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('--role_module',
type=str,
default='',
help='pretrained role 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')
parser.add_argument('--dataset_folder',
type=str,
default='./imSitu',
help='Location of annotations')
parser.add_argument('--imgset_dir',
type=str,
default='./resized_256',
help='Location of original images')
parser.add_argument('--frcnn_feat_dir',
type=str,
help='Location of output from detectron')
#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 = 15
clip_norm = 0.5
weight_decay = 1e-4
n_epoch = 500
n_worker = 3
#dataset_folder = 'imSitu'
#imgset_folder = 'resized_256'
dataset_folder = args.dataset_folder
imgset_folder = args.imgset_dir
print('model spec :, top down att with role q ')
train_set = json.load(open(dataset_folder + "/updated_train_new.json"))
imsitu_roleq = json.load(open("imsitu_data/imsitu_questions_prev.json"))
verb_templates = json.load(
open("imsitu_data/verb_questions_template.json"))
encoder = imsitu_encoder(train_set, imsitu_roleq, verb_templates)
model = model_verbq_final_deprole_verbqaft.BaseModel(encoder, args.gpuid)
# To group up the features
cnn_features, role_features = utils.group_features_noun(model)
#cnn_features, role_features = utils.group_features_joint_reverb(model)
train_set = imsitu_loader_roleq_updated(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_roleq_updated(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_updated(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_updated(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)
if args.resume_training:
print('Resume training ')
args.train_all = True
'''if len(args.resume_model) == 0:
raise Exception('[pretrained verb module] not specified')'''
utils.load_net_deproleqa0td(args.resume_model, [model])
optimizer_select = 0
model_name = 'resume_all'
else:
utils.load_net(args.verb_module, [model.verb_module])
utils.load_net(args.role_module, [model.role_module])
model_name = 'train_full'
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
torch.manual_seed(1234)
if args.gpuid >= 0:
#print('GPU enabled')
model.cuda()
torch.cuda.manual_seed(1234)
torch.backends.cudnn.deterministic = True
'''optimizer = torch.optim.Adam([
{'params': cnn_features, 'lr': 5e-5},
{'params': role_features}
], lr=1e-3)'''
utils.set_trainable_param(cnn_features, False)
optimizer = torch.optim.Adam(role_features, 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', 'finetune'],
required=True)
parser.add_argument("--batch_size", '-b', type=int, default=64)
parser.add_argument("--weights_file", help="the model to start from")
parser.add_argument(
'--finetune_verb',
action='store_true',
help='verb classifier train from the scratch, all others fixed')
parser.add_argument('--verb_module',
type=str,
default='',
help='pretrained verb module')
args = parser.parse_args()
batch_size = args.batch_size
#lr = 1e-5
lr = 1e-4
lr_max = 5e-4
lr_gamma = 0.1
lr_step = 25
clip_norm = 50
weight_decay = 1e-5
n_epoch = 500
n_worker = 4
# print('LR scheme : lr decay, vgg, fc as per gnn paper batch 64', 1e-5, 0.1,25)
dataset_folder = 'imSitu'
imgset_folder = 'resized_256'
model_dir = 'trained_models'
train_set = json.load(open(dataset_folder + "/train.json"))
encoder = imsitu_encoder(train_set)
model = model_verb_embd.RelationNetworks(encoder, args.gpuid)
train_set = imsitu_loader(imgset_folder, train_set, encoder,
model.train_preprocess())
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=batch_size,
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=batch_size,
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=batch_size,
shuffle=True,
num_workers=n_worker)
if args.command == "resume":
print("loading model weights...")
model.load_state_dict(torch.load(args.weights_file))
elif args.finetune_verb:
print(
'CNN fix, Verb fc fixed, train verb classifier layer from the scratch from: {}'
.format(args.verb_module))
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'])
#print(model)
if args.gpuid >= 0:
print('GPU enabled')
model.cuda()
# optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
utils.set_trainable(model, False)
utils.set_trainable_param(model.classifier.parameters(), True)
utils.set_trainable_param(model.verb.parameters(), True)
optimizer = torch.optim.Adam([{
'params': model.classifier.parameters(),
'lr': 1e-3
}, {
'params': model.verb.parameters(),
'lr': 5e-5
}])
# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_step, gamma=lr_gamma)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
'''optimizer = utils.CosineAnnealingWR(0.01,1200000 , 50,
torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))'''
#gradient clipping, grad check
print('Model training started!')
train(model, train_loader, dev_loader, traindev_loader, optimizer,
scheduler, n_epoch, model_dir, encoder, args.gpuid, clip_norm,
lr_max)
