def main():
import argparse
parser = argparse.ArgumentParser(
description="imsitu VSRL. Training, evaluation and prediction.")
parser.add_argument("--gpuid",
default=-1,
help="put GPU id > -1 in GPU mode",
type=int)
#parser.add_argument("--command", choices = ["train", "eval", "resume", 'predict'], required = True)
parser.add_argument('--resume_training',
action='store_true',
help='Resume training from the model [resume_model]')
parser.add_argument('--resume_model',
type=str,
default='',
help='The model we resume')
parser.add_argument('--verb_module',
type=str,
default='',
help='pretrained verb module')
parser.add_argument('--noun_module',
type=str,
default='',
help='pretrained noun module')
parser.add_argument('--train_role',
action='store_true',
help='cnn fix, verb fix, role train from the scratch')
parser.add_argument(
'--finetune_verb',
action='store_true',
help='cnn fix, verb finetune, role train from the scratch')
parser.add_argument(
'--finetune_cnn',
action='store_true',
help='cnn finetune, verb finetune, role train from the scratch')
parser.add_argument('--finetune_both',
action='store_true',
help='cnn fix, verb finetune, role finetune')
parser.add_argument('--output_dir',
type=str,
default='./trained_models',
help='Location to output the model')
parser.add_argument('--evaluate',
action='store_true',
help='Only use the testing mode')
parser.add_argument('--test',
action='store_true',
help='Only use the testing mode')
#todo: train role module separately with gt verbs
args = parser.parse_args()
batch_size = 640
#lr = 5e-6
lr = 0.0001
lr_max = 5e-4
lr_gamma = 0.1
lr_step = 25
clip_norm = 50
weight_decay = 1e-4
n_epoch = 500
n_worker = 3
dataset_folder = 'imSitu'
imgset_folder = 'resized_256'
print('model spec :, mac net finetune pretrained verb and role labeller ')
train_set = json.load(open(dataset_folder + "/train.json"))
encoder = imsitu_encoder(train_set)
model = model_mac_finetune_both.E2ENetwork(encoder, args.gpuid)
# To group up the features
cnn_verb_features, cnn_noun_features, verb_features, role_features = utils.group_features(
model)
train_set = imsitu_loader(imgset_folder, train_set, encoder,
model.train_preprocess())
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=64,
shuffle=True,
num_workers=n_worker)
dev_set = json.load(open(dataset_folder + "/dev.json"))
dev_set = imsitu_loader(imgset_folder, dev_set, encoder,
model.dev_preprocess())
dev_loader = torch.utils.data.DataLoader(dev_set,
batch_size=64,
shuffle=True,
num_workers=n_worker)
test_set = json.load(open(dataset_folder + "/test.json"))
test_set = imsitu_loader(imgset_folder, test_set, encoder,
model.dev_preprocess())
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=64,
shuffle=True,
num_workers=n_worker)
traindev_set = json.load(open(dataset_folder + "/dev.json"))
traindev_set = imsitu_loader(imgset_folder, traindev_set, encoder,
model.dev_preprocess())
traindev_loader = torch.utils.data.DataLoader(traindev_set,
batch_size=8,
shuffle=True,
num_workers=n_worker)
utils.set_trainable(model, False)
if args.train_role:
print('CNN fix, Verb fix, train role from the scratch from: {}'.format(
args.verb_module))
args.train_all = False
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 1
model_name = 'cfx_vfx_rtrain'
elif args.finetune_verb:
print('CNN fix, Verb finetune, train role from the scratch from: {}'.
format(args.verb_module))
args.train_all = True
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 2
model_name = 'cfx_vft_rtrain'
elif args.finetune_cnn:
print(
'CNN finetune, Verb finetune, train role from the scratch from: {}'
.format(args.verb_module))
args.train_all = True
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 3
model_name = 'cft_vft_rtrain'
elif args.resume_training:
print('Resume training from: {}'.format(args.resume_model))
args.train_all = True
if len(args.resume_model) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.resume_model, [model])
optimizer_select = 0
model_name = 'resume_all'
elif args.finetune_both:
print('finetune model from pretrained verb and noun models')
args.train_all = True
if len(args.verb_module) == 0 or len(args.noun_module) == 0:
raise Exception('[pretrained verb or noun module] not specified')
utils.load_net(args.verb_module, [model.conv_verb, model.verb],
['conv', 'verb'])
utils.load_net(args.noun_module, [
model.conv_noun, model.role_lookup, model.verb_lookup,
model.role_labeller
], ['conv', 'role_lookup', 'verb_lookup', 'role_labeller'])
optimizer_select = 4
model_name = 'finetune_both'
else:
print('Training from the scratch.')
