def main():
TVT, TMO = set_devices((-1,))
model = Model(local_conv_out_channels=128,
num_classes=751)
model_w = DataParallel(model)
map_location = (lambda storage, loc: storage)
sd = torch.load('ckpt.pth', map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights')
ims = []
im1 = PIL.Image.open('demo/0.jpg')
ims.append(preprocess_image(im1))
im2 = PIL.Image.open('demo/1.jpg')
ims.append(preprocess_image(im2))
ims = Variable(TVT(torch.from_numpy(np.array(ims)).float()))
start = time.time()
global_feat, local_feat = model(ims)[:2]
global_feat = np.vstack(global_feat.data.cpu().numpy())
local_feat = local_feat.data.cpu().numpy()
distance = get_distance((global_feat[0],local_feat[0]),(global_feat[1],local_feat[1]))
print("distance: ", distance)
def get_extractor():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
if cfg.seed is not None:
set_seed(cfg.seed)
#######################
# print configuration #
#######################
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
##########
# Models #
##########
model = Model() # num_class is None, no need for local feature
model_w = DataParallel(model)
print(model.state_dict().keys())
# Transfer model to specify devices
TMO([model])
assert (cfg.model_weight_file != '')
map_location = (lambda storage, loc: storage)
sd = torch.load(cfg.model_weight_file, map_location=map_location)
if 'state_dicts' in sd: # load from checkpoint
print(len(sd['state_dicts']))
sd = sd['state_dicts'][0]
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(cfg.model_weight_file))
###########
# Extract #
###########
extractor = ExtractFeature(model_w, TVT)
return extractor
def main():
TVT, TMO = set_devices((-1,))
model = Model(local_conv_out_channels=128,
num_classes=751)
# Model wrapper
model_w = DataParallel(model)
map_location = (lambda storage, loc: storage)
sd = torch.load('ckpt.pth', map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights')
IMAGES_PATH = "galery/"
files = os.listdir(IMAGES_PATH)
files.sort();
data = []
formats = ["png", "jpg"]
curr_id = 0
ids = {}
for image_name in files:
if image_name.split('.')[1] in formats and int(image_name.split('_')[0]) > 0:
id = image_name.split('_')[0]
'''
if id not in ids:
ids[id] = 0
else:
if ids[id] > 20:
continue
ids[id]+=1
'''
im = PIL.Image.open(IMAGES_PATH + image_name)
im = im.resize((128,256))
#im.save('tmp.jpg')
I = np.asarray(im)
I = I - np.mean(I)
I = I/np.std(I)
I= np.expand_dims(I.transpose(2, 0, 1),axis = 0)
ims = Variable(TVT(torch.from_numpy(I).float()))
global_feat, local_feat = model(ims)[:2]
global_feat = np.vstack(global_feat.data.cpu().numpy())
local_feat = local_feat.data.cpu().numpy()
#print(features)
print("processing file: ", image_name)
data.append({"id" : id, "features" : (global_feat, local_feat),"path" : IMAGES_PATH + image_name})
filename = 'data'
outfile = open(filename,'wb')
pickle.dump(data,outfile)
outfile.close()
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 __init__(self, ):
os.chdir('./src/person_reid/AlignedReID-Re-Production-Pytorch-master/')
import torch
from torch.nn.parallel import DataParallel
from aligned_reid.model.Model import Model
from aligned_reid.utils.utils import load_state_dict
from aligned_reid.utils.utils import set_devices
os.chdir('../../../')
model = Model(local_conv_out_channels=128, num_classes=751)
# Model wrapper
model_w = DataParallel(model)
weight = './model/align_reid/model_weight.pth'
map_location = (lambda storage, loc: storage)
sd = torch.load(weight, map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(weight))
TVT, TMO = set_devices((0, ))
self.featureExtractor = AlignReid_Embedder.ExtractFeature(model_w, TVT)
def prepare():
model = Model(local_conv_out_channels)
model_w = DataParallel(model)
map_location = (lambda storage, loc: storage)
sd = torch.load(model_weight_file, map_location=map_location)
if 'state_dicts' in sd:
sd = sd['state_dicts'][0]
load_state_dict(model, sd)
print('Loaded model weight from {}'.format(model_weight_file))
TMO([model])
extractor = ExtractFeature(model_w, TVT)
preprocessor = PreProcessIm(resize_h_w,
scale=scale_im,
im_mean=im_mean,
im_std=im_std)
return extractor, preprocessor
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)
def __call__(self, ims):
print(len(ims))
