import os import cv2 import math import time import torch import torch.distributed as dist import numpy as np import random import argparse from model.RIFE import Model from dataset import * from torch.utils.data import DataLoader, Dataset from torch.utils.tensorboard import SummaryWriter from torch.utils.data.distributed import DistributedSampler device = torch.device("cuda") log_path = 'train_log' def get_learning_rate(step): if step < 2000: mul = step / 2000. return 3e-4 * mul else: mul = np.cos((step - 2000) / (args.epoch * args.step_per_epoch - 2000.) * math.pi) * 0.5 + 0.5 return (3e-4 - 3e-6) * mul + 3e-6 def flow2rgb(flow_map_np): h, w, _ = flow_map_np.shape rgb_map = np.ones((h, w, 3)).astype(np.float32) normalized_flow_map = flow_map_np / (np.abs(flow_map_np).max()) rgb_map[:, :, 0] += normalized_flow_map[:, :, 0] rgb_map[:, :, 1] -= 0.5 * (normalized_flow_map[:, :, 0] + normalized_flow_map[:, :, 1]) rgb_map[:, :, 2] += normalized_flow_map[:, :, 1] return rgb_map.clip(0, 1) def train(model, local_rank): if local_rank == 0: writer = SummaryWriter('train') writer_val = SummaryWriter('validate') else: writer = None writer_val = None step = 0 nr_eval = 0 dataset = VimeoDataset('train') sampler = DistributedSampler(dataset) train_data = DataLoader(dataset, batch_size=args.batch_size, num_workers=8, pin_memory=True, drop_last=True, sampler=sampler) args.step_per_epoch = train_data.__len__() dataset_val = VimeoDataset('validation') val_data = DataLoader(dataset_val, batch_size=16, pin_memory=True, num_workers=8) print('training...') time_stamp = time.time() for epoch in range(args.epoch): sampler.set_epoch(epoch) for i, data in enumerate(train_data): data_time_interval = time.time() - time_stamp time_stamp = time.time() data_gpu, timestep = data data_gpu = data_gpu.to(device, non_blocking=True) / 255. timestep = timestep.to(device, non_blocking=True) imgs = data_gpu[:, :6] gt = data_gpu[:, 6:9] learning_rate = get_learning_rate(step) * args.world_size / 4 pred, info = model.update(imgs, gt, learning_rate, training=True) # pass timestep if you are training RIFEm train_time_interval = time.time() - time_stamp time_stamp = time.time() if step % 200 == 1 and local_rank == 0: writer.add_scalar('learning_rate', learning_rate, step) writer.add_scalar('loss/l1', info['loss_l1'], step) writer.add_scalar('loss/tea', info['loss_tea'], step) writer.add_scalar('loss/distill', info['loss_distill'], step) if step % 1000 == 1 and local_rank == 0: gt = (gt.permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype('uint8') mask = (torch.cat((info['mask'], info['mask_tea']), 3).permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype('uint8') pred = (pred.permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype('uint8') merged_img = (info['merged_tea'].permute(0, 2, 3, 1).detach().cpu().numpy() * 255).astype('uint8') flow0 = info['flow'].permute(0, 2, 3, 1).detach().cpu().numpy() flow1 = info['flow_tea'].permute(0, 2, 3, 1).detach().cpu().numpy() for i in range(5): imgs = np.concatenate((merged_img[i], pred[i], gt[i]), 1)[:, :, ::-1] writer.add_image(str(i) + '/img', imgs, step, dataformats='HWC') writer.add_image(str(i) + '/flow', np.concatenate((flow2rgb(flow0[i]), flow2rgb(flow1[i])), 1), step, dataformats='HWC') writer.add_image(str(i) + '/mask', mask[i], step, dataformats='HWC') writer.flush() if local_rank == 0: print('epoch:{} {}/{} time:{:.2f}+{:.2f} loss_l1:{:.4e}'.format(epoch, i, args.step_per_epoch, data_time_interval, train_time_interval, info['loss_l1'])) step += 1 nr_eval += 1 if nr_eval % 5 == 0: evaluate(model, val_data, step, local_rank, writer_val) model.save_model(log_path, local_rank) dist.barrier() def evaluate(model, val_data, nr_eval, local_rank, writer_val): loss_l1_list = [] loss_distill_list = [] loss_tea_list = [] psnr_list = [] psnr_list_teacher = [] time_stamp = time.time() for i, data in enumerate(val_data): data_gpu, timestep = data data_gpu = data_gpu.to(device, non_blocking=True) / 255. imgs = data_gpu[:, :6] gt = data_gpu[:, 6:9] with torch.no_grad(): pred, info = model.update(imgs, gt, training=False) merged_img = info['merged_tea'] loss_l1_list.append(info['loss_l1'].cpu().numpy()) loss_tea_list.append(info['loss_tea'].cpu().numpy()) loss_distill_list.append(info['loss_distill'].cpu().numpy()) for j in range(gt.shape[0]): psnr = -10 * math.log10(torch.mean((gt[j] - pred[j]) * (gt[j] - pred[j])).cpu().data) psnr_list.append(psnr) psnr = -10 * math.log10(torch.mean((merged_img[j] - gt[j]) * (merged_img[j] - gt[j])).cpu().data) psnr_list_teacher.append(psnr) gt = (gt.permute(0, 2, 3, 1).cpu().numpy() * 255).astype('uint8') pred = (pred.permute(0, 2, 3, 1).cpu().numpy() * 255).astype('uint8') merged_img = (merged_img.permute(0, 2, 3, 1).cpu().numpy() * 255).astype('uint8') flow0 = info['flow'].permute(0, 2, 3, 1).cpu().numpy() flow1 = info['flow_tea'].permute(0, 2, 3, 1).cpu().numpy() if i == 0 and local_rank == 0: for j in range(10): imgs = np.concatenate((merged_img[j], pred[j], gt[j]), 1)[:, :, ::-1] writer_val.add_image(str(j) + '/img', imgs.copy(), nr_eval, dataformats='HWC') writer_val.add_image(str(j) + '/flow', flow2rgb(flow0[j][:, :, ::-1]), nr_eval, dataformats='HWC') eval_time_interval = time.time() - time_stamp if local_rank != 0: return writer_val.add_scalar('psnr', np.array(psnr_list).mean(), nr_eval) writer_val.add_scalar('psnr_teacher', np.array(psnr_list_teacher).mean(), nr_eval) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--epoch', default=300, type=int) parser.add_argument('--batch_size', default=16, type=int, help='minibatch size') parser.add_argument('--local_rank', default=0, type=int, help='local rank') parser.add_argument('--world_size', default=4, type=int, help='world size') args = parser.parse_args() torch.distributed.init_process_group(backend="nccl", world_size=args.world_size) torch.cuda.set_device(args.local_rank) seed = 1234 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.benchmark = True model = Model(args.local_rank) train(model, args.local_rank)