thirdparty/Metric3D/training/mono/utils/do_train.py

import os
import torch
import matplotlib.pyplot as plt
from mono.model.monodepth_model import get_configured_monodepth_model
from tensorboardX import SummaryWriter
from mono.utils.comm import TrainingStats
from mono.utils.avg_meter import MetricAverageMeter
from mono.utils.running import build_lr_schedule_with_cfg, build_optimizer_with_cfg, load_ckpt, save_ckpt
from mono.utils.comm import reduce_dict, main_process, get_rank
from mono.utils.visualization import save_val_imgs, visual_train_data, create_html, save_normal_val_imgs
import traceback
from mono.utils.visualization import create_dir_for_validate_meta
from mono.model.criterion import build_criterions
from mono.datasets.distributed_sampler import build_dataset_n_sampler_with_cfg, build_data_array
from mono.utils.logger import setup_logger
import logging
from .misc import NativeScalerWithGradNormCount, is_bf16_supported
import math
import sys
import random
import numpy as np
import torch.distributed as dist
import torch.nn.functional as F
from contextlib import nullcontext

def to_cuda(data):
    for k, v in data.items():
        if isinstance(v, torch.Tensor):
            data[k] = v.cuda(non_blocking=True)
        if isinstance(v, list) and len(v)>1 and isinstance(v[0], torch.Tensor):
            for i, l_i in enumerate(v):
                data[k][i] = l_i.cuda(non_blocking=True)
    return data

def do_train(local_rank: int, cfg: dict):

    logger = setup_logger(cfg.log_file)

    # build criterions
    criterions = build_criterions(cfg)
    
    # build model
    model = get_configured_monodepth_model(cfg,
                                           criterions,
                                           ) 
    
    # log model state_dict
    if main_process():
        logger.info(model.state_dict().keys())
    
    # build datasets
    train_dataset, train_sampler = build_dataset_n_sampler_with_cfg(cfg, 'train')
    if 'multi_dataset_eval' in cfg.evaluation and cfg.evaluation.multi_dataset_eval:
        val_dataset = build_data_array(cfg, 'val')
    else:
        val_dataset, val_sampler = build_dataset_n_sampler_with_cfg(cfg, 'val')
    # build data loaders
    g = torch.Generator()
    g.manual_seed(cfg.seed + cfg.dist_params.global_rank)
    train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset,
                                                   batch_size=cfg.batchsize_per_gpu,
                                                   num_workers=cfg.thread_per_gpu,
                                                   sampler=train_sampler,
                                                   drop_last=True, 
                                                   pin_memory=True,
                                                   generator=g,)
                                                #    collate_fn=collate_fn)
    if isinstance(val_dataset, list):
        val_dataloader = [torch.utils.data.DataLoader(dataset=val_dataset,
                                                      batch_size=1,
                                                      num_workers=0,
                                                      sampler=torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False),
                                                      drop_last=True,
                                                      pin_memory=True,) for val_group in val_dataset for val_dataset in val_group]
    else:
        val_dataloader = torch.utils.data.DataLoader(dataset=val_dataset,
                                                batch_size=1,
                                                num_workers=0,
                                                sampler=val_sampler,
                                                drop_last=True,
                                                pin_memory=True,)
    
    # build schedule
    lr_scheduler = build_lr_schedule_with_cfg(cfg)
    optimizer = build_optimizer_with_cfg(cfg, model)
   
    # config distributed training
    if cfg.distributed:
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
        model = torch.nn.parallel.DistributedDataParallel(model.cuda(), 
                                                          device_ids=[local_rank], 
                                                          output_device=local_rank, 
                                                          find_unused_parameters=False)
    else:
        model = torch.nn.DataParallel(model.cuda())
    
    # init automatic mix precision training
    # if 'AMP' in cfg.runner.type:
    #     loss_scaler = NativeScalerWithGradNormCount()
    # else:
    #     loss_scaler = None
    loss_scaler = None
    
