def train(model, train_loader, epoch):
# average meters to record the training statistics
batch_time = util.AverageMeter()
data_time = util.AverageMeter()
# switch to train mode
model.switch_to_train()
progbar = Progbar(len(train_loader.dataset))
end = time.time()
for i, train_data in enumerate(train_loader):
data_time.update(time.time() - end)
vis_input, txt_input, _, _, _ = train_data
loss = model.train(vis_input, txt_input)
progbar.add(vis_input.size(0), values=[('data_time', data_time.val), ('batch_time', batch_time.val), ('loss', loss)])
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# Record logs in tensorboard
writer.add_scalar('train/Loss', loss, model.iters)
def main(n_aggregation, dim_feature, n_epochs, batch_size, eps):
W = np.random.normal(0, 0.4, [dim_feature, dim_feature])
A = np.random.normal(0, 0.4, dim_feature)
b = np.array([0.])
model = GraphNeuralNetwork(W, A, b, n_aggregation=n_aggregation)
optimizer = Adam(model)
dataset = util.get_train_data('../../datasets')
train_data, valid_data = util.random_split(dataset, train_ratio=0.5)
print('train_size: %d, valid_size: %d' %
(len(train_data), len(valid_data)))
for epoch in range(n_epochs):
train_loss = util.AverageMeter()
train_acc = util.AverageMeter()
for graphs, labels in util.get_shuffled_batches(
train_data, batch_size):
grads_flat = 0
for graph, label in zip(graphs, labels):
x = np.zeros([len(graph), dim_feature])
x[:, 0] = 1
grads_flat += calc_grads(model, graph, x, label,
bce_with_logit, eps) / batch_size
outputs = model(graph, x)
train_loss.update(bce_with_logit(outputs, label), 1)
train_acc.update((sigmoid(outputs) > 0.5) == label, 1)
optimizer.update(grads_flat)
valid_loss, valid_acc = test(model, valid_data, dim_feature)
print(
'epoch: %d, train_loss: %f, train_acc: %f, valid_loss: %f, vald_acc: %f'
% (epoch, train_loss.avg, train_acc.avg, valid_loss, valid_acc))
def eval_ensemble(split, ens, dataloaders, args, eval_batch_size=4):
ens.eval()
eval_dataloader = torch.utils.data.DataLoader(dataloaders[split].dataset,
batch_size=eval_batch_size)
loss = nn.NLLLoss()
caps_to_score = data.get_responses(args.dataset, args.data_folder)
loss_meter = util.AverageMeter()
acc_meter = util.AverageMeter()
for (imgs, caps, caplens, *idx) in tqdm(eval_dataloader,
desc="Eval ensemble"):
batch_size = imgs.shape[0]
if args.cuda:
imgs = imgs.cuda()
# caps -> class indices
cidx = data.to_text(caps, ens.vocab)
cidx = torch.tensor([caps_to_score.index(i) for i in cidx],
dtype=torch.int64)
cidx = cidx.to(imgs.device)
with torch.no_grad():
ens_scores = ens.score_captions_for_imgs(imgs, caps_to_score)
ens_loss = loss(ens_scores.log(), cidx)
loss_meter.update(ens_loss.item(), batch_size)
acc = (ens_scores.argmax(1) == cidx).float().mean().item()
acc_meter.update(acc, batch_size)
return {"acc": acc_meter.avg, "loss": loss_meter.avg}
def test(val_loader, model, criterion, cuda, print_freq):
batch_time = util.AverageMeter()
losses = util.AverageMeter()
top1 = util.AverageMeter()
prfa = util.AverageMeterPRFA()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target, seq_lengths) in enumerate(val_loader):
if cuda:
input = input.cuda()
target = target.cuda()
# compute output
output = model(input, seq_lengths)
loss = criterion(output, target)
# measure accuracy and record loss
prfa_all = util.prf_multi_classify(output.data, target, topk=(1, ))
prfa.update(prfa_all, seq_lengths.size(0))
prec1 = util.accuracy(output.data, target, topk=(1, ))
losses.update(loss.data, input.size(0))
top1.update(prec1[0][0], input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
four_classify_loop_print(i, print_freq, val_loader, batch_time, losses,
top1, prfa)
four_classify_last_print(top1, prfa)
return top1.avg
def samplewise_perturbation_eval(random_noise, data_loader, model, eval_target='train_dataset', mask_cord_list=[]):
loss_meter = util.AverageMeter()
err_meter = util.AverageMeter()
# random_noise = random_noise.to(device)
model = model.to(device)
idx = 0
for i, (images, labels) in enumerate(data_loader[eval_target]):
images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True)