old_train_eval_model = self.model.training
# Set eval mode.
# Force all BN layers to use global mean and variance, also disable
# dropout.
self.model.eval()
ims = Variable(self.TVT(torch.from_numpy(ims).float()))
global_feat, local_feat = self.model(ims)[:2]
global_feat = global_feat.data.cpu().numpy()
local_feat = local_feat.data.cpu().numpy()
# Restore the model to its old train/eval mode.
self.model.train(old_train_eval_model)
return global_feat, local_feat
model = Model(local_conv_out_channels=128, num_classes=751)
# Model wrapper
model_w = DataParallel(model)
weight = './model_weight.pth'
map_location = (lambda storage, loc: storage)
sd = torch.load(weight, map_location=map_location)
load_state_dict(model, sd)
print('Loaded model weights from {}'.format(weight))
TVT, TMO = set_devices((0, ))
FeatureExtractor = ExtractFeature(model_w, TVT)
global_feat, local_feat = FeatureExtractor(XXX)
from aligned_reid.utils.distance import compute_dist, low_memory_matrix_op, local_dist, normalize from aligned_reid.utils.utils import load_state_dict, measure_time from aligned_reid.utils.utils import set_devices from torch.autograd import Variable import numpy as np ########### # 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)
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)
resized_image = cv2.resize(input_image, (128, 256))
# input_image = np.asarray(Image.open('gow_query.jpg'))
transposed = resized_image.transpose(2, 0, 1)
test_img = transposed[np.newaxis]
# cv2.imshow("preview", input_image)
# 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)
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
TVTs, TMOs, relative_device_ids = set_devices_for_ml(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 #
###########
models = [Model(local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=len(train_set.ids2labels))
for _ in range(cfg.num_models)]
# Model wrappers
model_ws = [DataParallel(models[i], device_ids=relative_device_ids[i])
for i in range(cfg.num_models)]
#############################
# Criteria and Optimizers #
#############################
id_criterion = nn.CrossEntropyLoss()
g_tri_loss = TripletLoss(margin=cfg.global_margin)
l_tri_loss = TripletLoss(margin=cfg.local_margin)
optimizers = [optim.Adam(m.parameters(),
lr=cfg.base_lr,
weight_decay=cfg.weight_decay)
for m in models]
# Bind them together just to save some codes in the following usage.
modules_optims = models + optimizers
################################
# 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 optimizers
# is to cope with the case when you load the checkpoint to a new device.
for TMO, model, optimizer in zip(TMOs, models, optimizers):
TMO([model, optimizer])
########
# Test #
########
# Test each model using different distance settings.
def test(load_model_weight=False):
if load_model_weight:
load_ckpt(modules_optims, cfg.ckpt_file)
use_local_distance = (cfg.l_loss_weight > 0) \
and cfg.local_dist_own_hard_sample
for i, (model_w, TVT) in enumerate(zip(model_ws, TVTs)):
for test_set, name in zip(test_sets, test_set_names):
test_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n=========> Test Model #{} on dataset: {} <=========\n'
.format(i + 1, 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 #
############
# Storing things that can be accessed cross threads.
ims_list = [None for _ in range(cfg.num_models)]
labels_list = [None for _ in range(cfg.num_models)]
done_list1 = [False for _ in range(cfg.num_models)]
done_list2 = [False for _ in range(cfg.num_models)]
probs_list = [None for _ in range(cfg.num_models)]
g_dist_mat_list = [None for _ in range(cfg.num_models)]
l_dist_mat_list = [None for _ in range(cfg.num_models)]
# Two phases for each model:
# 1) forward and single-model loss;
# 2) further add mutual loss and backward.
# The 2nd phase is only ready to start when the 1st is finished for
# all models.
run_event1 = threading.Event()
run_event2 = threading.Event()
# This event is meant to be set to stop threads. However, as I found, with
# `daemon` set to true when creating threads, manually stopping is
# unnecessary. I guess some main-thread variables required by sub-threads
# are destroyed when the main thread ends, thus the sub-threads throw errors
# and exit too.
# Real reason should be further explored.
exit_event = threading.Event()
# The function to be called by threads.
def thread_target(i):
while not exit_event.isSet():
# If the run event is not set, the thread just waits.
if not run_event1.wait(0.001): continue
######################################
# Phase 1: Forward and Separate Loss #
######################################
TVT = TVTs[i]
model_w = model_ws[i]
ims = ims_list[i]
labels = labels_list[i]
optimizer = optimizers[i]
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)
probs = F.softmax(logits, dim=1)
log_probs = F.log_softmax(logits, dim=1)
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, l_dist_mat = 0, 0
elif cfg.local_dist_own_hard_sample:
# Let local distance find its own hard samples.
l_loss, l_dist_ap, l_dist_an, l_dist_mat = 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)
l_dist_mat = 0
id_loss = 0
if cfg.id_loss_weight > 0:
id_loss = id_criterion(logits, labels_var)
