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train.py
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import torch
from torch.utils import data
from torchvision import transforms
import os
import time
import random
import numpy as np
from args import get_parser
from libs.dataset.data import get_dataset
from libs.utils.obj import Loss
from libs.utils.eval import db_eval_iou_multi
from libs.utils.utils import make_dir
from libs.utils.utils import get_optimizer
from libs.utils.utils import check_parallel, save_checkpoint_epoch, load_checkpoint_epoch
from libs.model.HCPN import EncoderNet, DecoderNet
def init_dataloaders(args):
loaders = {}
# init dataloaders for training and validation
for split in ['train', 'val']:
batch_size = args.batch_size
to_tensor = transforms.ToTensor()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
image_transforms = transforms.Compose([to_tensor, normalize])
target_transforms = transforms.Compose([to_tensor])
dataset = get_dataset(
args, split=split, image_transforms=image_transforms,
target_transforms=target_transforms,
augment=args.augment and split == 'train',
input_size=(512, 512), pre_train=pretrain)
shuffle = True if split == 'train' else False
loaders[split] = data.DataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=args.num_workers,
drop_last=True,
pin_memory=True,
)
return loaders
def trainIters(args):
print(args)
model_dir = os.path.join('model/', args.model_name)
make_dir(model_dir)
epoch_resume = 0
if args.resume:
encoder_dict, decoder_dict, enc_opt_dict, dec_opt_dict, load_args = \
load_checkpoint_epoch(args.model_name, args.epoch_resume,
args.use_gpu)
epoch_resume = args.epoch_resume
encoder = EncoderNet()
decoder = DecoderNet()
encoder_dict, decoder_dict = check_parallel(encoder_dict, decoder_dict)
encoder.load_state_dict(encoder_dict)
decoder.load_state_dict(decoder_dict)
else:
encoder = EncoderNet()
decoder = DecoderNet()
criterion = Loss()
if args.ngpus > 1 and args.use_gpu:
decoder = torch.nn.DataParallel(decoder, device_ids=[0, 1], output_device=0)
encoder = torch.nn.DataParallel(encoder, device_ids=[0, 1], output_device=0)
criterion = torch.nn.DataParallel(criterion, device_ids=[0, 1], output_device=0)
if args.use_gpu:
encoder.cuda()
decoder.cuda()
criterion.cuda()
if args.use_gpu:
torch.cuda.synchronize()
encoder_params = list(encoder.parameters())
decoder_params = list(decoder.parameters())
dec_opt = get_optimizer(args.optim, args.lr, decoder_params,
args.weight_decay)
enc_opt = get_optimizer(args.optim_cnn, args.lr_cnn, encoder_params,
args.weight_decay_cnn)
loaders = init_dataloaders(args)
best_iou = 0
start = time.time()
for e in range(0, args.max_epoch):
print("Epoch", e+1)
epoch_losses = {'train': {'total': [], 'iou': [],
'mask_loss': [], 'ctr_loss': []},
'val': {'total': [], 'iou': [],
'mask_loss': [], 'ctr_loss': []}}
for split in ['train', 'val']:
if split == 'train':
encoder.train(True)
decoder.train(True)
else:
encoder.train(False)
decoder.train(False)
for batch_idx, (im1, im2, flow, ms1, ms2, ctr1, ctr2, negative_pixels1, negative_pixels2) in\
enumerate(loaders[split]):
