def main():
#GENERAL
root = "/home/kuru/Desktop/veri-gms-master/"
train_dir = '/home/kuru/Desktop/veri-gms-master/VeRispan/image_train/'
source = {'veri'}
target = {'veri'}
workers = 2
height = 320
width = 320
train_sampler = 'RandomSampler'
#AUGMENTATION
random_erase = True
jitter = True
aug = True
#OPTIMIZATION
opt = 'adam'
lr = 0.0003
weight_decay = 5e-4
momentum = 0.9
sgd_damp = 0.0
nesterov = True
warmup_factor = 0.01
warmup_method = 'linear'
#HYPERPARAMETER
max_epoch = 80
start = 0
train_batch_size = 16
test_batch_size = 50
#SCHEDULER
lr_scheduler = 'multi_step'
stepsize = [30, 60]
gamma = 0.1
#LOSS
margin = 0.3
num_instances = 6
lambda_tri = 1
#MODEL
arch = 'resnet101_ibn_a'
no_pretrained = False
#TEST SETTINGS
load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet101_ibn_a.pth'
#load_weights = None
start_eval = 0
eval_freq = -1
#MISC
use_gpu = True
use_amp = True
print_freq = 50
seed = 1
resume = ''
save_dir = '/home/kuru/Desktop/veri-gms-master/logapex/'
gpu_id = 0
vis_rank = True
query_remove = True
evaluate = False
dataset_kwargs = {
'source_names': source,
'target_names': target,
'root': root,
'height': height,
'width': width,
'train_batch_size': train_batch_size,
'test_batch_size': test_batch_size,
'train_sampler': train_sampler,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
transform_kwargs = {
'height': height,
'width': width,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
optimizer_kwargs = {
'optim': opt,
'lr': lr,
'weight_decay': weight_decay,
'momentum': momentum,
'sgd_dampening': sgd_damp,
'sgd_nesterov': nesterov
}
lr_scheduler_kwargs = {
'lr_scheduler': lr_scheduler,
'stepsize': stepsize,
'gamma': gamma
}
use_gpu = torch.cuda.is_available()
log_name = 'log_test.txt' if evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(save_dir, log_name))
print('Currently using GPU ', gpu_id)
cudnn.benchmark = True
print('Initializing image data manager')
dataset = init_imgreid_dataset(root='/home/kuru/Desktop/veri-gms-master/',
name='veri')
train = []
num_train_pids = 0
num_train_cams = 0
for img_path, pid, camid in dataset.train:
path = img_path[52:77]
#print(path)
folder = path[1:4]
pid += num_train_pids
camid += num_train_cams
train.append((path, folder, pid, camid))
num_train_pids += dataset.num_train_pids
num_train_cams += dataset.num_train_cams
pid = 0
pidx = {}
for img_path, pid, camid in dataset.train:
path = img_path[52:77]
folder = path[1:4]
pidx[folder] = pid
pid += 1
path = '/home/kuru/Desktop/veri-gms-master/gms/'
pkl = {}
entries = os.listdir(path)
for name in entries:
f = open((path + name), 'rb')
if name == 'featureMatrix.pkl':
s = name[0:13]
else:
s = name[0:3]
pkl[s] = pickle.load(f)
f.close
transform_t = train_transforms(**transform_kwargs)
data_tfr = vd(
pkl_file='index.pkl',
dataset=train,
root_dir='/home/kuru/Desktop/veri-gms-master/VeRi/image_train/',
transform=transform_t)
trainloader = DataLoader(data_tfr,
sampler=None,
batch_size=train_batch_size,
shuffle=True,
num_workers=workers,
pin_memory=False,
drop_last=True)
#data_tfr = vd(pkl_file='index.pkl', dataset = train, root_dir=train_dir,transform=transforms.Compose([Rescale(64),RandomCrop(32),ToTensor()]))
#dataloader = DataLoader(data_tfr, batch_size=batch_size, shuffle=False, num_workers=0)
print('Initializing test data manager')
dm = ImageDataManager(use_gpu, **dataset_kwargs)
testloader_dict = dm.return_dataloaders()
print('Initializing model: {}'.format(arch))
model = models.init_model(name=arch,
num_classes=num_train_pids,
loss={'xent', 'htri'},
last_stride=1,
pretrained=not no_pretrained,
use_gpu=use_gpu)
print('Model size: {:.3f} M'.format(count_num_param(model)))
if load_weights is not None:
print("weights loaded")
load_pretrained_weights(model, load_weights)
model = (model).cuda() if use_gpu else model
#model = nn.DataParallel(model).cuda() if use_gpu else model
optimizer = init_optimizer(model, **optimizer_kwargs)
#optimizer = init_optimizer(model)
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
keep_batchnorm_fp32=True,
loss_scale="dynamic")
model = nn.DataParallel(model).cuda() if use_gpu else model
scheduler = init_lr_scheduler(optimizer, **lr_scheduler_kwargs)
criterion_xent = CrossEntropyLoss(num_classes=num_train_pids,
use_gpu=use_gpu,
label_smooth=True)
criterion_htri = TripletLoss(margin=margin)
ranking_loss = nn.MarginRankingLoss(margin=margin)
if evaluate:
print('Evaluate only')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
_, distmat = test(model,
queryloader,
galleryloader,
train_batch_size,
use_gpu,
return_distmat=True)
if vis_rank:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
save_dir, 'ranked_results', name),
topk=20)
return
time_start = time.time()
ranklogger = RankLogger(source, target)
print('=> Start training')
data_index = search(pkl)
for epoch in range(start, max_epoch):
losses = AverageMeter()
#xent_losses = AverageMeter()
