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 = 3
dataset_folder = 'imSitu'
imgset_folder = 'resized_256'
print(
'model spec :, mac 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 = mac_model_with_verb.E2ENetwork(encoder, args.gpuid)
# To group up the features
cnn_features, verb_features, role_features = utils.group_features_single(
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_single(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(), 'lr': 5e-5},
{'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)
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')
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_data'
imgset_folder = 'of500_images_resized'
print('model spec :, mac net v nlp role q. steps 5 with verb')
train_set = json.load(open(dataset_folder + "/train.json"))
imsitu_roleq = json.load(open("imsitu_data/imsitu_questions.json"))
encoder = imsitu_encoder(train_set, imsitu_roleq)
model = mac_vsrl_roleq.E2ENetwork(encoder, args.gpuid)
# To group up the features
cnn_features, verb_features, role_features = utils.group_features_single(model)
train_set = imsitu_loader_roleq(imgset_folder, train_set, encoder, model.train_preprocess())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=4, shuffle=True, num_workers=n_worker)
dev_set = json.load(open(dataset_folder +"/dev.json"))
dev_set = imsitu_loader_roleq(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_roleq(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_roleq(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_single(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)
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
role_dict = top1.role_dict
fail_val_all = top1.value_all_dict
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)
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"]
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)