if random_noise is not None:
for i, (image, label) in enumerate(zip(images, labels)):
if not torch.is_tensor(random_noise):
sample_noise = torch.tensor(random_noise[idx]).to(device)
else:
sample_noise = random_noise[idx].to(device)
c, h, w = image.shape[0], image.shape[1], image.shape[2]
mask = np.zeros((c, h, w), np.float32)
x1, x2, y1, y2 = mask_cord_list[idx]
mask[:, x1: x2, y1: y2] = sample_noise.cpu().numpy()
sample_noise = torch.from_numpy(mask).to(device)
images[i] = images[i] + sample_noise
idx += 1
pred = model(images)
err = (pred.data.max(1)[1] != labels.data).float().sum()
loss = torch.nn.CrossEntropyLoss()(pred, labels)
loss_meter.update(loss.item(), len(labels))
err_meter.update(err / len(labels))
return loss_meter.avg, err_meter.avg
def train_fn(self, train_data_loader, model, optimizer, device, scheduler):
model.train()
losses = util.AverageMeter()
jaccards = util.AverageMeter()
tk0 = tqdm(train_data_loader, total=len(train_data_loader))
for bi, d in enumerate(tk0):
ids = d["ids"]
token_type_ids = d["token_type_ids"]
mask = d["mask"]
targets_start = d["targets_start"]
targets_end = d["targets_end"]
sentiment = d["sentiment"]
orig_selected = d["orig_selected"]
orig_tweet = d["orig_tweet"]
targets_start = d["targets_start"]
targets_end = d["targets_end"]
offsets = d["offsets"]
ids = ids.to(device, dtype=torch.long)
token_type_ids = token_type_ids.to(device, dtype=torch.long)
mask = mask.to(device, dtype=torch.long)
targets_start = targets_start.to(device, dtype=torch.long)
targets_end = targets_end.to(device, dtype=torch.long)
model.zero_grad()
outputs_start, outputs_end = model(
ids=ids,
mask_token_type_ids=mask,
token_type_ids=token_type_ids,
)
loss = self.loss_fn(outputs_start, outputs_end, targets_start,
targets_end)
loss.backward()
optimizer.step()
scheduler.step()
outputs_start = torch.softmax(outputs_start,
dim=1).cpu().detach().numpy()
outputs_end = torch.softmax(outputs_end,
dim=1).cpu().detach().numpy()
jaccard_scores = []
for px, tweet in enumerate(orig_tweet):
selected_tweet = orig_selected[px]
tweet_sentiment = sentiment[px]
jaccard_score, _ = util.calculate_jaccard_score(
original_tweet=tweet,
target_string=selected_tweet,
sentiment_val=tweet_sentiment,
idx_start=np.argmax(outputs_start[px, :]),
idx_end=np.argmax(outputs_end[px, :]),
offsets=offsets[px])
jaccard_scores.append(jaccard_score)
jaccards.update(np.mean(jaccard_scores), ids.size(0))
losses.update(loss.item(), ids.size(0))
tk0.set_postfix(loss=losses.avg, jaccard=jaccards.avg)
def test_keann_kg(val_loader,
model,
criterion,
cuda,
print_freq,
pdtb_category=''):
batch_time = util.AverageMeter()
losses = util.AverageMeter()
top1 = util.AverageMeter()
prfa = util.AverageMeterPRFA()
if pdtb_category != '':
prfa = util.AverageBinaryMeterPRFA()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (arg1, arg2, target, seq_lengths, transE_tensor_arg1,
transE_tensor_arg2, batch_original) in enumerate(val_loader):
arg1 = Variable(arg1, requires_grad=False)
arg2 = Variable(arg2, requires_grad=False)
target = Variable(target, requires_grad=False)
if cuda:
arg1 = arg1.cuda()
arg2 = arg2.cuda()
target = target.cuda()
# compute output
output = model((arg1, arg2), seq_lengths,
(transE_tensor_arg1, transE_tensor_arg2), cuda)
# hot_image(model.encoder(arg1), model.encoder(arg2), batch_original, model.rand_matrix, model.kg_relation)
loss = criterion(output, target)
# measure accuracy and record loss
prfa_all = util.prf_multi_classify(output.data, target, topk=(1, ))
prfa.update(prfa_all, seq_lengths.size(0))
prec1 = util.accuracy(output.data, target, topk=(1, ))
losses.update(loss.data.cpu().numpy(), seq_lengths.size(0))
top1.update(prec1[0][0], seq_lengths.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if pdtb_category != '':
binary_classify_loop_print(i, print_freq, val_loader, batch_time,
losses, top1, prfa, pdtb_category)
else:
four_classify_loop_print(i, print_freq, val_loader, batch_time,
losses, top1, prfa)
if pdtb_category != '':
binary_classify_last_print(top1, prfa)
else:
four_classify_last_print(top1, prfa)
return top1.avg
def test(model, dataset, dim_feature):
