def eval(model, dev_loader, encoder, gpu_mode):
model.eval()
val_loss = 0
print('evaluating model...')
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
with torch.no_grad():
mx = len(dev_loader)
for i, (img, verb, roles, labels) in enumerate(dev_loader):
print("{}/{} batches\r".format(i + 1, mx)),
'''im_data = torch.squeeze(im_data,0)
im_info = torch.squeeze(im_info,0)
gt_boxes = torch.squeeze(gt_boxes,0)
num_boxes = torch.squeeze(num_boxes,0)
verb = torch.squeeze(verb,0)
roles = torch.squeeze(roles,0)
labels = torch.squeeze(labels,0)'''
if gpu_mode >= 0:
img = torch.autograd.Variable(img.cuda())
roles = torch.autograd.Variable(roles.cuda())
verb = torch.autograd.Variable(verb.cuda())
labels = torch.autograd.Variable(labels.cuda())
else:
img = torch.autograd.Variable(img)
verb = torch.autograd.Variable(verb)
roles = torch.autograd.Variable(roles)
labels = torch.autograd.Variable(labels)
verb_predict_ids, role_predict_ids = model.forward_eval_rolebeam(
img)
'''loss = model.calculate_eval_loss(verb_predict, verb, role_predict, labels)
val_loss += loss.item()'''
top1.add_point_eval_rolebeam(verb_predict_ids, verb,
role_predict_ids, labels)
top5.add_point_eval_rolebeam(verb_predict_ids, verb,
role_predict_ids, labels)
del verb_predict_ids, role_predict_ids, img, verb, roles, labels
break
#return top1, top5, val_loss/mx
return top1, top5, 0
def train(model,
train_loader,
dev_loader,
traindev_loader,
optimizer,
scheduler,
max_epoch,
model_dir,
encoder,
gpu_mode,
clip_norm,
lr_max,
model_name,
args,
eval_frequency=4000):
model.train()
train_loss = 0
total_steps = 0
print_freq = 400
dev_score_list = []
time_all = time.time()
'''if model.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
'''if scheduler.get_lr()[0] < lr_max:
scheduler.step()'''
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
'''print('init param data check :')
for f in model.parameters():
if f.requires_grad:
print(f.data.size())'''
for epoch in range(max_epoch):
#print('current sample : ', i, img.size(), verb.size(), roles.size(), labels.size())
#sizes batch_size*3*height*width, batch*504*1, batch*6*190*1, batch*3*6*lebale_count*1
mx = len(train_loader)
for i, (img, verb, roles, labels) in enumerate(train_loader):
#print("epoch{}-{}/{} batches\r".format(epoch,i+1,mx)) ,
t0 = time.time()
t1 = time.time()
total_steps += 1
if gpu_mode >= 0:
img = torch.autograd.Variable(img.cuda())
roles = torch.autograd.Variable(roles.cuda())
verb = torch.autograd.Variable(verb.cuda())
labels = torch.autograd.Variable(labels.cuda())
else:
img = torch.autograd.Variable(img)
verb = torch.autograd.Variable(verb)
roles = torch.autograd.Variable(roles)
labels = torch.autograd.Variable(labels)
'''print('all inputs')
print(img)
print('=========================================================================')
print(verb)
print('=========================================================================')
print(roles)
print('=========================================================================')
print(labels)'''
verb_predict, role_predict = pmodel(img, verb, roles)
#verb_predict, rol1pred, role_predict = pmodel.forward_eval5(img)
#print ("forward time = {}".format(time.time() - t1))
t1 = time.time()
'''g = make_dot(verb_predict, model.state_dict())
g.view()'''
loss = model.calculate_loss(verb_predict, verb, role_predict,
labels, args)
#loss = model.calculate_eval_loss_new(verb_predict, verb, rol1pred, labels, args)
#loss = loss_ * random.random() #try random loss
#print ("loss time = {}".format(time.time() - t1))
t1 = time.time()
#print('current loss = ', loss)
loss.backward()
#print ("backward time = {}".format(time.time() - t1))
#torch.nn.utils.clip_grad_norm_(model.parameters(), clip_norm)
'''for param in filter(lambda p: p.requires_grad,model.parameters()):
print(param.grad.data.sum())'''
#start debugger
#import pdb; pdb.set_trace()
optimizer.step()
optimizer.zero_grad()
'''print('grad check :')
for f in model.parameters():
print('data is')
print(f.data)
print('grad is')
print(f.grad)'''
train_loss += loss.item()
#top1.add_point_eval5(verb_predict, verb, role_predict, labels)
#top5.add_point_eval5(verb_predict, verb, role_predict, labels)
top1.add_point(verb_predict, verb, role_predict, labels)
top5.add_point(verb_predict, verb, role_predict, labels)
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"]
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-')))
#print('Dev loss :', val_loss)
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 +
"/{}_macnet4layer_gtv_finetune.model".format(
model_name))
print('New best model saved! {0}'.format(max_score))
#eval on the trainset
'''top1, top5, val_loss = eval(model, traindev_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 ('TRAINDEV {} average :{:.2f} {} {}'.format(total_steps-1, avg_score*100,
utils.format_dict(top1_avg,'{:.2f}', '1-'),
utils.format_dict(top5_avg, '{:.2f}', '5-')))'''
print('current train loss', train_loss)
train_loss = 0
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
del verb_predict, role_predict, loss, img, verb, roles, labels
#break
print('Epoch ', epoch, ' completed!')
