def test(load_model_weight=False):
if load_model_weight:
if cfg.model_weight_file != '':
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.model_weight_file, map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(cfg.model_weight_file))
else:
load_ckpt(modules_optims, cfg.ckpt_file)
use_local_distance = (cfg.l_loss_weight > 0) \
and cfg.local_dist_own_hard_sample
for test_set, name in zip(test_sets, test_set_names):
test_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n=========> Test on dataset: {} <=========\n'.format(name))
test_set.eval(
normalize_feat=cfg.normalize_feature,
use_local_distance=use_local_distance)
def main():
cfg = Config()
# Redirect logs to both console and file.
if cfg.log_to_file:
ReDirectSTD(cfg.stdout_file, 'stdout', False)
ReDirectSTD(cfg.stderr_file, 'stderr', False)
# Lazily create SummaryWriter
writer = None
TVT, TMO = set_devices(cfg.sys_device_ids)
if cfg.seed is not None:
set_seed(cfg.seed)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
###########
# Dataset #
###########
train_set = create_dataset(**cfg.train_set_kwargs)
test_sets = []
test_set_names = []
if cfg.dataset == 'combined':
for name in ['market1501', 'cuhk03', 'duke']:
cfg.test_set_kwargs['name'] = name
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(name)
else:
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(cfg.dataset)
###########
# Models #
###########
model = Model(local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=len(train_set.ids2labels))
# Model wrapper
#model_w = DataParallel(model)
#############################
# Criteria and Optimizers #
#############################
id_criterion = SoftmaxEntropyLoss()
id_criterion1 = nn.CrossEntropyLoss()
g_tri_loss = TripletLoss(margin=cfg.global_margin)
l_tri_loss = TripletLoss(margin=cfg.local_margin)
center_loss = CenterLoss(num_classes=len(train_set.ids2labels),
feat_dim=2048,
use_gpu=True)
d_input_size = 2048
d_hidden_size = 128
d_output_size = 4
d_learning_rate = 1e-4
sgd_momentum = 0.9
d_steps = 2
g_steps = 1
dfe, dre, ge = 0, 0, 0
d_real_data, d_fake_data, g_fake_data = None, None, None
discriminator_activation_function = nn.Sigmoid()
G = Generator(model)
D = Discriminator(input_size=d_input_size,
hidden_size=d_hidden_size,
output_size=d_output_size,
f=discriminator_activation_function)
G_w = DataParallel(G)
D_w = DataParallel(D)
g_optimizer = optim.Adam(G.parameters(),
lr=cfg.base_lr,
weight_decay=cfg.weight_decay)
d_optimizer = optim.SGD(D.parameters(),
lr=d_learning_rate,
momentum=sgd_momentum)
optimizer_centloss = torch.optim.SGD(center_loss.parameters(), lr=0.001)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, g_optimizer]
################################
# May Resume Models and Optims #
################################
if cfg.resume:
resume_ep, scores = load_ckpt(modules_optims, cfg.ckpt_file)
# May Transfer Models and Optims to Specified Device. Transferring optimizer
# is to cope with the case when you load the checkpoint to a new device.
TMO(modules_optims)
########
# Test #
########
def test(load_model_weight=False):
if load_model_weight:
if cfg.model_weight_file != '':
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.model_weight_file,
map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(
cfg.model_weight_file))
else:
load_ckpt(modules_optims, cfg.ckpt_file)
use_local_distance = (cfg.l_loss_weight > 0) \
and cfg.local_dist_own_hard_sample
for test_set, name in zip(test_sets, test_set_names):
test_set.set_feat_func(ExtractFeature(G_w, TVT))
