def main():
global args
print "Loading training set and testing set..."
train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome('small', 'test')
print "Done."
train_loader = torch.utils.data.DataLoader(train_set, batch_size=1, shuffle=True, num_workers=8, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=False, num_workers=8, pin_memory=True)
net = RPN(not args.use_normal_anchors)
if args.resume_training:
print 'Resume training from: {}'.format(args.resume_model)
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.resume_model, net)
optimizer = torch.optim.SGD([
{'params': list(net.parameters())[26:]},
], lr=args.lr, momentum=args.momentum, weight_decay=0.0005)
else:
print 'Training from scratch...Initializing network...'
optimizer = torch.optim.SGD(list(net.parameters())[26:], lr=args.lr, momentum=args.momentum, weight_decay=0.0005)
network.set_trainable(net.features, requires_grad=False)
net.cuda()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
best_recall = 0.0
for epoch in range(0, args.max_epoch):
# Training
# train(train_loader, net, optimizer, epoch)
# Testing
recall, RPN_precision, RPN_recall = test(test_loader, net)
print('Epoch[{epoch:d}]: '
'Recall: '
'object: {recall: .3f}%% (Best: {best_recall: .3f}%%)'.format(
epoch = epoch, recall=recall * 100, best_recall=best_recall * 100))
print('object: {precision: .3f}%% '
'object: {recall: .3f}%% '.format(precision=RPN_precision*100, recall=RPN_recall*100))
# update learning rate
if epoch % args.step_size == 0:
args.disable_clip_gradient = True
args.lr /= 10
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
save_name = os.path.join(args.output_dir, '{}_epoch_{}.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
print('save model: {}'.format(save_name))
if np.all(recall > best_recall):
best_recall = recall
save_name = os.path.join(args.output_dir, '{}_best.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
)
re_cnt = True
if use_tensorboard and step % log_interval == 0:
exp.add_scalar_value('train_loss', train_loss / step_cnt, step=step)
exp.add_scalar_value('learning_rate', lr, step=step)
if _DEBUG:
exp.add_scalar_value('true_positive', tp/fg*100., step=step)
exp.add_scalar_value('true_negative', tf/bg*100., step=step)
losses = {'rpn_cls': float(net.rpn.cross_entropy.data.cpu().numpy()[0]),
'rpn_box': float(net.rpn.loss_box.data.cpu().numpy()[0]),
'rcnn_cls': float(net.cross_entropy.data.cpu().numpy()[0]),
'rcnn_box': float(net.loss_box.data.cpu().numpy()[0])}
exp.add_scalar_dict(losses, step=step)
if (step % 10000 == 0) and step > 0:
save_name = os.path.join(output_dir, 'faster_rcnn_{}.h5'.format(step))
network.save_net(save_name, net)
print(('save model: {}'.format(save_name)))
if step in lr_decay_steps:
lr *= lr_decay
optimizer = torch.optim.SGD(params[8:], lr=lr, momentum=momentum, weight_decay=weight_decay)
if re_cnt:
tp, tf, fg, bg = 0., 0., 0, 0
train_loss = 0
step_cnt = 0
t.tic()
re_cnt = False
def main():
global args, optimizer_select
# To set the model name automatically
print args
lr = args.lr
args = get_model_name(args)
print 'Model name: {}'.format(args.model_name)
# To set the random seed
random.seed(args.seed)
torch.manual_seed(args.seed + 1)
torch.cuda.manual_seed(args.seed + 2)
print("Loading training set and testing set...")
train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome(args.dataset_option, 'test')
print("Done.")
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=1,
shuffle=True,
num_workers=8,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=True,
num_workers=8,
pin_memory=True)
net = Hierarchical_Descriptive_Model(
nhidden=args.mps_feature_len,
n_object_cats=train_set.num_object_classes,
n_predicate_cats=train_set.num_predicate_classes,
MPS_iter=args.MPS_iter,
object_loss_weight=train_set.inverse_weight_object,
predicate_loss_weight=train_set.inverse_weight_predicate,
dropout=args.dropout,
use_kmeans_anchors=args.use_kmeans_anchors,
base_model=args.base_model) #True
# params = list(net.parameters())
# for param in params:
# print param.size()
print net
# Setting the state of the training model
net.cuda()
net.train()
network.set_trainable(net, False)
# network.weights_normal_init(net, dev=0.01)
if args.resume_model:
print 'Resume training from: {}'.format(args.HDN_model)
if len(args.HDN_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.HDN_model, net)
# network.load_net(args.RPN_model, net.rpn)
args.train_all = True
optimizer_select = 3
elif args.load_RCNN:
print 'Loading pretrained RCNN: {}'.format(args.RCNN_model)
args.train_all = False
network.load_net(args.RCNN_model, net.rcnn)
optimizer_select = 2
elif args.load_RPN:
print 'Loading pretrained RPN: {}'.format(args.RPN_model)
args.train_all = False
network.load_net(args.RPN_model, net.rpn)
net.reinitialize_fc_layers()
optimizer_select = 1
else:
print 'Training from scratch.'
