import argparse import datetime import json import PIL.Image import numpy as np import os import time import random from pathlib import Path import math import sys from PIL import Image import torch import torch.backends.cudnn as cudnn from torch.utils.tensorboard import SummaryWriter import torch.nn.functional as F from torch.utils.data import Dataset import wandb import timm assert "0.4.5" <= timm.__version__ <= "0.4.9" # version check import timm.optim.optim_factory as optim_factory import util.misc as misc from util.misc import NativeScalerWithGradNormCount as NativeScaler import util.lr_sched as lr_sched from util.FSC147 import transform_pre_train import models_mae_noct def get_args_parser(): parser = argparse.ArgumentParser('MAE pre-training', add_help=False) parser.add_argument('--batch_size', default=8, type=int, help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus') parser.add_argument('--epochs', default=200, type=int) parser.add_argument('--accum_iter', default=1, type=int, help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)') # Model parameters parser.add_argument('--model', default='mae_vit_base_patch16', type=str, metavar='MODEL', help='Name of model to train') parser.add_argument('--mask_ratio', default=0.5, type=float, help='Masking ratio (percentage of removed patches).') parser.add_argument('--norm_pix_loss', action='store_true', help='Use (per-patch) normalized pixels as targets for computing loss') parser.set_defaults(norm_pix_loss=False) # Optimizer parameters parser.add_argument('--weight_decay', type=float, default=0.05, help='weight decay (default: 0.05)') parser.add_argument('--lr', type=float, default=None, metavar='LR', help='learning rate (absolute lr)') parser.add_argument('--blr', type=float, default=1e-3, metavar='LR', help='base learning rate: absolute_lr = base_lr * total_batch_size / 256') parser.add_argument('--min_lr', type=float, default=0., metavar='LR', help='lower lr bound for cyclic schedulers that hit 0') parser.add_argument('--warmup_epochs', type=int, default=10, metavar='N', help='epochs to warmup LR') # Dataset parameters parser.add_argument('--data_path', default='./data/FSC147/', type=str, help='dataset path') parser.add_argument('--anno_file', default='annotation_FSC147_384.json', type=str, help='annotation json file') parser.add_argument('--data_split_file', default='Train_Test_Val_FSC_147.json', type=str, help='data split json file') parser.add_argument('--im_dir', default='images_384_VarV2', type=str, help='images directory') parser.add_argument('--gt_dir', default='gt_density_map_adaptive_384_VarV2', type=str, help='ground truth directory') parser.add_argument('--output_dir', default='./data/out/pre_4_dir', help='path where to save, empty for no saving') parser.add_argument('--device', default='cuda:5', help='device to use for training / testing') parser.add_argument('--seed', default=0, type=int) parser.add_argument('--resume', default='./weights/mae_pretrain_vit_base_full.pth', # mae_visualize_vit_base help='resume from checkpoint') # Training parameters parser.add_argument('--start_epoch', default=0, type=int, metavar='N', help='start epoch') parser.add_argument('--num_workers', default=10, type=int) parser.add_argument('--pin_mem', action='store_true', help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.') parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem') parser.set_defaults(pin_mem=True) # Distributed training parameters parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') parser.add_argument('--local_rank', default=-1, type=int) parser.add_argument('--dist_on_itp', action='store_true') parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') # Logging parameters parser.add_argument('--log_dir', default='./logs/pre_4_dir', help='path where to tensorboard log') parser.add_argument("--title", default="CounTR_pretraining", type=str) parser.add_argument("--wandb", default="counting", type=str) parser.add_argument("--team", default="wsense", type=str) parser.add_argument("--wandb_id", default=None, type=str) parser.add_argument('--anno_file_negative', default='annotation_FSC147_negative1.json', type=str, help='annotation json file') return parser os.environ["CUDA_LAUNCH_BLOCKING"] = '5' class TrainData(Dataset): def __init__(self): self.img = data_split['train'] random.shuffle(self.img) self.img_dir = im_dir self.TransformPreTrain = transform_pre_train(data_path) def __len__(self): return len(self.img) def __getitem__(self, idx): im_id = self.img[idx] anno = annotations[im_id] bboxes = anno['box_examples_coordinates'] # box_coordinates = anno.get('box_examples_coordinates', {}) # 获取图像的边界框坐标信息 # # print(box_coordinates) # # 获取第一个类别的边界框坐标列表 # first_category = next(iter(box_coordinates), None) # # print(first_category) # first_category_bboxes = box_coordinates[first_category] # if first_category_bboxes: # # print(first_category_bboxes[0]) # bboxes = first_category_bboxes[0] # else: # bboxes = [] # # if first_category_bboxes: # # bboxes = first_category_bboxes[0] # # else: # # pass rects = list() for bbox in bboxes: x1 = bbox[0][0] y1 = bbox[0][1] x2 = bbox[2][0] y2 = bbox[2][1] rects.append([y1, x1, y2, x2]) image = Image.open('{}/{}'.format(im_dir, im_id)) image.load() density_path = gt_dir / (im_id.split(".jpg")[0] + ".npy") density = np.load(density_path).astype('float32') sample = {'image': image, 'lines_boxes': rects, 'gt_density': density} sample = self.TransformPreTrain(sample) return sample['image'] def main(args): misc.init_distributed_mode(args) print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) print("{}".format(args).replace(', ', ',\n')) device = torch.device(args.device) # fix