print('\n=========> Test on dataset: {} <=========\n'.format(name))
test_set.eval(normalize_feat=cfg.normalize_feature,
use_local_distance=use_local_distance)
if cfg.only_test:
test(load_model_weight=True)
return
############
# Training #
############
start_ep = resume_ep if cfg.resume else 0
for ep in range(start_ep, cfg.total_epochs):
# Adjust Learning Rate
if cfg.lr_decay_type == 'exp':
adjust_lr_exp(g_optimizer, cfg.base_lr, ep + 1, cfg.total_epochs,
cfg.exp_decay_at_epoch)
if cfg.lr_decay_type == 'exp':
adjust_lr_exp(optimizer_centloss, cfg.base_lr, ep + 1,
cfg.total_epochs, cfg.exp_decay_at_epoch)
else:
adjust_lr_staircase(g_optimizer, cfg.base_lr, ep + 1,
cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
adjust_lr_staircase(optimizer_centloss, cfg.base_lr, ep + 1,
cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
may_set_mode(modules_optims, 'train')
g_prec_meter = AverageMeter()
g_m_meter = AverageMeter()
g_dist_ap_meter = AverageMeter()
g_dist_an_meter = AverageMeter()
g_loss_meter = AverageMeter()
l_prec_meter = AverageMeter()
l_m_meter = AverageMeter()
l_dist_ap_meter = AverageMeter()
l_dist_an_meter = AverageMeter()
l_loss_meter = AverageMeter()
id_loss_meter = AverageMeter()
sift_loss_meter = AverageMeter()
c_loss_meter = AverageMeter()
d_err_meter = AverageMeter()
g_err_meter = AverageMeter()
loss_meter = AverageMeter()
ep_st = time.time()
step = 0
epoch_done = False
while not epoch_done:
step += 1
step_st = time.time()
ims, im_names, labels, mirrored, epoch_done = train_set.next_batch(
)
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
labels_t = TVT(torch.from_numpy(labels).long())
labels_var = Variable(labels_t)
global_feat, local_feat, logits = G_w(ims_var)
sift_func = ExtractSift()
sift = torch.from_numpy(sift_func(ims_var)).cuda()
g_loss, p_inds, n_inds, g_dist_ap, g_dist_an, g_dist_mat = global_loss(
g_tri_loss,
global_feat,
labels_t,
normalize_feature=cfg.normalize_feature)
if cfg.l_loss_weight == 0:
l_loss = 0
elif cfg.local_dist_own_hard_sample:
# Let local distance find its own hard samples.
l_loss, l_dist_ap, l_dist_an, _ = local_loss(
l_tri_loss,
local_feat,
None,
None,
labels_t,
normalize_feature=cfg.normalize_feature)
else:
l_loss, l_dist_ap, l_dist_an = local_loss(
l_tri_loss,
local_feat,
p_inds,
n_inds,
labels_t,
normalize_feature=cfg.normalize_feature)
id_loss = 0
if cfg.id_loss_weight > 0:
id_loss = id_criterion1(logits, labels_var)
sift_loss = 0
if cfg.sift_loss_weight > 0:
sift_loss = torch.norm(
normalize(global_feat, axis=-1) - normalize(sift, axis=-1))
c_loss = 0
if cfg.c_loss_weight > 0:
c_loss = center_loss(normalize(global_feat, axis=-1),
labels_var)
###########################################view classifier#################################
batch_size = 48
view_real_label_list = []
view_fake_label_list = []
view_label_array = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
dtype='float32')
for m in range(batch_size):
i = m % 4
label_real = view_label_array[i, :]
label_fake = np.array([0.25, 0.25, 0.25, 0.25],
dtype='float32')
view_real_label_list.append(label_real)
view_fake_label_list.append(label_fake)
real_label = np.vstack(view_real_label_list)
view_real_label = torch.from_numpy(real_label).cuda()
fake_label = np.vstack(view_fake_label_list)
view_fake_label = torch.from_numpy(fake_label).cuda()
g_error = 0
if cfg.dg_loss_weight > 0:
for d_index in range(d_steps):
# 1. Train D on real+fake
d_optimizer.zero_grad()
d_data = global_feat
