

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

        metric_logger.update(closs=c_loss_value)

        lr = optimizer.param_groups[0]["lr"]
        metric_logger.update(lr=lr)

        misc.all_reduce_mean(loss_value)
        c_loss_value_reduce = misc.all_reduce_mean(c_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("c_train_loss", c_loss_value_reduce, epoch_1000x)
            log_writer.add_scalar("lr", 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()}