# print(batch_idx)
im1, im2, flow, mask1, mask2, ctr1, ctr2, negative_pixels1, negative_pixels2 = \
im1.cuda(), im2.cuda(), flow.cuda(), ms1.cuda(), ms2.cuda(), \
ctr1.cuda(), ctr2.cuda(), negative_pixels1.cuda(), negative_pixels2.cuda()
if split == 'train':
h5_1, h4_1, h3_1, h2_1, h5_2, h4_2, h3_2, h2_2 = encoder(im1, im2, flow)
mask_1, c_1, mask_2, c_2 = decoder(h5_1, h4_1, h3_1, h2_1, h5_2, h4_2, h3_2, h2_2)
mask_loss1 = criterion(mask_1, mask1, negative_pixels1)
ctr_loss1 = criterion(c_1, ctr1, negative_pixels1)
mask_loss2 = criterion(mask_2, mask2, negative_pixels2)
ctr_loss2 = criterion(c_2, ctr2, negative_pixels2)
mask_loss = 0.5 * (mask_loss1 + mask_loss2)
ctr_loss = 0.5 * (ctr_loss1 + ctr_loss2)
loss = mask_loss + 0.5 * ctr_loss
iou = db_eval_iou_multi(mask1.cpu().detach().numpy(), mask_1.cpu().detach().numpy())
dec_opt.zero_grad()
enc_opt.zero_grad()
decoder.zero_grad()
loss.mean().backward()
enc_opt.step()
dec_opt.step()
else:
with torch.no_grad():
h5_1, h4_1, h3_1, h2_1, h5_2, h4_2, h3_2, h2_2 = encoder(im1, im2, flow)
mask_1, c_1, mask_2, c_2 = decoder(h5_1, h4_1, h3_1, h2_1, h5_2, h4_2, h3_2, h2_2)
mask_loss1 = criterion(mask_1, mask1, negative_pixels1)
ctr_loss1 = criterion(c_1, ctr1, negative_pixels1)
mask_loss2 = criterion(mask_2, mask2, negative_pixels2)
ctr_loss2 = criterion(c_2, ctr2, negative_pixels2)
mask_loss = 0.5 * (mask_loss1 + mask_loss2)
ctr_loss = 0.5 * (ctr_loss1 + ctr_loss2)
loss = mask_loss + 0.5 * ctr_loss
iou = db_eval_iou_multi(mask1.cpu().detach().numpy(),
mask_1.cpu().detach().numpy())
epoch_losses[split]['total'].append(loss.data.mean().item())
epoch_losses[split]['mask_loss'].append(mask_loss.data.mean().item())
epoch_losses[split]['ctr_loss'].append(ctr_loss.data.mean().item())
epoch_losses[split]['iou'].append(iou)
if (batch_idx + 1) % args.print_every == 0:
mt = np.mean(epoch_losses[split]['total'])
mmask = np.mean(epoch_losses[split]['mask_loss'])
mbdry = np.mean(epoch_losses[split]['ctr_loss'])
miou = np.mean(epoch_losses[split]['iou'])
te = time.time() - start
print('Epoch: [{}/{}][{}/{}]\tTime {:.3f}s\tLoss: {:.4f}'
'\tMask Loss: {:.4f}\tCTR Loss: {:.4f}'
'\tIOU: {:.4f}'.format(e+1, args.max_epoch, batch_idx,
len(loaders[split]), te, mt,
mmask, mbdry, miou))
start = time.time()
miou = np.mean(epoch_losses['val']['iou'])
print(miou)
if pretrain:
save_checkpoint_epoch(args, encoder, decoder,
enc_opt, dec_opt, miou, False)
else:
if miou > best_iou:
best_iou = miou
save_checkpoint_epoch(args, encoder, decoder,
enc_opt, dec_opt, e+1, False)
if __name__ == '__main__':
parser = get_parser()
args = parser.parse_args()
torch.manual_seed(args.seed)
random.seed(args.seed)
args.model_name = 'HCPN'
args.batch_size = 10
args.ngpus = 2
args.max_epoch = 25
args.year = '2016'
args.num_works = 16
# first pretrain on YouTube-VOS (pretrain = True).
# max_epoch all sets to 25.
pretrain = True
# then train on davis dataset (pretrain = False)
# epoch_resume set to miou and uncomment the following two lines.
# args.resume = True
# args.epoch_resume = 0.6513122544336745 # miou
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
torch.cuda.manual_seed(args.seed)
trainIters(args)