d_decision = D_w(d_data)
d_error = id_criterion(d_decision,
view_real_label) # ones = true
d_error.backward(
retain_graph=True
) # compute/store gradients, but don't change params
d_optimizer.step(
) # Only optimizes D's parameters; changes based on stored gradients from backward()
# 2. Train G on D's response (but DO NOT train D on these labels)
g_error = id_criterion(
d_decision,
view_fake_label) # Train G to pretend it's genuine
loss = g_loss * cfg.g_loss_weight \
+ l_loss * cfg.l_loss_weight \
+ id_loss * cfg.id_loss_weight \
+ sift_loss * cfg.sift_loss_weight \
+ c_loss * cfg.c_loss_weight \
+ g_error * cfg.dg_loss_weight
g_optimizer.zero_grad()
optimizer_centloss.zero_grad()
loss.backward(retain_graph=True)
g_optimizer.step()
for param in center_loss.parameters():
param.grad.data *= (1 / cfg.c_loss_weight)
optimizer_centloss.step()
############
# Step Log #
############
# precision
g_prec = (g_dist_an > g_dist_ap).data.float().mean()
# the proportion of triplets that satisfy margin
g_m = (g_dist_an >
g_dist_ap + cfg.global_margin).data.float().mean()
g_d_ap = g_dist_ap.data.mean()
g_d_an = g_dist_an.data.mean()
g_prec_meter.update(g_prec)
g_m_meter.update(g_m)
g_dist_ap_meter.update(g_d_ap)
g_dist_an_meter.update(g_d_an)
g_loss_meter.update(to_scalar(g_loss))
if cfg.l_loss_weight > 0:
# precision
l_prec = (l_dist_an > l_dist_ap).data.float().mean()
# the proportion of triplets that satisfy margin
l_m = (l_dist_an >
l_dist_ap + cfg.local_margin).data.float().mean()
l_d_ap = l_dist_ap.data.mean()
l_d_an = l_dist_an.data.mean()
l_prec_meter.update(l_prec)
l_m_meter.update(l_m)
l_dist_ap_meter.update(l_d_ap)
l_dist_an_meter.update(l_d_an)
l_loss_meter.update(to_scalar(l_loss))
if cfg.id_loss_weight > 0:
id_loss_meter.update(to_scalar(id_loss))
if cfg.sift_loss_weight > 0:
sift_loss_meter.update(to_scalar(sift_loss))
if cfg.c_loss_weight > 0:
c_loss_meter.update(to_scalar(c_loss))
if cfg.dg_loss_weight > 0:
d_err_meter.update(to_scalar(d_error))
g_err_meter.update(to_scalar(g_error))
loss_meter.update(to_scalar(loss))
if step % cfg.log_steps == 0:
time_log = '\tStep {}/Ep {}, {:.2f}s'.format(
step,
ep + 1,
time.time() - step_st,
)
if cfg.g_loss_weight > 0:
g_log = (', gp {:.2%}, gm {:.2%}, '
'gd_ap {:.4f}, gd_an {:.4f}, '
'gL {:.4f}'.format(
g_prec_meter.val,
g_m_meter.val,
g_dist_ap_meter.val,
g_dist_an_meter.val,
g_loss_meter.val,
))
else:
g_log = ''
if cfg.l_loss_weight > 0:
l_log = (', lp {:.2%}, lm {:.2%}, '
'ld_ap {:.4f}, ld_an {:.4f}, '
'lL {:.4f}'.format(
l_prec_meter.val,
l_m_meter.val,
l_dist_ap_meter.val,
l_dist_an_meter.val,
l_loss_meter.val,
))
else:
l_log = ''
if cfg.id_loss_weight > 0:
id_log = (', idL {:.4f}'.format(id_loss_meter.val))
else:
id_log = ''
if cfg.sift_loss_weight > 0:
sift_log = (', sL {:.4f}'.format(sift_loss_meter.val))
else:
sift_log = ''
if cfg.c_loss_weight > 0:
c_log = (', cL {:.4f}'.format(c_loss_meter.val))
else:
c_log = ''
if cfg.c_loss_weight > 0:
d_g_log = (', d_err {:.4f}, g_err {:.4f}'.format(
d_err_meter.val, g_err_meter.val))
else:
d_g_log = ''
total_loss_log = ', loss {:.4f}'.format(loss_meter.val)
log = time_log + \
g_log + l_log + id_log + \
d_g_log + sift_log + c_log + \
total_loss_log
print(log)
#############
# Epoch Log #
#############
time_log = 'Ep {}, {:.2f}s'.format(
ep + 1,
time.time() - ep_st,
)
if cfg.g_loss_weight > 0:
g_log = (', gp {:.2%}, gm {:.2%}, '
'gd_ap {:.4f}, gd_an {:.4f}, '
'gL {:.4f}'.format(
g_prec_meter.avg,
g_m_meter.avg,
g_dist_ap_meter.avg,
g_dist_an_meter.avg,