    # load ckpt
    if cfg.load_from and cfg.resume_from is None:
        model, _, _, loss_scaler = load_ckpt(cfg.load_from, model, optimizer=None, scheduler=None, strict_match=False, loss_scaler=loss_scaler)
    elif cfg.resume_from:
        model, optimizer, lr_scheduler, loss_scaler = load_ckpt(
            cfg.resume_from, 
            model, 
            optimizer=optimizer, 
            scheduler=lr_scheduler, 
            strict_match=False, 
            loss_scaler=loss_scaler)

    if cfg.runner.type == 'IterBasedRunner':
        train_by_iters(cfg,
                    model, 
                    optimizer, 
                    lr_scheduler,
                    train_dataloader,
                    val_dataloader,
                    )
    elif cfg.runner.type == 'IterBasedRunner_MultiSize':
        train_by_iters_multisize(cfg,
                    model, 
                    optimizer, 
                    lr_scheduler,
                    train_dataloader,
                    val_dataloader,
                    )
    elif cfg.runner.type == 'IterBasedRunner_AMP':
        train_by_iters_amp(
            cfg = cfg,
            model=model, 
            optimizer=optimizer, 
            lr_scheduler=lr_scheduler,
            train_dataloader=train_dataloader,
            val_dataloader=val_dataloader,
            loss_scaler=loss_scaler
        )
    elif cfg.runner.type == 'IterBasedRunner_AMP_MultiSize':
        train_by_iters_amp_multisize(
            cfg = cfg,
            model=model, 
            optimizer=optimizer, 
            lr_scheduler=lr_scheduler,
            train_dataloader=train_dataloader,
            val_dataloader=val_dataloader,
            loss_scaler=loss_scaler
        )
    elif cfg.runner.type == 'EpochBasedRunner':
        raise RuntimeError('It is not supported currently. :)')
    else:
        raise RuntimeError('It is not supported currently. :)')


def train_by_iters(cfg, model, optimizer, lr_scheduler, train_dataloader, val_dataloader):
    """
    Do the training by iterations.
    """
    logger = logging.getLogger()
    tb_logger = None
    if cfg.use_tensorboard and main_process():
        tb_logger = SummaryWriter(cfg.tensorboard_dir)
    if main_process():
        training_stats = TrainingStats(log_period=cfg.log_interval, tensorboard_logger=tb_logger)
    
    lr_scheduler.before_run(optimizer)
    
    # set training steps
    max_iters = cfg.runner.max_iters
    start_iter = lr_scheduler._step_count

    save_interval = cfg.checkpoint_config.interval
    eval_interval = cfg.evaluation.interval
    epoch = 0
    logger.info('Create iterator.')
    dataloader_iterator = iter(train_dataloader)

    val_err = {}
    logger.info('Start training.')

    try:
        # for step in range(start_iter, max_iters):
        # keep same step in all processes, avoid stuck during eval barrier
        step = start_iter 
        while step < max_iters:
            if main_process():
                training_stats.IterTic()
            
            # get the data batch
            try:
                data = next(dataloader_iterator)
            except StopIteration:
                dataloader_iterator = iter(train_dataloader)
                data = next(dataloader_iterator)
            except Exception as e:
                logger.info('When load training data: ', e)
                continue
            except:
                logger.info('Some training data errors exist in the current iter!')
                continue
            data = to_cuda(data)
            # set random crop size
            # if step % 10 == 0:
            #     set_random_crop_size_for_iter(train_dataloader, step, size_sample_list[step])
            
            # check training data
            #for i in range(data['target'].shape[0]):
                # if 'DDAD' in data['dataset'][i] or \
                #     'Lyft' in data['dataset'][i] or \
                #     'DSEC' in data['dataset'][i] or \
                #     'Argovers2' in data['dataset'][i]:
                #     replace = True
                # else:
                #     replace = False
                #visual_train_data(data['target'][i, ...], data['input'][i,...], data['filename'][i], cfg.work_dir, replace=replace)