scheduler.step()
def train(model,
train_loader,
dev_loader,
traindev_loader,
optimizer,
scheduler,
max_epoch,
model_dir,
encoder,
gpu_mode,
clip_norm,
lr_max,
model_name,
args,
eval_frequency=2000):
model.train()
train_loss = 0
total_steps = 0
print_freq = 400
dev_score_list = []
'''if model.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(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
'''print('init param data check :')
for f in model.parameters():
if f.requires_grad:
print(f.data.size())'''
for epoch in range(max_epoch):
#print('current sample : ', i, img.size(), verb.size(), roles.size(), labels.size())
#sizes batch_size*3*height*width, batch*504*1, batch*6*190*1, batch*3*6*lebale_count*1
mx = len(train_loader)
for i, (_id, img, verb, verbq) in enumerate(train_loader):
#print("epoch{}-{}/{} batches\r".format(epoch,i+1,mx)) ,
total_steps += 1
if gpu_mode >= 0:
img = torch.autograd.Variable(img.cuda())
verb = torch.autograd.Variable(verb.cuda())
verbq = torch.autograd.Variable(verbq.cuda())
else:
img = torch.autograd.Variable(img)
verb = torch.autograd.Variable(verb)
verbq = torch.autograd.Variable(verbq)
#optimizer.zero_grad()
'''print('all inputs')
print(img)
print('=========================================================================')
print(verb)
print('=========================================================================')
print(roles)
print('=========================================================================')
print(labels)'''
verb_predict = pmodel(img, verbq)
'''g = make_dot(verb_predict, model.state_dict())
g.view()'''
loss = model.calculate_loss(verb_predict, verb)
#print('current loss = ', loss)
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(verb_predict, verb)
top5.add_point_verb_only(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()
#todo : top 5
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 = top1_avg["value*"]
avg_score /= 2
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-')))
#print('Dev loss :', val_loss)
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 +
"/{}_agent2verbq_td_predagent_agentobj.model".format(
model_name))
print('New best model saved! {0}'.format(max_score))
#eval on the trainset
'''top1, top5, val_loss = eval(model, traindev_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 ('TRAINDEV {} average :{:.2f} {} {}'.format(total_steps-1, avg_score*100,
utils.format_dict(top1_avg,'{:.2f}', '1-'),
utils.format_dict(top5_avg, '{:.2f}', '5-')))'''
print('current train loss', train_loss)
train_loss = 0
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
del loss, img
#break
print('Epoch ', epoch, ' completed!')
scheduler.step()
def train(model,
train_loader,
dev_loader,
optimizer,
scheduler,
max_epoch,
model_dir,
encoder,
gpu_mode,
eval_frequency=500):
model.train()
train_loss = 0
total_steps = 0
print_freq = 50
dev_score_list = []
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
'''print('init param data check :')
for f in model.parameters():
if f.requires_grad:
print(f.data.size())'''
for epoch in range(max_epoch):
#print('current sample : ', i, img.size(), verb.size(), roles.size(), labels.size())
#sizes batch_size*3*height*width, batch*504*1, batch*6*190*1, batch*3*6*lebale_count*1
mx = len(train_loader)
for i, (img, verb, roles, labels) in enumerate(train_loader):
#print("epoch{}-{}/{} batches\r".format(epoch,i+1,mx)) ,
total_steps += 1
if gpu_mode >= 0:
img = torch.autograd.Variable(img.cuda())
roles = torch.autograd.Variable(roles.cuda())
verb = torch.autograd.Variable(verb.cuda())
labels = torch.autograd.Variable(labels.cuda())
else:
img = torch.autograd.Variable(img)
verb = torch.autograd.Variable(verb)
roles = torch.autograd.Variable(roles)
labels = torch.autograd.Variable(labels)
optimizer.zero_grad()
verb_predict, role_predict = model(img, verb, roles)
loss = model.calculate_loss(verb_predict, verb, role_predict,
labels)
#print('current loss = ', loss)
loss.backward()
optimizer.step()
'''print('grad check :')
for f in model.parameters():
print('data is')
print(f.data)
print('grad is')
print(f.grad)'''
train_loss += loss.data.item()
top1.add_point(verb_predict, verb, role_predict, labels, roles)
top5.add_point(verb_predict, verb, role_predict, labels, roles)
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-')))
print('Dev loss :', val_loss)
dev_score_list.append(avg_score)
max_score = max(dev_score_list)
if max_score == dev_score_list[-1]:
checkpoint_name = os.path.join(
model_dir, '{}_devloss_cnngraph_{}.h5'.format(
'baseline', len(dev_score_list)))
utils.save_net(checkpoint_name, model)
print('New best model saved! {0}'.format(max_score))
print('current train loss', train_loss)
train_loss = 0
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
del verb_predict, role_predict, loss, img, verb, roles, labels
#break
scheduler.step()
print('Epoch ', epoch, ' completed!')