optimizer_select = 0
args.train_all = True
model_name = 'train_full'
optimizer = utils.get_optimizer(lr, weight_decay, optimizer_select,
cnn_verb_features, cnn_noun_features,
verb_features, role_features)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
if args.gpuid >= 0:
#print('GPU enabled')
model.cuda()
'''optimizer = torch.optim.Adam([{'params': model.verb.parameters(), 'lr': 5e-5},
{'params': model.role_lookup.parameters(), 'lr': 5e-5},
{'params': model.verb_lookup.parameters(), 'lr': 5e-5},
{'params': model.role_labeller.parameters(), 'lr': 5e-5}])'''
#optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_step, gamma=lr_gamma)
#gradient clipping, grad check
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
if args.evaluate:
top1, top5, val_loss = eval(model,
dev_loader,
encoder,
args.gpuid,
write_to_file=True)
top1_avg = top1.get_average_results()
top5_avg = top5.get_average_results()
avg_score = top1_avg["verb"] + top1_avg["value"] + top1_avg["value-all"] + top5_avg["verb"] + \
top5_avg["value"] + top5_avg["value-all"] + top5_avg["value*"] + top5_avg["value-all*"]
avg_score /= 8
print('Dev average :{:.2f} {} {}'.format(
avg_score * 100, utils.format_dict(top1_avg, '{:.2f}', '1-'),
utils.format_dict(top5_avg, '{:.2f}', '5-')))
#write results to csv file
role_dict = top1.role_dict
fail_val_all = top1.value_all_dict
pass_val_dict = top1.vall_all_correct
with open('role_pred_data.json', 'w') as fp:
json.dump(role_dict, fp, indent=4)
with open('fail_val_all.json', 'w') as fp:
json.dump(fail_val_all, fp, indent=4)
with open('pass_val_all.json', 'w') as fp:
json.dump(pass_val_dict, fp, indent=4)
print('Writing predictions to file completed !')
elif args.test:
top1, top5, val_loss = eval(model,
test_loader,
encoder,
args.gpuid,
write_to_file=True)
top1_avg = top1.get_average_results()
top5_avg = top5.get_average_results()
avg_score = top1_avg["verb"] + top1_avg["value"] + top1_avg["value-all"] + top5_avg["verb"] + \
top5_avg["value"] + top5_avg["value-all"] + top5_avg["value*"] + top5_avg["value-all*"]
avg_score /= 8
print('Test average :{:.2f} {} {}'.format(
avg_score * 100, utils.format_dict(top1_avg, '{:.2f}', '1-'),
utils.format_dict(top5_avg, '{:.2f}', '5-')))
else:
print('Model training started!')
train(model, train_loader, dev_loader, traindev_loader, optimizer,
scheduler, n_epoch, args.output_dir, encoder, args.gpuid,
clip_norm, lr_max, model_name, args)
def main():
import argparse
parser = argparse.ArgumentParser(description="imsitu VSRL. Training, evaluation and prediction.")
parser.add_argument("--gpuid", default=-1, help="put GPU id > -1 in GPU mode", type=int)
#parser.add_argument("--command", choices = ["train", "eval", "resume", 'predict'], required = True)
parser.add_argument('--resume_training', action='store_true', help='Resume training from the model [resume_model]')
parser.add_argument('--resume_model', type=str, default='', help='The model we resume')
parser.add_argument('--verb_module', type=str, default='', help='pretrained verb module')
parser.add_argument('--train_role', action='store_true', help='cnn fix, verb fix, role train from the scratch')
parser.add_argument('--finetune_verb', action='store_true', help='cnn fix, verb finetune, role train from the scratch')
parser.add_argument('--finetune_cnn', action='store_true', help='cnn finetune, verb finetune, role train from the scratch')
parser.add_argument('--output_dir', type=str, default='./trained_models', help='Location to output the model')
#todo: train role module separately with gt verbs
args = parser.parse_args()
batch_size = 640
#lr = 5e-6
lr = 0.0001
lr_max = 5e-4
lr_gamma = 0.1
lr_step = 25
clip_norm = 50
weight_decay = 1e-4
n_epoch = 500
n_worker = 4
dataset_folder = 'imSitu'
imgset_folder = 'resized_256'
print('model spec :, gmac net v pred for training and loss calc normalizing from only matching role count ')
train_set = json.load(open(dataset_folder + "/train.json"))
encoder = imsitu_encoder(train_set)
model = gmac_model_with_verb.E2ENetwork(encoder, args.gpuid)
# To group up the features
cnn_features, verb_features, role_features = utils.group_features(model)