            # forward
            pred_depth, losses_dict, conf = model(data)
                
            optimizer.zero_grad()
            losses_dict['total_loss'].backward()
            # if step > 100 and step % 10 == 0:
            #     for param in model.parameters():
            #         print(param.grad.max(), torch.norm(param.grad))
            torch.nn.utils.clip_grad_norm_(model.parameters(), 10)
            optimizer.step()

            # reduce losses over all GPUs for logging purposes
            loss_dict_reduced = reduce_dict(losses_dict)

            lr_scheduler.after_train_iter(optimizer)
            if main_process():
                training_stats.update_iter_stats(loss_dict_reduced)
                training_stats.IterToc()
                training_stats.log_iter_stats(step, optimizer, max_iters, val_err)

            # validate the model
            if cfg.evaluation.online_eval and \
                (step+1) % eval_interval == 0 and \
                val_dataloader is not None:
                if isinstance(val_dataloader, list):
                    val_err = validate_multiple_dataset(cfg, step+1, model, val_dataloader, tb_logger)
                else:
                    val_err = validate(cfg, step+1, model, val_dataloader, tb_logger)
                if main_process():
                    training_stats.tb_log_stats(val_err, step)

            # save checkpoint
            if main_process():
                if ((step+1) % save_interval == 0) or ((step+1)==max_iters):
                    save_ckpt(cfg, model, optimizer, lr_scheduler, step+1, epoch)
            
            step += 1

    except (RuntimeError, KeyboardInterrupt):
        stack_trace = traceback.format_exc()
        print(stack_trace)

def train_by_iters_amp(cfg, model, optimizer, lr_scheduler, train_dataloader, val_dataloader, loss_scaler):
    """
    Do the training by iterations.
    Mix precision is employed.
    """
    # set up logger
    tb_logger = None
    if cfg.use_tensorboard and main_process():
        tb_logger = SummaryWriter(cfg.tensorboard_dir)
    logger = logging.getLogger()
    # training status
    if main_process():
        training_stats = TrainingStats(log_period=cfg.log_interval, tensorboard_logger=tb_logger)

    # learning schedule
    lr_scheduler.before_run(optimizer)
    
    # set training steps
    max_iters = cfg.runner.max_iters
    start_iter = lr_scheduler._step_count

    save_interval = cfg.checkpoint_config.interval
    eval_interval = cfg.evaluation.interval
    epoch = 0

    # If it's too slow try lowering num_worker
    # see https://discuss.pytorch.org/t/define-iterator-on-dataloader-is-very-slow/52238
    logger.info('Create iterator.')
    dataloader_iterator = iter(train_dataloader)

    val_err = {}
    # torch.cuda.empty_cache()
    logger.info('Start training.')

    try:
        acc_batch = cfg.acc_batch
    except:
        acc_batch = 1

    try:
        # for step in range(start_iter, max_iters):
        # keep same step in all processes, avoid stuck during eval barrier
        step = start_iter *  acc_batch
        #while step < max_iters:
        while True:
            
            if main_process():
                training_stats.IterTic()

            # get the data batch
            try:
                data = next(dataloader_iterator)
            except StopIteration:
                dataloader_iterator = iter(train_dataloader)
                data = next(dataloader_iterator)
            except Exception as e:
                logger.info('When load training data: ', e)
                continue
            except:
                logger.info('Some training data errors exist in the current iter!')
                continue

            data = to_cuda(data)

            with torch.cuda.amp.autocast(dtype=torch.bfloat16):
                pred_depth, losses_dict, conf = model(data)

            total_loss = losses_dict['total_loss'] / acc_batch

            if not math.isfinite(total_loss):
                logger.info("Loss is {}, skiping this batch training".format(total_loss))
                continue
            
            # optimize, backward
            if (step+1-start_iter) % acc_batch == 0:
                optimizer.zero_grad()
            if loss_scaler == None:
                total_loss.backward()
                try:
                    if (step+1-start_iter) % acc_batch == 0:
                        torch.nn.utils.clip_grad_norm_(model.parameters(), 2.5, error_if_nonfinite=True)
                        optimizer.step()
                except:
                    print('NAN gradient, skipping optimizer.step() for this round...')
            else:
                loss_scaler(total_loss, optimizer, clip_grad=5, parameters=model.parameters(), update_grad=True)

