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train_multi_warping.py
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train_multi_warping.py
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import sys
import os
from optparse import OptionParser
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
import time
import cPickle as pickle
import math
# from eval import eval_net
sys.path.insert(0,'./ref_utils/')
sys.path.insert(0,'./Model/')
from Model import MultiscaleWarpingNet
#from utils import get_ids, split_ids, split_train_val, get_imgs_and_masks, batch
from LFDataset import LFDataset
from FlowerDataset import FlowerDataset
import matplotlib.pyplot as plt
import CustomLoss
def psnr(img1, img2):
mse = np.mean( (img1 - img2) ** 2 )
if mse == 0:
return 100
PIXEL_MAX = 1.0
if mse > 1000:
return -100
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def train_net(net, gpu=False, config={}):
dataset_train = config['dataset_train']
dataset_test = config['dataset_test']
print('Starting training...')
if config['optim'] == 'SGD':
optimizer = optim.SGD(net.parameters(), lr=config['lr'], momentum=0.9, weight_decay=0.0005)
elif config['optim'] == 'Adam':
optimizer = optim.Adam(net.parameters(), lr = config['lr'], weight_decay = 0.00005)
if config['loss'] == 'EuclideanLoss':
criterion = CustomLoss.EuclideanLoss()
elif config['loss'] == 'CharbonnierLoss':
criterion = CustomLoss.CharbonnierLoss()
elif config['loss'] == 'MSELoss':
criterion = nn.MSELoss()
else:
print 'None loss type'
sys.exit(0)
loss_count = 0
time_start = time.time()
#flag = 1
#buff_list = []
for iter_ in range(config['checkpoint'],config['max_iter']):
# reset the generators
#train = get_imgs_and_masks(iddataset['train'], dir_img, dir_mask, img_scale)
#val = get_imgs_and_masks(iddataset['val'], dir_img, dir_mask, img_scale)
buff = dataset_train.nextBatch_new(batchsize=config['batch_size'], shuffle=True, view_mode = 'Random', augmentation = True, offset_augmentation=config['data_displacement_augmentation'], crop_shape = config['train_data_crop_shape'],Dual = config['Dual'])
#buff_list.append(buff)
#flag += 1
#if flag > 5:
# file_ = open('./train_buff','wb')
# pickle.dump(buff_list,file_)
# file_.close()
# break
label_img = buff['input_img1_HR']
label_img = torch.from_numpy(label_img)
if gpu:
label_img = label_img.cuda()
net_pred = net(buff,mode = 'input_img1_HR')
# net_pred_flat = net_pred.view(-1)
# label_img_flat = label_img.view(-1)
loss = criterion(net_pred, label_img)
#print (loss)
loss_count += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (iter_ + 1) % config['snapshot'] == 0:
torch.save(net.state_dict(),
config['checkpoints_dir'] + 'CP{}.pth'.format(iter_ + 1))
print('Checkpoint {} saved !'.format(iter_ + 1))
if (iter_ + 1) % config['display'] == 0:
time_end = time.time()
time_cost = time_end - time_start
#------------------------------------------------
pre_npy = net_pred.data.cpu().numpy()
label_img_npy = label_img.data.cpu().numpy()
psnr_ = 0
for i in range(pre_npy.shape[0]):
psnr_ += psnr(pre_npy[i],label_img_npy[i]) / pre_npy.shape[0]
# print (i,psnr(pre_npy[i],label_img_npy[i]))
#------------------------------------------------------
#buff_val = dataset_test.nextBatch_new(batchsize=config['batch_size'], shuffle=True, view_mode = 'Random', augmentation = False, offset_augmentation=config['data_displacement_augmentation'], crop_shape = config['train_data_crop_shape'])
#val_img1_LR = buff_val['input_img1_LR']
#val_img2_HR = buff_val['input_img2_HR']
#val_img = np.concatenate((val_img1_LR,val_img2_HR),axis = 1)
#val_label_img = buff['input_img1_HR']
#val_img = torch.from_numpy(val_img)
#val_img = val_img.cuda()
#with torch.no_grad():
# val_pred = net(val_img)
# val_pred_npy = val_pred.cpu().numpy()
# psnr_ = 0
# for i in range(val_pred_npy.shape[0]):
# psnr_ += psnr(val_pred_npy[i],val_label_img[i]) / val_pred_npy.shape[0]
#print (i,psnr(val_pred_npy[i],val_label_img[i]))