train_set = imsitu_loader(imgset_folder, train_set, encoder, model.train_preprocess())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True, num_workers=n_worker)
dev_set = json.load(open(dataset_folder +"/dev.json"))
dev_set = imsitu_loader(imgset_folder, dev_set, encoder, model.dev_preprocess())
dev_loader = torch.utils.data.DataLoader(dev_set, batch_size=64, shuffle=True, num_workers=n_worker)
traindev_set = json.load(open(dataset_folder +"/dev.json"))
traindev_set = imsitu_loader(imgset_folder, traindev_set, encoder, model.dev_preprocess())
traindev_loader = torch.utils.data.DataLoader(traindev_set, batch_size=8, shuffle=True, num_workers=n_worker)
utils.set_trainable(model, False)
if args.train_role:
print('CNN fix, Verb fix, train role from the scratch from: {}'.format(args.verb_module))
args.train_all = False
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb], ['conv', 'verb'])
optimizer_select = 1
model_name = 'cfx_vfx_rtrain'
elif args.finetune_verb:
print('CNN fix, Verb finetune, train role from the scratch from: {}'.format(args.verb_module))
args.train_all = True
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb], ['conv', 'verb'])
optimizer_select = 2
model_name = 'cfx_vft_rtrain'
elif args.finetune_cnn:
print('CNN finetune, Verb finetune, train role from the scratch from: {}'.format(args.verb_module))
args.train_all = True
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb], ['conv', 'verb'])
optimizer_select = 3
model_name = 'cft_vft_rtrain'
elif args.resume_training:
print('Resume training from: {}'.format(args.resume_model))
args.train_all = True
if len(args.resume_model) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.resume_model, [model])
optimizer_select = 0
model_name = 'resume_all'
else:
print('Training from the scratch.')
optimizer_select = 0
args.train_all = True
model_name = 'train_full'
optimizer = utils.get_optimizer(lr,weight_decay,optimizer_select,
cnn_features, verb_features, role_features)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
if args.gpuid >= 0:
#print('GPU enabled')
model.cuda()
optimizer = torch.optim.Adam([{'params': model.conv.parameters(), 'lr': 5e-5},
{'params': model.verb.parameters()},
{'params': model.role_labeller.parameters()}],
lr=1e-3)
#optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_step, gamma=lr_gamma)
#gradient clipping, grad check
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
print('Model training started!')
train(model, train_loader, dev_loader, traindev_loader, optimizer, scheduler, n_epoch, args.output_dir, encoder, args.gpuid, clip_norm, lr_max, model_name, args)
model.load_state_dict(torch.load(args.weights_file))
dataset_train = imSituSituation(args.image_dir, train_set, encoder, model.train_preprocess())
dataset_dev = imSituSituation(args.image_dir, dev_set, encoder, model.dev_preprocess())
ngpus = 1
device_array = [i for i in range(0,ngpus)]
#batch_size = args.batch_size*ngpus
batch_size = 1
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size = batch_size, shuffle = True, num_workers = 1)
dev_loader = torch.utils.data.DataLoader(dataset_dev, batch_size = batch_size, shuffle = True, num_workers = 1)
model.cuda()
# need to make f rcnn params fixed
frcnn_features, crf_features = group_features(model)
optimizer = network.get_optimizer_dvsrl(args.learning_rate, 0, 1, args,
frcnn_features, crf_features, args.weight_decay)
#optimizer = optim.Adam(model.parameters(), lr = args.learning_rate , weight_decay = args.weight_decay)
train_model(args.training_epochs, args.eval_frequency, train_loader, dev_loader, model, encoder, optimizer, args.output_dir)
elif args.command == "eval":
print "command = evaluating"
eval_file = json.load(open(args.dataset_dir + "/" + args.eval_file))
if args.encoding_file is None:
print "expecting encoder file to run evaluation"
exit()
else:
encoder = torch.load(args.encoding_file)
print "creating model..."
def main():
import argparse
parser = argparse.ArgumentParser(
description="imsitu VSRL. Training, evaluation and prediction.")
