def validate(val_loader,
model,
criterion,
epoch,
log_freq=1,
print_sum=True,
device=None,
stereo=True):
losses = AverageMeter()
# set model to evaluation
model.eval()
with torch.no_grad():
epoch_time = time.time()
end = time.time()
for idx, (batch_images, batch_poses) in enumerate(val_loader):
data_time = time.time() - end
if stereo:
batch_images = [x.to(device) for x in batch_images]
batch_poses = [x.to(device) for x in batch_poses]
else:
batch_images = batch_images.to(device)
batch_poses = batch_poses.to(device)
# compute model output
out = model(batch_images)
loss = criterion(out, batch_poses)
losses.update(
loss.data[0],
len(batch_images) *
batch_images[0].size(0) if stereo else batch_images.size(0))
batch_time = time.time() - end
end = time.time()
if log_freq != 0 and idx % log_freq == 0:
print('Val Epoch: {}\t'
'Time: {batch_time:.3f}\t'
'Data Time: {data_time:.3f}\t'
'Loss: {losses.val:.3f}\t'
'Avg Loss: {losses.avg:.3f}'.format(
epoch,
batch_time=batch_time,
data_time=data_time,
losses=losses))
# if idx == 0:
# break
if print_sum:
print(
'Epoch: [{}]\tValidation Loss: {:.3f}\tEpoch time: {:.3f}'.format(
epoch, losses.avg, (time.time() - epoch_time)))
def eval_model(val_loader, model, criterion, eval_metric, epoch, use_cuda):
losses = AverageMeter()
y_pred = []
y_label = []
model.train(False)
torch.set_grad_enabled(False)
for i, data in enumerate(val_loader):
xi, xv, y = data[0], data[1], data[2]
if use_cuda:
xi, xv, y = xi.cuda(), xv.cuda(), y.cuda()
outputs = model(xi, xv)
loss = criterion(outputs, y)
pred = torch.sigmoid(outputs).cpu()
y_pred.extend(pred.data.numpy())
y_label.extend(y.data.numpy())
losses.update(loss.item(), y.shape[0])
total_metric = eval_metric(y_label, y_pred)
return losses.avg, total_metric
def train_epoch(train_loader, model, criterion, optimizer, epoch, use_cuda):
losses = AverageMeter()
model.train(True)
torch.set_grad_enabled(True)
for i, data in enumerate(train_loader):
xi, xv, y = data[0], data[1], data[2]
if use_cuda:
xi, xv, y = xi.cuda(), xv.cuda(), y.cuda()
optimizer.zero_grad()
outputs = model(xi, xv)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
losses.update(loss.item(), y.shape[0])
progress_bar(i, len(train_loader),
'batch {}, train loss {:.5f}'.format(i, losses.avg))
logging.info('Epoch: [{0}]\t Loss {loss.avg:.4f}\t'.format(epoch,
loss=losses))
def train(args):
if args.batch_size % args.num_instance != 0:
new_batch_size = (args.batch_size //
args.num_instance) * args.num_instance
print(
f"given batch size is {args.batch_size} and num_instances is {args.num_instance}."
+
f"Batch size must be divided into {args.num_instance}. Batch size will be replaced into {new_batch_size}"
)
args.batch_size = new_batch_size
# prepare dataset
train_loader, val_loader, num_query, train_data_len, num_classes = make_data_loader(
args)
model = build_model(args, num_classes)
print("model size: {:.5f}M".format(
sum(p.numel() for p in model.parameters()) / 1e6))
loss_fn, center_criterion = make_loss(args, num_classes)
optimizer, optimizer_center = make_optimizer(args, model, center_criterion)
if args.cuda:
model = model.cuda()
if args.amp:
if args.center_loss:
model, [optimizer, optimizer_center] = \
amp.initialize(model, [optimizer, optimizer_center], opt_level="O1")
else:
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O1")
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
if args.center_loss:
center_criterion = center_criterion.cuda()
for state in optimizer_center.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
model_state_dict = model.state_dict()
optim_state_dict = optimizer.state_dict()
if args.center_loss:
optim_center_state_dict = optimizer_center.state_dict()
center_state_dict = center_criterion.state_dict()
reid_evaluator = ReIDEvaluator(args, model, num_query)
start_epoch = 0
global_step = 0
if args.pretrain != '': # load pre-trained model
weights = torch.load(args.pretrain)
model_state_dict = weights["state_dict"]
model.load_state_dict(model_state_dict)
if args.center_loss:
center_criterion.load_state_dict(
