maskrcnn_benchmark/engine/alter_trainer.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time

import torch
import torch.distributed as dist

from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.metric_logger import MetricLogger


def reduce_loss_dict(all_loss_dict):
    """
    Reduce the loss dictionary from all processes so that process with rank
    0 has the averaged results. Returns a dict with the same fields as
    loss_dict, after reduction.
    """
    world_size = get_world_size()
    with torch.no_grad():
        loss_names = []
        all_losses = []
        for loss_dict in all_loss_dict:
            for k in sorted(loss_dict.keys()):
                loss_names.append(k)
                all_losses.append(loss_dict[k])
        all_losses = torch.stack(all_losses, dim=0)
        if world_size > 1:
            dist.reduce(all_losses, dst=0)
            if dist.get_rank() == 0:
                # only main process gets accumulated, so only divide by
                # world_size in this case
                all_losses /= world_size

        reduced_losses = {}
        for k, v in zip(loss_names, all_losses):
            if k not in reduced_losses:
                reduced_losses[k] = v / len(all_loss_dict)
            reduced_losses[k] += v / len(all_loss_dict)

    return reduced_losses


def do_train(
        model,
        data_loader,
        optimizer,
        scheduler,
        checkpointer,
        device,
        checkpoint_period,
        arguments,
):
    logger = logging.getLogger("maskrcnn_benchmark.trainer")
    logger.info("Start training")
    meters = MetricLogger(delimiter="  ")
    max_iter = min(len(task_loader) for task_loader in data_loader)
    start_iter = arguments["iteration"]
    model.train()
    start_training_time = time.time()
    end = time.time()
    for iteration, task_loader in enumerate(zip(*data_loader), start_iter):
        data_time = time.time() - end
        iteration = iteration + 1
        arguments["iteration"] = iteration

        all_task_loss_dict = []
        for task, (images, targets, _) in enumerate(task_loader, 1):
            if all(len(target) < 1 for target in targets):
                logger.warning('Sampled all negative batches, skip')
                continue

            images = images.to(device)
            targets = [target.to(device) for target in targets]

            loss_dict = model(images, targets, task)
            all_task_loss_dict.append(loss_dict)

        losses = sum(loss for loss_dict in all_task_loss_dict for loss in loss_dict.values())

        # reduce losses over all GPUs for logging purposes
        loss_dict_reduced = reduce_loss_dict(all_task_loss_dict)
        losses_reduced = sum(loss for loss in loss_dict_reduced.values())
        meters.update(loss=losses_reduced, **loss_dict_reduced)

        optimizer.zero_grad()
        losses.backward()
        optimizer.step()
        scheduler.step()

        batch_time = time.time() - end
        end = time.time()
        meters.update(time=batch_time, data=data_time)

        eta_seconds = meters.time.global_avg * (max_iter - iteration)
        eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))

        if iteration % 20 == 0 or iteration == max_iter:
            logger.info(
                meters.delimiter.join(
                    [
                        "eta: {eta}",
                        "iter: {iter}",
                        "{meters}",
                        "lr: {lr:.6f}",
                        "max mem: {memory:.0f}",
                    ]
                ).format(
                    eta=eta_string,
                    iter=iteration,
                    meters=str(meters),
                    lr=optimizer.param_groups[0]["lr"],
                    memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
                )
            )
        if iteration % checkpoint_period == 0:
            checkpointer.save("model_{:07d}".format(iteration), **arguments)
        if iteration == max_iter:
            checkpointer.save("model_final", **arguments)

    total_training_time = time.time() - start_training_time
    total_time_str = str(datetime.timedelta(seconds=total_training_time))
    logger.info(
        "Total training time: {} ({:.4f} s / it)".format(
            total_time_str, total_training_time / (max_iter)
        )
    )