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)
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 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)
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)
ckpt_file = '/home/eric/Disk100G/githubProject/AlignedReID-Re-Production-Pytorch/model/trainSet_conbined/Resnet-50/GL-0.7_LL-0.3_NNF_TWGD_EP-150_LDOHS-true_CP-0.3_CR-0.7_gm-lm-0.4_staircase_warm_up/200_ckpt.pth'
load_ckpt(modules_optims, 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.volatile = True
test_set.eval(normalize_feat=cfg.normalize_feature,
use_local_distance=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)