def main(argv=None):
print("Parameters:")
for k, v in FLAGS.__flags.items():
print(k, "=", v)
print()
#sess = tf.InteractiveSession()
input_generator = inputs.train_pipeline(batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs)
classifier_model = ImageClassifier(NUM_CLASSES,
IMAGE_SIZE,
batch_size=FLAGS.batch_size)
sess = tf.Session()
summary_dir = FLAGS.output_dir + FLAGS.summary_train_dir
train_writer = tf.summary.FileWriter(summary_dir, sess.graph)
coord = tf.train.Coordinator()
with sess.as_default():
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
step = 0
start = datetime.now()
for batch in input_generator:
accuracy, loss, summary, run_metadata, embeddings = classifier_model.train(
sess, batch)
if step % FLAGS.report_every is 0:
now = datetime.now()
elapsed = now - start
average = elapsed / step if not step is 0 else 0
print(
"Step %08d, Accuracy %.6f, Loss %.6f, Average Time %s/step, Elapsed Time %s%s"
% (step, accuracy * 100, loss, average, elapsed,
", Created Summary"
if step % FLAGS.summary_every is 0 else ""))
sys.stdout.flush()
if step % FLAGS.summary_every is 0:
train_writer.add_run_metadata(run_metadata, 'step%d' % step)
train_writer.add_summary(summary, step)
if step % FLAGS.checkpoint_every is 0:
classifier_model.save(sess, global_step=step)
step += 1
# MODE = 'eval'
MODE = 'classify'
if MODE == 'train':
classes = ['diverse_normal_0.06', 'sensity']
# classes = ['1-normal', 'sensity']
# classes = ['2-bikini', 'all_naked']
# classes = ['2-bikini', '4-naked', '6-sex']
pclassifier = ImageClassifier(
model_name='diversed_init_stage_porn_classifier',
classes=classes,
to_log_file=True)
pclassifier.train(save_gen_imgs=False, use_tensorboard=True)
elif MODE == 'eval':
# classes = ['1-normal', 'sensity']
classes = ['2-bikini', 'all_naked']
# classes = ['2-bikini', '4-naked', '6-sex']
# model_id = 'soft_porn_classifier.20190622035912.01-1.0523' # (0.075, 0.9826)
# model_id = 'soft_porn_classifier.20190622035912.02-0.5248' # (0.1830, 0.9304)
# model_id = 'soft_porn_classifier.20190622035912.04-0.4564' # (0.2270, 0.9130)
model_id = 'soft_porn_classifier.20190622035912.05-0.3722' # (0.3144, 0.8696)
# model_id = 'soft_porn_classifier.20190622035912.06-0.3613' # (0.4999, 0.8348)
pclassifier = ImageClassifier(model_id=model_id,
classes=classes,
def train(train_source_iter: ForeverDataIterator,
train_target_iter: ForeverDataIterator, model: ImageClassifier,
domain_adv: DomainAdversarialLoss, esem, optimizer: SGD,
lr_scheduler: StepwiseLR, epoch: int, source_class_weight,
args: argparse.Namespace):
batch_time = AverageMeter('Time', ':5.2f')
data_time = AverageMeter('Data', ':5.2f')
losses = AverageMeter('Loss', ':6.2f')
cls_accs = AverageMeter('Cls Acc', ':3.1f')
domain_accs = AverageMeter('Domain Acc', ':3.1f')
progress = ProgressMeter(
args.iters_per_epoch,
[batch_time, data_time, losses, cls_accs, domain_accs],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
domain_adv.train()
esem.eval()
end = time.time()
for i in range(args.iters_per_epoch):
lr_scheduler.step()
# measure data loading time
data_time.update(time.time() - end)
x_s, labels_s = next(train_source_iter)
x_t, _ = next(train_target_iter)
x_s = x_s.to(device)
x_t = x_t.to(device)
labels_s = labels_s.to(device)
# compute output
# x = torch.cat((x_s, x_t), dim=0)
# y, f = model(x)
# y_s, y_t = y.chunk(2, dim=0)
# f_s, f_t = f.chunk(2, dim=0)
y_s, f_s = model(x_s)
y_t, f_t = model(x_t)
with torch.no_grad():
yt_1, yt_2, yt_3, yt_4, yt_5 = esem(f_t)
confidece = get_confidence(yt_1, yt_2, yt_3, yt_4, yt_5)
entropy = get_entropy(yt_1, yt_2, yt_3, yt_4, yt_5)
consistency = get_consistency(yt_1, yt_2, yt_3, yt_4, yt_5)
w_t = (1 - entropy + 1 - consistency + confidece) / 3
w_s = torch.tensor([source_class_weight[i]
for i in labels_s]).to(device)
cls_loss = F.cross_entropy(y_s, labels_s)
transfer_loss = domain_adv(f_s, f_t, w_s.detach(),
w_t.to(device).detach())
domain_acc = domain_adv.domain_discriminator_accuracy
loss = cls_loss + transfer_loss * args.trade_off
cls_acc = accuracy(y_s, labels_s)[0]
losses.update(loss.item(), x_s.size(0))
cls_accs.update(cls_acc.item(), x_s.size(0))
domain_accs.update(domain_acc.item(), x_s.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
