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 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')
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')
parser.add_argument('--batch_size', type=int, default=64)
#todo: train role module separately with gt verbs
args = parser.parse_args()
batch_size = args.batch_size
#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'
dataset_folder = args.dataset_folder
imgset_folder = args.imgset_dir
train_set = json.load(open(dataset_folder + "/train_new_2000_all.json"))
imsitu_roleq = json.load(open("imsitu_data/imsitu_questions_prev.json"))
encoder = imsitu_encoder(train_set, imsitu_roleq)
model = model_vgg_featextractor4ggnn.BaseModel(encoder, args.gpuid)
# To group up the features
train_set = imsitu_loader_resnet_featextract(imgset_folder, train_set,
model.train_preprocess())
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=batch_size,
shuffle=False,
num_workers=n_worker)
dev_set = json.load(open(dataset_folder + "/dev_new_2000_all.json"))
dev_set = imsitu_loader_resnet_featextract(imgset_folder, dev_set,
model.dev_preprocess())
dev_loader = torch.utils.data.DataLoader(dev_set,
batch_size=batch_size,
shuffle=False,
num_workers=n_worker)
test_set = json.load(open(dataset_folder + "/test_new_2000_all.json"))
test_set = imsitu_loader_resnet_featextract(imgset_folder, test_set,
model.dev_preprocess())
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=batch_size,
shuffle=False,
num_workers=n_worker)
utils.set_trainable(model, False)
'''if 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])'''
#load verb and role modules
utils.load_net(args.verb_module, [model.conv_verbs], ['conv'])
utils.load_net(args.role_module, [model.conv_nouns], ['conv'])
if args.gpuid >= 0:
model.cuda()
extract_features(model, [train_loader, dev_loader, test_loader],
args.gpuid)
'''print('rechecking')
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')
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')
parser.add_argument('--batch_size', type=int, default=64)
#todo: train role module separately with gt verbs
args = parser.parse_args()
batch_size = args.batch_size
#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'
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 + "/train_new_2000.json"))
imsitu_roleq = json.load(open("imsitu_data/imsitu_questions_prev.json"))
encoder = imsitu_encoder(train_set, imsitu_roleq)
model = model_ft_cnn4imsitu.BaseModel(encoder, args.gpuid)
# To group up the features
train_set = imsitu_loader_objects(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_new_2000.json"))
dev_set = imsitu_loader_objects(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)
test_set = json.load(open(dataset_folder +"/test.json"))
test_set = imsitu_loader_objects(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_objects(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 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
utils.set_trainable(model, True)
utils.set_trainable(model.conv.vgg_features, False)
model_name = 'train_full'
optimizer = torch.optim.SGD([
{'params': model.conv.conv_exp.parameters()},
{'params': model.conv.vgg_classifier.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=20, gamma=0.1)
#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_nouns()
top5_avg = top5.get_average_results_nouns()
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_nouns()
top5_avg = top5.get_average_results_nouns()
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)