def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(net=cfg.net, pretrained=False)
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
model = model.base
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
###################
# Extract Feature #
###################
im_dir = osp.expanduser(cfg.image_dir)
im_paths = get_im_names(im_dir,
pattern='*.jpg',
return_path=True,
return_np=False)
all_feat = []
import scipy.io as sci
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
# all_feat = normalize(all_feat, axis=1)
# You can save your im_paths and features, or do anything else ...
sci.savemat(cfg.saved_feature_mat_file, {'features': all_feat})
def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(net=cfg.net, pretrained=False)
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
model = model.base
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
#######################
# Extract Image Crops #
#######################
all_feat = []
dataset = BoundingBoxCrops(cfg)
loader = DataLoader(dataset=dataset,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=cfg.num_workers)
print('Processing detections')
t = tqdm(iter(loader), leave=False, total=len(loader))
for i, data in enumerate(t):
imgs, bad_boxes = data
imgs = Variable(TVT(imgs))
with torch.no_grad():
feats = model(imgs)
feats = feats.data.cpu().numpy()
for j in range(len(bad_boxes)):
if bad_boxes[j] == True:
feats[j,:] = np.zeros(1280)
sci.savemat(cfg.saved_feature_mat_path+'/'+"{:05d}".format(i)+'.mat', {'features':feats})
def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride)
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
im_dir = osp.expanduser('~/Dataset/market1501/Market-1501-v15.09.15/query')
im_paths = get_im_names(im_dir,
pattern='*.jpg',
return_path=True,
return_np=False)
all_feat = []
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
all_feat = normalize(all_feat, axis=1)
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 #
###########
if not cfg.only_test:
train_set = create_dataset(**cfg.train_set_kwargs)
# The combined dataset does not provide val set currently.
val_set = None if cfg.dataset == 'combined' else create_dataset(
**cfg.val_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(last_conv_stride=cfg.last_conv_stride)
# Model wrapper
model_w = DataParallel(model)
#############################
# Criteria and Optimizers #
#############################
tri_loss = TripletLoss(margin=cfg.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.format(200))
# 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)
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, verbose=True)
def validate():
if val_set.extract_feat_func is None:
val_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n=========> Test on validation set <=========\n')
mAP, cmc_scores, _, _ = val_set.eval(
normalize_feat=cfg.normalize_feature,
to_re_rank=False,
verbose=False)
print()
return mAP, cmc_scores[0]
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')
# For recording precision, satisfying margin, etc
prec_meter = AverageMeter()
sm_meter = AverageMeter()
dist_ap_meter = AverageMeter()
dist_an_meter = AverageMeter()
dist_rec_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, masks, im_names, labels, mirrored, epoch_done = train_set.next_batch(
)
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
masks_var = Variable(TVT(torch.from_numpy(masks).float()))
labels_t = TVT(torch.from_numpy(labels).long())
# feat = model_w(ims_var, masks_var)
feat = model_w(ims_var, masks_var)