net.rpn.initialize_parameters()
net.reinitialize_fc_layers()
optimizer_select = 0
args.train_all = True
# To group up the features
# vgg_features_fix, vgg_features_var, rpn_features, hdn_features = group_features(net)
basenet_features, rpn_features, rcnn_feature, hdn_features = group_features(
net)
optimizer = network.get_optimizer(lr, optimizer_select, args,
basenet_features, rpn_features,
rcnn_feature, hdn_features)
target_net = net
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
top_Ns = [50, 100]
best_recall = np.zeros(len(top_Ns))
if args.evaluate:
recall = test(test_loader, target_net, top_Ns,
train_set.object_classes)
print('======= Testing Result =======')
for idx, top_N in enumerate(top_Ns):
print(
'[Recall@{top_N:d}] {recall:2.3f}%% (best: {best_recall:2.3f}%%)'
.format(top_N=top_N,
recall=recall[idx] * 100,
best_recall=best_recall[idx] * 100))
print('==============================')
else:
for epoch in range(0, args.max_epoch):
# Training
train(train_loader, target_net, optimizer, epoch)
# snapshot the state
save_name = os.path.join(
args.output_dir,
'{}_epoch_{}.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
print('save model: {}'.format(save_name))
recall = test(test_loader, target_net, top_Ns,
train_set.object_classes)
if np.all(recall > best_recall):
best_recall = recall
save_name = os.path.join(args.output_dir,
'{}_best.h5'.format(args.model_name))
network.save_net(save_name, net)
print('\nsave model: {}'.format(save_name))
print('Epoch[{epoch:d}]:'.format(epoch=epoch)),
for idx, top_N in enumerate(top_Ns):
print(
'\t[Recall@{top_N:d}] {recall:2.3f}%% (best: {best_recall:2.3f}%%)'
.format(top_N=top_N,
recall=recall[idx] * 100,
best_recall=best_recall[idx] * 100))
# updating learning policy
if (epoch + 1) % args.step_size == 0 or (epoch + 1) % (
args.step_size + 2) == 0:
lr /= 10
args.lr = lr
print '[learning rate: {}]'.format(lr)
args.enable_clip_gradient = False
args.train_all = False
optimizer_select = 2
# update optimizer and correponding requires_grad state
optimizer = network.get_optimizer(lr, optimizer_select, args,
basenet_features,
rpn_features, rcnn_feature,
hdn_features)
def main():
global args, optimizer_select
# To set the model name automatically
print args
lr = args.lr
args = get_model_name(args)
print 'Model name: {}'.format(args.model_name)
# To set the random seed
random.seed(args.seed)
torch.manual_seed(args.seed + 1)
torch.cuda.manual_seed(args.seed + 2)
print("Loading training set and testing set..."),
train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome('small', 'test')
print("Done.")
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=1,
shuffle=True,
num_workers=8,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=False,
num_workers=8,
pin_memory=True)
# Model declaration
net = Hierarchical_Descriptive_Model(
nhidden=args.mps_feature_len,
n_object_cats=train_set.num_object_classes,
n_predicate_cats=train_set.num_predicate_classes,
n_vocab=train_set.voc_size,
voc_sign=train_set.voc_sign,
max_word_length=train_set.max_size,
MPS_iter=args.MPS_iter,
use_language_loss=not args.disable_language_model,
object_loss_weight=train_set.inverse_weight_object,
predicate_loss_weight=train_set.inverse_weight_predicate,
dropout=args.dropout,
use_kmeans_anchors=not args.use_normal_anchors,
gate_width=args.gate_width,
nhidden_caption=args.nhidden_caption,
nembedding=args.nembedding,
rnn_type=args.rnn_type,
rnn_droptout=args.caption_use_dropout,
rnn_bias=args.caption_use_bias,
use_region_reg=args.region_bbox_reg,
use_kernel=args.use_kernel_function)
params = list(net.parameters())
for param in params:
print param.size()
print net
# To group up the features
vgg_features_fix, vgg_features_var, rpn_features, hdn_features, language_features = group_features(
net)
# Setting the state of the training model
net.cuda()
net.train()
logger_path = "log/logger/{}".format(args.model_name)
if os.path.exists(logger_path):
shutil.rmtree(logger_path)
configure(logger_path, flush_secs=5) # setting up the logger
network.set_trainable(net, False)
# network.weights_normal_init(net, dev=0.01)
if args.finetune_language_model:
print 'Only finetuning the language model from: {}'.format(
args.resume_model)
args.train_all = False
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.resume_model, net)
optimizer_select = 3
elif args.load_RPN:
print 'Loading pretrained RPN: {}'.format(args.saved_model_path)
args.train_all = False
network.load_net(args.saved_model_path, net.rpn)
net.reinitialize_fc_layers()
optimizer_select = 1
elif args.resume_training:
print 'Resume training from: {}'.format(args.resume_model)
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.resume_model, net)
args.train_all = True
optimizer_select = 2
else:
print 'Training from scratch.'