"""Function for model evaluation"""
acc = util.AverageMeter()
loss = util.AverageMeter()
for graph, label in dataset:
x = np.zeros([len(graph), dim_feature])
x[:, 0] = 1
outputs = model(graph, x)
loss.update(bce_with_logit(outputs, label), 1)
acc.update((sigmoid(outputs) > 0.5) == label, 1)
return loss.avg, acc.avg
def train(train_loader, model, criterion, optimizer, epoch, args, logger,
time_logger):
''' -------------------------averageMeter 선언.-----------------------------'''
batch_time = util.AverageMeter('Time', ':6.3f')
data_time = util.AverageMeter('Data', ':6.3f')
losses = util.AverageMeter('Loss', ':.4f')
top1 = util.AverageMeter('Acc@1', ':6.2f')
top5 = util.AverageMeter('Acc@5', ':6.2f')
''' -------------------------출력 progress 선언.-----------------------------'''
progress = util.ProgressMeter(len(train_loader),
[batch_time, data_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch))
''' -------------------------학습 시작.-----------------------------'''
model.train()
end = time.time()
for i, (images, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
output = model(images)
loss = criterion(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Gradient averaging
average_gradients(model)
''' -------------------------이미지넷 top1, top5 accuracy----------------------------'''
acc1, acc5, correct = util.accuracy(output, target, topk=(1, 5))
''' -------------------------각 GPU log 합쳐주기-----------------------------'''
reduced_loss = reduce_tensor(loss.data)
reduced_top1 = reduce_tensor(acc1[0].data)
reduced_top5 = reduce_tensor(acc5[0].data)
''' ------------------------- averageMeter에 업데이트 -----------------------------'''
losses.update(reduced_loss.item(), images.size(0))
top1.update(reduced_top1.item(), images.size(0))
top5.update(reduced_top5.item(), images.size(0))
batch_time.update(time.time() - end)
end = time.time()
''' ------------------------- gpu 하나로만 출력하기. (rank == 0 : 0번 gpu에서만 출력하도록.)-----------------------------'''
if dist.get_rank() == 0:
if i % args.print_freq == 0:
progress.display(i)
''' ------------------------- logger 에 업데이트-----------------------------'''
if dist.get_rank() == 0:
logger.write([epoch, losses.avg, top1.avg, top5.avg])
time_logger.write([epoch, batch_time.avg, data_time.avg])
def train_keann(train_loader, model, criterion, optimizer, epoch, cuda, clip,
print_freq):
batch_time = util.AverageMeter()
data_time = util.AverageMeter()
losses = util.AverageMeter()
top1 = util.AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (arg1, arg2, target, seq_lengths) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if cuda:
arg1 = arg1.cuda()
arg2 = arg2.cuda()
target = target.cuda()
# compute output
output = model((arg1, arg2), seq_lengths)
loss = criterion(output, target)
# measure accuracy and record loss
prec1 = util.accuracy(output.data, target, topk=(1, ))
losses.update(loss.data, seq_lengths.size(0))
top1.update(prec1[0][0], seq_lengths.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i != 0 and i % print_freq == 0:
print(
'Epoch: [{0}][{1}/{2}] Time {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) Loss {loss.val:.4f} ({loss.avg:.4f}) '
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1))
gc.collect()
def __init__(self, data_loader, logger, config):
self.loss_meters = util.AverageMeter()
self.acc_meters = util.AverageMeter()
self.acc5_meters = util.AverageMeter()
self.criterion = torch.nn.CrossEntropyLoss()
self.data_loader = data_loader
self.logger = logger
self.log_frequency = config.log_frequency if config.log_frequency is not None else 100
self.config = config
self.current_acc = 0
self.current_acc_top5 = 0
self.confusion_matrix = torch.zeros(config.num_classes, config.num_classes)
return
def __init__(self, args):
super(TestLogger, self).__init__(args)
self.steps_per_print = args.steps_per_print
self.steps_per_visual = args.steps_per_visual
self.experiment_name = args.name
self.num_epochs = args.num_epochs
self.masked_loss_meter = util.AverageMeter()
self.full_loss_meter = util.AverageMeter()
self.obscured_loss_meter = util.AverageMeter()
self.pbar = tqdm(total=int(self.num_epochs / self.steps_per_print))