loss, p_inds, n_inds, rec_inds, dist_ap, dist_an, dist_rec, dist_mat = global_loss(
tri_loss,
feat,
labels_t,
normalize_feature=cfg.normalize_feature)
optimizer.zero_grad()
loss.backward()
optimizer.step()
############
# Step Log #
############
# precision
prec = (dist_an > dist_ap).data.float().mean()
# the proportion of triplets that satisfy margin
sm = (dist_an > dist_ap + cfg.margin).data.float().mean()
# average (anchor, positive) distance
d_ap = dist_ap.data.mean()
# average (anchor, negative) distance
d_an = dist_an.data.mean()
# average rec distance
d_rec = dist_rec.data.mean()
prec_meter.update(prec)
sm_meter.update(sm)
dist_ap_meter.update(d_ap)
dist_an_meter.update(d_an)
dist_rec_meter.update(d_rec)
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
time_log = '\tStep {}/Ep {}, {:.2f}s'.format(
step,
ep + 1,
time.time() - step_st,
)
tri_log = (', prec {:.2%}, sm {:.2%}, '
'd_ap {:.4f}, d_an {:.4f}, d_rec {:.4f}, '
'loss {:.4f}'.format(
prec_meter.val,
sm_meter.val,
dist_ap_meter.val,
dist_an_meter.val,
dist_rec_meter.val,
loss_meter.val,
))
log = time_log + tri_log
print(log)
#############
# Epoch Log #
#############
time_log = 'Ep {}, {:.2f}s'.format(ep + 1, time.time() - ep_st)
tri_log = (', prec {:.2%}, sm {:.2%}, '
'd_ap {:.4f}, d_an {:.4f}, d_rec {:.4f}, '
'loss {:.4f}'.format(
prec_meter.avg,
sm_meter.avg,
dist_ap_meter.avg,
dist_an_meter.avg,
dist_rec_meter.avg,
loss_meter.avg,
))
log = time_log + tri_log
print(log)
##########################
# Test on Validation Set #
##########################
mAP, Rank1 = 0, 0
if ((ep + 1) % cfg.epochs_per_val == 0) and (val_set is not None):
mAP, Rank1 = validate()
# save ckpt
if cfg.log_to_file and ((ep + 1) % 50 == 0):
print('Saving model for epoch {}'.format(ep + 1))
save_ckpt(modules_optims, ep + 1, 0, cfg.ckpt_file.format(ep + 1))
# 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('val scores', dict(mAP=mAP, Rank1=Rank1), ep)
writer.add_scalars('loss', dict(loss=loss_meter.avg, ), ep)
writer.add_scalars('precision', dict(precision=prec_meter.avg, ),
ep)
writer.add_scalars('satisfy_margin',
dict(satisfy_margin=sm_meter.avg, ), ep)
writer.add_scalars(
'average_distance',
dict(
dist_ap=dist_ap_meter.avg,
dist_an=dist_an_meter.avg,
), ep)
########
# Test #
########
test(load_model_weight=False)
def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride)
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
im_dir = osp.expanduser('../../5Labeled Data/labeledNew')
im_paths = get_im_names(im_dir,
pattern='*.png',
return_path=True,
return_np=False)
currentData = {}
imagesKnown = set()
with open('../../7Data Features/dataFeatures.json') as json_file:
currentData = json.load(json_file)
for image in currentData["images"]:
imagesKnown.add(image["imageName"])
all_feat = []
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
all_feat = normalize(all_feat, axis=1)
# You can save your im_paths and features, or do anything else ...
for i, im_path in enumerate(im_paths):
if (im_path in imagesKnown):
print("image", im_path, "already exists")
pass
else:
currentData['images'].append({
'imageName': im_path,
'features': all_feat[i].tolist()
})
if (i + 1) % 100 == 0:
print('{}/{} images saved'.format(i, len(im_paths)))
with open('../../7Data Features/dataFeatures.json', 'w') as outfile:
json.dump(currentData, outfile)
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 #
###########
if not cfg.only_test:
train_set = create_dataset(**cfg.train_set_kwargs)