parser.add_argument("--gpuid",
default=-1,
help="put GPU id > -1 in GPU mode",
type=int)
#parser.add_argument("--command", choices = ["train", "eval", "resume", 'predict'], required = True)
parser.add_argument('--resume_training',
action='store_true',
help='Resume training from the model [resume_model]')
parser.add_argument('--resume_model',
type=str,
default='',
help='The model we resume')
parser.add_argument('--verb_module',
type=str,
default='',
help='pretrained verb module')
parser.add_argument('--train_role',
action='store_true',
help='cnn fix, verb fix, role train from the scratch')
parser.add_argument(
'--finetune_verb',
action='store_true',
help='cnn fix, verb finetune, role train from the scratch')
parser.add_argument(
'--finetune_cnn',
action='store_true',
help='cnn finetune, verb finetune, role train from the scratch')
parser.add_argument('--output_dir',
type=str,
default='./trained_models',
help='Location to output the model')
parser.add_argument('--evaluate',
action='store_true',
help='Only use the testing mode')
#todo: train role module separately with gt verbs
args = parser.parse_args()
batch_size = 640
#lr = 5e-6
lr = 0.0001
lr_max = 5e-4
lr_gamma = 0.1
lr_step = 25
clip_norm = 50
weight_decay = 1e-4
n_epoch = 500
n_worker = 3
dataset_folder = 'imSitu'
imgset_folder = 'resized_256'
train_set = json.load(open(dataset_folder + "/train.json"))
encoder = imsitu_encoder(train_set)
model = model_vsrl_small_finetune.RelationNetworks(encoder, args.gpuid)
# To group up the features
cnn_features, verb_features, role_features = utils.group_features(model)
train_set = imsitu_loader(imgset_folder, train_set, encoder,
model.train_preprocess())
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=32,
shuffle=True,
num_workers=n_worker)
dev_set = json.load(open(dataset_folder + "/dev.json"))
dev_set = imsitu_loader(imgset_folder, dev_set, encoder,
model.train_preprocess())
dev_loader = torch.utils.data.DataLoader(dev_set,
batch_size=32,
shuffle=True,
num_workers=n_worker)
traindev_set = json.load(open(dataset_folder + "/dev.json"))
traindev_set = imsitu_loader(imgset_folder, traindev_set, encoder,
model.train_preprocess())
traindev_loader = torch.utils.data.DataLoader(traindev_set,
batch_size=8,
shuffle=True,
num_workers=n_worker)
utils.set_trainable(model, False)
if args.train_role:
print('CNN fix, Verb fix, train role from the scratch from: {}'.format(
args.verb_module))
args.train_all = False
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 1
model_name = 'cfx_vfx_rtrain'
elif args.finetune_verb:
print('CNN fix, Verb finetune, train role from the scratch from: {}'.
format(args.verb_module))
args.train_all = True
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 2
model_name = 'cfx_vft_rtrain'
elif args.finetune_cnn:
print(
'CNN finetune, Verb finetune, train role from the scratch from: {}'
.format(args.verb_module))
args.train_all = True
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 3
model_name = 'cft_vft_rtrain'
elif args.resume_training:
print('Resume training from: {}'.format(args.resume_model))
args.train_all = True
if len(args.resume_model) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.resume_model, [model])
optimizer_select = 0
model_name = 'resume_all'
else:
if not args.evaluate:
print('Training from the scratch.')
optimizer_select = 0
args.train_all = True
model_name = 'train_full'
optimizer = utils.get_optimizer(lr, weight_decay, optimizer_select,
cnn_features, verb_features, role_features)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
if args.gpuid >= 0:
#print('GPU enabled')
model.cuda()
#optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=lr_step,
gamma=lr_gamma)
#gradient clipping, grad check
if args.evaluate:
top1, top5, val_loss = eval(model,
dev_loader,
encoder,
args.gpuid,
write_to_file=True)
top1_avg = top1.get_average_results()
top5_avg = top5.get_average_results()
avg_score = top1_avg["verb"] + top1_avg["value"] + top1_avg["value-all"] + top5_avg["verb"] + \
top5_avg["value"] + top5_avg["value-all"]
avg_score /= 8
print('Dev average :{:.2f} {} {}'.format(
avg_score * 100, utils.format_dict(top1_avg, '{:.2f}', '1-'),
utils.format_dict(top5_avg, '{:.2f}', '5-')))
#write results to csv file
gt_labels = top1.gt_situation
pred_labels = top1.predicted_situation
verb_pred = top1.verb_pred
with open("gt_rn_only.csv", "w") as f:
writer = csv.writer(f)
writer.writerows(gt_labels)
with open("pred_rn_only.csv", "w") as f:
writer = csv.writer(f)
writer.writerows(pred_labels)
with open("verbpred_rn_only.csv", "w") as f:
writer = csv.writer(f)
writer.writerow(['verb', 'total', 'predicted'])
for key, value in verb_pred.items():
writer.writerow([key, value[0], value[1]])
print('Writing predictions to file completed !')
else:
print('Model training started!')
