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train.py
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train.py
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import torch
import torch.optim as optim
from torch.nn import DataParallel
import numpy as np
import os
import time
import random
from tensorboardX import SummaryWriter
from model import resnet_model
from data import make_dataloader
from loss import make_loss
from config import get_parser
from eval import eval
def init_seed(args, gids):
"""
Set random seed for torch and numpy.
"""
random.seed(args.manual_seed)
np.random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
if gids is not None:
torch.cuda.manual_seed_all(args.manual_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def make_model(args, gids=None):
"""
Initialize ResNet-50 model.
"""
if 'softmax' in args.loss_type:
if args.dataset == 'market1501':
num_classes = 751
elif args.dataset == 'duke':
num_classes = 702
else:
raise NotImplementedError
model = resnet_model(num_classes=num_classes, include_top=True,
remove_downsample=args.remove_downsample)
else:
model = resnet_model(remove_downsample=args.remove_downsample)
if gids is not None:
model = model.cuda(gids[0])
if len(gids) > 1:
model = DataParallel(model, gids)
return model
def adjust_lr_exp(optimizer, base_lr, epoch, num_epochs, decay_start_epoch):
"""
Adjust learning rate exponentially from a given epoch to the end of training.
"""
if epoch < 1:
raise Exception('Current epoch number should be no less than 1.')
if epoch < decay_start_epoch:
return
for g in optimizer.param_groups:
g['lr'] = base_lr * (0.005 ** (float(epoch + 1 - decay_start_epoch)
/ (num_epochs + 1 - decay_start_epoch)))
print('=====> lr adjusted to {:.9f}'.format(g['lr']).rstrip('0'))
def train(args, model, optimizer, criterion, gids=None):
"""
Training
"""
tb = SummaryWriter(comment='_{}'.format(args.loss_type))
model.train()
train_loss = []
t0 = int(time.time())
for epoch in range(args.num_epochs):
if epoch % 10 == 0:
dataloader = make_dataloader(args, epoch)
print('=== Epoch {}/{} ==='.format(epoch, args.num_epochs))
adjust_lr_exp(optimizer, args.lr, epoch+1, args.num_epochs, args.lr_decay_start_epoch)
for iteration, (image, label) in enumerate(dataloader):
if args.cuda:
image, label = image.cuda(gids[0]), label.cuda(gids[0])
if args.loss_type == 'softmax':
_, logits = model(image)
loss = criterion(logits, label)
elif args.loss_type == 'softmax-triplet':
feat, logits = model(image)
loss = args.alpha * criterion['softmax'](logits, label) \
+ (1 - args.alpha) * criterion['triplet'](feat, label)
else:
feat = model(image)
loss = criterion(feat, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# print training info
train_loss.append(loss.item())
if args.loss_type == 'dmml':
print('Episode: {}, Loss: {:.6f}'.format(iteration, loss.item()))
else:
print('Batch: {}, Loss: {:.6f}'.format(iteration, loss.item()))
avg_training_loss = np.mean(train_loss)
print('Average loss: {:.6f}'.format(avg_training_loss))
tb.add_scalar('Train loss', avg_training_loss, epoch+1)
train_loss = []
t = int(time.time())
print('Time elapsed: {}h {}m'.format((t - t0) // 3600, ((t - t0) % 3600) // 60))
if epoch % 100 == 0 and epoch >= args.num_epochs // 2:
model_save_path = os.path.join(args.exp_root, 'model_{}.pth'.format(epoch))
if gids is not None and len(gids) > 1:
torch.save(model.module.state_dict(), model_save_path)
else:
torch.save(model.state_dict(), model_save_path)
print('Model {} saved.'.format(epoch))
model_save_path = os.path.join(args.exp_root, 'model_last.pth'.format(epoch))
if gids is not None and len(gids) > 1:
torch.save(model.module.state_dict(), model_save_path)
else:
torch.save(model.state_dict(), model_save_path)
print('Final model saved.')
tb.close()
eval(gid=gids[0], dataset=args.dataset, dataset_root=args.dataset_root, which='last', exp_dir=args.exp_root)
def main():
"""
Configs
"""
args = get_parser().parse_args()
if not os.path.exists(args.exp_root):
os.makedirs(args.exp_root)
if torch.cuda.is_available() and not args.cuda:
print("\nStrongly recommend to run with '--cuda' if you have a device with CUDA support.")
# print configs
print('='*40)
print('Dataset: {}'.format(args.dataset))
print('Model: ResNet-50')
print('Optimizer: Adam')
print('Image height: {}'.format(args.img_height))
print('Image width: {}'.format(args.img_width))
print('Loss: {}'.format(args.loss_type))
if args.loss_type == 'softmax-triplet':
print(' alpha: {}'.format(args.alpha))
if args.loss_type in ['contrastive', 'triplet', 'dmml']:
print(' margin: {}'.format(args.margin))
print(' class number: {}'.format(args.num_classes))
if args.loss_type == 'npair':
pass
elif args.loss_type == 'dmml':
print(' support number: {}'.format(args.num_support))
print(' query number: {}'.format(args.num_query))
print(' distance_mode: {}'.format(args.distance_mode))
else:
print(' instance number: {}'.format(args.num_instances))
print('Epochs: {}'.format(args.num_epochs))
print('Learning rate: {}'.format(args.lr))
print(' decay beginning epoch: {}'.format(args.lr_decay_start_epoch))
print('Weight decay: {}'.format(args.weight_decay))
if args.cuda:
print('GPU(s): {}'.format(args.gpu))
print('='*40)
"""
Initialization
"""
print('Initializing...')
if args.cuda:
gpus = ''.join(args.gpu.split())
gids = [int(gid) for gid in gpus.split(',')]
else:
gids = None
init_seed(args, gids)
model = make_model(args, gids)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
criterion = make_loss(args, gids)
print('Done.')
"""
Training
"""
print('Starting training...')
train(args, model, optimizer, criterion, gids)
print('Training completed.')
if __name__ == '__main__':
main()