htri_losses = AverageMeter()
accs = AverageMeter()
batch_time = AverageMeter()
model.train()
for p in model.parameters():
p.requires_grad = True # open all layers
end = time.time()
for batch_idx, (img, label, index, pid, cid) in enumerate(trainloader):
trainX, trainY = torch.zeros(
(train_batch_size * 3, 3, height, width),
dtype=torch.float32), torch.zeros((train_batch_size * 3),
dtype=torch.int64)
#pids = torch.zeros((batch_size*3), dtype = torch.int16)
for i in range(train_batch_size):
labelx = str(label[i])
indexx = int(index[i])
cidx = int(pid[i])
if indexx > len(pkl[labelx]) - 1:
indexx = len(pkl[labelx]) - 1
#maxx = np.argmax(pkl[labelx][indexx])
a = pkl[labelx][indexx]
minpos = np.argmax(ma.masked_where(a == 0, a))
pos_dic = data_tfr[data_index[cidx][1] + minpos]
neg_label = int(labelx)
while True:
neg_label = random.choice(range(1, 770))
if neg_label is not int(labelx) and os.path.isdir(
os.path.join(
'/home/kuru/Desktop/adiusb/veri-split/train',
strint(neg_label))) is True:
break
negative_label = strint(neg_label)
neg_cid = pidx[negative_label]
neg_index = random.choice(range(0, len(pkl[negative_label])))
neg_dic = data_tfr[data_index[neg_cid][1] + neg_index]
trainX[i] = img[i]
trainX[i + train_batch_size] = pos_dic[0]
trainX[i + (train_batch_size * 2)] = neg_dic[0]
trainY[i] = cidx
trainY[i + train_batch_size] = pos_dic[3]
trainY[i + (train_batch_size * 2)] = neg_dic[3]
#print("anc",labelx,'posdic', pos_dic[1],pos_dic[2],'negdic', neg_dic[1],neg_dic[2])
trainX = trainX.cuda()
trainY = trainY.cuda()
outputs, features = model(trainX)
xent_loss = criterion_xent(outputs[0:train_batch_size],
trainY[0:train_batch_size])
htri_loss = criterion_htri(features, trainY)
#tri_loss = ranking_loss(features)
#ent_loss = xent_loss(outputs[0:batch_size], trainY[0:batch_size], num_train_pids)
loss = htri_loss + xent_loss
optimizer.zero_grad()
if use_amp:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
#loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
losses.update(loss.item(), trainY.size(0))
htri_losses.update(htri_loss.item(), trainY.size(0))
accs.update(
accuracy(outputs[0:train_batch_size],
trainY[0:train_batch_size])[0])
if (batch_idx) % print_freq == 0:
print('Train ', end=" ")
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc {acc.val:.2f} ({acc.avg:.2f})\t'.format(
epoch + 1,
batch_idx + 1,
len(trainloader),
batch_time=batch_time,
loss=htri_losses,
acc=accs))
end = time.time()
scheduler.step()
print('=> Test')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
rank2, distmat2 = test_rerank(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank2)
del queryloader
del galleryloader
del distmat
#print(torch.cuda.memory_allocated(),torch.cuda.memory_cached())
torch.cuda.empty_cache()
if (epoch + 1) == max_epoch:
#if (epoch + 1) % 10 == 0:
print('=> Test')
save_checkpoint(
{
'state_dict': model.state_dict(),
'rank1': rank1,
'epoch': epoch + 1,
'arch': arch,
'optimizer': optimizer.state_dict(),
}, save_dir)
if vis_rank:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
save_dir, 'ranked_results', name),
topk=20)
def main():
#GENERAL
torch.cuda.empty_cache()
root = "/home/kuru/Desktop/veri-gms-master_noise/"
train_dir = '/home/kuru/Desktop/veri-gms-master_noise/VeRispan/image_train/'
source = {'verispan'}
target = {'verispan'}
workers = 4
height = 280
width = 280
train_size = 32
train_sampler = 'RandomSampler'
#AUGMENTATION
random_erase = True
jitter = True
aug = True
#OPTIMIZATION
opt = 'adam'
lr = 0.0003
weight_decay = 5e-4
momentum = 0.9
sgd_damp = 0.0
nesterov = True
warmup_factor = 0.01
warmup_method = 'linear'
#HYPERPARAMETER
max_epoch = 80
start = 0
train_batch_size = 8
test_batch_size = 100
#SCHEDULER
lr_scheduler = 'multi_step'
stepsize = [30, 60]
gamma = 0.1
#LOSS
margin = 0.3
num_instances = 4
lambda_tri = 1
#MODEL
#arch = 'resnet101'
arch='resnet101_ibn_a'
no_pretrained = False
#TEST SETTINGS
load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet101_ibn_a.pth'
#load_weights = None
start_eval = 0
eval_freq = -1
#MISC
use_gpu = True
print_freq = 10
seed = 1
resume = ''
save_dir = '/home/kuru/Desktop/veri-gms-master_noise/spanningtree_verinoise_101_stride2/'
gpu_id = 0,1
vis_rank = True
query_remove = True
evaluate = False
dataset_kwargs = {
'source_names': source,
'target_names': target,
'root': root,
'height': height,
'width': width,
'train_batch_size': train_batch_size,
'test_batch_size': test_batch_size,
'train_sampler': train_sampler,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
transform_kwargs = {
'height': height,
'width': width,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
optimizer_kwargs = {
'optim': opt,
'lr': lr,
'weight_decay': weight_decay,
'momentum': momentum,
'sgd_dampening': sgd_damp,
'sgd_nesterov': nesterov
}
lr_scheduler_kwargs = {
'lr_scheduler': lr_scheduler,
'stepsize': stepsize,
'gamma': gamma
}
