def load_network(network, model_name):
save_path = os.path.join('./logs', model_name, 'net_last.pth')
network.load_state_dict(torch.load(save_path))
return network
######################################################################
# Finetuning the convnet
# --------
#
opt.nclasses = len(class_names)
if opt.backbone == 'ResNet50':
model = PCB(opt)
elif opt.backbone == 'EfficientNet-B0':
model = PCB_Effi(opt)
if opt.LSTM:
# model_name = 'PCB-128_dim_cls'
# model = load_network(model, model_name)
model = PCB_Effi_LSTM(model, opt)
# model_name = 'LSTM'
# model = load_network(model, model_name)
if opt.GGNN:
# model_name = 'PCB-128_dim_cls'
# model = load_network(model, model_name)
model = PCB_Effi_GGNN(model, opt)
# model_name = 'LSTM'
def load_network(network, model_name):
save_path = os.path.join('./logs', model_name, 'net_last.pth')
network.load_state_dict(torch.load(save_path))
return network
######################################################################
# Finetuning the convnet
# --------
#
opt.nclasses = dataset.num_train_pids
if opt.backbone == 'ResNet50':
model = PCB(opt)
elif opt.backbone == 'EfficientNet-B0':
model = PCB_Effi(opt)
# model_name = 'test-pcb-ac'
# model = load_network(model, model_name)
model = VRidGGNN(model)
# model_name = 'VRid'
# model = load_network(model, model_name)
print(model)
if opt.single_cls:
if opt.backbone == 'ResNet50':
ignored_params = list(map(id, model.model.fc.parameters()))
elif opt.backbone == 'EfficientNet-B0':
# gallery_path = image_datasets['gallery'].imgs
# query_path = image_datasets['query'].imgs
# gallery_cam, gallery_label = get_id(gallery_path)
# query_cam, query_label = get_id(query_path)
######################################################################
# Load Collected data Trained model
# --------
#
print('-------test-----------')
if opt.backbone == 'ResNet50':
model_structure = PCB(opt)
elif opt.backbone == 'EfficientNet-B0':
model_structure = PCB_Effi(opt)
model_structure = VRidGGNN(model_structure)
model = load_network(model_structure)
model = VRidGGNN_test(model)
# Change to test mode
model = model.eval()
if use_gpu:
model = model.cuda()
# Extract feature
with torch.no_grad():
gallery_feature = extract_feature(model, galleryloader)
