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('--output_dir', type=str, default='./trained_models', help='Location to output the model')
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('--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('--train_file', default="train_freq2000.json", type=str, help='trainfile name')
parser.add_argument('--dev_file', default="dev_freq2000.json", type=str, help='dev file name')
parser.add_argument('--test_file', default="test_freq2000.json", type=str, help='test file name')
parser.add_argument('--model_saving_name', type=str, help='saving name of the outpul model')
parser.add_argument('--epochs', type=int, default=500)
parser.add_argument('--model', type=str, default='ggnn_baseline')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--clip_norm', type=float, default=0.25)
parser.add_argument('--num_workers', type=int, default=3)
args = parser.parse_args()
n_epoch = args.epochs
batch_size = args.batch_size
clip_norm = args.clip_norm
n_worker = args.num_workers
dataset_folder = args.dataset_folder
imgset_folder = args.imgset_dir
train_set = json.load(open(dataset_folder + '/' + args.train_file))
encoder = imsitu_encoder.imsitu_encoder(train_set)
train_set = imsitu_loader.imsitu_loader(imgset_folder, train_set, encoder,'train', encoder.train_transform)
constructor = 'build_%s' % args.model
model = getattr(caq_neighbour, constructor)(encoder.get_num_roles(),encoder.get_num_verbs(), encoder.get_num_labels(), encoder)
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 + '/' + args.dev_file))
dev_set = imsitu_loader.imsitu_loader(imgset_folder, dev_set, encoder, 'val', encoder.dev_transform)
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 + '/' + args.test_file))
test_set = imsitu_loader.imsitu_loader(imgset_folder, test_set, encoder, 'test', encoder.dev_transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=True, num_workers=n_worker)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
torch.manual_seed(args.seed)
if args.gpuid >= 0:
model.cuda()
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
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 module] not specified')
utils.load_net(args.resume_model, [model])
optimizer = torch.optim.Adamax(model.parameters(), lr=1e-3)
model_name = 'resume_all'
else:
print('Training from the scratch.')
model_name = 'train_full'
utils.set_trainable(model, True)
optimizer = torch.optim.Adamax([
{'params': model.convnet.parameters(), 'lr': 5e-5},
{'params': model.role_emb.parameters()},
{'params': model.verb_emb.parameters()},
{'params': model.ggnn.parameters()},
{'params': model.classifier.parameters()}
], lr=1e-3)
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(args.model_saving_name+'_role_pred_data.json', 'w') as fp:
json.dump(role_dict, fp, indent=4)
with open(args.model_saving_name+'_fail_val_all.json', 'w') as fp:
json.dump(fail_val_all, fp, indent=4)
with open(args.model_saving_name+'_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, optimizer, scheduler, n_epoch, args.output_dir, encoder, args.gpuid, clip_norm, model_name, args.model_saving_name,
)
def train(model, train_loader, dev_loader, optimizer, scheduler, max_epoch, model_dir, encoder, gpu_mode, clip_norm, model_name, model_saving_name, eval_frequency=4000):
model.train()
train_loss = 0
total_steps = 0
print_freq = 400
dev_score_list = []
if gpu_mode >= 0 :
ngpus = 2
device_array = [i for i in range(0,ngpus)]
pmodel = torch.nn.DataParallel(model, device_ids=device_array)
else:
pmodel = model
#pmodel = model
all = count_parameters(model)
cnn = count_parameters(model.convnet)
print('model parameters - all, cnn, base ', all, cnn)
top1 = imsitu_scorer.imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer.imsitu_scorer(encoder, 5, 3)
for epoch in range(max_epoch):
t = time.time()
for i, (_, img, verb, labels) in enumerate(train_loader):
total_steps += 1
#min_t = time.time()
if gpu_mode >= 0:
img = torch.autograd.Variable(img.cuda())
verb = torch.autograd.Variable(verb.cuda())
labels = torch.autograd.Variable(labels.cuda())
else:
img = torch.autograd.Variable(img)
verb = torch.autograd.Variable(verb)
labels = torch.autograd.Variable(labels)
role_predict = pmodel(img, verb)
loss = model.calculate_loss(verb, role_predict, labels)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip_norm)
optimizer.step()
optimizer.zero_grad()
train_loss += loss.item()
top1.add_point_noun(verb, role_predict, labels)
top5.add_point_noun(verb, role_predict, labels)
#print('after minibatch ', i, time.time() - min_t)
if total_steps % print_freq == 0:
top1_a = top1.get_average_results_nouns()
top5_a = top5.get_average_results_nouns()
print ("{},{},{}, {} , {}, loss = {:.2f}, avg loss = {:.2f}"
.format(total_steps-1,epoch,i, utils.format_dict(top1_a, "{:.2f}", "1-"),
utils.format_dict(top5_a,"{:.2f}","5-"), loss.item(),
train_loss / ((total_steps-1)%eval_frequency) ))
if total_steps % eval_frequency == 0:
top1, top5, val_loss = eval(model, dev_loader, encoder, gpu_mode)
model.train()
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(total_steps-1, avg_score*100,
utils.format_dict(top1_avg,'{:.2f}', '1-'),
utils.format_dict(top5_avg, '{:.2f}', '5-')))
dev_score_list.append(avg_score)
max_score = max(dev_score_list)
if max_score == dev_score_list[-1]:
torch.save(model.state_dict(), model_dir + "/{}_{}.model".format( model_name, model_saving_name))
print ('New best model saved! {0}'.format(max_score))
print('current train loss', train_loss)
train_loss = 0
top1 = imsitu_scorer.imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer.imsitu_scorer(encoder, 5, 3)
del role_predict, loss, img, verb, labels
print('epoch %d, time: %.2f' % (epoch, time.time()-t))
print('Epoch ', epoch, ' completed!')