def eval(model, checkpoint_list, model_dir, model_name, dev_loader, encoder,
gpu_mode):
model.eval()
val_loss = 0
model_list = []
for chkp in checkpoint_list:
new_model = model
new_model.load_state_dict(
torch.load(model_dir + model_name.format(chkp)))
model_list.append(new_model)
print('evaluating model...')
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
with torch.no_grad():
mx = len(dev_loader)
for i, (img, verb, roles, labels) in enumerate(dev_loader):
#print("{}/{} batches\r".format(i+1,mx)) ,
'''im_data = torch.squeeze(im_data,0)
im_info = torch.squeeze(im_info,0)
gt_boxes = torch.squeeze(gt_boxes,0)
num_boxes = torch.squeeze(num_boxes,0)
verb = torch.squeeze(verb,0)
roles = torch.squeeze(roles,0)
labels = torch.squeeze(labels,0)'''
if gpu_mode >= 0:
img = torch.autograd.Variable(img.cuda())
roles = torch.autograd.Variable(roles.cuda())
verb = torch.autograd.Variable(verb.cuda())
labels = torch.autograd.Variable(labels.cuda())
else:
img = torch.autograd.Variable(img)
verb = torch.autograd.Variable(verb)
roles = torch.autograd.Variable(roles)
labels = torch.autograd.Variable(labels)
batch_size = img.size(0)
verb_count = 504
for j in range(0, len(model_list)):
if j == 0:
verb_predict = model(img)
else:
verb_predict = torch.cat(
(verb_predict.clone(), model(img)), 1)
verb_pred_all = verb_predict.view(batch_size, -1, verb_count)
#print('verb_pred_all', verb_pred_all.size())
ensemble = torch.mean(verb_pred_all, 1)
#print('ensemble', ensemble.size())
'''loss = model.calculate_eval_loss(verb_predict, verb, role_predict, labels)
val_loss += loss.item()'''
top1.add_point_verb_only(ensemble, verb)
top5.add_point_verb_only(ensemble, verb)
del verb_predict, verb_pred_all, ensemble, img, verb, roles, labels
#break
#return top1, top5, val_loss/mx
return top1, top5, 0
def train(model,
train_loader,
dev_loader,
traindev_loader,
optimizer,
scheduler,
max_epoch,
model_dir,
encoder,
gpu_mode,
clip_norm,
lr_max,
checkpoint_at,
eval_frequency=4000):
model.train()
train_loss = 0
total_steps = 0
print_freq = 5
dev_score_list = []
checkp_list = []
'''if model.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
'''if scheduler.get_lr()[0] < lr_max:
scheduler.step()'''
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
'''print('init param data check :')
for f in model.parameters():
if f.requires_grad:
print(f.data.size())'''
for epoch in range(max_epoch):
#print('current sample : ', i, img.size(), verb.size(), roles.size(), labels.size())
#sizes batch_size*3*height*width, batch*504*1, batch*6*190*1, batch*3*6*lebale_count*1
mx = len(train_loader)
for i, (img, verb, roles, labels) in enumerate(train_loader):
#print("epoch{}-{}/{} batches\r".format(epoch,i+1,mx)) ,
total_steps += 1
if gpu_mode >= 0:
img = torch.autograd.Variable(img.cuda())
roles = torch.autograd.Variable(roles.cuda())
verb = torch.autograd.Variable(verb.cuda())
labels = torch.autograd.Variable(labels.cuda())
else:
img = torch.autograd.Variable(img)
verb = torch.autograd.Variable(verb)
roles = torch.autograd.Variable(roles)
labels = torch.autograd.Variable(labels)
#optimizer.zero_grad()
'''print('all inputs')
print(img)
print('=========================================================================')
print(verb)
print('=========================================================================')
print(roles)
print('=========================================================================')
print(labels)'''
verb_predict = pmodel(img)
'''g = make_dot(verb_predict, model.state_dict())
g.view()'''
loss = model.calculate_loss(verb_predict, verb)
#print('current loss = ', loss)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip_norm)
'''for param in filter(lambda p: p.requires_grad,model.parameters()):
print(param.grad.data.sum())'''
#start debugger
#import pdb; pdb.set_trace()
optimizer.step()
optimizer.optimizer.zero_grad()
#optimizer.step()
#optimizer.zero_grad()
'''print('grad check :')
for f in model.parameters():
print('data is')