use_gpu = torch.cuda.is_available()
log_name = 'log_test.txt' if evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(save_dir, log_name))
print('Currently using GPU ', gpu_id)
cudnn.benchmark = True
print('Initializing image data manager')
dataset = init_imgreid_dataset(root='/home/kuru/Desktop/veri-gms-master_noise/', name='verispan')
train = []
num_train_pids = 0
num_train_cams = 0
print(len( dataset.train))
for img_path, pid, camid, subid, countid in dataset.train:
#print(img_path)
path = img_path[56+6:90+6]
#print(path)
folder = path[1:4]
#print(folder)
pid += num_train_pids
newidd=0
train.append((path, folder, pid, camid,subid,countid))
num_train_pids += dataset.num_train_pids
num_train_cams += dataset.num_train_cams
pid = 0
pidx = {}
for img_path, pid, camid, subid, countid in dataset.train:
path = img_path[56+6:90+6]
folder = path[1:4]
pidx[folder] = pid
pid+= 1
sub=[]
final=0
xx=dataset.train
newids=[]
print(train[0:2])
train2={}
for k in range(0,770):
for img_path, pid, camid, subid, countid in dataset.train:
if k==pid:
newid=final+subid
sub.append(newid)
#print(pid,subid,newid)
newids.append(newid)
train2[img_path]= newid
#print(img_path, pid, camid, subid, countid, newid)
final=max(sub)
#print(final)
print(len(newids),final)
#train=train2
#print(train2)
train3=[]
for img_path, pid, camid, subid, countid in dataset.train:
#print(img_path,pid,train2[img_path])
path = img_path[56:90+6]
#print(path)
folder = path[1:4]
newid=train2[img_path]
#print((path, folder, pid, camid, subid, countid,newid ))
train3.append((path, folder, pid, camid, subid, countid,newid ))
train = train3
path = '/home/kuru/Desktop/adhi/veri-final-draft-master_noise/gmsNoise776/'
pkl = {}
#pkl[0] = pickle.load('/home/kuru/Desktop/veri-gms-master/gms/620.pkl')
entries = os.listdir(path)
for name in entries:
f = open((path+name), 'rb')
ccc=(path+name)
#print(ccc)
if name=='featureMatrix.pkl':
s = name[0:13]
else:
s = name[0:3]
#print(s)
#with open (ccc,"rb") as ff:
# pkl[s] = pickle.load(ff)
#print(pkl[s])
pkl[s] = pickle.load(f)
f.close
#print(len(pkl))
with open('cids.pkl', 'rb') as handle:
b = pickle.load(handle)
#print(b)
with open('index.pkl', 'rb') as handle:
c = pickle.load(handle)
transform_t = train_transforms(**transform_kwargs)
data_tfr = vdspan(pkl_file='index_veryspan_noise.pkl', dataset = train, root_dir='/home/kuru/Desktop/veri-gms-master_noise/VeRispan/image_train/', transform=transform_t)
print("lllllllllllllllllllllllllllllllllllllllllllline 433")
df2=[]
data_tfr_old=data_tfr
for (img,label,index,pid, cid,subid,countid,newid) in data_tfr :
#print((img,label,index,pid, cid,subid,countid,newid) )
#print("datframe",(label))
#print(countid)
if countid > 4 :
#print(countid)
df2.append((img,label,index,pid, cid,subid,countid,newid))
print("filtered final trainset length",len(df2))
data_tfr=df2
trainloader = DataLoader(data_tfr, sampler=None,batch_size=train_batch_size, shuffle=True, num_workers=workers,pin_memory=True, drop_last=True)
#data_tfr = vd(pkl_file='index.pkl', dataset = train, root_dir=train_dir,transform=transforms.Compose([Rescale(64),RandomCrop(32),ToTensor()]))
#dataloader = DataLoader(data_tfr, batch_size=batch_size, shuffle=False, num_workers=0)
for batch_idx, (img,label,index,pid, cid,subid,countid,newid) in enumerate(trainloader):
#print("trainloader",batch_idx, (label,index,pid, cid,subid,countid,newid))
print("trainloader",batch_idx, (label))
break
print('Initializing test data manager')
dm = ImageDataManager(use_gpu, **dataset_kwargs)
testloader_dict = dm.return_dataloaders()
print('Initializing model: {}'.format(arch))
model = models.init_model(name=arch, num_classes=num_train_pids, loss={'xent', 'htri'},
pretrained=not no_pretrained, last_stride =2 )
print('Model size: {:.3f} M'.format(count_num_param(model)))
if load_weights is not None:
print("weights loaded")
load_pretrained_weights(model, load_weights)
print(torch.cuda.device_count())
model = nn.DataParallel(model).cuda() if use_gpu else model
optimizer = init_optimizer(model, **optimizer_kwargs)
#optimizer = init_optimizer(model)
scheduler = init_lr_scheduler(optimizer, **lr_scheduler_kwargs)
criterion_xent = CrossEntropyLoss(num_classes=num_train_pids, use_gpu=use_gpu, label_smooth=True)
criterion_htri = TripletLoss(margin=margin)
ranking_loss = nn.MarginRankingLoss(margin = margin)
if evaluate:
print('Evaluate only')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
_, distmat = test(model, queryloader, galleryloader, train_batch_size, use_gpu, return_distmat=True)
if vis_rank:
visualize_ranked_results(
distmat, dm.return_testdataset_by_name(name),
save_dir=osp.join(save_dir, 'ranked_results', name),
topk=20
)
return
time_start = time.time()
ranklogger = RankLogger(source, target)
print('=> Start training')
data_index = search(pkl)
print(len(data_index))
for epoch in range(start, max_epoch):
losses = AverageMeter()
