def print_eval(prepare_data_fun, out_label):
model_file = os.path.join(snapshot_dir, "best_model.pth")
pkl_res_file = os.path.join(snapshot_dir,
"best_model_predict_%s.pkl" % out_label)
out_file = os.path.join(snapshot_dir,
"best_model_predict_%s.json" % out_label)
data_set_test = prepare_data_fun(**cfg['data'],
**cfg['model'],
verbose=True)
data_reader_test = DataLoader(data_set_test,
shuffle=False,
batch_size=cfg.data.batch_size,
num_workers=cfg.data.num_workers)
ans_dic = data_set_test.answer_dict
model = build_model(cfg, data_set_test)
model.load_state_dict(torch.load(model_file)['state_dict'])
model.eval()
question_ids, soft_max_result = run_model(model,
data_reader_test,
ans_dic.UNK_idx)
print_result(question_ids,
soft_max_result,
ans_dic,
out_file,
json_only=False,
pkl_res_file=pkl_res_file)
def print_eval(prepare_data_fun, out_label):
model_file = os.path.join(snapshot_dir, "best_model.pth")
pkl_res_file = os.path.join(snapshot_dir,
"best_model_predict_%s.pkl" % out_label)
out_file = os.path.join(snapshot_dir,
"best_model_predict_%s.json" % out_label)
data_set_test = prepare_data_fun(**cfg["data"],
**cfg["model"],
verbose=True)
data_reader_test = DataLoader(
data_set_test,
shuffle=False,
batch_size=cfg.data.batch_size,
num_workers=cfg.data.num_workers,
)
ans_dic = data_set_test.answer_dict
model = build_model(cfg, data_set_test)
model.load_state_dict(torch.load(model_file)["state_dict"])
model.eval()
question_ids, soft_max_result = run_model(model, data_reader_test,
ans_dic.UNK_idx)
print_result(
question_ids,
soft_max_result,
ans_dic,
out_file,
json_only=False,
pkl_res_file=pkl_res_file,
)
config_file = config_files[0]
with open(config_file, "r") as f:
config = yaml.load(f)
batch_size = config["data"]["batch_size"]
data_set_test = prepare_eval_data_set(**config["data"],
**config["model"],
verbose=True)
data_reader_test = DataLoader(data_set_test,
shuffle=False,
batch_size=batch_size,
num_workers=5)
ans_dic = data_set_test.answer_dict
accumulated_softmax = None
final_result = {}
n_model = 0
for c_file, model_file in zip(config_files, model_pths):
with open(c_file, "r") as f:
config = yaml.load(f)
myModel = build_model(config, data_set_test)
myModel.load_state_dict(torch.load(model_file)["state_dict"])
question_ids, soft_max_result = run_model(myModel, data_reader_test,
ans_dic.UNK_idx)
if n_model == 0:
final_result = soft_max_result