def start():
###############
# preparation #
###############
model = Model(last_conv_stride=last_conv_stride,
num_stripes=num_stripes,
local_conv_out_channels=local_conv_out_channels)
model_w = DataParallel(model)
TMO([model])
# preprocessing
preprocessor = PreProcessIm(resize_h_w=resize_h_w,
scale=scale_im,
im_mean=im_mean,
im_std=im_std)
# load model
map_location = (lambda storage, loc: storage)
sd = torch.load(model_weight_file, map_location=map_location)
load_state_dict(model, sd['state_dicts'][0])
print('Loaded model weight from {}'.format(model_weight_file))
extractor = ExtractFeature(model_w, TVT)
return preprocessor, extractor
def _initModel(self):
# Current dataset name:
dname = self.dataset_nm
# Number of classes
num_classes = self.num_classes_dict[dname]
# Parameters
cfg = self.cfg
# ReID model
TVT, TMO = set_devices(cfg.sys_device_ids)
if cfg.seed is not None:
set_seed(cfg.seed)
model = Model(
last_conv_stride=cfg.last_conv_stride,
num_stripes=cfg.num_stripes,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=num_classes
)
# Model wrapper
model_w = DataParallel(model)
# Criteria and Optimizers
criterion = torch.nn.CrossEntropyLoss()
# To finetune from ImageNet weights
finetuned_params = list(model.base.parameters())
# To train from scratch
new_params = [p for n, p in model.named_parameters()
if not n.startswith('base.')]
param_groups = [{'params': finetuned_params, \
'lr': cfg.finetuned_params_lr}, \
{'params': new_params, 'lr': cfg.new_params_lr}]
optimizer = optim.SGD(
param_groups,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
# Bind them together just to save some codes in the following usage.
modules_optims = [model, optimizer]
# 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)
self.model_w = model_w
self.modules_optims = modules_optims
self.criterion = criterion
self.TVT = TVT
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)
num_classes = len(train_set.ids2labels)
# 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,
num_stripes=cfg.num_stripes,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=num_classes)
# Model wrapper
model_w = DataParallel(model)
#############################
# Criteria and Optimizers #
#############################
criterion = torch.nn.CrossEntropyLoss()
# To finetune from ImageNet weights
finetuned_params = list(model.base.parameters())
# To train from scratch
new_params = [
p for n, p in model.named_parameters() if not n.startswith('base.')
]
param_groups = [{
'params': finetuned_params,
'lr': cfg.finetuned_params_lr
}, {
'params': new_params,
'lr': cfg.new_params_lr
}]
optimizer = optim.SGD(param_groups,
momentum=cfg.momentum,
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))
test_set.eval(normalize_feat=True, 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=True,
to_re_rank=False,
verbose=True)
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
adjust_lr_staircase(optimizer.param_groups,
[cfg.finetuned_params_lr, cfg.new_params_lr],
ep + 1, cfg.staircase_decay_at_epochs,
cfg.staircase_decay_multiply_factor)
may_set_mode(modules_optims, 'train')
# For recording loss
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_var = Variable(TVT(torch.from_numpy(labels).long()))
_, logits_list = model_w(ims_var)
loss = torch.sum(
torch.cat(
[criterion(logits, labels_var) for logits in logits_list]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
############
# Step Log #
############
loss_meter.update(to_scalar(loss))
if step % cfg.steps_per_log == 0:
log = '\tStep {}/Ep {}, {:.2f}s, loss {:.4f}'.format(
step, ep + 1,
time.time() - step_st, loss_meter.val)
print(log)
#############
# Epoch Log #
#############
log = 'Ep {}, {:.2f}s, loss {:.4f}'.format(ep + 1,
time.time() - ep_st,
loss_meter.avg)
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)
# 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,
num_stripes=cfg.num_stripes,
local_conv_out_channels=cfg.local_conv_out_channels,
num_classes=0)
# 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)