scheduler.step()
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('--output_dir', type=str, default='./trained_models', help='Location to output the model')
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('--evaluate', action='store_true', help='Only use the testing mode')
parser.add_argument('--evaluate_rare', 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('--train_file', default="train_freq2000.json", type=str, help='trainfile name')
parser.add_argument('--dev_file', default="dev_freq2000.json", type=str, help='dev file name')
parser.add_argument('--test_file', default="test_freq2000.json", type=str, help='test file name')
parser.add_argument('--org_train_file', default="train.json", type=str, help='org train file name')
parser.add_argument('--org_test_file', default="test.json", type=str, help='org test file name')
parser.add_argument('--model_saving_name', type=str, help='saving name of the outpul model')
parser.add_argument('--epochs', type=int, default=500)
parser.add_argument('--model', type=str, default='vgg_caq_joint')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--clip_norm', type=float, default=0.25)
parser.add_argument('--num_workers', type=int, default=3)
parser.add_argument('--vgg_verb_model', type=str, default='', help='Pretrained vgg verb model')
parser.add_argument('--tda_verb_model', type=str, default='', help='Pretrained topdown verb model')
parser.add_argument('--caq_model', type=str, default='', help='Pretrained CAQ model')
args = parser.parse_args()
n_epoch = args.epochs
batch_size = args.batch_size
clip_norm = args.clip_norm
n_worker = args.num_workers
dataset_folder = args.dataset_folder
imgset_folder = args.imgset_dir
train_set = json.load(open(dataset_folder + '/' + args.train_file))
encoder = imsitu_encoder.imsitu_encoder(train_set)
train_set = imsitu_loader.imsitu_loader(imgset_folder, train_set, encoder,'train', encoder.train_transform)
constructor = 'build_single_role_classifier'
vgg_verb_model = getattr(single_role_vgg_classifier, constructor)(len(encoder.verb_list))
constructor = 'build_top_down_baseline'
role_module = getattr(top_down_baseline_addemb, constructor)(encoder.get_num_roles(),encoder.get_num_verbs(), encoder.get_num_labels(), encoder)
constructor = 'build_top_down_baseline_verb'
tda_verb_model = getattr(top_down_verb_pred_agentplace, constructor)(encoder.get_num_labels(), encoder.get_num_verbs(), role_module)
constructor = 'build_top_down_baseline'
tda_role_module = getattr(top_down_baseline, constructor)(encoder.get_num_roles(),encoder.get_num_verbs(), encoder.get_num_labels(), encoder)
constructor = 'build_top_down_query_context_only_baseline'
caq_model = getattr(top_down_query_context_pretrained_baseline, constructor)(encoder.get_num_roles(),encoder.get_num_verbs(), encoder.get_num_labels(), encoder, tda_role_module)
constructor = 'build_%s' % args.model
model = getattr(revgg_caq_joint_eval, constructor)(vgg_verb_model, tda_verb_model, caq_model)
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 + '/' + args.dev_file))
dev_set = imsitu_loader.imsitu_loader(imgset_folder, dev_set, encoder, 'val', encoder.dev_transform)
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 + '/' + args.test_file))
test_set = imsitu_loader.imsitu_loader(imgset_folder, test_set, encoder, 'test', encoder.dev_transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=True, num_workers=n_worker)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
torch.manual_seed(args.seed)
if args.gpuid >= 0:
model.cuda()
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
#load models
utils.load_net(args.vgg_verb_model, [model.vgg_model])
print('successfully loaded vgg_verb_model!')
utils.load_net(args.tda_verb_model, [model.tda_model])
print('successfully loaded tda_verb_model!')
utils.load_net(args.caq_model, [model.caq_model])
print('successfully loaded caq_model!')
