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train.py
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train.py
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import os
import cv2
import math
import time
import torch
import torch.distributed as dist
import numpy as np
import random
import argparse
from tqdm import tqdm
from torch.utils.data import DataLoader, Dataset
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
import gc
from model.trainer import Model
from dataset.DualRealDataset import *
from utils.distributed_utils import (broadcast_scalar, is_main_process,
reduce_dict, synchronize)
from model.pytorch_msssim import ssim_matlab
from utils.logger import Logger
from utils.timer import (Timer,Epoch_Timer)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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('--batch_size_val', 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')
parser.add_argument('--input_num', default=2, type=int, help='input images number')
parser.add_argument('--input_dir', default='/media/zhongyi/D/data/realBR', type=str, required=True, help='path to the input dataset folder')
parser.add_argument('--dataset_name', default='realBR', type=str, required=True, help='Name of dataset to be used')
parser.add_argument('--data_mode1', default='Blur', type=str, help='Mode of input data')
parser.add_argument('--data_mode2', default='RS', type=str, help='Mode of input data')
parser.add_argument('--learning_rate', default=1e-4, type=float)
parser.add_argument('--weight_decay', default=0 , type=float)
parser.add_argument('--temporal', action='store_true',default=False) #### Read consecutive images and taken as input.
parser.add_argument('--training', default=True, type=bool)
parser.add_argument('--output_num', default=7, type=int, help='final output channel of the network')
parser.add_argument('--output_dir', default='', type=str, required=True, help='path to save training output')
parser.add_argument('--should_log', default=True, type=bool)
parser.add_argument('--resume', default=False, type=bool)
parser.add_argument('--resume_file', default=None, type=str, help='path to resumed model')
args = parser.parse_args()
# Gradually reduce the learning rate using cosine annealing
def get_learning_rate(step):
step =step+1
if step < 3000:
mul = step / 3000.
return args.learning_rate * mul
else:
mul = np.cos((step - 3000) / (args.epoch * args.step_per_epoch - 3000.) * math.pi) * 0.5 + 0.5
return (args.learning_rate - 1e-6) * mul + 1e-6
def _summarize_report(prefix="", should_print=True, extra={},log_writer=None,current_iteration=0,max_iterations=0):
if not is_main_process():
return
if not should_print:
return
print_str = []
if len(prefix):
print_str += [prefix + ":"]
print_str += ["{}/{}".format(current_iteration, max_iterations)]
print_str += ["{}: {}".format(key, value) for key, value in extra.items()]
log_writer.write(','.join(print_str))
def train(model):
log_writer = Logger(args)
log_writer.write("Torch version is: " + torch.__version__)
log_writer.write("===== Model =====")
log_writer.write(model.net_model)
#resume
if args.resume is True:
log_writer.write("Restore traing from saved model")
if args.resume_file is None:
dir_name = args.dataset_name+'_'+args.data_mode1+'-'+args.data_mode2+'_'+str(args.input_num)+'_'+str(args.output_num)
checkpoint_path = os.path.join(args.output_dir,dir_name,'best.ckpt')
else:
checkpoint_path = args.resume_file
checkpoint_info = model.load_model(path=checkpoint_path)
if is_main_process():
writer = SummaryWriter('./tensorboard_log/train')
writer_val = SummaryWriter('./tensorboard_log/validate')
else:
writer = None
writer_val = None
data_root = os.path.join(args.input_dir, args.dataset_name)
if args.dataset_name == 'realBR':
args.inter_num = 16
args.intra_num = 9
dataset = DualRealDataset(dataset_cls='train',\
input_num=args.input_num,\
output_num=args.output_num,\
data_root=data_root,\
data_mode1 = args.data_mode1,\
data_mode2 = args.data_mode2,\
inter_num = args.inter_num,\
intra_num = args.intra_num,temp=args.temporal)
dataset_val = DualRealDataset(dataset_cls='validate',\
input_num=args.input_num,\
output_num=args.output_num,\
data_root=data_root,\
data_mode1 = args.data_mode1,\
data_mode2 = args.data_mode2,\
inter_num = args.inter_num,\
intra_num = args.intra_num,temp=args.temporal)
elif args.dataset_name == 'GOPRO-VFI_copy':
if args.output_num >8:
raise Exception('Wrong output number!!!')
args.inter_num = 0
args.intra_num = 8
dataset = DualRealDataset(dataset_cls='train',\
input_num=args.input_num,\
output_num=args.output_num,\
data_root=data_root,\
data_mode1 = args.data_mode1,\
data_mode2 = args.data_mode2,\
inter_num = args.inter_num,\
intra_num = args.intra_num,temp=args.temporal)
dataset_val = DualRealDataset(dataset_cls='test',\
input_num=args.input_num,\
output_num=args.output_num,\
data_root=data_root,\
data_mode1 = args.data_mode1,\
data_mode2 = args.data_mode2,\
inter_num = args.inter_num,\
intra_num = args.intra_num,temp=args.temporal)
else:
raise Exception("Not supported dataset!!!!")