g_loss_meter.avg,
))
else:
g_log = ''
if cfg.l_loss_weight > 0:
l_log = (', lp {:.2%}, lm {:.2%}, '
'ld_ap {:.4f}, ld_an {:.4f}, '
'lL {:.4f}'.format(
l_prec_meter.avg,
l_m_meter.avg,
l_dist_ap_meter.avg,
l_dist_an_meter.avg,
l_loss_meter.avg,
))
else:
l_log = ''
if cfg.id_loss_weight > 0:
id_log = (', idL {:.4f}'.format(id_loss_meter.avg))
else:
id_log = ''
if cfg.sift_loss_weight > 0:
sift_log = (', sL {:.4f}'.format(sift_loss_meter.avg))
else:
sift_log = ''
if cfg.c_loss_weight > 0:
c_log = (', cL {:.4f}'.format(c_loss_meter.avg))
else:
c_log = ''
if cfg.dg_loss_weight > 0:
d_g_log = (', d_err {:.4f}, g_err {:.4f}'.format(
d_err_meter.avg, g_err_meter.avg))
else:
d_g_log = ''
total_loss_log = ', loss {:.4f}'.format(loss_meter.avg)
log = time_log + \
g_log + l_log + id_log + \
d_g_log + sift_log + c_log + \
total_loss_log
print(log)
# Log to TensorBoard
if cfg.log_to_file:
if writer is None:
writer = SummaryWriter(
log_dir=osp.join(cfg.exp_dir, 'tensorboard'))
writer.add_scalars(
'loss',
dict(
global_loss=g_loss_meter.avg,
local_loss=l_loss_meter.avg,
id_loss=id_loss_meter.avg,
d_err=d_err_meter.avg,
g_err=g_err_meter.avg,
sift_loss=sift_loss_meter.avg,
c_loss=c_loss_meter.avg,
loss=loss_meter.avg,
), ep)
writer.add_scalars(
'tri_precision',
dict(
global_precision=g_prec_meter.avg,
local_precision=l_prec_meter.avg,
), ep)
writer.add_scalars(
'satisfy_margin',
dict(
global_satisfy_margin=g_m_meter.avg,
local_satisfy_margin=l_m_meter.avg,
), ep)
writer.add_scalars(
'global_dist',
dict(
global_dist_ap=g_dist_ap_meter.avg,
global_dist_an=g_dist_an_meter.avg,
), ep)
writer.add_scalars(
'local_dist',
dict(
local_dist_ap=l_dist_ap_meter.avg,
local_dist_an=l_dist_an_meter.avg,
), ep)
# save ckpt
if cfg.log_to_file:
save_ckpt(modules_optims, ep + 1, 0, cfg.ckpt_file)
########
# Test #
########
test(load_model_weight=False)
def main():
cfg = Config()
# Redirect logs to both console and file.
if cfg.log_to_file:
ReDirectSTD(cfg.stdout_file, 'stdout', False)
ReDirectSTD(cfg.stderr_file, 'stderr', False)
# Lazily create SummaryWriter
writer = None
TVT, TMO = set_devices(cfg.sys_device_ids)
if cfg.seed is not None:
set_seed(cfg.seed)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
###########
# Dataset #
###########
train_set = create_dataset(**cfg.train_set_kwargs)
test_sets = []
test_set_names = []
if cfg.dataset == 'combined':
for name in ['market1501', 'cuhk03', 'duke']:
cfg.test_set_kwargs['name'] = name
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(name)
else:
test_sets.append(create_dataset(**cfg.test_set_kwargs))
test_set_names.append(cfg.dataset)
###########
# Models #
###########
model = Model(local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=len(train_set.ids2labels))
# Model wrapper
model_w = DataParallel(model)
#############################
# Criteria and Optimizers #
#############################
id_criterion = nn.CrossEntropyLoss()
g_tri_loss = TripletLoss(margin=cfg.global_margin)
l_tri_loss = TripletLoss(margin=cfg.local_margin)
center_loss = CenterLoss(num_classes=len(train_set.ids2labels),
feat_dim=2048,
use_gpu=True)
optimizer = optim.Adam(model.parameters(),
lr=cfg.base_lr,
weight_decay=cfg.weight_decay)
optimizer_centloss = torch.optim.SGD(center_loss.parameters(), lr=0.001)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
################################
# May Resume Models and Optims #
################################
if cfg.resume:
resume_ep, scores = load_ckpt(modules_optims, cfg.ckpt_file)
