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train_MMBS.py
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train_MMBS.py
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import os
import random
import time
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import utils
import json
from torch.optim.lr_scheduler import MultiStepLR
# standard cross-entropy loss
def instance_bce(logits, labels):
assert logits.dim() == 2
cross_entropy_loss = nn.CrossEntropyLoss()
prediction_ans_k, top_ans_ind = torch.topk(F.softmax(labels, dim=-1), k=1, dim=-1, sorted=False)
ce_loss = cross_entropy_loss(logits, top_ans_ind.squeeze(-1))
return ce_loss
# multi-label soft loss
def instance_bce_with_logits(logits, labels, reduction='mean'):
assert logits.dim() == 2
loss = nn.functional.binary_cross_entropy_with_logits(logits, labels, reduction=reduction)
if reduction == 'mean':
loss *= labels.size(1)
return loss
def compute_score_with_logits(logits, labels):
logits = torch.max(logits, 1)[1].data # argmax
one_hots = torch.zeros(*labels.size()).cuda()
one_hots.scatter_(1, logits.view(-1, 1), 1)
scores = (one_hots * labels)
return scores
def compute_self_loss(logits_neg, a):
prediction_ans_k, top_ans_ind = torch.topk(F.softmax(a, dim=-1), k=1, dim=-1, sorted=False)
neg_top_k = torch.gather(F.softmax(logits_neg,dim=-1), 1, top_ans_ind).sum(1)
qice_loss = neg_top_k.mean()
return qice_loss
def train(model, train_loader, eval_loader, opt):
utils.create_dir(opt.output)
optim = torch.optim.Adam(model.parameters(), lr=opt.learning_rate, betas=(0.9, 0.999), eps=1e-08,
weight_decay=opt.weight_decay)
logger = utils.Logger(os.path.join(opt.output, 'UpDn_MMBS.txt'))
utils.print_model(model, logger)
# load snapshot
if opt.checkpoint_path is not None:
print('loading %s' % opt.checkpoint_path)
model_data = torch.load(opt.checkpoint_path)
model.load_state_dict(model_data.get('model_state', model_data))
optim.load_state_dict(model_data.get('optimizer_state', model_data))
opt.s_epoch = model_data['epoch'] + 1
for param_group in optim.param_groups:
param_group['lr'] = opt.learning_rate
scheduler = MultiStepLR(optim, milestones=[10,15,20,25,30,35], gamma=0.5)
scheduler.last_epoch = opt.s_epoch
best_eval_score = 0
for epoch in range(opt.s_epoch, opt.num_epochs):
total_loss = 0
total_bce_loss = 0
total_cl_loss = 0
train_score_orig = 0
train_score_Shuffling = 0
train_score_Removal = 0
total_norm = 0
count_norm = 0
t = time.time()
N = len(train_loader.dataset)
scheduler.step()
for i, (v, b, q, a, _, _, Shuffling_q, Removal_q, positive_q, bias) in enumerate(train_loader):
v = v.cuda()
q = q.cuda()
a = a.cuda()
Shuffling_q = Shuffling_q.cuda()
Removal_q = Removal_q.cuda()
positive_q = positive_q.cuda()
bias = bias.cuda()
logits_orig, logits_Shuffling, logits_Removal, logits_positive, cl_loss = model(q, Shuffling_q, Removal_q, positive_q, v, temperature=0.5, estimator='easy', tau_plus=0.1, beta=1)
bce_loss_orig = instance_bce_with_logits(logits_orig, a, reduction='mean')
loss = bce_loss_orig + 1 * cl_loss
loss.backward()
total_norm += nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
count_norm += 1
optim.step()
optim.zero_grad()
score_orig = compute_score_with_logits(logits_orig, a.data).sum()
score_Shuffling = compute_score_with_logits(logits_Shuffling, a.data).sum()
score_Removal = compute_score_with_logits(logits_Removal, a.data).sum()
train_score_orig += score_orig.item()