torch.load(args.pretrain.replace(
'model', 'center_param'))["state_dict"])
if args.resume:
start_epoch = weights["epoch"]
global_step = weights["global_step"]
optimizer.load_state_dict(
torch.load(args.pretrain.replace('model',
'optimizer'))["state_dict"])
if args.center_loss:
optimizer_center.load_state_dict(
torch.load(
args.pretrain.replace(
'model', 'optimizer_center'))["state_dict"])
print(f'Start epoch: {start_epoch}, Start step: {global_step}')
scheduler = WarmupMultiStepLR(optimizer, args.steps, args.gamma,
args.warmup_factor, args.warmup_step,
"linear",
-1 if start_epoch == 0 else start_epoch)
current_epoch = start_epoch
best_epoch = 0
best_rank1 = 0
best_mAP = 0
if args.resume:
rank, mAP = reid_evaluator.evaluate(val_loader)
best_rank1 = rank[0]
best_mAP = mAP
best_epoch = current_epoch + 1
batch_time = AverageMeter()
total_losses = AverageMeter()
model_save_dir = os.path.join(args.save_dir, 'ckpts')
os.makedirs(model_save_dir, exist_ok=True)
summary_writer = SummaryWriter(log_dir=os.path.join(
args.save_dir, "tensorboard_log"),
purge_step=global_step)
def summary_loss(score, feat, labels, top_name='global'):
loss = 0.0
losses = loss_fn(score, feat, labels)
for loss_name, loss_val in losses.items():
if loss_name.lower() == "accuracy":
summary_writer.add_scalar(f"Score/{top_name}/triplet",
loss_val, global_step)
continue
if "dist" in loss_name.lower():
summary_writer.add_histogram(f"Distance/{loss_name}", loss_val,
global_step)
continue
loss += loss_val
summary_writer.add_scalar(f"losses/{top_name}/{loss_name}",
loss_val, global_step)
ohe_labels = torch.zeros_like(score)
ohe_labels.scatter_(1, labels.unsqueeze(1), 1.0)
cls_score = torch.softmax(score, dim=1)
cls_score = torch.sum(cls_score * ohe_labels, dim=1).mean()
summary_writer.add_scalar(f"Score/{top_name}/X-entropy", cls_score,
global_step)
return loss
def save_weights(file_name, eph, steps):
torch.save(
{
"state_dict": model_state_dict,
"epoch": eph + 1,
"global_step": steps
}, file_name)
torch.save({"state_dict": optim_state_dict},
file_name.replace("model", "optimizer"))
if args.center_loss:
torch.save({"state_dict": center_state_dict},
file_name.replace("model", "optimizer_center"))
torch.save({"state_dict": optim_center_state_dict},
file_name.replace("model", "center_param"))
# training start
for epoch in range(start_epoch, args.max_epoch):
model.train()
t0 = time.time()
for i, (inputs, labels, _, _) in enumerate(train_loader):
if args.cuda:
inputs = inputs.cuda()
labels = labels.cuda()
cls_scores, features = model(inputs, labels)
# losses
total_loss = summary_loss(cls_scores[0], features[0], labels,
'global')
if args.use_local_feat:
total_loss += summary_loss(cls_scores[1], features[1], labels,
'local')
optimizer.zero_grad()
if args.center_loss:
optimizer_center.zero_grad()
# backward with global loss
if args.amp:
optimizers = [optimizer]
if args.center_loss:
optimizers.append(optimizer_center)
with amp.scale_loss(total_loss, optimizers) as scaled_loss:
scaled_loss.backward()
else:
with torch.autograd.detect_anomaly():
total_loss.backward()
# optimization
optimizer.step()
if args.center_loss:
for name, param in center_criterion.named_parameters():
try:
param.grad.data *= (1. / args.center_loss_weight)
except AttributeError:
continue
optimizer_center.step()
batch_time.update(time.time() - t0)
total_losses.update(total_loss.item())
# learning_rate
current_lr = optimizer.param_groups[0]['lr']
summary_writer.add_scalar("lr", current_lr, global_step)
t0 = time.time()
if (i + 1) % args.log_period == 0:
print(
f"Epoch: [{epoch}][{i+1}/{train_data_len}] " +
f"Batch Time {batch_time.val:.3f} ({batch_time.mean:.3f}) "
+
f"Total_loss {total_losses.val:.3f} ({total_losses.mean:.3f})"
)
global_step += 1
print(
f"Epoch: [{epoch}]\tEpoch Time {batch_time.sum:.3f} s\tLoss {total_losses.mean:.3f}\tLr {current_lr:.2e}"
)
if args.eval_period > 0 and (epoch + 1) % args.eval_period == 0 or (
epoch + 1) == args.max_epoch:
rank, mAP = reid_evaluator.evaluate(
val_loader,
mode="retrieval" if args.dataset_name == "cub200" else "reid")