# May Transfer Models and Optims to Specified Device. Transferring optimizer
# is to cope with the case when you load the checkpoint to a new device.
TMO(modules_optims)
########
# Test #
########
def test(load_model_weight=False):
if load_model_weight:
if cfg.model_weight_file != '':
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.model_weight_file,
map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(
cfg.model_weight_file))
else:
load_ckpt(modules_optims, cfg.ckpt_file)
use_local_distance = (cfg.l_loss_weight > 0) \
and cfg.local_dist_own_hard_sample
for test_set, name in zip(test_sets, test_set_names):
test_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n=========> Test on dataset: {} <=========\n'.format(name))
test_set.eval(normalize_feat=cfg.normalize_feature,
use_local_distance=use_local_distance)
if cfg.only_test:
test(load_model_weight=True)
return
############
# Training #
############
start_ep = resume_ep if cfg.resume else 0
for ep in range(start_ep, cfg.total_epochs):
# Adjust Learning Rate
if cfg.lr_decay_type == 'exp':
adjust_lr_exp(optimizer, cfg.base_lr, ep + 1, cfg.total_epochs,
cfg.exp_decay_at_epoch)
if cfg.lr_decay_type == 'exp':
adjust_lr_exp(optimizer_centloss, cfg.base_lr, ep + 1,
cfg.total_epochs, cfg.exp_decay_at_epoch)
else:
adjust_lr_staircase(optimizer, cfg.base_lr, ep + 1,
cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
adjust_lr_staircase(optimizer_centloss, cfg.base_lr, ep + 1,
cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
may_set_mode(modules_optims, 'train')
g_prec_meter = AverageMeter()
g_m_meter = AverageMeter()
g_dist_ap_meter = AverageMeter()
g_dist_an_meter = AverageMeter()
g_loss_meter = AverageMeter()
l_prec_meter = AverageMeter()
l_m_meter = AverageMeter()
l_dist_ap_meter = AverageMeter()
l_dist_an_meter = AverageMeter()
l_loss_meter = AverageMeter()
id_loss_meter = AverageMeter()
c_loss_meter = AverageMeter()
loss_meter = AverageMeter()
ep_st = time.time()
step = 0
epoch_done = False
while not epoch_done:
step += 1
step_st = time.time()
ims, im_names, labels, mirrored, epoch_done = train_set.next_batch(
)
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
labels_t = TVT(torch.from_numpy(labels).long())
labels_var = Variable(labels_t)
feat, global_feat, local_feat, logits = model_w(ims_var)
g_loss, p_inds, n_inds, g_dist_ap, g_dist_an, g_dist_mat = global_loss(
g_tri_loss,
global_feat,
labels_t,
normalize_feature=cfg.normalize_feature)
if cfg.l_loss_weight == 0:
l_loss = 0
elif cfg.local_dist_own_hard_sample:
# Let local distance find its own hard samples.
l_loss, l_dist_ap, l_dist_an, _ = local_loss(
l_tri_loss,
local_feat,
None,
None,
labels_t,
normalize_feature=cfg.normalize_feature)
else:
l_loss, l_dist_ap, l_dist_an = local_loss(
l_tri_loss,
local_feat,
p_inds,
n_inds,
labels_t,
normalize_feature=cfg.normalize_feature)
id_loss = 0
if cfg.id_loss_weight > 0:
id_loss = id_criterion(logits, labels_var)
c_loss = 0
if cfg.c_loss_weight > 0:
c_loss = center_loss(normalize(global_feat, axis=-1),
labels_var)
loss = g_loss * cfg.g_loss_weight \
+ l_loss * cfg.l_loss_weight \
+ id_loss * cfg.id_loss_weight \
+ c_loss * cfg.c_loss_weight
optimizer.zero_grad()
optimizer_centloss.zero_grad()
loss.backward()
optimizer.step()
for param in center_loss.parameters():
param.grad.data *= (1 / cfg.c_loss_weight)
optimizer_centloss.step()
############
# Step Log #
############
# precision
g_prec = (g_dist_an > g_dist_ap).data.float().mean()
# the proportion of triplets that satisfy margin
g_m = (g_dist_an >