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-')))
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-')))
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('--output_dir',
type=str,
default='./trained_models',
help='Location to output the model')
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('--evaluate',
action='store_true',
help='Only use the testing mode')
parser.add_argument('--evaluate_rare',
action='store_true',
help='Only use the testing mode')
parser.add_argument('--evaluate_visualize',
action='store_true',
help='Only use the testing mode to visualize ')
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('--train_file',
default="train_freq2000.json",
type=str,
help='trainfile name')
parser.add_argument('--dev_file',
default="dev_freq2000.json",
type=str,
help='dev file name')
parser.add_argument('--test_file',
default="test_freq2000.json",
type=str,
help='test file name')
parser.add_argument('--org_train_file',
default="train.json",
type=str,
help='org train file name')
parser.add_argument('--org_test_file',
default="test.json",
type=str,
help='org test file name')
parser.add_argument('--model_saving_name',
type=str,
help='saving name of the outpul model')
parser.add_argument('--epochs', type=int, default=500)
parser.add_argument('--model',
type=str,
default='top_down_query_context_only_baseline')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--seed', type=int, default=1111, help='random seed')
parser.add_argument('--clip_norm', type=float, default=0.25)
parser.add_argument('--num_workers', type=int, default=3)
parser.add_argument('--baseline_model',
type=str,
default='',
help='Pretrained baseline topdown model')
args = parser.parse_args()
n_epoch = args.epochs
batch_size = args.batch_size
clip_norm = args.clip_norm
n_worker = args.num_workers
dataset_folder = args.dataset_folder
imgset_folder = args.imgset_dir
train_set = json.load(open(dataset_folder + '/' + args.train_file))
encoder = imsitu_encoder.imsitu_encoder(train_set)
train_set = imsitu_loader.imsitu_loader(imgset_folder, train_set, encoder,
'train', encoder.train_transform)
constructor = 'build_top_down_baseline'
baseline = getattr(top_down_baseline,
constructor)(encoder.get_num_roles(),
encoder.get_num_verbs(),
encoder.get_num_labels(), encoder)
constructor = 'build_%s' % args.model
model = getattr(top_down_image_context_pretrained_baseline,
constructor)(encoder.get_num_roles(),
encoder.get_num_verbs(),
encoder.get_num_labels(), encoder, baseline)
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 + '/' + args.dev_file))
dev_set = imsitu_loader.imsitu_loader(imgset_folder, dev_set, encoder,
'val', encoder.dev_transform)
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 + '/' + args.test_file))
test_set = imsitu_loader.imsitu_loader(imgset_folder, test_set, encoder,
'test', encoder.dev_transform)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=batch_size,
shuffle=True,
num_workers=n_worker)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
torch.manual_seed(args.seed)
if args.gpuid >= 0:
model.cuda()
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
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 module] not specified')
utils.load_net(args.resume_model, [model])
optimizer = torch.optim.Adamax(model.parameters(), lr=1e-3)
model_name = 'resume_all'
else:
print('Training from the scratch.')
model_name = 'train_full'
utils.set_trainable(model, True)
utils.load_net(args.baseline_model, [model.baseline_model])
utils.set_trainable(model.baseline_model, False)
optimizer = torch.optim.Adamax(
[{
'params': model.convnet.parameters(),
'lr': 5e-5
}, {
'params': model.role_emb.parameters()
}, {
'params': model.verb_emb.parameters()
}, {
'params': model.query_composer.parameters()
}, {
'params': model.v_att.parameters()
}, {
'params': model.q_net.parameters()
}, {
'params': model.v_net.parameters()
}, {
'params': model.resize_ctx.parameters()
}, {
'params': model.neighbour_attention.parameters()
}, {
'params': model.classifier.parameters()
}],
lr=1e-3)
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.all_res
fail_val_all = top1.value_all_dict
pass_val_dict = top1.vall_all_correct
with open(args.model_saving_name + '_role_pred_data.json', 'w') as fp:
json.dump(role_dict, fp, indent=4)
'''with open(args.model_saving_name+'_fail_val_all.json', 'w') as fp:
json.dump(fail_val_all, fp, indent=4)
with open(args.model_saving_name+'_pass_val_all.json', 'w') as fp:
json.dump(pass_val_dict, fp, indent=4)'''
print('Writing predictions to file completed !')