rank_string = ""
for r in (1, 2, 4, 5, 8, 10, 16, 20):
rank_string += f"Rank-{r:<3}: {rank[r-1]:.1%}"
if r != 20:
rank_string += " "
summary_writer.add_text("Recall@K", rank_string, global_step)
summary_writer.add_scalar("Rank-1", rank[0], (epoch + 1))
rank1 = rank[0]
is_best = rank1 > best_rank1
if is_best:
best_rank1 = rank1
best_mAP = mAP
best_epoch = epoch + 1
if (epoch + 1) % args.save_period == 0 or (epoch +
1) == args.max_epoch:
pth_file_name = os.path.join(
model_save_dir,
f"{args.backbone}_model_{epoch + 1}.pth.tar")
save_weights(pth_file_name, eph=epoch, steps=global_step)
if is_best:
pth_file_name = os.path.join(
model_save_dir, f"{args.backbone}_model_best.pth.tar")
save_weights(pth_file_name, eph=epoch, steps=global_step)
# end epoch
current_epoch += 1
batch_time.reset()
total_losses.reset()
torch.cuda.empty_cache()
# update learning rate
scheduler.step()
print(f"Best rank-1 {best_rank1:.1%}, achived at epoch {best_epoch}")
summary_writer.add_hparams(
{
"dataset_name": args.dataset_name,
"triplet_dim": args.triplet_dim,
"margin": args.margin,
"base_lr": args.base_lr,
"use_attn": args.use_attn,
"use_mask": args.use_mask,
"use_local_feat": args.use_local_feat
}, {
"mAP": best_mAP,
"Rank1": best_rank1
})
def test(model,
loader_test,
data_length,
device,
criterion,
batch_size,
print_logger,
step,
use_top5=False,
verbose=False):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
t1 = time.time()
with torch.no_grad():
# switch to evaluate mode
model.eval()
end = time.time()
for i, data in enumerate(loader_test):
inputs = data[0]["data"].to(device)
targets = data[0]["label"].squeeze().long().to(device)
# for i, (inputs, targets) in enumerate(loader_test, 1):
# inputs = inputs.to(device)
# targets = targets.to(device)
# compute output
output = model(inputs)
loss = criterion(output, targets)
#measure accuracy and record loss
prec1, prec5 = accuracy(output, targets, topk=(1, 5))
losses.update(loss.item(), batch_size)
top1.update(prec1[0], batch_size)
top5.update(prec5[0], batch_size)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# plot progress
# measure elapsed time
t2 = time.time()
print_logger.info('Test Step [{0}]: '
'Loss {loss.avg:.4f} '
'Prec@1(1,5) {top1.avg:.2f}, {top5.avg:.2f} '
'Time {time}'.format(step,
loss=losses,
top1=top1,
top5=top5,
time=t2 - t1))
loader_test.reset()
return top1.avg
def finetune(model,loader_train,data_length,device,criterion,optimizer,scheduler,\
print_freq, print_logger,step,batch_size,epochs=1,use_top5=False,verbose=True):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
best_acc = 0.
# switch to train mode
model.train()
end = time.time()
t1 = time.time()
num_iterations = int(data_length / batch_size)
for epoch in range(epochs):
scheduler.step(epoch)
for i, data in enumerate(loader_train):
inputs = data[0]["data"].to(device)
targets = data[0]["label"].squeeze().long().to(device)
# measure data loading time
data_time.update(time.time() - end)
optimizer.zero_grad()
# compute output
output = model(inputs)
loss = criterion(output, targets)
# compute gradient
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), CLIP_VALUE)
optimizer.step()
optimizer.moment = []
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, targets, topk=(1, 5))
losses.update(loss.item(), batch_size)
top1.update(prec1.item(), batch_size)
top5.update(prec5.item(), batch_size)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0:
print_logger.info(
'Finetune Step [{0}] Epoch [{1}|{2}] ({3}/{4}): '
'Loss {loss.avg:.4f} '
'Prec@1(1,5) {top1.avg:.2f}, {top5.avg:.2f} '.format(
step,
epoch,
epochs,
i,
num_iterations,
loss=losses,
top1=top1,
top5=top5))
if use_top5:
if top5.avg > best_acc:
best_acc = top5.avg
else:
if top1.avg > best_acc:
best_acc = top1.avg
loader_train.reset()
return model, top1.avg
def finetune_one_batch(model,
pre_params,
loader_train,
data_length,
device,
criterion,
optimizer,
scheduler,
print_freq,
print_logger,
step,
batch_size,
epochs=1,
use_top5=False,
verbose=True):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
best_acc = 0.