g_dist_ap + cfg.global_margin).data.float().mean()
g_d_ap = g_dist_ap.data.mean()
g_d_an = g_dist_an.data.mean()
g_prec_meter.update(g_prec)
g_m_meter.update(g_m)
g_dist_ap_meter.update(g_d_ap)
g_dist_an_meter.update(g_d_an)
g_loss_meter.update(to_scalar(g_loss))
if cfg.l_loss_weight > 0:
# precision
l_prec = (l_dist_an > l_dist_ap).data.float().mean()
# the proportion of triplets that satisfy margin
l_m = (l_dist_an >
l_dist_ap + cfg.local_margin).data.float().mean()
l_d_ap = l_dist_ap.data.mean()
l_d_an = l_dist_an.data.mean()
l_prec_meter.update(l_prec)
l_m_meter.update(l_m)
l_dist_ap_meter.update(l_d_ap)
l_dist_an_meter.update(l_d_an)
l_loss_meter.update(to_scalar(l_loss))
if cfg.id_loss_weight > 0:
id_loss_meter.update(to_scalar(id_loss))
if cfg.c_loss_weight > 0:
c_loss_meter.update(to_scalar(c_loss))
loss_meter.update(to_scalar(loss))
if step % cfg.log_steps == 0:
time_log = '\tStep {}/Ep {}, {:.2f}s'.format(
step,
ep + 1,
time.time() - step_st,
)
if cfg.g_loss_weight > 0:
g_log = (', gp {:.2%}, gm {:.2%}, '
'gd_ap {:.4f}, gd_an {:.4f}, '
'gL {:.4f}'.format(
g_prec_meter.val,
g_m_meter.val,
g_dist_ap_meter.val,
g_dist_an_meter.val,
g_loss_meter.val,
))
else:
g_log = ''
if cfg.l_loss_weight > 0:
l_log = (', lp {:.2%}, lm {:.2%}, '
'ld_ap {:.4f}, ld_an {:.4f}, '
'lL {:.4f}'.format(
l_prec_meter.val,
l_m_meter.val,
l_dist_ap_meter.val,
l_dist_an_meter.val,
l_loss_meter.val,
))
else:
l_log = ''
if cfg.id_loss_weight > 0:
id_log = (', idL {:.4f}'.format(id_loss_meter.val))
else:
id_log = ''
if cfg.c_loss_weight > 0:
c_log = (', cL {:.4f}'.format(c_loss_meter.val))
else:
c_log = ''
total_loss_log = ', loss {:.4f}'.format(loss_meter.val)
log = time_log + \
g_log + l_log + id_log + \
total_loss_log
print(log)
#############
# Epoch Log #
#############
time_log = 'Ep {}, {:.2f}s'.format(
ep + 1,
time.time() - ep_st,
)
if cfg.g_loss_weight > 0:
g_log = (', gp {:.2%}, gm {:.2%}, '
'gd_ap {:.4f}, gd_an {:.4f}, '
'gL {:.4f}'.format(
g_prec_meter.avg,
g_m_meter.avg,
g_dist_ap_meter.avg,
g_dist_an_meter.avg,
g_loss_meter.avg,
))
else:
g_log = ''
if cfg.l_loss_weight > 0:
l_log = (', lp {:.2%}, lm {:.2%}, '
'ld_ap {:.4f}, ld_an {:.4f}, '
'lL {:.4f}'.format(
l_prec_meter.avg,
l_m_meter.avg,
l_dist_ap_meter.avg,
l_dist_an_meter.avg,
l_loss_meter.avg,
))
else:
l_log = ''
if cfg.id_loss_weight > 0:
id_log = (', idL {:.4f}'.format(id_loss_meter.avg))
else:
id_log = ''
if cfg.c_loss_weight > 0:
c_log = (', cL {:.4f}'.format(c_loss_meter.avg))
else:
c_log = ''
total_loss_log = ', loss {:.4f}'.format(loss_meter.avg)
log = time_log + \
g_log + l_log + id_log + \
c_log + total_loss_log
print(log)
# Log to TensorBoard
if cfg.log_to_file:
if writer is None:
writer = SummaryWriter(
log_dir=osp.join(cfg.exp_dir, 'tensorboard'))
writer.add_scalars(
'loss',
dict(
global_loss=g_loss_meter.avg,
local_loss=l_loss_meter.avg,
id_loss=id_loss_meter.avg,
c_loss=c_loss_meter.avg,
loss=loss_meter.avg,
), ep)
writer.add_scalars(
'tri_precision',
dict(
global_precision=g_prec_meter.avg,
local_precision=l_prec_meter.avg,
), ep)
writer.add_scalars(
'satisfy_margin',
dict(
global_satisfy_margin=g_m_meter.avg,
local_satisfy_margin=l_m_meter.avg,
), ep)
writer.add_scalars(
'global_dist',
dict(
global_dist_ap=g_dist_ap_meter.avg,
global_dist_an=g_dist_an_meter.avg,
), ep)
writer.add_scalars(
'local_dist',
dict(
local_dist_ap=l_dist_ap_meter.avg,
local_dist_an=l_dist_an_meter.avg,
), ep)
# save ckpt
if cfg.log_to_file:
save_ckpt(modules_optims, ep + 1, 0, cfg.ckpt_file)
########
# Test #
########
test(load_model_weight=False)