#xent_losses = AverageMeter()
htri_losses = AverageMeter()
accs = AverageMeter()
batch_time = AverageMeter()
xent_losses=AverageMeter()
model.train()
for p in model.parameters():
p.requires_grad = True # open all layers
end = time.time()
for batch_idx, (img,label,index,pid, cid,subid,countid,newid) in enumerate(trainloader):
trainX, trainY = torch.zeros((train_batch_size*3,3,height, width), dtype=torch.float32), torch.zeros((train_batch_size*3), dtype = torch.int64)
#pids = torch.zeros((batch_size*3), dtype = torch.int16)
for i in range(train_batch_size):
#print("dfdsfs")
labelx = label[i]
indexx = index[i]
cidx = pid[i]
if indexx >len(pkl[labelx])-1:
indexx = len(pkl[labelx])-1
#maxx = np.argmax(pkl[labelx][indexx])
a = pkl[labelx][indexx]
minpos = np.argmin(ma.masked_where(a==0, a))
#print(minpos)
#print(np.array(data_index).shape)
#print(data_index[cidx][1])
pos_dic = data_tfr_old[data_index[cidx][1]+minpos]
neg_label = int(labelx)
while True:
neg_label = random.choice(range(1, 770))
#print(neg_label)
if neg_label is not int(labelx) and os.path.isdir(os.path.join('/home/kuru/Desktop/adiusb/veri-split/train', strint(neg_label))) is True:
break
negative_label = strint(neg_label)
neg_cid = pidx[negative_label]
neg_index = random.choice(range(0, len(pkl[negative_label])))
neg_dic = data_tfr_old[data_index[neg_cid][1]+neg_index]
trainX[i] = img[i]
trainX[i+train_batch_size] = pos_dic[0]
trainX[i+(train_batch_size*2)] = neg_dic[0]
trainY[i] = cidx
trainY[i+train_batch_size] = pos_dic[3]
trainY[i+(train_batch_size*2)] = neg_dic[3]
trainX = trainX.cuda()
trainY = trainY.cuda()
outputs, features = model(trainX)
xent_loss = criterion_xent(outputs[0:train_batch_size], trainY[0:train_batch_size])
htri_loss = criterion_htri(features, trainY)
#tri_loss = ranking_loss(features)
#ent_loss = xent_loss(outputs[0:batch_size], trainY[0:batch_size], num_train_pids)
loss = htri_loss+xent_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
losses.update(loss.item(), trainY.size(0))
htri_losses.update(htri_loss.item(), trainY.size(0))
xent_losses.update(xent_loss.item(), trainY.size(0))
accs.update(accuracy(outputs[0:train_batch_size], trainY[0:train_batch_size])[0])
if (batch_idx) % 50 == 0:
print('Train ', end=" ")
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'TriLoss {loss.val:.4f} ({loss.avg:.4f})\t'
'XLoss {xloss.val:.4f} ({xloss.avg:.4f})\t'
'OveralLoss {oloss.val:.4f} ({oloss.avg:.4f})\t'
'Acc {acc.val:.2f} ({acc.avg:.2f})\t'
'lr {lrrr} \t'.format(
epoch + 1, batch_idx + 1, len(trainloader),
batch_time=batch_time,
loss = htri_losses,
xloss = xent_losses,
oloss = losses,
acc=accs ,
lrrr=lrrr,
))
end = time.time()
scheduler.step()
print('=> Test')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader, test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
rank2, distmat2 = test_rerank(model, queryloader, galleryloader, test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank2)
#if (epoch + 1) == max_epoch:
if (epoch + 1) % 2 == 0:
print('=> Test')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader, test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
# if vis_rank:
# visualize_ranked_results(
# distmat, dm.return_testdataset_by_name(name),
# save_dir=osp.join(save_dir, 'ranked_results', name),
# topk=20)
save_checkpoint({
'state_dict': model.state_dict(),
'rank1': rank1,
'epoch': epoch + 1,
'arch': arch,
'optimizer': optimizer.state_dict(),
}, save_dir)
def main():
#GENERAL
torch.cuda.empty_cache()
root = "/home/kuru/Desktop/veri-gms-master/"
train_dir = '/home/kuru/Desktop/veri-gms-master/VeRispan/image_train/'
source = {'verispan'}
target = {'verispan'}
workers = 4
height = 320
width = 320
train_sampler = 'RandomSampler'
#AUGMENTATION
random_erase = True
jitter = True
aug = True
#OPTIMIZATION
opt = 'adam'
lr = 0.001
weight_decay = 5e-4
momentum = 0.9
sgd_damp = 0.0
nesterov = True
warmup_factor = 0.01
warmup_method = 'linear'
STEPS = (30, 60)
GAMMA = 0.1
WARMUP_FACTOR = 0.01
WARMUP_EPOCHS = 10
WARMUP_METHOD = 'linear'
#HYPERPARAMETER
max_epoch = 80
start = 0
train_batch_size = 16
test_batch_size = 50
#SCHEDULER
lr_scheduler = 'multi_step'
stepsize = [30, 60]
gamma = 0.1
#LOSS
margin = 0.3
num_instances = 4
lambda_tri = 1
#MODEL
#arch = 'resnet101'
arch = 'resnet50_ibn_a'
no_pretrained = False
#TEST SETTINGS
#load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet101_ibn_a.pth'
#load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet101_ibn_a.pth'
load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet50_ibn_a.pth'
#load_weights = None
start_eval = 0
eval_freq = -1
num_classes = 776
feat_dim = 2048
CENTER_LR = 0.5
CENTER_LOSS_WEIGHT = 0.0005
center_criterion = CenterLoss(num_classes=num_classes,
feat_dim=feat_dim,
use_gpu=True)
optimizer_center = torch.optim.SGD(center_criterion.parameters(),
lr=CENTER_LR)
#MISC
use_gpu = True
#use_gpu = False