print ('iter:%d time: %.2fs / %diters lr: %.8f %s: %.7f psnr: %.2f'%(iter_ + 1,time_cost,config['display'],config['lr'],config['loss'],loss_count / config['display'], psnr_))
loss_count = 0
time_start = time.time()
if (iter_ + 1) % config['step_size'] == 0:
config['lr'] = config['lr'] * config['gamma']
if config['optim'] == 'SGD':
optimizer = optim.SGD(net.parameters(), lr=config['lr'] * config['gamma'], momentum=0.9, weight_decay=0.0005)
elif config['optim'] == 'Adam':
optimizer = optim.Adam(net.parameters(), lr = config['lr'], weight_decay = 0.00005)
def get_args():
parser = OptionParser()
parser.add_option('--batch_size', dest='batch_size', default=8,
type='int', help='batch size')
parser.add_option('--lr', dest='lr', default=0.0001,
type='float', help='learning rate')
parser.add_option('--gpu', action='store_true', dest='gpu',
default=True, help='use cuda')
parser.add_option('--checkpoint_file', dest='load',
default=False, help='load file model')
parser.add_option('--checkpoint',dest = 'checkpoint',default = 0,type = 'int',help = 'snapshot')
parser.add_option('-s', '--scale', dest='scale', type='float',
default= 8 , help='downscaling factor of LR')
parser.add_option('--loss',dest = 'loss',default='EuclideanLoss',help = 'loss type')
parser.add_option('--dataset',dest = 'dataset',default = 'LFvideo',help = 'dataset type')
parser.add_option('--gamma',dest = 'gamma',type = 'float', default = 0.2,help = 'lr decay')
parser.add_option('--step_size',dest = 'step_size',type = 'float',default = 60000,help = 'step_size')
parser.add_option('--max_iter',dest = 'max_iter',default = 1000000,type = 'int',help = 'max_iter')
parser.add_option('--checkpoints_dir',dest = 'checkpoints_dir',default = './checkpoints/',help = 'checkpoints_dir')
parser.add_option('--snapshot',dest = 'snapshot',default = 5000,type = 'float',help = 'snapshot')
parser.add_option('--display',dest = 'display',default = 10,type = 'float',help = 'display')
parser.add_option('--optim', dest = 'optim', default = 'SGD', help = 'optimizer type')
(options, args) = parser.parse_args()
return options
if __name__ == '__main__':
args = get_args()
net = MultiscaleWarpingNet()
dataset_name = args.dataset
scale = args.scale
if dataset_name=='LFvideo':
dataset_train = LFDataset(filename = '/fileserver/haitian/dataset/lf_video_dataset/train_x4_x8.h5', scale = scale)
dataset_test = LFDataset(filename = '/fileserver/haitian/dataset/lf_video_dataset/test_x4_x8.h5', scale = scale)
H,W = (320,512)
elif dataset_name=='Flower':
dataset_train = FlowerDataset(filename = '/fileserver/haitian/dataset/flower_dataset/train_x4_x8.h5', scale = scale)
dataset_test = FlowerDataset(filename = '/fileserver/haitian/dataset/flower_dataset/test_x4_x8.h5', scale = scale)
H,W = (320,512)
config = {}
config['dataset_train'] = dataset_train
config['dataset_test'] = dataset_test
config['data_displacement_augmentation'] = False
config['train_data_crop_shape'] = [H,W]
config['max_iter'] = args.max_iter
config['snapshot'] = args.snapshot
config['display'] = args.display
config['lr'] = args.lr
config['batch_size'] = args.batch_size
config['step_size'] = args.step_size
config['gamma'] = args.gamma
config['checkpoints_dir'] = args.checkpoints_dir
config['loss'] = args.loss
config['checkpoint'] = args.checkpoint
config['optim'] = args.optim
config['Dual'] = False
if args.load:
net.load_state_dict(torch.load(args.load))
print('Model loaded from {}'.format(args.load))
if args.gpu:
net.cuda()
#net = nn.DataParallel(net)
# cudnn.benchmark = True # faster convolutions, but more memory
try:
train_net(net=net,gpu=args.gpu,config = config)
except KeyboardInterrupt:
torch.save(net.state_dict(), 'INTERRUPTED.pth')
print('Saved interrupt')
try:
sys.exit(0)
except SystemExit:
os._exit(0)