self.train_start_time = time()
def test_keann(val_loader,
model,
criterion,
cuda,
print_freq,
pdtb_category=''):
batch_time = util.AverageMeter()
losses = util.AverageMeter()
top1 = util.AverageMeter()
prfa = util.AverageMeterPRFA()
if pdtb_category != '':
prfa = util.AverageBinaryMeterPRFA()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (arg1, arg2, target, seq_lengths) in enumerate(val_loader):
if cuda:
arg1 = arg1.cuda()
arg2 = arg2.cuda()
target = target.cuda()
# compute output
output = model((arg1, arg2), seq_lengths)
loss = criterion(output, target)
# measure accuracy and record loss
prfa_all = util.prf_multi_classify(output.data, target, topk=(1, ))
prfa.update(prfa_all, seq_lengths.size(0))
prec1 = util.accuracy(output.data, target, topk=(1, ))
losses.update(loss.data, seq_lengths.size(0))
top1.update(prec1[0][0], seq_lengths.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if pdtb_category != '':
binary_classify_loop_print(i, print_freq, val_loader, batch_time,
losses, top1, prfa, pdtb_category)
else:
four_classify_loop_print(i, print_freq, val_loader, batch_time,
losses, top1, prfa)
if pdtb_category != '':
binary_classify_last_print(top1, prfa)
else:
four_classify_last_print(top1, prfa)
return top1.avg
def validate(val_loader, model, criterion, epoch, args, logger, time_logger):
batch_time = util.AverageMeter('Time', ':6.3f')
data_time = util.AverageMeter('Data', ':6.3f')
losses = util.AverageMeter('Loss', ':.4f')
top1 = util.AverageMeter('Acc@1', ':6.2f')
top5 = util.AverageMeter('Acc@5', ':6.2f')
progress = util.ProgressMeter(len(val_loader),
[batch_time, data_time, losses, top1, top5],
prefix='Test: ')
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
data_time.update(time.time() - end)
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
output = model(images)
loss = criterion(output, target)
acc1, acc5, correct = util.accuracy(output, target, topk=(1, 5))
reduced_loss = reduce_tensor(loss.data)
reduced_top1 = reduce_tensor(acc1[0].data)
reduced_top5 = reduce_tensor(acc5[0].data)
losses.update(reduced_loss.item(), images.size(0))
top1.update(reduced_top1.item(), images.size(0))
top5.update(reduced_top5.item(), images.size(0))
batch_time.update(time.time() - end)
end = time.time()
if dist.get_rank() == 0:
if i % args.print_freq == 0:
progress.display(i)
if dist.get_rank() == 0:
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(
top1=top1, top5=top5))
if dist.get_rank() == 0:
logger.write([epoch, losses.avg, top1.avg, top5.avg])
time_logger.write([epoch, batch_time.avg, data_time.avg])
return top1.avg
def evaluate(model, data_loader, device):
nll_meter = util.AverageMeter()
model.eval()
with torch.no_grad(), \
tqdm(total=len(data_loader.dataset)) as progress_bar:
for features, y in data_loader:
# Setup for forward
batch_size = y.shape[0]
# Forward
outputs = model(features)
y = y.to(device)
loss = loss_fn(outputs, y)
nll_meter.update(loss.item(), batch_size)
# Log info
progress_bar.update(batch_size)
progress_bar.set_postfix(NLL=nll_meter.avg)
model.train()
results_list = [('NLL', nll_meter.avg)]
results = OrderedDict(results_list)
return results
def train_on_epoch(self, optimizer, loader, epoch, validation=False):
print(f"Starting epoch {epoch}, validation: {validation} " + "=" * 30, flush=True)
loss_value = util.AverageMeter()
# house keeping
self.model.run()
if self.lr_schedule(epoch + 1) != self.lr_schedule(epoch):
files.save_checkpoint_all(
self.checkpoint_dir, self.model, args.arch,
optimizer, self.L, epoch, lowest=False, save_str='pre-lr-drop')
lr = self.lr_schedule(epoch)
for pg in optimizer.param_groups:
pg['lr'] = lr
criterion_fn = torch.nn.CrossEntropyLoss()
for index, (data, label, selected) in enumerate(loader):
start_tm = time.time()
global_step = epoch * len(loader) + index
if global_step * args.batch_size >= self.optimize_times[-1]:
# optimize labels #########################################
self.model.headcount = 1
print('Optimizaton starting', flush=True)
with torch.no_grad():
_ = self.optimize_times.pop()
self.update_assignment(global_step)
data = data.to(self.device)