probs_list[i] = probs
g_dist_mat_list[i] = g_dist_mat
l_dist_mat_list[i] = l_dist_mat
done_list1[i] = True
# Wait for event to be set, meanwhile checking if need to exit.
while True:
phase2_ready = run_event2.wait(0.001)
if exit_event.isSet():
return
if phase2_ready:
break
#####################################
# Phase 2: Mutual Loss and Backward #
#####################################
# Probability Mutual Loss (KL Loss)
pm_loss = 0
if (cfg.num_models > 1) and (cfg.pm_loss_weight > 0):
for j in range(cfg.num_models):
if j != i:
pm_loss += F.kl_div(log_probs, TVT(probs_list[j]).detach(), False)
pm_loss /= 1. * (cfg.num_models - 1) * len(ims)
# Global Distance Mutual Loss (L2 Loss)
gdm_loss = 0
if (cfg.num_models > 1) and (cfg.gdm_loss_weight > 0):
for j in range(cfg.num_models):
if j != i:
gdm_loss += torch.sum(torch.pow(
g_dist_mat - TVT(g_dist_mat_list[j]).detach(), 2))
gdm_loss /= 1. * (cfg.num_models - 1) * len(ims) * len(ims)
# Local Distance Mutual Loss (L2 Loss)
ldm_loss = 0
if (cfg.num_models > 1) \
and cfg.local_dist_own_hard_sample \
and (cfg.ldm_loss_weight > 0):
for j in range(cfg.num_models):
if j != i:
ldm_loss += torch.sum(torch.pow(
l_dist_mat - TVT(l_dist_mat_list[j]).detach(), 2))
ldm_loss /= 1. * (cfg.num_models - 1) * len(ims) * len(ims)
loss = g_loss * cfg.g_loss_weight \
+ l_loss * cfg.l_loss_weight \
+ id_loss * cfg.id_loss_weight \
+ pm_loss * cfg.pm_loss_weight \
+ gdm_loss * cfg.gdm_loss_weight \
+ ldm_loss * cfg.ldm_loss_weight
optimizer.zero_grad()
loss.backward()
optimizer.step()
##################################
# Step Log For One of the Models #
##################################
# Just record for the first model
if i == 0:
# 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()
# These meters are outer-scope objects
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.num_models > 1) and (cfg.pm_loss_weight > 0):
pm_loss_meter.update(to_scalar(pm_loss))
if (cfg.num_models > 1) and (cfg.gdm_loss_weight > 0):
gdm_loss_meter.update(to_scalar(gdm_loss))
if (cfg.num_models > 1) \
and cfg.local_dist_own_hard_sample \
and (cfg.ldm_loss_weight > 0):
ldm_loss_meter.update(to_scalar(ldm_loss))
loss_meter.update(to_scalar(loss))
###################
# End Up One Step #
###################
run_event1.clear()
run_event2.clear()
done_list2[i] = True
threads = []
for i in range(cfg.num_models):
thread = threading.Thread(target=thread_target, args=(i,))
# Set the thread in daemon mode, so that the main program ends normally.
thread.daemon = True
thread.start()
threads.append(thread)
start_ep = resume_ep if cfg.resume else 0
for ep in range(start_ep, cfg.total_epochs):
# Adjust Learning Rate
for optimizer in optimizers:
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')
epoch_done = False
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()
# Global Distance Mutual Loss
gdm_loss_meter = AverageMeter()
# Local Distance Mutual Loss
ldm_loss_meter = AverageMeter()
# Probability Mutual Loss
pm_loss_meter = AverageMeter()
loss_meter = AverageMeter()
ep_st = time.time()
step = 0
while not epoch_done:
step += 1
step_st = time.time()
ims, im_names, labels, mirrored, epoch_done = train_set.next_batch()
for i in range(cfg.num_models):
ims_list[i] = ims
labels_list[i] = labels
done_list1[i] = False
done_list2[i] = False
run_event1.set()
# Waiting for phase 1 done
while not all(done_list1): continue
run_event2.set()
# Waiting for phase 2 done
while not all(done_list2): continue
############
# Step Log #
############
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.num_models > 1) and (cfg.pm_loss_weight > 0):
pm_log = (', pmL {:.4f}'.format(pm_loss_meter.val))
else:
pm_log = ''
if (cfg.num_models > 1) and (cfg.gdm_loss_weight > 0):
gdm_log = (', gdmL {:.4f}'.format(gdm_loss_meter.val))
else:
gdm_log = ''
if (cfg.num_models > 1) \
and cfg.local_dist_own_hard_sample \
and (cfg.ldm_loss_weight > 0):
ldm_log = (', ldmL {:.4f}'.format(ldm_loss_meter.val))
else:
ldm_log = ''
total_loss_log = ', loss {:.4f}'.format(loss_meter.val)
log = time_log + \
g_log + l_log + id_log + \
pm_log + gdm_log + ldm_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.num_models > 1) and (cfg.pm_loss_weight > 0):
pm_log = (', pmL {:.4f}'.format(pm_loss_meter.avg))
else:
pm_log = ''
if (cfg.num_models > 1) and (cfg.gdm_loss_weight > 0):
gdm_log = (', gdmL {:.4f}'.format(gdm_loss_meter.avg))
else:
gdm_log = ''
if (cfg.num_models > 1) \
and cfg.local_dist_own_hard_sample \
and (cfg.ldm_loss_weight > 0):
ldm_log = (', ldmL {:.4f}'.format(ldm_loss_meter.avg))
else:
ldm_log = ''
total_loss_log = ', loss {:.4f}'.format(loss_meter.avg)
log = time_log + \
g_log + l_log + id_log + \
pm_log + gdm_log + ldm_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,
pm_loss=pm_loss_meter.avg,
gdm_loss=gdm_loss_meter.avg,
ldm_loss=ldm_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)