            # reduce losses over all GPUs for logging purposes
            if (step+1-start_iter) % acc_batch == 0:
                loss_dict_reduced = reduce_dict(losses_dict)
                lr_scheduler.after_train_iter(optimizer)

                if main_process():
                    training_stats.update_iter_stats(loss_dict_reduced)
                    training_stats.IterToc()
                    training_stats.log_iter_stats(step//acc_batch, optimizer, max_iters, val_err)

            # validate the model
                if cfg.evaluation.online_eval and \
                    ((step+acc_batch)//acc_batch) % eval_interval == 0 and \
                    val_dataloader is not None:
                # if True:
                    if isinstance(val_dataloader, list):
                        val_err = validate_multiple_dataset(cfg, ((step+acc_batch)//acc_batch), model, val_dataloader, tb_logger)
                    else:
                        val_err = validate(cfg, ((step+acc_batch)//acc_batch), model, val_dataloader, tb_logger)
                    if main_process():
                        training_stats.tb_log_stats(val_err, step)

                # save checkpoint
                if main_process():
                    if (((step+acc_batch)//acc_batch) % save_interval == 0) or (((step+acc_batch)//acc_batch)==max_iters):
                        save_ckpt(cfg, model, optimizer, lr_scheduler, ((step+acc_batch)//acc_batch), epoch, loss_scaler=loss_scaler)

            step += 1
            

    except (RuntimeError, KeyboardInterrupt):
        stack_trace = traceback.format_exc()
        print(stack_trace)

def validate_multiple_dataset(cfg, iter, model, val_dataloaders, tb_logger):
    val_errs = {}
    for val_dataloader in val_dataloaders:
        val_err = validate(cfg, iter, model, val_dataloader, tb_logger)
        val_errs.update(val_err)
    # mean of all dataset
    mean_val_err = {}
    for k, v in val_errs.items():
        metric = 'AllData_eval/' + k.split('/')[-1]
        if metric not in mean_val_err.keys():
            mean_val_err[metric] = 0
        mean_val_err[metric] += v / len(val_dataloaders)
    val_errs.update(mean_val_err)
    
    return val_errs


def validate(cfg, iter, model, val_dataloader, tb_logger):
    """
    Validate the model on single dataset
    """
    model.eval()
    dist.barrier()
    logger = logging.getLogger()
    # prepare dir for visualization data
    save_val_meta_data_dir = create_dir_for_validate_meta(cfg.work_dir, iter)
    # save_html_path = save_val_meta_data_dir + '.html'
    dataset_name = val_dataloader.dataset.data_name

    save_point = max(int(len(val_dataloader) / 5), 1)
    # save_point = 2
    # depth metric meter
    dam = MetricAverageMeter(cfg.evaluation.metrics)
    # dam_disp = MetricAverageMeter([m for m in cfg.evaluation.metrics if m[:6]!='normal'])
    for i, data in enumerate(val_dataloader):
        if i % 10 == 0:
            logger.info(f'Validation step on {dataset_name}: {i}')
        data = to_cuda(data)
        output = model.module.inference(data)
        pred_depth = output['prediction']
        pred_depth = pred_depth.squeeze()
        gt_depth = data['target'].cuda(non_blocking=True).squeeze()
        
        pad = data['pad'].squeeze()
        H, W = pred_depth.shape
        pred_depth = pred_depth[pad[0]:H-pad[1], pad[2]:W-pad[3]]
        gt_depth = gt_depth[pad[0]:H-pad[1], pad[2]:W-pad[3]]
        rgb = data['input'][0, :, pad[0]:H-pad[1], pad[2]:W-pad[3]]
        mask = gt_depth > 0
        #pred_depth_resize = cv2.resize(pred_depth.cpu().numpy(), (torch.squeeze(data['B_raw']).shape[1], torch.squeeze(data['B_raw']).shape[0]))
        dam.update_metrics_gpu(pred_depth, gt_depth, mask, cfg.distributed)

