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 test_record(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['state_dicts'][0])
print('Loaded model weights from {}'.format(cfg.model_weight_file))
else:
load_ckpt(modules_optims, cfg.ckpt_file)
#record trainval feat
epoch_done = False
print('\n=========> Record on Trainval: <=========\n')
i = 0
feat_list = []
h_list = []
im_names_list = []
while not epoch_done:
ims, im_names, labels, mirrored, epoch_done = train_set.next_batch()
# print('the size of ims:{}'.format(ims.size()))
print(ims.shape)
ims_var = Variable(TVT(torch.from_numpy(ims).float()))
print(ims_var.shape)
feat,_,h,_ = model(ims_var)
h = [lf.cpu().data.numpy() for lf in h]
h = np.concatenate(h, axis=2)
feat_list.append(feat.numpy())
h_list.append(h)
im_names_list.append(im_names)
feat_list=np.concatenate(feat_list, axis=0)
h_list=np.concatenate(h_list, axis=0)
im_names_list=np.concatenate(im_names_list, axis=0)
save_mat(1,cfg.record_file,feat_list,h_list,im_names_list)
print('saved successfully.......')
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 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 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)
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)