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__() # total number of steps per epoch
val_data = DataLoader(dataset_val, batch_size=args.batch_size_val, pin_memory=True, num_workers=8)
if torch.device("cuda") == device:
rank = args.local_rank if args.local_rank >=0 else 0
device_info = "CUDA Device {} is: {}".format(rank, torch.cuda.get_device_name(args.local_rank))
log_writer.write(device_info, log_all=True)
log_writer.write("Starting training...")
log_writer.write("Each epoch includes {} iterations".format(args.step_per_epoch))
train_timer = Timer()
snapshot_timer = Timer()
max_step = args.step_per_epoch*args.epoch
if args.resume is True:
step = checkpoint_info['best_monitored_iteration'] + 1
start_epoch = checkpoint_info['best_monitored_epoch']
best_dict = checkpoint_info
else:
step = 0 # total training steps across all epochs
start_epoch = 0
best_dict={
'best_monitored_value': 0,
'best_psnr':0,
'best_ssim':0,
'best_monitored_iteration':-1,
'best_monitored_epoch':-1,
'best_monitored_epoch_step':-1,
}
epoch_timer = Epoch_Timer('m')
for epoch in range(start_epoch,args.epoch):
sampler.set_epoch(epoch) ## to shuffle data
if step > max_step:
break
epoch_timer.tic()
for i, all_data in enumerate(train_data):
data = all_data[0]
img_ids = all_data[1]
# data: 4d tensor,(batch_size,3*input_num+3*output_num,h,w) BGR format
data_gpu = data.to(device, non_blocking=True) / 255.
imgs_tensor = data_gpu[:, :3*args.input_num] # (t2b,b2t)/(blur,RS)/(pre,cur) (batch_size,3*2,h,w)
gts_tensor = data_gpu[:, 3*args.input_num:] # multi gts (batch_size,3*output_num,h,w)
learning_rate = get_learning_rate(step)
##### Temporal-order encoding
batch,_,height,width = imgs_tensor.shape
rs_encode = torch.arange(0,height).type_as(imgs_tensor).unsqueeze(1).repeat(1,width) ##(h,w)
latent_gs_encode = []
for out_i in range(0,args.output_num):
gs_encode = torch.Tensor([(height-1)//(args.output_num-1)*out_i]).type_as(imgs_tensor).unsqueeze(0).repeat(height,width) #(h,w)
latent_gs_encode.append(gs_encode)
latent_gs_encodes = torch.stack(latent_gs_encode,dim=0) ##(output_num,h,w)
### Relative location of i_th latent gs to input rs
latent_gs_encodes = rs_encode.unsqueeze(0) - latent_gs_encodes ##(output_num*1,h,w)
latent_gs_encodes = latent_gs_encodes.unsqueeze(0).repeat(batch,1,1,1) ##(batch,output_num*1,h,w)
pred, info = model.update(imgs_tensor,latent_gs_encodes, gts_tensor, learning_rate, training=True)
img_height = pred.shape[-2]
MAX_DIFF = 1
mse = ((gts_tensor - pred) * (gts_tensor - pred)).reshape(args.batch_size*args.output_num,3,img_height,-1)
mse = torch.mean(torch.mean(torch.mean(mse,-1),-1),-1).detach().cpu().data
psnr_aa = 10* torch.log10( MAX_DIFF**2 / mse )
psnr = torch.mean(psnr_aa)
ssim = ssim_matlab(gts_tensor.contiguous().view(args.batch_size*args.output_num,3,img_height,-1)\
,pred.contiguous().view(args.batch_size*args.output_num,3,img_height,-1)).detach().cpu().numpy()
##### Write summary to tensorboard
if is_main_process():
writer.add_scalar('learning_rate', learning_rate, step)
writer.add_scalar('loss/charb', info['loss_charb'], step)
writer.add_scalar('loss/total', info['loss_total'], step)
writer.add_scalar('psnr', psnr, step)
writer.add_scalar('ssim', float(ssim), step)
### Log traing info to screen and file
should_print = (step % 1000 == 0 and step !=0) # 1000
extra = {}
if should_print is True:
extra.update(
{
"lr": "{:.2e}".format(learning_rate),
"time": train_timer.get_time_since_start(),
"train/total_loss":format(info['loss_total'].detach().cpu().numpy(), '.4f' ),
"train/loss_charb":format(info['loss_charb'].detach().cpu().numpy(),'.4f'),
"train/psnr":format(psnr,'.4f'),
"train/ssim":format(ssim,'.4f'),
}
)
train_timer.reset()
val_infor = evaluate(model, val_data, step,writer_val,True)
extra.update(val_infor)
_summarize_report(
should_print=should_print,
extra=extra,
prefix=args.dataset_name+'_'+args.data_mode1+'-'+args.data_mode2+'_'+str(args.input_num)+'_'+str(args.output_num),
log_writer = log_writer,
current_iteration=step,
max_iterations=max_step
)
#### Conduct full evaluation and save checkpoint
if step % 5000 == 0 and step !=0: # 5000
log_writer.write("Evaluation time. Running on full validation set...")