elif args.evaluate_visualize:
top1, top5, val_loss = eval_output(model,
dev_loader,
encoder,
args.gpuid,
write_to_file=True)
elif args.evaluate_rare:
org_train_set = json.load(
open(dataset_folder + '/' + args.org_train_file))
#compute sparsity statistics
verb_role_noun_freq = {}
for image, frames in org_train_set.items():
v = frames["verb"]
items = set()
for frame in frames["frames"]:
for (r, n) in frame.items():
key = (v, r, n)
items.add(key)
for key in items:
if key not in verb_role_noun_freq: verb_role_noun_freq[key] = 0
verb_role_noun_freq[key] += 1
#per role it is the most frequent prediction
#and among roles its the most rare
org_eval_dataset = json.load(
open(dataset_folder + '/' + args.org_test_file))
image_sparsity = {}
for image, frames in org_eval_dataset.items():
v = frames["verb"]
role_max = {}
for frame in frames["frames"]:
for (r, n) in frame.items():
key = (v, r, n)
if key not in verb_role_noun_freq: freq = 0
else: freq = verb_role_noun_freq[key]
if r not in role_max or role_max[r] < freq:
role_max[r] = freq
min_val = -1
for (r, f) in role_max.items():
if min_val == -1 or f < min_val: min_val = f
image_sparsity[image] = min_val
sparsity_max = 10
x = range(0, sparsity_max + 1)
print(
"evaluating images where most rare verb-role-noun in training is x , s.t. {} <= x <= {}"
.format(0, sparsity_max))
n = 0
for (k, v) in image_sparsity.items():
if v in x:
n += 1
print("total images = {}".format(n))
top1, top5, val_loss = eval_rare(model, test_loader, encoder,
args.gpuid, image_sparsity)
top1_avg = top1.get_average_results(range(0, sparsity_max + 1))
top5_avg = top5.get_average_results(range(0, sparsity_max + 1))
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 rare average :{:.2f} {} {}'.format(
avg_score * 100, utils.format_dict(top1_avg, '{:.2f}', '1-'),
utils.format_dict(top5_avg, '{:.2f}', '5-')))
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,
optimizer,
scheduler,
n_epoch,
args.output_dir,
encoder,
args.gpuid,
clip_norm,
model_name,
args.model_saving_name,
)
def train(model,
train_loader,
dev_loader,
optimizer,
scheduler,
max_epoch,
model_dir,
encoder,
gpu_mode,
clip_norm,
model_name,
model_saving_name,
eval_frequency=4000):
model.train()
train_loss = 0
total_steps = 0
print_freq = 400
dev_score_list = []
'''if gpu_mode >= 0 :
ngpus = 2
device_array = [i for i in range(0,ngpus)]
pmodel = torch.nn.DataParallel(model, device_ids=device_array)
else:
pmodel = model'''
pmodel = model
top1 = imsitu_scorer.imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer.imsitu_scorer(encoder, 5, 3)
for epoch in range(max_epoch):
for i, (img_id, img, verb_pred, verb) in enumerate(train_loader):
total_steps += 1
if gpu_mode >= 0:
img = torch.autograd.Variable(img.cuda())
verb_pred = torch.autograd.Variable(verb_pred.cuda())
verb = torch.autograd.Variable(verb.cuda())
else:
img = torch.autograd.Variable(img)
verb_pred = torch.autograd.Variable(verb_pred)
verb = torch.autograd.Variable(verb)
verb_predict = pmodel(img, verb_pred)
loss = model.calculate_verb_loss(verb_predict, verb)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip_norm)
optimizer.step()
optimizer.zero_grad()
train_loss += loss.item()
top1.add_point_verb_only_eval(img_id, verb_predict, verb)
top5.add_point_verb_only_eval(img_id, verb_predict, verb)
if total_steps % print_freq == 0:
top1_a = top1.get_average_results()
top5_a = top5.get_average_results()
print("{},{},{}, {} , {}, loss = {:.2f}, avg loss = {:.2f}".
format(total_steps - 1, epoch, i,
utils.format_dict(top1_a, "{:.2f}", "1-"),
utils.format_dict(top5_a, "{:.2f}", "5-"),
loss.item(), train_loss /
((total_steps - 1) % eval_frequency)))
if total_steps % eval_frequency == 0:
top1, top5, val_loss = eval(model, dev_loader, encoder,
gpu_mode)
model.train()
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(
total_steps - 1, avg_score * 100,
utils.format_dict(top1_avg, '{:.2f}', '1-'),
utils.format_dict(top5_avg, '{:.2f}', '5-')))
dev_score_list.append(avg_score)
max_score = max(dev_score_list)
if max_score == dev_score_list[-1]:
torch.save(
model.state_dict(), model_dir +
"/{}_{}.model".format(model_name, model_saving_name))
print('New best model saved! {0}'.format(max_score))
print('current train loss', train_loss)
train_loss = 0
top1 = imsitu_scorer.imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer.imsitu_scorer(encoder, 5, 3)
del verb_predict, loss, img, verb
print('Epoch ', epoch, ' completed!')
scheduler.step()