mass = data.size(0)
outputs = self.model(data)
# train CNN ####################################################
if self.num_heads == 1:
loss = criterion_fn(outputs, self.L[0, selected])
else:
loss = torch.mean(torch.stack([
criterion_fn(outputs[head_index], self.L[head_index, selected]) for head_index in
range(self.num_heads)]
))
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_value.update(loss.item(), mass)
data = 0
# some logging stuff ##############################################################
if index % args.log_iter == 0 and self.writer:
self.writer.add_scalar('lr', self.lr_schedule(epoch), global_step)
print(global_step, f" Loss: {loss.item():.3f}", flush=True)
print(global_step, f" Freq: {mass / (time.time() - start_tm):.2f}", flush=True)
if writer:
self.writer.add_scalar('Loss', loss.item(), global_step)
if index > 0:
self.writer.add_scalar('Freq(Hz)', mass / (time.time() - start_tm), global_step)
# end of epoch logging ################################################################
if self.writer and (epoch % args.log_intv == 0):
util.write_conv(self.writer, self.model, epoch=epoch)
files.save_checkpoint_all(self.checkpoint_dir, self.model, args.arch, optimizer, self.L, epoch, lowest=False)
return {'loss': loss_value.avg}
def optimize_epoch(self, optimizer, loader, epoch, validation=False):
print(f"Starting epoch {epoch}, validation: {validation} " + "="*30,flush=True)
loss_value = util.AverageMeter()
# house keeping
self.model.train()
if self.lr_schedule(epoch+1) != self.lr_schedule(epoch):
files.save_checkpoint_all(self.checkpoint_dir, self.model, args.arch,
optimizer, self.L, epoch, lowest=False, save_str='pre-lr-drop')
lr = self.lr_schedule(epoch)
for pg in optimizer.param_groups:
pg['lr'] = lr
XE = torch.nn.CrossEntropyLoss()
for iter, (data, label, selected) in enumerate(loader):
now = time.time()
niter = epoch * len(loader) + iter
if niter*args.batch_size >= self.optimize_times[-1]:
############ optimize labels #########################################
self.model.headcount = 1
print('Optimizaton starting', flush=True)
with torch.no_grad():
_ = self.optimize_times.pop()
self.optimize_labels(niter)
data = data.to(self.dev)
mass = data.size(0)
final = self.model(data)
#################### train CNN ####################################################
if self.hc == 1:
loss = XE(final, self.L[0, selected])
else:
loss = torch.mean(torch.stack([XE(final[h],
self.L[h, selected]) for h in range(self.hc)]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_value.update(loss.item(), mass)
data = 0
# some logging stuff ##############################################################
if iter % args.log_iter == 0:
if self.writer:
self.writer.add_scalar('lr', self.lr_schedule(epoch), niter)
print(niter, " Loss: {0:.3f}".format(loss.item()), flush=True)
print(niter, " Freq: {0:.2f}".format(mass/(time.time() - now)), flush=True)
if writer:
self.writer.add_scalar('Loss', loss.item(), niter)
if iter > 0:
self.writer.add_scalar('Freq(Hz)', mass/(time.time() - now), niter)
# end of epoch logging ################################################################
if self.writer and (epoch % args.log_intv == 0):
util.write_conv(self.writer, self.model, epoch=epoch)
files.save_checkpoint_all(self.checkpoint_dir, self.model, args.arch,
optimizer, self.L, epoch, lowest=False)
return {'loss': loss_value.avg}
def train(epoch, net, trainloader, device, optimizer, scheduler, loss_fn, max_grad_norm):
global global_step
print('\nEpoch: %d' % epoch)
net.train()
loss_meter = util.AverageMeter()
with tqdm(total=len(trainloader.dataset)) as progress_bar:
for x, cond_x in trainloader:
x , cond_x = x.to(device), cond_x.to(device)
optimizer.zero_grad()
z, sldj = net(x, cond_x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
loss.backward()
if max_grad_norm > 0:
util.clip_grad_norm(optimizer, max_grad_norm)
optimizer.step()
scheduler.step(global_step)
progress_bar.set_postfix(nll=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg),
lr=optimizer.param_groups[0]['lr'])
progress_bar.update(x.size(0))
global_step += x.size(0)
print('Saving...')