train_score_Shuffling += score_Shuffling.item()
train_score_Removal += score_Removal.item()
total_loss += loss.item() * v.size(0)
total_bce_loss += bce_loss_orig.item() * v.size(0)
total_cl_loss += cl_loss.item() * v.size(0)
if i != 0 and i % 100 == 0:
print(time.strftime("%a %b %d %H:%M:%S %Y", time.localtime()) )
print(
'traing: %d/%d, train_loss: %.6f, cl_loss: %.6f, bce_loss: %.6f, orig_train_acc: %.6f, Shuffling_train_acc: %.6f, Removal_train_acc: %.6f' %
(i, len(train_loader), total_loss / (i * v.size(0)),
total_cl_loss / (i * v.size(0)),
total_bce_loss / (i * v.size(0)),
100 * train_score_orig / (i * v.size(0)),
100 * train_score_Shuffling / (i * v.size(0)),
100 * train_score_Removal / (i * v.size(0))
))
total_loss /= N
total_bce_loss /= N
train_score_orig = 100 * train_score_orig / N
train_score_Shuffling = 100 * train_score_Shuffling / N
train_score_Removal = 100 * train_score_Removal / N
if None != eval_loader:
model.train(False)
eval_score, eval_score_Shuffling, eval_score_Removal, bound, entropy = evaluate(model, eval_loader)
model.train(True)
logger.write('\nlr: %.7f' % optim.param_groups[0]['lr'])
logger.write('epoch %d, time: %.2f' % (epoch, time.time() - t))
logger.write(
'\ttrain_loss: %.2f, norm: %.4f, score: %.2f, Shuffling_score: %.2f, Removal_score: %.2f' % (total_loss, total_norm / count_norm, train_score_orig, train_score_Shuffling, train_score_Removal))
if eval_loader is not None:
logger.write('\teval score: %.2f (%.2f)' % (100 * eval_score, 100 * bound))
logger.write('\teval Shuffling score: %.2f (%.2f)' % (100 * eval_score_Shuffling, 100 * bound))
logger.write('\teval Removal score: %.2f (%.2f)' % (100 * eval_score_Removal, 100 * bound))
if eval_loader is not None and entropy is not None:
info = '' + ' %.2f' % entropy
logger.write('\tentropy: ' + info)
if (eval_loader is not None and eval_score > best_eval_score):
model_path = os.path.join(opt.output, 'MMBS_best_model.pth')
utils.save_model(model_path, model, epoch, optim)
if eval_loader is not None:
best_eval_score = eval_score
@torch.no_grad()
def evaluate(model, dataloader):
score = 0
Shuffling_score = 0
Removal_score = 0
upper_bound = 0
num_data = 0
entropy = 0
for i, (v, b, q, a, q_id, _, Shuffling_q, Removal_q, positive_q, bias) in enumerate(dataloader):
v = v.cuda()
b = b.cuda()
q = q.cuda()
Shuffling_q = Shuffling_q.cuda()
Removal_q = Removal_q.cuda()
q_id = q_id.cuda()
bias = bias.cuda()
pred, pred_Shuffling, pred_Removal,pred_positive, _ = model(q, Shuffling_q, Removal_q, positive_q, v, temperature=0.5, estimator='easy', tau_plus=0.1, beta=1 )
batch_score = compute_score_with_logits(pred, a.cuda()).sum()
batch_score_Shuffling = compute_score_with_logits(pred_Shuffling, a.cuda()).sum()
batch_score_Removal = compute_score_with_logits(pred_Removal, a.cuda()).sum()
score += batch_score.item()
Shuffling_score += batch_score_Shuffling.item()
Removal_score += batch_score_Removal.item()
upper_bound += (a.max(1)[0]).sum().item()
num_data += pred.size(0)
score = score / len(dataloader.dataset)
Shuffling_score = Shuffling_score / len(dataloader.dataset)
Removal_score = Removal_score / len(dataloader.dataset)
upper_bound = upper_bound / len(dataloader.dataset)
return score, Shuffling_score, Removal_score,upper_bound, entropy
def calc_entropy(att): # size(att) = [b x v x q]
sizes = att.size()
eps = 1e-8
# att = att.unsqueeze(-1)
p = att.view(-1, sizes[1] * sizes[2])
return (-p * (p + eps).log()).sum(1).sum(0) # g