        # save evaluation results
        if i%save_point == 0 and main_process():
            save_val_imgs(iter, 
                          pred_depth, 
                          gt_depth, 
                          rgb, # data['input'], 
                          dataset_name + '_' + data['filename'][0], 
                          save_val_meta_data_dir,
                          tb_logger=tb_logger)

        ## surface normal
        if "normal_out_list" in output.keys():
            normal_out_list = output['normal_out_list']
            pred_normal = normal_out_list[-1][:, :3, :, :] # (B, 3, H, W)
            gt_normal = data['normal'].cuda(non_blocking=True)
            # if pred_normal.shape != gt_normal.shape:
            #     pred_normal = F.interpolate(pred_normal, size=[gt_normal.size(2), gt_normal.size(3)], mode='bilinear', align_corners=True)

            H, W = pred_normal.shape[2:]
            pred_normal = pred_normal[:, :, pad[0]:H-pad[1], pad[2]:W-pad[3]]
            gt_normal = gt_normal[:, :, pad[0]:H-pad[1], pad[2]:W-pad[3]]
            gt_normal_mask = ~torch.all(gt_normal == 0, dim=1, keepdim=True)
            dam.update_normal_metrics_gpu(pred_normal, gt_normal, gt_normal_mask, cfg.distributed)

            # save valiad normal
            if i%save_point == 0 and main_process():
                save_normal_val_imgs(iter, 
                                    pred_normal, 
                                    gt_normal, 
                                    rgb, # data['input'], 
                                    dataset_name + '_normal_' + data['filename'][0], 
                                    save_val_meta_data_dir,
                                    tb_logger=tb_logger)

    # create html for visualization
    merged_rgb_pred_gt = os.path.join(save_val_meta_data_dir, '*_merge.jpg')
    name2path = dict(merg=merged_rgb_pred_gt) #dict(rgbs=rgbs, pred=pred, gt=gt)
    # if main_process():
    #    create_html(name2path, save_path=save_html_path, size=(256*3, 512))

    # get validation error
    eval_error = dam.get_metrics()
    eval_error = {f'{dataset_name}_eval/{k}': v for k,v in eval_error.items()}
    # eval_disp_error = {f'{dataset_name}_eval/disp_{k}': v for k,v in dam_disp.get_metrics().items()}
    # eval_error.update(eval_disp_error)

    model.train()
    
    if 'exclude' in cfg.evaluation and dataset_name in cfg.evaluation.exclude:
        return {}
    return eval_error

def set_random_crop_size_for_iter(dataloader: torch.utils.data.dataloader.DataLoader, iter: int, size_pool=None):
    if size_pool is None:
        size_pool = [
            # [504, 504], [560, 1008], [840, 1512], [1120, 2016],
            [560, 1008], [840, 1512], [1120, 2016],
            # [480, 768], [480, 960], 
            # [480, 992], [480, 1024], 
            # [480, 1120], 
            # [480, 1280], 
            # [480, 1312],
            # [512, 512], [512, 640], 
            # [512, 960], 
            # [512, 992], 
            # [512, 1024], [512, 1120], 
            # [512, 1216], 
            # [512, 1280],
            # [576, 640], [576, 960], 
            # [576, 992], 
            # [576, 1024],
            # [608, 608], [608, 640], 
            # [608, 960], [608, 1024],
        ]
    random.seed(iter)
    sample = random.choice(size_pool)
    # idx = (iter // 10) % len(size_pool)
    #sample = size_pool[size_idx]
    
    # random.seed(iter)
    # flg = random.random() <= 1.0
    # if flg:
    crop_size = sample
    # else:
    #     crop_size = [sample[1], sample[0]]

    # set crop size for each dataset
    datasets_groups = len(dataloader.dataset.datasets)
    for i in range(datasets_groups):
        for j in range(len(dataloader.dataset.datasets[i].datasets)):
            dataloader.dataset.datasets[i].datasets[j].set_random_crop_size(crop_size)
    return crop_size