pickle_path = os.path.join(
'./model', name,
'train_features' + str(i) + '.pkl')
with open(pickle_path, "rb") as f:
temp = pickle.load(f)
features = torch.cat((features, temp))
features = features.view(-1, features.size(2))
print("Feature extraction finished")
print(
"Feeding extracted feature vectors to KMeans clustering model to generate clusters"
)
# start clustering with features vector
centers_path = train_cluster(features, centers_path)
print("Clusters generated")
model = model.convert_to_rpp_cluster()
model.avgpool.centers_path = centers_path
model = model.cuda()
print(model)
# step4: whole net training #
print("STARTING STEP 4 OF PCB:")
stage = 'full'
full_train(model, criterion, stage, opt.epochs)
else:
model = PCB(len(class_names),
num_bottleneck=256,
num_parts=opt.parts,
parts_ver=opt.PCB_Ver,
checkerboard=opt.CB,
share_conv=opt.share_conv)
elif opt.use_ftnet:
model_structure = ft_net(opt.nclasses, stride=opt.stride)
cluster = False
if opt.PCB:
model_structure = PCB(opt.nclasses,
num_bottleneck=256,
num_parts=opt.parts,
parts_ver=opt.PCB_Ver,
checkerboard=opt.CB,
share_conv=opt.share_conv)
if opt.RPP:
model_structure = model_structure.convert_to_rpp()
if opt.cluster:
model_structure = model_structure.convert_to_rpp_cluster()
print(model_structure)
model_structure.avgpool.cluster_plots = opt.cluster_plots
centers_path = os.path.join('./model', name, 'centers.pkl')
model_structure.avgpool.centers_path = centers_path
cluster = True
#if opt.fp16:
# model_structure = network_to_half(model_structure)
model = load_network(model_structure)
# Remove the final fc layer and classifier layer
if opt.PCB:
#if opt.fp16:
# model = PCB_test(model[1])