# The combined dataset does not provide val set currently.
val_set = None if cfg.dataset == 'combined' else create_dataset(
**cfg.val_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(last_conv_stride=cfg.last_conv_stride)
if cfg.dataset == 'market1501':
nr_class = 751
elif cfg.dataset == 'duke':
nr_class = 702
elif cfg.dataset == 'cuhk03':
nr_class = 767
elif cfg.dataset == 'combined':
nr_class = 2220
model = get_scp_model(nr_class)
# load pretrained ImageNet weights
model_dict = model.state_dict() # original
pretrained_dict = torch.load('models/resnet50-19c8e357.pth') # pretrained
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
if k in model_dict and model_dict[k].size() == v.size()
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
# Model wrapper
model_w = DataParallel(model)
#############################
# Criteria and Optimizers #
#############################
criterion_cls = torch.nn.CrossEntropyLoss()
criterion_feature = torch.nn.MSELoss()
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)
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))
mAP, cmc_scores, mq_mAP, mq_cmc_scores = test_set.eval(
normalize_feat=cfg.normalize_feature, verbose=True)
return mAP, cmc_scores, mq_mAP, mq_cmc_scores
def validate():
if val_set.extract_feat_func is None:
val_set.set_feat_func(ExtractFeature(model_w, TVT))
print('\n=========> Test on validation set <=========\n')
mAP, cmc_scores, _, _ = val_set.eval(
normalize_feat=cfg.normalize_feature,
to_re_rank=False,
verbose=False)
print()
return mAP, cmc_scores[0]
if cfg.only_test:
mAP, cmc_scores, mq_mAP, mq_cmc_scores = 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)
#
if ep < 20:
lr = 1e-4 * (ep + 1) / 2
elif ep < 80:
lr = 1e-3
elif 80 <= ep <= 180:
lr = 1e-4
else:
lr = 1e-5
for g in optimizer.param_groups:
g['lr'] = lr
may_set_mode(modules_optims, 'train')
# For recording precision, satisfying margin, etc
prec_meter = AverageMeter()
sm_meter = AverageMeter()
dist_ap_meter = AverageMeter()
dist_an_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 = Variable(TVT(torch.from_numpy(labels).long()))
feat = model_w(ims_var)
loss, prec = scp_loss(feat, labels_t, criterion_cls,
criterion_feature, cfg.ids_per_batch,
cfg.ims_per_id)
optimizer.zero_grad()
loss.backward()
optimizer.step()
############
# Step Log #
############
prec_meter.update(prec)
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
time_log = '\tStep {}/Ep {}, {:.2f}s'.format(
step,
ep + 1,
time.time() - step_st,
)
tri_log = (', prec {:.2%}, sm {:.2%}, '
'd_ap {:.4f}, d_an {:.4f}, '
'loss {:.4f}'.format(
prec_meter.val,
sm_meter.val,
dist_ap_meter.val,
dist_an_meter.val,
loss_meter.val,
))
log = time_log + tri_log
print(log)
#############
# Epoch Log #
#############
time_log = 'Ep {}, {:.2f}s'.format(ep + 1, time.time() - ep_st)
tri_log = (', prec {:.2%}, sm {:.2%}, '
'd_ap {:.4f}, d_an {:.4f}, '
'loss {:.4f}'.format(
prec_meter.avg,
sm_meter.avg,
dist_ap_meter.avg,
dist_an_meter.avg,
loss_meter.avg,
))
log = time_log + tri_log
print(log)
##########################
# Test on Validation Set #
##########################
mAP, Rank1 = 0, 0
if ((ep + 1) % cfg.epochs_per_val == 0) and (val_set is not None):
mAP, Rank1 = validate()
# 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('val scores', dict(mAP=mAP, Rank1=Rank1), ep)
writer.add_scalars('loss', dict(loss=loss_meter.avg, ), ep)
writer.add_scalars('precision', dict(precision=prec_meter.avg, ),
ep)
writer.add_scalars('satisfy_margin',
dict(satisfy_margin=sm_meter.avg, ), ep)
writer.add_scalars(
'average_distance',
dict(
dist_ap=dist_ap_meter.avg,
dist_an=dist_an_meter.avg,
), ep)
# save ckpt
if cfg.log_to_file:
save_ckpt(modules_optims, ep + 1, 0, cfg.ckpt_file)
########
# Test #
########
test(load_model_weight=False)
def main():
cfg = Config()
# Redirect logs to both console and file.
if cfg.log_to_file:
ReDirectSTD(cfg.stdout_file, 'stdout', False)
ReDirectSTD(cfg.stderr_file, 'stderr', False)
TVT, TMO = set_devices(cfg.sys_device_ids)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
###########
# Dataset #
###########
test_set = create_dataset(**cfg.test_set_kwargs)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride)
# Model wrapper
model_w = DataParallel(model)
# May Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
# To first load weights to CPU
map_location = (lambda storage, loc: storage)
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=map_location)
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
test_set.set_feat_func(ExtractFeature(model_w, TVT))
with measure_time('Extracting feature...', verbose=True):
feat, ids, cams, im_names, marks = test_set.extract_feat(True, verbose=True)
