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':
ignored_params = list(map(id, model.model._fc.parameters()))
ignored_params += (list(map(id, model.classifier.parameters())))
# 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)
query_feature = extract_feature(model, queryloader)
# ----------------------
#
# Load a pretrainied model and reset final fully connected layer.
#
opt.nclasses = len(class_names)
if opt.use_dense:
model = ft_net_dense(opt.nclasses, opt.droprate)
elif opt.use_NAS:
model = ft_net_NAS(opt.nclasses, opt.droprate)
else:
model = ft_net(opt.nclasses, opt.droprate, opt.stride)
if opt.PCB:
model = PCB_Effi(opt.nclasses)
if opt.PCB and (opt.LSTM or opt.GGNN):
model_name = 'PCB-128_dim_cls'
model = load_network(model, model_name)
model = PCB_Effi_LSTM(model) if opt.LSTM else PCB_Effi_GGNN(model)
# model_name = 'LSTM' or 'GGNN'
# model = load_network(model, model_name)
print(model)
if not opt.multi_loss:
ignored_params = list(map(id, model.model._fc.parameters()))
ignored_params += (list(map(id, model.classifier.parameters())) +
list(map(id, model.model.parameters())))
base_params = filter(lambda p: id(p) not in ignored_params,
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.PCB:
model = PCB_Effi(opt.nclasses)
if opt.LSTM:
# model_name = 'PCB-128_dim_cls'
# model = load_network(model, model_name)
model = PCB_Effi_LSTM(model, opt.train_backbone)
# 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.train_backbone)
# model_name = 'LSTM'
# model = load_network(model, model_name)