print_freq = 10
seed = 1
resume = ''
save_dir = '/home/kuru/Desktop/veri-gms-master_noise/spanningtree_veri_pure/'
gpu_id = 0, 1
vis_rank = True
query_remove = True
evaluate = False
dataset_kwargs = {
'source_names': source,
'target_names': target,
'root': root,
'height': height,
'width': width,
'train_batch_size': train_batch_size,
'test_batch_size': test_batch_size,
'train_sampler': train_sampler,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
transform_kwargs = {
'height': height,
'width': width,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
optimizer_kwargs = {
'optim': opt,
'lr': lr,
'weight_decay': weight_decay,
'momentum': momentum,
'sgd_dampening': sgd_damp,
'sgd_nesterov': nesterov
}
lr_scheduler_kwargs = {
'lr_scheduler': lr_scheduler,
'stepsize': stepsize,
'gamma': gamma
}
use_gpu = torch.cuda.is_available()
log_name = 'log_test.txt' if evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(save_dir, log_name))
print('Currently using GPU ', gpu_id)
cudnn.benchmark = True
print('Initializing image data manager')
#dataset = init_imgreid_dataset(root='/home/kuru/Desktop/veri-gms-master/', name='veri')
dataset = init_imgreid_dataset(root='/home/kuru/Desktop/veri-gms-master/',
name='verispan')
train = []
num_train_pids = 0
num_train_cams = 0
print(len(dataset.train))
for img_path, pid, camid, subid, countid in dataset.train:
#print(img_path)
path = img_path[56:90 + 6]
#print(path)
folder = path[1:4]
#print(folder)
#print(img_path, pid, camid,subid,countid)
pid += num_train_pids
camid += num_train_cams
newidd = 0
train.append((path, folder, pid, camid, subid, countid))
#print(train)
#break
num_train_pids += dataset.num_train_pids
num_train_cams += dataset.num_train_cams
pid = 0
pidx = {}
for img_path, pid, camid, subid, countid in dataset.train:
path = img_path[56:90 + 6]
folder = path[1:4]
pidx[folder] = pid
pid += 1
#print(pidx)
sub = []
final = 0
xx = dataset.train
newids = []
print(train[0:2])
train2 = {}
for k in range(0, 770):
for img_path, pid, camid, subid, countid in dataset.train:
if k == pid:
newid = final + subid
sub.append(newid)
#print(pid,subid,newid)
newids.append(newid)
train2[img_path] = newid
#print(img_path, pid, camid, subid, countid, newid)
final = max(sub)
#print(final)
print(len(newids), final)
#train=train2
#print(train2)
train3 = []
for img_path, pid, camid, subid, countid in dataset.train:
#print(img_path,pid,train2[img_path])
path = img_path[56:90 + 6]
#print(path)
folder = path[1:4]
newid = train2[img_path]
#print((path, folder, pid, camid, subid, countid,newid ))
train3.append((path, folder, pid, camid, subid, countid, newid))
train = train3
# for (path, folder, pid, camid, subid, countid,newid) in train:
# print(path, folder)
#path = '/home/kuru/Desktop/adhi/veri-final-draft-master_noise/gmsNoise776/'
path = '/home/kuru/Desktop/veri-gms-master/gms/'
pkl = {}
#pkl[0] = pickle.load('/home/kuru/Desktop/veri-gms-master/gms/620.pkl')
entries = os.listdir(path)
for name in entries:
f = open((path + name), 'rb')
ccc = (path + name)
#print(ccc)
if name == 'featureMatrix.pkl':
s = name[0:13]
else:
s = name[0:3]
#print(s)
#with open (ccc,"rb") as ff:
# pkl[s] = pickle.load(ff)
#print(pkl[s])
pkl[s] = pickle.load(f)
f.close
#print(len(pkl))
print('=> pickle indexing')
data_index = search(pkl)
print(len(data_index))
transform_t = train_transforms(**transform_kwargs)
#print(train[0],train[10])
#data_tfr = vd(pkl_file='index.pkl', dataset = train, root_dir='/home/kuru/Desktop/veri-gms-master/VeRi/image_train/', transform=transform_t)
data_tfr = vdspan(
pkl_file='index_veryspan.pkl',
dataset=train,
root_dir='/home/kuru/Desktop/veri-gms-master/VeRispan/image_train/',
transform=transform_t)
#print(data_tfr)
#print(trainloader)
#data_tfr2=list(data_tfr)
print("lllllllllllllllllllllllllllllllllllllllllllline 433")
df2 = []
data_tfr_old = data_tfr
for (img, label, index, pid, cid, subid, countid, newid) in data_tfr:
#print((img,label,index,pid, cid,subid,countid,newid) )
#print("datframe",(label))
#print(countid)
if countid > 4:
#print(countid)
df2.append((img, label, index, pid, cid, subid, countid, newid))
print("filtered final trainset length", len(df2))
data_tfr = df2
# with open('df2noise_ex.pkl', 'wb') as handle:
# b = pickle.dump(df2, handle, protocol=pickle.HIGHEST_PROTOCOL)
# with open('df2noise.pkl', 'rb') as handle:
# df2 = pickle.load(handle)
# data_tfr=df2
# for (img,label,index,pid, cid,subid,countid,newid) in data_tfr :
# print("datframe",(label))
#data_tfr = vdspansort( dataset = train, root_dir='/home/kuru/Desktop/veri-gms-master_noise/VeRispan/image_train/', transform=transform_t)
#trainloader = DataLoader(df2, sampler=None,batch_size=train_batch_size, shuffle=True, num_workers=workers,pin_memory=True, drop_last=True)
trainloader = DataLoader(data_tfr,
sampler=None,
batch_size=train_batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