#######################
# Select Query Images #
#######################
# Fix some query images, so that the visualization for different models can
# be compared.
# Sort in the order of image names
inds = np.argsort(im_names)
feat, ids, cams, im_names, marks = \
feat[inds], ids[inds], cams[inds], im_names[inds], marks[inds]
# query, gallery index mask
is_q = marks == 0
is_g = marks == 1
prng = np.random.RandomState(1)
# selected query indices
sel_q_inds = prng.permutation(range(np.sum(is_q)))[:cfg.num_queries]
q_ids = ids[is_q][sel_q_inds]
q_cams = cams[is_q][sel_q_inds]
q_feat = feat[is_q][sel_q_inds]
q_im_names = im_names[is_q][sel_q_inds]
####################
# Compute Distance #
####################
# query-gallery distance
q_g_dist = compute_dist(q_feat, feat[is_g], type='euclidean')
###########################
# Save Rank List as Image #
###########################
q_im_paths = [ospj(test_set.im_dir, n) for n in q_im_names]
save_paths = [ospj(cfg.exp_dir, 'rank_lists', n) for n in q_im_names]
g_im_paths = [ospj(test_set.im_dir, n) for n in im_names[is_g]]
for dist_vec, q_id, q_cam, q_im_path, save_path in zip(
q_g_dist, q_ids, q_cams, q_im_paths, save_paths):
rank_list, same_id = get_rank_list(
dist_vec, q_id, q_cam, ids[is_g], cams[is_g], cfg.rank_list_size)
save_rank_list_to_im(rank_list, same_id, q_im_path, g_im_paths, save_path)
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)
TVT, TMO = set_devices(cfg.sys_device_ids)
# Dump the configurations to log.
import pprint
print('-' * 60)
print('cfg.__dict__')
pprint.pprint(cfg.__dict__)
print('-' * 60)
###########
# Dataset #
###########
test_set = create_dataset(**cfg.test_set_kwargs)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride)
# Model wrapper
model_w = DataParallel(model)
# May Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
# To first load weights to CPU
map_location = (lambda storage, loc: storage)
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=map_location)
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
test_set.set_feat_func(ExtractFeature(model_w, TVT))
with measure_time('Extracting feature...', verbose=True):
feat, ids, cams, im_names, marks = test_set.extract_feat(True, verbose=True)
#######################
# Select Query Images #
#######################
# Fix some query images, so that the visualization for different models can
# be compared.
# Sort in the order of image names
inds = np.argsort(im_names)
feat, ids, cams, im_names, marks = \
feat[inds], ids[inds], cams[inds], im_names[inds], marks[inds]
# query, gallery index mask
is_q = marks == 0
is_g = marks == 1
prng = np.random.RandomState(1)
# selected query indices
sel_q_inds = prng.permutation(range(np.sum(is_q)))[:cfg.num_queries]
q_ids = ids[is_q][sel_q_inds]
q_cams = cams[is_q][sel_q_inds]
q_feat = feat[is_q][sel_q_inds]
q_im_names = im_names[is_q][sel_q_inds]
####################
# Compute Distance #
####################
# query-gallery distance
f= open("../../4Scripts/Data Labeling Process/imagesDesc2.txt","w+")
#for controlIndex in range(len(im_names[is_q])):
for controlIndex in range(0,6):
f.write(im_names[is_q][controlIndex] + "|" + "|".join(list(map(str, feat[is_q][controlIndex])))+"\n")
#for controlIndex in range(len(im_names[is_g])):
for controlIndex in range(0,6):
f.write(im_names[is_g][controlIndex] + "|" + "|".join(list(map(str, feat[is_g][controlIndex])))+"\n")
f.close()