the seed for reproducibility seed = args.seed + misc.get_rank() torch.manual_seed(seed) np.random.seed(seed) cudnn.benchmark = True dataset_train = TrainData() print(dataset_train) if True: # args.distributed: num_tasks = misc.get_world_size() global_rank = misc.get_rank() sampler_train = torch.utils.data.DistributedSampler( dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True ) print("Sampler_train = %s" % str(sampler_train)) else: sampler_train = torch.utils.data.RandomSampler(dataset_train) if global_rank == 0: if args.log_dir is not None: os.makedirs(args.log_dir, exist_ok=True) log_writer = SummaryWriter(log_dir=args.log_dir) else: log_writer = None if args.wandb is not None: wandb_run = wandb.init( config=args, resume="allow", project=args.wandb, name=args.title, # entity=args.team, tags=["CounTR", "pretraining"], id=args.wandb_id, ) else: wandb_run = None data_loader_train = torch.utils.data.DataLoader( dataset_train, sampler=sampler_train, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False, ) # define the model model = models_mae_noct.__dict__[args.model](norm_pix_loss=args.norm_pix_loss) model.to(device) model_without_ddp = model print("Model = %s" % str(model_without_ddp)) eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() if args.lr is None: # only base_lr is specified args.lr = args.blr * eff_batch_size / 256 print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) print("actual lr: %.2e" % args.lr) print("accumulate grad iterations: %d" % args.accum_iter) print("effective batch size: %d" % eff_batch_size) if args.distributed: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) model_without_ddp = model.module # following timm: set wd as 0 for bias and norm layers param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) print(optimizer) loss_scaler = NativeScaler() misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler) print(f"Start training for {args.epochs} epochs") start_time = time.time() for epoch in range(args.start_epoch, args.epochs): if args.distributed: data_loader_train.sampler.set_epoch(epoch) # train one epoch 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 = 20 accum_iter = args.accum_iter optimizer.zero_grad() if log_writer is not None: print('log_dir: {}'.format(log_writer.log_dir)) model_ = getattr(models_mae_noct, args.model)() for data_iter_step, samples in enumerate(metric_logger.log_every(data_loader_train, print_freq, header)): epoch_1000x = int((data_iter_step / len(data_loader_train) + epoch) * 1000) if data_iter_step % accum_iter == 0: lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader_train) + epoch, args) samples = samples.to(device, non_blocking=True) with torch.cuda.amp.autocast(): loss, pred, mask = model(samples, mask_ratio=args.mask_ratio) loss_value = loss.item() if data_iter_step % 2000 == 0: preds = model_.unpatchify(pred) preds = preds.float() preds = torch.einsum('nchw->nhwc', preds) preds = torch.clip(preds, 0, 1) if log_writer is not None: log_writer.add_images('reconstruction', preds, int(epoch), dataformats='NHWC') if wandb_run is not None: wandb_images = [] w_samples = torch.einsum('nchw->nhwc', samples.float()).clip(0, 1) masks = F.interpolate( mask.reshape(shape=(mask.shape[0], 1, int(mask.shape[1] ** .5), int(mask.shape[1] ** .5))), size=(preds.shape[1], preds.shape[2])) masks = torch.einsum('nchw->nhwc', masks.float()) combos = (w_samples + masks.repeat(1, 1, 1, 3)).clip(0, 1) w_images = (torch.cat([w_samples, combos, preds], dim=2) * 255).detach().cpu() print("w_images:", w_samples.shape, combos.shape, preds.shape, "-->", w_images.shape) for i in range(w_images.shape[0]): wi = w_images[i, :, :, :] wandb_images += [wandb.Image(wi.numpy().astype(np.uint8), caption=f"Prediction {i} at epoch {epoch}")] wandb.log({f"reconstruction": wandb_images}, step=epoch_1000x, commit=False) 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(loss=loss_value) lr = optimizer.param_groups[0]["lr"] metric_logger.update(lr=lr) loss_value_reduce = misc.all_reduce_mean(loss_value) if (data_iter_step + 1) % accum_iter == 0: if log_writer is not None: """ We use epoch_1000x as the x-axis in tensorboard. This calibrates different curves when batch size changes. """ log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) log_writer.add_scalar('lr', lr, epoch_1000x) if wandb_run is not None: log = {"train/loss": loss_value_reduce, "train/lr": lr} wandb.log(log, step=epoch_1000x, commit=True if data_iter_step == 0 else False) metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) train_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} # save train status and model if args.output_dir and (epoch % 100 == 0 or epoch + 1 == args.epochs): misc.save_model(args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch, suffix=f"pretraining_{epoch}") log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, 'epoch': epoch, } if args.output_dir and misc.is_main_process(): if log_writer is not None: log_writer.flush() with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: f.write(json.dumps(log_stats) + "\n") total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print('Training time {}'.format(total_time_str)) wandb.run.finish() if __name__ == '__main__': args = get_args_parser() args = args.parse_args() # load data data_path = Path(args.data_path) anno_file = data_path / args.anno_file data_split_file = data_path / args.data_split_file im_dir = data_path / args.im_dir gt_dir = data_path / args.gt_dir with open(anno_file) as f: annotations = json.load(f) with open(data_split_file) as f: data_split = json.load(f) if args.output_dir: Path(args.output_dir).mkdir(parents=True, exist_ok=True) main(args)