drop_last=True)
for batch_idx, (img, label, index, pid, cid, subid, countid,
newid) in enumerate(trainloader):
#print("trainloader",batch_idx, (label,index,pid, cid,subid,countid,newid))
print("trainloader", batch_idx, (label))
break
print('Initializing test data manager')
dm = ImageDataManager(use_gpu, **dataset_kwargs)
testloader_dict = dm.return_dataloaders()
print('Initializing model: {}'.format(arch))
model = models.init_model(name=arch,
num_classes=num_train_pids,
loss={'xent', 'htri'},
pretrained=not no_pretrained,
last_stride=2)
print('Model size: {:.3f} M'.format(count_num_param(model)))
if load_weights is not None:
print("weights loaded")
load_pretrained_weights(model, load_weights)
#checkpoint = torch.load('/home/kuru/Desktop/veri-gms-master/logg/model.pth.tar-19')
#model._load_from_state_dict(checkpoint['state_dict'])
#model.load_state_dict(checkpoint['state_dict'])
#optimizer.load_state_dict(checkpoint['optimizer'])
#print(checkpoint['epoch'])
#print(checkpoint['rank1'])
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
print(torch.cuda.device_count())
model = nn.DataParallel(model).cuda() if use_gpu else model
optimizer = init_optimizer(model, **optimizer_kwargs)
#optimizer = init_optimizer(model)
#optimizer.load_state_dict(checkpoint['optimizer'])
scheduler = init_lr_scheduler(optimizer, **lr_scheduler_kwargs)
# scheduler = WarmupMultiStepLR(optimizer, STEPS, GAMMA,
# WARMUP_FACTOR,
# WARMUP_EPOCHS, WARMUP_METHOD)
criterion_xent = CrossEntropyLoss(num_classes=num_train_pids,
use_gpu=use_gpu,
label_smooth=True)
criterion_htri = TripletLoss(margin=margin)
ranking_loss = nn.MarginRankingLoss(margin=margin)
if evaluate:
print('Evaluate only')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
_, distmat = test(model,
queryloader,
galleryloader,
train_batch_size,
use_gpu,
return_distmat=True)
if vis_rank:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
save_dir, 'ranked_results', name),
topk=20)
return
time_start = time.time()
ranklogger = RankLogger(source, target)
# # checkpoint = torch.load('/home/kuru/Desktop/market_all/ibna_model/model.pth.tar-79')
# # model.load_state_dict(checkpoint['state_dict'])
# # optimizer.load_state_dict(checkpoint['optimizer'])
# # print(checkpoint['epoch'])
# # start_epoch=checkpoint['epoch']
# # start=start_epoch
# checkpoint = torch.load('/home/kuru/Desktop/veri-gms-master/spanningtreeveri/model.pth.tar-2')
# model.load_state_dict(checkpoint['state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer'])
# print(checkpoint['epoch'])
# start_epoch=checkpoint['epoch']
# start=start_epoch
##start_epoch=resume_from_checkpoint('/home/kuru/Desktop/veri-gms-master/logg/model.pth.tar-20', model, optimizer=None)
print('=> Start training')
for epoch in range(start, max_epoch):
print(epoch, scheduler.get_lr()[0])
#print( torch.cuda.memory_allocated(0))
losses = AverageMeter()
#xent_losses = AverageMeter()
htri_losses = AverageMeter()
accs = AverageMeter()
batch_time = AverageMeter()
xent_losses = AverageMeter()
model.train()
for p in model.parameters():
p.requires_grad = True # open all layers
end = time.time()
for batch_idx, (img, label, index, pid, cid, subid, countid,
newid) in enumerate(trainloader):
trainX, trainY = torch.zeros(
(train_batch_size * 3, 3, height, width),
dtype=torch.float32), torch.zeros((train_batch_size * 3),
dtype=torch.int64)
#pids = torch.zeros((batch_size*3), dtype = torch.int16)
#batchcount=0
for i in range(train_batch_size):
if (countid[i] > 4):
#batchcount=batchcount+1
#print("dfdsfs")
labelx = label[i]
indexx = index[i]
cidx = pid[i]
if indexx > len(pkl[labelx]) - 1:
indexx = len(pkl[labelx]) - 1
#maxx = np.argmax(pkl[labelx][indexx])
a = pkl[labelx][indexx]
minpos = np.argmin(ma.masked_where(a == 0, a))
# print(len(a))
# print(a)
# print(ma.masked_where(a==0, a))
# print(labelx,index,pid,cidx,minpos)
# print(np.array(data_index).shape)
# print(data_index[cidx][1])
pos_dic = data_tfr_old[data_index[cidx][1] + minpos]
#print('posdic', pos_dic)
neg_label = int(labelx)
while True:
neg_label = random.choice(range(1, 770))
#print(neg_label)
if neg_label is not int(labelx) and os.path.isdir(
os.path.join(
'/home/kuru/Desktop/veri-gms-master_noise/veriNoise_train_spanning_folder',
strint(neg_label))) is True:
break
negative_label = strint(neg_label)
neg_cid = pidx[negative_label]
neg_index = random.choice(
range(0, len(pkl[negative_label])))
#print(negative_label,neg_cid,neg_index,data_index[neg_cid] )
neg_dic = data_tfr_old[data_index[neg_cid][1] + neg_index]
#print('negdic', neg_dic)
trainX[i] = img[i]
trainX[i + train_batch_size] = pos_dic[0]
trainX[i + (train_batch_size * 2)] = neg_dic[0]
trainY[i] = cidx
trainY[i + train_batch_size] = pos_dic[3]
trainY[i + (train_batch_size * 2)] = neg_dic[3]
# trainY[i+train_batch_size] = pos_dic[7]
# trainY[i+(train_batch_size*2)] = neg_dic[7]
#break