state = {
'net': net.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
}
torch.save(state, 'savemodel/cINN/checkpoint_' + str(epoch) + '.tar')
def _init_loss_meters(self):
loss_meters = {}
if self.do_classify:
loss_meters['cls_loss'] = util.AverageMeter()
return loss_meters
def evaluate(test_loader,
model,
criterion,
n_iter=-1,
verbose=False,
device='cuda'):
"""
Standard evaluation loop.
"""
loss_meter = util.AverageMeter()
# switch to evaluate mode
model.train()
with torch.no_grad():
#end = time.time()
#bpd = 0
for i, (x, target) in enumerate(test_loader):
# early stop
if i >= 100: break
x = x.to('cuda')
z, sldj = model(x, reverse=False)
loss = criterion(z, sldj)
loss_meter.update(loss.item(), x.size(0))
bpd = util.bits_per_dim(x, loss_meter.avg)
return bpd
def test(epoch, net, testloader, device, loss_fn, num_samples, save_dir):
global best_loss
net.eval()
loss_meter = util.AverageMeter()
with tqdm(total=len(testloader.dataset)) as progress_bar:
for x, _ in testloader:
x = x.to(device)
z, sldj = net(x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
progress_bar.set_postfix(nll=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg))
progress_bar.update(x.size(0))
# Save checkpoint
if loss_meter.avg < best_loss:
print('Saving...')
state = {
'net': net.state_dict(),
'test_loss': loss_meter.avg,
'epoch': epoch,
}
os.makedirs('save', exist_ok=True)
torch.save(state, 'save/best.pth.tar')
best_loss = loss_meter.avg
# Save samples and data
images = sample(net, num_samples, device)
os.makedirs(save_dir, exist_ok=True)
images_concat = torchvision.utils.make_grid(images, nrow=int(num_samples ** 0.5), padding=2, pad_value=255)
torchvision.utils.save_image(images_concat, os.path.join(save_dir, 'epoch_{}.png'.format(epoch)))
def train(epoch, net, trainloader, device, optimizer, scheduler, loss_fn, max_grad_norm):
global global_step
print('\nEpoch: %d' % epoch)
net.train()
loss_meter = util.AverageMeter()
with tqdm(total=len(trainloader.dataset)) as progress_bar:
for x, _ in trainloader:
x = x.to(device)
optimizer.zero_grad()
z, sldj = net(x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
loss.backward()
if max_grad_norm > 0:
util.clip_grad_norm(optimizer, max_grad_norm)
optimizer.step()
progress_bar.set_postfix(nll=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg),
lr=optimizer.param_groups[0]['lr'])
progress_bar.update(x.size(0))
scheduler.step(global_step)
global_step += x.size(0)
def test(epoch, net, testloader, device, num_samples, best_loss):
net.eval()
loss_meter = util.AverageMeter()
with tqdm(total=len(testloader.dataset)) as progress_bar:
for x, _ in testloader:
x = x.to(device)
x_q = sample(net, m=64, n_ch=3, im_w=32, im_h=32, K=100, device=device)
loss = net(x_q).mean() - net(x).mean()
loss_meter.update(loss.item(), x.size(0))
progress_bar.set_postfix(nll=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg))
progress_bar.update(x.size(0))
# Save checkpoint
if loss_meter.avg < best_loss:
print('Saving...')