informance = 0.0
params = []
model.train()
end = time.time()
t1 = time.time()
for epoch in range(epochs):
if scheduler is not None:
scheduler.step(epoch)
for batch_idx, data in enumerate(loader_train, 0):
# for i,(inputs,targets) in enumerate(loader_train,0):
# pdb.set_trace()
inputs, targets = data
inputs = inputs.to(device)
targets = targets.to(device)
# measure data loading time
data_time.update(time.time() - end)
optimizer.zero_grad()
# compute output
output = model(inputs)
loss = criterion(output, targets)
# compute gradient
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), CLIP_VALUE)
params = []
optimizer.step()
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, targets, topk=(1, 5))
losses.update(loss.item(), batch_size)
top1.update(prec1.item(), batch_size)
top5.update(prec5.item(), batch_size)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
print_logger.info(
'Finetune One Batch Step [{0}]: '
'Loss {loss.avg:.4f} '
'Prec@1(1,5) {top1.avg:.2f}, {top5.avg:.2f} '.format(
step, loss=losses, top1=top1, top5=top5))
for _, p in model.named_parameters():
params.append(p)
moment = optimizer.moment
informance = [0.0 for i in range(len(moment))]
suminfo = 0.0
for i in range(len(moment)):
informance[i] = moment[i] * torch.pow(
(pre_params[i] - params[i]), 2)
suminfo = 0.0
for info in informance:
suminfo += torch.sum(info).item()
if use_top5:
if top5.avg > best_acc:
best_acc = top5.avg
else:
if top1.avg > best_acc:
best_acc = top1.avg
optimizer.moment = []
return model, suminfo, top1.avg
def train(train_loader,
model,
criterion,
optimizer,
epoch,
max_epoch,
log_freq=1,
print_sum=True,
poses_mean=None,
poses_std=None,
device=None,
stereo=True):
# switch model to training
model.train()
losses = AverageMeter()
epoch_time = time.time()
gt_poses = np.empty((0, 7))
pred_poses = np.empty((0, 7))
end = time.time()
for idx, (batch_images, batch_poses) in enumerate(train_loader):
data_time = (time.time() - end)
if stereo:
batch_images = [x.to(device) for x in batch_images]
batch_poses = [x.to(device) for x in batch_poses]
else:
batch_images = batch_images.to(device)
batch_poses = batch_poses.to(device)
out = model(batch_images)
loss = criterion(out, batch_poses)
# print('loss = {}'.format(loss))
# Make an optimization step
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.update(
loss.data[0],
len(batch_images) *
batch_images[0].size(0) if stereo else batch_images.size(0))
# move data to cpu & numpy
if stereo:
bp = [x.detach().cpu().numpy() for x in batch_poses]
outp = [x.detach().cpu().numpy() for x in out]
gt_poses = np.vstack((gt_poses, *bp))
pred_poses = np.vstack((pred_poses, *outp))
else:
bp = batch_poses.detach().cpu().numpy()
outp = out.detach().cpu().numpy()
gt_poses = np.vstack((gt_poses, bp))
pred_poses = np.vstack((pred_poses, outp))
batch_time = (time.time() - end)
end = time.time()
if log_freq != 0 and idx % log_freq == 0:
print('Epoch: [{}/{}]\tBatch: [{}/{}]\t'
'Time: {batch_time:.3f}\t'
'Data Time: {data_time:.3f}\t'
'Loss: {losses.val:.3f}\t'
'Avg Loss: {losses.avg:.3f}\t'.format(epoch,
max_epoch - 1,
idx,
len(train_loader) - 1,
batch_time=batch_time,
data_time=data_time,
losses=losses))
# if idx == 0:
# break
# un-normalize translation
unnorm = (poses_mean is not None) and (poses_std is not None)
if unnorm:
gt_poses[:, :3] = gt_poses[:, :3] * poses_std + poses_mean
pred_poses[:, :3] = pred_poses[:, :3] * poses_std + poses_mean
t_loss = np.asarray([
np.linalg.norm(p - t)
for p, t in zip(pred_poses[:, :3], gt_poses[:, :3])
])
q_loss = np.asarray([
quaternion_angular_error(p, t)
for p, t in zip(pred_poses[:, 3:], gt_poses[:, 3:])
])
# if unnorm:
# print('poses_std = {:.3f}'.format(np.linalg.norm(poses_std)))
# print('T: median = {:.3f}, mean = {:.3f}'.format(np.median(t_loss), np.mean(t_loss)))
# print('R: median = {:.3f}, mean = {:.3f}'.format(np.median(q_loss), np.mean(q_loss)))
if print_sum:
print(
'Ep: [{}/{}]\tTrain Loss: {:.3f}\tTe: {:.3f}\tRe: {:.3f}\t Et: {:.2f}s\t\
{criterion_sx:.5f}:{criterion_sq:.5f}'.format(
epoch,
max_epoch - 1,
losses.avg,
np.mean(t_loss),
np.mean(q_loss), (time.time() - epoch_time),
criterion_sx=criterion.sx.data[0],
criterion_sq=criterion.sq.data[0]))