# else:
# print("skiped",countid[i],subid[i],label[i])
#break
#print(batchcount)
trainX = trainX.cuda()
trainY = trainY.cuda()
outputs, features = model(trainX)
xent_loss = criterion_xent(outputs[0:train_batch_size],
trainY[0:train_batch_size])
htri_loss = criterion_htri(features, trainY)
centerloss = CENTER_LOSS_WEIGHT * center_criterion(
features, trainY)
#tri_loss = ranking_loss(features)
#ent_loss = xent_loss(outputs[0:batch_size], trainY[0:batch_size], num_train_pids)
loss = htri_loss + xent_loss + centerloss
loss = htri_loss + xent_loss
optimizer.zero_grad()
optimizer_center.zero_grad()
loss.backward()
optimizer.step()
# for param in center_criterion.parameters():
# param.grad.data *= (1. /CENTER_LOSS_WEIGHT)
# optimizer_center.step()
for param_group in optimizer.param_groups:
#print(param_group['lr'] )
lrrr = str(param_group['lr'])
batch_time.update(time.time() - end)
losses.update(loss.item(), trainY.size(0))
htri_losses.update(htri_loss.item(), trainY.size(0))
xent_losses.update(xent_loss.item(), trainY.size(0))
accs.update(
accuracy(outputs[0:train_batch_size],
trainY[0:train_batch_size])[0])
if (batch_idx) % 50 == 0:
print('Train ', end=" ")
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'TriLoss {loss.val:.4f} ({loss.avg:.4f})\t'
'XLoss {xloss.val:.4f} ({xloss.avg:.4f})\t'
'OveralLoss {oloss.val:.4f} ({oloss.avg:.4f})\t'
'Acc {acc.val:.2f} ({acc.avg:.2f})\t'
'lr {lrrr} \t'.format(
epoch + 1,
batch_idx + 1,
len(trainloader),
batch_time=batch_time,
loss=htri_losses,
xloss=xent_losses,
oloss=losses,
acc=accs,
lrrr=lrrr,
))
end = time.time()
# del loss
# del htri_loss
# del xent_loss
# del htri_losses
# del losses
# del outputs
# del features
# del accs
# del trainX
# del trainY
scheduler.step()
print('=> Test')
save_checkpoint(
{
'state_dict': model.state_dict(),
#'rank1': rank1,
'epoch': epoch + 1,
'arch': arch,
'optimizer': optimizer.state_dict(),
},
save_dir)
GPUtil.showUtilization()
print(torch.cuda.memory_allocated(), torch.cuda.memory_cached())
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
rank2, distmat2 = test_rerank(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank2)
del queryloader
del galleryloader
del distmat
print(torch.cuda.memory_allocated(), torch.cuda.memory_cached())
torch.cuda.empty_cache()
if (epoch + 1) == max_epoch:
#if (epoch + 1) % 10 == 0:
print('=> Test')
save_checkpoint(
{
'state_dict': model.state_dict(),
'rank1': rank1,
'epoch': epoch + 1,
'arch': arch,
'optimizer': optimizer.state_dict(),
}, save_dir)
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
# del queryloader
# del galleryloader
# del distmat
if vis_rank:
visualize_ranked_results(
distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(save_dir, 'ranked_results', name),
topk=20)
def main():
torch.backends.cudnn.deterministic = True
cudnn.benchmark = True
#parser = argparse.ArgumentParser(description="ReID Baseline Training")
#parser.add_argument(
#"--config_file", default="", help="path to config file", type=str)
#parser.add_argument("opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER)
#args = parser.parse_args()
config_file = 'configs/baseline_veri_r101_a.yml'
if config_file != "":
cfg.merge_from_file(config_file)
#cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = setup_logger("reid_baseline", output_dir, if_train=True)
logger.info("Saving model in the path :{}".format(cfg.OUTPUT_DIR))
logger.info(config_file)
if config_file != "":
logger.info("Loaded configuration file {}".format(config_file))
with open(config_file, 'r') as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
path = 'D:/Python_SMU/Veri/verigms/gms/'
pkl = {}
entries = os.listdir(path)
for name in entries:
f = open((path + name), 'rb')
if name == 'featureMatrix.pkl':
s = name[0:13]
else:
s = name[0:3]
pkl[s] = pickle.load(f)
f.close
with open('cids.pkl', 'rb') as handle:
b = pickle.load(handle)
with open('index.pkl', 'rb') as handle:
c = pickle.load(handle)
train_transforms, val_transforms, dataset, train_set, val_set = make_dataset(
cfg, pkl_file='index.pkl')
num_workers = cfg.DATALOADER.NUM_WORKERS
num_classes = dataset.num_train_pids
#pkl_f = 'index.pkl'
pid = 0
pidx = {}
for img_path, pid, _, _ in dataset.train:
path = img_path.split('\\')[-1]
folder = path[1:4]
pidx[folder] = pid
pid += 1
if 'triplet' in cfg.DATALOADER.SAMPLER:
train_loader = DataLoader(train_set,
batch_size=cfg.SOLVER.IMS_PER_BATCH,
sampler=RandomIdentitySampler(
dataset.train, cfg.SOLVER.IMS_PER_BATCH,
cfg.DATALOADER.NUM_INSTANCE),
num_workers=num_workers,
pin_memory=True,
collate_fn=train_collate_fn)
elif cfg.DATALOADER.SAMPLER == 'softmax':
print('using softmax sampler')
train_loader = DataLoader(train_set,
batch_size=cfg.SOLVER.IMS_PER_BATCH,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
collate_fn=train_collate_fn)
else:
print('unsupported sampler! expected softmax or triplet but got {}'.
