def eval(self,
saver: Saver,
iteration: int,
verbose: bool,
do_tb: bool = True):
if self.perform_x2v:
cmc_scores_i2v, mAP_i2v = self.evaluate_i2v(verbose=verbose)
if do_tb:
saver.dump_metric_tb(mAP_i2v, iteration, 'i2v', f'mAP')
self.tb_cmc(saver, cmc_scores_i2v, iteration, 'i2v')
cmc_scores_v2v, mAP_v2v = self.evaluate_v2v(verbose=verbose)
if do_tb:
saver.dump_metric_tb(mAP_v2v, iteration, 'v2v', f'mAP')
self.tb_cmc(saver, cmc_scores_v2v, iteration, 'v2v')
if self.perform_x2i:
cmc_scores_i2i, mAP_i2i = self.evaluate_i2i(verbose=verbose)
if do_tb:
saver.dump_metric_tb(mAP_i2i, iteration, 'i2i', f'mAP')
self.tb_cmc(saver, cmc_scores_i2i, iteration, 'i2i')
cmc_scores_v2i, mAP_v2i = self.evaluate_v2i(verbose=verbose)
if do_tb:
saver.dump_metric_tb(mAP_v2i, iteration, 'v2i', f'mAP')
self.tb_cmc(saver, cmc_scores_v2i, iteration, 'v2i')
def main():
conf = Conf()
args = parse(conf)
device = conf.get_device()
conf.suppress_random(set_determinism=args.set_determinism)
saver = Saver(conf.log_path, args.exp_name)
train_loader, query_loader, gallery_loader, queryimg_loader, galleryimg_loader = \
get_dataloaders(args.dataset_name, conf.nas_path, device, args)
num_pids = train_loader.dataset.get_num_pids()
net = nn.DataParallel(get_model(args, num_pids))
net = net.to(device)
saver.write_logs(net.module, vars(args))
opt = Adam(net.parameters(), lr=1e-4, weight_decay=args.wd)
milestones = list(
range(args.first_milestone, args.num_epochs, args.step_milestone))
scheduler = lr_scheduler.MultiStepLR(opt,
milestones=milestones,
gamma=args.gamma)
triplet_loss = OnlineTripletLoss('soft', True, reduction='mean').to(device)
class_loss = nn.CrossEntropyLoss(reduction='mean').to(device)
print("EXP_NAME: ", args.exp_name)
for e in range(args.num_epochs):
if e % args.eval_epoch_interval == 0 and e > 0:
ev = Evaluator(net, query_loader, gallery_loader, queryimg_loader,
galleryimg_loader, DATA_CONFS[args.dataset_name],
device)
ev.eval(saver, e, args.verbose)
if e % args.save_epoch_interval == 0 and e > 0:
saver.save_net(net.module, f'chk_{e // args.save_epoch_interval}')
avm = AvgMeter(['triplet', 'class'])
for it, (x, y, cams) in enumerate(train_loader):
net.train()
x, y = x.to(device), y.to(device)
opt.zero_grad()
embeddings, f_class = net(x, return_logits=True)
triplet_loss_batch = triplet_loss(embeddings, y)
class_loss_batch = class_loss(f_class, y)
loss = triplet_loss_batch + class_loss_batch
avm.add([triplet_loss_batch.item(), class_loss_batch.item()])
loss.backward()
opt.step()
if e % args.print_epoch_interval == 0:
stats = avm()
str_ = f"Epoch: {e}"
for (l, m) in stats:
str_ += f" - {l} {m:.2f}"
saver.dump_metric_tb(m, e, 'losses', f"avg_{l}")
saver.dump_metric_tb(opt.param_groups[0]['lr'], e, 'lr', 'lr')
print(str_)
scheduler.step()
ev = Evaluator(net, query_loader, gallery_loader, queryimg_loader,
galleryimg_loader, DATA_CONFS[args.dataset_name], device)
ev.eval(saver, e, args.verbose)
saver.save_net(net.module, 'chk_end')
saver.writer.close()
