def save_siamese_rpn():
# load config
rpn_path = root_dir + 'experiments/siamrpn_alex_dwxcorr_16gpu/pre_train/checkpoint_e45.pth'
gru_rpn = root_dir + 'experiments/siamrpn_alex_dwxcorr_16gpu/config.yaml'
cfg.merge_from_file(gru_rpn)
# create model
model_rpn = ModelBuilder()
model_rpn = load_pretrain(model_rpn, rpn_path).cuda().eval()
gru_path = root_dir + 'experiments/siamrpn_alex_dwxcorr_16gpu/gru_snapshot/gru_10.pth'
gru_cfg = root_dir + 'experiments/siamrpn_alex_dwxcorr_16gpu/config_gru.yaml'
cfg.merge_from_file(gru_cfg)
# create model
model_gru = ModelBuilder()
model_gru = load_pretrain(model_gru, gru_path).cuda().eval()
for key, item in model_gru.named_parameters():
# print(key.find("grus"))
print(key, item.shape)
for key, item in model_rpn.named_parameters():
# print(key.find("grus"))
print(key, item.shape)
model_gru_dict = model_gru.state_dict()
model_rpn_dict = model_rpn.state_dict()
for key in model_gru_dict:
if key.find("grus") != -1:
print("fix:", key)
else:
print("change:", key)
model_gru_dict[key] = model_rpn_dict[key]
# name_map={}
# model_legacy_dict = model_legacy.state_dict()
# model_alexnet_dict = model_alexnet.state_dict()
# for para1,para2 in zip(model_legacy.named_parameters(),model_alexnet.named_parameters()):
# # print(para1[0],para1[1].shape)
# print(para1[0])
# print(para2[0])
# print(para1[1].shape)
# print(para2[1].shape)
# print("--"*40)
# # print("['{}'--->'{}']".format(para1[0], para2[0]),para1[1].shape, para2[1].shape)
# name_map[para1[0]]=para2[0]
# print(name_map)
#
#
# for key,val in name_map.items():
# model_alexnet_dict[val]=model_legacy_dict[key]
torch.save(model_gru_dict, "siamese_gru10_rpn45.pth")
def save_siamese_rpn():
# load config
cfg.merge_from_file(args.config)
cfg.BACKBONE.TYPE = 'alexnetlegacy'
# create model
model_legacy = ModelBuilder()
# load model
model_legacy = load_pretrain(model_legacy, args.snapshot).cuda().eval()
cfg.BACKBONE.TYPE = 'alexnet'
# create model
model_alexnet = ModelBuilder()
#
# for key ,item in model.named_parameters():
# print(key,item.shape)
for key, item in model_alexnet.named_parameters():
print(key, item.shape)
name_map = {}
model_legacy_dict = model_legacy.state_dict()
model_alexnet_dict = model_alexnet.state_dict()
for para1, para2 in zip(model_legacy.named_parameters(),
model_alexnet.named_parameters()):
# print(para1[0],para1[1].shape)
print(para1[0])
print(para2[0])
print(para1[1].shape)
print(para2[1].shape)
print("--" * 40)
# print("['{}'--->'{}']".format(para1[0], para2[0]),para1[1].shape, para2[1].shape)
name_map[para1[0]] = para2[0]
print(name_map)
for key, val in name_map.items():
model_alexnet_dict[val] = model_legacy_dict[key]
torch.save(model_alexnet_dict, "siamese_alexnet_rpn.pth")