train(model, train_loader, dev_loader, traindev_loader, optimizer,
scheduler, n_epoch, args.output_dir, encoder, args.gpuid,
clip_norm, lr_max, model_name, args)
def main():
import argparse
parser = argparse.ArgumentParser(
description="imsitu VSRL. Training, evaluation and prediction.")
parser.add_argument("--gpuid",
default=-1,
help="put GPU id > -1 in GPU mode",
type=int)
#parser.add_argument("--command", choices = ["train", "eval", "resume", 'predict'], required = True)
parser.add_argument('--resume_training',
action='store_true',
help='Resume training from the model [resume_model]')
parser.add_argument('--resume_model',
type=str,
default='',
help='The model we resume')
parser.add_argument('--verb_module',
type=str,
default='',
help='pretrained verb module')
parser.add_argument('--train_role',
action='store_true',
help='cnn fix, verb fix, role train from the scratch')
parser.add_argument(
'--finetune_verb',
action='store_true',
help='cnn fix, verb finetune, role train from the scratch')
parser.add_argument(
'--finetune_cnn',
action='store_true',
help='cnn finetune, verb finetune, role train from the scratch')
parser.add_argument('--output_dir',
type=str,
default='./trained_models',
help='Location to output the model')
#todo: train role module separately with gt verbs
args = parser.parse_args()
batch_size = 640
#lr = 5e-6
lr = 0
lr_max = 5e-4
lr_gamma = 0.1
lr_step = 25
clip_norm = 50
weight_decay = 1e-4
n_epoch = 500
n_worker = 3
dataset_folder = 'imSitu'
imgset_folder = 'resized_256'
print(
'model spec :, 256 hidden, 1e-4 init lr, 25 epoch decay, 4 layer mlp for g,2mlp f1, 3 att layers with res connections param init xavier uni 2 heads dropout 0.5 mask 6loss maskb4g transformopt'
)
train_set = json.load(open(dataset_folder + "/train.json"))
encoder = imsitu_encoder(train_set)
model = model_vsrl_finetune_selfatt_ff.RelationNetworks(
encoder, args.gpuid)
# To group up the features
cnn_features, verb_features, role_features = utils.group_features(model)
train_set = imsitu_loader(imgset_folder, train_set, encoder,
model.train_preprocess())
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=24,
shuffle=True,
num_workers=n_worker)
dev_set = json.load(open(dataset_folder + "/dev.json"))
dev_set = imsitu_loader(imgset_folder, dev_set, encoder,
model.train_preprocess())
dev_loader = torch.utils.data.DataLoader(dev_set,
batch_size=24,
shuffle=True,
num_workers=n_worker)
traindev_set = json.load(open(dataset_folder + "/dev.json"))
traindev_set = imsitu_loader(imgset_folder, traindev_set, encoder,
model.train_preprocess())
traindev_loader = torch.utils.data.DataLoader(traindev_set,
batch_size=8,
shuffle=True,
num_workers=n_worker)
utils.set_trainable(model, False)
if args.train_role:
print('CNN fix, Verb fix, train role from the scratch from: {}'.format(
args.verb_module))
args.train_all = False
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 1
model_name = 'cfx_vfx_rtrain'
elif args.finetune_verb:
print('CNN fix, Verb finetune, train role from the scratch from: {}'.
format(args.verb_module))
args.train_all = True
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 2
model_name = 'cfx_vft_rtrain'
elif args.finetune_cnn:
print(
'CNN finetune, Verb finetune, train role from the scratch from: {}'
.format(args.verb_module))
args.train_all = True
if len(args.verb_module) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.verb_module, [model.conv, model.verb],
['conv', 'verb'])
optimizer_select = 3
model_name = 'cft_vft_rtrain'
elif args.resume_training:
print('Resume training from: {}'.format(args.resume_model))
args.train_all = True
if len(args.resume_model) == 0:
raise Exception('[pretrained verb module] not specified')
utils.load_net(args.resume_model, [model])
optimizer_select = 0
model_name = 'resume_all'
else:
print('Training from the scratch.')
optimizer_select = 0
args.train_all = True
model_name = 'train_full'
optimizer = utils.get_optimizer(lr, weight_decay, optimizer_select,
cnn_features, verb_features, role_features)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
if args.gpuid >= 0:
#print('GPU enabled')
model.cuda()
opt = utils.NoamOpt(256, 1, 4000, optimizer)
#optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=lr_step,
gamma=lr_gamma)
#gradient clipping, grad check
print('Model training started!')
train(model, train_loader, dev_loader, traindev_loader, opt, scheduler,
n_epoch, args.output_dir, encoder, args.gpuid, clip_norm, lr_max,
model_name, args)