state = {
'net': net.state_dict(),
'test_loss': loss_meter.avg,
'epoch': epoch,
}
os.makedirs('ckpts', exist_ok=True)
torch.save(state, 'ckpts/best_ebm.pth.tar')
best_loss = loss_meter.avg
# Save samples and data
images = sample(net, m=64, n_ch=3, im_w=32, im_h=32, K=100, device=device)
os.makedirs('ebm_samples', exist_ok=True)
images_concat = torchvision.utils.make_grid(images, nrow=int(num_samples ** 0.5), padding=2, pad_value=255)
torchvision.utils.save_image(images_concat, 'ebm_samples/epoch_{}.png'.format(epoch))
return best_loss
def evaluate(model, data_loader, device, eval_file, max_len, use_squad_v2):
nll_meter = util.AverageMeter()
model.eval()
pred_dict = {}
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(data_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
# Forward
log_p = model(cw_idxs, qw_idxs, cc_idxs, qc_idxs)
y1, y2 = y1.to(device), y2.to(device)
#print("ckpt 1")
ans_lens = y2 - y1
loss = 0
for i in range(max_len):
mask = ((torch.ones_like(y1) * i) == ans_lens).type(
torch.cuda.LongTensor)
y = y1 * mask
loss += F.nll_loss(log_p[:, :, i], y)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
log_p, ans_len = torch.max(log_p, dim=-1)
starts = torch.max(log_p, dim=-1)[1]
ends = starts
for i in range(starts.size(0)):
ends[i] += ans_len.type(torch.cuda.LongTensor)[i, starts[i]]
# print("starts and ends:", starts, ends, starts.size(), ends.size())
# starts, ends = util.discretize(p, p + ans_lens, max_len, use_squad_v2)
# Log info
progress_bar.update(batch_size)
progress_bar.set_postfix(NLL=nll_meter.avg)
preds, _ = util.convert_tokens(gold_dict, ids.tolist(),
starts.tolist(), ends.tolist(),
use_squad_v2)
pred_dict.update(preds)
model.train()
results = util.eval_dicts(gold_dict, pred_dict, use_squad_v2)
results_list = [('NLL', nll_meter.avg), ('F1', results['F1']),
('EM', results['EM'])]
if use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
return results, pred_dict
def evaluate(model, data_loader, device, eval_file, max_len, use_squad_v2):
nll_meter = util.AverageMeter()
model.eval()
pred_dict = {}
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(data_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ans, ids in data_loader:
# Setup for forward
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
ans = ans.to(device)
batch_size = cw_idxs.size(0)
# Forward
logits = model(cw_idxs, qw_idxs,cc_idxs,qc_idxs)
# y1, y2 = y1.to(device), y2.to(device)
# loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss = nn.CrossEntropyLoss()(logits,ans.long())
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
# p1, p2 = log_p1.exp(), log_p2.exp()
# starts, ends = util.discretize(p1, p2, max_len, use_squad_v2)
# Log info
progress_bar.update(batch_size)
progress_bar.set_postfix(NLL=nll_meter.avg)
# preds, _ = util.convert_tokens(gold_dict,
# ids.tolist(),
# starts.tolist(),
# ends.tolist(),
# use_squad_v2)
preds, _ = util.convert_class(gold_dict,
ids.tolist(),
logits.tolist())
pred_dict.update(preds)
model.train()
results = util.eval_class(gold_dict, pred_dict)
results_list = [('NLL', nll_meter.avg),
('F1', results['F1']),
('EM', results['EM'])]
# if use_squad_v2:
# results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
return results, pred_dict
def __init__(self, args, epoch, dataset_len):
super(TrainLogger, self).__init__(args, epoch, dataset_len)
self.iters_per_print = args.iters_per_print
self.experiment_name = args.name
self.max_eval = args.max_eval
self.num_epochs = args.num_epochs
self.loss_meter = util.AverageMeter()
def test(epoch, net, testloader, device, loss_fn, num_samples, in_channels,
base_path, args):
global best_loss
global hists
net.eval()
loss_meters = [util.AverageMeter() for _ in range(3)]
with tqdm(total=len(testloader.dataset)) as progress_bar:
for x in testloader:
if len(x) == 2 and type(x) is list:
x = x[0]
x = x.to(device)
with torch.no_grad():
x_hat, mu, logvar, output_var = net(x)
loss, reconstruction_loss, kl_loss = loss_fn(
x, x_hat, mu, logvar, output_var)
loss_meters[0].update(loss.item(), x.size(0))
loss_meters[1].update(reconstruction_loss.item(), x.size(0))
loss_meters[2].update(kl_loss.item(), x.size(0))
progress_bar.set_postfix(loss=loss_meters[0].avg,
rc_loss=loss_meters[1].avg,
kl_loss=loss_meters[2].avg)
progress_bar.update(x.size(0))
# Save checkpoint
if loss_meters[0].avg < best_loss:
print('Saving...')
