Siamese-Image-Modeling/engine_pretrain.py at main · OpenGVLab/Siamese-Image-Modeling · GitHub

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# ------------------------------------------------------------------------
# SiameseIM
# Copyright (c) SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from MAE (https://github.com/facebookresearch/mae)
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
# References:
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# DeiT: https://github.com/facebookresearch/deit
# ------------------------------------------------------------------------


import math
import os
import sys
from turtle import update
from typing import Iterable
from pathlib import Path

import torch

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


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 = 50

    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, data in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        if args.with_blockwise_mask:
            samples, labels, mask = data
        else:
            samples, labels = data
            mask = None

        # 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)

            if args.mmschedule == 'const':
                mm = args.mm
            elif args.mmschedule == 'cosine':
                mm = 1. - 0.5 * (1. + math.cos(math.pi * (data_iter_step / len(data_loader) + epoch) / args.epochs)) * (1. - args.mm)
            metric_logger.update(mm=mm)
        update_mm = (data_iter_step % accum_iter == 0)

        if args.loss_type in ['sim',]:
            x1, x2, delta_i, delta_j, delta_h, delta_w, relative_flip, flip_delta_j = samples
            x1 = x1.to(device, non_blocking=True)
            x2 = x2.to(device, non_blocking=True)
            delta_i = delta_i.to(x1)
            delta_j = delta_j.to(x1)
            delta_h = delta_h.to(x1)
            delta_w = delta_w.to(x1)
            flip_delta_j = flip_delta_j.to(x1)

            rel_pos_21 = (delta_i, delta_j, delta_h, delta_w, relative_flip, flip_delta_j)

            with torch.cuda.amp.autocast(enabled=(not args.fp32)):
                loss, outputs = model(x1, x2, rel_pos_21, mm, update_mm, mask=mask)
                metric_logger.update(**outputs)
        else:
            samples = samples.to(device, non_blocking=True)

            with torch.cuda.amp.autocast(enabled=(not args.fp32)):
                loss, _, _ = model(samples, mask_ratio=args.mask_ratio)

        loss_value = loss.item()

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

        loss /= accum_iter
        grad_norm = loss_scaler(loss, optimizer, parameters=model.parameters(),
                    update_grad=(data_iter_step + 1) % accum_iter == 0, clip_grad=args.clip_grad)
        if args.fp32:
            loss_scale = None
        else:
            loss_scale = loss_scaler.state_dict()['scale']

        metric_logger.update(grad_norm=grad_norm)
        metric_logger.update(loss_scale=loss_scale)

        if (data_iter_step + 1) % accum_iter == 0:
            optimizer.zero_grad()

        torch.cuda.synchronize()

        metric_logger.update(loss=loss_value)

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

        loss_value_reduce = misc.all_reduce_mean(loss_value)
        outputs_reduced = {k_: misc.all_reduce_mean(v_) for k_, v_ in outputs.items()}
        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('train_loss', loss_value_reduce, epoch_1000x)
            log_writer.add_scalar('lr', lr, epoch_1000x)
            log_writer.add_scalar('grad_norm', grad_norm, epoch_1000x)
            if loss_scale is not None:
                log_writer.add_scalar('loss_scale', loss_scale, epoch_1000x)
            log_writer.add_scalar('mm', mm, epoch_1000x)
            for k_, v_ in outputs_reduced.items():
                log_writer.add_scalar(f'train/{k_}', v_, 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()}