print(f.data)
print('grad is')
print(f.grad)'''
train_loss += loss.item()
top1.add_point_verb_only(verb_predict, verb)
top5.add_point_verb_only(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 % checkpoint_at == 0:
#assuming this is the best model at each cycle
checkpoint_number = math.ceil(total_steps / checkpoint_at)
torch.save(
model.state_dict(), model_dir +
"/verb_only256_cosanwr_p1_chkp{0}.model".format(
checkpoint_number))
print('New model saved at cycle {0}'.format(checkpoint_number))
checkp_list.append(checkpoint_number)
top1, top5, val_loss = eval(
model, checkp_list, model_dir,
"/verb_only256_cosanwr_p1_chkp{0}.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"] + top5_avg["verb"]
avg_score /= 2
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-')))
print('current train loss', train_loss)
train_loss = 0
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
del verb_predict, loss, img, verb, roles, labels
#break
print('Epoch ', epoch, ' completed!')
def train(model,
train_loader,
dev_loader,
traindev_loader,
optimizer,
scheduler,
max_epoch,
model_dir,
encoder,
gpu_mode,
clip_norm,
lr_max,
eval_frequency=4000):
model.train()
train_loss = 0
total_steps = 0
print_freq = 400
dev_score_list = []
'''if model.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(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
'''print('init param data check :')
for f in model.parameters():
if f.requires_grad:
print(f.data.size())'''
for epoch in range(max_epoch):
#print('current sample : ', i, img.size(), verb.size(), roles.size(), labels.size())
#sizes batch_size*3*height*width, batch*504*1, batch*6*190*1, batch*3*6*lebale_count*1
mx = len(train_loader)
for i, (p_img, p_verb, n_img, n_verb) in enumerate(train_loader):
#print("epoch{}-{}/{} batches\r".format(epoch,i+1,mx)) ,
total_steps += 1
if gpu_mode >= 0:
p_img = torch.autograd.Variable(p_img.cuda())
p_verb = torch.autograd.Variable(p_verb.cuda())
n_img = torch.autograd.Variable(n_img.cuda())
n_verb = torch.autograd.Variable(n_verb.cuda())
else:
p_img = torch.autograd.Variable(p_img)
p_verb = torch.autograd.Variable(p_verb)
n_img = torch.autograd.Variable(n_img)
n_verb = torch.autograd.Variable(n_verb)
#optimizer.zero_grad()
'''print('all inputs')
print(img)
print('=========================================================================')
print(verb)
print('=========================================================================')
print(roles)
print('=========================================================================')
print(labels)'''
p_img_rep, p_verb_rep = pmodel(p_img, p_verb)
n_img_rep, n_verb_rep = pmodel(n_img, n_verb)
p_img_pred = pmodel.classifier_forward(p_img)
n_img_pred = pmodel.classifier_forward(n_img)
#print('came here')
'''g = make_dot(verb_predict, model.state_dict())
g.view()'''
predicted_labels = torch.cat([p_img_pred, n_img_pred], 0)
gt_labels = torch.cat([p_verb, n_verb], 0)
p_loss = model.calculate_loss(predicted_labels, gt_labels)
#n_loss = model.calculate_loss(n_img_pred, n_verb)
triplet_loss = model.triplet_loss(p_img_rep, p_verb_rep, n_img_rep,
n_verb_rep)
loss = triplet_loss + p_loss
#loss = p_loss
#loss = triplet_loss
#print('current loss = ', loss)
loss.backward()
#print('done')
# torch.nn.utils.clip_grad_norm_(model.parameters(), clip_norm)
optimizer.step()
optimizer.zero_grad()
train_loss += loss.item()
top1.add_point_verb_only(p_img_pred, p_verb)
top5.add_point_verb_only(p_img_pred, p_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)))
#del verb_predict, loss, img, verb, roles, labels
#break
print('Epoch ', epoch, ' completed!')
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"] + top5_avg["verb"]
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 + "/verbonly_vgg16_rank.model")
print('New best model saved! {0}'.format(max_score))
print('current train loss', train_loss)
train_loss = 0
top1 = imsitu_scorer(encoder, 1, 3)
top5 = imsitu_scorer(encoder, 5, 3)
scheduler.step()