state = {
'net': net.state_dict(),
'test_loss': loss_meters[0].avg,
'epoch': epoch,
}
ckpt_path = base_path / 'ckpts'
ckpt_path.mkdir(exist_ok=True)
best_path_ckpt = ckpt_path / 'best.pth.tar'
torch.save(state, best_path_ckpt)
best_loss = loss_meters[0].avg
# Save samples and data
sample_latent = args.latent_dim if args.model == 'VAE' else None
images = sample(net, num_samples, device, in_channels, sample_latent)
if images.shape[1] == 2:
images = images[:, :1, :, :]
if images.shape[1] == 6:
images = images[:, :3, :, :]
image_vals = images.detach().cpu().numpy().flatten()
hist = np.histogram(image_vals, bins=100)
hists.append(hist)
hists_path = base_path / 'hists.pkl'
with hists_path.open('wb') as f:
pickle.dump(hists, f)
samples_path = base_path / 'samples'
samples_path.mkdir(exist_ok=True)
epoch_path = samples_path / f'epoch_{epoch}.png'
images_concat = torchvision.utils.make_grid(images,
nrow=int(num_samples**0.5),
padding=2,
pad_value=255)
torchvision.utils.save_image(images_concat, epoch_path)
def __init__(self,
criterion,
data_loader,
logger,
config,
global_step=0,
target='train_dataset'):
self.criterion = criterion
self.data_loader = data_loader
self.logger = logger
self.config = config
self.log_frequency = config.log_frequency if config.log_frequency is not None else 100
self.loss_meters = util.AverageMeter()
self.acc_meters = util.AverageMeter()
self.acc5_meters = util.AverageMeter()
self.global_step = global_step
self.target = target
print(self.target)
def run(split, epoch, model, optimizer, criterion, dataloaders, args):
training = split == "train"
if training:
ctx = nullcontext
model.train()
else:
ctx = torch.no_grad
model.eval()
ranger = tqdm(dataloaders[split], desc=f"{split} epoch {epoch}")
loss_meter = util.AverageMeter()
acc_meter = util.AverageMeter()
for (s1, s1len, s2, s2len, targets) in ranger:
if args.cuda:
s1 = s1.cuda()
s1len = s1len.cuda()
s2 = s2.cuda()
s2len = s2len.cuda()
targets = targets.cuda()
batch_size = targets.shape[0]
with ctx():
logits = model(s1, s1len, s2, s2len)
loss = criterion(logits, targets)
if training:
optimizer.zero_grad()
loss.backward()
optimizer.step()
preds = logits.argmax(1)
acc = (preds == targets).float().mean()
loss_meter.update(loss.item(), batch_size)
acc_meter.update(acc.item(), batch_size)
ranger.set_description(
f"{split} epoch {epoch} loss {loss_meter.avg:.3f} acc {acc_meter.avg:.3f}"
)
return {"loss": loss_meter.avg, "acc": acc_meter.avg}
def evaluate(model, data_loader, device, eval_file, max_len, use_squad_v2,
args):
nll_meter = util.AverageMeter()
model.eval()
pred_dict = {}
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(data_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
## Additions for BERT ##
max_context_len, max_question_len = args.para_limit, args.ques_limit
if "bert" in args.model_type:
bert_dev_embeddings = get_embeddings("dev", ids,
args.para_limit,
args.ques_limit)
else:
bert_dev_embeddings = None
# Forward
log_p1, log_p2 = model(cw_idxs, qw_idxs, bert_dev_embeddings, \
max_context_len, max_question_len, device)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
p1, p2 = log_p1.exp(), log_p2.exp()
starts, ends = util.discretize(p1, p2, max_len, use_squad_v2)
# Log info
progress_bar.update(batch_size)
progress_bar.set_postfix(NLL=nll_meter.avg)
preds, _ = util.convert_tokens(gold_dict, ids.tolist(),
starts.tolist(), ends.tolist(),
use_squad_v2)
pred_dict.update(preds)
model.train()
results = util.eval_dicts(gold_dict, pred_dict, use_squad_v2)
results_list = [('NLL', nll_meter.avg), ('F1', results['F1']),
('EM', results['EM'])]
if use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
return results, pred_dict