all_val_infor = evaluate(model, val_data, step,writer_val,False,use_tqdm=True)
val_extra = {"validation time":snapshot_timer.get_time_since_start()}
if (all_val_infor['val/ssim']+all_val_infor['val/psnr'])/2 > best_dict['best_monitored_value']:
best_dict['best_monitored_iteration'] = step
best_dict['best_monitored_epoch_step'] = i
best_dict['best_monitored_epoch'] = epoch
best_dict['best_monitored_value'] = float(format((all_val_infor['val/ssim']+all_val_infor['val/psnr'])/2,'.4f'))
best_dict['best_ssim'] = all_val_infor['val/ssim']
best_dict['best_psnr'] =all_val_infor['val/psnr']
model.save_model(args,step,best_dict, update_best=True)
else:
model.save_model(args,step,best_dict, update_best=False)
val_extra.update(
{'current_psnr':all_val_infor['val/psnr'],
'current_ssim':all_val_infor['val/ssim'],
}
)
val_extra.update(best_dict)
prefix = "{}: full val".format(args.dataset_name+'_'+args.data_mode1+'-'+args.data_mode2+'_'+str(args.input_num)+'_'+str(args.output_num))
_summarize_report(
extra=val_extra,
prefix=prefix,
log_writer = log_writer,
current_iteration=step,
max_iterations=max_step
)
snapshot_timer.reset()
gc.collect() # clear up memory
if device == torch.device("cuda"):
torch.cuda.empty_cache()
step += 1
if step > max_step:
break
if is_main_process():
print("EPOCH: %02d Elapsed time: %4.2f " % (epoch+1, epoch_timer.toc()))
dist.barrier()
def evaluate(model, val_data, step,writer_val,single_batch,use_tqdm=False):
psnr_list = []
ssim_list = []
disable_tqdm = not use_tqdm
for i, all_data in enumerate(tqdm(val_data,disable=disable_tqdm)):
data = all_data[0]
img_ids = all_data[1]
data_gpu = data.to(device, non_blocking=True) / 255.
imgs_tensor = data_gpu[:, :3*args.input_num]
gts_tensor = data_gpu[:, 3*args.input_num:]
##### Temporal-order encoding
batch,_,height,width = imgs_tensor.shape
rs_encode = torch.arange(0,height).type_as(imgs_tensor).unsqueeze(1).repeat(1,width) ##(h,w)
latent_gs_encode = []
for out_i in range(0,args.output_num):
gs_encode = torch.Tensor([(height-1)//(args.output_num-1)*out_i]).type_as(imgs_tensor).unsqueeze(0).repeat(height,width) #(h,w)
latent_gs_encode.append(gs_encode)
latent_gs_encodes = torch.stack(latent_gs_encode,dim=0) ##(output_num,h,w)
### relative location of ith latent gs to input rs
latent_gs_encodes = rs_encode.unsqueeze(0) - latent_gs_encodes ##(output_num*1,h,w)
latent_gs_encodes = latent_gs_encodes.unsqueeze(0).repeat(batch,1,1,1) ##(batch,output_num*1,h,w)
with torch.no_grad():
pred, info = model.update(imgs_tensor,latent_gs_encodes, gts_tensor, training=False)
img_height = pred.shape[-2]
for j in range(gts_tensor.shape[0]):
MAX_DIFF = 1
mse = ((gts_tensor[j] - pred[j]) * (gts_tensor[j] - pred[j])).reshape(args.output_num,3,img_height,-1)
mse = torch.mean(torch.mean(torch.mean(mse,-1),-1),-1).detach().cpu().data
psnr_aa = 10* torch.log10( MAX_DIFF**2 / mse )
psnr = torch.mean(psnr_aa)
psnr_list.append(psnr)
ssim = ssim_matlab(gts_tensor[j].contiguous().view(args.output_num,3,img_height,-1),\
pred[j].contiguous().view(args.output_num,3,img_height,-1)).cpu().numpy()
ssim_list.append(ssim)
if single_batch is True:
break
if is_main_process() and single_batch is False:
writer_val.add_scalar('psnr', np.array(psnr_list).mean(), step)
writer_val.add_scalar('ssim', np.array(ssim_list).mean(), step)
return {
'val/ssim': float(format(np.mean(ssim_list),'.4f')),
'val/psnr': float(format(np.mean(psnr_list),'.4f')),
}
if __name__ == "__main__":
torch.distributed.init_process_group(backend="nccl", world_size=args.world_size)
torch.cuda.set_device(args.local_rank)
# For reproduction
seed = 1234
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# To accelerate training process when network structure and inputsize are fixed
torch.backends.cudnn.benchmark = True
model = Model(config=args,\
local_rank=args.local_rank)
train(model)