format(cfg.SAMPLER))
print("train loader loaded successfully")
val_loader = DataLoader(val_set,
batch_size=cfg.TEST.IMS_PER_BATCH,
shuffle=False,
num_workers=num_workers,
pin_memory=True,
collate_fn=train_collate_fn)
print("val loader loaded successfully")
if cfg.MODEL.PRETRAIN_CHOICE == 'finetune':
model = make_model(cfg, num_class=576)
model.load_param_finetune(cfg.MODEL.PRETRAIN_PATH)
print('Loading pretrained model for finetuning......')
else:
model = make_model(cfg, num_class=num_classes)
loss_func, center_criterion = make_loss(cfg, num_classes=num_classes)
optimizer, optimizer_center = make_optimizer(cfg, model, center_criterion)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_EPOCHS,
cfg.SOLVER.WARMUP_METHOD)
print("model,optimizer, loss, scheduler loaded successfully")
height, width = cfg.INPUT.SIZE_TRAIN
log_period = cfg.SOLVER.LOG_PERIOD
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
eval_period = cfg.SOLVER.EVAL_PERIOD
device = "cuda"
epochs = cfg.SOLVER.MAX_EPOCHS
logger = logging.getLogger("reid_baseline.train")
logger.info('start training')
if device:
if torch.cuda.device_count() > 1:
print('Using {} GPUs for training'.format(
torch.cuda.device_count()))
model = nn.DataParallel(model)
model.to(device)
loss_meter = AverageMeter()
acc_meter = AverageMeter()
evaluator = R1_mAP_eval(len(dataset.query),
max_rank=50,
feat_norm=cfg.TEST.FEAT_NORM)
model.base._freeze_stages()
logger.info('Freezing the stages number:{}'.format(cfg.MODEL.FROZEN))
data_index = search(pkl)
print("Ready for training")
for epoch in range(1, epochs + 1):
start_time = time.time()
loss_meter.reset()
acc_meter.reset()
evaluator.reset()
scheduler.step()
model.train()
for n_iter, (img, label, index, pid, cid) in enumerate(train_loader):
optimizer.zero_grad()
optimizer_center.zero_grad()
#img = img.to(device)
#target = vid.to(device)
trainX, trainY = torch.zeros(
(train_loader.batch_size * 3, 3, height, width),
dtype=torch.float32), torch.zeros(
(train_loader.batch_size * 3), dtype=torch.int64)
for i in range(train_loader.batch_size):
labelx = label[i]
indexx = index[i]
cidx = pid[i]
if indexx > len(pkl[labelx]) - 1:
indexx = len(pkl[labelx]) - 1
a = pkl[labelx][indexx]
minpos = np.argmin(ma.masked_where(a == 0, a))
pos_dic = train_set[data_index[cidx][1] + minpos]
#print(pos_dic[1])
neg_label = int(labelx)
while True:
neg_label = random.choice(range(1, 770))
if neg_label is not int(labelx) and os.path.isdir(
os.path.join('D:/datasets/veri-split/train',
strint(neg_label))) is True:
break
negative_label = strint(neg_label)
neg_cid = pidx[negative_label]
neg_index = random.choice(range(0, len(pkl[negative_label])))
neg_dic = train_set[data_index[neg_cid][1] + neg_index]
trainX[i] = img[i]
trainX[i + train_loader.batch_size] = pos_dic[0]
trainX[i + (train_loader.batch_size * 2)] = neg_dic[0]
trainY[i] = cidx
trainY[i + train_loader.batch_size] = pos_dic[3]
trainY[i + (train_loader.batch_size * 2)] = neg_dic[3]
#print(trainY)
trainX = trainX.cuda()
trainY = trainY.cuda()
score, feat = model(trainX, trainY)
loss = loss_func(score, feat, trainY)
loss.backward()
optimizer.step()
if 'center' in cfg.MODEL.METRIC_LOSS_TYPE:
for param in center_criterion.parameters():
param.grad.data *= (1. / cfg.SOLVER.CENTER_LOSS_WEIGHT)
optimizer_center.step()
acc = (score.max(1)[1] == trainY).float().mean()
loss_meter.update(loss.item(), img.shape[0])
acc_meter.update(acc, 1)
if (n_iter + 1) % log_period == 0:
logger.info(
"Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}"
.format(epoch, (n_iter + 1), len(train_loader),
loss_meter.avg, acc_meter.avg,
scheduler.get_lr()[0]))
end_time = time.time()
time_per_batch = (end_time - start_time) / (n_iter + 1)
logger.info(
"Epoch {} done. Time per batch: {:.3f}[s] Speed: {:.1f}[samples/s]"
.format(epoch, time_per_batch,
train_loader.batch_size / time_per_batch))
if epoch % checkpoint_period == 0:
torch.save(
model.state_dict(),
os.path.join(cfg.OUTPUT_DIR,
cfg.MODEL.NAME + '_{}.pth'.format(epoch)))
if epoch % eval_period == 0:
model.eval()
for n_iter, (img, vid, camid, _, _) in enumerate(val_loader):
with torch.no_grad():
img = img.to(device)
feat = model(img)
evaluator.update((feat, vid, camid))
cmc, mAP, _, _, _, _, _ = evaluator.compute()
logger.info("Validation Results - Epoch: {}".format(epoch))
logger.info("mAP: {:.1%}".format(mAP))
for r in [1, 5, 10]:
logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(
r, cmc[r - 1]))