net.rpn.initialize_parameters()
net.reinitialize_fc_layers()
optimizer_select = 0
args.train_all = True
optimizer = network.get_optimizer(lr, optimizer_select, args,
vgg_features_var, rpn_features,
hdn_features, language_features)
target_net = net
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
top_Ns = [50, 100]
best_recall = np.zeros(len(top_Ns))
if args.evaluate:
recall = test(test_loader, net, top_Ns)
print('======= Testing Result =======')
for idx, top_N in enumerate(top_Ns):
print(
'[Recall@{top_N:d}] {recall:2.3f}%% (best: {best_recall:2.3f}%%)'
.format(top_N=top_N,
recall=recall[idx] * 100,
best_recall=best_recall[idx] * 100))
print('==============================')
else:
for epoch in range(0, args.max_epoch):
# Training
train(train_loader, target_net, optimizer, epoch)
# snapshot the state
save_name = os.path.join(
args.output_dir,
'{}_epoch_{}.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
print('save model: {}'.format(save_name))
# Testing
# network.set_trainable(net, False) # Without backward(), requires_grad takes no effect
recall = test(test_loader, net, top_Ns)
if np.all(recall > best_recall):
best_recall = recall
save_name = os.path.join(args.output_dir,
'{}_best.h5'.format(args.model_name))
network.save_net(save_name, net)
print('\nsave model: {}'.format(save_name))
print('Epoch[{epoch:d}]:'.format(epoch=epoch)),
for idx, top_N in enumerate(top_Ns):
print(
'\t[Recall@{top_N:d}] {recall:2.3f}%% (best: {best_recall:2.3f}%%)'
.format(top_N=top_N,
recall=recall[idx] * 100,
best_recall=best_recall[idx] * 100)),
# updating learning policy
if epoch % args.step_size == 0 and epoch > 0:
lr /= 10
args.lr = lr
print '[learning rate: {}]'.format(lr)
args.enable_clip_gradient = False
if not args.finetune_language_model:
args.train_all = True
optimizer_select = 2
# update optimizer and correponding requires_grad state
optimizer = network.get_optimizer(lr, optimizer_select, args,
vgg_features_var,
rpn_features, hdn_features,
language_features)
def main():
global args
print "Loading training set and testing set..."
train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome(args.dataset_option, 'test')
object_classes = test_set.object_classes
print "Done."
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=1,
shuffle=True,
num_workers=8,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=False,
num_workers=8,
pin_memory=True)
net = FasterRCNN(nhidden=args.mps_feature_len,
use_kmeans_anchors=args.use_kmeans_anchors,
n_classes=len(object_classes),
model=args.base_model)
if args.resume_model:
print 'Resume training from: {}'.format(args.resume_model)
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.detection_model, net)
# optimizer = torch.optim.SGD([
# {'params': list(net.parameters())},
# ], lr=args.lr, momentum=args.momentum, weight_decay=0.0005)
else:
print 'Training from scratch...Initializing network...'
optimizer = torch.optim.SGD(list(net.parameters()),
lr=args.lr,
momentum=args.momentum,
weight_decay=0.0005)
# network.set_trainable(net.features, requires_grad=True)
net.cuda()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
best_map = 0.0
for epoch in range(0, args.max_epoch):
# Training
train(train_loader, net, optimizer, epoch)
# update learning rate
if epoch % args.step_size == args.step_size - 1:
args.clip_gradient = False
args.lr /= 5
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
save_name = os.path.join(
args.output_dir, '{}_epoch_{}.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
print('save model: {}'.format(save_name))
try:
# Testing
map = evaluate(test_loader, net, object_classes)
print(
'Epoch[{epoch:d}]: '
'Recall: '
'object: {map: .3f}%% (Best: {best_map: .3f}%%)'.format(
epoch=epoch, map=map * 100, best_map=best_map * 100))
if map > best_map:
best_map = map
save_name = os.path.join(
args.output_dir,
'{}_best.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
except:
continue
