ychenhq's picture
Upload folder using huggingface_hub
c62dd62 verified
raw
history blame
7.22 kB
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)