def main():
# name_data = '20180525184611494'
name_data = '20180717103311494'
TVT, TMO = set_devices((0,))
dataset_kwargs = dict(
name = name_data,
resize_h_w = (256, 128),
scale = True,
im_mean = [0.486, 0.459, 0.408],
im_std = [0.229, 0.224, 0.225],
batch_dims = 'NCHW',
num_prefetch_threads = 1)
test_set_kwargs = dict(
part = 'val',
batch_size = 32,
final_batch = True,
shuffle = False,
mirror_type = ['random', 'always', None][2],
prng = np.random)
test_set_kwargs.update(dataset_kwargs)
test_set = create_dataset(**test_set_kwargs)
with measure_time('Load model'):
model = Model(local_conv_out_channels = 128,
num_classes = 1000)
model_w = DataParallel(model)
optimizer = optim.Adam(model.parameters(),
lr = 2e-4,
weight_decay = 0.0005)
modules_optims = [model, optimizer]
with measure_time('Load checkpoint'):
load_ckpt(modules_optims, 'ckpt.pth')
test_set.set_feat_func(ExtractFeature(model_w, TVT))
exp_summary(test_set)
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)
optimizer = optim.Adam(model.parameters(),
lr=cfg.base_lr,
weight_decay=cfg.weight_decay)
# 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)
else:
adjust_lr_staircase(
optimizer,
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()
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 = 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)
loss = g_loss * cfg.g_loss_weight \
+ l_loss * cfg.l_loss_weight \
+ id_loss * cfg.id_loss_weight
optimizer.zero_grad()
loss.backward()
optimizer.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))
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 = ''
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 = ''
total_loss_log = ', loss {:.4f}'.format(loss_meter.avg)
log = time_log + \
g_log + l_log + id_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,
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)
###########
# Models #
###########
local_conv_out_channels = 128
num_classes = 3
model = Model(local_conv_out_channels=local_conv_out_channels, num_classes=num_classes)
# Model wrapper
model_w = DataParallel(model)
base_lr = 2e-4
weight_decay = 0.0005
optimizer = optim.Adam(model.parameters(), lr=base_lr, weight_decay=weight_decay)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
model_weight_file = '../../model_weight.pth'
map_location = (lambda storage, loc: storage)
sd = torch.load(model_weight_file, map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(model_weight_file))
sys_device_ids = (0,)
TVT, TMO = set_devices(sys_device_ids)
def main():
# reranking_mAP_list = []
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)
optimizer = optim.Adam(model.parameters(),
lr=cfg.base_lr,
weight_decay=cfg.weight_decay)
# 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)
# reranking_mAP_list.append(mAP)
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)
else:
adjust_lr_staircase(optimizer, 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()
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 = 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)
loss = g_loss * cfg.g_loss_weight \
+ l_loss * cfg.l_loss_weight \
+ id_loss * cfg.id_loss_weight
optimizer.zero_grad()
loss.backward()
optimizer.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))
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 = ''
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 = ''
total_loss_log = ', loss {:.4f}'.format(loss_meter.avg)
log = time_log + \
g_log + l_log + id_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,
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)
#if (ep+1)%1==0:
# test(load_model_weight=False)
# if (ep+1)%10==0:
# print(reranking_mAP_list)
########
# Test #
########
test(load_model_weight=False)
# key = cv2.waitKey(0)
###########
# Models #
###########
local_conv_out_channels = 128
num_classes = 3
model = Model(local_conv_out_channels=local_conv_out_channels,
num_classes=num_classes)
# Model wrapper
model_w = DataParallel(model)
base_lr = 2e-4
weight_decay = 0.0005
optimizer = optim.Adam(model.parameters(),
lr=base_lr,
weight_decay=weight_decay)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
model_weight_file = '/home/niruhan/AlignedReID-Re-Production-Pytorch/model_weight.pth'
map_location = (lambda storage, loc: storage)
sd = torch.load(model_weight_file, map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(model_weight_file))
sys_device_ids = (0, )